Reviewerly/exHarmony huggingface dataset:
An adaptive knowledge distillation algorithm for text classification

Using knowledge distillation to compress pre-trained models such as Bert has proven to be highly effective in text classification tasks. However, the overhead of tuning parameters manually still hinders their application in practice. To alleviate the cost of manual tuning of parameters in training tasks, inspired by the inverse decrease of the word frequency of TF-IDF, this paper proposes an adaptive knowledge distillation method (AKD). This core idea of the method is based on the Cosine similarity score which is calculated by the probabilistic outputs similarity measurement in two networks. The higher the score, the closer the student model&#x0027;s understanding of knowledge is to the teacher model, and the lower the degree of imitation of the teacher model. On the contrary, we need to increase the degree to which the student model imitates the teacher model. Interestingly, this method can improve distillation model quality. Experimental results show that the proposed method significantly improves the precision, recall and F1 value of text classification tasks. However, training speed of AKD is slightly slower than baseline models. This study provides new insights into knowledge distillation.
Scrum Best Practices Recommendation: a Media and Community Startup Case Study

XYZ Company is a media and community company that applies Scrum in the process of developing key business support applications. The results of problem identification through interviews with the Chief Technology Officer (CTO) and Chief Product Officer (CPO) showed that one of the root causes of the problem was the Scrum team did not implement the Scrum guidelines strictly. This research evaluates the maturity level of Scrum implementation using the Standard CMMI Appraisal Method for Process Improvement (SCAMPI C). Researchers used the Scrum Maturity Model (SMM) as a model for calculating maturity. This SMM has been updated by following the Scrum Guide 2020 and best practices in 2020-2021 obtained from the literature study. To affirm the appraisal result, This research also distributed questionnaires to 33 respondents from the product team and the technology team. This research recommends implementation improvements using the Plan, Do, Check, Act (PDCA) Cycle approach. The results of the Scrum implementation maturity assessment are on Level 2. This research recommends some practices to be implemented and improved in the objectives: (a) Scrum Role Exist; (b) Scrum Meetings Occur and are Participated, and (c) Daily Scrum Succeed. If applied consistently, XYZ will have a quality improvement in Scrum implementation.
A Collision Avoidance Algorithm for Idle Robots in Multi-robot System

Multi-robot systems (MRSs) play vital roles in manufacturing systems and logistics systems. In order to improve the efficiency of MRSs, we propose an idle robot collision avoidance algorithm(IRCAA) for idle robots to give way for working robots. In the proposed method, idle robots stop in place for new tasks without returning to robot stations. Once an idle robot gets in the way of working robots, IRCAA will find an avoidance point (AP) for the idle robot to give way. Additionally, no new block or deadlock occurs when the idle robot arrives at the AP. The proposed method in this paper is effective and able to improve the efficiency of MRSs, which is verified by both simulation experiments and actual industrial projects.
Development and Application of Facial Makeup Transfer

Facial makeup transfer is a technology that uses image processing technology to transfer the known makeup to the target face. The makeup transfer based on image editing has widespread applications in entertainment, social contact, identity authentication, and other fields. Firstly, this article briefly introduced the basic principles and practical application of makeup transfer, summarized the representative algorithms and models of facial makeup transfer in recent years, and then analyzed qualitative and quantitative evaluation criteria. Finally, the current situation in the technology at this stage are discussed, and look forward the future development direction of facial makeup transfer.
Chinese Authorship

The modern Chinese term “zuo zhe” (author) is derived from the ancient word “zuo,” meaning to compose, to do, or to engage in – all commanding a notion of power and authority. These semantic threads collectively underpin the long-standing contention that Chinese authorship began with Confucius. Mencius (390–305 BCE) was the first person to claim that Confucius was the author of the Spring and Autumn Annals: “When the world declined and the Way fell into obscurity, heresies and violence again arose. […] Confucius was apprehensive and composed [zuo] the Spring and Autumn Annals.”1 The Han historian Sima Qian (c.145–c.86 BCE) inherited this concept of “zuo,” acknowledging that “when Confucius was in straits he wrote (zuo) the Spring and Autumn Annals.”2
Classification of Modulation Error Rate Measurement using Convolutional Neural Networks in ISDB-T

