Reviewerly/exHarmony huggingface dataset:
Improved collision detection of MD5 with additional sufficient conditions

&lt;abstract&gt;&lt;p&gt;One application of counter-cryptanalysis is detecting whether a message block is involved in a collision attack, such as the detection of MD5 and SHA-1. Stevens and Shumow speeded up the detection of SHA-1 by introducing unavoidable conditions in message blocks. They left a challenge: how to determine unavoidable conditions for MD5. Later, Shen et al. found that the unavoidable conditions of MD5 were the sufficient conditions located in the last round of differential paths. In this paper, we made further work. We discover sufficient conditions in the second round that can also be used as unavoidable conditions. With additional sufficient conditions, we subdivide three sets and distinguish seven more classes. As a result, compared with Shen's collision detection algorithm, our improved algorithm reduces the collision detection cost by 8.18%. Finally, we find that they do exist in the differential paths constructed by the automatic tool "HashClash".&lt;/p&gt;&lt;/abstract&gt;
The Impact of Selected Macroeconomic Variables and FDI on Foreign Trade in Jordan for the Period (2010-2020)

Unit Root and Cointegration tests as well as autoregressive distributed lag (ARDL) are used in this study to assess how selected macroeconomic variables and foreign direct investment (FDI) impact foreign trade (FT) of Jordan for the period (2010-2020). The automatic (ARDL) (E-view) method was used to assess the long-run relationship between the variables, represented in GDP, inflation rate (INFR), interest rate (IR), and FDI as independent variables, and international trade as a dependent variable. Study findings showed a positive and significant relationship between selected macroeconomic factors and direct investment in foreign commerce. This suggests that an increase in FDI by 1% results in a (0.13%) increase in foreign commerce provided that all other conditions remain constant. Gross domestic product (GDP) and foreign trade (FT) have a statistically significant positive association. The association between inflation rate (INFR), interest rate (IR), and international trade are negative and statistically significant. According to the data modified R-square (option R2) amounted to (84.2%), means that independent variables account for (84.2%) of the variation in GDP shares derived from international trade.
Simulation-Based Method for Analyzing Timing Attack Against Pass-Code Breaking System

Rate of diversification and evolution among various embedded and IOT devices have brought new concerns and challenges in the field of security of embedded devices. Not only do these devices need protection against software attacks or network attacks but equal attention needs to be paid against emerging type of attacks known as hardware attacks. Hardware attacks also known as side channel attacks extract the secret information through side channels which act as leakage parameters and convey information such as power emanation of devices, EM emanation, etc. Least studied timing attack needs attention due to its trait of less expertise and easy conduction. In this work methodology is proposed for Simple (SPA) and Differential Power Analysis (DPA) against evaluation and testing of timing attack for pass-code key breaker. A model depicting timing attack as real-time scenario which captures timing information as a side channel leakage parameter against microcontroller is presented. We also presented assembly code (.asm) for generation and evaluation of timing attack. We conclude our paper by presenting a model of timing attack that can be modeled practically for deeply investigating and analyzing experimental results against timing attack. After performing over several timing-based iterations we were able to recover secret key with 100% match.
HeadStart: Efficiently Verifiable and Low-Latency Participatory Randomness Generation at Scale

Generating randomness by public participation allows participants to directly contribute randomness and verify the result's security.Ideally, the difficulty of participating in such activities should be as low as possible to reduce the computational burden of being a contributor.However, existing randomness generation protocols are unsuitable for this scenario because of scalability or usability issues.Hence, this paper presents HeadStart, a participatory randomness protocol designed for public participation at scale.HeadStart allows contributors to verify the result on commodity devices efficiently and provides a parameter L that can make the result-publication latency L times lower.Additionally, we propose two implementation improvements to speed up the verification further and reduce the proof size.The verification complexity of HeadStart is only O(L × polylog(T ) + log C) for a contribution phase lasting for time T with C contributions.
Dung Beetle-Inspired Local Search in PSO for LSSMTWTS Problem

The large-scale single machine total weighted tardiness scheduling problem (LSSMTWTSP) is a complex NP-Hard problem. Instead, one should set a set of unrelated tasks on a machine with different criteria. The goal of the problem is to find the minimum possible weighted retardation. For the past few decades, the particle swarm optimization algorithm (PSOA) has shown commendable performance in optimization. Researchers are creating many new forms of PSO to solve complex problems. This paper covers an impressive local search technique inspired by Dung beetle orientation and foraging activities in PSOs. The strategy designed is named the Dung beetle-inspired PSO (DBPSO) algorithm. The efficiency and accuracy of the employed DBPSO strategy have experimented on the LSSMTWTS problem, which shows that DBPSO can be considered an efficient method for determining combinatorial optimization dilemmas.
'It's Problematic but I'm not Concerned': University Perspectives on Account Sharing

