Reviewerly/exHarmony huggingface dataset:
An Alphapose-Based Pedestrian Fall Detection Algorithm

In order to identify the falling behavior quickly and accurately from the surveillance video, an optimized model of alphapose is proposed here. First, the pedestrian target detection model and pose estimation model are accelerated by the lightweight YOLOv4-tiny model that we adopted. Then, the human pose joint point coordinate data obtained by the alphapose model is used to judge the occurrence of a falling action by the proposed judging algorithm. We use relationship between the head joint point line velocity and the crotch joint line velocity at the moment of a human fall and the change of angle between the perpendicular bisector and the x-axis of the image to judge the occurrence of the fall action. The algorithm proposed in this paper was compared with the main human posture-based fall detection algorithms for comparative analysis, with an image resolution of 320 × 240. The results show that the model proposed in this paper can timely and accurately detect the occurrence of pedestrian fall behavior.
Neural Fusion for Voice Cloning

Voice cloning is a technique to build text-to-speech applications for individuals. When only very limited training data is available, it is challenging to preserve both high speech quality and high speaker similarity. We propose a neural fusion architecture to incorporate a unit concatenation method into a parametric text-to-speech model to address this issue. Unlike the hybrid unit concatenation system, the proposed fusion architecture is still an end-to-end neural network model. It consists of a text encoder, an acoustic decoder, and a phoneme-level reference encoder. The reference encoder extracts phoneme-level embeddings corresponding to the cloning audio segments, and the text encoder infers phoneme-level embeddings from the input text. One of the two embeddings is then selected and sent to the decoder. We use auto-regressive distribution modeling and decoder refinement after the selection stage to overcome the concatenation discontinuity problem. Experimental results show that the neural fusion system significantly improves the speaker similarity using the selected units with the highest probability. The speech naturalness remains similar to the directly decoded systems.
Realization of Single Image Super-Resolution Reconstruction Based on Wavelet Transform and Coupled Dictionary

In various tasks of machine vision, image resolution is one of the important factors that affect the performance of the model. Generally, crop images with low resolution and lack of detail informa-tion may be collected. The picture is not good for the accuracy of yield prediction and crop pest identification. In this paper, tomato leaves are used as the target image, and the super-resolution reconstruction process takes advantage of the sharpness of the image, which has the characteristic of Scale invariance. Firstly, each image block is classified by using clustering algorithm accord-ing to the sharpness value of the image, and then wavelet transform is used to extract image features from each class of image blocks to get wavelet subbands respectively, subbands of each class not only train a union dictionary, but also learn a separate mapping function. Joint dictionary training and separate mapping matrix learning are helpful to optimize the high resolution and low resolution sparse coefficients. In the Reconstruction Stage: in order to reduce the image reconstruction time, the wavelet transform is only applied to the image blocks with a certain sharpness value, while the image reconstruction performance is basically unchanged, then the high-resolution image blocks are reconstructed by using the mapping function, coupled dictionary and the sparse representation coefficients of the image blocks. When the sharpness of the image block is lower than a certain sharpness value, the high and middle resolution image blocks will be superimposed to finally get the high resolution image.In the various tasks of machine vision, image resolution is one of the important factors that affect average PSNR value of the algorithm in this paper is 3.94 dB, 3.54 dB, 3.36 dB, 3.23 dB, 3.01 dB and 1.51 dB higher respectively.
Being Polite: Modeling Politeness Variation in a Personalized Dialog Agent

Politeness enhances interactions by improving relations between the participants. If there is a display of rudeness, even the finest conversation can fall through. In addition, if lathered with kindness, even the most angst-prone circumstance can be expressed with far less suffering. Previously, researchers have focused upon including politeness in conversations. But the existing research does not focus on variations in politeness according to the user profile. Therefore, in this article, we propose a novel task of generating polite personalized dialog responses in accordance with the user profile and consistent with the conversational history. We design a novel Polite Personalized Dialog Generation (PoPe-DG) framework that employs a reinforced deliberation network. We create human-annotated politeness templates according to user profiles to induce politeness variation in the generated responses for the proposed task. Precisely, the personality profile is transformed and normalized into a vector using the fusion attention combined with dialogue utterances to build context. Furthermore, the context modules and the annotated templates are appended to initialize the deliberation decoder. Experimental analysis validates that our proposed approach inculcates politeness in responses in accordance with the user profile and the conversational history.
Explaining Image Classifications with Near Misses, Near Hits and Prototypes

