Reviewerly/exHarmony huggingface dataset:
CompaSeC

Fault-injection attacks are a risk for any computing system executing security-relevant tasks, such as a secure boot process. While hardware-based countermeasures to these invasive attacks have been found to be a suitable option, they have to be implemented via hardware extensions and are thus not available in most Commonly used Off-The-Shelf (COTS) components. Software Implemented Hardware Fault Tolerance (SIHFT) is therefore the only valid option to enhance a COTS system's resilience against fault attacks. Established SIHFT techniques usually target the detection of random hardware errors for functional safety and not targeted attacks. Using the example of a secure boot system running on a RISC-V processor, in this work we first show that when the software is hardened by these existing techniques from the safety domain, the number of vulnerabilities in the boot process to single, double, triple, and quadruple instruction skips cannot be fully closed. We extend these techniques to the security domain and propose Compiler-assisted Security Countermeasure (CompaSeC). We demonstrate that CompaSeC can close all vulnerabilities for the studied secure boot system. To further reduce performance and memory overheads we additionally propose a method for CompaSeC to selectively harden individual vulnerable functions without compromising the security against the considered instruction skip faults.
ViraEye

This paper presents the ViraEye chip, an energy-efficient stereo vision accelerator based on the binary neural network (BNN) to achieve high-quality and real-time stereo estimation. This stereo vision accelerator is designed as an end-to-end full pipeline architecture where all processing procedures, including stereo rectification, BNNs, cost aggregation and post-processing, are implemented on the ViraEye chip. ViraEye allows for top level pipelining between accelerator and image sensors, and no external CPUs or GPUs are required. The accelerator is implemented using SMIC 55nm CMOS technology and achieves top-performing processing speed in terms of million disparity estimations per second (MDE/s) metric among the existing ASIC in the open literature.
Graph Partitioning Approach for Fast Quantum Circuit Simulation

Owing to the exponential increase in computational complexity, the fast simulation of the large quantum circuit has become very difficult. This is an important challenge for the utilization of quantum computers because it is closely related to the verification of quantum computation by classical machines. The Hybrid Schrödinger-Feynman simulation seems to be a promising solution, but its application is very limited. To solve this drawback, we propose an improved simulation method based on graph partitioning. Experimental results show that our approach significantly reduces the simulation time of the Hybrid Schrödinger-Feynman simulation.
Measuring material thickness changes through tri-aperture digital speckle pattern interferometry

A configuration for the measurement of thickness changes in materials through one-shot digital speckle pattern interferometry (DSPI) was developed. The phase maps calculation was made by adding carrier fringes by the multiple aperture principle and Fourier Transform Method (FTM). With this setup, interferometry configurations verified that the simultaneous and instantaneous visualization of two opposite faces of a surface is possible. In addition, the combination of the simultaneous results obtained from both sides of the material makes it possible to determine displacements with greater sensitivity or to identify changes in their thickness. The validation and demonstrative tests were carried out with a 1-mm-thick aluminum plate with a 5-mm diameter through hole coated. Thickness changes to 2 μm were measured.
Convolutional Neural Network Knowledge Graph Link Prediction Model Based on Relational Memory

A knowledge graph is a collection of fact triples, a semantic network composed of nodes and edges. Link prediction from knowledge graphs is used to reason about missing parts of triples. Common knowledge graph link prediction models include translation models, semantics matching models, and neural network models. However, the translation models and semantic matching models have relatively simple structures and poor expressiveness. The neural network model can easily ignore the overall structural characteristics of triples and cannot capture the links between entities and relations in low-dimensional space. In response to the above problems, we propose a knowledge graph embedding model based on a relational memory network and convolutional neural network (RMCNN). We encode triple embedding vectors using a relational memory network and decode using a convolutional neural network. First, we will obtain entity and relation vectors by encoding the latent dependencies between entities and relations and some critical information and keeping the translation properties of triples. Then, we compose a matrix of head entity encoding embedding vector, relation encoding embedding vector, and tail entity embedding encoding vector as the input of the convolutional neural network. Finally, we use a convolutional neural network as the decoder and a dimension conversion strategy to improve the information interaction capability of entities and relations in more dimensions. Experiments show that our model achieves significant progress and outperforms existing models and methods on several metrics.
Toward Selective Adversarial Attack for Gait Recognition Systems Based on Deep Neural Network

