Reviewerly/exHarmony huggingface dataset:
Adversarial Sample Detection for BERT Model Based on Sample Sensitivity Characteristics

Deep learning technology has developed rapidly in the field of natural language processing. Studies have shown that even high-performance language models are vulnerable to adversarial attacks. Text adversarial attacks lead language models to make mistakes in the sentiment judgment of sentences, posing a threat to the security of language processing systems. We found that when adversarial examples attacked Bidirectional Encoder Representation from Transformers (BERT) models with different parameters, the output distribution was significantly different from that of normal samples. Therefore, we exploit this discrepancy phenomenon to propose an adversarial example detection method to detect whether the current sample is an adversarial example for the BERT model. First, feature sensitivity metrics are designed by extracting representative adversarial examples. Then, we train an SVM-based adversarial example classification detector and corresponding text adversarial sample detection algorithm is designed. We validate the differential performance of normal samples and adversarial samples on a heterogeneous BERT model set through experiments on the SST dataset. The experiment results demonstrate that our adversarial sample detection method designed based on the sample sensitivity metric could be over 90% performance on the validation dataset.
Using Counterexample Generation and Theory Exploration to Suggest Missing Hypotheses

Newcomers to ACL2 are sometimes surprised that ACL2 rejects formulas that they believe should be theorems, such as (reverse (reverse x)) = x.Experienced ACL2 users will recognize that the theorem only holds for intended values of x, and given ACL2's total logic, there are many counterexamples for which this formula is simply not true.Counterexample generation (cgen) is a technique that helps by giving the user a number of counterexamples (and also witnesses) to the formula, e.g., letting the user know that the intended theorem is false when x is equal to 10.In this paper we describe a tool called DrLA that goes further by suggesting additional hypotheses that will make the theorem true.In this case, for example, DrLA may suggest that x needs to be either a true-list or a string.The suggestions are discovered using the ideas of theory exploration and subsumption from automated theorem proving.
The Dichotomy of Crypto and NN: War and Peace

In recent years, NNs, as the basic components in AI models and cryptographic schemes, have come together in war and peace; a cross-impact forms a dichotomy deserving a comprehensive study. NNs can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by NNs. On the other hand, NNs and cryptographic algorithms can mutually support each other. NNs can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of NNs. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current studies that take a comprehensive look at the many ways NNs are currently interacting with cryptography. This part aims to fill that niche by providing an overview on the state of the cross-impact between NNs and cryptosystems. To this end, this chapter will highlight the current areas where progress is being made in research on the crypto-NN dichotomy.
Unveiling Culinary Patterns: Implementation Of K-Means Clustering Algorithm on Food Products in Cafes

Barcode Se'i and Coffee is one of the cafes on JL. Major Utarya, No. 48, Empangsari, District. Tawang, Tasikmalaya. Barcode Se'i and Coffee is quite famous because the concept of the place is nice, comfortable, and instagrammable. Not only that, but the Barcode cafÃ© was also the first to create cow sei in Tasikmalaya. By analyzing the cafe menu groupings, information can be found regarding the level of menu sales. This type of analysis, capable of assessing sales levels, involves the use of data mining techniques such as clustering. Data mining is a data processing stage that aims to identify and extract patterns from a certain set of data. One of the methods included in data mining is the clustering technique. Reclassification techniques are used to group objects into several groups based on observed indicators, ensuring that all objects have a significant level of similarity compared to objects placed in different groups. With Rapidminer software and using the k-means algorithm with sales data for 11 months with the calculations carried out producing 5 clusters. Based on the comparison results of 3 K-Means algorithms with different K values, namely 3, 4, 5, the result from Davies Bouldin with a value close to 0 is a value with K 5, with the result from Davies Bouldin being - 0.912.
Unmasking the Deception: The Interplay between Fake Reviews, Rating Dispersion, and Consumer Demand

