Reviewerly/exHarmony huggingface dataset:
Sentiment analysis of public opinions on the welfare of honorary educators using Naive Bayes

Abstract The development of the internet in Indonesia is very rapid, this is marked by the number of emerging social media. One of the most popular social media is Twitter. Many Twitter users who tweet their expression and opinion on a product, service, political issues or things that are trending. Even the current government is not spared from public comment on social media, one of which is an honorary teacher at an elementary school in Pandeglang Regency, who lives next to his school toilet. This case was widely discussed by almost all Indonesians through offline and online media, especially on social media. The impact of this case is the difference in response in the form of public sentiment towards public services, especially in improving the welfare of educators. Various sentiments of Netizens (internet users) appear on social media ranging from praise, criticism, suggestions, satire and even expressions of hatred towards the Pandeglang Government. This research was conducted to determine netizens’ sentiments towards the Pandeglang Regency government based on the case of honorary teacher news in Pandeglang Regency who live in school toilets. The approach used is Naive Bayes. The results of sentiment analysis showed that there are 16 Twitter posts grouped in neutral class, 19 posts in positive class and the remaining 16 posts are in the negative class. The level of accuracy generated from the sentiment analysis model formed is 88.24%.
The extension of existing end-user mobility dataset based on generative adversarial networks

The end-user mobility patterns play an key role in the process of 5G network design. Massive increase of the RAN infastructure complexity creates additional requirements on precise network planning and overall orchestration of the network as such. The possible solution to enhance the statistics feeding the network planning process is to generate massive dataset of the end-user mobility patterns. Unfortunately, we are still constrained with few number of commercially available datasets, far from the sample statistics needed for machine learning training purposes. Our solution to overcome this problem is to generate additional end user traffic statistics by using generative adversial networks approach. Given the existing sample-constrained end user mobility datasets, GAN network learns to generate new data with the same statistics as the training set. Thus, by leveraging this approach we are able to generate theoretically unlimited samples of realistic end user mobility trajectories. This artificial data jointly with existing limited datasets have the potential to be used for the training blocks of machine learning within the process of the network planning optimization.
GEOSPATIAL EVALUATION OF BELGRADE FOR THE PURPOSES OF DETERMINATION OF SUITABLE LOCATIONS FOR THE CONSTRUCTION OF PV PLANTS

&amp;lt;p&amp;gt;The aim of this paper is to identify the most suitable locations for the construction of Solar Photovoltaic Plants (PVP) on the territory of the City of Belgrade (Republic of Serbia). The city is rich in natural resources and abundant in energy potential. The climatic and spatial characteristics favor the use of solar energy: the average annual solar radiation in Belgrade is 1446.8 kWh/m2 /year, and the sunshine duration is around 2200 h/year.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The analysis covered the area of 3240.7 km2 (administrative area of Belgrade). Geospatial evaluation was based on three basic criteria: aspect, slope and land use. The Method of elimination in combination with modern methods in geospatial research (GIS) was applied. Based on the mentioned geospatial factors, thematic maps were made and later, their overlapping resulted in obtaining a synthesis Map of the suitability of the terrain for the construction of the PV plants. Of the total considered area, 13.68% were evaluated as suitable for the given purpose.&amp;lt;/p&amp;gt;
Privacy preserving targeted advertising and recommendations

Recommendation systems form the centerpiece of a rapidly growing trillion dollar online advertisement industry. Curating and storing profile information of users on web portals can seriously breach their privacy. Modifying such systems to achieve private recommendations without extensive redesign of the recommendation process typically requires communication of large encrypted information, making the whole process inefficient due to high latency. In this paper, we present an efficient recommendation system redesign, in which user profiles are maintained entirely on their device/web-browsers, and appropriate recommendations are fetched from web portals in an efficient privacy-preserving manner. We base this approach on precomputing compressed data structures from historical data and running low latency lookups when providing recommendations in real-time.
Correlations between Complexity and Entanglement in a One-Dimensional XY Model

Many people believe that the study of complex quantum systems may be simplified by first analyzing the static and dynamic entanglement present in those systems [Phys. Rev. A 66 (2002) 032110]. In this paper, we attempt to complement such notion by adding an order–disorder quantifier called statistical complexity and studying how it is correlated with the degree of entanglement as measured by the concurrence quantifier. We perform such an analysis with reference to a representative system chosen from condensed matter theory, the so-called X Y model. Some interesting insight is obtained as the concurrence and the complexity become correlated in an unexpected fashion.
Toward a formal proof for multi-secret sharing in the random oracle model

