Reviewerly/exHarmony huggingface dataset:
Investigating Public Speaking Anxiety Factors among EFL University Students through Online Learning

As in many other countries, universities in Indonesia have been closed since March 2020 due to the COVID-19 pandemic lockdown, and lecturers have been forced to adapt to online learning. Students in this online course admitted to feeling anxious when studying public speaking. This descriptive quantitative study aims to analyze the potential factors (English oral proficiency, personality, lecturer's scoring criteria, classmates' responses, pronunciation, audience familiarity, unfamiliar speech topic, preparedness, English grammar, vocabulary) affecting the students' public speaking anxiety at four different stages. The respondents of this study were 60 undergraduate students. A questionnaire adapted from McCroskey (1970) was used, and data were analyzed using descriptive statistics of IBM SPSS statistics 26 software. The findings of this study revealed that the factors affecting the students' public speaking anxiety through online learning varied by stages. Through online learning, English oral proficiency and topic familiarity could be the most influential factors on students' public speaking anxiety. The findings of this study are useful for educators in helping EFL students overcome their public speaking anxiety and enabling them to perform better in public speaking.
Machine learning approach for identifying suspicious uniform resource locators (URLs) on Reddit social network

The applications and advantages of the Internet for real-time information sharing can never be over-emphasized. These great benefits are too numerous to mention but they are being seriously hampered and made vulnerable due to phishing that is ravaging cyberspace. This development is, undoubtedly, frustrating the efforts of the Global Cyber Alliance – an agency with a singular purpose of reducing cyber risk. Consequently, various researchers have attempted to proffer solutions to phishing. These solutions are considered inefficient and unreliable as evident in the conflicting claims by the authors. Against this backdrop, this work has attempted to find the best approach to solving the challenge of identifying suspicious uniform resource locators (URLs) on Reddit social networks. In an effort to handle this challenge, attempts have been made to address two major problems. The first is how can the suspicious URLs be identified on Reddit social networks with machine learning techniques? And the second is how can internet users be safeguarded from unreliable and fake URLs on the Reddit social network? This work adopted six machine learning algorithms – AdaBoost, Gradient Boost, Random Forest, Linear SVM, Decision Tree, and Naïve Bayes Classifier – for training using features obtained from Reddit social network and for additional processing. A total sum of 532,403 posts were analyzed. At the end of the analysis, only 87,083 posts were considered suitable for training the models. After the experimentation, the best performing algorithm was AdaBoost with an accuracy level of 95.5% and a precision of 97.57%.
Automatic Gender Classification from Handwritten Images: A Case Study

Using a handwritten sample to automatically classify the writer’s gender is an essential task in a wide range of areas, e.g., psychology, historical documents classification, and forensic analysis. The challenge of gender prediction from offline handwriting can be demonstrated by the relatively low (below 90%) performance of state-of-the-art systems. Despite a high interest within a broad spectrum of research communities, the published works in this area generally concentrate on English and Arabic languages. Most of the existing approaches focus on manual feature selection. In this work, we study an application of deep neural networks for gender classification, where we investigate cross-domain transfer learning with ImageNet pre-training. The study was performed on two datasets, the QUWI dataset, consisting of handwritten documents in English and Arabic, and a new dataset of documents in Hebrew script. We perform extensive experiments, analyze and compare the results obtained with different neural networks. We demonstrate that advanced deep-learning models outperform conventional machine learning approaches that were used in previous studies. We also compare the obtained results against human-level performance and show that the problem is challenging for non-experts.
Mission Engineering and Design using Real-Time Strategy Games: An Explainable-AI Approach

Abstract In this paper, we adapt computational design approaches, widely used by the engineering design community, to address the unique challenges associated with mission design using RTS games. Specifically, we present a modeling approach that combines experimental design techniques, meta-modeling using convolutional neural networks (CNNs), uncertainty quantification, and explainable AI (XAI). We illustrate the approach using an open-source real-time strategy (RTS) game called microRTS. The modeling approach consists of microRTS player agents (bots), design of experiments that arranges games between identical agents with asymmetric initial conditions, and an AI infused layer comprising CNNs, XAI, and uncertainty analysis through Monte Carlo Dropout Network analysis that allows analysis of game balance. A sample balanced game and corresponding predictions and SHapley Additive exPlanations (SHAP) are presented in this study. Three additional perturbations were introduced to this balanced gameplay and the observations about important features of the game using SHAP are presented. Our results show that this analysis can successfully predict probability of win for self-play microRTS games, as well as capture uncertainty in predictions that can be used to guide additional data collection to improve the model, or refine the game balance measure.
Russian Federal Remote E-voting Scheme of 2021 – Protocol Description and Analysis

