Reviewerly/exHarmony huggingface dataset:
NAYA as a Tool of Software Cost Automatic Analysis

A software project failure can be caused by an inappropriate and ineffective management approach in developing software. One of the failures of software projects that have the greatest capacity is the error in estimating financing and scheduling software. Representations of software financing calculations with good accuracy can be represented by the COCOMO II and Use Case Point (UCP) methods. The COCOMO II and UCP methods have a long steps calculation process with a lot of data collection, this obstacle is the main factor in the cost calculation process in the software and also, the lack of information about software cost calculation methods and techniques makes this section often overlooked by software developers. NAYA application developed by researchers has guided developers to write supporting data on the calculation components that have been provided. This application was developed with an iterative development model for implementation because application development requires feedback from the user as an improvement of specific functions by the system. The advantages NAYA application with COCOMO II in this application are simple data that must be prepared by the user, layout of calculations with minimum wages, and a comprehensive presentation of calculation results. Cost calculation with UCP has the advantage of implementing the Re-UCP method so that the components used are very comprehensive and complete so that it can remind UCP value and effort. NAYA applications can help software developers to calculate and arrange to finance for a software project quickly and accurately.
Design of an efficient trapping and analysis system for tobacco pests

The damage of tobacco pests seriously restricts the high quality development of enterprises. The effective pest control is based on accurate and timely monitoring information of the pest, the traditional monitoring process for information of the pest has many problems, such as heavy workload, low efficiency and high error rate. In this paper, an efficient trapping and analysis system is designed, which consists of five modules: tobacco pests trapping, power supply voltage stabilization, image acquisition, wireless communication and image processing, all modules work together to catch tobacco pests automatically, take pictures of the trap board and upload them to the host computer, after image recognition, the number of tobacco pests is calculated, the result can guide us to take effective pest control work. The design of the system has improved the degree of automation and accuracy, reduce the manual workload and work efficiency has also been greatly improved.
Towards Similar User Utterance Augmentation for Out-of-Domain Detection

Data scarcity is a common issue in the development of Dialogue Systems from scratch, where it is difficult to find dialogue data. This scenario is more likely to happen when the system’s language differs from English. This paper proposes a first text augmentation approach that selects samples similar to annotated user utterances from existing corpora, even if they differ in style, domain or content, in order to improve the detection of Out-of-Domain (OOD) user inputs. Three different sampling methods based on word-vectors extracted from BERT language representation model are compared. The evaluation is carried out using a Spanish chatbot corpus for OOD utterances detection, which has been artificially reduced to simulate various scenarios with different amounts of data. The presented approach is shown to be capable of enhancing the detection of OOD user utterances, achieving greater improvements when less annotated data is available.
<scp>ABR</scp> optimization for live sports events with <scp>ICN‐enabled</scp> network framework

The global networked live sports businesses have been developed in the explosive mode, where the data retrieval of live video streaming has played the important role. Although some specialized techniques have been proposed to guarantee the effective video transmission, it is very necessary to pay attention to the Adaptive Bit Rate (ABR) optimization in order to enhance the quality of watching. In this paper, we use the Q‐learning method based on neuronal population coding to optimize ABR. Besides, we also introduce the Information Centric Networking (ICN) paradigm to improve the traditional distribution framework in order to store the hot sports streaming at the edge caches. We make the experiments by collecting the real live sports events, and the results show that the proposed scheme has better transmission time and quality of watching than the previous schemes.
Video Game DRM: Analysis and Paradigm Solution

Video game piracy is the illegal downloading and sharing of video game content, a type of copyright infringement. It is a major problem faced by video game publishers when selling their games, owing to the ease of downloading games through torrents or DDL websites. Digital Rights Management (DRM) tools are used to hinder piracy. And yet DRM remains a false barrier as crackers invariably break the DRM and release the video game on P2P file sharing platforms and torrent websites within few days of the launch of the video game. This research paper investigates the existing DRM or copy protection methods used in video games and presents new solutions that could be implemented with DRM thereby making the video games harder to crack.
Expertise in education: theoretical analysis of basic concepts

