Reviewerly/exHarmony huggingface dataset:
Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Summary Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non‐trivial. With a marked increase in the size, subjectivity, and diversity of social web‐data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well‐structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high‐quality studies to address specific research questions pertaining to the defined research domain.
“How may i help you?” Says a robot: Examining language styles in the service encounter

Is figurative or literal language more effective in the service encounter context? The current research reveals that literal language used by a service provider might be more congruent with conversational norms, thus leading to better customer evaluation. Most importantly, the effectiveness of language styles (literal vs. figurative) is contingent on a service agent type (human vs. robot vs. kiosk). Specifically, consumers respond more favorably to human service agents who use literal (vs. figurative) language, and due to the notion of anthropomorphism such an effect extends to service robots. However, the language style effect is not observed among service kiosks as they lack humanlike features. Furthermore, perceived credibility is the underlying mechanism explaining the language style effect among service agents.
High-Performance FPGA-Based CNN Accelerator With Block-Floating-Point Arithmetic

Convolutional neural networks (CNNs) are widely used and have achieved great success in computer vision and speech processing applications. However, deploying the large-scale CNN model in the embedded system is subject to the constraints of computation and memory. An optimized block-floating-point (BFP) arithmetic is adopted in our accelerator for efficient inference of deep neural networks in this paper. The feature maps and model parameters are represented in 16-bit and 8-bit formats, respectively, in the off-chip memory, which can reduce memory and off-chip bandwidth requirements by 50% and 75% compared to the 32-bit FP counterpart. The proposed 8-bit BFP arithmetic with optimized rounding and shifting-operation-based quantization schemes improves the energy and hardware efficiency by three times. One CNN model can be deployed in our accelerator without retraining at the cost of an accuracy loss of not more than 0.12%. The proposed reconfigurable accelerator with three parallelism dimensions, ping-pong off-chip DDR3 memory access, and an optimized on-chip buffer group is implemented on the Xilinx VC709 evaluation board. Our accelerator achieves a performance of 760.83 GOP/s and 82.88 GOP/s/W under a 200-MHz working frequency, significantly outperforming previous accelerators.
Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction

This paper proposes a novel multimodal framework for rating prediction of consumer products by fusing different data sources, namely physiological signals, global reviews obtained separately for the product and its brand. The reviews posted by global viewers are retrieved and processed using Natural Language Processing (NLP) technique to compute compound score considered as global rating. Also, electroencephalogram (EEG) signals of the participants were recorded simultaneously while watching different products on computer’s screen. From EEG, valence scores in terms of product rating are obtained using self-report towards each viewed product for acquiring local rating. A higher valence score corresponds to intrinsic attractiveness of the participant towards a product. Random forest based regression techniques is used to model EEG data to build a rating prediction framework considered as local rating. Furthermore, Artificial Bee Colony (ABC) based optimization algorithm is used to boost the overall performance of the framework by fusing global and local ratings. EEG dataset of 40 participants including 25 male and 15 female is recorded while viewing 42 different products available on e-commerce website. Experiment results are encouraging and suggest that the proposed ABC optimization approach can achieve lower Root Mean Square Error (RMSE) in rating prediction as compared to individual unimodal schemes.
New binary whale optimization algorithm for discrete optimization problems

The whale optimization algorithm (WOA) is an intelligence-based technique that simulates the hunting behaviour of humpback whales in nature. In this article, an adaptation of the original version of the WOA is made for handling binary optimization problems. For this purpose, two transfer functions (S-shaped and V-shaped) are presented to map a continuous search space to a binary one. To illustrate the functionality and performance of the proposed binary whale optimization algorithm (bWOA), its results when applied on twenty-two benchmark functions, three engineering optimization problems and a real-world travelling salesman problem are found. Furthermore, the proposed bWOA is compared with five well-known metaheuristic algorithms. The experimental results show its superiority in comparison with other state-of-the-art metaheuristics in terms of accuracy and speed. Finally, Wilcoxon's rank-sum non-parametric statistical test is carried out at the 5% significance level to judge whether the results of the proposed algorithm differ from those of the other comparison algorithms in a statistically significant way.
Corruption, natural resources and economic growth: Evidence from OIC countries

