Reviewerly/exHarmony huggingface dataset:
Research on Overfitting Problem and Correction in Machine Learning

Abstract Machine learning is the key technology of artificial intelligence, which uses learning and training data model to find the problem to achieve the law. In practical applications, there is always a difference between the input data of the model and the training data. Based on the biased training data, overfitting will occur, which will cause the machine learning to fail to achieve the expected goals. Generalization is the process of ensuring that the model can fully reflect the characteristics of the actual input data. Therefore, the ability of the machine learning model depends to a large extent on the effectiveness of generalization. In order to fully understand the learning and generalization model, we conducted a study by the polynomial curve fitting. First, based on the training data, we analyzed using matrix theory and analytical solutions derived polynomial fit. Then, numerical and maximum likelihood theoretical analysis was carried out for the overfitting problem. Finally, combined with the objective function, a typical regularization method to overcome overfitting and improve generalization ability is elaborated.
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer.

Text style transfer is the task that generates a sentence by preserving the content of the input sentence and transferring the style. Most existing studies are progressing on non-parallel datasets because parallel datasets are limited and hard to construct. In this work, we introduce a method that follows two stages in non-parallel datasets. The first stage is to delete attribute markers of a sentence directly through a classifier. The second stage is to generate a transferred sentence by combining the content tokens and the target style. We experiment on two benchmark datasets and evaluate context, style, fluency, and semantic. It is difficult to select the best system using only these automatic metrics, but it is possible to select stable systems. We consider only robust systems in all automatic evaluation metrics to be the minimum conditions that can be used in real applications. Many previous systems are difficult to use in certain situations because performance is significantly lower in several evaluation metrics. However, our system is stable in all automatic evaluation metrics and has results comparable to other models. Also, we compare the performance results of our system and the unstable system through human evaluation. Our code and data are available at the link (this https URL).
Implementation of A Neuron Using Sigmoid Activation Function with CMOS

A multi-input neuron circuit with high-precision Sigmoid activation function (AF) is presented in this paper. The proposed circuit composed of input signal weighting circuit, current-voltage conversion circuit, and Sigmoid AF fitting circuit. Designed circuit can fit Sigmoid function based on the current-voltage relationship of differential pairs. The circuit is simulated in TSMC 0.18 um CMOS technology. The post-simulation shows that the error between the output of Sigmoid AF fitting circuit and the ideal Sigmoid function is 1.76%. The area of the layout is 375 μm ×238 μm. The maximum error caused by noise in the output of the circuit is 80 pA/√{Hz}.
Security-Performance Trade-offs of Kubernetes Container Runtimes

The extreme adoption rate of container technologies along with raised security concerns have resulted in the development of multiple alternative container runtimes targeting security through additional layers of indirection. In an apples-to-apples comparison, we deploy three runtimes in the same Kubernetes cluster, the security focused Kata and gVisor, as well as the default Kubernetes runtime runC. Our evaluation based on three real applications demonstrate that runC outperforms the more secure alternatives up to 5x, that gVisor deploys containers up to 2x faster than Kata, but that Kata executes container up to 1.6x faster than gVisor. Our work illustrates that alternative, more secure, runtimes can be used in a plug-and-play manner in Kubernetes, but at a significant performance penalty. Our study is useful both to practitioners - to understand the current state of the technology in order to make the right decision in the selection, operation and/or design of platforms - and to scholars to illustrate how these technologies evolved over time.
Cyber Security Canvas for SMEs

In an increasingly digitalized and networked world, information security and cyber security pose ever greater challenges to organizations. Cyber-attacks cause high economic damage and can bring organizations to ruin. Many small and medium-sized enterprises (SME) are under the illusion that only the large companies are the victims of an attack; they protect their valuable data against this background only poorly. But even in research, the focus is generally not on SMEs. In the context of this work, an easy-to-use Cyber Security Canvas is therefore being modelled to close this gap and to allow SMEs pragmatic access to the topic. The model framework is supplemented with modular building blocks. The building blocks can be put together individually according to the requirements and needs of the SMEs using them, with the model dividing them up according to priority. The newly designed Cyber Security Canvas was put through an application test with a European-based SME in order to gain first insights into its practical suitability in the European context. The model proved to be successful, and was well received by the participant. Nevertheless, it has potential for improvement.
Performance improvement of two-axis solar tracking system by using flat-mirror reflectors