This article proposes to perform detection and recognition of the modulations of the ISDB-T system and its measurement of the Modulation Error Rate (MER) through deep learning. Initially, a data set of 30,000 constellation images of the QPSK, 16-QAM, and 64-QAM modulations with different identified MER values in dB was generated. These data sets are used to train and validate a convolutional neural network based on the transfer learning in AlexNet network architecture, destined to recognize different types of images. The validation results and a test set obtained from the same database were highly satisfactory. Most of them approach 100% accuracy in the classification, which showed a good detection of modulation and especially discrimination of the MER value when evaluating constellations. ISDB-T signals transmitted by broadcast in the city of Quito-Ecuador and by the laboratory were also evaluated. A professional ISDB-T analyzer and a receiver designed with software-defined radio (SDR) ADALM-PLUTO were used for reception. The results in the receiving equipment show an accuracy of 100% of the detected modulation and very close values between the measured MER values and those obtained by the neural network.
Data Distribution Modelling in Supervised Learning Algorithm is for The classification of Prospective Recipient Candidate

Indonesia is a country with the largest Muslim population in the world. The number of practices of worship in Islam may affect several things. One of these practices is zakat. This is because zakat can help people with lower economic levels, even zakat has its role in reducing poverty. For this reason, zakat management is very important to be optimized. This research applies a classification to the data of prospective mustahik of BAZNAS Riau 2020. The purpose of this research was to find out the performance of classification algorithm in determining the feasibility of tithe recipient and to give the knowledge to the stakeholder in this case is BAZNAS Riau. The classification algorithms used are Probabilistic Neural Network (PNN), K-Nearest Neighbor (KNN), and Naive Bayes Classifier (NBC). The division of training and testing data is carried out using K-fold Cross-Validation and Hold out. The findings obtained are that the NBC algorithm has better performance with an accuracy of 97.12% based on the K-fold cross-validation division technique.
Every child a reader

What are the most effective strategies to ensure that every child leaves primary school a fluent reader? In this Best Practice Focus, Robbie Burns dissects the science, principles and essential elements of effective whole-school reading strategies before advising us how we might best implement these approaches across the primary school
Only pay for what you need: Detecting and removing unnecessary TEE-based code

A Trusted Execution Environment (TEE) provides an isolated hardware environment for sensitive code and data to protect a system’s integrity and confidentiality. As we discovered, programmers tend to overuse TEE protection. When they place non-sensitive code in TEE, the trusted computing base (TCB) grows unnecessarily, leading to long execution latencies and large attack surfaces. To address this problem, we first study a representative sample of open-source projects to uncover how TEE is utilized in real-world software. To facilitate the process of removing non-sensitive code from TEE, we introduce TEE Insourcing , a new type of software refactoring that identifies and removes the unnecessary program parts out of TEE. We implemented TEE Insourcing as the TEE-DRUP framework, which operates in three phases: (1) a variable sensitivity analysis designates each variable as sensitive or non-sensitive; (2) a TEE-aware taint analysis identifies non-sensitive TEE-based functions; (3) a fully-declarative program transformation automatically moves these functions out of TEE. Our evaluation demonstrates that our approach is correct, effective, and usable. By deploying TEE-DRUP to discover and remove the unnecessary TEE code, programmers can both reduce the TCB’s size and improve system performance. • A study of Trusted Execution Environments (TEE) usage in real-world software. • An identification of the problem of misplacing non-sensitive code in TEE. • A variable sensitivity analysis that designates (non-)sensitive program variables. • A compiler-based tool for removing non-sensitive code from TEEs.
Dropout Token To Improve Neural Language Model

Dropout, as an effective avoid over-fitting method for training a neural network, is widely used in both computer vision and natural language progressing. The typical approach is dropping out hidden and visible units. Specifically, the neural language model usually applies dropout for hidden units. However, few research applies dropout for input. In this study, we employ dropout on input token sequence. This is similar to mask but the critical difference is the masked tokens will not be predicted at all. Two benchmark dataset, EMNLP2017 WMT News and Penn Tree Bank, are experimented with. The experimental results show that our method outperforms baseline model significantly.
Improving the Speed of Machine Learning Algorithms using Bio-Inspired Techniques

Today, digital data is exploding at a breakneck speed. Traditional data analytics techniques, unfortunately, are rapidly losing their capabilities and efficiencies when dealing with large datasets. This problem has prompted several researchers to develop more effective, efficient, and fast big data analytics tools. Machine Learning (ML)-based approaches are among the most dependable techniques used to extract usable insights from large datasets. However, some of them cannot efficiently handle large datasets, and their training time grows with dataset size. This paper presents two Nature-Inspired techniques for improving the training time of ML algorithms and the processing time of big datasets. The techniques are evaluated on four ML algorithms and large or medium-scale datasets. Results show that the training time of the four ML algorithms was reduced without a significant drop in classification accuracy. Moreover, the proposed methods are significantly faster than two well-known instance selection methods. Furthermore, statistical analysis reveals that the techniques reduced data size significantly, making them suitable for processing large datasets.
The Effect of Discounting Actor-loss in Actor-Critic Algorithm