Deep neural networks (DNNs) perform well for image recognition, speech recognition, and pattern analysis. However, such neural networks are vulnerable to adversarial examples. An adversarial example is a data sample created by adding a small amount of noise to an original sample in such a way that it is difficult for humans to identify but that will cause the sample to be misclassified by a target model. In a military environment, adversarial examples that are correctly classified by a friendly model while deceiving an enemy model may be useful. In this paper, we propose a method for generating a selective adversarial example that is correctly classified by a friendly gait recognition system and misclassified by an enemy gait recognition system. The proposed scheme generates the selective adversarial example by combining the loss for correct classification by the friendly gait recognition system with the loss for misclassification by the enemy gait recognition system. In our experiments, we used the CASIA Gait Database as the dataset and TensorFlow as the machine learning library. The results show that the proposed method can generate selective adversarial examples that have a 98.5% attack success rate against an enemy gait recognition system and are classified with 87.3% accuracy by a friendly gait recognition system.
Development of a Short-term Solar Irradiance Forecasting using Satellite Image in Combination with Numerical Weather Prediction Model

A short-term solar irradiance forecasting method has been developed to provide highly accurate prediction, using satellite image prediction model in combination with numerical weather prediction model. This method was applied and evaluated using the solar irradiance observational site at the CRIEPI Akagi Testing Center (Gunma Prefecture, Japan).
Face Detection and Recognition in Near Infra-Red Image

Face detection and recognition is an extensively researched topic in Artificial Intelligence. The use of AI in detection and mapping of faces or any objects can reduce the time spent in video auditing. A face recognition system maps facial traits from a picture or video using biometrics. To identify a match, it compares the data with a database of recognized faces. In any situation, facial recognition technology can intelligently assist in confirming a person's identification. Infra-Red camera or night vision in security cameras uses infrared light to capture near-infrared images in the dark and also through fog, dust and smoke, such that camera works in all conditions. Cameras operating in visual spectrum will work well only when the image capturing works well, but IR camera images can capture quality images at all times. The paper presents our work in creating a facial recognition system in near-infrared images from a surveillance camera, in which database was created in live Close Circuit Television footage for different individuals and the same was tested with different face detection techniques, classify images using the facial embeddings from VGG-face model to determine the faces.
Integration of Chatbots in the Procurement Stage of a Supply Chain

Procurement, one of the core supply chain operations, involves acquiring goods and services in the interest of carrying out business activities, which when effectively planned can lead to numerous benefits which include streamlining the process, selecting and identifying the right suppliers as well as reducing the cost amongst others. Therefore, by shifting from a transactional to strategy-oriented procurement process, Artificial Intelligence (AI) can aid in adding value to the procurement process. Chatbots powered by AI could be integrated with current Enterprise Resource Planning (ERP) systems to automate the process. The focus of this study is with respect to medium-sized enterprises, the goal being to maximize advantages. In order to understand recent developments, gaps as well as the various tools and techniques currently in use, a literature review of salient work in the field of procurement was first conducted. This was followed by the formulation of the null and alternative hypotheses in order to study the relationship between the use of chatbots and the procurement stage in the supply chain. The responses were then used to generate a variety of relationships, which were then examined using Tableau and factor analysis, respectively, revealing the positive impact digitalization had on reducing supply chain costs, inventory and sales. Finally, a chatbot with features including supplier preference order, by implementing Analytic Hierarchy Process (AHP), answers to frequently asked queries, and key procurement metrics was constructed. The responses indicated that most companies have not incorporated Robotic Process Automation (RPA) in their supply chain process and believe that the supplier relation domain has the highest chance of being most efficient.
Evaluation of a Random Number Generator based on an Internal Linear Feedback Shift Register

A previous study presented a random number generator based on the fluctuation in the sampling interval of an internal linear feedback shift register (LFSR). They reported that the derived random numbers passed the Diehard test; however, the design guidelines were not specified. The previous study examined two configurations of LFSR, which are far from satisfactory to determine the appropriate length and feedback polynomial. Moreover, the minimal sampling period was not known. The present study focuses on these aspects and elucidates the requirements of LFSRs to generate high-quality random numbers. Extensive experiments were conducted, and the following results were derived. (1) The selection of the characteristic polynomial of an LFSR affects the quality of random numbers. (2) The length of an LFSR should be 48 bits or greater with a constant sampling period. (3) The sampling period should be 32 cycles or larger. (4) The quality of random number sequence is considerably improved by a fluctuating sampling period. (5) The properly designed random number generator passes the NIST test.
What Is the Internet Water Army? A Practical Feature-Based Detection of Large-Scale Fake Reviews

