Reviewerly/exHarmony huggingface dataset:
EvAn: Neuromorphic Event-Based Sparse Anomaly Detection

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application—video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on the motion history at each pixel. To enable this, we also put forward a generic unsupervised deep learning solution to learn a novel memory surface known as Deep Learning (DL) memory surface. DL memory surface encodes the temporal information readily available from these sensors while retaining the sparsity of event data. Since there is no existing dataset for anomaly detection in the event domain, we also provide an anomaly detection event dataset with a set of anomalies. We empirically validate our anomaly detection architecture, composed of sparse convolutional layers, on this proposed and online dataset. Careful analysis of the anomaly detection network reveals that the presented method results in a massive reduction in computational complexity with good performance compared to previous state-of-the-art conventional frame-based anomaly detection networks.
Applied AI matters: AI4Code

The marriage of Artificial Intelligence (AI) techniques to problems surrounding the generation, maintenance, and use of source code has come to the fore in recent years as an important AI application area1. A large chunk of this recent attention can be attributed to contemporaneous advancements in Natural Language Processing (NLP) techniques and sub-fields. The naturalness hypothesis, which states that "software is a form of human communication" and that code exhibits patterns that are similar to (human) natural languages (Devanbu, 2015; Hindle, Barr, Gabel, Su, &amp; Devanbu, 2016), has allowed for the application of many of these NLP advances to code-centric usecases. This development has contributed to a spate of work in the community --- much of it captured in a survey by Allamanis, Barr, Devanbu, and Sutton (2018) that focuses on classifying these approaches by the type of probabilistic model applied to source code. This increase in the variety of AI techniques applied to source code has found various manifestations in the industry at large. Code and software form the backbone that underpins almost all modern technical advancements: it is thus natural that breakthroughs in this area should reflect in the emergence of real world deployments.
A Blockchain Based Secure IoT Solution for Water Quality Management

Water pollution is a major issue all over the world, and one of the great causes of water deterioration is industrial waste as the polluted water is being dumped into surface water such as seas, springs, and dams. Consequently, this will affect the whole planet, starting from destroying the agricultural crop up to the endangerment of humankind. There are plenty of industries all over Saudi Arabia, many of which are of a high category that generates dangerous fluids and other materials in their daily production activities. By discharging polluted components in water, the industry is considered to be violating the General Authority of Meteorology Environmental Protection (GAMEP)’s laws and regulations. It’s a revolutionary step to stop establishments and industries from contributing to water pollution. Currently, industries violating these laws are difficult to identify due to the manual nature of measuring industrial water tanks. This produces difficulties in pulling out a sample to measure. To tackle the aforementioned issue, a system has been proposed as it is not only detecting water pollution but will take action in order to prevent the continuation of water pollution. The integration between two technologies has been introduced in this system. First, IoT (internet of things) is used to measure water components in industrial tanks and detect any violation. Second, blockchain is applied to the appropriate penalty of the violating industry and maintain the transparency, integrity, and reliability of the violation records. The system will be capable of measuring water quality in real-time and facilitate the instant detection of any violations to carry out the appropriate penalty. An introductory experiment shows prominent results that can be used to decrease the number of water violation cases to save the planet and secure a promising future.
Insights into the role of gender in aesthetic design: a participatory study on the design of digital health wearables

Abstract The role of artificial intelligence (AI) in facilitating the real-time processing of data is revolutionising the future of healthcare through mobile diagnostics, remote monitoring devices, and wearable technology products. The rise in digital wearables for remote healthcare is evolving at an increasing pace towards patient-centred and personalised care with connected patients. This transformation is creating new opportunities for designers to increase patients' participation and sustain their engagement in remote healthcare. In this paper, the authors have investigated the role of gender in aesthetic design in the context of digital health wearables to enhance user engagement and interaction. The investigations were conducted through participatory design sessions and showed a constructive relationship between aesthetic preferences and understanding the influence of gender as a means of facilitating user engagement with digital health wearables. This paper presents a novel user response model that leads to suggestions for future work, including research in the areas of gender awareness in aesthetics to move beyond traditional, stereotypical, and pre-identified gendered characteristics related to femininity and masculinity. The findings conclude with a path forwards for design research to promote gender awareness in aesthetic design for the realisation of healthcare wearables of the future .
A Precise Calibration Method for Line Scan Cameras

