Reviewerly/exHarmony huggingface dataset:
Decoding the Public interest of Aarogya Setu, contact tracing app for managing the COVID19 pandemic in India

At the time of the writing of this paper, India is the country worldwide with more COVI19 new cases, more new deaths and the second just after the United States for total deaths. In 2020 India followed its Asian and Western peers in relying on mobile apps to operate one of the pandemics and epidemics management strategies: contact tracing. Contact tracing apps have been a widespread tool adopted by national governments across the world to aid in the COVID19 pandemic management. As these apps function by creating the trace of individuals who have been in physical contact with a person tested as positive to inform the possibility of spreading, possible misuse or secondary uses have raised concern More than situating the app in two binary points, this paper seeks to respond to the question of how the use of this app delivers public interest in India. Results show that to do this a definition of what Public Interest could mean in India should happen first and further research to assess the app in these terms can take place after.
Moving Design Research: GIFs as Research Tools

Animated GIFs are often viewed as a nod to early internet culture or as tools for digital communication, but in this pictorial, we highlight a new use of GIFs, as tools for design research. We walk through four case studies from our own research that exemplify GIFs used throughout the design process as empirical probes, prototypes, communication tools, and finalized artifacts. By conducting a collaborative, reflexive analysis of these cases, we present an annotated portfolio of the goals, crafting and aesthetic choices of our GIFs and how creating GIFs added to our research. We conclude by noting that both the aesthetics of movement and the rich, concise, and contextualized nature of gifs added to our depth of thinking and ability to communicate speculative and imaginative concepts. Finally, we also suggest that research dissemination, especially for design research, would be enriched by supporting more diverse knowledge-production artifacts such as GIFs.
Deep triplet residual quantization

• Offline training achieves promising results compared with online training. • Local information help to improve the triplet numbers and quality. • Optimize norm and angle separately provide flexible learning objectives. Quantization techniques have been widely used in the approximate near neighbor similarity search, data compression, etc. Recently, metric learning based deep hashing methods take advantage of quantization techniques to accelerate the computation with minimal accuracy loss. However, most of the existing deep quantization methods are designed for Euclidean distance, which may not lead to good performance in maximum inner product search (MIPS). In addition, metric learning requires an elaborated training strategy for sample selection, which matters in learning high-quality feature representation and boosting the convergent speed of the network. In this paper, we propose a novel deep triplet residual quantization (DTRQ) model that integrates the residual quantization (RQ) into the triplet selection strategy and the quantization error control of MIPS. Specifically, instead of randomly grouping the samples as in DTQ, we group the samples based on the geographical information provided by RQ so that each group can generate more high-quality triplets for faster convergence. Furthermore, we decompose the triplet quantization loss into the norm and angle aspect, which especially reduce the codeword redundancy in MIPS ranking. By stringing the residual quantization through the triplet selection stage and quantization error control, DTRQ can generate high-quality and compact binary codes, which yields promising image retrieval performance on three benchmark datasets, NUS-WIDE, CIFAR-10, and MS-COCO.
Unpaired Image-to-Image Translation Network for Semantic-based Face Adversarial Examples Generation

Recent studies have shown that neural networks are vulnerable to adversarial example (AE) attacks. However, the existing AE generation techniques restrict the pixel perturbation to improve imperceptibility, resulting in low attack success rates. Although increasing perturbations can improve the attack success rate, the imperceptibility of AEs will be reduced. In order to mitigate this contradiction, we propose a new attack method, named AttAdvGAN, which uses adversarial-consistency loss for unpaired image-to-image translation to generate semantic-based AEs for faces, encouraging the generated image contains important features of the original image and hiding adversarial perturbations into shared feature in the target domain. Experiment results show that the proposed approach can generate imperceptible face AEs on the CelebA dataset with high attack success rate in fooling the state-of-the-art face recognition model. In addition, our proposed method can also be used for facial privacy protection.
Detecting Software Security Vulnerability during an Agile Development by Testing the Changes to the Security Posture of Software Systems

