Reviewerly/exHarmony huggingface dataset:
A Systematic Survey of e-Commerce Applications with Blockchain in Agro Sector

With the advent of new technologies, e-Commerce plays a major role in agricultural sector. The aim of this survey is to review the works done related to e-Commerce in agriculture using various technologies and the benefits of direct marketing in the period between 2017-2021. The study includes (a) how Blockchain could be used in agriculture (b)Developing e-commerce platform using various technologies (c)prospects of direct marketing.
Syntactically Coherent Text Augmentation for Sequence Classification

In this article, we address the problem of data scarcity for the sequence classification tasks. We propose AugmentGAN, a simple-yet-effective generative adversarial network-based text augmentation model, which ensures syntactic coherency in the newly generated samples. Given an input with a label, AugmentGAN aims to generate a semantically similar sequence that follows the syntactic structure of the original sample. Exhaustive task-based evaluation is conducted to show the efficacy of AugmentGAN—we employ 12 different datasets across five classification tasks, i.e., sentiment analysis, emotion recognition, sarcasm detection, intent classification, and spam detection. We observe that, compared to the existing text augmentation techniques, AugmentGAN yields an improved performance across datasets for all the tasks. AugmentGAN also turns out to be effective for multiple languages, i.e., English, Hindi, and Bengali.
Design of a Decentralized and Predictive Real-Time Framework for Air Pollution Spikes Monitoring

Exposure to air pollution spikes cause health problems to regularly exposed organisms, raising the need to monitor them in real-time. Existing air pollution monitors mainly use a cloud-centric design considering relatively constant pollution, therefore duty-cycling sensors with long sleep periods to save their batteries. Such design is however inefficient for monitoring pollution spikes. Furthermore, since spikes vanish rapidly, integrity of monitoring data is very important. This paper presents a framework integrating edge-centric design and blockchain in monitoring air pollution spikes, while using short-term prediction artificial intelligence to timely alert pollution emitters about exceeding long-term average pollution limits defined by standards.
A Pronoun Replacement-Based Special Tagging System for Bengali Language Processing (BLP)

Natural language processing (NLP) is one of the most important thing for human machine interaction and a very important thing for machine learning system. In the world, over 27 crore people use Bengali as their first and mother language, and it has its own written system, so it is very much important to process Bengali language for natural language processing. In this research work, we have tried to demonstrate an upgraded parts of speech tagging system (POS) for Bengali language, where we have used special tagging system with general grammatical parts of speech based on many different things like—Considering suffixes for verb, where get 68% success rate. We have also added places name, occupation name, Bengali Name, Bengali repeated word, digit of Bengali in both written and digit form, English acronym, organization name for both cases. The success rate of tagging for genera tagging is 70 and 76% for special tagging which is ever highest. This tagging system can be used for Bengali language processing (BLP) like—sentiment analysis for Bengali, Bengali text summarization, etc.
Novel Approach to Data Hiding in Binary Images Minimizing Distortion

In today’s digital communication, much important information is sent as picture images in binary form. To secure this information, data is hidden in these images to preserve originality, authenticity of their visible content or to covert communication as invisible contents. The proposed idea is to hide data in such a way that the flipping pixels in the cover are minimized. The very first ciphertext is generated by encrypting original data with a key. Then, the binary image is scanned in four ways, and out of these four scannings, one that is having maximum pixel and ciphertext bit matching count is selected for hiding the data by flipping the unmatched pixel. The header with cipher embedding details and its length is embedded from the end of the image in reverse order applying odd–even feature to 3 × 3 blocks. During extraction, the exact opposite method is applied so that the hidden data can be accurately extracted.
Enhancing agile software development in the banking sector—A comprehensive case study at LHV

Abstract FinTech companies are challenging established financial institutions' dominance by offering the same products with a superior customer experience and delivering new features faster. The adoption of agile software development partially enables this competitive advantage. In response to this challenge, banks explore how they can improve their agile processes. LHV, a mid‐sized bank, uses agile practices but faces the challenge of further improvement to stay competitive with FinTech companies. In this article, we explore how LHV can improve its agile software development process. We conduct a case study at LHV where we first derive eight change proposals based on a literature review and interviews. Then, we report on how LHV implemented the change proposals and their perceived impact. Our results stress the importance of taking a coherent approach to improving agile processes by considering both business units and operations involved in the product life‐cycle. It is also necessary to align organizational structures to enable team autonomy by, for instance, decentralizing decision authority. Finally, it is beneficial to adapt agile practices to their context and have an IT architecture and technology supporting the agile approach.
Smart toys and children’s understanding of personal data