With the advantages of ultrahigh resolution and acquisition rate, line scan cameras are increasingly being deployed for precision measurement. In order to achieve high precision, the intrinsic parameters must be calibrated properly. However, the existing calibration models that only calibrate the principle points and the distortion in 1-D direction and ignore the other directions result in limited adaptability and accuracy, which are especially serious for multiline cameras. To conquer this challenge, this article proposes a precise calibration method that is qualified for both multiline cameras and single-line cameras. Through the analysis of the relative position relationship between the pixel array and principle point, a method for calibrating the principle points and the distortion in 2-D direction is proposed and a flexible calibration device is designed based on a theodolite. The calibration quality is improved by the novel calibration method that effectively establishes the correspondence relationship between the rays and pixels by recording the incident angle of the collimated beam and its image, respectively. Moreover, a robust optimization algorithm is proposed to enhance the stability of parameter estimation. The proposed method is verified by Monte-Carlo simulation, real experimental data, and flight. The results show that the 2-D calibration model can improve the calibration accuracy of the multiline cameras by 45 pixels at most, and its reprojection measurement uncertainty is as low as 0.1 pixels. The comparative results of the experiments demonstrate that the proposed method has the advantages of high accuracy, low cost, and easy implementation.
Redundant Information Neural Estimation

We introduce the Redundant Information Neural Estimator (RINE), a method that allows efficient estimation for the component of information about a target variable that is common to a set of sources, known as the “redundant information”. We show that existing definitions of the redundant information can be recast in terms of an optimization over a family of functions. In contrast to previous information decompositions, which can only be evaluated for discrete variables over small alphabets, we show that optimizing over functions enables the approximation of the redundant information for high-dimensional and continuous predictors. We demonstrate this on high-dimensional image classification and motor-neuroscience tasks.
Mobile Application-Based Virtual Assistant Using Deep Learning

Autonomous machines have become a reality rather than a distant fantasy these days. Machines’ understanding and subsequent reaction to its surroundings are its major features. Due to this, scene perception is a widely researched topic in the branch of computer vision nowadays. Identifying the environment around, finds application in a variety of fields like robotics, virtual assistants for the visually impaired, etc. This paper proposes leveraging the advanced convolutional neural networks required to perform scene perception. The objective is to develop an Android-based app that can identify the indoor surroundings around the mobile by accessing its camera.
Constraint answer set programming as a tool to improve legislative drafting

"Rules as Code" in this paper is used to refer to a proposed methodology of legislative and regulatory drafting.1 That legislation can be represented in declarative code for automation has long been recognized [6], as has the opportunity for improving the quality of legal drafting with the techniques of formal representation [1].
Enhanced Detection Range for EM Side-channel Attack Probes utilizing Co-planar Capacitive Asymmetry Sensing

Electromagnetic (EM) side-channel analysis (SCA) attack, which breaks cryptographic implementations, has become a major concern in the design of circuits and systems. This paper focuses on EM SCA and proposes the detection of an approaching EM probe even before an attack is performed. The proposed method of co-planar capacitive asymmetry sensing consists of a grid of four metal plates of the same size and dimension. As an EM probe approaches the sensing metal plates, the symmetry of the sensing metal plate system breaks, and the capacitance between each pair diverge from their baseline capacitances. Using Ansys Maxwell Finite Element Method (FEM) simulations, we demonstrate that the co-planar capacitive asymmetry sensing has an enhanced detection range compared to other sensing methods. At a distance of 1 mm between the sensing metal plates and the approaching EM probe, it shows >17 % change in capacitance, leading to a > 10 × improvement in detection range over the existing inductive sensing methods. At a distance of 0.1 mm, a > 45% change in capacitance is observed, leading to a > 3 × and > 11 × sensitivity improvement over capacitive parallel sensing and inductive sensing respectively. Finally, we show that the co-planar capacitive asymmetry sensing is sensitive to both E-field and H-field probes, unlike inductive sensing which cannot detect an E-field probe.
Automated three-dimensional vessel reconstruction based on deep segmentation and bi-plane angiographic projections

