Reviewerly/exHarmony huggingface dataset:
Abusive Comments Classification in Social Media Using Neural Networks

Social media is progressively exposed to issues of damaging conduct, for example, private assaults and cyberbullying. It is troublesome and time consuming to manually check which of the comments need to be blocked. Therefore, automating the process of identifying the abusive comments and blocking them would not only save time, but also provide safety to the users. Our project focuses on solving this problem by using deep learning technique. The Twitter dataset is used to train the model. The comments are classified as abusive and non-abusive using convolutional neural network (CNN) model. This project is thus able to detect and moderate abusive content on social media automatically.
Ordering Method and Empirical Study on Multiple Factor Sensitivity of Group Social Attitudes Based on Entropy Theory

When studying the various factors affecting a group’s social attitudes, minor changes in a factor will easily cause changes to other factors due to their association and relevance to each other; therefore, such a factor is... | Find, read and cite all the research you need on Tech Science Press
A Digital Start-up Project–CARM Tool as an Innovative Approach to Digital Government Transformation

The digital revolution impacts modern workplaces through constant advances in technology. This study uses a digital start-up project experience to demonstrate how a Digital Government Transformation can be beneficial. The increase of administrative requirements, compliance, and capability readiness has prompted the development of the Digital start–up project known as the CARM Tool (Compliance, Assurance and Risk Management). CARM introduces a low risk low cost and high productivity solution, which can directly and immediately benefit stakeholders in a large complex government enterprise. It aims to elevate staff performance, customer service and provide qualification between financial statements, asset management and alignment with the Digital Government Transformation strategy. The digital start-up inspired, innovative CARM tool is an original idea developed by the author to utilise emerging technologies to modernise and consolidate the manual-based business processes and legacy systems, to improve visibility and accountability within the enterprise and to advance workforce skills throughout the organisation. In this paper, the author presents a detailed start-up project approach that draws the public sector specifically Defence into the digital transformation age through the innovative CARM tool from concept, to prototype to game-changing enterprise modernisation.
Open source evolutionary structured optimization

Nevergrad is a derivative-free optimization platform gathering both a wide range of optimization methods and a wide range of test functions to evaluate them upon. Some of these functions have very particular structures which standard methods are not able to use. The most recent feature of Nevergrad is the ability to conveniently define a search domain, so that many algorithms in Nevergrad can automatically rescale variables and/or take into account their possibly logarithmic nature or their discrete nature, but also take into account any user-defined mutation or recombination operator. Since many problems are efficiently solved using specific operators, Nevergrad therefore now enables using specific operators within generic algorithms: the underlying structure of the problem is user-defined information that several families of optimization methods can use and benefit upon. We explain how this API can help analyze optimization methods and how to use it for the optimization of a structured Photonics physical testbed, and show that this can produce significant improvements.
Text-Independent Voice Authentication System Using MFCC Features

Voice authentication is the process of identifying the user based on their voice. There have been many successful attempts with remarkable accuracies. Since, more and more information is getting available on internet, and the secure authentication has become ever more necessary. In this voice authentication system (VAS), English pangrams are used to capture every alphabet utterance of the user. MFCC features are extracted from the collected audio files and a convolutional neural network (CNN) model is trained. While testing, the system generates two random words and concatenates it with the English pangrams to increase the security of the system. This gives the attacker no opportunity to pre-record the sentence and played back against the system to gain the access.
Developing an Adaptation Process for Real-Coded Genetic Algorithms

The genetic algorithm (GA) is a metaheuristic method which simulates the life cycle and the survival of the fittest in the nature for solving optimization problems. This study aimed to develop enhanced operation by modifying the current GA. This development process includes an adaptation method that contains certain developments and adds a new process to the classic algorithm. Individuals of a population will be trialed to adapt to the current solution of the problem by taking them separately for each generation. With this adaptation method, it is more likely to get better results in a shorter time. Experimental results show that this new process accelerated the algorithm and a certain solution has been reached in fewer generations. In addition, better solutions were achieved, especially for a certain number of generations.
LightBC: A Lightweight Hash-Based Blockchain for the Secured Internet of Things

