Reviewerly/exHarmony huggingface dataset:
Blockchain-based data aggregation scheme for fault-tolerant privacy-preserving in smart grid

Smart grid is the intelligence of the grid, which can realize fine-grained smart metering, facilitates the intelligent generation, distribution, and management of electricity. Data aggregation is one of the most effective methods to protect privacy and security. The fault tolerance is very important to improve the robustness of data aggregation scheme. Since all smart meters work around the clock, it is inevitable that some smart meters will fail. Existing solutions generally require the deployment of a trusted authority, which is impractical. Blockchain is a decentralized protocol in which data on the chain cannot be changed or forged. Therefore, this paper proposes a fault-tolerant privacy protection data aggregation scheme for smart grid based on blockchain. The security analysis shows that the scheme meets the privacy and security requirements of data aggregation in smart grid.
Multi-Object Tracking Based on a Novel Feature Image With Multi-Modal Information

Multi-object tracking technology plays a crucial role in many applications, such as autonomous vehicles and security monitoring. This paper proposes a multi-object tracking framework based on the multi-modal information of 3D point clouds and color images. At each sampling instant, the 3D point cloud and image acquired by a LiDAR and a camera are fused into a color point cloud, where objects are detected by the Point-GNN method. And, a novel height-intensity-density (HID) image is constructed from the bird's eye view. The HID image truly reflects the shapes and materials of objects and effectively avoids the influence of object occlusion, which is helpful to object tracking. In two sequential HID images, a new rotation kernel correlation filter is proposed to predict the objects. Furthermore, an object retention module and an object re-recognition module are developed to overcome the object matching failure in the in-between frames. The proposed method takes full advantage of the multi-modal data and effectively achieves the information complementation to improve the accuracy of multi-object tracking. The experiments with the KITTI dataset show that the proposed method has the best performance among the existing traditional multi-object tracking methods.
ML-Based System Failure Prediction Using Resource Utilization

Digital transactions are growing exponentially on a regular basis without any sign of interruption. Unfortunately, most online service providers were not able to operate 24/7 every day due to the limitation of system components. Online service providers can be highly benefited if an accurate system failure prediction can be obtained. The adverse effect of computer failure might be mitigated if a proper prediction could be made beforehand. In this paper, we propose a simple model training approach to detect the failure that might arise in a system by parsing the log files and conducting a probabilistic analysis of future performance values in advance. We utilize a Recurrent Neural Networks (RNN), namely, Long Short-Term Memory (LSTM) to provide the optimal solution for predicting system failure by reckoning the hardware performance utilization value and utilizing the prediction of log data system for representing the system benchmark. Consequently, the significant constituents that are considered for calculation are all utilization of CPU, MEM, DISK, and NET. Apart from utilization, another essential constituent that needs to examine is System Callout, which is a representative for displaying the alert signal to inform whenever the information system should ignore the incoming transactions to maintain several server systems.
Observations of Global Software Engineering Classes During the Pandemic: Students’ Perspective

In addition to traditional technical skills, like modeling in UML, programming, databases and APIs, students of software engineering also need to learn how to work effectively in physically separated teams. The global nature of the software industry also requires students to learn to communicate with team members and stakeholders in different countries, with different cultures. Travel restrictions imposed to combat the Covid-19 pandemic have made it very difficult for students to spend a semester abroad. To enable students to gain international experience without leaving their home countries, a distributed course on global software engineering was conducted by two universities. Experiences with a focus on the student’s perspectives are presented.
Using <i>The Electronic Resources Troubleshooting Guide</i> to Guide the Creation of Troubleshooting Training Strategies

"Using The Electronic Resources Troubleshooting Guide to Guide the Creation of Troubleshooting Training Strategies." Journal of Electronic Resources Librarianship, 35(1), pp. 66–67
Two-Stage Approach to Cluster Categorical Medical Data

Medical datasets, apart from interval data, usually contain variables of categorical kind, either nominal or ordinal, such as symptoms and stage of disease, diagnosis, medications used, gender, etc. The attempts to cluster such data brings specific challenges that are not feasible with sets consisting of interval variables only, because assumptions behind most clustering algorithms, such as Gaussianity of noise, are violated, and the idea of a prototype vector (centroid) to represent a cluster becomes unclear. A reliable clustering technique could be extremely useful e.g. in diagnostics of cardiological illnesses. Here, we proposed overcoming the issues mentioned above using a two-stage clustering procedure. The first stage was to project multidimensional categorical vectors into a 2D continuous space. Then, in the second stage, clustering was performed on continuous-value vectors. The additional advantage of this procedure was visual inspection of the data distribution, facilitating the selection of clusters.
Implementation of Deep Learning Models for Real-Time Face Mask Detection System Using Raspberry Pi