We propose a method for explaining the results of black box image classifiers to domain experts and end users, combining two example-based explanatory approaches: Firstly, prototypes as representative data points for classes, and secondly, contrastive example comparisons in the form of near misses and near hits. A prototype globally explains the relevant characteristics for a entire class, whereas near hit and near miss explain the local decision boundary of a specific prediction. To combine both types of explanations within one framework is novel and we propose that presenting both types of explanations is especially helpful for domain experts in visual domains. To improve the faithfulness of the explanations, we investigated an unbiased, generic embedding and a model-related (model-specific) embedding for handling the images. The proposed approaches are evaluated regarding parameter selection and suitability on two different data sets – the well-known MNIST and a real-world industrial quality control data set. Finally, it is shown how global and local example-based explanation can be combined and realized within a demonstrator.
Buddy - A Personal Companion to Match People with Cognitive Disabilities and AT

Abstract This paper presents a novel web-based repository and recommender system to match the requirements of users with cognitive disabilities with available AT solutions when working on the web. Based on personal data from users (questionnaire, simple games) stored in a profile and tool data provided by AT solution providers and AT vendors, Buddy recommends tools supporting particular cognitive needs of target users. Buddy intends to bridge the gap between the growing amount of AT solutions and the actual low level of uptake by involving all stakeholder groups in the value chain on the Buddy platform.
SRInpaintor: When Super-Resolution Meets Transformer for Image Inpainting

Recent image inpainting methods have achieved remarkable improvements by using generative adversarial networks (GAN). Most of them have been designed to produce plausible results from high-level semantic features using only high-resolution (HR) supervision. However, because abundant details are lost in large holes, it is difficult to simultaneously synthesize details while preserving structural coherence in HR space. Besides, the correlations between the inside and outside of the missing region play a critical role in transferring relevant known information to generate semantic-coherent textures, especially in patch matching-based methods. In this work, we present SRInpaintor which inherits the merits of super-resolution (SR) and transformer for high-fidelity image inpainting. The SRInpaintor starts from global structure reasoning with low-resolution (LR) input and progressively refines the local textures in HR space, constituting a multi-stage framework with SR supervision. The bottom stage recovers coarse SR results that provide structural information as an appearance prior, and is combined with the higher-resolution corrupted image at the next stage to render available textures for the missing region. Such a design can analyse the image from LR to HR with the increase of stages, enabling coarse-to-fine information propagation and detail refinement. In addition, we propose a hierarchical transformer (HieFormer) to model the long-term correlations between distant contexts and holes. By embedding it into a compact latent space in a cross-scale manner, we can ensure reliable relevant texture transformation and robust appearance consistency. Experimental results demonstrate the superiority of our method compared with recent state-of-the-art methods. Code will be available on <uri>https://github.com/lifengshiwo/SRInpaintor</uri>.
Enhancing Mobile App Bug Reporting via Real-Time Understanding of Reproduction Steps

One of the primary mechanisms by which developers receive feedback about in-field failures of software from users is through bug reports. Unfortunately, the quality of manually written bug reports can vary widely due to the effort required to include essential pieces of information, such as detailed reproduction steps (S2Rs). Despite the difficulty faced by reporters, few existing bug reporting systems attempt to offer automated assistance to users in crafting easily readable, and conveniently reproducible bug reports. To address the need for proactive bug reporting systems that actively aid the user in capturing crucial information, we introduce a novel bug reporting approach called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBug</small> . <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBug</small> assists reporters in writing S2Rs for mobile applications by analyzing natural language information entered by reporters in real-time, and linking this data to information extracted via a combination of static and dynamic program analyses. As reporters write S2Rs, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBug</small> is capable of automatically suggesting potential future steps using predictive models trained on realistic app usages. To evaluate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBug</small> , we performed two user studies based on 20 failures from 11 real-world apps. The empirical studies involved ten participants that submitted ten bug reports each and ten developers that reproduced the submitted bug reports. In the studies, we found that reporters were able to construct bug reports 31% <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">faster</i> with <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBug</small> as compared to the state-of-the-art bug reporting system used as a baseline. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBug</small> 's reports were also <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">more reproducible</i> with respect to the ones generated with the baseline. Furthermore, we compared <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBug</small> 's prediction models to other predictive modeling approaches and found that, overall, the predictive models of our approach outperformed the baseline approaches. Our results are promising and demonstrate the feasibility and potential benefits provided by proactively assistive bug reporting systems.
Secure Mobile Internet Banking System Using QR Code and Biometric Authentication

