User System Research Articles

Information control in networked discrete event systems

How to control information exchange among different users is an important problem in networked systems with many users/agents. Generally speaking, there are several considerations in control of information exchange in a networked system, including (1) to ensure a friend user has sufficient information to perform its tasks, (2) to deprive an adversary user its information to perform its tasks, (3) to minimize information exchange among friend users so that the risk of information leaking is minimized, and (4) to maximize information broadcasted to all users to achieve maximum transparency. In this paper, we investigate the information control problems in the framework of discrete event systems. Based on the problem at hand, we divide users in a networked system into two or more groups. Users in the same group are consider as friends and users in a different group are consider as adversaries. Several information control problems are investigated and solved using a systematic and rigorous approach. Methods are developed to design controllers that send minimum information to its friends to help them to perform their tasks and broadcast maximum information without helping its adversaries.

ИНТЕГРАЦИЯ СОЦИОЛОГИЧЕСКИХ ТЕОРИЙ В ПРАКТИКУ СОЦИАЛЬНОЙ РАБОТЫ С НАРКОПОТРЕБИТЕЛЯМИ В СИСТЕМЕ ПРОБАЦИИ

The research’s relevance is caused by the need to improve the effectiveness of social work with drug users in the probation system. Drug users face problems such as social stigma, weak social ties, and limited resources. The study is aimed to analyze sociological theories for application in the practice of social work with drug users in the probation system. A literature review and analysis of documents on sociological theories and problems of drug addiction were used. The following results were achieved. The theory of stigmatization helps to analyze the impact of stigma on drug users and develop methods to reduce stigma. Social network theory provides insight into the role of social connections. Recovery capital theory analyzes the available resources and support. The article concludes that these sociological theories contribute to a deeper understanding of drug users’ problems and the development of more effective strategies for rehabilitation and reintegration into society.

Enhancing Network Security Through Router-Based Firewalls: An Investigation into Design, Effectiveness, and Human Factors

A firewall serves as a crucial computer program designed to block malicious software, thereby safeguarding the user's system. It helps prevent the unauthorized dissemination of personal information and the incursion of harmful viruses. Taking inspiration from China's "Great Firewall"—a sophisticated system utilizing advanced software and hardware to automatically filter, censor, and monitor internet content—this research aims to devise a router-based firewall system capable of autonomously shielding users' computers from viruses and unauthorized websites. A comprehensive questionnaire was deployed to unearth the predominant sources of information leakage among computer users and ascertain the role of firewalls within computer systems. The analytical findings underscore that the majority of information leakage security risks stem from a significant lack of personal privacy awareness and a deficient understanding of firewall security systems. The proposed firewall system harnesses router-based control to address the limitations inherent in PC software-based firmware systems. This innovation enhances the control of viral threats and the prevention of user privacy leaks. Serving as a tangible barrier, the router-based firewall obstructs the route of viruses or malevolent programs before reaching the user's computer. This strategic design substantially reduces user intervention, ensuring the continuous operation of the firewall to steadfastly protect the user's computing environment.

SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.

Perceptions of vulnerable roadway users on autonomous vehicle regulations

Introduction: Development and implementation of autonomous vehicle (AV) related regulations are necessary to ensure safe AV deployment and wide acceptance among all roadway users. Assessment of vulnerable roadway users’ perceptions on AV regulations could inform policymakers the development of appropriate AV regulations that facilitate the safety of diverse users in a multimodal transportation system. Method: This research evaluated pedestrians’ and bicyclists’ perceptions on six AV regulations (i.e., capping AV speed limit, operating AV in manual mode in the sensitive areas, having both pilot and co-pilot while operating AVs, and three data-sharing regulations). In addition, pedestrians’ and bicyclists’ perceptions of testing AVs in public streets were evaluated. Statistical testing and modeling techniques were applied to accomplish the research objectives. Results: Compared to the other AV regulations assessed in this research, strong support for AV-related data sharing regulations was identified. Older respondents showed higher approval of AV testing on public roadways and less support for regulating AVs. AV technology familiarity and safe road sharing perceptions with AVs resulted in lower support for AV regulations. Conclusions: Policymakers and AV technology developers could develop effective educational tools/resources to inform pedestrians and bicyclists about AV technology reliability and soften their stance, especially on AV regulations, which could delay technology development. Practical Applications: The findings of this research could be used to develop informed AV regulations and develop policies that could improve pedestrians’ and bicyclists’ attitudes/perceptions on regulating AVs and promoting AV technology deployments.