In online marketplaces, consumers rely on reviews to make informed purchase decisions, rendering the presence of fake reviews detrimental. Meanwhile, products that have acquired fake reviews may exhibit different characteristics of the review distributions, such as a higher rating dispersion, and consumers may take this into account when making their purchase decisions. In this paper, we explore the interplay between fake reviews and rating dispersion, and their impact on consumer demand, while controlling for average product ratings. First, using a data set with fake review labels, we find that product rating dispersion is positively correlated with the likelihood that the product has acquired fake reviews. Then, by employing rating dispersion changes due to rating distribution rounding as an identification strategy, we isolate the effect of rating dispersion on product sales. Our findings indicate that rating dispersion negatively affects sales. To further investigate the underlying mechanism, we conduct experiments in which participants are shown products with varying rating distributions and are asked about their choices, willingness to pay, and concerns about fake reviews. Additionally, we incorporate an information treatment, which later functions as an instrumental variable, to determine the impact of consumer suspicion of fake reviews on their demand. The experimental results demonstrate that rating dispersion significantly impacts consumer demand, with heightened dispersion in ratings leading to concerns about fake reviews. These findings align with the outcomes from our observational study, underscoring the importance of comprehending the interplay between fake reviews, rating dispersion, and consumer demand in online marketplaces.
Towards Transferable Adversarial Attacks on Image and Video Transformers

The transferability of adversarial examples across different convolutional neural networks (CNNs) makes it feasible to perform black-box attacks, resulting in security threats for CNNs. However, fewer endeavors have been made to investigate transferable attacks for vision transformers (ViTs), which achieve superior performance on various computer vision tasks. Unlike CNNs, ViTs establish relationships between patches extracted from inputs by the self-attention module. Thus, adversarial examples crafted on CNNs might hardly attack ViTs. To assess the security of ViTs comprehensively, we investigate the transferability across different ViTs in both untargetd and targeted scenarios. More specifically, we propose a Pay No Attention (PNA) attack, which ignores attention gradients during backpropagation to improve the linearity of backpropagation. Additionally, we introduce a PatchOut/CubeOut attack for image/video ViTs. They optimize perturbations within a randomly selected subset of patches/cubes during each iteration, preventing over-fitting to the white-box surrogate ViT model. Furthermore, we maximize the L2 norm of perturbations, ensuring that the generated adversarial examples deviate significantly from the benign ones. These strategies are designed to be harmoniously compatible. Combining them can enhance transferability by jointly considering patch-based inputs and the self-attention of ViTs. Moreover, the proposed combined attack seamlessly integrates with existing transferable attacks, providing an additional boost to transferability. We conduct experiments on ImageNet and Kinetics-400 for image and video ViTs, respectively. Experimental results demonstrate the effectiveness of the proposed method.
Evaluating a Fine-Tuned Whisper Model on Underrepresented Romanian Speech

Speech datasets available for training Romanian automatic speech recognition (ASR) systems are constructed around similar demographics (male voices, age between 19-29 years). In this paper, we present a dataset of underrepresented Romanian speech (USPDATRO), constructed from open data. We fine-tune a state-of-the-art Whisper model using existing datasets and evaluate the resulting model on the dataset of underrepresented speech. Results indicate that more such data is needed to improve the performance of Romanian ASR sytems.
Development of a PSO Modification with Soft Particle Reinitialization

A modification of particle swarm optimization (PSO) with soft reinitialization of particles was developed in the article. It forces random particles (but not the best ones) to change their positions and velocities. In the frame of the investigation (for four benchmarks and thirty particles in a swarm) the reasonable number of particles to reinitialize equals one. A comparative study shows the advantages and a drawback of developed PSO modification.
A Comparative Study of Bigdata Tools: Hadoop Vs Spark Vs Storm

Big data is expanding quickly in terms of volume, value, veracity, and velocity, making it challenging to process, acquire, and analyze the data. Big data analytics is becoming more and more well-liked as a technique for researching enormous amounts of data on demand. The Apache Hadoop, Apache Spark, and Apache Storm frameworks are three of the most popular large data processing frameworks. Despite the fact that all three of them allow large data processing, three frameworks have different uses and underlying architectures for those uses. Numerous studies have spent time and energy comparing different big data frameworks by assessing them for a specified key performance measure. Since a few years ago, Hadoop, Spark, and Storm frameworks have been developed and used for big data processing in this area. These platforms are producing encouraging outcomes. This study compares the Hadoop, Spark, and Storm frameworks and explores the development of big data computing platforms. When compared to the Apache Hadoop and Apache Storm frameworks, our research showed that Spark was superior in terms of processing time, CPU usage, latency, execution time and job performance.
Measuring Content Validity: Tadabbur Al Quran and Sejahtera Living

