Reviewerly/exHarmony huggingface dataset:
AN EXPERIMENTAL ANALYSIS ON ECOMMERCE REVIEWS,WITH SENTIMENT CLASSIFICATION USING OPINION MINING ON WEB

Sentiment analysis is a big branch in the field of natural language processing. Sentiment analysis mainly text based analysis, but there are some challenges that make it difficult as compared to traditional text based analysis. This paper empathizes on the need of an attempt to improve research process and progress of sentiment analysis on the basis of investigation. Outcome of the analysis are summarized in this paper. This paper analyze the reviews of products manually by collecting data in the form of a excel file. Then it will produce and classify the reviews as positive or negative comments to get the best product. Now it’s more relevant to automate reviews data it is growing exponentially. This method works by web scrapping reviews from e-commerce website. Data cleaning is applied to remove the unwanted data known as stop words. The features are identified. The feature can be camera, battery life etc. Obtain frequency across all the products and for all the reviews per feature. The intended work is to extract the features from the reviews and detecting the polarity for each aspect, thus resulting in feature extraction matrix (FEM). FEM matrix has each row as an observation for a product and each of the columns represent the feature. List of Products based on highest value of FEM for searched features and product recommendations are generated based on the user searched feature.
ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback

By combining recent advances in Natural Language Processing and Conversational Agent (CAs), we suggest a new form of human-computer interaction for individuals to receive formative feedback on their argumentation to help them to foster their logical reasoning skills. Hence, we introduce ArgueBot, a conversational agent, that provides adaptive feedback on students' logical argumentation. We, therefore, 1) leveraged a corpus of argumentative student-written peer-reviews in German, 2) trained, tuned, and benchmarked a model that identifies claims, premises and non-argumentative sections of a given text, and 3) built a conversational feedback tool. We evaluated ArgueBot in a proof-of-concept evaluation with students. The evaluation results regarding technology acceptance, the performance of our trained model, and the qualitative feedback indicate the potential of leveraging recent advances in Natural Language Processing for new human-computer interaction use cases for scalable educational feedback.
Impact of Training Set Design in CNN-Based Sar Image Despeckling

The rise of deep learning has impacted profoundly all aspects of image processing and remote sensing. Following this trend, in the last few years, a large number of data-driven methods have been proposed also for SAR image despeckling. However, in spite of this large effort, only limited performance gains have been observed. We believe this is mostly due to the use of training sets that are only partially fit to the task, and sometimes plain wrong. In this work we assess experimentally the impact of training set design on the performance of SAR image despeckling with the goal of highlighting solid guidelines for sensible training.
Open data in digital strategies against COVID-19: the case of Belgium

COVID-19 has highlighted the importance of digital in the fight against the pandemic (control at the border, automated tracing, creation of databases...). In this research, we analyze the Belgian response in terms of open data. First, we examine the open data publication strategy in Belgium (a federal state with a sometimes complex functioning, especially in health), second, we conduct a case study (anatomy of the pandemic in Belgium) in order to better understand the strengths and weaknesses of the main COVID-19 open data repository. And third, we analyze the obstacles to open data publication. Finally, we discuss the Belgian COVID-19 open data strategy in terms of data availability, data relevance and knowledge management. In particular, we show how difficult it is to optimize the latter in order to make the best use of governmental, private and academic open data in a way that has a positive impact on public health policy.
Belief Space Partitioning for Symbolic Motion Planning

We propose a memory-constrained partition-based method to extract symbolic representations of the belief state and its dynamics in order to solve planning problems in a partially observable Markov decision process (POMDP). Our K-means partitioning strategy uses a fixed number of symbols to represent the partitions of the belief space and ensures the parameterization of the belief dynamics does not grow exponentially as the system dimension increases. By casting our problem as a partitioning of the POMDP, we can then solve planning problems using traditional symbolic planning solvers (such as HTN or A* solvers). Our work is motivated by an autonomous underwater vehicle navigation problem where the vehicle is affected by uncertain flow conditions and receives severely limited position observations. Simulation experiments are provided to validate the performance of the proposed algorithms.
Does the layout of the Android unlock pattern affect the security and usability of the password?