With the digital technology explosion, Internet banking users increased exponentially due to worldwide accessibility and convenience. The leading challenge of online banking is to ensure security for online transactions and the accounts of customers. Phishing attempts to get user’s login credentials like username and password by disguising themselves as a trusted entity in the banking sector. SIM swap is a new cyber fraud, where the attacker collects the personal data of the bank customer and gets a new SIM card. The attacker can easily steal the user’s login credentials like user name and password using phishing attack and the OTP using the SIM swap fraud attack. This study analyzes the security of the online banking system and proposes a new anomaly-based fraud detection method to overcome phishing and SIM swap fraud attacks. The login attributes like IP address, device, cookie, operating system, and browser are used to generate and update the user’s profile. The primary user profile contains the most recently used login attributes, and the second profile contains the most frequently used login attributes. If the current login attributes match either the primary or secondary user profile, the user can access their account. Otherwise, additional security mechanisms like OTP with QR code or biometric authentication or both are used to identify the suspicious behavior of the user. The proposed method reduces the login burden of the user and provides better security for the online mobile banking system.
Performance Analysis of Hyperledger Fabric based Blockchain for Traceability in Food Supply Chain

Blockchain, a decentralized technology, has found application in various sectors like banking, healthcare, energy and supply chain management. Hyperledger fabric (HLF), an open-source platform to implement blockchain technology (BKCT), is gaining widespread attention in the industries. However, the analysis of performance of platforms as the like of HLF has been done in a restricted manner in the past. The performance analysis will indicate the effectiveness of these platforms to implement BKCT. This kind of study is especially useful in the field of supply chain management. Thus, this study proposes a BKCT enabled traceability in a food supply chain and evaluates the impact of latest version of Hyperledger Fabric implementing BKCT on performance metrics such as throughput, latency and organizational scalability. To evaluate the goal of performance analysis, the tests are run on a blockchain based traceability model for a food supply chain. The results indicate that this version can support a wide range of transactions from 50 to 10,00,000 with minimum latency of 67.3 ms. This study will guide the practitioners of supply chain management in appropriate selection of a suitable blockchain platform.
An Ontology-Driven Question Answering System For Computer Network Module

The field of "question and answering" has become a popular area of research in recent years.The main reason for this is that the search through the ”question answering" system is found to be more efficient than the normal search. The Question Answering (QA) Systems can be two types in general such as open-domain QA systems and closed-domain QA systems. Search engines are generally open-domain QA systems that are used to search and retrieve the data we need. However, instead of search engines giving accurate and precise answers to the user queries, they often returned the list of links. Then, the user clicks on each link one by one to get the answer. This method of searching can sometimes not give the precise answers to the user queries, or the user may have to spend more time searching for the answer, and hence the users may experience discomfort. This situation can be avoided by using the semantic concept. The normal web data are machine-readable and can be understood by humans, whereas the semantic web information is machine-readable and understandable. Ontology is the main component of the semantic web and it can be described as the structure of knowledge-representation of a particular domain or subject. It clearly describes concepts, roles, instances, and the relationships between them. The Question Answering (QA) system is one of the popular applications of ontology. Here, the QA system is used to extract the precise answer to the user queries from the data repository. The system can be developed using different techniques like NLP with IR, reasoning with the NLP, web-based QA System, ontology-based QA System, and more. This particular question answering system is developed using the ontology model. This ontology-driven question answering (QA) system provides the facility to the users to find accurate and concise answers for their queries in the Computer Network module of ICT Subject.The same questions that are asked in the ontology-based question answering system can be asked in the web-based system. The reason is that both of these methods are used in this system. This will make it easier for users to understand the difference between the two systems.The performance results of both these systems further strengthen the statement
Browser Fingerprinting Identification Using Incremental Clustering Algorithm Based on Autoencoder