The purpose of this quantitative quasi-experimental study is to identify the possible correlation between software changes and the likelihood that software releases developed using an agile methodology like DevOps will introduce vulnerabilities into the software application when integrated. There are several scholarly articles that provide details on how Agile development methodologies like scrum and DevOps rely on automated testing for security. The majority of literature on the subject recommend manual security and penetration testing, but there is currently no objective measure to determine when this manual testing should take place. In Agile scrum and in DevOps, manual security testing is usually conducted after a large feature is completed and integrated into production. If a correlation can be found between aspects of software changes and their propensity to introduce vulnerabilities into a software application, then that data can be used to build an objective process for measuring when manual security testing should be performed in Agile development.
Model-based Automation of Test Script Generation Across Product Variants: a Railway Perspective

In this work, we report on our experience in defining and applying a model-based approach for the automatic generation of test scripts for product variants in software product lines. The proposed approach is the result of an effort leveraging the experiences and results from the technology transfer activities with our industrial partner Bombardier Transportation. The proposed approach employs metamodelling and model transformations for representing different testing artefacts and making their generation automatic. We demonstrate the industrial applicability and efficiency of the proposed approach using the Bombardier Transportation Aventra software product line. We observe that the proposed approach mitigates the development effort, time consumption and consistency drawbacks typical of traditional strategies.
Anonymous device authorization for cellular networks

Cellular networks connect nearly every human on the planet; they consequently have visibility into location data and voice, SMS, and data contacts and communications. Such near-universal visibility represents a significant threat to the privacy of mobile subscribers. In 5G networks, end-user mobile device manufacturers assign a Permanent Equipment Identifier (PEI) to every new device. Mobile operators legitimately use the PEI to blocklist stolen devices from the network to discourage device theft, but the static PEI also provides a mechanism to uniquely identify and track subscribers. Advertisers and data brokers have also historically abused the PEI for data fusion of location and analytics data, including private data sold by cellular providers.
Fixed-Horizon Active Hypothesis Testing

Two active hypothesis testing problems are formulated. In these problems, the agent can perform a fixed number of experiments and then decide on one of the hypotheses or declare its experiments inconclusive. The first problem is an asymmetric formulation in which the objective is to minimize the probability of incorrectly declaring a particular hypothesis to be true while ensuring that the probability of correctly declaring that hypothesis is moderately high. This formulation is a generalization of the formulation in the Chernoff–Stein lemma to an active setting. The second problem is a symmetric formulation in which the objective is to minimize the misclassification probability while ensuring that the true hypothesis is declared conclusively with moderately high probability. For these problems, lower and upper bounds on the optimal misclassification probabilities are derived and these bounds are shown to be asymptotically tight. Novel experiment selection strategies are provided. It is shown that these strategies are asymptotically optimal and using numerical experiments, it is demonstrated that these strategies have a significantly better performance in the nonasymptotic regime.
Learning Path Recommender Systems: A Systematic Mapping

Learning Path Recommender Systems (LPRS) are systems that make recommendations of learning resources to be consumed in a determined sequence. Such kind of recommendation is useful in scenarios where we need to personalize the learning especially when the students need to be guided faced an overwhelming amount of resources. LPRS are gaining more attention in the last years because of the popularity of e-learning, and such need to guide, motivate and engage students in big data scenarios. The systematic mapping proposed in this paper tries to understand how LPRS are done and how they are evaluated. Our findings suggest that the papers use mostly content-based algorithms and there is a lack of discussion on explainable and trustworthy LPRS.
Compositional Distributional Semantics with Syntactic Dependencies and Selectional Preferences

This article describes a compositional model based on syntactic dependencies which has been designed to build contextualized word vectors, by following linguistic principles related to the concept of selectional preferences. The compositional strategy proposed in the current work has been evaluated on a syntactically controlled and multilingual dataset, and compared with Transformer BERT-like models, such as Sentence BERT, the state-of-the-art in sentence similarity. For this purpose, we created two new test datasets for Portuguese and Spanish on the basis of that defined for the English language, containing expressions with noun-verb-noun transitive constructions. The results we have obtained show that the linguistic-based compositional approach turns out to be competitive with Transformer models.
Sentiment Analysis from Bangla Text Review Using Feedback Recurrent Neural Network Model