Account sharing is a common, if officially unsanctioned, practice among workgroups, but so far understudied in higher education. We interview 23 workgroup members about their account sharing practices at a U.S. university. Our study is the first to explicitly compare IT and non-IT observations of account sharing as a "normal and easy" workgroup practice, as well as to compare student practices with those of full-time employees. We contrast our results with those in prior works and offer recommendations for security design and for IT messaging. Our findings that account sharing is perceived as low risk by our participants and that security is seen as secondary to other priorities offer insights into the gap between technical affordances and social needs in an academic workplace such as this.?
Effectiveness of Action Bible Game Board Media to Introduce Bible Characters to Children

This study aims to determine the Action Bible Game Board media's effectiveness in introducing Old Testament Bible characters to children aged 9 -12 years old at the Pentecostal Church in Indonesia (GPdI) Ungaran and Indonesian Bethel Church (GBI) Kana Ungaran.The author uses experimental quantitative research to find the effect of specific treatments on others under controlled conditions.The results show a significant difference between the pre-test and post-test for the experimental group.A significant difference was found between the control and the experimental groups.Sunday school children taught using the Action Bible Game Board media had better recognition than those taught using ordinary media.
Manta Ray Foraging Optimization with Quasi-reflected Opposition Strategy for Global Optimization

This paper proposes an extension of Manta Ray Foraging Optimization (MRFO) using Oppositional-based Learning (OBL) technique called Quasi Reflected Opposition (QRO). MRFO is a new algorithm that developed based on the nature of a species in cartilaginous fish called Manta Ray. Manta ray employs three foraging strategies which are chain, cyclone and somersault foraging. Nonetheless, MRFO is tends to getting trap into local optima due to the redundant of intensification of the search agents in the search space. On the other side, OBL is a prominent technique in reducing chance of local optimum while increasing the convergence speed. Thus, QRO is synergized into MRFO to form QR-MRFO, in objective to improve MRFO in term of finding better accuracy of solution and faster convergence rate. Latter, QR-MRFO was performed on a series of benchmark functions and analyzed using statistical non-parametric test of Wilcoxon to measure the significant level of improvement. Results from the test shows that MRFO is undoubtedly defeated by QR-MRFO in term of accuracy.
Out of the Dark: The Effect of Law Enforcement Actions on Cryptocurrency Market Prices

The susceptibility of cryptocurrencies to criminal activity is a vigorously debated issue of high policy relevance. Not only the share of cryptocurrency turnover linked to crime is unknown, also the question which of several cryptocurrencies are prevalent on the darknet, and hence should be prioritized in building analytical capability for law enforcement, calls for empirical research. Using the event study methodology, we estimate the market reaction on cryptocurrency exchanges to news about successful law enforcement actions of systemic relevance for the cybercriminal ecosystem. The events studied include seizures of darknet marketplaces and shutdowns of cybercriminal data centers and mixers. Although the number of relevant events is still small, we observe significant cumulative abnormal returns to such news over the past years. We cautiously interpret the obtained results by cryptocurrency and direction of the effect, and derive implications for future research and policy.
Distribution Considerations

We define a linguistic distribution as the range of values for a quantitative linguistic variable across the texts in a corpus. An accurate parameter estimate means that the measures based on the corpus are close to the actual values of a parameter in the domain. Precision refers to whether or not the corpus is large enough to reliably capture the distribution of a particular linguistic feature. Distribution considerations relate to the question of how many texts are needed. The answer will vary depending on the nature of the linguistic variable of interest. Linguistic variables can be categorized broadly as linguistic tokens (rates of occurrence for a feature) and linguistic types (the number of different items that occur). The distribution considerations for linguistic tokens and linguistic types are fundamentally different. Corpora can be “undersampled” or “oversampled” – neither of which is desirable. Statistical measures can be used to evaluate corpus size relative to research goals – one set of measures enables researchers to determine the required sample size for a new corpus, while another provides a means to determine precision for an existing corpus. The adage “bigger is better” aptly captures our best recommendation for studies of words and other linguistic types.
Fake Restaurant Review Detection Using Deep Neural Networks with Hybrid Feature Fusion Method

