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Knowledge-based systems and their ontologies evolve due to different reasons. Ontology evolution is the adaptation of an ontology and the propagation of these changes to dependent artifacts such as queries and other ontologies. Besides identifying basic/simple changes, it is imperative to identify complex changes between two versions of the same ontology to make this adaptation possible. There are many definitions of complex changes applied to ontologies in the literature. However, their specifications across works vary both in formalization and textual description. Some works also use different terminologies to refer to a change, while others use the same vocabulary to refer to distinct changes. Therefore, there is a lack of a unified list of complex changes. The main goals of this paper are: (i) present the primary documents that identify complex changes; (ii) provide critical analyses about the set of the complex changes proposed in the literature and the documents mentioning them; (iii) provide a unified list of complex changes mapping different sets of complex changes proposed by several authors; (iv) present a classification for those complex changes; and (v) describe some open directions of the area. The mappings between the complex changes provide a mechanism to relate and compare different proposals. The unified list is thus a reference for the complex changes published in the literature. It may assist the development of tools to identify changes between two versions of the same ontology and enable the adaptation of artifacts that depend on the evolved ontology.
Geographically distributed teams often face challenges in coordination and collaboration, lowering their productivity. Understanding the relationship between team dispersion and productivity is critical for supporting such teams. Extensive prior research has studied these relations in lab settings or using qualitative measures. This paper extends prior work by contributing an empirical case study in a real-world organization, using quantitative measures. We studied 117 new research project teams from the same discipline within an industrial research lab for 6 months. During this time, all teams shared one goal: submitting research papers to the same target conference. We analyzed these teams' dispersion-related characteristics as well as team productivity. Interestingly, we found little statistical evidence that geographic and time differences relate to team productivity. However, organizational and functional distances are predictive of the productivity of the dispersed teams we studied. We discuss the open research questions these findings revealed and their implications for future research.
Personal Medical Records (PMR) manage an individual’s medical information in digital form and allow patients to view their medical information and doctors to diagnose diseases. Today’s institution-dependent centralized storage, fails to give trustworthy, secure, reliable, and traceable patient controls. This leads to a serious disadvantage in diagnosing and preventing diseases. The proposed blockchain technique forms a secured network between doctors of the same specialization for gathering opinions on a particular diagnosis by sharing the PMR with consent to provide better care to patients. To finalize the disease prediction, members can approve the diagnosis. The smart contract access control allows doctors to view and access the PMR. The scalability issue is resolved by the Huffman code data compression technique, and security of the PMR is achieved by an advanced encryption standard. The proposed techniques’ requirements, latency time, compression ratio and security analysis have been compared with existing techniques.
In this paper, we have suggested a method for simultaneous quantification using high-performance liquid chromatography for Nebivolol and Cilnidipine in pharmaceutical dosage forms that is accurate, easy, and fast. The separation is achieved using a mobile phase of 50:30:20 v/v acetonitrile, methanol, and potassium dihydrogen orthophosphate buffer, with pH 4.0 adjusted with orthophosphoric acid (10%) on a Phenomenex-luna C18 (250 mm * 4.6 mm, 5) column. We have used a flow rate of e 1.2 mL min−1, with UV detection at 283 nm. Nebivolol and Cilnidipine have retention times of 2.37 and 7.69 min, respectively. For both Nebivolol and Cilnidipine, a linear response is seen over the concentration ranges of 2–10 g/mL (R2 = 0.998) and 4–20 g/mL (R2 = 0.997). For Nebivolol, the limit of quantitation (LOQ) and limit of detection (LOD) are 0.13 and 0.40 g/mL, respectively, and for Cilnidipine, they are 0.11 and 0.35 g/mL. The percent recovery for Nebivolol is 101.4–101.7%, while for Cilnidipine it is 100.5–100.9%. It is found that the procedure is accurate, exact, linear, sensitive, and robust.KeywordsNebivolol (NBV)Cilnidipine (CIL)Liquid chromatographyCombined dosage formMethod validation
The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data (historical data) related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this paper, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100-8500 kW). The volume of big-data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression (L2 regularization). In addition, the proposed novel XGBoost forecast system decreased the root mean square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) neural networks for same data sets. Furthermore, the CoD determination coefficient ( $R^{2}$ ) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA).