The proliferation of smart toys has led to an increase in young children’s exposure to technologies that ask for their personal data, thus creating privacy concerns and efforts to minimise the exploitation of children’s data. Research that addresses the ways in which young children (below the age of five or six) could come to understand a concept as abstract as ‘personal data’ is scarce but this is fundamental to the approach presented here. The paper makes three main contributions: (i) a discussion of what we mean by children’s understanding of personal data from smart toys, (ii) a theoretical account of approaches for supporting children’s understanding of data based on developmental and child interaction design literature and, building on this foundation, (iii) a description of a previously unreported prototype smart toy – Edi the Bear – along with a small-scale pilot study to explore the extent to which the toy can support children’s understanding of personal data. Preliminary findings suggest that revealing the mechanisms by which personal data is collected can help children to develop their capacity to understand that their data has value as it can be used by unknown others to learn about who they are. We describe the main functionality of the prototype and consider the ways in which it could be used by educators to communicate concepts of personal data in a way that is meaningful to young children.
An Innovative Employment of the NetLogo AIDS Model in Developing a New Chain Code for Compression

In this paper, we utilize the NetLogo HIV model in constructing an environment for bi-level image encoding and employ it in compression. Our model considers converting an image into a virtual environment that comprises female agents testing positive and negative for HIV. Female agents are scattered according to the allocation of the pixels in the original images to be tested. The simulation considers introducing male agents that test positive for HIV, the purpose of which is to track their movements while infecting other HIV- female agents. The progressions of the HIV+ male agents within the simulation take advantage of the relative encoding approach previously used by other image processing and agent-based modeling researchers. That is to say, the simulation allows generating a high proportion of similar movement forms that are similarly encoded regardless of the movements of agents. This is followed up by applying Huffman coding to the obtained chains of movement strings for further reduction. The ultimate results reveal that our product could outperform existing benchmarks using all the images we employed in testing.
Multimodal Machine Learning for Pedestrian Detection

Designing and developing autonomous vehicles that are capable of moving safely on roads by sensing the environment has motivated researchers to focus on pedestrian detection systems so they can detect people as fast and accurately as possible. However, for pedestrian detection, it is crucial to consider not only the pedestrians themselves but their color as well, because color has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Therefore, considering skin color detection for implementing pedestrian detection systems is an essential required step in ensuring autonomous vehicles are further incorporated into our society. Detecting human skin has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, imaging conditions, and others. Recently it has been noted that pedestrian detection systems for autonomous vehicles perform poorly at detecting people with darker skin tones. Such findings indicate that there is a larger problem that is causing these issues: algorithmic bias. Algorithmic bias in pedestrian detection systems could be the leading factor of their poor performance due to the methods implemented and datasets used. Unfortunately, the algorithmic bias in this context has not been considered closely and it seems that many studies do not cover this aspect closely when discussing pedestrian detection systems for autonomous vehicles. To alleviate this, we attempt to explore different techniques that can be used to detect pedestrians while minimizing bias. In our experiment, we use both a YOLO v3 convolutional neural network and K-Means clustering for classifying skin-tones.
Pyramid fully residual network for single image de-raining

Rain removal from a single image is a challenging and significant task of image pre-processing. In this paper, we learn the multi-scale streaks from rainy images using feature pyramid, and to improve the effectiveness of the learning, we focus on the feature propagation re-usage and propagation in the extremely deep de-raining network. Specifically, we design a de-raining2 unit and propose a novel deep de-raining network, respectively, called Pyramid Fully Residual Unit and Network (PFR-Unit and PFR-Net). The PFR-Unit employs fully residual learning in each level of feature pyramid and the PFR-Net connects PFR-Units by a compact dense architecture. The fully residual learning encourages the feature re-usage in PFR-Unit by performing identity mapping for all available shortcuts. The compact dense connection strengthens the feature propagation between the PFR-Units and ensures the unicity of the learning space for the PFR-Units. Along with negative SSIM loss, the PFR-Net presents a good performance in single image de-raining. Comprehensive experimental results show that the PFR-Net outperforms the state-of-the-art single de-raining methods with a big margin on Rain100H, Rain100L and Rain1200 datasets.
Reciprocity in Instant Messaging Conversations

We tested how the introduction and removal of well-defined roles influenced contribution behaviors in instant messaging conversations. Pairs of participants worked on a referential communication task where one participant (the director) had more information than the other (the matcher). Next, these roles were removed and the participants were allowed to communicate freely. Participants then switched director/matcher roles and the procedure was repeated. On average participants in the director role wrote more than participants in the matcher role during the task. But instead of a balanced conversation during unstructured chat, which might have happened without a task preceding it, during off-task conversation former-matchers, on average, contributed more than former-directors. Results support the hypothesis that speech complementarity leads to efforts to redress imbalance, a process we call reciprocity.
Learnable Hypergraph Laplacian for Hypergraph Learning

HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data. However, most existing convolution filters are localized and determined by the pre-defined initial hypergraph topology, neglecting to explore implicit and long-range relations in real-world data. In this paper, we propose the first learning-based method tailored for constructing adaptive hypergraph structure, termed HypERgrAph Laplacian aDaptor (HERALD), which serves as a generic plug-and-play module for improving the representational power of HGCNNs. Specifically, HERALD adaptively optimizes the adjacency relationship between vertices and hyperedges in an end-to-end manner and thus the task-aware hypergraph is learned. Furthermore, HERALD employs the self-attention mechanism to capture the non-local paired-nodes relation. Extensive experiments on various popular hypergraph datasets for node classification and graph classification tasks demonstrate that our approach obtains consistent and considerable performance enhancement, proving its effectiveness and generalization ability.
Security Analysis and Improvement of a Redactable Consortium Blockchain for Industrial Internet-of-Things

Abstract A redactable consortium blockchain (RCB) can build a trust layer for industrial internet of things (IIoT) so as to enable IIoT to resist certain powerful attacks resulting in improper block content. The redactability is particularly important for blockchains applied in IIoT with valuable or sensitive activities such as financial IoT or energy-trading IoT. Huang et al. proposed a threshold chameleon hash (TCH) scheme and then constructed an accountable-and-sanitizable chameleon signature scheme based on TCH. These two primitives are further used as fundamental modules to build an RCB, which empowers IIoT devices to operate the blockchain in a controllable way. However, our paper shows that Huang et al.’s RCB suffers from a security problem that weakens the crucial redactability. Specifically, we find out that if a transaction in a given block is legally redacted by all authorized sensors who collectively hold the private redacting key, anyone (without any private information) can further redact this redacted transaction and delete any transaction within this redacted block and, meanwhile, any sensor user with a private signing (not redacting) key can insert a forged transaction into this redacted block. We further address this threat by replacing the TCH module in Huang et al.’s RCB with our designed TCH.
Spear Phishing Emails Detection Based on Machine Learning

Spear phishing emails target to specific individual or organization, they are more elaborated, targeted, and harmful than phishing emails. The attackers usually harvest information about the recipient in any available ways, then create a carefully camouflaged email and lure the recipient to perform dangerous actions. In this paper we present a new effective approach to detect spear phishing emails based on machine learning. Firstly we extracted 21 Stylometric features from email, 3 forwarding features from Email Forwarding Relationship Graph Database(EFRGD), and 3 reputation features from two third-party threat intelligence platforms, Virus Total(VT) and Phish Tank(PT). Then we made an improvement on Synthetic Minority Oversampling Technique(SMOTE) algorithm named KM-SMOTE to reduce the impact of unbalanced data. Finally we applied 4 machine learning algorithms to distinguish spear phishing emails from non-spear phishing emails. Our dataset consists of 417 spear phishing emails and 13916 non-spear phishing emails. We were able to achieve a maximum recall of 95.56%, precision of 98.85% and 97.16% of F1-score with the help of forwarding features, reputation features and KM-SMOTE algorithm.
From enfant terrible to problem-solver? Tracing the competing discourse to explain blockchain-related technological diffusion

Since the inception of blockchain-related technologies over a decade ago, investors’ uptake of the technologies has grown rapidly. But even with the advancement in standing from conceptual beginnings to real-world experimentation, mainstream adoption of the technologies in either organizational or individual contexts has yet to eventuate. To resolve this paradox, we examine progression of the technologies’ diffusion instead, and in the process, seek to uncover the underlying dynamics of sentiments at play. Diffusion involves multiple actions by different parties, with social discourses via diverse media playing an essential role. In demonstrating the interplay of support to resistance via media framing and the diffusion of innovation theory, findings from our paper contribute to the technology adoption literature. Understanding resistant behavior toward a new technology area could help resolve potential issues that may arise. This would hopefully lead to better technological implementation outcomes in future.
Implementation of IoT Based Smart Assistance Gloves for Disabled People

Communication between normal people and people with vocal and hearing troubles is a difficult task. The sign language used by these people is not understandable by the common people, so it creates a communication barrier. People who are paralyzed also require assistance regularly. For such people we have proposed Implementation of IOT based Smart assistance gloves for disabled people. The gloves we designed is very simple yet effective when compared to the existing system. With the help of flex sensors, the finger gesture is detected and the corresponding instructions are displayed in the android app with audio output. The proposed system is implemented by Arduino uno and Raspberry pi, where the communication between these two modules is done by wireless serial port module due to its secured data transmission. An alert message will be sent through the GSM module during emergency situation.
Research on English Teaching Model with Computer Aid