Pohrmen, Fabiola Hazel Saha, GoutamBlockchain technology is one of the key technologies that have the potential to solve many of the Internet of Things (IoT) challenges. The IoT environment consists of numerous resource-constrained devices. The security and privacy of these devices have become primary concerns among consumers and businesses. Although, Blockchain could provide better security and privacy to these devices, their limited memory, battery life, and processing capabilities make Blockchain-IoT integration very challenging. Current implementations of Blockchain use cryptographic schemes like SHA-256 and ECDSA. However, the resource-constrained nature of IoT devices demands lightweight versions of the Blockchain for IoT. In this work, a lightweight hash-based Blockchain (LightBC) is proposed for the IoT, which adapts the SPONGENT hash function. It has been emulated and compared with SHA-256 based Blockchain on a Blockchain emulator and satisfactory results were found for upto 8000 nodes. The IoT architecture has also been proposed for implementing the same.
A different approach to solving the PBVS control problem

This paper offers a different approach to solving the position-based visual servoing control problem. If focuses on the development of a free of coordinates solution that allows the direct use of point features 3D position without the necessity of recovering the pose of the object. The proposed approach makes use of the rigid body motion parameterization techniques based on the properties of dual Lie algebras. Considering an imposed velocity field over the motion of a rigid body, our work gives an algebraic close form free of coordinates solution for the position based visual control law. This solution allows the motion control to be stable while keeping the image features in the field of view. Also, it provides a natural decoupling of the visual sensor motion on all six degrees of freedom. A comparison by simulations is conducted using different scenarios. The results show how the proposed solution copes with the problems of the classic servoing techniques.
Machine Learning based Restaurant Revenue Prediction

Food industry has a crucial part in enhancing the financial progress of a country. This is very true for metropolitan cities than any small towns of our country. Despite the contribution of food industry to the economy, the revenue prediction of the restaurant has been limited. The agenda of this work is basically to detect the revenue for any upcoming setting of restaurant. There are three types of restaurant which have been encountered. They are inline, food court, and mobile. In our proposed solution, we take into consideration the various features of the datasets for the prediction. The input features were ordered based on their impact on the target attribute which was the restaurant revenue. Various other pre-processing techniques like Principal Component Analysis (PCA), feature selection and label encoding have been used. Without the proper analysis of Kaggle datasets pre-processing cannot be done. Algorithms are then evaluated on the test data after being trained on the training datasets. Random Forest (RF) was found to be the best performing model for revenue prediction when compared to linear regression model. The model accuracy does make a difference before pre-processing and after pre-processing. The accuracy increases after the applied methods of pre-processing.
Identifying Disinformation Websites Using Infrastructure Features

Platforms have struggled to keep pace with the spread of disinformation. Current responses like user reports, manual analysis, and third-party fact checking are slow and difficult to scale, and as a result, disinformation can spread unchecked for some time after being created. Automation is essential for enabling platforms to respond rapidly to disinformation. In this work, we explore a new direction for automated detection of disinformation websites: infrastructure features. Our hypothesis is that while disinformation websites may be perceptually similar to authentic news websites, there may also be significant non-perceptual differences in the domain registrations, TLS/SSL certificates, and web hosting configurations. Infrastructure features are particularly valuable for detecting disinformation websites because they are available before content goes live and reaches readers, enabling early detection. We demonstrate the feasibility of our approach on a large corpus of labeled website snapshots. We also present results from a preliminary real-time deployment, successfully discovering disinformation websites while highlighting unexplored challenges for automated disinformation detection.
Heuristics for the Design of Large Multipliers for FPGAs

This proposal presents a scalable methodology for the design of large multipliers by using tiling. Thereby, a multiplier of a given arbitrary size is partitioned into smaller DSP blocks or logic-based multipliers. This can be represented by tiling an area (defined by the size of the large multiplier) by using tiles of different shapes (defined by the small multipliers), each assigned with individual costs. Resource optimal solutions for this problem have been proposed for small multipliers by using integer linear programming (ILP) solvers, but the computational effort to solve the tiling problem for multipliers beyond 64x64 exceeds solving times of several days on current computers. Many applications like from cryptography require much larger multipliers. In addition, none of the previous methods exploit resource reductions from the well known Karatsuba scheme. Hence, it is first shown how the Karatsuba scheme can be included in the tiling optimization by considering it as a specific tile. Next, two fast and scalable tiling heuristics are presented to obtain good solutions in a reasonable time. Similar to previous work, the first heuristic is based on a greedy search. Based on that, the second heuristic improves these results by applying the idea of the beam search meta-heuristic. Both heuristics are capable to include the Karatsuba scheme, scale well to large multipliers and show significant improvements compared to state-of-the-art heuristics.
Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure

Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm &amp; Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the algorithm as compared to the previous algorithm. Proposed algorithm aims to improve in term of cost and execution time and reduces network cost. Methods: The proposed task scheduling algorithm in cloud uses ACO with network cost and execution cost as a fitness function. This work tries to improve the existing ACO that will give improved result in terms of performance and execution cost for cloud architecture. Our study includes a comparison between various other algorithms with our proposed ACO model. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The network cost and user requests measures the performance of the proposed model. Conclusion: The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost, network cost).
Perceptions and use of cloud services: an empirical study on the students of a public university in Bangladesh

Purpose The purpose of this study is to measure the perceptions and use of cloud services by the students of the University of Dhaka, Bangladesh. The specific objective of this study is to know what perceptions students have about cloud services, why and how they use cloud for academic activities. Design/methodology/approach For understanding the use of cloud services among the students of Dhaka University, a quantitative survey was carried out to collect data. Printed questionnaires were distributed among the students in different departments and university library. Both open- and close-ended questions were included in the questionnaire. A total of 329 students responded in this survey and the response rate was 85.5%. The collected data were analyzed using Statistical Package for the Social Sciences. Findings Findings revealed that students are generally aware of cloud services and majority of the respondents use cloud for different purposes. Students used cloud for storage, back up and collaboration purposes and some of Google features are widely used by majority of the students for academic purposes. Originality/value To the best of authors’ knowledge, this is the first time an effort has been made to measure the use of cloud services among the students of University of Dhaka. This study will help to raise cloud service awareness among the students and encourage the authorities to adopt appropriate strategies and policies to resolve cloud adoption in university setting. Specially, Center of Excellence in Teaching and Learning of Dhaka University can take some initiatives in this case.
Design and Implementation of Takagi-Sugeno Fuzzy Model Based Control Scheme for the Continuous Stirred Tank Reactor

In this work, the authors have designed and implemented a Takagi-Sugeno fuzzy model based control scheme (TS-MBC) for the continuous stirred tank reactor (CSTR) process. The proposed control scheme can be viewed as an alternative to the nonlinear model based control scheme proposed by Panda & Prakash1. In this work the nonlinear process is represented using a bank of linear discrete state space models combined using fuzzy membership function. The output of the proposed control scheme is computed in two steps. The bank of linear models weighted using fuzzy membership function is used to compute the value of the controller output in the first step. In the second step, the controller output computed in the first step is updated based on the measurement. The performance of the proposed control scheme has been compared with that of a nonlinear model based control scheme (NMBC) and nonlinear PID (NPID).
Coherence, entanglement, and complementarity in mixed classical light

The classical analogues of quantum correlations have been the subject of considerable interest in the past few years; however, all of these investigations consider the classical analogue of only pure quantum mechanical states. In this work, we study mixed classical light states and derive relations between the polarization coherence of the field and the (classical) entanglement between its degrees of freedom. We show, for a specific model of mixed states, where the purity is determined by a single parameter, how the coherence shared between polarization and entanglement shrinks as the level of purity decreases. The sum of the square of polarization and entanglement remains constant, a behavior consistent with previous results [ Phys. Rev. Lett. 117 , 153901 ( 2016 ) PRLTAO 0031-9007 10.1103/PhysRevLett.117.153901 ] of pure states, even though the constant for the mixed-state case is now smaller in value.
Enhancing the Performance of Grayscale Image Classification by 2D Charlier Moments Neural Networks

This paper presents a new model for 2D image classification based on 2D discrete Charlier moments and neural networks to enhance the classification accuracy of Grayscale images. Discrete Charlier moments have the ability to extract relevant features from an image even in lower orders, and with high efficiency of the neural networks; we can design the proposed efficient model. Experiments are carried out on Coil-20 and ORL datasets to demonstrate the performance of the proposed model. The obtained results show the capability of the proposed model to achieve high classification accuracy on both datasets, and to outperform other recent methods.
Policy-aware data lakes: a flexible approach to achieve legal interoperability for global research collaborations