We analyze and present an experimental approach to see the effect of limiting the Temporal Difference (TD) error in estimating actor-loss on an actor-critic-based agent. The limitation is done by reducing the loss value of an actor to the factor of an epsilon <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> constant. In this experiment, we chose four epsilon values, i.e., 0.01, 0.1, 0.5, and 1.0, where 1.0 means no discount at all. In the experiment, we spawn four agents to solve a trivial task for humans in a custom lightweight Windows Operating System (OS)-like simulation. Each agent receives inputs of the simulation's screen image and controls the cursor inside the simulation to reach for any rendered red circles. After 50 episodes, 50,000 steps in total, each agent achieved about the same success rate with slight differences. The agent given an epsilon value of 0.01 achieved the highest success rate, higher than one without discount learning (epsilon=1.0), although not much.
Methodology

This chapter discusses the methodological framework for the empirical research on cycling and motorcycling tourism. Taking a critical realist approach to create knowledge, it illustrates the four-phased, mixed-method strategy later used to collect empirical evidence. It reflects upon the rationale for every methodological choice, illustrates the data collection process and reflects upon the strengths and weaknesses of quantitative, qualitative and mixed research methods. Several of these methods are critically presented: case study research, mobile video ethnography, ride-alongs, bio-sensing techniques, and self-report and observation techniques for emotion measurement, including facial action coding systems. Finally, the introduction of thick description and personas design as tools to integrate quantitative and qualitative data allows for a structured planning of the convergence of evidence from the multiple methodological sources.
A Gold Multipurpose Arabic Corpus (GAC)

A corpus is a large collection of spoken or written one or more language, it is collected from different resources, then structured, stored, and treated automatically by special algorithm to reach the desired goal, this is why it is used by researchers in different domain such as grammar, semantics, lexicography, natural Language Processing and other language studies. Therefore, building a corpus was still a challenge for many researchers in those fields for many years. Our study in this paper aims to build a new gold standard Arabic corpus due to the lack of successful trials in compiling Arabic corpora. The corpus produced by our team, is a text corpus, collected from a set of Arabic Newspaper articles morphologically analyzed from eight Arabic countries, and it contains more than 18 million words in total, covering six categories (Religion, Economy, Culture, Sports, Local and International News). It was encoded with UTF -8 encoding and marked with two mark-up languages: JSON and XML. In the hope that this corpus can be used as an accurate reference for segmentation and validation and learning in the syntax analysis mainly for the word segmentation and part of speech tagging.
A Conflict-Reducing Path Planning Algorithm in Automated Warehouses

With the development of modern logistics industry, automated warehouses are becoming more and more widely used. An automated guided vehicle (AGV) is often used to carry goods in warehouse. Under specific limitations of the road layout, AGVs are prone to conflict when moving. According to the layout characteristics of automated warehouses, this paper proposes a conflict-reducing path planning algorithm, which is an improvement of A-star algorithm. The conflict cost and turn cost are introduced to the algorithm to reduce the conflict probability and the number of turns of paths. Through simulation comparison, it is verified that the algorithm can reduce the probability of conflicts among AGVs and improve the overall efficiency of multi-AGV systems.
Exploiting Visual Content for Travel Location Recommendation

Image has become an important source to improve the quality of travel location recommendation, it reflects user interests and travel location properties. However, little work exists for travel location recommendation by exploiting images. In this study, we propose a method for accurate and personalized travel location recommendations using visual content. Specifically, a convolutional neural network is used to extract visual content which is used to learn the latent feature representation of implicit feedback and uncover user interests and travel location properties. In addition, the third-party web service is used to extract age and gender features from the images to understanding user interest and the travel location properties. Experimental results on real-world datasets demonstrate the effectiveness of our method.
Conditional Data Augmentation For Sky Segmentation

Outdoor scene parsing is a very popular topic which algorithms seek to labels or identify objects in images. Sky segmentation is one of the popular outdoor scene parsing task. Sky segmentation models are usually trained on ideal datasets and produce high quality results. However, the performance of sky segmentation model decreases because of varying weather conditions, different time and scene changes due to seasonal weather or other issues in reality. This paper focuses on applying data augmentation methods to generate diversified images. A conditional data augmentation method based on BicycleGAN is proposed in this paper. The model considers mask loss and content loss for improving the quality and details of the generated images. The experimental results demonstrate that the quality of the generated image is better than the existing methods.
Review on Generative Adversarial Neural Networks (GAN) in Text-to-Image Synthesis

In recent years there is a spike in text-to-image synthesis research. Most of the researches use Generative Adversarial Network (GAN) because of its effectiveness in generating a realistic synthetic image. In this paper, we provide several recent papers that focus on GAN based text-to-image synthesis and discuss their architecture, advantages of the model, dataset, and evaluation metric.
An Enhanced Big Data Handling Architecture for Privacy Preservation of Cloud Data