Currently, Android unlock pattern is the most widely used graphical unlock pattern. But, as similar to text passwords, user-generated Android graphical passwords usually have lower entropy values. Most of them can be easily guessed by an attacker. In this paper, we mainly study how to change the 3 × 3 layout of Android unlock pattern based on nine points. And, we want to use this method to determine whether it will affect the security and usability of the graphical passwords. We conducted an online study on the university campus to evaluate the security and usability of the original Android unlock pattern and the other four patterns. We evaluated the recall success rate, setup time and recall time for the graphical passwords. Besides, we also established the 3-gram Markov model to evaluate the security of the passwords from the three indicators: β-success-rate, Min-entropy, and partial guessing entropy. Our research found that changing the layout of the original Android unlock pattern based on nine points can improve security to a certain extent, and it would not have much impact on usability.
Focusing the Argumentative Process: Neighborhood-Based Semantics in Abstract Argumentation

The introduction of abstract argumentation has allowed the study of many exciting characteristics of the argumentation process. Nevertheless, while helpful in many aspects, abstraction diminishes the knowledge representation capabilities available to describe naturally occurring features of argumentative dialogues. One of these elements is the consideration of the topics involved in a discussion. In studying dialogical processes, participants recognize that some topics are closely related to the original issue, while others are more distant to the central subject or simply refer to unrelated matters. Consequently, it is reasonable to study different argumentation semantics that consider the focus of a discussion to evaluate acceptability. In this work, we will introduce the necessary representational elements required to reflect the focus of a discussion, and we will propose an extension of the semantics for multi-topic abstract argumentation frameworks acknowledging that every argument has its own zone of relevance in the argumentation framework, leading to a concept of a neighborhood of legitimate defenses. Furthermore, other semantic elaborations are defined and discussed around this structure.
Research on image Recognition Technology Based on Artificial Intelligence

Image recognition technology is an important field in artificial intelligence. With the development of society and the progress of technology, image recognition technology has gradually been more widely used. This paper introduces the principle of image recognition technology and discusses the advantages of image recognition technology in artificial intelligence. The main function of image recognition technology is to liberate manpower. By using this technology to identify and process all kinds of information, labor intensity can be reduced, and labor efficiency and work quality can be improved. Based on this, this paper discusses the artificial intelligence image recognition technology and its specific application based on the principle of image recognition technology, hoping to help more people understand this technology and maximize the use value of this technology.
5G Security: FPGA Implementation of SNOW-V Stream Cipher

In this paper, a very compact architecture the newest member of the SNOW family of stream ciphers, called SNOW-V, is presented. The proposed architecture has a 128-bit datapath and is pipelined in key areas in order to achieve the maximum possible frequency while using only a small number of hardware resources. The design was coded using the VERILOG hardware description language and the BASYS3 board (Artix 7 XC7A35T) was the target of the hardware implementation. The proposed implementation utilizes only 2109 FPGA LUTs and 1352 FFs and reaches a data throughput of 2.6 Gbps at 224 MHz clock frequency.
Design of an Interactive Geo-Location Mobile Application for Civil Societies in East Africa

This paper reports the developed mobile application through the use of advanced technologies such as mobile computing, cloud computing and Global Positioning System (GPS) in order to solve the challenges.The purpose of this study is to find out how the recent advances and mass adoption of ICT by the public can best be leveraged to enhance the performance of Civil Society Organizations (CSOs) in East Africa.The data collection techniques used are through questionnaires, observations and conducting interviews with different stakeholders in the civil society arena i.e., the donors, the CSOs and the people in society that are given a voice by civil societies.In the system development phase, agile development methodology was used.Analysis of the data collected showed that there is a gap in the availability of a single or a centralized platform which can be easily accessed, user-friendly and reliable where different actors can readily get reliable and up-to-date information about the available CSOs they are interested in.To address this problem, an interactive online directory of CSOs has been developed.The platform is mobile based and enables CSOs to register and fill up their current details, thus ensuring that there is always correct and updated information.The platform is equipped with, among other features, a geo-mapping facility which enables users of the system to correctly geo-locate their civil societies of interest on a map view.The results of system evaluation showed that 88.125% of users were satisfied with the system basing on the evaluation criteria.
Get The Best of the Three Worlds

