Reviewerly/exHarmony huggingface dataset:
An Emergent Role of Knowledge Graph and Summarization Methodology to Simplify Recruitment for the Indian IT Industry

In the new age of information revolution, recruiters are getting many job applicants’ profiles from various sources. Recruiters invest considerable time and effort to evaluate and organize this amount of data in semi-structured or unstructured format in the information technologies industry. To understand and summarize job applicants’ profiles, a knowledge graph can help to provide instant screening. This paper proposes the use of a machine learning techniques for the generation of knowledge graph and extractive summarization of job applicants. This will help to have a quick knowledge graph visualization and short summary of relevant information from candidates’ profiles. The results of the study can significantly reduce the effort and time taken to manually screen profiles for matching jobs during a recruitment process.
High-level synthesis, cryptography, and side-channel countermeasures: A comprehensive evaluation

Side-channel attacks pose a severe threat to both software and hardware cryptographic implementations. Current literature presents various countermeasures against these kinds of attacks, based on approaches such as hiding or masking, implemented either in software, or on register–transfer level or gate level in hardware. However, emerging trends in hardware design lean towards a system-level approach, allowing for faster, less error-prone, design process, an efficient hardware/software co-design, or sophisticated validation, verification, and (co)simulation strategies. In this paper, we propose a Boolean masking scheme suitable for high-level synthesis of substitution–permutation network-based encryption. We implement both unprotected and protected PRESENT, AES/Rijndael and Serpent encryption in C language, utilizing the concept of dynamic logic reconfiguration, synthesize it for Xilinx FPGA, and we compare our results regarding time and area utilization. We evaluate the effectiveness of proposed countermeasures using both specific and non-specific t-test leakage assessment methodology. We discuss the leakage assessment results, and we identify and discuss the related limitations of the system-level approach and the high-level synthesis.
Lucid-GAN: An Adversarial Network for Enhanced Image Inpainting

Image in-painting has been a significant research area which is being explored in the past decade. It has many diversified applications such as damaged image repairing and object removal. The hallmark algorithm for this application is EdgeConnect which comprises an edge generator and an image completion network. But, EdgeConnect is used to repair only damaged images. The process of repairing damaged images also faces the pitfall of enormous quality reduction after reconstruction. This paper presents a custom Generative Adversarial Network (GAN) - named Lucid-GAN on top of EdgeConnect to upscale the quality of the image. This decreases the noise in the reconstructed image, while parallely improving the SSIM, PSNR and the MAE scores by a considerable margin.
Exploration and Exploitation Zones in a Minimalist Swarm Optimiser

The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the population’s dimensional behaviour in each iteration and each defined exploration-exploitation zone, and to subsequently offer improvements to the working of the optimiser. The derived variants, titled unified DFO or uDFO, are successfully applied to an extensive set of test functions, as well as high-dimensional tomographic reconstruction, which is an important inverse problem in medical and industrial imaging.
On semantics-based minimal revision for legal reasoning

When literal interpretation of statutes leads to counterintuitive consequences, judges, especially in high courts, may identify counterintuitive consequences and revise interpretation of statutes. Researchers have studied revisions for computational legal representation. Generally, studies on revision usually consider minimal revision to reflect limitation of judges' legislative power. However, those studies tend to minimize the number of operations used for changing rules rather than minimize the changes of semantics (the set of conclusions obtained from the program), which vary among cases. In this paper, we consider minimizing the changes of semantics of a rule-base written in a normal logic program. We consider that each possible fact-base (the representation of a case) has its corresponding semantics and corresponding dominant rule-base, which is a set of Horn clauses obtained from the subset of rule-base that is specific to the considered fact-base. Hence, we present a new sub type of semantics-based minimal revision called a dominant-based minimal revision. Furthermore, we present one guidance to obtain one dominant-based minimal revision by using legal debugging and Closed World Specification. We also compare the dominant-based minimal revision with the syntax-based minimal revision in Theory Distance Metric. As the syntax-based minimal revision minimizes the number of operations used for changing rules, the comparison shows that the syntax-based minimal revision may cause extra semantics changes compared to the dominant-based minimal revision, especially when the rule-base contains multiple rules for the same consequence. We discuss that such extra semantics changes can be considered as unintentional changes caused by the syntax-based minimal revision. Hence, legal reasoning systems can check with the user such extra semantics changes to confirm the user intention of changes.
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes

This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021.We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities.We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.
GPU accelerated 3DES encryption