This research aims to examine the content validity of students’ Tadabbur Al Quran and Sejahtera Living using the Content Validity Ratio (CVR). The research was conducted through the evaluation of 8 expert panels with a purposive sampling technique. Expert panels were divided into two categories: professional and field experts. Five professional experts are lecturers at International Islamic University Malaysia and are involved with their respective expertise in the tadabbur Al Quran. Three field experts consisted of one doctor of philosophy (PhD) and two master’s candidates who are studying at the International Islamic University Malaysia and specializing in particular fields of study (Al Quran and Sunnah). The scale contains two aspects: tadabbur Al Quran and students’ Sejahtera living. Tadabbur Al Quran is conceptualized in three constructs with 17 items. Meanwhile, there are five constructs with 21 items for students' Sejahtera living. The results of the research show that the scale has good content validity and proved that the scale has great potential to be promoted as a good scale of the Tadabbur Al Quran and students’ Sejahtera Living. It is suggested to apply more sophisticated statistical analysis, such as the Structural Equation Modelling for elaborating on quality items and model development.
Comparing Differentiable Logics for Learning Systems: A Research Preview

Extensive research on formal verification of machine learning (ML) systems indicates that learning from data alone often fails to capture underlying background knowledge.A variety of verifiers have been developed to ensure that a machine-learnt model satisfies correctness and safety properties, however, these verifiers typically assume a trained network with fixed weights.ML-enabled autonomous systems are required to not only detect incorrect predictions, but should also possess the ability to self-correct, continuously improving and adapting.A promising approach for creating ML models that inherently satisfy constraints is to encode background knowledge as logical constraints that guide the learning process via so-called differentiable logics.In this research preview, we compare and evaluate various logics from the literature in weakly-supervised contexts, presenting our findings and highlighting open problems for future work.Our experimental results are broadly consistent with results reported previously in literature; however, learning with differentiable logics introduces a new hyperparameter that is difficult to tune and has significant influence on the effectiveness of the logics.
Perception of CSR Activities, Organizational Trust, and Employee Outcome: Evidence from Iraq Telecom Market in Post-Pandemic COVID-19

Purpose: This study investigates the intermediary role of organizational trust in the relationship between corporate social responsibility (CSR) and employee outcomes. Theoretical framework: Building on Carroll’s pyramid adapted to the unique context of Iraq, the study explores the dimensions of organizational trust, CSR initiatives, and tangible employee outcomes. Design/Methodology/Approach: Employing a quantitative approach, the research was conducted within four telecommunications firms in Iraq, involving 359 employees with at least one year of work experience. Data collection spanned from September 2021 to October 2022, achieving a commendable 97.55% response rate. Structural equation modeling, utilizing the partial least squares (PLS) method and a bootstrapping technique involving 5,000 resamples, assessed the statistical significance of hypotheses. Findings: The study reveals a subtle yet noteworthy positive influence of CSR on employee loyalty, job satisfaction, and turnover intention. Importantly, organizational trust emerges as a robust mediator, influencing employee outcomes positively. Research, Practical &amp; Social implications: This research suggests extending the conceptual framework to diverse industry sectors and geographical settings. Additionally, the inclusion of alternative mediators, such as perceived organizational justice, warrants exploration in future studies. Originality/Value: In contrast to studies using Western-centric metrics for CSR, this research adopts an alternative conceptual framework tailored to the unique context of Iraq, contributing to a deeper understanding of the interplay between organizational trust, CSR initiatives, and employee outcomes.
Applying Classification Trees to Stress Patterns of Japanese Loanwords in English

This paper provides an analysis of the stress patterns of loanwords of Japanese origin in English. The ctree function of the party package in R is used to create classification trees. The stress pattern is not immediately obvious, so the algorithm is used to discover the significant predictor variables and create a classification tree. This illustration displays the stress pattern in a straightforward manner. The determining factor for the stress assignment of Japanese loanwords in English is syllable weight and vowel sonority. The presence of a final coda is the significant variable for the stress assignment in two-syllable loanwords, whereas vowel sonority is the significant variable for stress assignment in three and four-syllable loanwords. This provides support for Kenstowicz’s [1997] and deLacy’s [2002] universal sonority hierarchy.
Human Computer Interaction

