Reviewerly/exHarmony huggingface dataset:
Optimizing Iterative Algorithms for Social Network Sharding

There has recently been significant interest in applications that require computations on massive graph structures, including scenarios where the graph is too large to be processed on a single machine. In this case, the graph needs to be partitioned into subgraphs that can be assigned to individual machines, in a process called graph or social network sharding. Given the sizes of the graphs involved, it is necessary or at least highly desirable that the partitioning itself can also be performed in a distributed manner, instead of running a sequential partitioning algorithm on a single node.We study such distributed algorithms for graph sharding, where the goal is to create subgraphs of roughly equal size that minimize the number of edges crossing subgraph boundaries. In particular, we focus on two well-known approaches that can be efficiently i mplemented i n M apReduce a nd r elated distributed computing paradigms: the Balanced Label Propagation algorithm of Ugander and Backstrom, and the method of Duong et al. based on the Bayesian Stochastic Block Modeling approach of Hofman and Wiggins. Our contributions are as follows: (1) We perform the first direct experimental comparison of the two approaches, which were independently proposed and published. (2) We propose and evaluate several enhancements of Balanced Label Propagation that result in improved graph shardings. (3) We propose and evaluate hybrid methods that perform label propagation both on individual nodes, as suggested by Ugander and Backstrom, and on stochastic blocks inferred using the approach of Duong et al.
VGC AI Competition - A New Model of Meta-Game Balance AI Competition

This work presents a framework for a new type of meta-game balance AI Competition based on Pokémon, Pokémon battles can be viewed as adversarial games played by AIs. Around these games, there is also a meta-game: which Pokémon to include in a team for battles, which moves to pick for every Pokémon in the team, etc. This meta-game is itself a game with a set of rules that govern which Pokémon and which moves are available in the roster that can be selected from, or which attributes (health points, damage, etc.) a Pokémon or moves should have. The aim of the framework is to facilitate competitions in creating the most balanced meta-game possible; one where there is a large variety of Pokémon and moves to choose from, and many possible combinations that are effective. AI agents could assist human designers in achieving strategically expressive meta-games, and this type of benchmark could incentivize game designers and researchers alike to advance knowledge on this type of domain.
Securing Kafka with Encryption-at-Rest

Over the last decade Kafka has become a key component of Big Data processing pipelines. Kafka is increasingly used as a store of data, not just as a means of transferring it from one location to another. For cloud providers and enterprises this means that Kafka must conform to the same security and compliance requirements as conventional data storage systems such as relational databases. One important requirement is encryption-at-rest which is not currently supported by Kafka. We present an analysis of different ways to implement encryption in Kafka and then describe the first c omplete s ystem f or implementing encryption-at-rest at the granularity of a Kafka topic at scale. We demonstrate the challenges in implementing encryption policy, key distribution, key rotation and data re-encryption in Kafka, using our working implementation for illustration.
Improving the Elderly’s Fall Management through Innovative Personalized Remote Monitoring Solution

Falls are one of the major sources of severe injuries, pain and even death in the elderly’s category in an ageing population. Smart monitoring solutions provide functionalities for detecting and preventing the falls. In this paper we present how the capabilities of an underdevelopment system that aim to sustain a tailored remote monitoring of an elderly person in his home can be extended through a fall management functionality provided by a conceptual model based on the data collected by smart wearables/devices and Machine Learning algorithms. Prediction and detection/alert models are developed for ensuring an at-home personalized healthcare solution.
Differentially Private Federated Temporal Difference Learning

This paper considers a federated temporal difference() (TD()) learning algorithm and provides both asymptotic and finite-time analyses. To protect each worker agent cost information from being accessed by possible attackers, we propose a privacy-preserving variant of the algorithm by adding perturbation to the exchanged information. We show the rigorous differential privacy guarantee by using moments accountant and derive an upper bound of the utility loss for the privacy-preserving algorithm. Evaluations are also provided to corroborate the efficiency of the algorithms.
Design and Application of a Novel 4-Transistor Chaotic Map with Robust Performance