This paper presents the details of one of the two cryptographic remote e-voting protocols used in the Russian parliamentary elections of 2021. As the official full version of the scheme has never been published by the election organisers, our paper aims at putting together as complete picture as possible from various incomplete sources. As all the currently available sources are in Russian, our presentation also aims at serving the international community by making the description available in English for further studies. In the second part of the paper, we provide an initial analysis of the protocol, identifying the potential weaknesses under the assumptions of corruption of the relevant key components. As a result, we conclude that the biggest problems of the system stem from weak voter authentication. In addition, as it was possible to vote from any device with a browser and Internet access, the attack surface was relatively large in general.
A survey on functional encryption

Functional Encryption (FE) expands traditional public-key encryption in two different ways: it supports fine-grained access control and allows learning a function of the encrypted data.In this paper, we review all FE classes, describing their functionalities and main characteristics.In particular, we mention several schemes for each class, providing their security assumptions and comparing their properties.To our knowledge, this is the first survey that encompasses the entire FE family.
Optimal bandwidth estimators of kernel density functionals for contaminated data

In this study, we provide simulation-based exploration and characterization of the two most crucial kernel density functionals that play a central role in kernel density estimation, considering the probability density functions that are members of the location-scale family. Kernel density functional estimates are known to rely on the choice of preliminary bandwidth. Normal-scale estimators are commonly used to obtain preliminary bandwidth estimates, with the assumption that the data come from normal distribution. Here, we present an alternative approach, called the Cauchy-scale estimators, to obtain preliminary bandwidth estimates. In this approach, data are assumed to come from a Cauchy distribution. Furthermore, analysis results related to the sampling distribution of bandwidth estimators based on the normal- and Cauchy-scale approaches are presented. As a case study, we provide a comprehensive characterization of different contamination levels with a simulation study constructed for the random samples from normal distributions with various parameters and various contamination levels. The proposed preliminary bandwidth selection shows lower variance in both mixture and contaminated data in our simulations. Besides, functional bandwidth presents results similar to the simulation results in the applications we made on the real data set.
Structures of Spurious Local Minima in <i>k</i>-Means

The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\text {-means}$ </tex-math></inline-formula> clustering problem concerns finding a partition of the data points into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> clusters such that the total within-cluster squared distance is minimized. This optimization objective is non-convex, and not everywhere differentiable. In general, there exist spurious local solutions other than the global optimum. Moreover, the simplest and most popular algorithm for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\text {-means}$ </tex-math></inline-formula> , namely Lloyd’s algorithm, generally converges to such spurious local solutions both in theory and in practice. In this paper, we investigate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structures</i> of these spurious local solutions under a probabilistic generative model with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> ground truth clusters. As soon as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=3$ </tex-math></inline-formula> , spurious local minima provably exist, even for well-separated clusters. One such local minimum puts two centers at one true cluster, and the third center in the middle of the other two true clusters. We prove that this is essentially the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">only</i> type of spurious local minima under a separation condition. In particular, any local minimum solution only involves a configuration that puts multiple centers at a true cluster, and one center in the middle of multiple true clusters. Our results pertain to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\text {-means}$ </tex-math></inline-formula> formulation for mixtures of Gaussians or bounded distributions, and hold in the over- and under-parametrization regimes where the number of centers in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\text {-means}$ </tex-math></inline-formula> may not equal to the number of true clusters. Our theoretical results corroborate existing empirical observations and provide justification for popular heuristics for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\text {-means}$ </tex-math></inline-formula> clustering.
Learning Fashion Similarity Based on Hierarchical Attribute Embedding

