Sherief Abdallah

• PhD in Computer Science
• Professor at British University in Dubai

This paper examines the Internet of Things (IoT) and its application in the Internet of Vehicles (IoV). IoV combines AI and IoT for real-time monitoring and operation of vehicles. We discuss how the integration of AI and IoT enhances vehicle functionalities through the deployment of sensors, infrared gadgets, cameras, and heat detectors, facilitati...

Sentiment analysis is the process of examining people's opinions and emotions towards goods, services, organizations, individuals, and other things, through the use of textual data. It involves categorizing text as positive, negative, or neutral to quantify people's beliefs. Social media platforms have become an important source of sentiment analys...

The use of Big Data analytics is rabidly affecting our life and increasingly important to our day-today transactions. Big data is so-called the new weapon that could unlock many opportunities. On 11 March 2020, the World Health Organization has declared COVID-19 as a global health crisis. Although the 1st case was reported ion the 31st of December...

Predictive maintenance plays an essential role in maintaining the fleet and keeping it available to operate with minimum downtime. However, for one government organization in Dubai, corrective maintenance work orders are more than just preventive maintenance and are needed to increase the vehicles' availability and reduce the maintenance cost. This...

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational...

(1) Background: the ability to use social media to communicate without revealing one’s real identity has created an attractive setting for cyberbullying. Several studies targeted social media to collect their datasets with the aim of automatically detecting offensive language. However, the majority of the datasets were in English, not in Arabic. Ev...

Recently, extensive studies and research in the Arabic Natural Language Processing (ANLP) field have been conducted for text classification and sentiment analysis. Moreover, the number of studies that target Arabic dialects has also increased. In this research paper, we constructed the first manually annotated dataset of the Emirati dialect for the...

Understanding the key elements of successful business partnerships has significant economic value. In this paper we analyze, for the first time, real world trade license data as a social network. The data we analyze cover the license registrations from 2015 to 2017 in a major cosmopolitan city in the middle east. The dataset consists of more than t...

People in the Arab world prefer to use their dialects in writing while using social media sites which results in generating a significant amount of texts. Sentiment analysis in texts is one of the most important applications of the Natural Language Processing field of science. Sentiment Analysis involves classifying texts based upon emotions or pol...

Good coordination among school staff and families leads to increased learning quality and academic success for students with special education needs and disabilities (SEND). This pilot study aims to investigate the use of mobile technology for the coordination of therapy and learning for students with SEND. This study first follows a participatory...

Objectives: This study aimed to examine acceptance levels of and attitudes towards telemedicine among users in the United Arab Emirates (UAE) and assess associations between perceived usefulness (PU), perceived ease of use (PEOU), attitudes towards use (ATU) and behavioural intention of use (BIU) in relation to telemedicine technology. Methods:...

Social media is a weapon that is capable of construction as well as destruction. The real power of the prevailing social media platforms becomes evident by witnessing the influence created by these platforms on a large scale. It plays a significant role in everyday life. The rising popularity due to its ability to make people attached with kith and...

Educational data is considered by researchers and data scientists as an indicator for the future predictions. The current research study aims for classifying IT alumni students into employed and unemployed. The data collected from two universities in Jordan. 781 of IT alumni students in two universities in Jordan participate in the current study. T...

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational...

Social media attracts a lot of users around the world. Many reasons drive people to use social media sites such as expressing opinions and ideas, displaying their diaries and sharing them with others, social communication with family and friends and building new social relationships, learning and sharing knowledge. Written text is one of the most c...

One of the influential research fields is the use of Artificial Intelligence and Blockchain for transparency in governance. The standard mechanisms utilized in governance are required to be transformed in respect of assorted parameters such as availability of data to users further as information asymmetries between the users should be minimized. An...

Student retention is one the biggest challenges facing academic institutions worldwide. In this research, we present a novel data mining approach to predict retention among a homogeneous group of students with similar social and cultural background at an academic institution based in the Middle East. Several researchers have studied retention by fo...

Government open data is a booster for government performance, transparency, and innovation. The purpose of this study to investigate how researchers use Dubai open data on Dubai Pulse platform, Using Dubai open data for Knowledge and Human Development Authority in Dubai (KHDA) shows was not covered by academic researchers. To find the reasons behin...

