Reviewerly/exHarmony huggingface dataset:
The BitcoinHeist: Classifications of Ransomware Crime Families

Ransomware attacks are on the rise and attackers are hijacking valuable information from different critical infrastructures and businesses requiring ransom payments to release the encrypted files. Payments in cryptocurrencies are designed to evade tracing the transactions and the recipients. With anonymity being paramount, tracing cryptocurrencies payments due to malicious activity and criminal transactions is a complicated process. Therefore, the need to identify these transactions and label them is crucial to categorize them as legitimate digital currency trade and exchange or malicious activity operations. Machine learning techniques are utilized to train the machine to recognize specific transactions and trace them back to malicious transactions or benign ones. I propose to work on the Bitcoin Heist data set to classify the different malicious transactions. The different transactions features are analyzed to predict a classifier label among the classifiers that have been identified as ransomware or associated with malicious activity. I use decision tree classifiers and ensemble learning to implement a random forest classifier. Results are assessed to evaluate accuracy, precision, and recall. I limit the study design to known ransomware identified previously and made available under the Bitcoin transaction graph from January 2009 to December 2018.
Speaking Performance and Anxiety Levels of Chinese EFL Learners in Face-to-Face and Synchronous Voice-based Chat

In recent, mobile technology is experiencing a highly development, it is necessary to explore whether it holds the potential to boost EFL learners’ language acquisition based on its instant messaging apps in synchronous voice chat (SVC) mode. With a focus on Chinese EFL learners, this study aimed to compare their speaking performance in SVC and face-to-face (F2F) chat modes. It also explored the relationship between learners’ speaking performance and anxiety levels in these two chat modes which allow real-time communication. In this mixed methods study, WeChat instant messaging was used as the platform for SVC. Forty students from a public university in China participated in 4 chat sessions in SVC and F2F chat modes over 4 weeks. Quantitative data were collected through the oral scores of the participants’ performance in the chat sessions and anxiety questionnaires. Then, qualitative data were obtained from a focus group interview. The findings revealed a significant difference in learners’ speaking performance in SVC and F2F chat. Students’ speaking performance outperformed in SVC chat compared to F2F chat. This could be linked to students’ anxiety levels which were slightly higher in F2F chat. Despite that, most of the students preferred F2F chat to SVC chat due to the practicality of F2F chat.
Are BERTs Sensitive to Native Interference in L2 Production?

With the essays part from The International Corpus Network of Asian Learners of English (ICNALE) and the TOEFL11 corpus, we finetuned neural language models based on BERT to predict English learners' native languages.Results showed neural models can learn to represent and detect such native language impacts, but multilingually trained models have no advantage in doing so.
Principles of Somatic Movement Education for Architectural Design

An architect trying to predict the spatial effect of their design on its inhabitants often faces a dilemma. Their professional experience and personal feeling allows them to intuit its effect. Such intuition, however, might lack legitimacy in the dominant design practice. For over a century, the question of the felt space in architecture has been a topic of theoretical discussion, which led to the insight that the answer might lay not so much in studying the architectural structures, but rather in studying the bodies that inhabit them. And still the dominant architectural practice follows the outdated dualistic (mis-)understanding of the felt space. Another historical development took place in dance. Here, since the 1960s,the traditionally formalistic and objectifying understanding of dance has been strongly influenced by techniques of bodily sensitization, stemming from the field of somatics. In themselves rather diverse, these techniques have been institutionally delineated through the principles of somatic movement education. One of their characteristics is that somatic techniques are constantly re-emerging - not from a priori knowledge but from the study of one's own body and its interactions with the environment. This article envisages how such principles might be applied to architectural design practice and give rise to new embodied design practices - which might foster architects' sensory expertise and thus legitimize the felt knowledge in professional contexts.
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation

Multi-hop relation detection in KnowledgeBase Question Answering (KBQA) aims at retrieving the relation path starting from the topic entity to the answer node based on a given question, where the relation path may comprise multiple relations.Most of the existing methods treat it as a single-label learning problem while ignoring the fact that for some complex questions, there exist multiple correct relation paths in knowledge bases.Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem.However, performing multi-label multi-hop relation detection is challenging since the numbers of both the labels and the hops are unknown.To tackle this challenge, multi-label multi-hop relation detection is formulated as a sequence generation task.A relation-aware sequence relation generation model is proposed to solve the problem in an end-to-end manner.Experimental results show the effectiveness of the proposed method for relation detection and KBQA.
Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The former one is responsible for detecting any face, which is not wearing a mask. Whereas, the latter is exploited to manage the control for the entire building or a public area such as bus, train station, or airport by connecting a Closed-Circuit Television (CCTV) camera to the room of management. The work is implemented using a Core-i5 CPU workstation attached with a Webcam. Then, MATLAB software is programmed to instruct both Arduino and NodeMCU (Micro-Controller Unit) for remote control as IoT. In terms of deep learning, a 15-layer convolutional neural network is exploited to train 1,376 image samples to generate a reference model to use for comparison. Before deep learning, preprocessing operations for both image enhancement and scaling are applied to each image sample. For the training and testing of the proposed system, the Simulated Masked Face Recognition Dataset ( SMFRD) has been exploited. This dataset is published online. Then, the proposed deep learning system has an average accuracy of up to 98.98 %, where 80 % of the dataset was used for training and 20 % of the samples are dedicated to testing the proposed intelligent system. The IoT system is implemented using Arduino and NodeMCU_TX (for transmitter) and RX (for receiver) for the signal transferring through long distances. Several experiments have been conducted and showed that the results are reasonable and thus the model can be commercially applied
Challenges and Ethical Issues Related to COVID-19 Contact Tracing Teams in Turkey

The purpose of this study was to determine and evaluate the challenges and ethical issues faced by COVID-19 filiation (contact tracing) teams and report the experiences of team members.This is a descriptive study conducted to evaluate the performance of contact tracing field teams established by the Ministry of Health to identify, refer, and follow-up persons who tested positive for COVID-19 and their contacts. Reports were collected from various Turkish medical associations, the Turkish Ministry of Health, online news sources, and online interviews published between 11 March 2020 and 18 April 2021.A total of 32 reports published by the Turkish Ministry of Health, various medical associations in Turkey, and the Turkish Academy of Science, together with a number of online media reports on issues involving contact tracing teams, were reviewed. While the activities of the teams were overall found to be of benefit during the pandemic, numerous factors negatively impacting their success were identified. These factors included insufficient social support, wherein the fundamental bioethical values of solidarity, responsibility, respect, and trust were not adopted on a societal level, as well as issues involving the teams themselves, such as lack of time to properly conduct their work, inadequate communication skills and/or personal issues.Studies conducted by the contact tracing teams were of vital importance in managing the pandemic. The teams were especially effective in reaching both educated and uneducated segments of the society, in diagnosing cases, and in informing the public regarding the pandemic. Evaluating the experiences of the contact tracing teams deployed in Turkey is of vital importance to ensure better preparation for any future pandemics.
Context aware semantic adaptation network for cross domain implicit sentiment classification

Cross-domain sentiment classification could be attributed to two steps. The first step is used to extract the text representation, and the other is to reduce domain discrepancy. Existing methods mostly focus on learning the domain-invariant information, rarely consider using the domain-specific semantic information, which could help cross-domain sentiment classification; traditional adversarial-based models merely focus on aligning the global distribution ignore maximizing the class-specific decision boundaries. To solve these problems, we propose a context-aware semantic adaptation (CASA) network for cross-domain implicit sentiment classification (ISC). CASA can provide more semantic relationships and an accurate understanding of the emotion-changing process for ISC tasks lacking explicit emotion words. (1) To obtain inter- and intrasentence semantic associations, our model builds a context-aware heterogeneous graph (CAHG), which can aggregate the intrasentence dependency information and the intersentence node interaction information, followed by an attention mechanism that remains high-level domain-specific features. (2) Moreover, we conduct a new multigrain discriminator (MGD) to effectively reduce the interdomain distribution discrepancy and improve intradomain class discrimination. Experimental results demonstrate the effectiveness of different modules compared with existing models on the Chinese implicit emotion dataset and four public explicit datasets.
Constacyclic Codes over Finite Chain Rings of Characteristic p