Human-computer interaction (HCI) is the study of how people interact with computers and design systems that are usable, efficient, and enjoyable to use. HCI researchers study how people use computers, develop new technologies that let people use them in inventive ways, and evaluate the usability of existing systems. HCI is a multidisciplinary field that draws on research from a variety of disciplines, including computer science, psychology, design, and linguistics. HCI researchers use a variety of methods to study human-computer interaction, including user studies, usability testing, and ethnographic research. HCI research has played a major role in the development of many of the computer technologies that we use today, including graphical user interfaces, touch screens, and voice recognition. HCI research is also important for the development of new technologies, such as augmented reality and virtual reality.
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.
Pengembangan Multimedia Pembelajaran Interaktif Berbasis Macromedia Flash 8 pada Mata Pelajaran SKEP di Kota Cilegon

The creation of the 2013 curriculum for YPWKS Vocational High School students in the 2019/2020 school year. The study program promotes student competency development through SKEP (Electrical Pneumatic Control System) disciplines. Based on observations from oral interviews, there was no multimedia learning to assist learning in these subjects, therefore the goal of this research is to support learning in SKEP subjects. The development research, or R&amp;D (Research and Development), was carried out utilizing the Borg and Gall model, which was reduced from 10 to 7 stages. The model is divided into seven stages: one for research analysis and data collection, two for planning, three for product draft development, four for initial field trials, five for revising test results, six for product outcomes refining, and seven for dissemination and implementation. Three material experts and three media experts are involved in the feasibility of multimedia learning. Then the multimedia was tested on class XI students of the Industrial Automation Engineering competence program and the scale used in gauging the "Likert scale". Based on the results of validation by media experts and material experts, the feasibility of interactive learning multimedia in the media aspect was 73.99 (very good) from a score range of 65.00 X 80.00, and the feasibility of the material aspect was 58.99 (very good) from a score range of 52.00 X 64.00. Field According to trial data, students responded to the practicality of Interactive Learning Multimedia with an average score of 50% (very good) on a scale of 0-100%. These findings suggest that the created Electropneumatic Control System Learning Multimedia is appropriate for use in Electropneumatic Control System Learning activities, particularly for students in class XI Industrial Automation Engineering studying Pneumatic Control System material.
Pengembangan Aplikasi Web dan Mobile Pada Monitoring dan Evaluasi Program Tahfidz Quran

Madrasah Ulumul Qur'an (MUQ) Pidie Regency is one of the educational institutions that has a tahfidz program. Recently, the recording of the memorization of the Qur'an for each student is carried out on one sheet of paper. The existence of information technology requires an educational institution to implement IT applications properly. This study aims to build a fine monitoring and evaluation system for tahfidz qur'an in terms of presenting data to the MUQ of Pidie Regency and parents. Data information on the proposed web and mobile application system is in the form of information data in kinds of tables, text, graphs and reports of rote achievement. The research method used research and development approach with an application development model (the waterfall). The applications used the concept of web services that connected between web and mobile applications. The results showed that the development of monitoring and evaluation system in the form of web and mobile applications for data management on the development of tahfidz by presenting data in the form of text, graphs and tables. This effectively provides monitoring and evaluation functions which easier, faster and more efficient. The results of the application effectiveness test showed that the average value of the percentage of 19 questions was 96%. Based on the results of the questionnaire, the average value is above 70%. Generally, this application is very helpful for parents in monitoring the development of their child's memorization.
GAME SEBAGAI MEDIA PEMBELAJARAN KREATIF DAN MENARIK UNTUK MENINGKATKAN MINAT /MOTIVASI BELAJAR BAHASA INGGRIS PADA ANAK SEKOLAH DASAR

English is the language of all countries in the world in its position as a global lingua franca. Indonesia must also place English in the same position so that it can compete more in international competition. English education in Indonesia has been carried out since elementary school. The objectives of learning English through game media include: increasing students' enthusiasm in learning English, improving students' basic English skills through fast learning because it is packaged effectively, creatively, and fun. Educational games can be interactive English learning media, making the learning process more fun and effective. Games are an alternative way that can be used to see some of the positive aspects that will be obtained from the elements of game activities. Educational games or games can be very effective in helping convey material and increasing student motivation to study harder