The cloud is the leading technology in the IT world for storing and managing massive quantities of data. Protection and data privacy preservation are two of the most common concerns in big data. Confidential information must be secured from multiple unauthorized accesses in attempt to optimize its security. Different traditional cryptography algorithms have been used in the security of big data in the cloud to enhance privacy. Still, because of its reduced security, there are some privacy protection concerns. With the emergence of IoT-cloud-based devices, IoT has advanced significantly in the field of big data processing. The health-care system is one of the recent IoT-based Big data applications. To preserve the privacy of patient data, several studies are required. Data security and computing overheads are still major challenges in the IoT-cloud-based health system. To ensure the privacy of huge data, the ElGamal Elliptic Curve (EGEC) encryption technique is proposed. The results are analyzed and the comparison depicts the outperformance of the proposed system.
A Distributed Point-to-Point Energy Trading System Based on Blockchain Privacy Protection

Centralized traditional electricity trading systems often face many problems such as high maintenance costs, low transaction information transparency, and untimely clearing of funds when faced with massive, high-frequency, and small-value transactions of distributed energy [1]. In response to these problems, many scholars have proposed distributing energy trading schemes based on blockchain technology by virtue of its excellent features such as distributed storage, non-tampering, traceability, and credibility. However, ordinary blockchains such as early Bitcoin, with their open and transparent on-chain transactions, can easily make the privacy and security of data from multiple parties challenging. In response to this problem, this paper proposes a distributed point-to-point energy transaction system based on ChainSQL blockchain privacy protection, and proposes a high-security and strong privacy blockchain transaction system that encrypts transaction data and has data authority management. Making full use of smart contract technology, our scheme realizes automatic settlement, traceability, and auditability of electricity transaction data.
HWID Policymaking

This chapter provides policymakers with a HWID platform for regulating socio-technical HCI phenomena and issues. After a brief introduction to HCI and policymaking, the chapter presents policy work done on the HWID platform about sustainable digital work design. The HWID relation artefact Types I–IV are revisited from a policy perspective, and policymaking is discussed related to socio-technical HCI design for digital work environments, well-being at work, a notion of decent work, and more. The chapter ends with a brief discussion of geopolitical issues and reflections on benefits and challenges of HWID and policymaking.
Neurally Optimized Model Predictive Control for Autonomous Landing of a UAV on a moving platform

The uses of UAVs are developing rapidly and are now also used to extend human capabilities. Their control poses several problems that are still open because the system is subject to strong constraints. In this context landing on mobile platform is one of challenging one. In this paper an automated solution for autonomous landing of a UAV on a moving target based on camera vision only is proposed. The method uses a discrete Model Predictive Control (MPC) approach based on Linear Time Invariant (LTI) model. Constraints on inputs, input rates and states are considered and solved using a Neural Optimizer. Software In the Loop (SIL) experiments were performed and proved that the method is valid. Finally a tool-chain for implementation and testing using open-source components only is explained.
Spectral Ensemble Clustering with LDA-based Co-training for Multi-view Data Analysis

Multi-view data has attracted more and more attention in recent years due to its practical significance. Generally, ensemble clustering and co-training are effective ways to analyze this kind of data. Among ensemble clustering algorithms, Spectral Ensemble Clustering (SEC) shows good performance by leveraging co-association matrix, which is constructed with base clustering result of single-view space. However, SEC is also limited by base clustering results. When the base clustering results of some views deviate greatly from the actual data distribution, it will affect the accuracy of co-association matrix, making it difficult for SEC to learn good cluster structure. Therefore, this paper proposes a novel algorithm for multi-view data clustering, namely Spectral Ensemble Clustering with LDA-based Co-training (LSEC). This method introduces a novel co-training strategy into ensemble clustering analysis. Specifically, based on the assumption that the clustering result of SEC has global cluster information, we use the class label obtained by SEC as reference label to perform Linear Discriminant Analysis (LDA) on original data to get refined data. Subsequently, K-means algorithm is performed on the refined data to get new base clustering results. The basic idea of this strategy is to use global cluster information to co-train local feature space. Experimental results on seven real world multi-view data sets show the effectiveness of proposed method.
Toward Physical Implementation of Multi-Scroll Attractors

The study of systems of third-order differential equations is of interest due to its wide dynamic range and its applications in secure communication systems, cryptography, among others. A special class of these systems is the jerk system, which is easy and simple to achieve. In this paper, we present the physical implementation of a multi-scroll system in ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The system is simulated in Simulink and implemented in an electronic circuit. The solution of the ordinary differential equations is represented by the voltage of the physical solution of the implemented system. The experimental tests are compared against the numerical simulation and that of the implemented circuit.