Reviewerly/exHarmony huggingface dataset:
The Role of Telecentre in Developing Entrepreneurship

This paper evaluates the role of a telecentre project- the Union Digital Centres (UDC) in Bangladesh- in developing entrepreneurship among rural youths under Public-Private Partnership. Contrary to engaging qualified operators in terms of efficiency and investment the UDC has employed local youths who are small scale investors, home grown and amateur in skill. Many operators earn very low and some run off the project leading to closure of centres. In this context understanding the issue of entrepreneurship attains significance for the sustainability of the model. Entrepreneurship is defined in terms of operator's income, his/her satisfaction on income and the level of investment. The study hypothesizes that factors responsible for entrepreneurship development include ICT and internet, services, clients' turnout, stakeholders' involvement and experience of the operator. Based on an internet survey and interview of operators and a review of literature the study explores features of UDC facilitated entrepreneurship. It describes stakeholders' involvement in ensuring inputs and their roles in promoting entrepreneurship. An application of a structural equation model supports the hypotheses validating that operator's own initiative synergized with support from the government influence the development of entrepreneurship among operators. The results specify that the UDC bears potential to develop entrepreneurship among operators given they use a variety of equipment and better quality internet, provide a range of services to a greater volume of clients, receive stakeholder's support and demonstrate computer and managerial skills. The government's role in implementation, human resource management and monitoring of the project influences the process. The study also finds that the project experiences a range of challenges from inadequate involvement of partners. Since deficiency in any partner's role can affect the mission of entrepreneurship development, the author suggests that the government must take the lead role in engaging relevant partners to convert the UDC as an enterprise.
A hybrid of tropical-singular value decomposition method for salt and peppernoise removal

The unknown information contained in an image that causes the change of information in the image is called noise. In this paper, we propose a new method for removing salt and pepper noise by using singular value decomposition and the concept of tropical algebra operations. To determine the performance of the proposed method, 20 test images are used as samples. Then three different image quality assessments are used: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and image enhancement factor (IEF). In addition, six different filtering methods, i.e. MF, DWMF, PSMF, MDBUTM, NAFSM, and BPDF, are used to compare the performance of the proposed method. The experimental results show that the proposed method yields better results than the existing methods, in particular the BPDF filtering method.
Blockchain Based Cryptocurrency for IOT

Prior to blockchain technology coming into much usage, the earlier cryptographic digital cash schemes relied merely on trusted authorities. Bitcoin revolutionized this conventional view of a cryptocurrency, trading decentralized, distributed ledger, completely secured by the majority rule without predetermined authority or owner. Only standard cryptographic primitives such as signatures and hash functions were used to achieve security. Though other cryptocurrencies such as Bit-gold and B-money which are among the authority-free cryptocurrencies, Bitcoin is the first one to experience public acceptance on a large scale. There are various factors which contribute to Bitcoins success, though there is still a little question of doubt that the decentralized design of blockchain technology and perceived resilience to government or third-party intervention played an extremely crucial role in its uptake. Distributed crypto cash, more specifically in Blockchain technology has hailed as one key and most eminent and widely accepted innovation of Bitcoin. In this paper we have worked on the use of blockchain technology to implement cryptocurrency for IOT.
Discovering User Bias in Ordinal Voting Systems

Crowdsourcing systems increasingly rely on users to provide more subjective ground truth for intelligent systems - e.g. ratings, aspect of quality and perspectives on how expensive or lively a place feels, etc. We focus on the ubiquitous implementation of online user ordinal voting (e.g 1-5, 1 star-4 stars) on some aspect of an entity, to extract a relative truth, measured by a selected metric such as vote plurality or mean. We argue that this methodology can aggregate results that yield little information to the end user. In particular, ordinal user rankings often converge to a indistinguishable rating. This is demonstrated by the trend in certain cities for the majority of restaurants to all have a 4 star rating. Similarly, the rating of an establishment can be significantly affected by a few users [10]. User bias in voting is not spam, but rather a preference that can be harnessed to provide more information to users. We explore notions of both global skew and user bias. Leveraging these bias and preference concepts, the paper suggests explicit models for better personalization and more informative ratings.
An Analysis of Object Embeddings for Image Retrieval.