Sentiment analysis is one of the most discussed topics in natural language processing. A number of researches have already been made on sentiment analysis, and most of the works are on English language text. There are only a few works that have been found on sentiment analysis from Bangla text. Bangla is the seventh most communicated language in the world, so sentiment analysis on Bangla text plays an important role in detecting the opinion and sentiment of Bengali-spoken people about some products, services, or business. There are lots of microblogging sites and social networks where Bengali-spoken people write comments in Bangla texts. In our paper, we have proposed a special version of recurrent neural network (RNN) model, called long short-term memory (LSTM) to detect the sentiment from the text review dataset. In this regard, we have collected a total of 4000 comments from different online repositories. Our proposed model can successfully classify positive and negative sentiments from Bangla text with an accuracy of 84% and precision of 85%.
Exploiting Wi-Fi Signals for Human Activity Recognition

Human activity recognition is gaining much attention due to its role in medical alert systems, interactive video games, smart home systems, and many more. One of the main objectives of any human activity recognition system is recognizing the different human activities without affecting them. In this work, we utilize the information embedded in the overflowing Wi-Fi signal to determine which activity is being performed. A dataset obtained by observing 20 subjects performing activities in two different environments adds to this study's credibility. The performed experiments show that an average activity recognition accuracy of 94% is possible.
A Survey of incremental high-utility pattern mining based on storage structure

Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining.
Demand Matrix Optimization for Offchain Payments in Blockchain

Offchain networks are dominant as a solution to the scalability problem of blockchain systems, allowing users to perform transactions without their recording on the chain. Offchain networks consist of channels connecting pairs of users with frequent transactions. Transactions between users without a direct channel are also supported through paths of multiple channels but involve payment of fees to intermediate users. A set of pending transactions (the demand) is typically served one by one. This paper proposes a novel approach to reduce the number of offchain transactions and their corresponding fees while preserving the requested impact on user accounts, by performing optimizations directly on the demand matrix. We contribute an analytical characterization of the properties of such offchain payments demand matrix optimizations, present efficient algorithms and report on an empirical evaluation, demonstrating the simplicity and effectiveness of the approach.
Cascaded Convolutional Neural Network for Image Super-Resolution

Image super resolution is an important field of computer research. The current mainstream image super-resolution technology is to use deep learning to mine the deeper features of the image, and then use it for image restoration. However, most of these models mentioned above only trained the images in a specific scale and do not consider the relationships between different scales of images. In order to utilize the information of images at different scales, we design a cascade network structure and cascaded super-resolution convolutional neural networks. This network contains three cascaded FSRCNNs. Due to each sub FSRCNN can process a specific scale image, our network can simultaneously exploit three scale images, and can also use the information of three different scales of images. Experiments on multiple datasets confirmed that the proposed network can achieve better performance for image SR.
Topic-Guided Local-Global Graph Neural Network For Image Captioning

Image captioning is to generate textual descriptions for a given image by analyzing its visual semantics. It can be applied for numerous applications such as surveillance, where generating descriptions of images enables a more efficient workflow. However, accurate descriptions require to formulate the interactions among visual objects and semantics, which have not been adequately exploited yet. Therefore, a novel architecture is proposed, namely topic-guided local-global graph neural network, to address the interactions in a two-level scheme. Local information is characterized through visual objects and semantic graphs are introduced to formulate their relations. Global information is characterized with a topic graph to analyze captioning context and guides the semantic graphs for captioning. Particularly, graph convolutions and graph transformers with a connection between the adjacency matrices are explored. Experimental results on MS-COCO dataset demonstrate the effectiveness of our proposed method.
Preimage Attacks on 4-Round Keccak by Solving Multivariate Quadratic Systems