Browser fingerprinting technology is of great significance in the location of malicious traffic, and it also plays an important role in accurate advertising. The concealment or tampering of the explicit identifier leads to the failure of traditional fingerprinting identification technology. In this paper, we implement browser fingerprinting identification technology based on multiple implicit identifiers which are easily accessible. Especially, we designed an incremental clustering algorithm based on autoencoder. The new data point is treated as an incremental process for the clustering algorithm. Different from traditional clustering algorithms, the newcoming data point is compressed by autoencoder, and then treated as an incremental process so that we just need to compare the distance to the nearest cluster center with the distance threshold for fingerprinting identification, instead of traversing the entire dataset. Experimental results show that our incremental clustering algorithm has distinct advantages, especially in time consumption when comparing with K-means and DBSCAN algorithms, and prove that it is a lightweight algorithm for fingerprinting identification to adapt to the dynamic real environment and has better application prospects.
Semantic Analysis on Social Media

Sentiment research on social media provides businesses with a quick and easy way to track public opinion about their brand, business, directors, and other topics. In recent years, a variety of features and approaches for training sentiment classifiers on datasets have been investigated, with mixed results. In this research, we have proposed an approach for detecting emotion in text and predicting sentiment using semantics as extra characteristics for various datasets and a study on present methods for opinion mining like machine learning and lexicon-based methods.
Cyber Security: Terms, Laws, Threats and Protection

Looking at the current scenario of 21<sup>st</sup> century, people have revolutionized their daily life and activities with the help of internet and digitalization. As the digital era is advancing and getting more digitalized, the cyber space is often targeted to attacks in an unethical way for illegal benefits. The sudden spike in cyber-attacks has led to creation of cyber rules and regulations. The cunningness and effective attack by attackers make it nearly impossible to trace back the attacker. In this paper, a rigorous review has been conducted on cyber-crime, fundamental terminologies used in cyber, related laws and guidelines to be more secure on cyber space.
APT: a buzzword and a reality - A bibliometric review of the literature (2010–2020)

This paper performs a bibliometric analysis of the peer-reviewed literature on Advanced Persistent Threats (APTs) taken from 2010&#x2013;2020. APTs being a complex and tactical attack, require a multi-disciplinary perspective. In this study, we reveal several emerging trends based on 1205 literature papers. We report many popular bibliometric indicators, for example, publication trends, prolific authors, citation analysis, co-author analysis, and publication forums. An important indicator reported in our study is to find common research themes. To do that, we utilize the unsupervised Louvain algorithm. We believe our work will give insights into the current development in APT work, identifying common research themes and promote collaborations between researchers.
Minimizing Play Request Rejection through Workload Splitting in Edge-Cloud Gaming

Cloud gaming abstracts the concept of traditional gaming and places the gaming activities on remote rendering servers (RSes). Although this allows heterogeneous devices to gain access to multiple game titles, latency issue is always unavoidable. Each game input must go through a complete round trip between the player's device and the cloud gaming server. Hence, cloud games are not as responsive as traditional computer games where the game logic runs locally. Moreover, in order to have an acceptable level of game playability, the latency level must be within a certain threshold. This also prevents some players who are located in remote regions from playing the game due to high latency. Therefore, in this paper, we employ edge servers in order to reach those players by activating lower capability RSes which are more geographically distributed. Furthermore, we also allow workload splitting of foreground and background rendering between edge and cloud RSes to ease the burden of each individual RS with a trade-off between cost and latency constraints. From our experiments, our architecture and allocation scheme results in reduction of play request rejections for up to 28% compared to traditional cloud gaming approach.
A Filter is Better Than None: Improving Deep Learning-Based Product Recommendation Models by Using a User Preference Filter

Nowadays, recommendation systems are a central component in a wide range of online services. One major challenge for them is to generate suitable recommendations for individual users who differ, among other things, in their interests and preferences. In this paper, we propose a deep learning-based product-to-product recommendation system that uses product embeddings to incorporate content-based product information and user data in form of historical checkouts. On top of that, we add a filter that rearranges the models&#x0027; recommendations based on the individual user preferences to a product category that is derived from past activities. In order to evaluate and show the impact of our approach, we conduct tests on historical data and an A/B-test in a real online service that offers thousands of different customer packaged goods (CPG) to millions of users. The results show that our approach delivers suitable product recommendations to users and outperforms the current system in use.