The article is devoted to theoretical analysis of basic concepts of the problem of ex-pertise in the field of education. It is revealed that ambiguity in the definition of ex-pertise is traced in the scientific literature, as well as the substitution of this concept by similar ones, such as monitoring, diagnostics, inspection, evaluation, audit, control, consulting, etc. It is noted that the above concepts differ in their essence, purpose, tasks, functions, purpose, objects, content, participants, application, technologies, methods, tools and output. The author specifies the essence of theoretical concepts of «expert activity in education» and «expert in the field of education».
Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.
Semantic Data Structures for Knowledge Generation in Open World Information System

As the amount of information grows exponentially online, Information Systems role in support knowledge flows encouraged by linked data increases as a driver to innovation, culture, business practices and people behavior. Web search engines are particularly affected by the open world challenges, notably as part of the growing digital ecosystems of networks and platforms of technology, media, and telecommunications (TMT) companies delivering personalized and customized services (e.g. Amazon in retailing, Uber in ride service hailing, food delivery, and bicycle-sharing system, and Airbnb in lodging). To recognize search intent drawn from user’s behavior allows to provide personalized search results. The work presented in this paper has the purpose of exploring methods to represent semantic relationships between concepts indexed by Web search engines in order to aid them recognize search intent and display results that meet the search intent. The performance of two different types of data structures based on entity-centric indexing was compared. The data structures were: a knowledge base that used an entity-centric mapping of Wikipedia categories and the KBpedia Knowledge Graph. Through analysis of entity ranking and linking, we detected that the Knowledge Graph could identify approximately three times more properties and relationships, which increases Web search engines capability to “understand” what is being asked.
Deep Learning Approach of Sparse Autoencoders with Lp/L2 Regularization

In this paper, we put forward a novel deep learning approach with Lp / L2 regularization based on sparse autoencoders network, trained by a nonnegativity constraint algorithm. Since L2 norm regularization penalizes the negative weights with smaller magnitudes much weaker than those with bigger magnitudes, lots of the weights could take small negative values. In order to address this issue, non-Lipschitz nonconvex LP norm (0<p<1) regularization which could force most of the negative weights to become non-negative is introduced, and the combination of LP/L2 norm regularization is applied for nonnegativity constraint. The proposed approach is analyzed for accuracy on the MNIST dataset for image classification, the experimental results have indicated that using both LP and L2 regularizations could induce non-negativity of weights and have promising performance.
Stop bugging me! Evading modern-day wiretapping using adversarial perturbations

• Mass surveillance systems can be used to eavesdrop on private VoIP conversations. • Adversarial perturbations can be used as a viable privacy protection solution. • Several challenges need to be addressed to create a feasible real-world solution. • A Teensy-based device is used to obfuscate conversations on five VoIP applications. Mass surveillance systems for voice over IP (VoIP) conversations pose a great risk to privacy. These automated systems use learning models to analyze conversations, and calls that involve specific topics are routed to a human agent for further examination. In this study, we present an adversarial-learning-based framework for privacy protection for VoIP conversations. We present a novel method that finds a universal adversarial perturbation (UAP), which, when added to the audio stream, prevents an eavesdropper from automatically detecting the conversation’s topic. As shown in our experiments, the UAP is agnostic to the speaker or audio length, and its volume can be changed in real time, as needed. Our real-world solution uses a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real time. We examine different speakers, VoIP applications (Skype, Zoom, Slack, Google Meet, and Microsoft Teams), and audio lengths. Our results in the real world suggest that our approach is a feasible solution for privacy protection.
Herbalife Nutrition Product Recommender System using Mamdani Fuzzy Logic

A survey conducted by the Indonesian Ministry of Health shows that the obesity level of Indonesian citizens is increasing after time significant lately. In the modern era, Health factors and body weight maintenance becoming a high priority for professional people to perform and look better in public. Another survey also shows that Indonesia is having two nutrition problems, malnutrition, and obesity (overnutrition). To overcome obesity, many industries and companies have competed to release nutritional healthy product to maintain body weight. These products could be in the form of food or drinks. in the form of drinks, some popular products are powder or liquid milk. One company focusing on this industry is Herbalife Inc. Herbalife product sales through their official online and offline stores, and also through nutrition house club to easily getting closer to their customer needs. This research will focus on one nutrition house club called Fresh Club to develop a Herbalife product recommendation by predicting customer health. Some parameters used to predict customer health and these parameters are taken from Tanita Herbalife's body measures. All the parameters will be calculated and used to decide which product suits their need. the recommender system proposed will implement Mamdani Fuzzy logic. Some parameters/criteria used to recommend Herbalife product are age, water, fat, bone mass, stomach fat, and body posture. The experiment shows that the recommender system in Fresh Club using Mamdani Fuzzy Logic achieves 88.3% recommendation accuracy compare with the expert recommendation.
Which Information Help agile Teams the Most? An Experience Report on the Problems and Needs