We present an analysis of embeddings extracted from different pre-trained models for content-based image retrieval. Specifically, we study embeddings from image classification and object detection models. We discover that even with additional human annotations such as bounding boxes and segmentation masks, the discriminative power of the embeddings based on modern object detection models is significantly worse than their classification counterparts for the retrieval task. At the same time, our analysis also unearths that object detection model can help retrieval task by acting as a hard attention module for extracting object embeddings that focus on salient region from the convolutional feature map. In order to efficiently extract object embeddings, we introduce a simple guided student-teacher training paradigm for learning discriminative embeddings within the object detection framework. We support our findings with strong experimental results.
Robust measurement validation for radar target tracking using prior information

In this study, the authors propose a robust measurement validation algorithm for radar target tracking in a heavy-clutter environment. This algorithm defines the prior information with the radar measurement information from the estimated target trajectory. An additional validation gate is set up within the conventional validation gate using the statistics of the prior information then only a part of the validation measurement is selected to update the target state. To verify the effectiveness of the proposed algorithm, the authors designed a simulation for target tracking. The simulation results show that the proposed algorithm has a lower root mean squared position error compared to conventional algorithms in a heavy-clutter environment.
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification

This paper addresses the problem of modeling textual conversations and detecting emotions.Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turnbased conversational modeling for classifying the emotions.Our model was evaluated on the data provided by the SemEval-2019 shared task on contextual emotion detection in text.The model shows very competitive results.
Triggerflow: Regression Testing by Advanced Execution Path Inspection

Cryptographic libraries often feature multiple implementations of primitives to meet both the security needs of handling private information and the performance requirements of modern services when the handled information is public. OpenSSL, the de-facto standard free and open source cryptographic library, includes mechanisms to differentiate the confidential data and its control flow, including run-time flags, designed for hardening against timing side-channels, but repeatedly accidentally mishandled in the past. To analyze and prevent these accidents, we introduce Triggerflow, a tool for tracking execution paths that, assisted by source annotations, dynamically analyzes the binary through the debugger. We validate this approach with case studies demonstrating how adopting our method in the development pipeline would have promptly detected such accidents. We further show-case the value of the tooling by presenting two novel discoveries facilitated by Triggerflow: one leak and one defect.
Cross-dataset time series anomaly detection for cloud systems

In recent years, software applications are increasingly deployed as online services on cloud computing platforms. It is important to detect anomalies in cloud systems in order to maintain high service availability. However, given the velocity, volume, and diversified nature of cloud monitoring data, it is difficult to obtain sufficient labelled data to build an accurate anomaly detection model. In this paper, we propose cross-dataset anomaly detection: detect anomalies in a new unlabelled dataset (the target) by training an anomaly detection model on existing labelled datasets (the source). Our approach, called ATAD (Active Transfer Anomaly Detection), integrates both transfer learning and active learning techniques. Transfer learning is applied to transfer knowledge from the source dataset to the target dataset, and active learning is applied to determine informative labels of a small part of samples from unlabelled datasets. Through experiments, we show that ATAD is effective in cross-dataset time series anomaly detection. Furthermore, we only need to label about 1%-5% of unlabelled data and can still achieve significant performance improvement.
TCEC Cup 2

This is a report of the 'TCEC Cup 2' computational experiment, the second TCEC knockout event and the second major part of TCEC’s 14th season. It featured the best 32 engines of the ‘TCEC 14’ league event and the games were held at Blitz tempo. The different format called for different optimal settings in the engines, thereby enabling a comparison with performance at slower tempi. This event made history: the first major trophy win by a 'NN' Neural Network chess engine, LEELA CHESS ZERO, even though relatively disadvantaged by the faster tempo. All decisive games in the supplementary data have been played out by FRITZ17 at search depth 24 to benchmark endgame practice.
One Shot Crowd Counting with Deep Scale Adaptive Neural Network

This paper aims to utilize the deep learning architecture to break through the limitations of camera perspective, image background, uneven crowd density distribution and pedestrian occlusion to estimate crowd density accurately. In this paper, we proposed a new neural network called Deep Scale-Adaptive Convolutional Neural Network (DSA-CNN), which can convert a single crowd image to density map for crowd counting directly. For a crowd image with any size and resolution, our algorithm can output the density map of the crowd image by end-to-end method and finally estimate the number of the crowd in the image. The proposed DSA-CNN consists of two parts: the seven layers CNN network structure and DSA modules. In order to ensure the proposed method is robust to camera perspective effect, DSA-CNN has adopted different sizes of filters in the network and combines them ingeniously. In order to reduce the depth of the data to increase the speed of training, the proposed method utilized 1 × 1 filter in DSA module. To validate the effectiveness of the proposed model, we conducted comparative experiments on four popular public datasets (ShanghiTech dataset, UCF_CC_50 dataset, WorldExpo’10 dataset and UCSD dataset). We compare the proposed method with other well-known algorithms on the MAE and MSE indicators, such as MCNN, Switching-CNN, CSRNet, CP-CNN and Cascaded-MTL. Experimental results show that the proposed method has excellent performance. In addition, we found that the proposed model is easily trained, which further increases the usability of the proposed model.
Machine Learning: A Way of Dealing with Artificial Intelligence