This work studied the performance improvement of a two-axis solar tracking system by using flat-mirror reflectors. The two- and four-sided flat-mirror reflectors were set on the solar panel with angles of 90°, 120°, and 150° between the panel and flat-mirror reflectors. The highest efficiency, electrical power, and current were observed for four-sided flat mirrors at the angle of 120°, followed by two-sided flat mirrors at the angle of 120°, two-sided flat mirrors at the angle of 150°, two-sided flat mirrors at the angle of 90°, four-sided flat mirrors at the angle of 150°, and four-sided flat mirrors at the angle of 90°, respectively, because when the four-sided flat mirrors were mounted at a suitable degree of 120°, this improved the collected irradiance. At 90°, the four-sided flat mirrors blocked more solar rays than the two-sided ones. At 150°, the angle was too big for the solar ray to be reflected to the solar panel. The results indicated that the performance of the solar tracking system (efficiency, electrical power, and current) was affected by the number and angle of flat mirrors. The results of this study can be applied for the efficient performance of a novel solar tracking system design. This will be beneficial for the use of solar energy.
Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods

Bitcoin is invented in 2009 by the pseudonymous Satoshi Nakamoto. Bitcoin is a decentralized digital currency system [1]. Bitcoin is the most acknowledged cryptocurrency in the world, which provide it interesting for financier. The cryptocurrency market capitalization on date 22nd July 2020 value represents roughly USD 277 billion of dollars, bitcoin representing 62% of it. However, a disadvantage for investors is the difficulty of predicting the price of bitcoin due to the high volatility of the bitcoin exchange rate. Measurement, estimation, and modeling of currency exchange rate volatility compose a significant research area. For this reason, a lot of studies done about bitcoin price prediction both Machine Learning (ML) and Statistical Methods. In comparison studies, ML methods perform better in general. This review is a comprehensive study on how we can better predict bitcoin prices by grouping previously done studies. The presentation of Bitcoin price prediction studies in groups reveals, the difference from other review studies. These are statistical methods, ML and statistical methods, ML-ML, frequency effect of selected time, effect of social media and web search engine, causality, optimization of hyperparameters methods.
RSDS: Getting System Call Whitelist for Container Through Dynamic and Static Analysis

Container technology has been used for running multiple isolated operating system distros on a host or deploying large scale microservice-based applications. In most cases, containers share the same kernel with the host and other containers on the same host, and the application in the container can make system calls of the host kernel like a normal process on the host. Seccomp is a security mechanism for the Linux kernel, through which we can prohibit certain system calls from being executed by the program. Docker began to support the seccomp mechanism from version 1.10 and disables around 44 system calls out of 300+ by default. However, for a particular container, there are still many system calls that are unnecessary for running it allowed to be executed, and the abuse of system calls by a compromised container can trigger the security vulnerabilities of a host kernel. Unfortunately, Docker does not provide a way to get the necessary system calls for a particular container. In this paper, we propose RSDS, a method combining dynamic analysis and static analysis to get the necessary system calls for a particular container. Our experiments show that our solution can reduce system calls by 69.27%-85.89% compared to the default configuration on an x86-64 PC with Ubuntu 16.04 host OS and does not affect the functionalities of these containers.
VR Training Environment for Electrical Vehicle Assembly Training in EEC

Hands-on training is the best way of learning, especially the training in industry. One of the most common problems if a company must train a great amount of workers at the same time is the high cost of training sets. The emerging of electrical vehicle (EV) forces the traditional automotive industry to shift its manufacturing paradigm, especially in the Eastern Economic Corridor (EEC) area of Thailand. To enable the training to run parallelly, the concept of virtual reality (VR) training is constructed. This work demonstrates the effective approach of pedagogical and instructional training courses (ADDIE model) based on VR technology. This virtual reality training environment functions as expected and receives a remarkable high score of 4.7 out of 5.
A Structured Approach towards Robust Database Collection for Language Identification

Language Identification (LID) is a method by which the language is identified from an utterance communicated by an unidentified person. Language Identification systems classification is based on database collection methodologies, techniques of extraction of features from the speech signals and methods of classification. From the very beginning stage of database collection to the classification stage, it is evaluated that there is a great need to work on all the dimensions of language identification systems. This paper gives a review of different databases used in the area of Indian Language Identification systems. Also, the existing work delivers a structured method towards robust speech database collection for developing an efficient language identification system. The paper also presents several databases available for language identification systems.
Discrete Pigeon-Inspired Optimization-Simulated Annealing Algorithm and Optimal Reciprocal Collision Avoidance Scheme for Fixed-Wing UAV Formation Assembly