Automated three-dimensional (3D) blood vessel reconstruction to improve vascular diagnosis and therapeutics is a challenging task in which the real-time implementation of automatic segmentation and specific vessel tracking for matching artery sequences is essential. Recently, a deep learning-based segmentation technique has been proposed; however, existing state-of-the-art deep architectures exhibit reduced performance when they are employed using real in-vivo imaging because of serious issues such as low contrast and noise contamination of the X-ray images. To overcome these limitations, we propose a novel methodology composed of the de-haze image enhancement technique as pre-processing and multi-level thresholding as post-processing to be applied to the lightweight multi-resolution U-shaped architecture. Specifically, (1) bi-plane two-dimensional (2D) vessel images were extracted simultaneously using the deep architecture, (2) skeletons of the vessels were computed via a morphology operation, (3) the corresponding skeleton structure between image sequences was matched using the shape-context technique, and (4) the 3D centerline was reconstructed using stereo geometry. The method was validated using both in-vivo and in-vitro models. The results show that the proposed technique could improve the segmentation quality, reduce computation time, and reconstruct the 3D skeleton automatically. The algorithm accurately reconstructed the phantom model and the real mouse vessel in 3D in 2 s. Our proposed technique has the potential to allow therapeutic micro-agent navigation in clinical practice, thereby providing the 3D position and orientation of the vessel.
An AI and Computer Vision-based Face Mask Recognition &amp; Detection System

In the Current Scenario, when the whole world is going witnessing, it becomes a matter of paramount importance to control the spread of the disease. For the same, the governing authorities have been advising various regulations and guidelines. Among various guidelines, the most promising preventing measure is to cover the face with a mask to minimize the spread of COVID -19. For the same, various models are being developed by different researchers to automate the process of mask detection that will help to ensure that maximum people adhere to the advisories issued by leading health organizations. Here, in this paper, the authors propose an automated system that detects the mask. The proposed model identifies faces through facial features through technology such as video cameras. The proposed Face Mask Recognition System employs Deep Learning, a type of Artificial Intelligence (AI), to detect the person with or without a mask. The proposed model can be connected with any surveillance system installed at your premises. Further, the authorities can identify the person who is not wearing a mask to initiate any action if required. The system also sends an alert message to the administrator if someone enters the premises without a face mask. The proposed model has been tested to establish its efficiency and effectiveness. The accuracy of the proposed model is obtained to be in the range of 95% to 99% depending on the digital capabilities, establishing the supremacy of the proposed model.
Coffee forensics — Reconstructing data in IoT devices running Contiki OS

The ability to examine evidence and reconstruct files from novel IoT operating systems, such as Contiki with its Coffee File System, is becoming vital in digital forensic investigations. Two main challenges for an investigator facing such devices are that (i) the forensic artifacts of the file system are not well documented, and (ii) there is a lack of available forensic tools. To meet these challenges, we use code review and an emulator to gain insight into the Coffee file system, including its functionality, and implement reconstruction of deleted and modified data from extracted flash memory in software. We have integrated this into a forensic tool, COFFOR, and analyzed the Coffee File System to reconstruct deleted and modified files. This paper presents an overview of the artifacts in the file system and implements methods for the chronological ordering of the deleted file versions, and discusses these methods’ limitations. Our results demonstrate that forensic acquisition and analysis of devices running the Contiki operating system can reveal live and deleted files, as well as file version history. In some cases, a complete, chronological ordering of the version history can be reconstructed.
Survey on Theory and Applications of InterCriteria Analysis Approach

Short notes on the concept of InterCriteria Analysis (ICrA), a new method for estimating similarity with regards to estimations of criteria and objectives, are described. The main results of the theory development since ICrA's inception are considered. Several software implementations of ICrA are presented. Various applications of the approach to a wide variety of problems are discussed.
Institutional privacy risks in sharing DNS data

