Reviewerly/exHarmony huggingface dataset:
English-Vietnamese Cross-Lingual Similarity Estimation Using Language-Agnostic Representation from Multilingual Sentence Encoders

In this paper, we propose a method based on multilingual sentence encoders combined with distilling language-agnostic meaning embedding. First, we extract meaning embeddings from origin embeddings by removing language-specific information. Second, we train and validate model purely based on English-Vietnamese bilingual corpora without any human annotations. Finally, cross-lingual sentence similarity estimation uses a simple cosine similarity calculation. Reported results experimented on cross-lingual semantic textual similarity task and proved that our proposed method shows a strong performance compared with origin multilingual sentence encoders. In addition, our method also consistently improves many multilingual sentence encoder performances. With 11144 English-Vietnamese sentence pairs, our model helps to improve the Pearson correlation from 0.6699 to 0.7161.
Assessment and Management of Information Security Practices and Knowledge Gap: Evidence from GRID Telecommunications Department of Ghana GRID Company Limited

The cardinal aim of this paper is to access and effectively manage the Information Security knowledge gap of employees with access to database resources using Grid Telecommunications database portal as a case study. The method applied a novel approach using semi-structured interviews to collect data from selected employees for analysis. The study emphasized on social engineering attacks as a major means in which sensitive information can be acquired from employees by external entities. The final results showed very promising results as some major knowledge gaps of employees were highlighted and recommendations made to address them. The results can also assist authorities of any organization with similar knowledge gaps to enact and implement policies to close them for the protection of the organization&#x0027;s resources from external access.
Issues on Japanese COVID-19 Exposure Notifications Application (COCOA)

This study investigates problems related to COCOA, which is a smartphone app officially provided by Japan’s Ministry of Health, Labour and Welfare (MHLW) that is designed to notify users when they have been in close contact with coronavirus disease 2019 (COVID-19) positive persons, and thus help the government and healthcare organizations contain the spread of the virus. The information we have obtained thus far indicates that poor utilization rates of the app are due to significant program flaws, which caused the initial usage to be sluggish, as well as the failures of various health centers to adequately provide polymerase chain reaction (PCR) testing for COCOA notification recipients, which exacerbated sluggishness issues. Furthermore, a related survey revealed that although the government provides an integrated data system called the Health Center Real-time Information-sharing System on COVID-19 (Japanese abbreviation HER-SYS), information on fever outpatients (hospital names, locations, consultation times, presence or absence of PCR testing, etc.) corresponding to each local government is still not fully available.
Relevance Collaborative Filtering Algorithm for Medical Information Recommendation

Information overload makes it difficult for people to quickly find the appropriate medical information, and as a result, personalized recommendation algorithm has become one of the main demands from users. The existing label based collaborative filtering recommendation method does not consider the relevance of the labels, which makes the recommendation results assimilating rather than personalized. In this paper, a new relevance collaborative filtering algorithm was proposed for medical information recommendation. Label relevance was used to solve the problem of weight deviation. User and information representation based on label relevance feature representation was established, and the information similarity weighted by label relevance was calculated by feature similarity measurement. Finally, k-nearest method was used to predict user information preference. This method was applied to medical information recommendation system for experimental analysis, which solved the shortcomings of traditional project-based collaborative filtering method and improved the matching degree between medical information recommendation and users.
Semantic Knowledge Representation of Plants Used in Traditional Medicine in Somalia

In Somalia the traditional medicine is widely used to treat different types of diseases. The rural communities trust and rely more on traditional healing as its part of their cultural experiences. The traditional knowledge regarding the Somali herbal medicine is yet to be documented to reserve for the future generation. This paper discusses the semantic knowledge representation of plants used in traditional medicine in Somalia. The study uses Acanthaceae-Chenopodiaceae family as a case study for the development of the ontology of the Somali medical plants. The study investigates the scientific name and the family of the plant, the vernacular or (indigenous name), parts of the plant used, medicinal usage, the method of preparation of remedy and the dosage. METHONTOLOGY ontology process life cycle is used to create the knowledge representation applying Resource Description Framework (RDF), Web Ontology Language (OWL), and RDF Query Language (SPARQL). In this ontology, data was gathered from previous publication on inventory of plants used in traditional medicine in Somalia especially the Acanthaceae-Chenopodiaceae family of plants. A prototype of a knowledge-based system is constructed based on the ontology classes in order to provide a well-defined model for Somali herbal plants used for traditional medicine. The findings of this study help to establish an ontology for Somali traditional medicinal plants, which is needed by a variety of parties and supports the best possible protection and utilization of Somali's botanical resources.
A Case Study on the Multi-Vector Data Breach on Astoria