Multi-UAV system is an important part of unmanned system, which plays an indispensable role in military field and civil agriculture. First, task assignment model including complex constraints is established and Discrete Pigeon-Inspired Optimization-Simulated Annealing algorithm (DPIO-SA) is proposed to solve it, which updates the speed and position of pigeons through exchange and cross operations. Then, Genetic Algorithm (GA) is adopted to optimize the UAVs’ locations in formation. The experimental results show that the average fitness value of DPIO-SA is 13.5% higher than DPIO; After running the algorithm for 30 times, the number of times that DPIO-SA algorithm reaches the global optimum is 15, while DPIO is 2. Both mean DPIO-SA is easier to jump out of local extremum. To describe the fixed-wing UAV, the Unicycle model is adopted. PID control is used to control the fixed-wing’s heading and speed. Aiming at the collision avoidance, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is proposed, which allows fixed-wings to avoid collisions without having to communicate with each other. In the algorithm, the velocity region is divided by the definition of velocity obstacle, and the optimal velocity is obtained by linear planning algorithm. This enables the fixed-wings’ formation to find the right velocity in real time and effectively to avoid collision. Experiments show that 24 fixed-wings completed the formation assembly after running 16.4 s. Finally, according to the Contract Network Algorithm (CNA), the task scheduling problem is solved by the interaction among fixed-wings.
Sentiment Analysis of E-commerce Customer Reviews Based on Natural Language Processing

E-commerce can largely boost the economic development and customer behavior analysis is necessary for e-commerce marketing strategy. We used the dataset of Women's E-Commerce Clothing Reviews to study the sentiment analysis of customer recommendation. Five popular machine learning algorithms were applied to solve the problem, including Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost and LightGBM. These algorithms aimed at figuring out the insight correlation between review features and product recommendation based on natural language processing (NLP). The best result was achieved by LightGBM algorithm with highest AUC value and accuracy. The precision, recall and F1 score were all 0.97. Ridge Regression, Linear Kernel SVM and XGboost algorithms which had close performances with the accuracy of 0.94. This research can help generate a deeper comprehension of customer sentiment and grasp customer psychology in e-commerce transaction industry
A Novel Encryption and Decryption of Data using Mobile Cloud Computing Platform

As the need for super-fast mobile devices incorporating cloud computing technology continues to be the need of the hour, Mobile Cloud Computing (MCC) serves as the platform for mobile users to share data with others, store information on the cloud and also compute using the data. Over the years, the most widely preferred encryption that has proven to be reliable is Attribute Based Encryption (ABE). However, this encryption methodology requires expensive pairing operation which makes it unsuitable for MCC. As a result of this, MCC remains slow in reaching the crowd due to the challenge of resource-constrained mobile devices. To tackle this resource-constraint we propose a novel method of outsourcing operations to resource-rich cloud servers so that the constraint on resources does not hinder proper functioning of the mobile device. There are a number of advantages when data sharing is incorporated with lightweight fine-grain data sharing methodology. This method has a number of advantages such as CCA security level, resisting decryption key exposure and supporting verifiable outsourced decryption. Simulation results indicate that the performance analysis and concrete security proof is apt for MCC environment.
Weighted combination of BERT and N-GRAM features for Nuanced Arabic Dialect Identification

Around the Arab world, different Arabic dialects are spoken by more than 300M persons, and are increasingly popular in social media texts. However, Arabic dialects are considered to be low-resource languages, limiting the development of machine-learning based systems for these dialects. In this paper, we investigate the Arabic dialect identification task, from two perspectives: country-level dialect identification from 21 Arab countries, and province-level dialect identification from 100 provinces. We introduce an unified pipeline of state-of-the-art models, that can handle the two subtasks. Our experimental studies applied to the NADI shared task, show promising results both at the country-level (F1-score of 25.99%) and the province-level (F1-score of 6.39%), and thus allow us to be ranked 2nd for the country-level subtask, and 1st in the province-level subtask.
Towards Multimodal Simultaneous Neural Machine Translation

Simultaneous translation involves translating a sentence before the speaker’s utterance is completed in order to realize real-time understanding in multiple languages. This task is significantly more challenging than the general full sentence translation because of the shortage of input information during decoding. To alleviate this shortage, we propose multimodal simultaneous neural machine translation (MSNMT), which leverages visual information as an additional modality. Our experiments with the Multi30k dataset showed that MSNMT significantly outperforms its text-only counterpart in more timely translation situations with low latency. Furthermore, we verified the importance of visual information during decoding by performing an adversarial evaluation of MSNMT, where we studied how models behaved with incongruent input modality and analyzed the effect of different word order between source and target languages.
Toxic Comment Classification Using Hybrid Deep Learning Model