The Domain Name System (DNS) is used in every website visit and e-mail transmission, so privacy is an obvious concern. In DNS, users ask recursive resolvers (or "recursives") to make queries on their behalf. Prior analysis of DNS privacy focused on privacy risks to individual end-users, mainly in traffic between users and recursives. Recursives cache and aggregate traffic for many users, factors that are commonly assumed to protect end-user privacy above the recursive. We document institutional privacy as a new risk posed by DNS data collected at authoritative servers, even after caching and aggregation by DNS recursives. We are the first to demonstrate this risk by looking at leaks of e-mail exchanges which show communications patterns, and leaks from accessing sensitive websites, both of which can harm an institution's public image. We define a methodology to identify queries from institutions and identify leaks. We show the current practices of prefix-preserving anonymization of IP addresses and aggregation above the recursive are not sufficient to protect institutional privacy, suggesting the need for novel approaches. We demonstrate this claim by applying our methodology to real-world traffic from DNS servers that use partial prefix-preserving anonymization. Our work prompts additional privacy considerations for institutions that run their own resolvers and authoritative server operators that log and share DNS data.
Birth-Death MCMC Approach for Multivariate Beta Mixture Models in Medical Applications

Lately, data mining tools have received significant attention because of their capability in modeling and analyzing collected data in various fields including medical research. With the growing availability of medical data, it is crucial to develop models that can discover hidden patterns in data and analyze them. Among various techniques, mixture models have been widely used for categorization problems in statistical modeling. In this paper, a Bayesian learning framework is proposed for multivariate Beta mixture model. Previous works have shown that multivariate Beta distribution can be considered as an alternative to Gaussian due to the flexibility of its shape and convincing performance. In particular, we use the Birth and Death Markov Chain Monte Carlo (MCMC) algorithm, which allows simultaneous parameters estimation and model selection. Experimental results on medical applications demonstrate the effectiveness of the proposed algorithm.
Market Acceptance of the Blockchain Technology on the Example of Bitcoin

The paper is aimed at revealing market acceptance of blockchain technology in the digital economy on the example of bitcoin. The authors study features if cryptocurrencies and the pricing factors of bitcoin in the period of the rising interest of global media in 2016–2017. With the help of regression tools, the authors demonstrate the interconnection of the bitcoin price with the factors of global financial markets and internal expectation factors of the crypto industry dynamics in the course of early adoption of bitcoin by various industries of the economy. #CSOC1120
Straight-Line: A new global path planning algorithm for Mobile Robot

Today most organizations use mobile robots for many purposes and to perform difficult and repetitive tasks. Robots are used to transport goods from one place to another within factories and warehouses. It is also used to provide different services in government and private institutions, as it can interact with people and help them to obtain different services with high quality and very quickly without any trouble. For the robot performs its functions better, it needs a path planning algorithm to avoid obstacles and works faster. Therefore, we develop a new algorithm called straight line algorithm. The new algorithm can get the optimal path with shortest length in low running time. The new algorithm searches for the optimal solution in the critical region firstly. This way capable of finding the shortest path faster. We evaluate straight-line algorithm with Genetic algorithm GA. We find that the new algorithm outperforms better than the GA in terms, distance, speed, and running time.
Security Issues of Unified Payments Interface and Challenges: Case Study

NPCI, which stands for National Payment Corporation of India, was the organisation behind the idea of UPI, a user-friendly system in which funds can be directly transferred from the bank account to the account using a mobile phone. UPI is based on the concept of 1 click 2-factor authentication. The first factor is the user's mobile phone itself, and the second factor is MPIN or bio-metrics. It is based on the IMPS(Immediate Payment Service), but there are considerable differences between both services, and we will observe it. With a foresight to make the Indian economy cashless, it helps people transfer funds in an immediate and real-time process. It has played a major role in the revolution of cashless transactions in India. Although significant UPI users are minor and much lesser compared to the Indian population, over 2.07 billion transactions per month have been made by UPI by October 2020, which makes it our essential part of our day-to-day life. This paper will discuss the working of UPI, how UPI is different from conventional cashless transaction methods. After that, we will discuss how the attacker can find the UPI's loopholes (here we reviewed UPI BHIM 1.0) and empty the victim's bank account. The attacker can make these attacks remotely, and these attacks can affect a single user to multiple users. We will also discuss how the attacker can achieve his/her goal using a malicious App. In the end, we will see how UPI BHIM 2.0 update was successful in covering this security loophole.