Reviewerly/exHarmony huggingface dataset:
Implementation of clustering unsupervised learning using K-Means mapping techniques

Abstract The manufacturing sector is one of the major contributors to the Indonesian economy. Human work is still needed on the production floor in the manufacturing industry to ensure a smooth operation. This study explores the use of unregulated learning clustering techniques in data mining in the form of clusters of employees in the production industry. The data collection process is carried out through a survey conducted by the Central Statistical Agency (abbreviated as the BPS) with Url: https:/www.bps.go.id in Large Medium Industries and Micro &amp; Small Industries. The statistics used include 24 industrial classifications, with the number of manufacturing employees in the 2017-2019 industry as a percentage. The unregulated technique of learning clustering is k-means. The Large Cluster (E1) and the Low Cluster are the two labels used (E2). The Davies Bouldin Index (DBI) parameter with a dbi value of 0,929 was used to evaluate the cluster (k=2). The findings showed five manufacturing sectors of the high cluster in 5 cluster and 19 manufacturing sectors of the small cluster in 0 cluster. For each cluster the centroid value is 1.67; 1.64; 1.592 (cluster 1/E1) and 0.348; 0.343; 0.3447 (cluster 0/E2), respectively. The research findings will inform the government to improve labour absorption, which will reduce the unemployment rate by substantial numbers in each manufacturing industry.
How Can Software Development Teams Be Controlled During the COVID-19 Pandemic

The COVID-19 pandemic forced companies to rethink how and where the employees work as well as how to control them. This lead to an increased reliance on digital services. Drawing on the literature of control theory, this chapter presents a literature review using on control practices that have been used to control agile and/or traditional software development teams, that is, the teams that develop the very same services everyone now relies upon. Based on a review of 25 journal articles, I develop possible adaptions to remote environments for the identified control practices. 60 control practices are identified for agile software development, whereas 36 control practices are identified for traditional software development teams. Many control practices identified, especially those associated with formal or self-control, can be enacted during the pandemic without adaption. While some control practices need to be adapted, others—mostly those associated with clan control—can be emulated via digital services although the effectiveness is questionable.
Changing the Geometry of Representations: α-Embeddings for NLP Tasks

Word embeddings based on a conditional model are commonly used in Natural Language Processing (NLP) tasks to embed the words of a dictionary in a low dimensional linear space. Their computation is based on the maximization of the likelihood of a conditional probability distribution for each word of the dictionary. These distributions form a Riemannian statistical manifold, where word embeddings can be interpreted as vectors in the tangent space of a specific reference measure on the manifold. A novel family of word embeddings, called α-embeddings have been recently introduced as deriving from the geometrical deformation of the simplex of probabilities through a parameter α, using notions from Information Geometry. After introducing the α-embeddings, we show how the deformation of the simplex, controlled by α, provides an extra handle to increase the performances of several intrinsic and extrinsic tasks in NLP. We test the α-embeddings on different tasks with models of increasing complexity, showing that the advantages associated with the use of α-embeddings are present also for models with a large number of parameters. Finally, we show that tuning α allows for higher performances compared to the use of larger models in which additionally a transformation of the embeddings is learned during training, as experimentally verified in attention models.
Curve and Surface Fitting Techniques in Computer Vision

Curve and surface-fitting are classic problems of approximation that find use in many fields, including computer vision. There are two broad approaches to the problem — interpolation, which seeks to fit points exactly, and regression, which seeks a rougher approximation which is more robust to noise. This survey looks at several techniques of both kinds, with a particular focus on applications in computer vision. We make use of an empirical first-level evaluation approach which scores the techniques on multiple features based on how important they are to users of the technique and developers. This provides a quick summary of the broad applicability of the technique to most situations, rather than a deep evaluation of the performance and accuracy of the technique obtained by running it on several datasets.
An Attentive Deep Supervision based Semantic Matching Framework For Tag Recommendation in Software Information Sites

Tag recommendation in software information sites is a popular way to help developers classify software objects. Existing methods mostly consider tag recommendation as a multi-label classification task, which does not adequately leverage the semantic information of tags themselves. It is observed that the information granularity of tags is from abstract to specific and deep learning models have proven capable of automatically learning the features in different layers of an integrated network with different abstraction degrees. In this paper, we propose TagMatchRec, a deep semantic matching framework for tag recommendation instead of being based on classification. In our framework, multiple layers with different information granularities are directly connected to the output layer aiming at improving the quality of tag recommendation. Moreover, because the abstraction levels of semantic features learned by each layer may be different given different software objects and tags, an attentive deep supervision is introduced so that the dense connections from early layers to the output layer have directly weighted impact on loss function optimization. Comprehensive evaluations are conducted the datasets from four software information sites. The experimental results show that TagMatchRec has achieved better performance compared with the state-of-the-art approaches.
Beyond Probability Ranking Principle: Modeling the Dependencies among Documents