The activities of computers with Artificial Intelligence are intended for Speech recognition, Learning, Planning, Problem solving, etc. Machine Learning is a division/subset of AI. Deep learning is a part of AI that is concerned with coping the learning approach to facilitate human beings applies to gain certain kind of knowledge. This paper presents the idea of Machine Learning. The different methods of learning like supervised learning, unsupervised learning and reinforcement learning are explained with the concepts of regression, classification, clustering and association are given. The terminologies used in machine learning like statistic fit and dimensionality reduction are given with suitable examples.
TF‐CPABE: An efficient and secure data communication with policy updating in wireless body area networks

The major challenge in wireless body area networks (WBAN) is setting up a protected communication between data consumers and a body area network controller while meeting the security and privacy requirements. This paper proposes efficient and secure data communication in WBANs using a Twofish symmetric algorithm and ciphertext‐policy attribute‐based encryption with constant size ciphertext; in addition, the proposed scheme incorporates policy updating to update access policies. To the best of the author's knowledge, policy updating in WBAN has not been studied in earlier works. The proposed scheme is evaluated in terms of message size, energy consumption, and computation cost, and the results are compared with those of existing schemes. The result shows that the proposed method can achieve higher efficiency than conventional methods.
Identification of Dependencies in Task Allocation during Distributed Agile Software Development

There is a growing interest for Distributed Agile Software Development (DASD) in software industry due to multiple benefits such as availability of resources, low development cost, changeability and customer satisfaction. Carrying out DASD poses a challenge of effective task allocation, which requires active coordination between dispersed teams. Effective coordination can be achieved by identifying and managing dependencies in DASD environment. To understand the task allocation complexity due to dispersed teams at different sites, we identify different types of dependencies in DASD. These dependencies must be recognized as they have a profound influence on software product achievement using DASD. Identification and understanding them support management to timely recognize different types of issues that can cause delay, interruptions or even cancellation of Agile Sprints. DASD being an emerging practice, their understanding also leverages methodical task allocation that can optimize on quality concerns such as cost of development and quality of product.
Modelling the Impact of AI for Clinical Decision Support

Many AI (or ML) systems have been proposed for clinical decision support. Clinical usefulness is assessed in an ‘Impact Study’, a form of trial of a completed system. In development, in contrast, the focus is on AI accuracy measures, such as the AUC. To improve impact and to justify the cost of a study, the impact of a proposed AI system should be modelled during its development. We show that an Influence Diagram can be used for this and provide a small set of generic models for diagnostic AI systems. We show that how the AI interacts with clinical decision makers is at least as important as its predictive accuracy.
On Practical Aspects of PCFG Password Cracking

When users choose passwords to secure their computers, data, or Internet service accounts, they tend to create passwords that are easy to remember. Probabilistic methods for password cracking profit from this fact, and allow the attackers and forensic investigators to guess user passwords more precisely. In this paper, we present our additions to a technique based on probabilistic context-free grammars. By modification of existing principles, we show how to guess more passwords for the same time, and how to reduce the total number of guesses without significant impact on success rate.
Neural Network Approach for Extracting Aggregated Opinions from Analytical Articles

Large texts that analyze a situation in some domain, for example politics or economy, usually are full of opinions. In case of analytical articles, opinions usually are a kind of attitudes with source and target presented as named entities, both mentioned in the text. We present an application of the specific neural network model for sentiment attitude extraction. This problem is considered as a three-class machine learning task for the whole documents. Treating text attitudes as a list of related contexts, we first extract related sentiment contexts and then calculate the resulted attitude label. For sentiment context extraction, we use Piecewise Convolutional Neural Network (PCNN). We experiment with variety of functions that allows us to compose the attitude label, including recurrent neural network, which give the possibility to take into account additional context aspects. For experiments, the RuSentRel corpus was used, it contains Russian analytical texts in the domain of international relations.
A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into Dimensions