A general multi-secret sharing (GMS) scheme allows a dealer to share multiple secrets among a set of participants, such that any authorized subset of participants can reconstruct the secrets simultaneously, while any unauthorized subset of participants cannot get any information about the secrets. The first formal security model of multi-stage secret sharing (MSS) schemes with a computational security proof in the random oracle model (ROM) had been introduced recently by Herranz et al. To the best of our knowledge, so far, there exists no computational secure GMS with a formal proof in ROM in the literature. This is what we will do in this paper. After describing formally the computational security of a GMS, we propose a GMS with adding a formal proof of computational security in ROM.
Deformable Convolutional Neural Network for Fine-grained Image Recognition

In recent years, deep learning techniques have demonstrated their powerful feature extraction capabilities in a variety of visual analysis tasks. Especially in the field of fine-grained image classification, deep feature extraction technology has achieved great performance improvement. The depth features extracted based on the deep learning model have strong expressive power. Fine-grained image classification is intended to subclass large categories, such as different types of birds. This identification task is more challenging because these sub-classes have large interclass differences and small inter-class differences. This paper first introduces the research progress of fine-grained image classification. Then, the data set used in the experiment and the extended data set are introduced. A variable convolution method for fine-grained image classification is proposed, and the research direction of the next step is summarized and explored.
Research on Image Recognition of Supermarket Commodity Based on Convolutional Neural Network

For the actual shopping scenes in supermarkets, there will be more complex background environment, more diverse detection objects, and more changeable actual scenes, so the traditional image recognition network cannot meet the existing needs. In this paper, Convolutional Neural Network technology and YOLOv3 target detection algorithm are adopted to realize the recognition and detection of target objects in images, automated analysis and processing of these product image data with computer vision algorithms. The convolution neural network and COCO data set are used to train the neural network. Finally, the well-trained network is tested. Tested, the effect is good.
Abnormal Behavior Recognition in an Examination Based on Pose Spatio-Temporal Features

The following topics are dealt with: learning (artificial intelligence); data analysis; cloud computing; data mining; feature extraction; convolutional neural nets; Internet; Big Data; pattern classification; and power engineering computing.
Humanoid Control Module: An Abstraction Layer for Humanoid Robots

Humanoid robots promise a better integration of robots into our everyday life, but they come with additional challenges when compared with other mobile robots. We present a novel approach to simplify their usage by handling these challenges with the Humanoid Control Module and show its utility in the RoboCup Soccer Humanoid League. The paper also discusses previous approaches to this problem from inside and outside of the RoboCup domain.
Scale-adaptive and Color-corrected Retinex Defogging Algorithm

This paper proposes a Retinex defogging algorithm for scale adaptation and color correction.This algorithm can solve the problem that the Retinex defogging algorithm needs to manually set the Gaussian parameters and the color cast of the image after defogging. Firstly, the fog concentration is estimated by the transmittance of the dark channel prior theory, and the piecewise function between the fog concentration and the defogging scale is constructed, so that the scale is selected according to different fog concentrations adaptively; Secondly, the brightness and color of the image are adjusted to the level similar to the original image by using the numerical characteristics of the image. From the comparison with the MSRCR algorithm, the algorithm has better color recovery effect, outstanding detail and good visual effect.
How technology can impact customer-facing train crew experiences

Customer-facing train crew members have to follow strict procedures to guarantee that trains are safe and run on time. They are also responsible for revenue protection and customer care. Human factors and ergonomics research are instrumental to understand the safety-critical aspects and improve work. We bring user experience research and personas to describe how train crew perceive their routines and how new technology may impact them. We conducted 7 hours of interviews and 30 hours of shadowing observations with the train crew (N = 22) to provide an understanding of who are they and to define their experiences. We present the crew’s current routines and created two personas to represent them. One is slightly reluctant to adopt the proposed technology, whereas the other is more accepting. Results indicate how such technology may affect crew work ergonomics and experiences, and suggest which valuable aspects should be maintained, for example the positive interactions with passengers. Practitioner summary: This study investigated the work routines of the customer-facing train crew. Interviews and shadowing were conducted with 22 crew from a large operator in the UK. Personas were created to represent them. Results show their preferred activities and how these would be affected by the introduction of new technology. Abbreviations: CH; customer host (onboard catering staff); DOO: driver-only operation; ETA: estimated time of arrival; PTI: platform-train interface; TM: train manager (onboard customer-facing authority); UCD: user-centred design; UX: user experience
Paring-free certificateless security authentication scheme for WSN based on ECC