Information is passed by word-of-mouth figures prominently when consumers evaluate products through reviews. However, severe logistical problems are caused by the internet’s Water Army (i.e., literally people who are hired by individuals or organizations to compose false reviews), that flood the internet e-commerce websites. An array of internet e-commerce sites is flooded with inauthentic information, and false reviews are used to maliciously induce consumers to purchase specific products, that often contain some defects. Notwithstanding the fact that the internet Water Army first manifested in China, it can also exist in other countries. The rationale lies in the high profitability possible, in the minds of numerous organized underground paid poster groups, and in writing fake reviews to misinform consumers. It has become an increasingly daunting task to precisely spot the Water Army members, who often alter their writing style and posted content. In this paper, the authors devise a comprehensive set of features to characterize all users and compare the paid posters against the normal users on different dimensions; furthermore, an ensemble detection model equipped with seven disparate algorithms is put into place. Our model reached a score of 0.730 in the AUC measure, 0.691 in the F1 measure on the JD dataset, 0.926 in the AUC measure, and 0.871 in the F1 measure on the Amazon dataset, which outshines the measures in the existing research. The significance and contribution of this work are in advancing constructive solutions and recommendations for this major concern of the entire e-commerce industry.
Evaluation of Random Number Generator Utilizing Weather Data and LFSR

Entropy sources (e.g., physical phenomena) are essential for true random number (TRN) generation. An unpredictable random number (URN) generator was previously proposed, which uses processor internal registers as its entropy sources. Another study proposed to integrate a linear feedback shift register (LFSR) in a processor, and sample it to generate a URN sequence. The entropy source of this URN is the fluctuation of sampling period. The current study proposes to use weather data as the entropy source for URN generation, where the sampling period is modified by the wind direction data. The derived URN sequences passed the Diehard test when the least sampling period β > 29 with a 32-bit LFSR. It also passed the NIST test when the weather data were accessed with an appropriate hash function when β was 32.
Application of Mobile Learning in Higher English Education Systems Using Cognitive Web Services

Students choose from various learning pathways and resources while using mobile learning, which creates a universal learning environment. Machine learning techniques developed to handle AI-related challenges are known as cognitive web services. Adaptive education methods and good knowledge path design can help achieve the goal of studying whenever. In addition, mobile learning devices' display capabilities are becoming a critical determinant of students' attention and time to proficiency. Getting the necessary mobile learning components is now a hot issue. Current mobile learning environments do not include cognitive learning services to be self-adaptive to improvise the standard of the intended service. Student Performance ratio 92.11%, Students' efficiency ratio 89.9, Professional teaching ratio 95.23%, Error rate 43.86%, English learning ratio 93.32%, Interactive ratio 92.5% and Learning flexibility ratio 94.86% are obtained as the experimental results for the suggested method, which proves that the system is more efficient.
Multi-Objective Negotiation Mechanism in Manufacturing Enterprise Supply Chain Based on Multi-Agent

In the operation of manufacturing enterprise supply chain, there are lots of conflicts and differences between the node enterprises because of the different demands on the price, quality, cost, and other factors. These conflicts and differences can be effectively solved by negotiation. In this paper, the authors will abstract different entities in manufacturing enterprise supply chain as agents, and present a negotiation mode, and then discuss the negotiation tactics and procedures between the purchasing agent and supplier agent. Next, a practical example will be discussed and simulated for validating the negotiation model. Application of negotiation tactics and models will be helpful for resolving differences and conflicts, and improving negotiation efficiency. That will be used for optimizing the supply chain management, maximizing the benefits, and improving the operational efficiency of manufacturing enterprise supply chain.
Design of Automatic Education Classification Management System in Cognitive Web Services Platforms Using Machine Learning Techniques

Adequate learning resources help students develop their instructional methods, beliefs, attitudes, and general abilities to go beyond a superficial understanding of a subject. It is found that lack of knowledge, inadequate search skills, and lacks of time were all obstacles that hindered library patrons from easily accessing educational resources. It is discovered that patrons of libraries were unable to easily access educational resources because of a lack of knowledge, poor search skills, and a lack of time. Design of automatic education classification management system (D-AECMS) is a proposal in this paper to create and implement cloud-based educational resources and management strategies that support economic development in the classroom. Predictive model-based quality inspection in industrial manufacturing using machine learning techniques and edge cloud computing technology is now possible.
Generalized quantum PageRank algorithm with arbitrary phase rotations