Lossy image compression always faces a tradeoff between rate-distortion performance and compression/decompression speed. With the advent of neural image compression, hardware (GPU) becomes the new vertex in the tradeoff triangle. By resolving the high GPU dependency and improving the low speed of neural models, this paper proposes two non-GPU models that get the best of the three worlds. First, the CPU-friendly Independent Separable Down-Sampling (ISD) and Up-Sampling (ISU) modules are proposed to lighten the network while ensuring a large receptive field. Second, an asymmetric autoencoder architecture is adopted to boost the decoding speed. At last, the Inverse Quantization Residual (IQR) module is proposed to reduce the error caused by quantization. In terms of rate-distortion performance, our network surpasses the state-of-the-art real-time GPU neural compression work at medium and high bit rates. In terms of speed, our model's compression and decompression speeds surpass all other traditional compression methods except JPEG, using only CPUs. In terms of hardware, the proposed models are CPU friendly and perform stably well in a non-GPU environment. The code is publicly available at https://github.com/kengchikengchi/FasiNet.
Privacy-preserving Multi-cloud based Dynamic Symmetric Searchable Encryption

Symmetric searchable encryption (SSE) provides a secure and efficient way for users to search on the encrypted data stored on the cloud. Most of the existing SSE schemes reveal the search pattern and access pattern during the query of keywords, which can be used to derive the keyword to query or contained in documents. To solve this problem, we propose a privacy-preserving multi-cloud based dynamic SSE scheme, called PMD-SSE, which implements the privacy protection and dynamic update of SSE. To protect the search pattern and access pattern, we divide the index into blocks of the same size and distribute them to multiple clouds, and an index cache is conducted to improve the efficiency of shuffling. We shuffle and re-encrypt the entities in index cache after each query, and then redistribute them to clouds randomly. Furthermore, PMD-SSE supports dynamic update of SSE, and realizes the forward security in dynamic update through the pseudo-random function. At last, formal security analysis show that PMD-SSE is adaptive secure against malicious attacks.
Canonical Correlation Analysis with Bhattacharya Similarity Distance for Multiview Data Representation

Many problems can be correctly tackled only when the related information is available from various viewpoints. Thus, when a problem is to be solved by machine, the data from multiple views needs to be well presented to a machine learning algorithm that suitably processes and learns. For a machine learning algorithm to be able to process such data, finding complementary information between view pairs is important. In this paper, a similarity distance-based canonical correlation analysis (SDCCA) has been proposed to determine the complementary information by finding the similarity between view pairs for multiview data representation. The proposed approach uses the Bhattacharya similarity distance. It is evident from experimental results based on real-world datasets that the SDCCA approach performs better compared to existing canonical correlation analysis-based approaches. Thus is a more effective and promising approach for solving real-life problems where consideration of complementary information in multiple views is essential.
Service Employees Use Emoticons of Different Emotion Intensity in Online After-sales Service

This paper aims to investigate the use of emoticons of different emotion intensity in Taobao online after-sales service, and demonstrate their appropriateness. The study explores how to enhance the customer service experience through the correct use of emoticons in all phases of online after-sales service. The purpose of this paper is to provide suggestions for the use of emoticons to improve customer service outcomes while improving the efficiency and quality of communication in online after-sales service.
Stable Depth Estimation Within Consecutive Video Frames

Deep learning based depth estimation methods have been proven effective and promising, especially learning depth from monocular video. Depth-from-video is the real sense of unsupervised depth estimation, as it doesn’t need depth ground truth or stereo image pairs as supervision. However, most of existing depth-from-video methods did not think of frame-to-frame depth estimation stability. We found depths within temporally consecutive frames exist instability although single image depth can be estimated well by recent works. Thus, this work aims to solve this problem. Specifically, we define a temporal smoothness term for the depth map and propose a temporal stability loss to constrain depths of the same objects within consecutive frames to keep their stability. We also propose an inconsistency check processing according to the differences between synthetic view frames and their original RGB frame. Based on the inconsistency check, we propose a self-discovered mask to handle the moving and occluded objects. Experiments show that the proposed method is effective and can estimate stable depth results within temporally consecutive frames. Meanwhile, it achieves competitive performance on the KITTI dataset.
Model-based Reinforcement Learning with Provable Safety Guarantees via Control Barrier Functions