Subcarrier-users nomination process for downlink NOMA system

The non-orthogonal multiple access (NOMA) characteristics of 5G radio access allow for more efficient resource distribution. NOMA improves spectral efficiency by allowing Power domain users to transmit power concurrently with other users of time and spectrum resources. The selection of the subcarrier and the nomination of users to the subcarrier is of the utmost importance to maximize spectral efficiency fairness among the users and improve the data rate of weak users in the downlink NOMA system. The least gain subcarrier First-threshold-based Adaptive user grouping algorithm (LGSF-TBAUGA) has been devised for this purpose. This algorithm maintains a significant channel gain differential based on a threshold, thus avoiding successive interference cancellation (SIC) process imperfections. The computational complexity of the proposed technique is calculated and compared to other approaches. The power coefficients of the selected users are used to allocate power to them. The proposed method improves the weak user data and overall system sum rates. As a result, the spectrum efficiency and user fairness index have been greatly enhanced compared to some existing algorithms.

An investigation of progress related to stochastic stationary bandit algorithms

The Multi-armed Bandit algorithm stands as a consequential tool for informed decision-making, distinct from reliance on intuitive selections, given its systematic proclivity to meticulously assess accessible alternatives with the intent of discerning the most auspicious outcome. Amid the repertoire of algorithmic variations, the Stochastic Stationary Bandit algorithm assumes a foundational and enduring role, finding versatile application across diverse domains, including but not limited to digital advertising, price optimization, and recommendation systems. With these considerations in view, the present study embarks upon a comprehensive scrutiny of this subject matter. This paper reviews on the Explore-Then-Commit algorithm, Upper Confidence Bound algorithm, and Thompson Sampling algorithm by explaining, comparing their formulation, features, and expected results. Explore-Then-Commit algorithm has distinct phase to explore all the choices uniformly. Upper Confidence Bound algorithm make decisions by calculate an upper confidence index which is an overestimate for each choice. Thompson Sampling algorithm depends on randomness to make choices. Explore-Then-Commit algorithm faces the problem of when to explore and when to stop. Upper Confidence Bound algorithm and Thompson Sampling algorithm solve this problem by avoid certain phases. Multi-armed Bandit algorithm could deal with the process of displaying items of potential interest to users in a recommendation system, the delivery of resources in resource allocation, or the way to maximize revenue in a business.

User Behavior Mining

AbstractStudying the behavior of users in software systems has become an essential task for software vendors who want to mitigate usability problems and identify automation potentials, or for researchers who want to test behavioral theories. One approach to studying user behavior in a data-driven way is through the analysis of so-called user interaction (UI) logs, which record the low-level activities that a user performs while executing a task. In the paper, the authors refer to the analysis of UI logs as User Behavior Mining (UBM) and position it as a research topic. UBM is conceptualized by means of a four-component framework that elaborates how UBM data can be captured, which technologies can be applied to analyze it, which objectives UBM can accomplish, and how theories can guide the analytical process. The applicability of the framework is demonstrated by three exemplary applications from an ongoing research project with a partner company. Finally, the paper discusses practical challenges to UBM and derives an agenda for potential future research directions.

Online Learning and Detecting Corrupted Users for Conversational Recommendation Systems

Online Learning and Detecting Corrupted Users for Conversational Recommendation Systems

I Stalk Myself More than I Should

Today we find ourselves immersed in digital environments made available by centralised social media platforms on a daily basis. While these platforms did provide users expanded connectivity and visibility, they also confined the same user in an economic system focused on collection and commodification of personal data for profit, and in return used them as resources of free labour. In light of this analysis, is it possible to carry out an artistic practice within centralised social media platforms, therefore take an active part in them, while remaining critically engaged, in the attempt to highlight some of the structural dynamics and problems of these realities?In this paper some fundamental aspects of the aforementioned channels will be discussed through the analysis of selected works and two methods utilised by the author to avoid the culture of interveillance.

Effect of beta blockers on optical coherence tomography angiography in normal, suspect, and glaucomatous eyes: A prospective study.