The study analyzes the impact of corruption and natural resources on economic growth by incorporating the role of per capita income and Information Communication Technologies (ICT). We use panel time series data from 1984 to 2016 for 43-member countries of Organization of Islamic Corporation (OIC). We apply Cross Sectional Autoregressive Distributed Lags (CS-ARDL) approach for estimation of results. The finding shows that corruption impedes economic growth. Natural resources have positive and significant effect on economic growth. Moreover, the combine effect of corruption and natural resources is positive. We split sample size according to low and high ICT diffusion economies and found that natural resources are inversely related with economic growth in low ICT diffusion economies. All other results are consistent through-out the analysis. Our finding affirms the existing hypothesis that corruption impedes economic growth in OIC countries. Our analysis also corroborates that ICT plays significant contribution in nexus between corruption and economic growth within OIC countries.
An overview of augmented reality technology

Augmented reality is a technology that combines virtual reality with reality. In recent years, the rapid development of augmented reality technology has aroused people's high attention. This paper first expounds the research and progress of augmented reality at home and abroad. Secondly, it introduces the key technologies, development tools and application of augmented reality in some fields. Finally, it looks forward to the future development trend of augmented reality technologies such as AR cloud.
VR facial animation via multiview image translation

A key promise of Virtual Reality (VR) is the possibility of remote social interaction that is more immersive than any prior telecommunication media. However, existing social VR experiences are mediated by inauthentic digital representations of the user (i.e., stylized avatars). These stylized representations have limited the adoption of social VR applications in precisely those cases where immersion is most necessary (e.g., professional interactions and intimate conversations). In this work, we present a bidirectional system that can animate avatar heads of both users' full likeness using consumer-friendly headset mounted cameras (HMC). There are two main challenges in doing this: unaccommodating camera views and the image-to-avatar domain gap. We address both challenges by leveraging constraints imposed by multiview geometry to establish precise image-to-avatar correspondence, which are then used to learn an end-to-end model for real-time tracking. We present designs for a training HMC, aimed at data-collection and model building, and a tracking HMC for use during interactions in VR. Correspondence between the avatar and the HMC-acquired images are automatically found through self-supervised multiview image translation, which does not require manual annotation or one-to-one correspondence between domains. We evaluate the system on a variety of users and demonstrate significant improvements over prior work.
Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks

Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, we present a novel memory-augmented colorization model MemoPainter that can produce high-quality colorization with limited data. In particular, our model is able to capture rare instances and successfully colorize them. Also, we propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need for class labels. Experiments show that our model has superior quality in both few-shot and one-shot colorization tasks.
A Deep Learning Model for Estimating Story Points

Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating the effort required for completing user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in completing a user story or resolving an issue. In this paper, we propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is end-to-end trainable from raw input data to prediction outcomes without any manual feature engineering. We offer a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. An empirical evaluation demonstrates that our approach consistently outperforms three common baselines (Random Guessing, Mean, and Median methods) and six alternatives (e.g., using Doc2Vec and Random Forests) in Mean Absolute Error, Median Absolute Error, and the Standardized Accuracy.
Review of ontology-based recommender systems in e-learning

In recent years there has been an enormous increase in learning resources available online through massive open online courses and learning management systems. In this context, personalized resource recommendation has become an even more significant challenge, thereby increasing research in that direction. Recommender systems use ontology, artificial intelligence, among other techniques to provide personalized recommendations. Ontology is a way to model learners and learning resources, among others, which helps to retrieve details. This, in turn, generates more relevant materials to learners. Ontologies have benefits of reusability, reasoning ability, and supports inference mechanisms, which helps to provide enhanced recommendations. The comprehensive survey in this paper gives an overview of the research in progress using ontology to achieve personalization in recommender systems in the e-learning domain.
Double-Spending With a Sybil Attack in the Bitcoin Decentralized Network