Managing the security of big data has become increasingly more complex as threat actors continue to maliciously exploit legacy and outdated systems of smaller and medium-sized firms. In this paper, we explore Astoria Company’s data breach, whereby the attacker exfiltrated vast amounts of confidential user data by exploiting MySQL and PHP-based vulnerabilities in a popular database management tool. Furthermore, we analyze the attack methodology of the perpetrator and the range of impacts incurred by the breach. The novelty of this paper is centered primarily on the range of defensive solutions we propose for the firm and for similar companies to deter and mitigate such attacks. These defensive solutions and risk mitigation strategies range from relatively low-cost software updates to more wide-ranging IT governance changes. The aim of this paper is to highlight the increasing need to safeguard the security of the big data held by smaller businesses, which have become prime targets for such type of attacks.
Metrics for Assessing Security of System-on-Chip

Due to the increasing complexity of modern hetero-geneous System-on-Chips (SoC) and the growing vulnerabilities, security risk assessment and quantification is required to measure the trustworthiness of a SoC. This paper describes a systematic approach to model the security risk of a system for malicious hardware attacks. The proposed method uses graph analysis to assess the impact of an attack and the Common Vulnerability Scoring System (CVSS) is used to quantify the security level of the system. To demonstrate the applicability of the proposed metric, we consider two open source SoC benchmarks with different architectures. The overall risk is calculated using the proposed metric by computing the exploitability and impact of attack on critical components of a SoC.
Multi-level attention network for image steganalysis

While attention mechanisms have achieved excellent performance in image recognition, target tracking and natural language processing, little studies have been done on attention based steganalysis. In order to verify the performance of attention mechanism in deep learning-based steganalysis, this paper presents the first image steganalysis model based on multilayer attention mechanism, which applies a dual-residual structure network to effectively enhance the transmission of steganographic signals in the deep network, and introduces a multilayer attention module to aggregate convolutional features and obtain channel attention through an interactive local cross-channel strategy, and to feed the feature map with the aggregated channel information by pooling into the convolutional layer to obtain spatial attention, effectively improving the model’s ability to capture steganographic data. The experimental results on the BOSSbasev1.01 dataset and BOWS2 dataset demonstrate the effectiveness of the proposed method, which achieves comparable or leading results compared with the current mainstream algorithms.
HEOPC-VGGNet Based Image Enhancement Algorithm

With the advancement of technology, smart cars are becoming more and more important. Smart cars combine digital cameras, Light Detection and Ranging (LiDAR), and various sensors to make their functions more and more powerful. Since the information provided by the LiDAR is limited, color images are needed to obtain more information. Nowadays, in addition to the point cloud map, self-driving technology also needs to be combined with color images to determine the correct route. Especially the color images are often too dark at night, which leads to deviations in judging routes and obstacles. Therefore, image enhancement technology becomes more important to protect the safety of self-drivers and pedestrians through nighttime image calibration. The proposed method uses the histogram equalization combined with neural networks to enhance images specifically for nighttime environments in this paper. The histogram equalization method is quite simple and fast for image enhancement, and deep learning technology has the advantage of high accuracy. In this paper, the architecture combines the characteristics of both, and is divided into two main parts. In the first part, the scheme uses histogram equalization to enhance the image, and in the second part, the enhancement factor of the image is adjusted by neural network to finally achieve a brightness enhancement, suppress overexposure, and noise in the image.
Design of Fuzzy PID with Second-order Model Reference for Ball and Beam System

Fuzzy PID controller has been one of a popular technique in control system to produce great performance for a linear or non-linear system. However, the design cannot guarantee to achieve any desired specifications other than the error. Hence, this paper presents a new approach in Fuzzy PID controller design that can guide the system to achieve a desired output. The proposed structure guides the fuzzy with a second-order model reference input rather than a step input signal. Hence, fuzzy will tune the PID as guided by the reference model input to achieve the desired output. This study was simulated on a ball and beam model to control the ball position. Several sets of second-order model reference were evaluated to see how close the output can track the input signal. The results show good performance in input signal tracking and thus, all the desired specifications were fulfilled.
ISO-Cloud: An Intelligent System for Optimizing the Overall Income in Cloud Providers