Amidst this COVID-19 pandemic, it is of utmost importance to wear facemasks and follow precautionary and preventive measures to decrease the further spread of this virus. In recent years Convolutional Neural networks (CNN) has impacted tremendously in various fields for classification and detection systems. In this paper we propose a facemask detection system using deep learning algorithms and a comparative study of various metrics for these deep learning algorithms. Algorithms like VGG, Resnet, Inception, Nasnet and Densenet, and its variations have been used. Using these deep learning models as a base and fine tuning the output layers of these models we construct an architecture for deep learning. Hyperparameter tuning and other methods like data augmentation have also helped in achieving better results. Various metrics like precision, recall, F1score, Average precision, accuracy and hamming loss has been evaluated for the models trained. An accuracy of 93.76% and average precision of 90.99% is achieved for the Denset201. Furthermore, we propose a standalone facemask detection system using the Raspberry Pi and a camera by fitting in the Nasnet mobile model into the detection system. Many applications for this system can be foreseen in places like hospitals, malls, restaurants and other places of public interest as an authentication or entry access criteria system.
Redesigned Monitoring System for Television Usage and Light/Dark Room for Older People Living Alone

For older people who live alone, we previously designed a system that enabled family members at distant locations to monitor television (TV) usage and other activities of the older people, and the system has been used for over 10 years. This system comprises personal computers (PCs) that run on Microsoft Windows® operating system (OS) and a few sensors. The system forwards the TV usage data of the older person to family members through free e-mail. To operate the PCs safely for a longer period, regular updating of the OS and antivirus environment is necessary. Furthermore, the previous systems connected to the internet should be protected against cyberattacks to ensure information security and continuous system operation. To reduce the needs for system maintenance, we redesigned the existing monitoring system by incorporating sensors and microcomputers with an Arduino integrated development environment. A feasibility test was conducted for the new system, in which a 76-year-old woman and her family participated. Our findings showed that the new system was significantly maintenance-free, and the device size, when installed next to the TV, was approximately 90% smaller than the previous design. In addition, the new system performed as efficiently as the previous system.
Developing a Meta-AR Space Construction System and Mapping Coverage Visualization

Visualizing the local environment for remote people is necessary for augmented reality (AR) and remote people between local and remote participants. Three dimensional (3D) reconstruction is a promising method for capturing the shapes and appearances in a local space, but accurate reconstruction requires high-precision sensors and high-performance computers. However, general-purpose smartphones and tablets have distance sensors like LiDAR, and we realized a meta-AR space based on such devices. Through synchronized AR and virtual spaces, local and remote users can collaborate in our meta-AR space. The performance and coverage of a smartphone-based 3D reconstruction system are two issues, though. First, 3D reconstruction using a conventional ray cast is slow; however, using a depth map, improvement is possible. Second, users are unable to see the captured space's coverage; however, doing so is useful. The system cannot adequately reconstruct the space when the coverage is too low. This paper describes a quick 3D reconstruction system that supports the reconstruction with a depth map and mapping coverage visualization. Our experimental findings show its effectiveness, mapping coverage, and utility. The mapping coverage is adequate and our proposed method is 258 times faster than the ray cast method. Moreover, our mapping-coverage visualization increased the texture by 15%.
Listen to the noise – Demonstrating an end to end multi-platform and multilingual sentiment analysis approach

This paper demonstrates a holistic approach for conducting multi-platform and multilingual sentiment analysis of a main stream football club in the United Arab Emirates. The paper reports first iterations of the research project which anticipates to build an automated sentiment analysis system for football clubs to detect and prevent a potential social media crisis at an early stage. The article illustrates a schematic of how state of the art machine learning algorithms, methods and techniques can be put together to provide a basis for automated data collection, classification, sentiment analysis and visual reporting. The article also presents that the time-consuming and costly process of human text labelling can be effectively replaced by lexicon-based sentiment analysis. This paper reports the complete process from data collection to visual reports to discuss challenges and opportunities associated with design and development of such artefacts.
ADASEML: Hospitalization Period Prediction of COVID-19 Patients Using ADASYN and Stacking Based Ensemble Learning