Because wireless sensor network (WSN) nodes have the limits of low storage resources, weak computing power and less electric power, the paper proposes a pairing-free certificateless security authentication scheme for WSN based on ECC algorithm. The scheme realizes mutual authentication, key agreement and session key update of nodes in the network using ECC point multiplication algorithm and AES symmetric encryption algorithm. Basing on this, we analyze the correctness and security, it shows that the scheme has two-party authentication, forward security, perfect-forward security, known-key security, anti-key leakage camouflage and other security attributes. Finally, we make the formal analysis of the performance of the scheme, and then we implement the simulation on the platform of OMNET++ basing on the language of C++. Comparing with other schemes, our scheme has less energy consumption and execution time, which can meet the needs of WSN better.
Mary, Hugo, and Hugo*: Learning to schedule distributed data‐parallel processing jobs on shared clusters

Summary Distributed data‐parallel processing systems like MapReduce, Spark, and Flink are popular for analyzing large datasets using cluster resources. Resource management systems like YARN or Mesos in turn allow multiple data‐parallel processing jobs to share cluster resources in temporary containers. Often, the containers do not isolate resource usage to achieve high degrees of overall resource utilization despite overprovisioning and the often fluctuating utilization of specific jobs. However, some combinations of jobs utilize resources better and interfere less with each other when running on the same shared nodes than others. This article presents an approach for improving the resource utilization and job throughput when scheduling recurring distributed data‐parallel processing jobs in shared clusters. The approach is based on reinforcement learning and a measure of co‐location goodness to have cluster schedulers learn over time which jobs are best executed together on shared resources. We evaluated this approach over the last years with three prototype schedulers that build on each other: Mary, Hugo, and Hugo*. For the evaluation we used exemplary Flink and Spark jobs from different application domains and clusters of commodity nodes managed by YARN. The results of these experiments show that our approach can increase resource utilization and job throughput significantly.
Application of augmented reality (AR) media on conformation of alkanes and cycloalkanes concepts to improve student’s spatial ability

Abstract The purpose of this study is to apply the Augmented Reality (AR) media to improve the student’s spatial ability on alkanes and cycloalkane conformation. The method of this study was quasi-experimental control group pretest-posttest design for the second-semester students of the Chemical Education at UIN Sunan Gunung Djati Bandung. Data carried out through tests and non-test (worksheet and observation), and further analyzed using statistical methods by mean of SPSS version 23. The results showed that the student’s spatial ability on conformation of alkanes and cycloalkanes was increased with N-gain results categorized as moderate (mean 0.58). This means that AR conformation can visualize the movement of an object.
Novel Dummy Rounds Schemes as a DPA Countermeasure in PRESENT Cipher

The Dummy Rounds Side-Channel Attacks countermeasure scheme for digital design has been proposed in earlier work. Its experimental evaluation and analysis revealed weaknesses that resulted in the proposal of an enhanced Dummy Rounds scheme. In this paper, we present the implementation of the proposed enhancement of Dummy Rounds scheme in PRESENT cipher and provide its experimental evaluation using Welch's t-test. We further propose several novel modifications of Dummy Rounds scheme as a solution to other security problems we have encountered. Novel Dummy Rounds scheme, namely its modifications proposed in this paper, are superior to earlier proposed schemes in terms of side-channel leakage prevention.
Teaching Technology in “Poorly Resourced” Contexts

There is a widely held perception that teaching Technology in “poorly resourced” contexts is nearly unfeasible. In this chapter, I present a counter view to conventional understandings of “poorly resourced”. Communities inhabiting such contexts engage localised technologies on daily basis to sustain their livelihoods. Teachers can use such resources to teach Technology too. This chapter contributes a paradigm shift from disregarding these contexts and the transformative and creative approaches to the teaching of Technology.
State of the Art in Logics for Verification of Resource-Bounded Multi-Agent Systems

Approaches to the verification of multi-agent systems are typically based on games or transition systems defined in terms of states and actions. However such approaches often ignore a key aspect of multi-agent systems, namely that the agents’ actions require (and sometimes produce) resources. We survey previous work on the verification of multi-agent systems that takes resources into account, extending substantially a survey from 2016 [9].
A Two-stage Approach for Virtual Resources Adaptive Scheduling in Container Cloud