A new one dimensional discrete chaotic map circuit is presented. The design is done in a 45 nm CMOS process but the proposed topology is generally applicable for any technology node. The design is hardware-efficient as it contains only four MOS transistors and offers robust chaotic performance with a wide chaotic range. The chaotic performance is analyzed using bifurcation plot, Lyapunov exponent, correlation coefficient, and sample entropy. These different qualitative and quantitative measures clearly demonstrate excellent ergodic properties across a wide chaotic parameter range. The proposed map is also used in designing a reconfigurable logic generator and its wide chaotic window is shown to significantly enhance the functionality space of the logic generator.
Modeling and Verification of Real-Time Systems with the Event Calculus and s(CASP)

Modeling a cyber-physical system’s requirement specifications makes it possible to verify its properties w.r.t. the expected behavior. Standard modeling approaches based on automata theory model these systems at the system architecture level, as they have to explicitly encode the notion of states and define explicit transitions between these states. Event Calculus encoding using Answer Set Programming (ASP) allows for elegant and succinct modeling of these dynamic systems at the requirements specification level, thanks to the near-zero semantics gap between the system’s requirement specifications and the Event Calculus encoding. In this work we propose a framework that uses the EARS notation to describe the system requirements, and an Event Calculus reasoner based on s(CASP), a goal-directed Constraint Answer Set Programming reasoner over the rationals/reals, to directly model these requirements. We evaluate our proposal by (i) modeling the well-known Train-Gate-Controller system, a railroad crossing problem, using the EARS notation and Event Calculus, (ii) translating the specifications into s(CASP), and (iii) checking safety and liveness of the system.
Case Study Application of Business Intelligence in Digital Advertising

This paper demonstrates the application of business intelligence in decision-making in digital advertising through a case study. Data used for analysis was collected during a test phase of an advertising platform. The study analyzes multiple types of traffic, related to countries, browsers, household incomes, and days of a week. Beside tabular reports, the paper presents how to visualize those results using Python libraries to make them more visually appealing. Furthermore, logistic regression was used to build models to detect relationships between the number of impressions and clicks. Finally, the authors propose multiple combinations of data that could be used to create different reports that lead to smarter decision-making and cost-effectiveness.
Information Transmission Protection Using Linguistic Steganography With Arithmetic Encoding And Decoding Approach

Security of Internet of Things and Internet space in general is one of the main areas of research in the field of information technology today and steganography - one of the best methods of message encryption and transmission. Steganography algorithms are used to hide data in cover media, such as text, images, videos, etc., as cover media and change the structure and features of a text message so that it is not identified by human eyes and machines. However, the use of text media as accompanying text is relatively difficult compared to other methods of information coverage due to the lack of available redundant information in the text data. At the same time, text data is being transmitted the fastest and most frequently, so it is ideal for quickly transmitting short important messagesю In this research, an overview of existing approaches to textual steganography and our approaches to steganography methods are presented. New approaches to text steganography, which easily hide the text-coverage and also take less time to encrypt, are presented. A new method of steganography is shown and tested; a software implementation of this method is created. The advantages of using a new method of steganography are substantiated. It is confirmed that this algorithm is faster when working with large amounts of data, comparing to existing algorithms. At the same time, it allows to choose different parameters and settings, increasing its protective potential.
Advertisement Click Fraud Detection Using Machine Learning Techniques

Web browsing has become the integral part of our daily activities. Users come across various advertisement while accessing these web pages. Various companies advertise their products on web pages to increase their sales. The companies hire other Advertisement Agencies for the task of publishing the advertisement. The Advertisement Agency charges the company for clicks made on the Advertisement links using pay-per-click model. Few Advertisements Agency indulge in malpractices to increase their profit by manipulating the number of clicks, this leads to financial loss to the company advertising their products. Click fraud detection helps in identifying the genuine clicks made by the user and the counterfeit clicks made using click bots or other software. In this paper, a comparison is conducted between different machine learning algorithms- Logistic Regression, RNN and various Boosting Algorithm on the basis of accuracy, precision, recall rate, f1-score, subsample, n_estimator, max depth and Learning Rate. The system was experimented on TalkingData AdTracking Fraud Detection Dataset from Kaggle. After experimental analysis it is observed that Logistic Regression shows superiority over the models in comparison.
Proactive Robot Assistance: Affordance-Aware Augmented Reality User Interfaces

