Reviewerly/exHarmony huggingface dataset:
An Automated Framework for Migrating Java Applications to Ethereum Solidity Applications

With the advancement of blockchain technology at a breakneck pace, numerous organizations are now experimenting with blockchain solutions for their operations. In this nascent stage, due to the scarcity of blockchain domain experts, there is a growing trend towards an automated conversion of legacy systems to blockchain-based systems. To this aim, this paper presents an automated framework that facilitates the migration of centralized Java-based applications to decentralized Solidity-based Ethereum applications. The experimental results are encouraging, demonstrating its ability to handle large-scale Java codebase. To the best of our knowledge, this is the first proposal of its kind that enables the translation of Java source codes to Solidity source codes.
A strong call-by-need calculus

We present a call-by-need $\lambda$-calculus that enables strong reduction (that is, reduction inside the body of abstractions) and guarantees that arguments are only evaluated if needed and at most once. This calculus uses explicit substitutions and subsumes the existing strong-call-by-need strategy, but allows for more reduction sequences, and often shorter ones, while preserving the neededness. The calculus is shown to be normalizing in a strong sense: Whenever a $\lambda$-term t admits a normal form n in the $\lambda$-calculus, then any reduction sequence from t in the calculus eventually reaches a representative of the normal form n. We also exhibit a restriction of this calculus that has the diamond property and that only performs reduction sequences of minimal length, which makes it systematically better than the existing strategy. We have used the Abella proof assistant to formalize part of this calculus, and discuss how this experiment affected its design. In particular, it led us to derive a new description of call-by-need reduction based on inductive rules.
New BBO Algorithm with Mixed Migration and Gaussian Mutation

This paper proposes a new type of biogeography-based optimization with mixed migration and Gaussian mutation. First, a hyperbolic tangent migration model is adopted, which is more in line with natural science; secondly, for the core step of the algorithm-migration operation, a hybrid migration mode with the addition of a perturbation factor \(\gamma\) is used to enhance the algorithm's global search capabilities, At the same time, Gaussian mutation is added to the original BBO mutation operation. These two mutation methods work together on the mutation operation, and the final results show that the proposed improvement strategy can on the one hand improve searching accuracy, on the other hand accelerate convergence speed of the algorithm.
Node Classification Using Gated Attention Network for Discussions

In recent years, large-scale discussions on theWeb have attracted much attention. In this situation, research on large-scale online consensus building support systems has been actively conducted. Facilitators are essential to discuss smoothly in the discussion. However, it is difficult for humans to play the role of facilitator in a large-scale discussion because participants post hundreds or thousands of opinions and the discussion may be held all day long. Therefore, developing automated facilitation agent that can automatically facilitate discussions is required. In order to develop automated facilitation agent, there are previous studies on extracting discussion as a graph structure. The purpose of this study is to perform node classification, which is one of the subtask of discussion structure extraction, with high accuracy. In the proposed method, we use Bidirectional Encoder Representations from Transformers (BERT), which was the state of the art in the field of natural language processing at the time of its release and is still widely studied and used today, to obtain distributed representations from sentences. We also use Gated Attention Network (GaAN), which has an attention mechanism and can propagate and calculate the information of neighboring nodes including the importance, in order to classify the obtained distributed representations. We adopt the Issue-Based Information System (IBIS) structure, which is designed to promote creative and constructive discussions, for the discussion structure. The experimental results show that our method can classify opinions with higher accuracy than previous studies and other general classification methods.
An Enhanced Multi-frequency Learned Image Compression Method

Learned image compression methods have represented the potential to outperform the traditional image compression methods in recent times. However, current learned image compression methods utilize the same spatial resolution for latent variables, which contains some redundancies. By representing different frequency latent variables with different spatial resolutions, the spatial redundancy is reduced, which improves the R-D performance. Based on the recently introduced generalized octave convolutions, which factorize latent variables into different frequency components, an enhanced multi-frequency learned image compression method is introduced. In this paper, we incorporate the channel attention module into multi-frequency learned image compression network to improve the performance of adaptive code word assignment. By using the attention module to capture the global correlation of latent variables, complex parts of the image such as textures and boundaries can be better reconstructed. Besides, an enhancement module on decoder side is utilized to generate gains. Our method shows the great visual appearance and achieves a better grade on the MS-SSIM distortion metrics at low bit rates than other standard codecs and learning-based image compression methods.
Automated Sign Language Alphabet Detection