Nowadays, cloud systems are increasing their role as cost-effective solutions to deploy applications and services for a wide spectrum of companies and business models. Hence, properly designing and configuring the key parts of the cloud, such as the data center, the collection of offered VMs, and the SLAs, among others, is crucial to obtain the expected income. The main difficulty of this task lies in analyzing and studying the high number of inter-related parameters required to provide a proper cloud configuration. In this work, we focus on the impact of SLAs configuration on the global income, and we propose ISO-Cloud, an Intelligent System for optimizing the overall income of cloud providers, which uses simulation techniques to analyze how the different configurations of SLAs impact the obtained profits. Thus, ISO-Cloud automatically creates different cloud scenarios for certain cloud architecture and, then, potential SLAs configurations to improve the obtained profits are investigated. A thorough experimental evaluation is presented, which shows promising results. In this case, ISO-Cloud significantly improves the overall income in three different SLAs provided by an expert.
System on Chip (SoC) Security Architecture Framework for Isolated Domains Against Threats

This paper presents a definition of a secure system and design principles, which help govern security policies within an embedded system. By understanding a secure system, a common system on chip (SoC) architecture is evaluated and their vulnerabilities explored. This effort helped define requirements for a framework for a secure and isolated SoC architecture for users to develop in. Throughout this paper, a SoC architecture framework for isolated domains has been proposed and its robustness verified against different attack scenarios. To support different levels of criticality and complexity in developing user applications, three computing domains were proposed: security and safety critical (SSC) domain, high performance (HP) domain, and sandbox domain. These domains allow for complex applications to be realized with varying levels of security. Isolation between different computing domains is established using consumer off the shelf (COTS) techniques and architectural components provided by the Zynq Ultrascale+ (ZU+) multiprocessor SoC (MPSoC). To the best of our knowledge, this is the first work that implements a secure system design on the ZU+ platform. There have been many other implementations in hardware security to mitigate certain attack scenarios such as side channel attacks, temporal attacks, hardware trojans, etc. However, our work is different than others, as it establishes the framework for isolated computing domains for secure applications and also verifies system security by attacking one domain from the others.
Knowledge Simplification: A Conceptual Framework

As knowledge can be expressed in different ways, this sometimes induce complexity into the knowledge base system. Among the complexity induced into the knowledge base is the duplication of representation. This has been a very difficult issue to comprehend and handle, due to high level of complexities that accrue after the execution of computable expression in some formal language. Based on different conceptual view, a couple of simplification technique has been proposed to simplify knowledge-based system. Knowledge simplification is a complete design methodology process that is applicable to the knowledge base system. There is need to unify all the knowledge simplification techniques. Thus, the realization of a conceptual framework is very crucial as it supports knowledge simplification by eliminating all unnecessary complexity in a knowledge base system. However, complexity associated with the knowledgebase system has been identified to start from having duplicates right from when the raw knowledge is acquired. Obviously, designing and developing a single framework that integrates all simplification technique is of paramount need. In this article, we design and develop a generic conceptual framework for knowledge simplification regardless of the conceptual model used.
Characterizing Side-Channel Leakage of DNN Classifiers though Performance Counters

Rapid advancements in Deep Neural Networks (DNN) have led to their deployment in a wide range of com-mercial applications. DNN classifiers are powerful tools that drive a broad spectrum of important applications, from image recognition to autonomous vehicles. Like other applications, they have been shown to be vulnerable to side-channel information leakage. There have been several proof-of-concept attacks demon-strating the extraction of their model parameters and input data. However, no prior study has examined the possibility of using side-channels to extract the DNN classifier's decision or output. In this initial study, we aim to understand if there exists a correlation between the output class selected by a classifier and side-channel information collected while running the inference process on a CPU. Our initial evaluation shows that with the proposed approach it is possible to accurately recover the output class for model inputs via multiple side-channels: primarily power, but also branch mispredictions and cache misses.
Adaptive Matched Filters for VHR Image Classification