The study aimed to assess the effect of hypertension, and use of systemic beta blockers (BB) and other antihypertensives on ocular perfusion by optical coherence tomography angiography (OCTA) in normal, suspect, and glaucomatous eyes. Cross-sectional study in tertiary eye care center. Prospectively recruited consenting subjects between 18 and 90 years with or without glaucoma. Measured the optic nerve peripapillary perfusion and flux and macular vessel density (MVD: 6 × 6 and 3 × 3 mm) in the superficial retinal layer using OCTA. Included 200 eyes (112 patients). Compared to nonhypertensives or those on non-BB antihypertensives (NBB), hypertensives on BB had lower peripapillary perfusion (43.45,43.40, 42.05%, P = 0.003), and MVD (6 × 6 mm: 16.65, 16.70,15.75 mm/mm 2 , P = 0.002; 3 × 3 mm: 18.70, 18.50, 18.00 mm/mm 2 , P = 0.025). Those on systemic BB with vasodilatory properties had similar perfusion parameters as nonhypertensives and NBB. Those on systemic BB without vasodilating properties had significantly lower peripapillary perfusion (42.05 vs 43.30%, P = 0.011) and MVD (6 × 6 mm: 15.15 vs 16.60 mm/mm 2 , P < 0.001; 3 × 3 mm: 17.40 vs 18.70 mm/mm 2 , P = 0.005) compared to nonhypertensives. On multivariate analysis, peripapillary perfusion increased with increase in diastolic blood pressure (β:0.051, p: 0.04) and increasing age was the only factor found to be significantly associated with decreased peripapillary and macular perfusion parameters. Systemic BB users have worse ocular perfusion parameters compared to those on other medications or nonhypertensives on univariate analysis but similar perfusion on multivariate analysis. Those on BB with vasodilation have better ocular perfusion parameters. All BB cannot be considered equally detrimental to ocular perfusion. Further well-controlled prospective studies are needed to reassess the effects of BB with or without vasodilation on ocular perfusion.

Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization

Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect.

Clustering Approach for Reliable Wireless Communication

Multiple access techniques for 5G systems are based on principles related to security, efficiency, and performance increase. Multi-user shared access (MUSA) is included in code-domain non-orthogonal multiple access (CD-NOMA) techniques with multiple benefits including multiple-access interference minimization and system capacity increase. The additional benefits of MUSA include its grant-free nature, which makes it applicable to scenarios involving random access channels, minimal latency communications, etc. Depending on the high-frequency diversity of the fading-affected channels, this technique is also used to lessen their effects. This paper illustrates the concept of code correlation used in MUSA by proposing a method to allocate MUSA codes to active users in an uplink data transmission system based on clusters. This clustering approach (grouping of 3GPP TR 38.812 spreading codewords) enables performance improvement in different system configurations by implementing various scenarios and providing reliable results. It is demonstrated that four-length spreading code selection from 3GPP family codes is a satisfactory solution, and this cluster grouping can be designed in a less overloaded multi-user system. The challenges, advantages, and limitations of the proposed method are outlined throughout the study.

Filter design and power allocation in multi-user universal filtered multi-carrier (MU-UFMC) systems

Universal Filtered Multi-Carrier (UFMC) system has been introduced as a candidate for the new generation. UFMC is a multi-carrier system, which can overcome challenges in wireless communications. In fact, the advantages of multi-carrier systems over single-carrier systems include higher bandwidth gain and higher robustness against frequency-selective fading. Generally, most multi-carrier systems suffer from notoriously sensitive to symbol-time offset (STO) and carrier-frequency offset (CFO). However, since UFMC systems utilize subband filters, sensitivity to CFO and STO can be reduced by designing efficient subband filters. In this paper, filter design problem is addressed in order to mitigate interference caused by effect of CFO in a multi-user UFMC (MU-UFMC) system under fair optimization framework. Since filter design problem is formulated as a non-convex optimization problem, we present two different approach in order to solve it. In the first approach, the formulated problem is relaxed so that one is converted to a standard second-order-cone programming (SOCP). In the second approach, we present a proposed algorithm with low complexity based on some lemma and relaxation. Then power allocation problem is solved to maximize the minimum achievable rate among all the users in a MU-UFMC system. The simulation results show the proposed schemes for filter design and power allocation outperform the benchmark schemes.