Probability Ranking Principle (PRP), which assumes that each document has a unique and independent probability to satisfy a particular information need, is one of the fundamental principles for ranking. Traditionally, heuristic ranking features and well-known learning-to-rank approaches have been designed by following the PRP principle. Recently, neural IR models, which adopt deep learning to enhance the ranking performances, also obey the PRP principle. Though it has been widely used for nearly five decades, in-depth analysis shows that PRP is not an optimal principle for ranking, due to its independent assumption that each document should be independent of the rest candidates. Counter examples include pseudo relevance feedback, interactive information retrieval, search result diversification, etc. To solve the problem, researchers recently proposed to model the dependencies among the documents during the designing of ranking models. A number of ranking models have been proposed and state-of-the-art ranking performances have been achieved. This tutorial aims to give a comprehensive survey on these recently developed ranking models that go beyond the PRP principle. The tutorial tries to categorize these models based on their intrinsic assumptions: assuming that the documents are independent, sequentially dependent, or globally dependent. In this way, we expect the researchers focusing on ranking in search and recommendation can have a novel angle of view on the designing of ranking models, and therefore can stimulate new ideas on developing novel ranking models. The material of this tutorial can be found in https://github.com/pl8787/wsdm2021-beyond-prp-tutorial.
Bridging the Generation Gap with Net Students

The article presented aims at describing the educational needs of the new generation of learners, specifically Generation Net. We examine generational differences between the Boom Generation, Generation Y, Millennial and Generation Z cohorts. The Strauss-Howe generational theory constituting the basis of our research, we give the interpretation of the generation gap in Russian reality. We present the statistics on faculty-student belonging to a particular generation cohort at Minin University, Russia, and explore the relationship between the former and latter generations. We conclude that the traditional sage-on-the-stage approach used in most Russian universities does not work in dealing with the current Generation Net representatives. There is an urgent need to find new teaching approaches to reach the new generation of learners that will help meet their educational needs, goals and values. We see the way of improving the quality of teaching in introducing the latest technologies that are able to bridge the generation gap, boosting academic achievement.
To Ensemble or Not Ensemble: When Does End-to-End Training Fail?

End-to-End training (E2E) is becoming more and more popular to train complex Deep Network architectures. An interesting question is whether this trend will continue—are there any clear failure cases for E2E training? We study this question in depth, for the specific case of E2E training an ensemble of networks. Our strategy is to blend the gradient smoothly in between two extremes: from independent training of the networks, up to to full E2E training. We find clear failure cases, where overparameterized models cannot be trained E2E. A surprising result is that the optimum can sometimes lie in between the two, neither an ensemble or an E2E system. The work also uncovers links to Dropout, and raises questions around the nature of ensemble diversity and multi-branch networks.
Hybrid Biogeography-Based Optimization and Genetic Algorithm for Feature Selection in Mammographic Breast Density Classification

It can be acknowledged from the literature that the high density of breast tissue is a root cause for the escalation of breast cancer among the women, imparting its prime role in Cancer Death among women. Moreover, in this era where computer-aided diagnosis systems have become the right hand of the radiologists, the researchers still find room for improvement in the feature selection techniques. This research aspires to propose hybrid versions of Biogeography-Based Optimization and Genetic Algorithm for feature selection in Breast Density Classification, to get rid of redundant and irrelevant features from the dataset; along with it to achieve the superior classification accuracy or to uphold the same accuracy with lesser number of features. For experimentation, 322 mammogram images from mini-MIAS database are chosen, and then Region of Interests (ROI) of seven different sizes are extracted to extract a set of 45 texture features corresponding to each ROI. Subsequently, the proposed algorithms are used to extract an optimal subset of features from the hefty set of features corresponding to each ROI. The results indicate the outperformance of the proposed algorithms when results were compared with some of the other nature-inspired metaheuristic algorithms using various parameters.
A centralised training algorithm with D3QN for scalable regular unmanned ground vehicle formation maintenance