A double-spend attack is one of the major security issues in most blockchain systems, but it is difficult to successfully launch unless an adversary has massive computing power. In this paper, we introduce a new attack model that combines a double-spend attack with a Sybil attack in the Bitcoin network. We present analysis results that a double-spending attacker can make a block propagation delay by conducting Sybil attacks and increase the probability of winning the mining race, thus successfully launching the double-spend attack. We develop the probability of success of this new attack in mathematics forms and analyze this attack model from the perspective of economics. We present the attacker's break-even point in various situations and demonstrate the effect of the proposed attack.
AnoPCN

Video anomaly detection is a challenging problem due to the ambiguity and complexity of how anomalies are defined. Recent approaches for this task mainly utilize deep reconstruction methods and deep prediction ones, but their performances suffer when they cannot guarantee either higher reconstruction errors for abnormal events or lower prediction errors for normal events. Inspired by the predictive coding mechanism explaining how brains detect events violating regularities, we address the Anomaly detection problem with a novel deep Predictive Coding Network, termed as AnoPCN, which consists of a Predictive Coding Module (PCM) and an Error Refinement Module (ERM). Specifically, PCM is designed as a convolutional recurrent neural network with feedback connections carrying frame predictions and feedforward connections carrying prediction errors. By using motion information explicitly, PCM yields better prediction results. To further solve the problem of narrow regularity score gaps in deep reconstruction methods, we decompose reconstruction into prediction and refinement, introducing ERM to reconstruct current prediction error and refine the coarse prediction. AnoPCN unifies reconstruction and prediction methods in an end-to-end framework, and it achieves state-of-the-art performance with better prediction results and larger regularity score gaps on three benchmark datasets including ShanghaiTech Campus, CUHK Avenue, and UCSD Ped2.
Crowdsourcing Mechanism for Trust Evaluation in CPCS Based on Intelligent Mobile Edge Computing

Both academia and industry have directed tremendous interest toward the combination of Cyber Physical Systems and Cloud Computing, which enables a new breed of applications and services. However, due to the relative long distance between remote cloud and end nodes, Cloud Computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this article, we first propose a novel trust evaluation mechanism using crowdsourcing and Intelligent Mobile Edge Computing. The mobile edge users with relatively strong computation and storage ability are exploited to provide direct management for end nodes. Through close access to end nodes, mobile edge users can obtain various information of the end nodes and determine whether the node is trustworthy. Then, two incentive mechanisms, i.e., Trustworthy Incentive and Quality-Aware Trustworthy Incentive Mechanisms, are proposed for motivating mobile edge users to conduct trust evaluation. The first one aims to motivate edge users to upload their real information about their capability and costs. The purpose of the second one is to motivate edge users to make trustworthy effort to conduct tasks and report results. Detailed theoretical analysis demonstrates the validity of Quality-Aware Trustworthy Incentive Mechanism from data trustfulness, effort trustfulness, and quality trustfulness, respectively. Extensive experiments are carried out to validate the proposed trust evaluation and incentive mechanisms. The results corroborate that the proposed mechanisms can efficiently stimulate mobile edge users to perform evaluation task and improve the accuracy of trust evaluation.
A Neural Temporal Model for Human Motion Prediction

We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation. Key aspects of our proposed system include: 1) a novel, two-level processing architecture that aids in generating planned trajectories, 2) a simple set of easily computable features that integrate derivative information, and 3) a novel multi-objective loss function that helps the model to slowly progress from simple next-step prediction to the harder task of multi-step, closed-loop prediction. Our results demonstrate that these innovations improve the modeling of long-term motion trajectories. Finally, we propose a novel metric, called Normalized Power Spectrum Similarity (NPSS), to evaluate the long-term predictive ability of motion synthesis models, complementing the popular mean-squared error (MSE) measure of Euler joint angles over time. We conduct a user study to determine if the proposed NPSS correlates with human evaluation of long-term motion more strongly than MSE and find that it indeed does. We release code and additional results (visualizations) for this paper at: https://github.com/cr7anand/neural_temporal_models.
A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification

The best way to acquire knowledge about an algorithm is feeding it data and checking the result. In a layman's language machine learning can be called as an ideological child or evolution of the idea of understanding algorithm through data. Machine learning can be subdivided into two paradigms, supervised learning and unsupervised learning. Supervised learning is implemented to classify data using algorithms like support vector machines (SVM), linear regression, logistic regression, neural networks, nearest neighbor etc. Supervised learning algorithm uses the concepts of classification and regression. Linear classification was earlier used to form the decision plane but was bidimensional. But a particular dataset might have required a non linear decision plane. This gave the idea of the support vector machine algorithm which can be used to generate a non linear decision boundary using the kernel function. SVM is a vast concept and can be implemented on various real world problems like face detection, handwriting detection and many more. This paper surveys the various concepts of support vector machines, some of its real life applications and future aspects of SVM.
Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system

Cryptocurrency has gained considerable popularity in the past decade. The untraceable and uncontrolled nature of cryptocurrency attracts millions of people around the world. Research in cryptocurrency is dedicated to finding the ether and predicting its price according to the cryptocurrency's past price inflations. In this study, price prediction is performed with two machine learning methods, namely linear regression (LR) and support vector machine (SVM), by using a time series consisting of daily ether cryptocurrency closing prices. Different window lengths are used in ether cryptocurrency price prediction by using filters with different weight coefficients. In the training phase, a cross-validation method is used to construct a high-performance model independent of the data set. The proposed model is implemented using two machine learning techniques. When using the proposed model, the SVM method has a higher accuracy (96.06%) than the LR method (85.46%). Furthermore, the accuracy score of the proposed model can be increased up to 99% by adding features to the SVM method.
Decision Support for Blockchain Platform Selection: Three Industry Case Studies

Blockchain technology has received significant attention recently, as it offers a reliable decentralized infrastructure for all kinds of business transactions. Software-producing organizations are increasingly considering blockchain technology for inclusion into their software products. Selecting the best fitting blockchain platform requires the assessment of its functionality, adaptability, and compatibility to the existing software product. Novice software developers and architects are not experts in every domain, so they should either consult external experts or acquire knowledge themselves. The decision-making process gets more complicated as the number of decision-makers, alternatives, and criteria increases. Hence, a decision model is required to externalize and organize knowledge regarding the blockchain platform selection context. Recently, we designed a decision support system to use such decision models to support decision-makers with their technology selection problems in software production. In this article, we introduce a decision model for the blockchain platform selection problem. The decision model has been evaluated through three real-world case studies at three software-producing organizations. The case-study participants asserted that the approach provides significantly more insight into the blockchain platform selection process, provides a richer prioritized option list than if they had done their research independently, and reduces the time and cost of the decision-making process.
Machine learning for assisting cervical cancer diagnosis: An ensemble approach

Cervical cancer remains one of the most prevalent gynecologic malignancies, worldwide. As cervical cancer is a highly preventable disease, therefore, early screening represents the most effective strategy to minimize the global burden of cervical cancer. However, due to scarce awareness, lack of access to medical centers, and highly expensive procedures in developing countries, the vulnerable patient populations cannot afford to undergo examination regularly. A novel ensemble approach is presented in this paper to predict the risk of cervical cancer. By adopting a voting strategy, this method addresses the challenges associated with previous studies on cervical cancer. A data correction mechanism is proposed to improve the performance of the prediction. A gene-assistance module is also included as an optional strategy to enhance the robustness of the prediction. Multiple measurements are performed to evaluate the proposed method. The results indicate that the likelihood of developing cervical cancer can be effectively predicted using the voting strategy. Compared with other methods, the proposed method is more scalable and practical.