POSSIBLE EVALUATION OF THE CORRECTNESS OF EXPLANATIONS TO THE END USER IN AN ARTIFICIAL INTELLIGENCE SYSTEM

The subject of this paper is the process of evaluation of explanations in an artificial intelligence system. The aim is to develop a method for forming a possible evaluation of the correctness of explanations for the end user in an artificial intelligence system. The evaluation of the correctness of explanations makes it possible to increase the user's confidence in the solution of an artificial intelligence system and, as a result, to create conditions for the effective use of this solution. Aims: to structure explanations according to the user's needs; to develop an indicator of the correctness of explanations using the theory of possibilities; to develop a method for evaluating the correctness of explanations using the possibilities approach. The approaches used are a set-theoretic approach to describe the elements of explanations in an artificial intelligence system; a possibility approach to provide a representation of the criterion for evaluating explanations in an intelligent system; a probabilistic approach to describe the probabilistic component of the evaluation of explanations. The following results are obtained. The explanations are structured according to the needs of the user. It is shown that the explanation of the decision process is used by specialists in the development of intelligent systems. Such an explanation represents a complete or partial sequence of steps to derive a decision in an artificial intelligence system. End users mostly use explanations of the result presented by an intelligent system. Such explanations usually define the relationship between the values of input variables and the resulting prediction. The article discusses the requirements for evaluating explanations, considering the needs of internal and external users of an artificial intelligence system. It is shown that it is advisable to use explanation fidelity evaluation for specialists in the development of such systems, and explanation correctness evaluation for external users. An explanation correctness assessment is proposed that uses the necessity indicator in the theory of possibilities. A method for evaluation of explanation fidelity is developed. Conclusions. The scientific novelty of the obtained results is as follows. A possible method for assessing the correctness of an explanation in an artificial intelligence system using the indicators of possibility and necessity is proposed. The method calculates the necessity of using the target value of the input variable in the explanation, taking into account the possibility of choosing alternative values of the variables, which makes it possible to ensure that the target value of the input variable is necessary for the explanation and that the explanation is correct.

Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol

IntroductionMultimorbidity is defined as the presence of two or more chronic diseases. Co-occurring diseases can have synergistic negative effects, and are associated with significant impacts on individual health outcomes and...

Community-Enhanced Contrastive Learning for Graph Collaborative Filtering

Graph collaborative filtering can efficiently find the hidden interests of users for recommender systems in recent years. This method can learn complex interactions between nodes in the graph, identify user preferences, and provide satisfactory recommendations. However, recommender systems face the challenge of data sparsity. To address this, recent studies have utilized contrastive learning to make use of unlabeled data structures. However, the existing positive and negative example sampling methods are not reasonable. Random-based or data augmentation-based sampling cannot make use of useful latent information. Clustering-based sampling methods ignore the semantics of node features and the relationship between global and local information. To utilize the latent structures in the data, we introduce a novel Community-Enhanced Contrastive Learning method to help the recommendation main task called CECL which uses a community detection algorithm to sample examples with semantic and global information, using both known and hidden community connections in the bipartite interaction graph. Extensive experiments are conducted on two well-known datasets, the results of which show a 12% and 8% performance improvement compared to that of the existing baseline methods.

ANALYSIS AND DESIGN OF THE BPK RI LIBRARY INFORMATION SYSTEM REPRESENTATIVE OF CENTRAL KALIMANTAN PROVINCE ANDROID BASED

Library staff record books and it takes a relatively long time and is less accurate. In addition, visitors have not been able to get the information provided by the library directly, be it announcement information, news about the library, agenda of events carried out in the library. Library's web can only be accessed by library employees. Using the PIECES analysis method, and tools using UML (Unified Modeling Language), as well as interface design using Figma and draw,io are used to describe data flow diagrams. Android which can be used by the office as a reference if you want to develop or implement it into a program. The results of this research has been presented to the office, where they have also provided feedback through filling out questionnaires. Overall, based on the results of the questionnaire analysis, it can be concluded that the design of an Android-based library obtains an interpretation value of 96% so that it can be concluded that the design has followed the needs of the user's system.

Нормативно-правовые основы социальной работы с наркопотребителями в Кыргызской Республике

The article discusses the regulatory framework for organizing social work with drug users in Kyrgyzstan. The regulatory and legal documents that regulate the activities of social services and organizations involved in helping drug users in Kyrgyzstan were analyzed. The author of the article examines the legal regulation of social work with drug users in the prison system and probation system. The article is also devoted to the study of social work with drug users within harm reduction programs - methadone maintenance therapy and syringe/needle exchange. Opioid overdose prevention standard, social institution standard, outreach work standard, and psycho-social support standard are reviewed.

Enhanced Signal Area Estimation in Radio-Communication Spectrograms Based on Morphological Image Processing

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.