This paper presents an innovative method for the design and application of matched filters. We extend the use of the matched filters from single object enhancement to multi-object enhancement, and show their potential in improving the pixel-based classification. The challenging step in de-signing the matched filters is the choice of the coefficients, to which we propose a technique based on the use of an optimization algorithm, particle swarm optimization. The classification of the image is performed through the Support Vector Machines. We generated adaptive 2D kernels, used varying sizes and assessed their impact on the classification accuracy. A significant improvement has been achieved using our method compared to the original features alone, which is represented by a gain that ranges between 2.96&#x0025;-9.43&#x0025;, and 3.52&#x0025;-11.7&#x0025;, for respectively overall and average classification accuracy. Moreover, a very important improvement of the per-class classification accuracy has been achieved, reaching 31.48&#x0025; gain. Therefore, our extracted spatial features are very discriminative.
Oblivious Intrusion Detection System

Intrusion Detection Systems (IDSs) are capable of monitoring network traffic and matching it against rules. Obliv-ious IDSs perform the same tasks of IDSs while using encrypted rules and producing encrypted results without being able to decrypt the rules or the results. Current implementations to this technology suffer from slow searching speeds and/or lack of generality. In this paper we present a generic approach to implement privacy-preserving intrusion detection system based on hybrid binary gates along with an application algorithm for hybrid text matching. Benchmarking showed a wide variety of trade-offs with minimum searching time of 897 msecs for 1-byte encrypted rule through 10- bytes plaintext.
Nonlinear synthetic photonic lattices for optical neural networks

We reveal that a synthetic photonic lattice based on coupled waveguide loops can serve as a feed-forward neural network for processing of optical pulse sequences in time domain. We demonstrate the capabilities of the system in solving three different problems. We perform efficient training for dispersion compensation for the pulse train distorted in a linear fiber-optic link. We also show that the optical network with an intrinsic Kerr-type nonlinearity can perform a flexible nonlinear transformation of the pulse amplitude as well as discrimination of different pulse sequences.
A Federated Leaning Perspective for Intelligent Data Communication Framework in IoT Ecosystem

Edge intelligence propelled federated learning as a promising technology for embedding distributed intelligence in the Internet of Things (IoT) ecosystem. The multidimensional data generated by IoT devices is enormous in volume and personalized in nature. Thus, integrating federated learning to train the learning model for performing analysis on source data can be helpful. Despite the above reasons, the current schemes are centralized and depend on the server for aggregation of local parameters. So, in this paper, we have proposed a model that enables the sensor to be part of a defined cluster (based on the type of data generated by the sensor) during the registration process. In this approach, the aggregation is performed at the edge server for sub-global aggregation, which further communicates the aggregated parameters for global aggregation. The sub-global model is trained by selecting an optimal value for local iterations, batch size, and appropriate model selection. The experimental setup based on the tensor flow federated framework is verified on MNSIT-10 datasets for the validity of the proposed methodology.
A Cyber Security Cognizance among College Teachers and Students in Embracing Online Education

Cyber security is everybody&#x0027;s responsibility. It is the capability of the person to protect or secure the use of cyberspace from cyber-attacks. Cyber security awareness is the combination of both knowing and doing to safeguard one&#x0027;s personal information or assets. Online threats continue to rise in the Philippines which is the focus of this study, to identify the level of cyber security awareness among the students and teachers of Occidental Mindoro State College (OMSC) Philippines. Results shows that the level of cyber security awareness in terms of Knowledge, majority of the students and teachers got the passing score and above however there are almost fifty percent got below the passing score. In terms of Practices, both the teachers and the students need to strengthen the awareness of system and browser updates to boost the security level of the devices used. More than half of the IT students are aware of the basic cyber security protocol but there is a big percentage in the Non-IT students which is to be considered. Majority of the teachers are aware of the basic cyber security protocols however the remaining number must be looked into. There is a need to intensity the awareness of the students in the proper etiquette in using the social media. Boost the basic cyber security awareness training to all students and teachers to avoid cybercrime victims.
Influence of inherent parameters within virtual systems on cognitive rehabilitation

The study is based on a theoretical review of the current literature in the field of virtual systems applied to cognitive rehabilitation, focusing on the influence of different parameters such as immersion, presence, cues, and distractors. To this end, it is proposed to design applications with adjustable features that allow different levels of inherent parameters to be evaluated. For experimental purposes, it is initially proposed to use the application with healthy patients and then continue with patients with a neurodegenerative disease.