Fast feedback promotes agile teams to improve their work during the software process, making it crucial for team success. Information systems accelerate the availability of information that result in compact knowledge sources. In practice, feedback in Sprints is often limited to sole progress and performance measures, e.g., burndown charts or velocity diagrams. Sprint insights related to team dynamics are rarely considered, even though they frequently cause project failures, e.g., lack of social interaction. In this paper, we describe a survey study conducted with international members of the software engineering community to reveal which information helps agile teams the most and provides practical support in Sprints. We describe results in an experience report highlighting the frequent information problems and needs of agile teams, considering the perspective of 90 researchers and practitioners. The responses were quantitatively interpreted. The report promotes understanding about how or what kind of information would be useful for agile development teams. Moreover, it reveals what information problems were perceived as crucial for project success and avoidable, considering proper team feedback. The study endorses practical needs for system-aided feedback that supplies knowledge on the human factors in Sprints. The findings are relevant for practitioners and researchers that struggle on improving team feedback based on information needs.
Embedded Landmark implementation for Deep Learning pre-processing

Due to the evolution of information technology, it is becoming increasingly easy to use new platforms in order to set up efficient systems that are well adapted to the expected needs. As part of improving security and facilitating the detection of potentially dangerous persons, an intelligent application for on-board facial recognition is being developed. It is within this framework that we propose this paper. The objective of the proposed work is twofold. On the one hand, we propose to develop a module for the detection of relevant facial characteristics, which is the first step of an intelligent video surveillance application. Based on the detection of points of interest of the Landmark algorithm, a software optimization of the work is proposed. On the other hand, this application will be decomposed in order to be embedded on a multiprocessor architecture. In order to validate the multiprocessor-based approach, a comparison with other existing powerful processor architectures will allow to validate the best approach. This work will be the input for an intelligent embedded face detection application based on Machine Learning.
Survey and Gap Analysis on Event Prediction of English Unstructured Texts

In the age of big data analytics, text mining on large sets of digital textual data does not suffice all the analytic purposes. Prediction on unstructured texts and interlinking of the information in various domains like strategic, political, medical, financial, etc. is very pertinent to the users seeking analytics beyond retrieval. Along with analytics and pattern recognition from the textual data, there is a need to formulate this data and explore the possibility of predicting future event(s). Event prediction can best be defined as the domain of predicting the occurrence of an event from the textual data. This paper spans over two main sections of surveying technical literature and existing tools/technologies functioning in the domain of prediction and analytics based on unstructured text. The survey also highlights fundamental research gaps from the reviewed literature. A systematic comparison of different technical approaches has been listed in a tabular form. The gap analysis provides future scope of more optimized algorithms for textual event prediction.
Countermeasures Against Information Leakage Induced by Data Serialization Effects in a RISC CPU

Side-channel attacks (SCAs) utilize the side-channel information leakage of devices to obtain sensitive information, which have become one of the most prominent threats to the security of embedded systems. Information leakage induced by data serialization effects is a critical problem in designing countermeasures against SCAs. In this paper, information leakage induced by data serialization effects in a general-purpose RISC CPU with a three-stage pipeline is studied. The side-channel analysis is based on the netlist-level simulation to guarantee a "clean room" environment. Based on the implementation of SCAs by using correlation power analysis (CPA) method, information leakage is significant in the CPU and the correct key is successfully guessed with the help of only tens of power traces. Three countermeasures based on software and hardware are proposed and compared with consideration of CPU security, performance and power consumption. After implementing the countermeasures, the information leakage is reduced significantly and the anti-attack ability of the CPU is improved (up to four orders of magnitude). Moreover, when the countermeasures are implemented in actual noisy environment, the CPU security will be further improved. Reasonable compromise needs to be made between the CPU security and implementation overhead to choose suitable SCA-resistant countermeasures under different conditions.
Reliability Evaluation of Power Security and Stability Control Devices Based on the AHP