Abstract A popular model for global scientific repositories is the data commons, which pools or connects many datasets alongside supporting infrastructure. A data commons must establish legally interoperability between datasets to ensure researchers can aggregate and reuse them. This is usually achieved by establishing a shared governance structure. Unfortunately, governance often takes years to negotiate and involves a trade-off between data inclusion and data availability. It can also be difficult for repositories to modify governance structures in response to changing scientific priorities, data sharing practices, or legal frameworks. This problem has been laid bare by the sudden shock of the COVID-19 pandemic. This paper proposes a rapid and flexible strategy for scientific repositories to achieve legal interoperability: the policy-aware data lake. This strategy draws on technical concepts of modularity, metadata, and data lakes. Datasets are treated as independent modules, which can be subject to distinctive legal requirements. Each module must, however, be described using standard legal metadata. This allows legally compatible datasets to be rapidly combined and made available on a just-in-time basis to certain researchers for certain purposes. Global scientific repositories increasingly need such flexibility to manage scientific, organizational, and legal complexity, and to improve their responsiveness to global pandemics.
Iterative Path Planning of a Serial Manipulator in a Cluttered Known Environment

Robot path planning in a cluttered environment can be a challenging task. In this paper, a new algorithm is proposed to perform path planning of a point-to-point motion of an industrial manipulator. The algorithm starts from a simple joint interpolated movement and, by computing collision detection along the path, creates a set of via-points that form a path which allows the robot to move to the end position without any collision. In the paper, the new algorithm is presented and compared in simulation to other methods.
The heat is on: towards the realization of non-cryogenic photonic quantum technologies

Abstract Solid-state devices capable of emitting single photons on demand are poised to allow the development of several photonic quantum technologies. Although high-performance devices have been reported in laboratory settings, the vast majority of experimental demonstrations performed to date have required the assistance of cryogenic cooling. In this perspective article we discuss the general progress and future challenges for the development of single photon emitters capable of operation at higher temperatures: negating the need for costly and cumbersome cryogenic cooling systems and their related vacuum requirements.
Automatic Detection of Sensitive Information in Educative Social Networks

Detecting sensitive information with privacy in mind is a relevant issue on Social Networks. It is often difficult for users to manage the privacy associated with their posts on social networks taking into account their possible consequences. The main objective of this work is to provide users information about the sensitivity of the information they will share when they decide to publish a message in online media. For this purpose, an assistant agent to detect sensitive information based on different types of categories detected in the message (i.e., location, personal data, health, personal attacks, emotions, etc.) is proposed. Entity recognition libraries, ontologies, dictionaries, and sentiment analysis will be used to detect the different categories. This agent is integrated into the social network Pesedia, aimed for children and teenagers, and through a soft-paternalism mechanism provides information to users about the sensitivity of certain content and help them in making decisions about its publication. The agent decision process will be evaluated with a dataset elaborated from messages of the social network Twitter.
Amortized learning of neural causal representations.

Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models are often represented as Bayesian networks and learning them scales poorly with the number of variables. Moreover, these approaches cannot leverage previously learned knowledge to help with learning new causal models. In order to tackle these challenges, we represent a novel algorithm called \textit{causal relational networks} (CRN) for learning causal models using neural networks. The CRN represent causal models using continuous representations and hence could scale much better with the number of variables. These models also take in previously learned information to facilitate learning of new causal models. Finally, we propose a decoding-based metric to evaluate causal models with continuous representations. We test our method on synthetic data achieving high accuracy and quick adaptation to previously unseen causal models.
Automated Chinese Seal Carving Art Creation with AI Assistance

Artificial intelligence is providing new possibilities for pre-serving and reviving traditional arts and crafts. Here we present a method to automate the creation of seal carving artworks with deep learning generated glyphs, an interactive user interface for seal design, and fast mechanical engraving to turn the design into a physical entity. People with no prior experience of seal carving created personalized Han-dynasty-style seals using the interface we developed. Our method allows novices to be engaged in the creative design of Chinese seals and greatly improve the efficiency of the seal carving process, which used to be time-consuming and require high expertise.