Let R be a finite commutative chain ring of characteristic p with invariants p,r, and k. In this paper, we study λ-constacyclic codes of an arbitrary length N over R, where λ is a unit of R. We first reduce this to investigate constacyclic codes of length ps (N=n1ps,p∤n1) over a certain finite chain ring CR(uk,rb) of characteristic p, which is an extension of R. Then we use discrete Fourier transform (DFT) to construct an isomorphism γ between R[x]/&lt;xN−λ&gt; and a direct sum ⊕b∈IS(rb) of certain local rings, where I is the complete set of representatives of p-cyclotomic cosets modulo n1. By this isomorphism, all codes over R and their dual codes are obtained from the ideals of S(rb). In addition, we determine explicitly the inverse of γ so that the unique polynomial representations of λ-constacyclic codes may be calculated. Finally, for k=2 the exact number of such codes is provided.
Time Waits for No One! Analysis and Challenges of Temporal Misalignment

When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance.In this work, we establish a suite of eight diverse tasks across different domains (social media, science papers, news, and reviews) and periods of time (spanning five years or more) to quantify the effects of temporal misalignment.Our study is focused on the ubiquitous setting where a pretrained model is optionally adapted through continued domainspecific pretraining, followed by task-specific finetuning.We establish a suite of tasks across multiple domains to study temporal misalignment in modern NLP systems.We find stronger effects of temporal misalignment on task performance than have been previously reported.We also find that, while temporal adaptation through continued pretraining can help, these gains are small compared to task-specific finetuning on data from the target time period.Our findings motivate continued research to improve temporal robustness of NLP models. 1
MLP-Mixer: An all-MLP Architecture for Vision

Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary. We present MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs). MLP-Mixer contains two types of layers: one with MLPs applied independently to image patches (i.e. mixing the per-location features), and one with MLPs applied across patches (i.e. mixing spatial information). When trained on large datasets, or with modern regularization schemes, MLP-Mixer attains competitive scores on image classification benchmarks, with pre-training and inference cost comparable to state-of-the-art models. We hope that these results spark further research beyond the realms of well established CNNs and Transformers.
ETHICAL ASPECTS OF DISTANCE EDUCATION IN THE EPOCH OF THE COVID-19 PANDEMIC

The article focuses on the ethical issues of distance learning as a challenge faced by education during the COVID-19 pandemic. The author considers the moral and value aspects of distance learning at different levels - from problems of an international nature to individual communication between teacher and student. The range of problems related to the ethical component of distance learning is quite wide: digital inequality, digital literacy, pedagogical relations between "digital aborigines" and "digital immigrants", features of the use of digital material, etc. The author demonstrates the ambiguity of the problems that arose in the era of the pandemic. Thus, on the one hand, distance education requires a complete transition to digital format, on the other - both sides of the educational process are not ready for this format, because they do not have the necessary technical knowledge. Ethical issues are often financially related, such as addressing digital inequality or digital literacy. The article emphasizes that the digitalization of education requires a high level of responsibility and trust from all participants in the educational process. The most important condition of ethical behavior is awareness of digital etiquette and the effectiveness of penalties for its violation or incentives for its observance: compliance with ethical standards is a fundamental condition for success in the format of distance learning. At the same time, this requires first of all the development of distance learning principles and a corresponding code of ethics at the level of a specific educational institution.
Experimental Demonstration of Fine-Grained Steering Inequality of Two-Qubit Mixed States

Quantum steering, as a cornerstone of quantum information, is usually used to witness the quantum correlation of bipartite and multi-partite states. Here, we experimentally demonstrate the quantum steering inequality of two-qubit mixed states based on the fine-grained uncertainty relation. Our experimental results show that the steering inequality has potent sensitivity to Werner states and Bell diagonal states. The steering strategy exhibits a strong ability to identify that Werner states are steerable when the decoherence coefficient a&gt;12. Compared to the steering inequality obtained by another stratagem, the steering witness criteria of mixed states based on the fine-grained uncertainty relation demonstrated in our experiment has better precision and accuracy. Moreover, the detection efficiency in our measurement setup is only required to be 50% to close the detection loophole, which means our approach needs less detector efficiency to certificate the steerability of mixed states.
Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