A decision support system is used by decision makers for a long time. But, in some cases, the originally designed multidimensional schema does not cover the entire needs of decision makers, which can change over time. One such unfulfilled need, is using facts to describe dimension members. In this article, we propose a methodology to transform the constellation schema of a data warehouse by integrating factual data into a dimension. The proposed methodology and algorithms enrich a constellation multidimensional schema with new analytical possibilities for decision makers. This enrichment has repercussions for the entire multidimensional schema that are managed by multidimensional modeling, hierarchy calculation and the hierarchy version. In this article, we present a theoretical view of the proposed methodology supported by a case study, an implemented prototype and a complete evaluation based on a standard benchmark.
Online Handwritten Bangla Character Recognition Using Frechet Distance and Distance Based Features

This paper inspects the impact of feature vector produced by Frechet Distance (FD) along with the conventional distance based features to recognize online handwritten Bangla characters. FD based feature computation starts with dividing a character sample into different rectangular zones. Then FD values are computed from each zone to every other zones. In distance based feature extraction technique also a character sample is divided into several segments and distances are measured from a particular segment to all other segments. Feature vectors so produced are experimented on 10000 online handwritten Bangla characters. SVM (Support Vector Machine) classifier produces the reasonably satisfactory recognition accuracy of 98.98% when FD based features are combined with distance based features.
Simultaneous 3D Motion Detection, Long-Term Tracking and Model Reconstruction for Multi-Objects

Motion detection and object tracking play important roles in unsupervised human–machine interaction systems. Nevertheless, the human–machine interaction would become invalid when the system fails to detect the scene objects correctly due to occlusion and limited field of view. Thus, robust long-term tracking of scene objects is vital. In this paper, we present a 3D motion detection and long-term tracking system with simultaneous 3D reconstruction of dynamic objects. In order to achieve the high precision motion detection, an optimization framework with a novel motion pose estimation energy function is provided in the proposed method by which the 3D motion pose of each object can be estimated independently. We also develop an accurate object-tracking method which combines 2D visual information and depth. We incorporate a novel boundary-optimization segmentation based on 2D visual information and depth to improve the robustness of tracking significantly. Besides, we also introduce a new fusion and updating strategy in the 3D reconstruction process. This strategy brings higher robustness to 3D motion detection. Experiments results show that, for synthetic sequences, the root-mean-square error (RMSE) of our system is much smaller than Co-Fusion (CF); our system performs extremely well in 3D motion detection accuracy. In the case of occlusion or out-of-view on real scene data, CF will suffer the loss of tracking or object-label changing, by contrast, our system can always keep the robust tracking and maintain the correct labels for each dynamic object. Therefore, our system is robust to occlusion and out-of-view application scenarios.
Giving Camel to Artifacts for Industry 4.0 Integration Challenges

Interoperability is a key factor of Cyber-Physical System (CPS) concept. Based on studies that use Multi-Agent System (MAS) as the core of a CPS, we are proposing to model many resources of the factories following the well-known Agents and Artifacts model for integrating agents and their environment. To enhance the interoperability of this system, we use the Apache Camel framework, a middleware to define routes to integrate a wide range of endpoints using different protocols. In this paper, we are demonstrating our camel component for artifacts in the context of Industry 4.0.
Organizational planning for quality management in the digital age

Purpose Quality managers today require a holistic, overreaching view on the organization and its goals in order to plan the quality management organization (QMO) effectively. The purpose of this paper is to present the concept of a web application that aims to support quality managers in organizational planning tasks. The basis for the implementation is a previously developed planning method that builds on Product and Technology Roadmapping as well as Quality Function Deployment. It supports the QMO’s strategic development in line with the organization’s overall strategy. Design/methodology/approach Based on previous research that systematically identified the requirements toward a suitable planning method and a consecutively designed framework, a web application was developed. This process followed agile software development approaches, including test-driven iteration cycles and validation through a case-based user trial. Findings Standardized quality criteria for software development together with identified requirements for a suitable planning method provided a specified guideline to develop a web application. This application enables quality managers to effectively deploy the previously developed planning method. It proved successful in an initial field trial and creates a basis for integrated, computer-aided QMO planning. Originality/value With an integrated planning method that was implemented through a web application, quality managers could be effectively supported in developing the QMO in line with the overall strategy. As organizational planning tasks are reoccurring but not day-to-day business, moderated workshops that use the web application as a guiding tool and enable quality managers to focus on delivering information inputs appear to be a highly promising approach.