In this paper, we present preimage attacks on 4-round Keccak-224/256 as well as 4-round Keccak[ $$r = 640,c = 160,l = 80$$ ] in the preimage challenges. We revisit the Crossbred algorithm for solving the Boolean multivariate quadratic (MQ) system and elaborate the computational complexity for the case $$D = 2$$ . The result shows that the Crossbred algorithm has advantages when n is small and m outperforms n with feasible memory costs. In our attacks, we construct Boolean MQ systems in order to make full use of variables. With the help of solving MQ systems, we successfully improve preimage attacks on Keccak-224/256 reduced to 4 rounds. Moreover, we implement the preimage attack on 4-round Keccak[ $$r = 640,c = 160,l = 80$$ ], an instance in the Keccak preimage challenges, and find 78-bit matched near preimages.
Robust Distributed Optimization With Randomly Corrupted Gradients

In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all the participating agents are prone to failure. We model each agent's state over time as a two-state Markov chain that indicates Byzantine or trustworthy behaviors at different time instants. We set no restrictions on the maximum number of Byzantine agents at any given time. We design our method based on three layers of defense: 1) temporal robust aggregation, 2) spatial robust aggregation, and 3) gradient normalization. We study two settings for stochastic optimization, namely Sample Average Approximation and Stochastic Approximation. We provide convergence guarantees of our method for strongly convex and smooth non-convex cost functions.
AI-Based Container Orchestration for Federated Cloud Environments

This paper proposes an architecture for artificial-intelligence-based container orchestration in cloud federation environments. The paper adapts the architecture proposed by Kurze et al. [1] and enhances it with two subcomponents in the federation layer to enable resource identification and container orchestration across cloud federation members. These two subcomponents are the AI Resource Identifier and the Container Orchestrator. The AI Resource Identifier is responsible for identifying the available resource in the federated environment whenever needed. It optimizes resource allocation of federation members by using Artificial Intelligence (AI) techniques for learning from the past. The Container Orchestrator sub-component is responsible for organizing jobs to be allocated along with all the necessary files and dependencies in a containerized way. This, in turn, enhances scalability and portability in a federated environment.
A multi-image hiding technique in dilated video regions based on cooperative game-theoretic approach

This paper presents a cooperative game theory approach to design an optimal video steganography framework. An ideal and perfect video steganography system should provide enough space to embed the secret data without degrading the visual quality. However, in a steganographic system, the payload capacity and visual quality are always inversely proportional to each other. This paper proposes a novel game-theoretic approach using strategy adaption to address this issue. This approach utilizes the Iterative Elimination of Strictly Dominant Strategies (IESDS) method to acquire the optimal solution. The obtained optimal solution comes up with the best trade-off between capacity and visual quality. Also, experimental results infer that the optimal solution outperforms the contemporary methods by attaining significant outcomes.
Identifying users’ domain expertise from dialogues

Nowadays, many companies are offering chatbots and voicebots to their customers. Despite much recent success in natural language processing and dialogue research, the communication between a human and a machine is still in its infancy. In this context, dialogue personalization could be a key to bridge some of the gap, making sense of users' experiences, needs, interests and mental models when engaged in a conversation. On this line, we propose to automatically learn user's features directly from the dialogue with the chatbot, in order to enable the adaptation of the response accordingly and thus improve the interaction with the user. In this paper, we focus on the user's domain expertise and, assuming that expertise affects linguistic features of the language, we propose a vocabulary-centered model joint with a Deep Learning method for the automatic classification of the users expertise at word- and message-level. An experimentation over 5000 real messages taken from a telco commercial chatbot carried to high accuracy scores, demonstrating the feasibility of the proposed task and paving the way for novel user-aware applications.
Content Related Feature Analysis for Fake Online Consumer Review Detection

Due to the advancements of Internet, majority of people will prefer to buy and sell the commodities through e-commerce websites. In general, people mostly trust on reviews before taking the decisions. Fraudulent reviewers will consider this as an opportunity to write fake reviews for misleading both the customers and producers. There is a necessity to identify fake reviews before making it available for decision making. This research focuses on fake review detection by using content-related features, which includes linguistic features, POS features, and sentiment analysis features. Ontology-based method is used for performing the aspect-wise sentiment analysis. All the features of reviews are calculated and incorporated into the ontology, and fake review detection is also accelerated through the rule-based classifier by inferencing and querying the ontology. Due to the issues related with labeled dataset, the outliers from an unlabeled dataset were selected as fake reviews. The performance measure of the rule-based classifier outperforms by incorporating all the content-related features.