The possibility of using sensor-based data acquisition to supply the necessary information to an Artificial Neural Network (ANN), for South African Sign Language (SASL) alphabet recognition, is investigated. To accomplish this, a data glove was designed and implemented to generate the necessary sensory information. Thereafter, training the ANN to recognize a selection of signs in the SASL alphabet was achieved. The system was able to recognize six signs with an accuracy of 96%. This result provides a foundation for a system that will enable bilateral communication between the deaf and hearing demographic to mitigate the communication hindrance.
High-speed camouflage detection with checkerboard projection

Camouflaged objects can hide into the surrounding environments by changing their appearances. It is difficult to discover them with traditional detection methods. To address the problem, this paper proposes an effective checkerboard projection method for high-speed camouflage detection. Specifically, the projector illuminates two complementary checkerboard patterns onto the monitoring region, meanwhile the camera records the deformed patterns. Then we develop an image analysis algorithm to extract the local variations of the deformed patterns, which can indicate the locations and contours of camouflaged objects. Both simulations and experiments verify that the proposed method can effectively detect the objects camouflaged in complex environments with the advantages of high speed, good robustness, and easy to use.
Assessing the Health of the Dark Web:

Abstract A hidden part of the World Wide Web is known as the Dark Web, featuring websites that cannot be indexed by traditional search engines. Many open source software products are used to access and navigate through the Dark Web. Together they form the Dark Web open source software ecosystem. Research on this ecosystem is scarce and research on the ecosystem health is non-existent, even though ecosystem health is an useful indicator of the livelihood of an ecosystem. The goal of this research is to evaluate the health of the ecosystem through an assessment of Tor, I2P and GitHub. The Open Source Ecosystem Health Operationalization framework is used to help perform this assessment. Eight metrics from the framework are selected, which are measured using the data collected. Analysis of Tor and I2P metrics suggest that there has been an increase in Tor and I2P user activity in the recent past. Added knowledge, spin offs and forks and usage indicate active participation and interest in Tor and I2P. There has also been an increase in the number of active GitHub Dark Web projects. However, these GitHub projects are not well-connected and only a small number of projects have a large number of contributors. There is some variety among the GitHub software projects. The framework proves to be adequately capable of determining the health of the Dark Web open source ecosystem with the available data.
DSL to Support the Process of Creating Security Documentation for Classified Systems

In this article, we will focus on creating a Domain-Specific Language (DSL) that can be used to streamline the demanding process of creating security documentation for classified systems. The article is mainly focused on the work of the language designer, so you will not find a detailed description of the domain for which the language is designed. More importantly, the article provides an example of the possible use of our DSL.
Data Augmentation Technology for Dysarthria Assistive Systems

Voice-driven communication aids are one of the methods commonly used by patients with dysarthria. However, this type of assistive devices demands a large amount of voice data from patients to increase the effectiveness. In the meantime, this will sink patients into an overwhelming recording burden. Due to those difficulties, this research proposes a voice augmentation system to conquer the aforementioned concern. Furthermore, the system can improve the recognition efficiency. The results of this research reveal that the proposed speech generator system for dysarthria can launch corpus to be more similarities to the patient’s speech. Moreover, the recognition rate, in duplicate sentences, has been improved and promoted to the higher level. The word error rate can be reduced from 64.42% to 4.39% in the case of patients with Free-talk. According to these results, our proposed system can provide more reliable and helpful technique for the development of communication aids.
Adversarial Training for Image Captioning Incorporating Relation Attention

Image captioning methods with attention mechanism are leading this field, especially models with global and local attention. But there are few conventional models to integrate the relationship information between various regions of the image. In this paper, this kind of relationship features are embedded into the fused attention mechanism to explore the internal visual and semantic relations between different object regions. Besides, to alleviate the exposure bias problem and make the training process more efficient, we combine Generative Adversarial Network with Reinforcement Learning and employ the greedy decoding method to generate a dynamic baseline reward for self-critical training. Finally, experiments on MSCOCO datasets show that the model can generate more accurate and vivid image captioning sentences and perform better in multiple prevailing metrics than the previous advanced models.
A Novel Approach to Detect Low Quality Deepfake Videos