Embedding items directly into a common feature space, and then measuring the similarity by calculating the feature distance in this space, has become the main method for similarity learning in current fashion retrieval tasks. The method is simple and efficient, but it ignores the correlation among fashion attributes and the impact of these correlations on the feature space, thereby reducing the accuracy of retrieval. Since the number of fashion attributes is large and the semantic granularity is also different, how to capture the relationship between fashion attributes and perform refined embedding to accurately represent fashion items is a challenge. In this paper, by constructing an attribute tree, we propose a hierarchical attribute embedding method for representing fashion items to enhance the relationship between attributes and use masking technology to disentangle different attributes. Based on these modules, we propose a hierarchical attribute-aware embedding network (HAEN) which takes images and attributes as input, learns multiple attribute-specific embedding spaces, and measures fine-grained similarity in the corresponding spaces. The extensive experimental result on two fashion-related public datasets FashionAI and DARN shows the superiority (+5.11&#x0025; and +3.09&#x0025; in MAP, respectively) of our proposed HAEN compared with state-of-the-art methods.
Modeling-framework for model-based software engineering of complex Internet of things systems

Internet of things (IoT) systems are composed of variety of units from different domains. While developing a complete IoT system, different professionals from different domains may have to work in collaboration. In this paper we provide a framework which allows using discrete and continuous time modeling and simulation approaches in combination for IoT systems. The proposed framework demonstrates on how to model Ad-hoc and general IoT systems for software engineering purpose. We demonstrate that model-based software engineering on one hand can provide a common platform to overcome communication gaps among collaborating stakeholders whereas, on the other hand can model and integrate heterogeneous components of IoT systems. While modeling heterogeneous IoT systems, one of the major challenges is to apply continuous and discrete time modeling on intrinsically varying components of the system. Another difficulty may be how to compose these heterogeneous components into one whole system. The proposed framework provides a road-map to model discrete, continuous, Ad-hoc, general systems along with composition mechanism of heterogeneous subsystems. The framework uses a combination of Agent-based modeling, Aspect-oriented modeling, contract-based modeling and services-oriented modeling concepts. We used this framework to model a scenario example of a service-oriented IoT system as proof of concept. We analyzed our framework with existing systems and discussed it in details. Our framework provides a mechanism to model different viewpoints. The framework also enhances the completeness and consistency of the IoT software models.
Cyber Threat Exploitation and Growth during COVID-19 Times

The COVID-19 emergency has seen a significant rise in cyber threats. Throughout the world, there has been a marked growth in ransomware, fake COVID-19 apps (FCA), and targeted phishing scams, and different attacks. A global crisis seems to present cybercriminals with a big opportunity. Security Organization (SO) has observed instances of cybercrime has jumped by as much as 300%. After the end of February, phishing attempts have risen by more than 600%, including conventional impersonation scams along with enterprise email compromise (BEC) and extort ambusher to design web viruses. This article discusses the most important and divisive manufacturing activities to control the epidemic and minimize its consequences by first offering legal perspectives into the broader debate on the relation between constitutional authentications. An emphasis on rising cybernated infringement during the epidemic gives insights into the necessary threats and solutions for the protection of personal data. The research continues to use the above as a case study to illustrate how cybercriminals used important incidents and political statements to strategically craft and design cybercrime strategies. The vulnerabilities generated by the COVID-19 lockout are abused by criminals, raising the possibility of cybernated infringement, currency concealment, and terrorist funding (TF). Forums around the world responded by calling attention to these challenges from financial institutions and offering advice on how to enhance cyber protection and mitigate ML and TF risks.
Query Pixel Guided Stroke Extraction with Model-Based Matching for Offline Handwritten Chinese Characters

Stroke extraction and matching are critical for structural interpretation based applications of handwritten Chinese characters, such as Chinese character education and calligraphy analysis. Stroke extraction from offline handwritten Chinese characters is difficult because of the missing of temporal information, the multi-stroke structures and the distortion of handwritten shapes. In this paper, we propose a comprehensive scheme for solving the stroke extraction problem for handwritten Chinese characters. The method consists of three main steps: (1) fully convolutional network (FCN) based skeletonization; (2) query pixel guided stroke extraction; (3) model-based stroke matching. Specifically, based on a recently proposed architecture of FCN, both the stroke skeletons and cross regions are firstly extracted from the character image by the proposed SkeNet and CrossNet, respectively. Stroke extraction is solved by simulating the human perception that once given a certain pixel from non-cross region of a stroke, the whole stroke containing the pixel can be traced. To realize this idea, we formulate stroke extraction as a problem of pairing and connecting skeleton-wise stroke segments which are adjacent to the same cross region, where the pairing consistency between stroke segments is measured using a PathNet [1]. To reduce the ambiguity of stroke extraction, the extracted candidate strokes are matched with a character model consisting of standard strokes by tree search to identify the correct strokes. For verifying the effectiveness of the proposed method, we train and test our models on character images with stroke segmentation annotations generated from the online handwriting datasets CASIA-OLHWDB and ICDAR13-Online, as well as a dataset of Regularly-Written online handwritten characters (RW-OLHWDB). The experimental results demonstrate the effectiveness of the proposed method and provide several benchmarks. Particularly, the precisions of stroke extraction for ICDAR13-Online and RW-OLHWDB are 89.0% and 94.9%, respectively.
Impact of temporal and spatial variability of solar resource on technical sizing of isolated solar installations in Senegal using satellite data