The unmanned ground vehicle (UGV) has been widely used to accomplish various missions in civilian or military environments. Formation of the UGVs group is an important technique to support the broad applications of multi-functional UGVs. This study proposes a scalable regular UGV formation maintenance (SRUFM) algorithm based on deep reinforcement learning (DRL), which aims to use a unified DRL framework to improve the lateral control and longitudinal control performance of UGV in different situations of the formation. Based on the tailored design of state, action space, related inter-vehicle information, and the dueling double deep Q-network (D3QN), SRUFM outperforms nature deep Q-network and double deep Q-network in the exploration efficiency and the convergence speed in same CARLA training environments with fixed size formation. Furthermore, when the formation's scale is extended with similar initialisation conditions, the SRUFM can still get a nearly 90% success rate to execute all experiment formation maintenance missions after 4000 episodes of training. Each UGV in the formation can keep distance within the upper and lower error threshold of 0.15 m. The simulation experiments show that the proposed centralised training frame with D3QN is suitable to solve scalable regular UGV formation maintenance missions.
A Study and Comparison of Various Types of Load Balancers

Load Balancer, critical component of distributed system plays a very important role in equally dividing the traffic among various servers thus minimizing server response time and maximizing the throughput. Different types of load balancers such as hardware load balancer, software Load balancer and virtual load balancer exist in the market. In this paper a study and comparisons of few lead load balancers has been made to enable IT companies to choose the best load balancer for their organizations.
Multi-modal Unsupervised Domain Adaptation for Deformable Registration Based on Maximum Classifier Discrepancy

The scarce availability of labeled data makes multi-modal domain adaptation an interesting approach in medical image analysis. Deep learning-based registration methods, however, still struggle to outperform their non-trained counterparts. Supervised domain adaptation also requires labeled- or other ground truth data. Hence, unsupervised domain adaptation is a valuable goal, that has so far mainly shown success in classification tasks. We are the first to report unsupervised domain adaptation for discrete displacement registration using classifier discrepancy in medical imaging. We train our model with mono-modal registration supervision. For cross-modal registration no supervision is required, instead we use the discrepancy between two classifiers as training loss. We also present a new projected Earth Mover's distance for measuring classifier discrepancy. By projecting the 2D distributions to 1D histograms, the EMD L1 distance can be computed using their cumulative sums.
Compact and Secure Generic Discrete Gaussian Sampler based on HW/SW Co-design

In this paper, we present the first Hardware (HW) / Software (SW) co-design based generic discrete Gaussian sampler architecture on the Xilinx Zynq platform. The area optimized and secure sampler can produce a distribution based on an arbitrary standard deviation and center given as input. We use multi-level logic optimization on Knuth-Yao algorithm's Discrete Distribution Generating (DDG) tree travel-based Boolean mapping of random bits and samples instead of the previous two-level logic optimization to reduce the resource utilization. This method results in nearly 60% lesser LUT utilization compared to the previous designs on Xilinx FPGAs. Further, we introduce improvements in the shuffling algorithm leveraging the HW/SW co-design methodology compared to the existing shuffling architectures for randomizing Gaussian samples to protect against timing-based side-channel attacks.
An Encrypted Field Locating Algorithm for Private Protocol Data Based on Data Reconstruction and Moment Eigenvector

Encrypted network data classification has received considerable attention in the industry and research communities for a long time.However, the emergence of new private applications and encryption protocols has brought more new challenges.The primary task of classification is to determine whether the sample is encrypted.When the specification of private protocol is unpublished, only the whole sample can be processed, and thus the unencrypted field which coexists with the encrypted field will seriously lower the classification effect.To tackle this problem, an algorithm based on data reconstruction and moment eigenvector is proposed, which can not only estimate the encryption result but also locate the encrypted field in each data sample.In the algorithm, the encryption probability sequence is calculated firstly by data reconstruction and CNN (Convolutional Neural Network) model transfer.Then, based on the derivative of the encryption probability sequence, the suspected encrypted field set is generated.Finally, encrypted field matching is performed based on the similarity of the four-dimensional moment eigenvector, and thus the possible encrypted field of each sample is obtained.The proposed algorithm achieved a recall rate of 93% and a precision rate of 72% in an experiment of distinguishing the encrypted/unencrypted ones of complex data.The forward coverage, reverse coverage, and F1 value in identifying encrypted field reached 89%, 90%, and 90%, respectively.Compared with the encrypted field matching methods based on the K-Nearest Neighbor algorithm, Dynamic Time Warping, Runs test, and Frequency test, the method proposed in this paper exhibited salient advantages.
Blockchain-Based Fine-Grained Data Sharing for Multiple Groups in Internet of Things