Student retention is one of the biggest challenges facing academic institutions worldwide as it does not only affects the student negatively but also hinders institutional quality and reputation. In this paper, we use classification techniques to predict retention at an academic institution based in the Middle East. Our study relies solely on pre-c...

Crowd evacuation in emergencies may lead to fatalities if the evacuation plans were not test-ed and evaluated. Traditionally, evacuation drills have been, and still are, being used to assess evacuation plans. However, in recent years the simulation of evacuation plans during emergencies has emerged as a strong alternative that is cost effective and...

The rise of social media offered new channels of communication between a government and its citizens. The social media channels are interactive, inclusive, low-cost, and unconstrained by time or place. This two-way communication between governments and citizens is referred to as electronic citizen participation, or e-participation. E-participation...

Recently, the government of United Arab of Emirates (UAE) is focusing on Artificial Intelligence (AI) strategy for future projects that will serve various sectors. Health care sector is one of the significant sectors they are focusing on and the planned (AI) projects of it is aiming to minimize chronic and early prediction of dangerous diseases aff...

Users on Twitter post millions of tweets every day from all over the world. Analyzing such data has received significant attention in the last decade. While most of the previous work focused on business-related analysis, our study focuses on the perspective of governments, with Dubai as a case study. We collected corpus of tweets related to Dubai,...

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], drivi...

Customers’ Segmentation is an important concept for designing marketing campaigns to improve businesses and increase revenue. Clustering algorithms can help marketing experts to achieve this goal. The rapid growth of high dimensional databases and data warehouses, such as Customer Relationship Management (CRM), stressed the need for advanced data a...

There has been an explosion of websites that manage classifieds in general, and real estate listings in particular. Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In fact, we are not aware of any work that attemp...

Nowadays, the broadcasting of news via social media networks is almost provided in a textual format. The nature of the broadcasted text is considered as unstructured text. Text mining techniques play an essential role in converting the unstructured text into informative knowledge. It has been observed that there is no research has addressed the tex...

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], drivi...

This paper focuses on the problem of quantifying how certain words in a text affect, positively or negatively, some numeric signal. These words can lead to important decisions for significant applications such as E-commerce. For example, consider the corpus of real-estate classifieds, which we developed as a case study. Each classified has a descri...

Analyzing the learning dynamics in multi-agent systems (MASs) has received growing attention in recent years. Theoretical analysis of the dynamics was only possible in simple domains and simple algorithms. When one or more of these restrictions do not apply, theoretical analysis becomes prohibitively difficult, and researchers rely on experimental...

Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to optimal policies in Markov decision processes. However, QL exhibits an artifact: in expectation, the effective rate of updating the value of an action depends on the probability of choosing that action. In other words, there is a tight coupling between t...

Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and the unavailability of large public databases of handwritten characters and words. The use of synthetic data for training and testing handwritten character recognition systems is one of the possible solutions to provid...

Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In this paper we propose a two-stage regression model that exploits the textual data in real estate classifieds. We show how our model can be used to predict the pri...

Currently in UAE many higher educational institutes are replacing the traditional teaching and learning system with iPad based teaching and learning system. The focus of this paper is to study the effect of class size on the effectiveness of iPad based learning process in UAE high school classrooms. We conducted quantitative analysis in one of the...

Purpose – The purpose of this paper is to articulate clear understanding about the role of enterprise information systems (EIS) in developing innovative business practices. Particularly, it aims to explore the different ways that make EIS enables innovation development. Design/methodology/approach – The study adopted exploratory case study, base...

Networks are seen everywhere in our modern life, including the Internet, the Grid, P2P file sharing, and sensor networks. Consequently, researchers in Artificial Intelligence (and Multi-Agent Systems in particular) have been actively seeking methods for optimizing the performance of these networks. A promising yet challenging optimization direction...

While Artificial Intelligence has successfully outperformed humans in complex combinatorial games (such as chess and checkers), humans have retained their supremacy in social interactions that require intuition and adaptation, such as cooperation and coordination games. Despite significant advances in learning algorithms, most algorithms adapt at t...

Centralized sanctioning institutions have been shown to emerge naturally through social learning, displace all other forms of punishment and lead to stable cooperation. However, this result provokes a number of questions. If centralized sanctioning is so successful, then why do many highly authoritarian states suffer from low levels of cooperation?...