The blockchain is tremendous technology for securing data transmission over a decentralized cloud environment. A PHR can offer easy data exchange and storage. However, unauthorised individuals may steal sensitive users' data from cloud devices, leading to significant privacy leakage risks. The main problem is that the non-authentication policy leads to key leakages due to unauthorised entries through intruders. To resolve this problem, we propose Blockchain-Based Decentralized Security (BCDS) using Crypto-Proof of Stake (CPoS) for securing sensitive Personal health care records. First, to process the security, the lattice construction phase is used based on blockchain decentralized. The Random shuffle block padding is intended to rehash the block to score with padding bits to block-level encryption. Then the sensitive terms are marginalized by threshold labels being highly encrypted by Optimized Starvation Point Link Encryption (OSPLE) to protect the data. By integrating Master Node Key Aggregation Policy (MNKAP) to verify the contact proof of staking authentication to the peer end to authenticate the security. The Delegate proof of stack provides higher security to protect the sensitive terms by accessing PHR records transactions to improve the integrity proofing to the admittance of the key to deliver the data. The key signature verification then validates the key for authentication to decrypt the protected data. This proves a higher security protocol and authentication principle than any other method as well as security proofing.
BACKGROUND: Direct oral anticoagulants (DOACs) have become widely used for the prevention of stroke in nonvalvular atrial fibrillation (AF) and for the treatment of venous thromboembolism (VTE). Warfarin, the standard of care prior to DOACs, requires monitoring and dose adjustment to ensure patients remain appropriately anticoagulated. DOACs do not require monitoring but are significantly more expensive. We sought to examine real-world effectiveness and costs of DOACs and warfarin in patients with AF and VTE. OBJECTIVE: To examine clinical and economic outcomes. The clinical objectives were to determine the bleeding and thrombotic event rates associated with DOACs vs warfarin. The economic objectives were to determine the cost associated with these events, as well as the all-cause medical and pharmacy costs associated with DOACs vs warfarin. METHODS: This analysis was an observational, propensity-matched comparison of retrospective medical and pharmacy claims data for members enrolled in an integrated health plan between October 1, 2015, and September 30, 2020. Members who were older than 18 years of age with at least 1 30-day supply of warfarin or a DOAC filled within 30 days of a new diagnosis of VTE or nonvalvular AF were eligible for the analysis. Cox hazard ratios were used to compare differences in clinical outcomes, where paired t-tests were used to evaluate economic outcomes. RESULTS: After matching, there were 893 patients in each group. Among matched members, warfarin was associated with increased risk of nonmajor bleeds relative to apixaban (hazard ratio [HR] = 1.526; P = 0.0048) and increased risk of pulmonary embolism relative to both DOACs (apixaban: HR = 1.941 [P = 0.0328]; rivaroxaban: HR = 1.833 [P = 0.0489]). No statistically significant difference was observed in hospitalizations or in length of stay between warfarin and either DOAC. The difference-in-difference (DID) in total costs of care per member per month for apixaban and rivaroxaban relative to warfarin were $801.64 (P = 0.0178) and $534.23 (P = 0.0998) more, respectively. DID in VTE-related cost for apixaban was $177.09 less, relative to warfarin (P = 0.0098). DID in all-cause pharmacy costs for apixaban and rivaroxaban relative to warfarin were $342.47 (P < 0.0001) and $386.42 (P < 0.001) more, respectively. CONCLUSIONS: Warfarin use was associated with a significant decrease in total cost of care despite a significant increase in VTE-related costs vs apixaban. Warfarin was also associated with a significant increase in other nonmajor bleeds relative to apixaban, as well as a significant increase in pulmonary embolism relative to both DOACs. Warfarin was associated with a significant reduction in all-cause pharmacy cost compared with either DOAC. DISCLOSURES: The authors of this study have nothing to disclose.