The COVID-19 pandemic places additional constraints on hospitals and medical services. Understanding the period for support requirements for COVID-19 infected admitted to hospitals is critical for resource distribution planning in hospitals, particularly in resource-reserved settings. Machine Learning techniques are being used to approximate a patient’s duration of stay in the hospital. This research uses Decision Tree, Random Forest and K-Nearest Neighbors, Voting classifiers, and Stacking classifiers to predict patients’ length of stay in the hospital. Due to the imbalance in the dataset, Adaptive Synthetic (ADASYN) was used to resolve the issue, and the permutation feature importance method was employed to find the feature importance scores in identifying important features during the models’ development process. The proposed “ADASEML” has shown superior performance to the earlier works, with an accuracy of 80%, precision of 78%, and recall of 80%.
Classification of Quora Insincere Questionnaire Using Soft Computing Paradigm

The global system of interconnection covers an enormous scope of multiple data resources and feasible access mechanisms. It provides a direction for developing robust frameworks to interact and exchange knowledge in a diversified domain. But a major problem arises when a fast response to queries are raised by end users from different corners of the worldwide forum. Quora is one of the web based platform for receiving questions and suggesting answers accordingly. Many a times it is observed that the queries seems to be very irrelevant and creates chaos situation. Researchers are still struggling to equip new policies and mechanisms to deal with virulent and non-virulent queries. The cognition behind this study, aims at designing of a model to classify sincere and insincere questions using soft computing mechanisms. In this paper, seven algorithms out of which three ensemble techniques are employed on the Kaggle dataset.
Game Theory as a Method for Assessing the Security of Database Systems

This paper addresses the topic of database security, which is a critical component of many systems. The goal of the work is to investigate the effectiveness of methods for ensuring database security. The work introduces an innovative way of evaluating the effectiveness of the launched attacks. A literature review identifies current solutions in areas relevant to database security and identifies the state of the art and science. The final analysis introduced cost estimation and transformation of the results into a payoff matrix, which allowed the use of decision-making methods from the field of game theory. No party, defender or attacker, obtained a dominant strategy, but the application of the min-max criterion showed that the defenders’ best strategy is to implement all means of protection, given the assumptions introduced. The presented evaluation method can be applied to decision making in cyber security and contribute to cost optimization in the organization.
Building an intelligent system for answering specialized questions about COVID-19

The paper discusses the design and implementation process of an intelligent system for answering specialized questions about COVID-19. The system is based on deep learning and transfer learning techniques and uses the popular CORD-19 dataset as a source of scientific knowledge about the problem domain. The experiments performed with the pilot version of the system are presented and the obtained results are analyzed. Conclusions are formulated about the applicability and the opportunities for improvement of the proposed approach.
Visualization of Attention Distraction Points obtained by Modelling and Analysis of an Intelligent Bicycle Assistant System

Multimodal data based attention analysis, evaluation and visualization is currently predominant in various domains. The SmartHelm Project aims to present results from new kinds of data such as distraction from cargo bike riders in regular logistics processes. Because of regulatory and legal constraints there was no data collected in real traffic scenarios so far, therefore an alternative methodology is implemented in this work. ‘Pseudo distractions’ generated through four random numbers denoting the distraction type encoded with GPS information obtained from experiments are visualized on a map. To gain more knowledge about real distractions, the feedback gained from interviews with experts and riders in city logistics is visualized on a map to show hotspots in the city. SmartHelm test studies gathered first biophysical data based on Electroencephalography (EEG) and eye-tracking. This paper describes how this data is processed and how the related data management system will be used for the data obtained from forthcoming inner city studies. The outlook describes how we aim to publish biophysical data on an open data platform and to present the data analysis results on a dashboard.
Consumer Cyber Insurance as Risk Transfer: A Coverage Analysis

The internet has become an integral part of most people's lives. Although this has many advantages, such as constant entertainment and easy access to resources, the internet also has its downsides. Studies indicate that people tend to be more concerned with the cyber risks that arise from their internet usage than before. While cyber insurance for businesses has been on the market for several years, the novel type of consumer cyber insurance (CCI) mitigates cyber risks to a residual level. However, we argue that both supply and demand sides do not have a shared understanding of CCIs due to the complexity and dynamics of cyber risks. Therefore, we conduct a content analysis regarding the coverage of CCI to (1) demonstrate the potential value of such insurance policies for private households and (2) increase consumer awareness of the dynamically changing cyber risk situation.
Sparse Warcasting