Exceptional improvements in the domain of Deep Learning (DL) have resulted in the surge of highly realistic Artificial Intelligence (AI) generated fraudulent videos, generally known as Deepfakes. Although this technology has infinite useful applications, there are also notable concerns about the drawbacks of the same. The generation of false videos about public figures such as politicians or actors to despoil their character and to trick the public is one such application. So there is an urgent need to develop a system that would detect and mitigate the negative impact of these AI-generated videos on society. High quality Deepfake videos were easily detected by the previously created models. But the efficiency of the system would gradually decline when tested for low quality videos. The videos that are transferred through various social media sites are of low quality, so the detection of such videos becomes challenging. So, in this system using the concept of super resolution along with Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), detection of the low quality Deepfakes is carried out. In this, the concept super resolution increases the quality of videos and then the CNN architecture with the help of LSTM will classify the videos as Deepfake or not by considering the given facial features. For this project, the proposed framework was used on the FaceForensics++ dataset. This system has thus aimed to develop a model that detects such low quality Deepfake videos with better accuracy.
Self-compassion and body responsiveness on premenstrual syndrome among female.

The reason for this examination was to distinguish the impacts of Objectified Body Consciousness and Self-sympathy on premenstrual condition among female undergrads and components that impact the Premenstrual Syndrome among understudy. Strategies: The subjects of this investigation were the 525 quantities of undergrads in the one college and two junior college in D city. Poll overview was directed from May through June, 2017. Information were dissected utilizing SPSS variant 20.0 for enlightening insights, t-test, single direction ANOVA. To look at impacting factors on premenstrual condition, Pearson's connection coefficient and progressive relapse technique were utilized. Results: On the one hand, there were positive connections between’ s body surveillance (r=. 179, p<.001), body shame (r=.327, p<.001) and premenstrual condition. Likewise, there was positive relationship between’ s body observation and body shame (r=.346, p<.001)
Gapoera: Application programming interface for AI environment of Indonesian board game

Currently, the development of computer games has shown a tremendous surge. The ease and speed of internet access today have also influenced the development of computer games, especially computer games that are played online. Internet technology has allowed computer games to be played in multiplayer mode. Interaction between players in a computer game can be built in several ways, one of which is by providing balanced opponents. Opponents can be developed using intelligent agents. On the other hand, research on developing intelligent agents is also growing rapidly. In computer game development, one of the easiest ways to measure the performance of an intelligent agent is to develop a virtual environment that allows the intelligent agent to interact with other players. In this research, we try to develop an intelligent agent and virtual environment for the board game. To be easily accessible, the intelligent agent and virtual environment are then developed into an Application Programming Interface (API) service called Gapoera API. The Gapoera API service that is built is expected to help game developers develop a game without having to think much about the artificial intelligence that will be embedded in the game. This service provides a basic multilevel intelligent agent that can provide users with playing board games commonly played in Indonesia. Although the Gapoera API can be used for various types of games, in this paper, we will focus on the discussion on a popular traditional board game in Indonesia, namely Mancala. The test results conclude that the multilevel agent concept developed has worked as expected. On the other hand, the development of the Gapoera API service has also been successfully accessed on several game platforms.
Collaborative Positional-Motion Excitation Module for Efficient Action Recognition

Massive progress for vision-based action recognition has been made in the last few years, owing to the advancement of deep convolutional neural networks (CNNs). In contrast with 2D CNN-based approaches, 3D CNN-based approaches can effectively capture spatial and temporal features. However, they are computationally intensive. To boost 2D-CNN performance, most of the existing methods leverage channel attention (e.g. squeeze and excitation), which despite its strong impact on the model performance, operates only on the channel space and ignores the spatial space. In this work, we design a generic and collaborative excitation module, namely the Collaborative Positional-Motion Excitation Module (CPME) for action recognition. CPME is a dual-pathway excitation module designed to embed the crucial types of information, mainly the positional information and the motion information, for efficient action recognition. Positional Enhancement Pathway (PEP), the first pathway of CPME, considers encoding direction-aware and position-sensitive information. Motion Enhancement Pathway (MEP), the second pathway, encodes the motion information by emphasizing the informative features in each frame and excite motion-sensitive channels. We integrate the proposed CPME into 2D CNNs to form a simple yet effective CPME-Net with limited extra computational cost. Finally, a discriminative and diverse video-level representation for action recognition is generated by end-to-end training. Experiments on two popular action recognition datasets demonstrate that CPME blocks bring performance improvements on 2D CNN baseline, and our method achieves competitive results against the state-of-the-art methods.
Deep Classification of Gun Carried by Moving Persons Using Proposed TUVD-CSA Dataset