Object-centric processes (also known as Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. Interactions take place through bridging events where instances exchange data. Object-centric processes are recently gaining popularity in academia and industry, because their nature is observed in many application scenarios. This poses significant challenges in predictive analytics due to the complex intricacy of the process instances that relate to each other via many-to-many associations. Existing research is unable to directly exploit the benefits of these interactions, thus limiting the prediction quality. This paper proposes an approach to incorporate the information about the object interactions into the predictive models. The approach is assessed on real-life object-centric process event data, using different Key Performance Indicators (KPIs). The results are compared with a naïve approach that overlooks the object interactions, thus illustrating the benefits of their use on the prediction quality.
This is the first work that incorporates recent advancements in “explainability” of machine learning (ML) to build a routing obfuscator called ObfusX. We adopt a recent metric—the SHAP value—which explains to what extent each layout feature can reveal each unknown connection for a recent ML-based split manufacturing attack model. The unique benefits of SHAP-based analysis include the ability to identify the best candidates for obfuscation, together with the dominant layout features which make them vulnerable. As a result, ObfusX can achieve better hit rate (97% lower) while perturbing significantly fewer nets when obfuscating using a via perturbation scheme, compared to prior work. When imposing the same wirelength limit using a wire lifting scheme, ObfusX performs significantly better in performance metrics (e.g., 2.2 times more reduction on average in percentage of netlist recovery).
Cloud computing offers "pay as you go" IT services to users worldwide. To enable this, resource providers host myriad applications from various domains, and there is a rising phenomenon of mega data centers. These data centers hosting Cloud applications consume massive amounts of electrical energy, contributing to high operational costs and carbon footprints. Companies bringing Cloud computing, particularly Infrastructure as a Service (IaaS), directly to users, and as a result, the number of cloud users is rising steeply. Further, Cloud resource providers need to ensure efficient price discovery and allocate their capacity to maximize revenue. This directly translates to a need for an efficient pricing model, that satisfies both the user's and the cloud provider's needs.This paper presents an auction-based approach to ensure truthful price discovery and maximize revenue. Further, it proposes energy-aware heuristics to shut down the new servers so that the total energy cost for the resource provider is minimized. Additionally, this approach has been validated by conducting a simulation. The results demonstrate that the auction-based mechanism has immense potential as it offers significantly better revenue realization.
KI befi ndet sich im Spannungsfeld zwischen Vertrauen und Innovationskraft. In diesem Artikel werden anschaulich verschiedene Dimensionen von Vertrauenswürdiger KI beleuchtet: Fairness, Transparenz, Erklärbarkeit, Robustheit, Datenschutz und Nachhaltigkeit. Unter dem Druck des aktuell diskutierten Regulierungsvorschlags AI ACT der EU müssen sich Unternehmen mit Vertrauenswürdiger KI auseinandersetzen. Wie dies gelingen kann, wird in einem ganzheitlichen Lösungsansatz dargestellt
Cloud technologies and artificial intelligence are transforming call centers into intelligent relationship hubs. Such transformation requires call-center employees to be strategically connected based on the distribution of expertise. However, today’s organizations lack a real-time method to agilely and pervasively map knowledge-distribution and optimize information flow. We present a pilot study showing that the interactions captured by Zigbee and infrared (IR) Internet of things (IoT) sensors on sociometric badges (a business-card-sized printed circuit board) worn by 36 employees in a call center can be used to characterize interactions and their impact on employee performance. Specifically, we quantify an employee’s centrality, weak ties, and strong ties from sensor network data and analyze the effects on average task-processing time. Further analysis reveals insights into interactions among workers that were previously limited by coarse qualitative data in survey studies. This study points to the potential of a “living lab” approach for investigating the effects of employee interactions and behaviors on their performance quantitatively in the real word using ubiquitous IoT.