Safety is a critical property in applications including robotics, transportation, and energy. Safety is especially challenging in reinforcement learning (RL) settings, in which uncertainty of the system dynamics may cause safety violations during exploration. Control Barrier Functions (CBFs), which enforce safety by constraining the control actions at each time step, are a promising approach for safety-critical control. This technique has been applied to ensure the safety of model-free RL, however, it has not been integrated into model-based RL. In this paper, we propose Uncertainty-Tolerant Control Barrier Functions (UTCBFs), a new class of CBFs to incorporate model uncertainty and provide provable safety guarantees with desired probability. Furthermore, we introduce an algorithm for model-based RL to guarantee safety by integrating CBFs with gradient-based policy search. Our approach is verified through a numerical study of a cart-pole system and an inverted pendulum system with comparison to state-of-the-art RL algorithms.
Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of Terrains

The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps.
A Security Risk Management Framework for Permissioned Blockchain Applications

As permissioned blockchain becomes a common foundation of blockchain-based applications for current organizations, related stakeholders need a means to assess the security risks of the applications. Therefore, this study proposes a security risk management framework for permissioned blockchain applications. The framework divides itself into different implementation stacks and provides guidelines to control the security risks of permissioned blockchain applications. According to the best of our knowledge, this study is the first research that provides a means to evaluate the security risks of permissioned blockchain applications from a holistic point of view. If users can trust the applications that adopted this framework, this study can hopefully contribute to the adoption of permissioned blockchain technologies.
Improved Resource Scheduling for Lightweight SMT-COP

Simultaneous multithreading (SMT) processors target resource under-utilization related performance problems of superscalar processors by allowing various datapath resources to be simultaneously shared among several threads with ease. This approach exploits thread-level parallelism on top of the well-studied instruction level parallelism. However, when datapath resources are shared among threads, there is always a possibility that corunning ill-intended programs may try to sniff on other programs with sensitive information like passwords or cryptocurrency information. By stress-testing and measuring many of the shared resources, this is quite achievable. These approaches are known as side-channel attacks. SMT-COP, a study published recently, is a secure architectural implementation based on resource scheduling that prevents these kind of attacks based on execution logic with a modest performance loss of nearly 10%. In this study, we aim to improve SMT-COP’s resource distribution scheme with several approaches, providing a quite close sense of security to that of SMT-COP while improving the performance by up to 8.86% on the average, over the original SMT-COP design.
Hierarchical self attention based sequential labelling model for Bhojpuri, Maithili and Magahi languages

Sequential labelling plays a vital role in solving numerous Natural Language Processing (NLP) applications such as Machine Translation and Information Extraction etc. One of these is Part-of-Speech (POS) tagging, which assigns a sequence of grammatical categories to the given sentence, and Chunking which groups them into ‘chunks’ or what can be called minimal phrases. Bhojpuri, Maithili and Magahi are low resource languages and widely spoken in central north-eastern India, belonging to the Indo-Aryan language family. The creation of an annotated corpus for POS tagging and Chunking, and then building an initial automatic tool for these problems is the first attempt towards building language technology tools for these languages. The annotated corpus used to develop POS Taggers and Chunkers, based on various machine learning algorithms (TnT, CRF, MEMM and Structured SVM) and state-of-the-art LSTM-CNN-CRF model, and then these compared with the obtained results on two new proposed deep learning-based models, Self-Attention Hierarchical Bi-LSTM CRF (SAHBiLC) and a fine-tuned version of it, Fine-SAHBiLC. The SAHBiLC and Fine-SAHBiLC models outperform on Bhojpuri (Accuracy for POS and Chunking is 0.86% and 0.94%, respectively) and Maithili (Accuracy for POS and Chunking is 0.86% and 0.95%, respectively) and Magahi (Accuracy for POS is 0.86%).
Web-ECA: A Web-based ECA Platform

Running an embodied conversation agent (ECA) on a client–server model has the following advantages: 1) the experimenter need not bring a computer with installing the ECA to the experimental site, 2) the user need not own a high-performance computer, 3) it is easy to make changes to the ECA for system maintenance and operation, and 4) data collection using an ECA becomes easier. To realize these benefits, we propose a platform for executing ECA on a server–client model, i.e., a platform for ECA that can be accessed through the Internet. This paper describes the system configuration and an application of this proposed platform.