Online review websites all over the world have developed by leaps and bounds in the past decade, followed by more and more fake reviews that are very difficult to identify. This not only affects the decision-making of consumers, but also seriously undermines the fairness of the consumption market. Most existing research ignored the abundant metadata in reviews and its effective combination with the review text. In this paper, we firstly collect reviews from one of the most popular Chinese review platforms Dianping, and release two fake review datasets we construct, which makes up for the relative lack of datasets in this field. Afterward, we present some novel and effective features for fake review detection by analyzing the characteristics of the collected reviews. In addition, we propose a multimodal fake review detection model BAM (BERT + Attention + MLP), which takes the review text and the extracted features as input, and uses neural networks as well as multimodal fusion technology to realize the recognition of fake reviews. The experimental results show that BAM has low dependence on the size of the dataset. Compared with baseline models, it can be found that BAM has a better detection performance.
Sentiment analysis on feedback of higher education teaching conduct: An empirical evaluation of methods

Sentiment analysis aims to automatically identify and classify the tone or polarity of people’s opinion in an unstructured text. This paper focuses on evaluating the accuracy of sentiment analysis methods to classify Indonesian higher education teaching conduct. In this context, we make the following contributions: (1) evaluation on the impact of text preparation methods in term of accuracy of sentiment analysis results, (2) evaluation the accuracy of three popular sentiment analysis methods (i.e., Naive Bayes, Support Vector Machine, Decision Tree) in classifying Indonesian text, and (3) proposal and evaluation on the effectiveness of combining the results from the three methods considered in this study with hope to improve the results’ accuracy. Finally we analyzed cases where all classifiers suggested incorrect sentiment classification and highlighted areas for future works to improve the accuracy of sentiment analysis, in particular for Indonesian Text.
Rethinking Importance Weighting for Transfer Learning

A key assumption in supervised learning is that training and test data follow the same probability distribution. However, this fundamental assumption is not always satisfied in practice, e.g., due to changing environments, sample selection bias, privacy concerns, or high labeling costs. Transfer learning (TL) relaxes this assumption and allows us to learn under distribution shift. Classical TL methods typically rely on importance weighting—a predictor is trained based on the training losses weighted according to the importance (i.e., the test-over-training density ratio). However, as real-world machine learning tasks are becoming increasingly complex, high-dimensional, and dynamical, novel approaches are explored to cope with such challenges recently. In this chapter, after introducing the foundation of TL based on importance weighting, we review recent advances on joint and dynamic importance-predictor estimation. Furthermore, we introduce a method of causal mechanism transfer that incorporates causal structure in TL. Finally, we discuss future perspectives of TL research.
Contrastive learning-based generative network for single image deraining

The crux of image deraining stems from the challenge of recognizing the diverse rain patterns within the rainy image. Most methods for image deraining remain visible rain residuals in the restored image, which suffers from insufficient modeling of rain streaks. In this work, we propose contrastive learning-based generative network (CLGNet), which follows a coarse-to-fine framework. In the coarse phase, our CLGNet employs the hierarchical encoder–decoder structure to remove obvious rain patterns, and first generates the coarse background image. Then, we introduce a well-designed multiscale feature aggregation module in the refining phase to extract and integrate global information dependencies from different scales. In additon, to facilitate the intra-stage and cross-stage information interaction, we propose the intra-stage feature fusion module and the cross-stage feature fusion module to encode broad contextual information. More importantly, we propose an innovative contrastive learning strategy and apply it to each stage of our proposed CLGNet to enhance the decoupling ability of the encoder and help the model recognize complex rain patterns. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed CLGNet than other state-of-the-art methods for single image deraining on both the visual quality and quantitative evaluation.
Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic

Model-based reinforcement learning algorithms, which aim to learn a model of the environment to make decisions, are more sample efficient than their model-free counterparts. The sample efficiency of model-based approaches relies on whether the model can well approximate the environment. However, learning an accurate model is challenging, especially in complex and noisy environments. To tackle this problem, we propose the conservative model-based actor-critic (CMBAC), a novel approach that achieves high sample efficiency without the strong reliance on accurate learned models. Specifically, CMBAC learns multiple estimates of the Q-value function from a set of inaccurate models and uses the average of the bottom-k estimates---a conservative estimate---to optimize the policy. An appealing feature of CMBAC is that the conservative estimates effectively encourage the agent to avoid unreliable “promising actions”---whose values are high in only a small fraction of the models. Experiments demonstrate that CMBAC significantly outperforms state-of-the-art approaches in terms of sample efficiency on several challenging control tasks, and the proposed method is more robust than previous methods in noisy environments.
PF-VTON: Toward High-Quality Parser-Free Virtual Try-On Network