The ability to detect handheld gun or gun on the other body parts is an ordinary human skill; the same detection problem presents an exceptional challenge for computer vision. Very few works has been reported in the area of real-time detection of scene where persons carrying gun, although it has several implication in the area of security and surveillance. Using real-time gun detection to improve surveillance methods is a promising application of Convolutional Neural Networks (CNNs). In the present scope of this article, we are particularly interested in the real-time scene detection where person with gun appears. As a result, a comparison is made between the existing state-of-the art classification techniques based on CNNs architecture using our created mimic dataset Tripura University Video Dataset for Crime Scene Analysis (TUVD-CSA). For an endways fine-tuning using contemporary architectures, we witness the direct proportion of performance and network complication.
Machine Learning Theory in the Strategic Management of Regional Risk Factors Measurement

Under the background of the state’s strong support for entrepreneurship, domestic small- and medium-sized enterprises ushered in the climax of development, but there are still crises coexisting with opportunities. According to statistics, most small- and medium-sized enterprises cannot survive the first three years of the initial stage of entrepreneurship. It can be said that risks exist all the time for enterprises. How to face the risk crisis and effectively avoid these regional risks has become an important factor for enterprises to survive for a long time. The accelerating pace of global economic integration has not only brought opportunities to enterprises but also brought challenges to the survival of enterprises. At present, there are few studies on regional risk in China and most of them are qualitative studies; there is no more specific quantitative study on risk factors. In view of this situation, this paper will study the quantitative evaluation model of regional risk factors based on machine learning. The development of this model adopts the method of support vector machine, which is a more commonly used risk assessment machine learning method. In order to better assess the risk, this paper also establishes a risk assessment index system, which classifies the factors of regional risk in detail and gives the specific evaluation method. Through the combination of modern technologies such as intelligent computing, semisupervised learning, and strategic center organization, the final model is established. After four risk prediction experiments including measuring the net profit margin of total assets of enterprise a, the data shows that the accuracy of the risk assessment model in this paper has been greatly improved compared with the traditional way and shows that the short-term prediction is higher than the long-term prediction and the overall prediction effect is relatively ideal, which can be applied to the practical management of regional risk prediction of enterprises.
Machine Learning based Speaker Gender Classification using Transformed Features

Speech and image processing are fundamental components of artificial intelligence technology. Speech processing can be deployed to acquire unique features of a person's voice. These can then be used for speaker identification as well as gender and age classification. This paper studies the effect of the relative degree of correlation in speech features on gender classification. To this end, gender classification performance is evaluated using orthogonally transformed speech features. The performance is then compared to the case when speech features are used without transformation. Two machine learning approaches are used in the evaluation. One of them primarily depends on Gaussian Mixture Models (GMM) and the other one uses Support Vector Machines (SVM). The results show that less correlated speech features, obtained after the orthogonal transformation, provides better classification performance.
Research on Cross-Project Software Defect Prediction Based on Machine Learning

In recent years, machine learning technology has developed vigorously. The research on software defect prediction in the field of software engineering is increasingly adopting various algorithms of machine learning. This article has carried out a systematic literature review on the field of defect prediction. First, this article studies the development process of defect prediction, from correlation to prediction model. then this article studies the development process of cross-project defect prediction based on machine learning algorithms (naive Bayes, decision tree, random forest, neural network, etc.). Finally, this paper looks forward to the research difficulties and future directions of software defect prediction, such as imbalance in classification, cost of data labeling, and cross-project data distribution.
Bitcoin Blackout: Proof-of-Work and the Centralization of Mining

Electricity constitutes the main input factor for miners of proof-of-work cryptocurrencies like Bitcoin, so they gravitate towards countries with cheap energy. We analyze risks associated with this geographical centralization of mining by exploiting an exogenous shock to electricity supply in a relatively small region with heavy Bitcoin mining activity. We first document a drop of about 25% in the total computing power of the Bitcoin network during a blackout that lasts several days. Compared to a control group consisting of a proof-of-stake cryptocurrency, we find evidence of blockchain congestion as fees increase substantially while the number and value of transactions decrease. We also document an impact on exchange trading activity. Trading volume and especially exchange rate volatility increase while liquidity deteriorates during the blackout, even though returns are mostly unaffected. Additionally, market integration drops as price differences between exchanges increase considerably.