Single convention convergence across different types of networks is a challenging multi-agent task. Our central hypothesis in this paper is that no simple distributed mechanism (such as the state-of-the-art Generalized Simple Majority (GSM) rule) can achieve this. We augment the agents with "network thinking" capability to solve this single convent...

We propose in this paper the use of a hierarchy of coordinators to improve the convergence of a network of agents to a global norm. A norm or a convention is an unwritten law that a society of agents agree on. Social norms are used by humans all the time. Choosing on which side of the road to drive a car and the right-of-way at an intersection are...

This paper studies how people reveal private information in strategic settings in which participants need to negotiate over resources, but are uncertain about each other's objectives. The study compared two negotiation protocols which differed in whether they allowed participants to disclose their objectives in a repeated negotiation setting of inc...

Q-learning is a very popular reinforcement learning algorithm being proven to converge to optimal policies in Markov decision processes. However, Q-learning shows artifacts in non-stationary environments, e.g., the probability of playing the optimal action may decrease if Q-values deviate significantly from the true values, a situation that may ari...

Several important complex network measures that helped discover-ing common patterns across real-world networks ignore edge weights, an important information in real-world networks. We propose a new methodology for generalizing measures of unweighted net-works through a generalization of the cardinality concept of a set of weights. The key observati...

Named Entity Recognition (NER) is a subtask of information extrac-tion that seeks to recognize and classify named entities in unstructured text into predefined categories such as the names of persons, organizations, locations, etc. The majority of researchers used machine learning, while few researchers used handcrafted rules to solve the NER probl...

Until recently, little work has been dedicated to the representation and interchange of informal, semi-structured arguments of the type found in natural language prose and dialogue. To redress this, the research community recently initiated work towards an Argument Interchange Format (AIF). The AIF aims to facilitate the exchange of semi-structured...

Unweighted network measures are commonly used to analyze real-world networks due to their simplicity and intuitiveness. This motivated the search for generalizations of unweighted network measures that take weights into account. We propose a new generalization methodology that captures how focused are the interactions over edges. The less focused t...

Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and nor...

This paper is about how groups solve global coordination problems such as the distributed graph coloring problem. We focused on scenarios in which agents are not able to communicate explicitly, but can rely on observing the momentary choices of their immediate neighbors in a social network. It has been reported that humans use two cognitive heurist...

Decentralized reinforcement learning (DRL) has been applied to a number of distributed applications. However, one of the main challenges faced by DRL is its convergence. Previous work has shown that hierarchically organizational control is an effective way of coordinating DRL to improve its speed, quality, and likelihood of convergence. In this pap...

Experimental analysis of networks of cooperative learning agents (to verify certain properties such as the system's stability) has been commonly used due to the complexity of theoretical analysis in such cases. Due to the large number of parameters to analyze, researchers used metrics that summarize the system in few parameters. Since in cooperativ...

Several important complex network measures that helped discovering common patterns across real-world networks ignore edge weights, an important information in real-world networks. We propose a new methodology for generalizing measures of unweighted networks through a generalization of the cardinality concept of a set of weights. The key observation...

Experimental verification has been the method of choice for verifying the stability of a multi-agent reinforcement learning (MARL) algorithm as the number of agents grows and theoretical analysis becomes prohibitively complex. For cooperative agents, where the ultimate goal is to optimize some global metric, the stability is usually verified by obs...

A key measure that has been used extensively in analyzing complex networks is the degree of a node (the number of the node's neighbors). Because of its discrete nature, when the degree measure was used in analyzing weighted networks, weights were either ignored or thresholded in order to retain or disregard an edge. Therefore, despite its popularit...

Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large- scale systems. In this work, we develop an organization-based control framework to speed up the convergence of MARL algo- rithms in a network of agents. Our framework defines a multi-level organizational structure for automate...

The taxi dispatch problem involves assigning taxis to callers waiting at different locations. A dispatch system currently in use by a major taxi company divides the city (in which the system operates) into regional dispatch areas. Each area has fixed designated adjacent areas hand-coded by human experts. When a local area does not have vacant cabs,...

This paper studies a novel negotiation protocol in settings in which players need to exchange resources in order to achieve their own objective, but are uncertain about the objectives of other participants. The protocol allows participants to request each other to disclose their interests at given points in the negotiation. Revealing information ab...

Argumentation is now a very fertile area of research in Ar- tificial Intelligence. Yet, most approaches to reasoning with arguments in AI are based on a normative perspective, re- lying on intuition as to what constitutes correct reasoning, sometimes aided by purpose-built hypothetical examples. For these models to be useful in agent-human argument...

Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents decisions. Due to the complexity of the problem, the majority of the previously developed MARL algorithms assumed agents either had some knowledge of the underlying game (such as Nash equilibria) and/or observed other agents actions and the rewards the...

Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Only a subset of these MARL algorithms both do not require agents to know the underlying environment and can learn a stochastic policy (a policy that chooses actions according to a probability distribution). Weighted Policy Learner (WPL) is...

Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large-scale systems. In this work, we develop a supervision framework to speed up the convergence of MARL algorithms in a network of agents. The framework defines an organizational structure for automated supervision and a communicat...

To cope with large scale, agents are usually organized in a network such that an agent interacts only with its immediate neighbors in the network. Reinforcement learning techniques have been commonly used to optimize agents local policies in such a network because they require little domain knowledge and can be fully distributed. However, all of th...

The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators. Servers execute tasks and may dier in their capabilities, e.g. one server may take more time than the other in ex- ecuting the same task. Mediators act on behalf of user...

This paper discusses a solution to the problems posed by sensor resource allocation in an adaptive, distributed radar array. We have formulated a variant of the classic resource allocation problem, called the setting-based resource allocation problem, which reflects the challenges posed in domains in which sensors have multiple settings, each of wh...

Agents can benefit by cooperating to solve a common problem [2, 11]. For example, several robots may cooperate to move a heavy object, sweep a specific area in short time, etc. However, as the number of agents increases, having all agents involved in a detailed coordination/negotiation process will limit the scalability of the system. It is better...

Mediation is the process of decomposing a task into subtasks, finding agents suitable for these subtasks and negotiating with agents to obtain commitments to execute these subtasks. This process involves several decisions to be made by a mediator including which tasks to mediate, when to interrupt the current task mediation to pursue a better task,...

We overview the software architecture for a network of low-powered radars (sensors) that collaboratively and adaptively sense the lowest few kilometers of the earth's atmosphere. We focus on the system's main control loop -- ingesting data from remote radars, identifying meteorological features in this data, and determining each radar's future scan...

A Multi-linked negotiation problem occurs when an agent needs to negotiate with multiple other agents about different subjects (tasks, conflicts, or resource requirements), and the negotiation over one subject has influence on negotiations over other subjects. The solution of the multi-linked negotiations problem will become increasingly important...

The coalition formation problem has received a considerable amount of attention in recent years. In this work we present a novel distributed algorithm that returns a solution in polynomial time and the quality of the returned solution increases as agents gain more experience. Our solution utilizes an underlying organization to guide the coalition f...

The coalition formation problem has received a considerable amount of attention in recent years. In this work we present a novel distributed algorithm that returns a solution in polynomial time and the quality of the returned solution increases as agents gain more experience. Our solution utilizes an underlying organization to guide the coalition f...

In this paper we show how imposing an organization (topology and search mechanism) on agents in a peer-to-peer network can improve system performance. The organizational structure is the underlying topolgy con-necting agents in the system. The search mechanism is how agents are traversed in such a topology. In par-ticular, we discuss our solutions...

Coalition formation methods allow agents to join together and are thus necessary in cases where tasks can only be performed cooperatively by groups. This is the case in the Request For Proposal (RFP) domain, where some requester business agent issues ...

This paper addresses the problem of coordinating a group of agents involved in a team. To achieve flexible teamwork, agents should synchronize their work and monitor their performance to avoid redundant work. Generalized Partial Global Planning (GPGP) is one of the most common techniques used in coordinating cooperative agents, however, no techniqu...

Several multiagent reinforcement learning (MARL) algorith-ms have been proposed to optimize agents' decisions. Only a subset of these MARL algorithms both do not require agents to know the underlying environment and can learn a stochastic policy (a policy that chooses actions accord-ing to a probability distribution). Weighted Policy Learner (WPL)...

Various Artificial Intelligence semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. These semantics can make conflicting predictions, as in the situa-tion known as floating reinstatement. We argue that the de-bate about which semantics makes the correct predi...

Mining and analyzing complex networks have received signif-icant attention in recent years due to the explosive growth of social networks and the discovery of common patterns that govern wide-range of real world networks. Most of the work that analyzed social networks relied on computing measures that capture some aspect of the network structure, s...