Forecasting economic activity during an invasion is a nontrivial exercise. The lack of timely statistical data and the expected nonlinear effect of military violence challenge the use of established nowcasting and short-term forecasting methodologies. The current study explores the use of partial least squares augmented with an additional sparsity step to nowcast quarterly Ukrainian GDP using Google search data. Model outputs are benchmarked against both static and dynamic factor models estimated via principal components. Preliminary results outline the usefulness of partial least squares in capturing the effects of large shocks in a setting rich in data, but poor in statistics.
X-Volt

Power side-channel (PSC) attacks are well-known threats to sensitive hardware like advanced encryption standard (AES) crypto cores. Given the significant impact of supply voltages (VCCs) on power profiles, various countermeasures based on VCC tuning have been proposed, among other defense strategies. Driver strengths of cells, however, have been largely overlooked, despite having direct and significant impact on power profiles as well.
Building Oscillatory Neural Networks

This talk is about a novel computing paradigm based on coupled oscillatory neural networks. Oscillatory neural networks (ONNs) are recurrent neural networks where each neuron is an oscillator and oscillator couplings are the synaptic weights. Inspired by Hopfield Neural Networks, ONNs make use of nonlinear dynamics to compute and solve computational problems such as associative memory tasks and combinatorial optimization problems difficult to address with conventional digital computers. An exciting direction in recent years has been to implement Ising machines based on the Ising model of coupled binary spins on magnets. In this talk, I cover the design aspects of building ONNs from devices to architecture to allow to benefit from the parallel computations with oscillators while implementing them in an energy efficient way.
Validating the Redundancy Assumption for HDL from Code Clone's Perspective

Automated program repair (APR) is being leveraged in hardware description languages (HDLs) to fix hardware bugs without human involvement. Most existing APR techniques search for donor code (i.e., code fragment for bug fixing) in the original program to generate repairs, which is based on the assumption that donor code can be found in existing source code. The redundancy assumption is the fundamental basis of most APR techniques, which has been widely studied in software by searching code clones of donor code. However, despite a large body of work on code clone detection, researchers have focused almost exclusively on repositories in traditional programming languages, such as C/C++ and Java, while few studies have been done on detecting code clones in HDLs. Furthermore, little attention has been paid on the repetitiveness of bug fixes in hardware designs, which limits automatic repair targeting HDLs. To validate the redundancy assumption for HDL, we perform an empirical study on code clones of real-world bug fixes in Verilog. On top of empirical results, we find that 17.71% of newly introduced code in bug fixes can be found from the clone pairs of buggy code in the original program, and 11.77% can be found in the file itself. The findings not only validate the assumption but also provides helpful insights for the design of APR targeting HDLs.
Scan-free verifiable public-key searchable encryption supporting efficient user updates in distributed systems

Searchable encryption (SE) is a promising technology that provides search functions while preserving information privacy. A small number of SE schemes can be applied to a distributed system. In such systems, multiple owners provide data that is encrypted by their secret keys to the system for retrieval by users. In this setup, however, users need to scan a searchable index for retrievals, which leads to linear search complexity. Moreover, updates of users (e.g., revoking an expired user from the system) generally incur substantial overhead to re-build the index. Accordingly, we propose a scan-free verifiable public-key searchable encryption scheme with an efficient user update function in a distributed system (i.e., in a multi-owner multi-user model). First, owners self-select secret keys to encrypt keywords. A master secret key is designed to support keyword searches among these keys. Additionally, a searchable index with an implicit relationship between the same keyword is proposed. There is no need to scan the index for keyword retrieval, which achieves sub-linear search complexity. It is also important that the updates of owners do not exert an influence on users and the other owners in our proposed scheme. As the key revocation mechanism, the master secret key is split into more partial secret keys that are distributed to users. Accordingly, the system only needs to re-split and re-distribute the key rather than re-building the index for updates of users. When making queries, not only are the content of queries and the master secret key concealed, but collusion between some users and the cloud server is resisted. The proposed scheme also supports an efficient result verification mechanism. The security and utility of our scheme were confirmed thru analysis and evaluation.