With the emergence of new information devices such as microcomputers, mobile smart devices, Wi-Fi, etc., the important impact it has brought to all walks of life has made our work and life richer and more convenient. In order to solve the problem of sharing English teaching resources between different schools in the same school, a comprehensive management system for English teaching resources information based on artificial intelligence is proposed. Based on the system operating environment design and database design, the system software design is realized. The results show that the proposed English teaching resource information integrated management system is more suitable for the sharing of English teaching resource information than the conventional English teaching resource information integrated management system.
Hybridized Cuckoo Search with Multi-Verse Optimization-Based Patch Matching and Deep Learning Concept for Enhancing Video Inpainting

Abstract This paper aims to develop a novel deep learning concept to deal with video inpainting. Initially, motion tracking is performed, which is the process of determining motion vectors that describe the transformation from adjacent frames in a video sequence. Further, the regions or patches of each frame are categorized using the optimized recurrent neural network (RNN), in which the region is split into a smooth and structure region. It is performed using the texture feature called grey-level co-occurrence matrix. The filling of the smooth region is accomplished by replacing with the mean pixels of unmasked region, and the structure region is done by optimized patch matching approach based on scale-invariant feature transform (SIFT). The main objective optimized patch matching is based on the minimized Euclidean distance between the extracted SIFT features of the original patch and reference patch, and the certain patch is inpainted by the optimized patch. Here, the hybridization of two meta-heuristic algorithms like cuckoo search algorithm and multi-verse optimization (MVO) called Cuckoo Search-based MVO is used to optimize the RNN and patch matching. Finally, the experimental results verify the reliability of the proposed algorithm over existing algorithms.
A Novel Actuating–Sensing Bone Conduction-Based System for Active Hand Pose Sensing and Material Densities Evaluation Through Hand Touch

We realized and tested a novel system aimed at discriminating different hand poses by means of an active actuating and sensing approach realized by converting electromagnetic to mechanical waves (and vice versa) and analyzing the characteristics of the waves after their travel through the bones and cartilage of the hand. The actuating part is realized through a transducer, placed on the dorsal part of the hand, performing electromagnetic-to-mechanical energy conversion. The sensing part is realized through an accelerometer, placed on a finger, performing mechanical-to-electromagnetic energy conversion. The way the sound propagates through the fingers mainly depends on the traveled path, the angles of fingers' flexion, and the (un)matched conditions with surroundings. Therefore, the investigation of the characteristics of the returned signal (in phase shifts and power spectral densities) can furnish information for discriminating among different finger poses and different densities of touched materials. The system highly performed in repeatability and reproducibility of the measures, well-discriminating among hand poses and among densities of touched materials.
Evaluating Critical Reinforcement Learning Framework in the Field

Reinforcement Learning (RL) is learning what action to take next by mapping situations to actions so as to maximize cumulative rewards. In recent years RL has achieved great success in inducing effective pedagogical policies for various interactive e-learning environments. However, it is often prohibitive to identify the critical pedagogical decisions that actually contribute to desirable learning outcomes. In this work, by utilizing the RL framework we defined critical decisions to be those states in which the agent has to take the optimal actions, and subsequently, the Critical policy as carrying out optimal actions in the critical states while acting randomly in others. We proposed a general Critical-RL framework for identifying critical decisions and inducing a Critical policy. The effectiveness of our Critical-RL framework is empirically evaluated from two perspectives: whether optimal actions must be carried out in critical states (the necessary hypothesis) and whether only carrying out optimal actions in critical states is as effective as a fully-executed RL policy (the sufficient hypothesis). Our results confirmed both hypotheses.
FARMER’S ASSISTANT using AI Voice Bot

In the year 2020,agriculture contributes about 19.9% of India's GDP and employed about 60% of the country's population which makes it important part of the country's economic growth. Till 2018, it was about 15.41% only but now it has been increased with the help of the latest technologies like IOT,AI,CHATBOT etc. The proposed system is a mobile application which was developed to assist the farmers by two ways, the voice bot and the suggestion bot. To respond the farmer's queries in the multi-language, we have created an agricultural multi linguistic voice bot using Google translator,pysttsx3 and Google search engines. Also, we have the suggestion bot to give a versatile suggestion to the answer of farmer's query related to weather, crop, fertilizer, soil etc. Using this mobile application, farmers will progress towards better farming practices and increase the agricultural production.