Image-based virtual try-on aims to transfer a target clothes onto a person has attracted increased attention. However, the existing methods are heavily based on accurate parsing results. It remains a big challenge to generate highly-realistic try-on images without human parser. To address this issue, we propose a new Parser-Free Virtual Try-On Network (PF-VTON), which is able to synthesize high-quality try-on images without relying on human parser. Compared to prior arts, we introduce two key innovations. One is that we introduce a new twice geometric matching module, which warps the pixels of the target clothes and the features of the preliminary warped clothes to obtain the final warped clothes with realistic texture and robust alignment. The other is that we design a new U-Transformer, which is highly effective for generating highly-realistic images in try-on synthesis. Extensive experiments show that our system outperforms the state-of-the-art methods both qualitatively and quantitatively.
A CUDA-Parallelized Fast AutoEncoder for Highly Efficient Latent Factor Analysis on High-Dimensional and Sparse Matrices from Recommender Systems

An AutoEncoder (AE)-based latent factor analysis model can precisely extract non-linear latent features from a High-dimensional and Sparse (HiDS) matrix from a recommender system. However, it requires prefilling an HiDS matrix's unknown data to achieve its compatibility with a GPU platform, which leads to tremendous consumption of computation and storage. To address this issue, this paper presents a CUDA-Parallelized Fast AutoEncoder (CPFAE) for highly efficient latent factor analysis on a high-dimensional and sparse matrix from a recommender system. Its main idea is two-fold: a) implementing mini-batch-based weight update in the form of efficient sparse matrix multiplication to train the neural network, and b) implementing an efficient computation model for a compressed sparse matrix to make full use of a GPU platform's computation power. Experimental results on two HiDS matrices from real applications demonstrate that compared with a state-of-the-art AE-based model, CPFAE achieves significant gain in computation and storage efficiency.
Caste-hate speech and digital media politics

Campaigns against hate speech related to race, gender groups and sexual orientation have received varying degrees of acknowledgement and recognition in global contexts. However, the same has not been true for caste-hate speech that affects a fifth of the world’s population. This article advocates that global policy-makers should consider caste a protected characteristic related to hate-speech policies. It further calls for recognition and inclusion of caste as a protected characteristic in all international covenants related to human rights and hate speech. It also sets out action plans to mitigate hate speech in everyday conversations that are increasingly mediated by and saturated in digital platforms.
QTBIPOC PD: Exploring the Intersections of Race, Gender, and Sexual Orientation in Participatory Design

As Human-Computer Interaction (HCI) research aims to be inclusive and representative of many marginalized identities, there is still a lack of available literature and research on intersectional considerations of race, gender, and sexual orientation, especially when it comes to participatory design. We aim to create a space to generate community recommendations for effectively and appropriately engaging Queer, Transgender, Black, Indigenous, People of Color (QTBIPOC) populations in participatory design, and discuss methods of dissemination for recommendations. Workshop participants will engage with critical race theory, queer theory, and feminist theory to reflect on current exclusionary HCI and participatory design methods and practices.
Acquisition performance of B1I abounding with 5G signals

The widespread 5G base stations can be potential jammers for the vulnerable BeiDou B1I receivers due to its low power. Therefore, a novel analytical model is derived for the 5G signal to evaluate its impact on acquisition performance under three decision methods. The good agreement between the Monte Carlo method (MCM) through software defined receiver (SDR) and the derived expressions validates the effectiveness of the proposed algorithm. It can be found that the receivers exhibit varied responses for different 5G waveforms and decision strategies. The receiver also shows the least endurances for some kind of 5G waveforms, however, this kind of adverse effect can be cancelled by a reduced interference signal ratio (ISR), an increased integration time or a larger accumulation times.
Cryptocurrency Response to COVID-19: A Test of Efficient Market Hypothesis

Cryptocurrencies are a newer form of investment avenues without any geographical boundary. The asset gives confidence to its investors because of blockchain technology. However, a higher level of volatility is observed in such currencies. Partly because of the lack of an underlying asset base and thus, making it highly speculative. The efficient market hypothesis (EMH) indicates the value of an asset to be consistent with available information. The volatility observed raises a concern about the information efficiency of cryptocurrencies. The COVID-19 pandemic provided a context with dynamic information flows and uncertainty to test the weak form of EMH. In the weak form, past returns influence future prices. This study tested if returns from different cryptocurrencies are consistent with the weak form of EMH for the period 01.01.2020 to 31.12.2020 marked by the pandemic. The study finds that returns from cryptocurrencies did not consistently conform to the weak form of EMH. Thus, the market has irrationality and inefficiency related to the dynamic information flow during the pandemic. The findings are significant for policymakers, economists and investors.