In container cloud, when allocating resources for virtual machines(VMs) and containers, it is very important to improve the computing resources utilization. The virtual resources allocating process could be divided into two stages. The first stage is to place the VMs on the physical machines(PMs), and this sub-process will be constrained by PMs' resources. The second stage is to place the containers on the virtual machines, and this sub-process will be constrained by VMs. We views this process as secondary bin packing problem, and use different packing strategies according the different characteristics of each stage. we proposed the two-stage virtual resources adaptive scheduling algorithm to deal with this problem. The experimental results show that the proposed algorithm can efficiently improve the data center's computing resources utilization.
Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks

This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. This is done by applying the methodological framework of the Simplified Graph Convolutional Neural Network (SGC) to two academic publication datasets: Cora and Citeseer. The performance of SGC is compared to the original Graph Convolutional Network (GCN) framework. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of SGC. When removal is based on a more localized selection of nodes, augmenting the network with both strong-ties and weak-ties provides a benefit, indicating that SGC successfully leverages local information of network nodes.
A framework for the analysis and synthesis of Swarm Intelligence algorithms

Over the last decades, the number of Swarm Intelligence algorithms proposed in the literature has increased considerably. However, most algorithms do not follow an adequate scientific rigour neither the principles of Swarm Intelligence, reproducing similar computational procedures of many other approaches, but by means of a different metaphor. In this scenario, the aim of this paper is to propose a framework for the analysis and synthesis of Swarm Intelligence algorithms that can contribute to a structured understanding (analysis) and development (synthesis) of these algorithms. The main objective of the proposed framework is to guide the construction of Swarm Intelligence metaphors in a consistent and well-founded way, as well as to contribute with the analysis and development of new algorithms that had better encompass the features and abilities of insects’ societies.
Emotiongan: Facial Expression Synthesis Based on Pre-Trained Generator

Abstract Since the Generative Adversarial Networks (GANs) was proposed, researches on image generation attract many scholars’ general attention and good graces. Traditional GANs generate a sample by playing a minimax game between generator and discriminator. In this paper, we propose a new method called EmotionGAN for generating facial expression. Specifically, the inverse of the generator is firstly utilized to establish the mapping between the input and feature vector. Then the Generalized Linear Model (GLM) is used to fit the changing direction of different expressions in the feature space, which provide a linear guidance to the feature vector along the expression axis, and thus spatial distribution consistence with the target feature vector is assured. Finally the generator is applied to reconstruct the facial image of the expression. By controlling the intensity of the feature vector, the generated image can be smoothly changed on a specific expression. Experiments have shown that EmotionGAN can quickly generate face images with arbitrary expressions while ensuring identity information is not changed, and the image attributes are more accurate and the resolution is higher.
RELATIONSHIP BETWEEN ARABIC LANGUAGE SPEAKING ANXIETY AND MOTIVATION AMONG RELIGIOUS SECONDARY SCHOOL STUDENTS IN MALAYSIA

Purpose of the study: This study aimed at identifying the level of Arabic language speaking anxiety and its relationship to motivation among students of religious secondary schools in Malaysia.&#x0D; Methodology: This quantitative study identifies the anxiety level in speaking Arabic and its relationship with motivation. A survey was done with a selected number of 500 Arabic students out of the total population throughout Malaysia. The survey was done using questionnaires that have been adapted and modified from past studies after completing its validity and reliability process.&#x0D; Main Findings: The findings indicate that the level of Arabic speaking anxiety among secondary religious school students in Malaysia is moderately high and also shows that students’ anxiety in speaking Arabic does not have any significant relationship with their motivation. This finding illustrates that Arabic speaking anxiety is unrelated to student motivation in learning the Arabic Language.&#x0D; Applications of this study: The study can be useful for Arabic Language teachers. Students with a high anxiety level will feel anxious and avoid taking part in learning activities that will then influence the quality of learning. Hence, speaking anxiety must be tackled separately by the Arabic Language teacher from motivation since there is no significant relationship between the two variables.&#x0D; Novelty/Originality of this study: This study produced instruments to assess the level of Arabic speaking anxiety. The result of this study is consistent with previous studies, which investigated the level of anxiety in learning a foreign language and speaking anxiety must be tackled separately from motivation because it has no significant relationship with individual students’ motivation.