The quantization of the PageRank algorithm is a promising tool for a future quantum internet. Here we present a modification of the quantum PageRank introducing arbitrary phase rotations (APR) in the underlying Szegedy's quantum walk. We define three different APR schemes with only one phase as a degree of freedom. We have analyzed the behavior of these algorithms in a small generic graph, observing that a decrease of the phase reduces the standard deviation of the instantaneous PageRank, so the nodes of the network can be distinguished better. However, the algorithm takes more time to converge, so the phase can not be decreased arbitrarily. With these results we choose a concrete value for the phase to later apply the algorithm to complex scale-free graphs. In these networks, the original quantum PageRank is able to break the degeneracy of the residual nodes and detect secondary hubs that the classical algorithm suppresses. Nevertheless, not all of the detected secondary hubs are real according to the PageRank's definition. Some APR schemes can overcome this problem, restoring the degeneration of the residual nodes and highlighting the truly secondary hubs of the networks. Finally, we have studied the stability of the new algorithms. The original quantum algorithm was known to be more stable than the classical. We have found that one of our algorithms, whose PageRank distribution resembles the classical one, has a stability similar to the original quantum algorithm.
Bayesian estimation of decay parameters in Hawkes processes

Hawkes processes with exponential kernels are a ubiquitous tool for modeling and predicting event times. However, estimating their decay parameter is challenging, and there is a remarkable variability among decay parameter estimates. Moreover, this variability increases substantially in cases of a small number of realizations of the process or due to sudden changes to a system under study, for example, in the presence of exogenous shocks. In this work, we demonstrate that these estimation difficulties relate to the noisy, non-convex shape of the Hawkes process’ log-likelihood as a function of the decay. To address uncertainty in the estimates, we propose to use a Bayesian approach to learn more about likely decay values. We show that our approach alleviates the decay estimation problem across a range of experiments with synthetic and real-world data. With our work, we support researchers and practitioners in their applications of Hawkes processes in general and in their interpretation of Hawkes process parameters in particular.
Walking on vertices and edges by continuous-time quantum walk

The quantum walk dynamics obey the laws of quantum mechanics with an extra locality constraint, which demands that the evolution operator is local in the sense that the walker must visit the neighboring locations before endeavoring to distant places. Usually, the Hamiltonian is obtained from either the adjacency or the laplacian matrix of the graph and the walker hops from vertices to neighboring vertices. In this work, we define a version of the continuous-time quantum walk that allows the walker to hop from vertices to edges and vice versa. As an application, we analyze the spatial search algorithm on the complete bipartite graph by modifying the new version of the Hamiltonian with an extra term that depends on the location of the marked vertex or marked edge, similar to what is done in the standard continuous-time quantum walk model. We show that the optimal running time to find either a vertex or an edge is $O(\sqrt{N_e})$ with success probability $1-o(1)$, where $N_e$ is the number of edges of the complete bipartite graph.
Report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022) at RecSys 2022

Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2022 workshop at the 16th ACM Conference on Recommender Systems brought together academia and industry to critically reflect on the evaluation of recommender systems. The primary goal of the workshop was to capture the current state of evaluation from different, and maybe even diverging or contradictory perspectives. Date: 22 September, 2022. Website: https://perspectives-ws.github.io/2022/.
Quality, Quantity and Impact in Engineering Research: Case Study of a Non-Doctoral University in the Arab World

Academics aim to increase their impact in their field by increasing the quantity and quality of their publications. However, the researchers’ productivity may be affected by the ranking and the location of the research institution they are involved in. The case of universities in the Arab world is not studied previously especially for non-doctoral institutions. Universities in developing countries have limited funding and hence a relatively low research output. This work investigates the associations between quality, quantity and impact of research in the field of Engineering for faculty members in a non-doctoral university in the Arab world. Results show that the Field-Weighted Citation Impact (FWCI) is more correlated with quality than quantity of publications while the h-index is more correlated with quantity than quality. Also, a positive correlation is reported between the quantity and quality of publications for the Engineering field. Finally, faculty members with a higher credit-load have relatively lower quality of publications.
Parts of speech tagging towards classical to quantum computing

Quantum computing is a fast-emerging field that follows the laws of quantum mechanics to solve complex problems for classical systems. In the last few years, several researchers have emerged in quantum computing in accordance with the artificial intelligence field. Natural language processing is one of the prominent subfields of artificial intelligence. Quantum computing can be applied to the applications of natural language processing for better performance. One of the vital applications of natural language processing is Parts-Of-Speech (POS) tagging. It is prerequired for many natural language processing applications. In this paper, we have performed POS tagging of the Mizo language using classical Long short-term memory (LSTM). Subsequently, quantum-enhanced long short-term memory (QLSTM) has also been used to perform POS tagging of the Mizo language. The approaches mentioned above have been tested on the Mizo-tagged corpus, and experimental results have shown that quantum computing approaches such as QLSTM need the inclusion of new technologies to achieve significant results.