Assistive robots have the potential to increase the autonomy and quality of life of people with disabilities <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . Their applications include rehabilitation robots, smart wheelchairs, companion robots, mobile manipulators, and educational robots <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . However, designing an intuitive user interface (UI) for the control of assistive robots remains a challenge, as most UIs leverage traditional control interfaces, such as joysticks and keyboards, which might be challenging and even impossible for some users. Augmented reality (AR) UIs introduce more natural interactions between people and assistive robots, potentially reaching a more diverse user base.
Performance analysis of texture characterization techniques for lung nodule classification

Abstract Lung cancer ranks very high on a global index for cancer-related casualties. With early detection of lung cancer, the rate of survival increases to 80-90%. The standard method for diagnosing lung cancer from Computed Tomography (CT) scans is by manual annotation and detection of the cancerous regions, which is a tedious task for radiologists. This paper proposes a machine learning approach for multi-class classification of the lung nodules into solid, semi-solid, and Ground Glass Object texture classes. We employ feature extraction techniques, such as gray-level co-occurrence matrix, Gabor filters, and local binary pattern, and validate the performance on the LNDb dataset. The best performing classifier displays an accuracy of 94% and an F1-score of 0.92. The proposed approach was compared with related work using the same dataset. The results are promising, and the proposed method can be used to diagnose lung cancer accurately.
A Gröbner-Bases Approach to Syndrome-Based Fast Chase Decoding of Reed–Solomon Codes

We present a simple syndrome-based fast Chase decoding algorithm for Reed–Solomon (RS) codes. Such an algorithm was initially presented by Wu (IEEE Trans. IT, Jan. 2012), building on properties of the Berlekamp–Massey (BM) algorithm. Wu devised a fast polynomial-update algorithm to construct the error-locator polynomial (ELP) as the solution of a certain linear-feedback shift register (LFSR) synthesis problem. This results in a conceptually complicated algorithm, divided into 8 subtly different cases. Moreover, Wu’s polynomial-update algorithm is not immediately suitable for working with vectors of evaluations. Therefore, complicated modifications were required in order to achieve a true “one-pass” Chase decoding algorithm, that is, a Chase decoding algorithm requiring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> operations per modified coordinate, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the RS code length. The main result of the current paper is a conceptually simple syndrome-based fast Chase decoding of RS codes. Instead of developing a theory from scratch, we use the well-established theory of Gröbner bases for modules over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}[X]$ </tex-math></inline-formula> (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> is the finite field of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> elements, for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> a prime power). The basic observation is that instead of Wu’s LFSR synthesis problem, it is much simpler to consider “the right” minimization problem over a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">module</i> . The solution to this minimization problem is a simple polynomial-update algorithm that avoids syndrome updates and works seamlessly with vectors of evaluations. As a result, we obtain a conceptually simple algorithm for one-pass Chase decoding of RS codes. Our algorithm is general enough to work with any algorithm that finds a Gröbner basis for the solution module of the key equation as the initial algorithm (including the Euclidean algorithm), and it is not tied only to the BM algorithm.
Using COVID-19 Masks Dataset to Implement Deep Convolutional Neural Networks For Facial Emotion Recognition

In light of the novel pandemic called COVID-19, the world has been instructed to wear protective facial masks to limit its spread. Doing so has reduced the effectiveness of traditional facial recognition technologies, especially in processing human facial emotions. This has rendered the usage of such technology obsolete in managing facial databases, relying on it for security purposes, and so on. It is then necessary to enhance the current generation of facial recognition to adapt to the protective masks. Speaking of the current facial recognition generation, most of its complex iterations heavily rely on deep learning, which is flawed since the existing facial databases are insufficient, making it even more inadequate to bypass facial masks. This is why the present research paper suggests implementing the Deep Convolutional Neural Networks (DCNN) algorithm using the Japanese Female Facial Expression (JAFFE) to simulate a masked face emotion recognition. This facial database is available free for academic research, was utilized to label the available images displaying various facial emotions under the umbrella of one of the seven basic human facial emotions, allowing for a more advanced facial technology. Consistent with the latest research findings, the proposed facial emotion recognition attains up to an accuracy of 71.35% due to its meticulous masked facial database.
Privacy-Preserving Mechanisms for Multi-Label Image Recognition

Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this gap, two privacy-preserving models, Privacy-Preserving Multi-label Graph Convolutional Networks (P2-ML-GCN) and Robust P2-ML-GCN (RP2-ML-GCN), are developed in this article, where differential privacy mechanism is implemented on the model’s outputs so as to defend black-box attack and avoid large aggregated noise simultaneously. In particular, a regularization term is exploited in the loss function of RP2-ML-GCN to increase the model prediction accuracy and robustness. After that, a proper differential privacy mechanism is designed with the intention of decreasing the bias of loss function in P2-ML-GCN and increasing prediction accuracy. Besides, we analyze that a bounded global sensitivity can mitigate excessive noise’s side effect and obtain a performance improvement for multi-label image recognition in our models. Theoretical proof shows that our two models can guarantee differential privacy for model’s outputs, weights and input features while preserving model robustness. Finally, comprehensive experiments are conducted to validate the advantages of our proposed models, including the implementation of differential privacy on model’s outputs, the incorporation of regularization term into loss function, and the adoption of bounded global sensitivity for multi-label image recognition.
Into the Nebula: Embodied perception of scenography in virtual environments

The introduction of three-dimensional digital imagery as performance sites through the increasing application of virtual reality (VR) technology offers a chance to open discussion about the perception of scenography in digital environments. Consisting solely of pixels, computer-generated settings allow the perceivers to step into the weightless state of physical inexistence in VR and leave the tangible world behind, at least to some extent. At first glance, a perceptual focus on sound and vision may give the impression that we have not moved far from the traditional way of experiencing scenography by simply looking at it. However, a more contemporary understanding of matter as doing, rather than as an innate object, provokes reconsideration of perception in this specific type of environment. While the development of media technology continues to focus on better integration of the human body into virtual reality, I think it can be debated whether the body, loaded with embodied memory of real-world encounters, is the medium to get us there and, if so, in which capacity? Besides fooling the body and our experience of embodiment, could our minds play a different role in our becoming digital beings? Using a variety of examples from the field of VR artistic practices—Analog Studios’ Notget, VRTOV’s The Turning Forest and Tom Crago’s Materials—this essay asks whether the expansion of scenographic horizons is grounded in designers’ capacity to rethink the meaning of matter and to mindfully recalibrate embodiment in order to enable the perceiver to see and feel things differently. I look closely into embodied perception of VR that catapults the perceiver into a vast universe of endless spatial possibilities, a place where potentially the physicality of the body may be exchanged for a scenographic body and the opportunity for active co-creation of digital worlds.
Architecture, Generative Model, and Deep Reinforcement Learning for IoT Applications: Deep Learning Perspective

Deep learning is a subclass of machine learning. In the last few years, this has come to prominence with the core availability of GPUs for computing. There are many applications in which deep learning is using such as a self-driving car in industry that generated caption for images automatically. The deep learning algorithm consists of artificial neural network (ANN) with a number of hidden layers. It is used for nonlinear with supervised and unsupervised learning. Deep learning models provide better results and improvement as compared to traditional machine learning in many areas such as language translation, speech recognition, stock market, healthcare, etc. Today’s most of the deep learning frameworks are open source and freely available for use. Deep learning frameworks provide building blocks for validation of deep learning neural networks, training, and designing with the interface of high-level programming. Some of the most used frameworks are MXNet, PyTorch, TensorFlow, etc. On another side, the Internet of Things (or Internet Connected Things) has attracted many applications and sectors for producing results efficiently, and these devices have made people’s life longer to live. These devices are generating a lot of data (called big data) which can be used for further prediction/forecast by modern deep learning models. In deep learning, complex problems can be solved with the help of modern tools. Differently, each framework is built for a different purpose. These frameworks are often evolving and getting better very rapidly. There are many criteria to choose a framework. In the chapter, we will discuss different types of framework, pros and cons of every framework, architecture, and different criteria to choose the better framework which will be useful for Internet of Things-based applications.
Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes

Although Content Based Image Retrieval (CBIR) is an active research field, application to images simultaneously containing multiple objects has received limited research inter- est. For such complex images, it is difficult to precisely convey the query intention, to encode all the image aspects into one compact global feature representation and to unambiguously define label similarity or dissimilarity. Motivated by the recent success on many visual benchmark tasks, we propose a self- supervised method to train a feature representation learning model. We propose usage of multiple query images, and use an attention based architecture to extract features from diverse image aspects that benefits from this. The method shows promising performance on road scene datasets, and, consistently improves when multiple query images are used instead of a single query image.