In this paper, the analytic hierarchy process (AHP) method is proposed to evaluate the reliability of the security and stability control devices (SSCDs) of power systems. The main factors that affect the reliability of the SSCDs are first summarized, and the index system of reliability evaluation is built based on the SSCD’s hidden failures. The AHP method is then applied to determine the index weight while the index value is calculated by Markov state space model. Finally, the reliability of the SCS-500E-type SSCD is evaluated.
A Hardware/Software Collaborative SM4 Implementation Resistant to Side-channel Attacks on ARM-FPGA Embedded SoC

The SM4 algorithm is the first commercial cryptographic algorithm officially announced in China for wireless local area network products. It is suitable for scenarios that require high real-time performance, such as wireless communication and IoT sensor nodes. It can be seen that the security research of the SM4 algorithm is of great significance to wireless devices in the IoT. Like other symmetric encryption algorithms, the SM4 algorithm faces some security threats, such as side-channel attacks. Among them, cache timing attacks and power/electromagnetic analysis attacks are becoming more and more threatening due to their low execution difficulty and powerful attack capabilities. Most implementations of anti-side channel attacks against the SM4 algorithm can only resist one of above two attacks. However, side-channel leakages associated with above attacks often coexist.Therefore in this paper, we present a hardware/software collaborative SM4 implementation on ARM-FPGA embedded SoC which can resist above two types of attacks simultaneously. It randomly divides the 32 rounds of SM4 encryption into three stages: the beginning software stage, the middle hardware stage, and the final software stage. Besides, we shuffle the order of some independent operations in each round of the software stages and add dummy rounds to the hardware stage. Finally, we conduct above two types of attacks on unprotected software/hardware SM4, shuffled software SM4 and our scheme, then evaluate their performance respectively. The data throughput of our scheme is 0.86 times that of the original software SM4, while the FPGA resource requirements of our scheme are 0.87 times that of the unprotected hardware implementation.
EasyGML

Despite the great success of Graph Machine Learning (GML) in a variety of applications, the industry is still seeking a platform which makes performing industrial-purpose GML convenient. In this demo, we present EasyGML, a fully-functional and easy-to-use platform for general AI practitioners to apply out-of-the-box GML models in industrial scenarios. Leveraging the distributed data warehouse as its data infrastructure, EasyGML adopts AGL, an integrated system for industrial-purpose graph learning, as its core GML engine, and develops a model zoo containing various GML models, supporting both node property prediction and link property prediction. It packs different steps of GML workflow into different components, and provides a user-friendly web-based GUI for users to build their GML workflows simply by connecting several components together, without any coding.
Path to Digital Watermarking

This chapter contains the introduction of digital watermarking, various types of digital watermarking, why watermarking is required, and various application areas where watermarking is useful. Various data hiding methods are also discussed. The basic principle of watermark embedding and watermark extraction is also explained. Watermarking classification depending on the various parameters and also the classification of watermarking is explained in this chapter.
Publishing Pedagogical Materials

Many teachers write materials for their own teaching situation, but relatively few take the steps to become a professional materials writer, writing materials that will be published and used by a wider audience. Writing coursebooks and other pedagogical materials can be a rewarding and exciting area of development within the ELT profession. The rewards are not only financial; the process of following a brief, meeting deadlines and applying strict pedagogical standards is an excellent way to improve as a teaching professional and member of a teaching team. In this chapter, Emma and Gary Pathare, both successful and prolific writers of coursebooks, exam items and supplementary materials for international publishers and examination boards, demystify the process and explore the skills and attitudes required to succeed as authors.
Regularized Multi-view Spectral Clustering With Noisy Data

Multi-view clustering has attracted much attention in recent years. When the data is noisy, traditional multi-view algorithm might not receive good results. An entropy perturbed multi-view spectral clustering method is proposed to solve this problem. In order to reduce the impact of noise on the data, entropy is used as a measure to perturb the Laplacian matrix within a threshold, trying to find a set of eigenvectors carrying largest information. Canonical angle is used to measure the difference in clustering ability between each view and the consensus view to assign weights for views. Experimental results show that the proposed method has good performance on both noisy and non-noisy datasets.