With the increasing availability of affordable data services and social media presence, our life is not untouched with ‘cyber,’ i.e., electronic technology. With it, various challenges and issues are faced, and the most sensitive among them is Cyberbullying. Cyberbullying is the form of ‘abusive,’ ‘offensive,’ ‘inappropriate,’ and ‘toxic’ comments that are present on the platforms. With the fear of online abuse and bullying, many people give-up on perceiving different opinions and stop expressing themselves. Nowadays, various online platforms like Quora, Wikipedia, Twitter, and Facebook have become part and parcel of everybody’s life. These stages battle to viably encourage discussions, driving numerous networks to restrict or shutdown client remarks. Unfortunately, online comments with toxicity cause online badgering, bullying, and personal attacks. Therefore, toxic comment classification problem has attracted the attention of many organizations from the past few years. Hence, in this paper, we present a hybrid Deep Learning model that will detect such toxic comments and classify them according to the type of toxicity. As an outcome, we achieved the best results with an accuracy of 98.39% and an f1 score of 79.91%.
HyperGrid Transformers: Towards A Single Model for Multiple Tasks

Achieving state-of-the-art performance on natural language understanding tasks typically relies on fine-tuning a fresh model for every task. Consequently, this approach leads to a higher overall parameter cost, along with higher technical maintenance for serving multiple models. Learning a single multi-task model that is able to do well for all the tasks has been a challenging and yet attractive proposition. In this paper, we propose HyperGrid Transformers, a new Transformer architecture that leverages task-conditioned hyper networks for controlling its feed-forward layers. Specifically, we propose a decomposable hypernetwork that learns grid-wise projections that help to specialize regions in weight matrices for different tasks. In order to construct the proposed hypernetwork, our method learns the interactions and composition between a global (task-agnostic) state and a local task-specific state. We conduct an extensive set of experiments on GLUE/SuperGLUE. On the SuperGLUE test set, we match the performance of the state-of-the-art while being 16 times more parameter efficient. Our method helps bridge the gap between fine-tuning and multi-task learning approaches.
Adaptive comprehensive particle swarm optimisation‐based functional‐link neural network filtre model for denoising ultrasound images

Multiplicative speckle is a dominant type of noise that spoils the inherent features of the medical ultrasound (US) images. Apart from the speckle, impulse and Gaussian noises also appear in the US image due to the error encountered during the data transmission and transition of switching circuits and sensors. The noise not only deteriorates the visual quality of the US but also creates complications in the diagnosis. In this study, an adaptive comprehensive particle swarm optimisation-based functional-link neural network (ACPSO-FLNN) filtre has been proposed and implemented in filtering noisy US images in different noise conditions. The proposed filtre is compared with some state-of-the-art filtering techniques. Quantitative and qualitative measures such as training time, time complexity, convergence rate, and statistical test are included to study the performance of the proposed filtre. Furthermore, sensitivity, computational complexity, and order of the proposed filtre are also investigated. Friedman's test with 50 images is performed for statistical validation. The lower rank, that is, 6 and critical value of 21 × 10–4 of the proposed ACPSO-FLNN filtre validates its dominance over other filtres.
Distributed Computation: Privacy, Straggler Mitigation, and Security Against Colluding Workers

In a distributed computation system, there is a master node who wants to compute a function of its own data by distributing the computation amongst several worker nodes. In distributed computation system considered here, the master wants to multiply two matrices, one owned by the master and other matrix is in the library of matrices shared by the worker nodes. The master divides the computation task among these nodes. After recovering the desired computation, any set of up to T colluding workers should not know which matrix in the library was desired by the master, referred to as demand privacy and also do not have any information about master's matrix, referred to as data privacy. In this paper, a distributed computing scheme is proposed that simultaneously ensures (1) data and demand privacy of the master against colluding workers (2) straggler mitigation and (3) security against malicious workers. Similar matrix multiplication scenario was considered against non-colluding worker nodes by M. Kim and J. Lee in Private Secure Coded Computation.
Bitcoin Could Be the First Cryptocurrency to Reach a Market Capitalization of One Trillion Dollars

Bitcoin blockchain possesses immense potential for future opportunities, well beyond its current use in financial services underpinning cryptocurrencies, i.e. replacing traditional trusted third parties with trusted machines. Despite over a decade has passed since Nakamoto Satoshi launched Bitcoin in January 2009, it still continues to face barriers, challenges as well as a major regulatory hurdle in the U.S. and Europe. This paper looked into three hypothetical scenarios where the price of bitcoin surges over $50,000 (scenario 1), $100,000 (scenario 2), and $1,000,000 (scenario 3). Although new path-breaking technologies and inventions (i.e. Bitcoin) will continue to forge ahead unabated regardless of doubters, doomsayers, skeptics, pessimists, disbelievers, and short-sighted politicians (President Trump); however, these scenarios can only become a reality if the Trump administration and law makers stop constantly running headlong into backlash to cryptocurrencies (Bitcoin and Libra coin in particular). For future price growth of Bitcoin and altcoins, cryptocurrency markets need more people like Christine Lagarde, the former Managing Director of the IMF (currently, president of the European Central Bank), who urged central banks not to ignore “winds of change” and consider looking into the case of central bank digital currency.