The use of visible images from geostationary satellites such as Meteosat makes it possible to observe practically the entire surface of the earth almost instantaneously. This study aims to analyze for the first time the spatial and temporal variability of solar resource at different scales (daily, interannual and seasonal) in Senegal. Senegal is a climatologically interesting area because of its heterogeneity in terms of climate, from desert type (Sahelian) in the north to tropical type in the south, its sea–land interface and also its north–south gradient in precipitation. It is therefore interesting to map the solar resource to identify the different zone (zoning) in terms of solar potential. We will then suggest a model to size a 100% solar microgrid for electricity production. A case study has been applied with this model for a village that has a requirement of 134 kWh/d. In this paper, GHI (Global Horizontal Irradiance) estimates obtained from satellite imagery allowed us to evaluate the solar resource and its daily, interannual and seasonal variability. The Helioclim-3 V5 (HC-3) database is used over a 14-year period (2006–2019). This database allows to estimate the solar resource components at any point of the globe and for longer time periods. HC-3 has a very good spatial resolution (about 3 km) and a high temporal resolution (15 min). This method takes into account the relief and integrates clouds, water vapors and aerosols. The results to fully cover the needs of this 100% solar village mini-grid, there is a variation of the peak power of 20.20% between the maximum and minimum values over the year and 46.56% over the seasons. Coefficient of variation (COV) maps expressed as a percentage also show a low dispersion of the solar resource over the study period. The COV shows a narrow distribution with a minimum value of 6% and a maximum value of 9% varying from area to area and season to season. To see how a data compares to others, we will see the concept of percentiles. The P5, P10, P50 and P90 percentiles are calculated and compared to the GHI average from 2006 to 2019. The results show that P10 and P90 are closer to the GHI average than P5 and P50.
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph

Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion.Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task.However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs.In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage.In this manner, two passages can mutually guide each other in the process of APE.Furthermore, we propose an inter-sentence relation graph to effectively model the interrelations between two sentences and thus facilitates the extraction of argument pairs.Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs.Experimental results show that our approach significantly outperforms the current state-of-the-art model.
Load-Step: A Precise TrustZone Execution Control Framework for Exploring New Side-channel Attacks Like Flush+Evict

Trusted execution environments (TEEs) are imported into processors to protect sensitive programs against a potentially malicious operating system (OS), though, they are announced not effective in defending microarchitecture ($\mu$ arch) side-channel attacks. Furthermore, TEE attackers often utilize their high privilege to strengthen attacks by interrupting the execution of victim programs. Maximum temporal resolution is achieved on the x86 platform, which interrupts and measures by every instruction. However, the capability of $\mu$ arch side-channel attacks and the precision a kernel-privileged attacker can achieve in the TrustZone system are still unexplored. In this paper, we propose Load-Step, a precise framework that periodically interrupts the victim program in the TrustZone system and then conducts $\mu$ arch side-channel attacks. Our self-designed benchmark shows that Load-Step can invoke interrupts with load-instruction precision. Based on Load-Step, we present Flush+Evict, a new side-channel attack detecting the Arm Cache Coherent Interconnect (ArmCCI). It outperforms Prime+Probe with much higher precision and 282 % of the profiling speed. When attacking the RSA decryption in the latest MbedTLS library, Load-Step can recover the full key by only a single trace in 7.5 seconds. Our work thus breaches the exponent blinding, which aims to defend RSA decryption against side-channel attacks in the MbedTLS library.