Tourists can easily find the hotels of any particular place and share their opinions on social media and other tourism apps. These reviews give an abstraction to the readers about a particular hotel. Somehow, the readers may get confused on choosing the right hotel due to ambiguity in many reviews. Sentiment classification methods will be used to differentiate the positive and negative sentiments. However, each hotel review might have a binary statement which makes it difficult to identify whether the statement is positive or negative. In order to tackle the ambiguity, the tokenized sentences are considered. Naïve Bayes classifier, a supervised machine learning algorithm, is applied to recognize the features and to classify the reviews into positive or negative. Then, the proposed framework will be implemented as a Web application using open-source methodologies and micro frameworks. The dashboard of the Web application comprises three areas, namely data, recommendation and a plot. Data shows the reviews along with the classified sentiments of the selected hotel. Recommendation part shows the levels of recommendations for the selected hotel, and the plot shows the positive and negative statements of that particular hotel. A word cloud will be presented for the effective visualization of key terminologies in the positive and negative statements. Previously, review classification was taken into consideration as a part of sentiment analysis. Here, in this approach, sentiment recommendation combines the techniques of sentiment classification and recommendation of deserved entity.
The worldwide COVID-19 outbreak has dramatically called for appropriate responses from governments. Scientists estimated both the basic reproduction number and the lethality of the virus. The former one depends on several factors (environment and social behavior, virus characteristics, removal rate). In the absence of specific treatments (vaccine, drugs) for COVID-19 there was a limited capability to control the likelihood of transmission or the recovery rate. Therefore, to limit the expected exponential spread of the disease and to reduce its consequences, most national authorities have adopted containment strategies that are mostly focused on social distancing measures. In this context, we performed an analysis of the effects of government lockdown policies in 5 European Countries (France, Germany, Italy, Spain, United Kingdom). We used phone mobility data, published by Apple Inc. and Google, as an indirect measure of social distancing over time since we believe they represent a good approximation of actual changes in social behaviors. (i) The responsiveness of the governments in taking decisions (ii) The coherence of the lockdown policy with changes in mobility data (iii) The lockdown implementation performance in each country. (iv) The effects of social distancing on the epidemic evolution These data were first analyzed in relation with the evolution of political recommendations and directives to both assess (i) responsiveness of governments in taking decisions and (ii) the implementation performance in each country. Subsequently, we used data made available by John Hopkins University in the attempt to compare changes in people behaviors with the evolution of COVID-19 epidemic (confirmed cases, new and cumulative) in each country in scope. Finally, we made an attempt to identify some key lockdown performance parameters in order to (i) establish responsiveness, efficiency and effectiveness of the lockdown measures (ii) model the latency occurring between the changes in social behaviors and the changes in growth rate of the disease.
While clustering is a powerful methodology used for grouping objects into families, it is hard to conceive a natural object grouping method without considering the context of a particular application. In high-dimensional problems with large volumes and rapidly evolving part flows, many clustering methods have traditionally been used to form part families considering similarities. In this paper, a two-phase clustering method is developed to optimize the grouping process under technological constraints, in a bid to improve resource planning. The first phase consists in forming part families using the Agglomerative Hierarchical Clustering approach, considering multidimensional parametrization of the part, while in the second phase, the optimal number of clusters is determined using ELECTRE III, which serves to handle uncertainty. Based on a real case study in the electronics industry, an improved production planning solution is proposed to validate the method’s efficiency. The solution was compared to the in-use method to highlight its added value.
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6,822 members
Jinwoo Choi
  • IBM Systems Group
Ramon Bertran
  • Reliability- and Power-Aware Microarchitectures
Xiao Hu Liu
  • Thomas J. Watson Research Center
Constantinos Evangelinos
  • Thomas J. Watson Research Center
Das Pemmaraju
  • Machine Learning Technologies
Armonk, United States