We can state undoubtedly that Bangla language is rich enough to work with and implement various Natural Language Processing (NLP) tasks. Though it needs proper attention, hardly NLP field has been explored with it. In this age of digitalization, large amount of Bangla news contents are generated in online platforms. Some of the contents are inappropriate for the children or aged people. With the motivation to filter out news contents easily, the aim of this work is to perform document level sentiment analysis (SA) on Bangla online news. In this respect, the dataset is created by collecting news from online Bangla newspaper archive. Further, the documents are manually annotated into positive and negative classes. Composite process technique of “Pipeline” class including Count Vectorizer, transformer (TF-IDF) and machine learning (ML) classifiers are employed to extract features and to train the dataset. Six supervised ML classifiers (i.e. Multinomial Naive Bayes (MNB), K-Nearest Neighbor (K-NN), Random Forest (RF), (C4.5) Decision Tree (DT), Logistic Regression (LR) and Linear Support Vector Machine (LSVM)) are used to analyze the best classifier for the proposed model. There has been very few works on SA of Bangla news. So, this work is a small attempt to contribute in this field. This model showed remarkable efficiency through better results in both the validation process of percentage split method and 10-fold cross validation. Among all six classifiers, RF has outperformed others by 99% accuracy. Even though LSVM has shown lowest accuracy of 80%, it is also considered as good output. However, this work has also exhibited surpassing outcome for recent and critical Bangla news indicating proper feature extraction to build up the model.
Tracing variation in discourse connectives in translation and interpreting through neural semantic spaces

In the present paper, we explore lexical contexts of discourse markers in translation and interpreting on the basis of word embeddings.Our special interest is on contextual variation of the same discourse markers in (written) translation vs. (simultaneous) interpreting.To explore this variation at the lexical level, we use a data-driven approach: we compare bilingual neural word embeddings trained on source-to-translation and source-tointerpreting aligned corpora.Our results show more variation of semantically related items in translation spaces vs. interpreting ones and a more consistent use of fewer connectives in interpreting.We also observe different trends with regard to the discourse relation types.
Perilaku Konsumtif Gamers Genshin Impact terhadap Pembelian Gacha

The purpose of this study is to empirically examine the effect of gamers' consumptive behavior on gacha purchases in the Genshin Impact Game. Gacha is a game based on the player's chance and luck. Most of these games are usually free-to-play games with a gacha system as an incentive to use real currency to collect rare (limited) items. The research method used is a quantitative type. The number of research samples is 250 people from the population of Genshin Impact players whose number is unknown, with the sampling technique is accidental sampling. The large influence of consumptive behavior on the purchase of gacha can be seen in the results of the coefficient of determination (R2) as a percentage of 74.5%. With the results of this coefficient, it can be concluded that the purchase of gacha by 74.5% is influenced by the consumptive behavior of the players, while the remaining 25.5% is contributed by other factors that can be explained by other variables outside the model
Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates

Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations.Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correlations, but the research was primarily used in human-computer interaction scenarios.It still remains underexplored whether or how such model interpretations can be used to automatically "unlearn" confounding features.In this work, we propose influence tuning-a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels.We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training. 1
A comparison of convolutional neural networks for Kazakh sign language recognition

For people with disabilities, sign language is the most important means of communication. Therefore, more and more authors of various papers and scientists around the world are proposing solutions to use intelligent hand gesture recognition systems. Such a system is aimed not only for those who wish to understand a sign language, but also speak using gesture recognition software. In this paper, a new benchmark dataset for Kazakh fingerspelling, able to train deep neural networks, is introduced. The dataset contains more than 10122 gesture samples for 42 alphabets. The alphabet has its own peculiarities as some characters are shown in motion, which may influence sign recognition. Research and analysis of convolutional neural networks, comparison, testing, results and analysis of LeNet, AlexNet, ResNet and EffectiveNet – EfficientNetB7 methods are described in the paper. EffectiveNet architecture is state-of-the-art (SOTA) and is supposed to be a new one compared to other architectures under consideration. On this dataset, we showed that the LeNet and EffectiveNet networks outperform other competing algorithms. Moreover, EffectiveNet can achieve state-of-the-art performance on nother hand gesture datasets. The architecture and operation principle of these algorithms reflect the effectiveness of their application in sign language recognition. The evaluation of the CNN model score is conducted by using the accuracy and penalty matrix. During training epochs, LeNet and EffectiveNet showed better results: accuracy and loss function had similar and close trends. The results of EffectiveNet were explained by the tools of the SHapley Additive exPlanations (SHAP) framework. SHAP explored the model to detect complex relationships between features in the images. Focusing on the SHAP tool may help to further improve the accuracy of the model