Cloud-based Internet of Things, which is considered as a promising paradigm these days, can provide various applications for our society. However, as massive sensitive and private data in IoT devices are collected and outsourced to cloud for data storage, processing, or sharing for cost saving, the data security has become a bottleneck for its further development. Moreover, in many large-scale IoT systems, multiple group data sharing is practical for users. Thus, how to ensure data security in multiple group data sharing remains an open problem, especially the fine-grained access control and data integrity verification with public auditing. Therefore, in this paper, we propose a blockchain-based fine-grained data sharing scheme for multiple groups in cloud-based IoT systems. In particular, we design a novel multiauthority large universe CP-ABE scheme to guarantee the fine-grained access control and data integrity across multiple groups by integrating group signature into our scheme. Moreover, to ease the need for a trusted third auditor in traditional data public auditing schemes, we introduce blockchain technique to enable a distributed data public auditing. In addition, with the group signature, our scheme also realizes anonymity and traitor tracing. The security analysis and performance evaluation show that our scheme is practical for large-scale IoT systems.
Recommendation of Online Educational Resources Based on Neural Network

In recent years, online education is developing rapidly. However, it is difficult for students to obtain suitable content from massive online educational resources. Therefore, it is of great significance to recommend personalized educational resources to students. How to effectively model the characteristics of students and educational resources, and to explore the deep relationship between them is one of the core challenges of the recommendation model. In this paper, we propose an educational resources recommendation model based on neural network. Based on the data of Spark Space, the online learning platform of Beijing University of Posts and Telecommunications, we first construct a knowledge concept maps according to the knowledge structure of the course. And then we combine the learning style scale and Bloom's taxonomy theory to construct the characteristic attributes of students and education resources. Finally, we use a multilayer perceptron network to make personalized recommendations to students. Experiments have proved that the recommendation model proposed achieves a great recommendation result.
A Combination Techniques of Intrusion Prevention and Detection for Cloud Computing

Abstract Cloud computing provides scalable, on-demand, and highly available computing services over the Internet to both the public and organizations on a pay-per-use basis. It provides a variety of services such as networking, storage space, and applications. The key issue for cloud computing is ensuring the confidentiality and privacy of cloud resources and data. Enticing the user to purchase cloud services requires their trust which cannot be guaranteed unless the infrastructure is effectively protected because attacks at this level will threaten the whole system. To this end, we propose the Integrated Intrusion Prevention and Detection System (IIPDS) to prevent and detect different types of attacks to the infrastructure level of the cloud system. The proposed design uses Trusted Third Party (TTP) services and SSL/TLS protocols as intrusion prevention methods to secure the communication between the cloud provider and the user. It also uses multiple Intrusion Detection Systems (IDS) distributed over multiple cloud regions. The IDS system is capable of detecting known and unknown attacks using anomaly and rule-based (signature) intrusion detection techniques. The simulation results proved the efficiency of the system in detecting a wide range of attacks with low false positive alerts and low computational overhead.
Estimating Sentiment in Social Media - a Case Study of the Migrant Caravans News on Twitter

Social media has empowered us to gain immense knowledge, criticism, and positivity from every corner of the world. Accessing social network sites like Twitter and Facebook has slowly become the norm, as people are now inclined towards looking for news and opinions in social media rather than reading traditional newspapers or watching TV news. There are many instances when a piece of news in these sites, irrespective of its reliability, has a significant impact on people's opinions towards it. In this work, we investigated some queried tweets of around 39 weeks of a sensational tragic issue of “Central American migrant caravans” and how the tweets played their critical roles in the overall sentiment on the twittersphere. We used Vader Sentiment Analysis to analyze the sentiment of each tweet before estimating the sentiment on Twitter toward this news. We show that such simple estimates can be very accurate.
Performance Analysis Of AES And DESede On The Sensitive Data Stored In HDFS

Hadoop is a distributed framework for storing and analyzing big data. Organizations store their sensitive information on HDFS and use MapReduce to analyze the data. Therefore, data encryption is used widely to prevent the leakage of sensitive data. In our paper we have compared two widely used data encryption algorithms AES and DESede on the sensitive information stored in HDFS. The comparison of two algorithms is done by considering factors like data size, decryption time and encryption time. Different file sizes are taken and some experimental studies are performed to validate the capabilities.
Vehicle Positioning Method of Roadside Monocular Camera

In this work, we propose an end-to-end detection method based on the 2D bounding box detection to locate vehicles with a roadside monocular camera. Our work is based on the fact that the shape of the vehicles bottom plane frame in the RGB image of roadside monocular camera is approximately parallelogram, which is validated by the dataset BoxCars116k. Compared with previous work in literature, the method takes full advantage of key points detection by convolutional neural network, which can estimate the vehicle position more simpler and efficiently. Furthermore, the method can make more accurate location for the vehicles on the road by camera calibration. The performance of our end-to-end detection method is validated by the experimental results on the BoxCars116k dataset.