Abstract Triple DES (3DES) is a NIST and ISO/IEC standard block cipher that is also used in some web browsers and several electronic payment applications. We propose an optimized bit‐level parallelization of 3DES for GPU accelerated encryption to allow processing high volumes of data. Since the block size of 3DES is 64 bits, our approach considers a kernel block as a 64‐bit 3DES block. Each kernel block performs XOR, permutation, and S‐box operations of this cipher in parallel and memory accesses are optimized by the use of constant and shared memory. Although table based and bitsliced implementations of block ciphers on GPUs outperform naive implementations, their performance vary significantly on different GPU models and architectures. Lack of publicly available source codes prohibit a fair comparison of the performance results for different implementations. In this work, we provide performance results on various GPU models and make our implementation publicly available for reproducibility and further comparisons. When compared against the baseline multi‐threaded CPU implementation, our optimization achieves an average of 15.95 speed‐up when encrypting large files using an RTX 2070 Super GPU. Moreover, when modified into a key search attack, more than 94.4 million 3DES key searches per second can be conducted on an RTX 2070 Super GPU.
Integration of User Experience and Agile Techniques for Requirements Analysis: A Systematic Review

User experience and agile techniques have grown over the last decade. However, there are not many articles that study how the integration of both proposals influences requirements engineering. The objective of this research is to structure the available literature on the integration of agile techniques and user experience in the domain of requirements engineering. A systematic literature review (SLR) was carried out considering duly indexed conference and journal publications. Likewise, the studies found in the four (4) selected databases followed a rigorous study selection procedure. The systematic review recovered a total of 25 primary studies after going through a quality evaluation and revealed the different practices and strategies most used in the requirements analysis integrating agile techniques and user experience. Besides, it allowed identifying the obstacles faced by professionals in requirements engineering. The study carried out concludes that the most used practices in requirements engineering are user stories, low-fidelity prototypes, and person. The literature provides a systematic summary and proposals for frameworks that can be incorporated into agile development projects by integrating user experience.
Learning Tasks in Intelligent Environments via Inverse Reinforcement Learning

One of the most common objective for an Intelligent Environment (IE), especially in case of applications of Ambient Assisted Living, is to support a user in everyday tasks. A complex task can be effectively represented as a workflow; i.e., a composition of simpler activities. This research activity aims at developing methodologies and tools able to make a robotic system learns a task from demonstrations in an IE. The large number of accessible sensors in the intelligent environment, as well as the availability of advanced services such as those for activity recognition and situation awareness, can facilitate either the recognition of the action to be executed at the i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> step of the workflow and the verification of the correct execution of the task. In this paper, we present an hybrid approach based on Inverse Reinforcement Learning (IRL) to learn from observations of the behavior of an expert and on forward Reinforcement Learning to correct and improve the learned behavior. We also present the high-level architecture of the IE that supports the learning process.
Quantum deep learning by sampling neural nets with a quantum annealer

We demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploit fast sampling times. We propose approaches to overcome two hurdles for high resolution image classification on a quantum processing unit (QPU): the required number and the binary nature of the model states. With this novel method we successfully transfer a pretrained convolutional neural network to the QPU. By taking advantage of the strengths of quantum annealing, we show the potential for classification speedup of at least one order of magnitude.
Trends and Prospects of the Educational Technologies Development

Authors of the present paper have examined the main existing learning formats and educational technologies regarding the prospects of their development. The existing formats have been analyzed together with the more promising ones, pointing out strengths and limitations of both. Research methodology includes data collection, analysis, and systematization from the open sources, combined with the author's personal experience in using various teaching formats in university. As the result of the present research, the most promising combinations of educational formats have been identified and described regarding pros and cons of each of them, as well as software suitable for organizing distanced and hybrid formats. The research has shown that hybrid format turned out to be the most effective one.
PICO

Memory safety violations such as buffer overflows are a threat to security to this day. A common solution to ensure memory safety for C is code instrumentation. However, this often causes high execution-time overhead and is therefore rarely used in production. Static analyses can reduce this overhead by proving some memory accesses in bounds at compile time. In practice, however, static analyses may fail to verify in-bounds accesses due to over-approximation. Therefore, it is important to additionally optimize the checks that reside in the program. In this article, we present PICO, an approach to eliminate and replace in-bounds checks. PICO exactly captures the spatial memory safety of accesses using Presburger formulas to either verify them statically or substitute existing checks with more efficient ones. Thereby, PICO can generate checks of which each covers multiple accesses and place them at infrequently executed locations. We evaluate our LLVM-based PICO prototype with the well-known SoftBound instrumentation on SPEC benchmarks commonly used in related work. PICO reduces the execution-time overhead introduced by SoftBound by 36% on average (and the code-size overhead by 24%). Our evaluation shows that the impact of substituting checks dominates that of removing provably redundant checks.
Eye Gaze and Interaction Differences of Holistic Versus Analytic Users in Image-Recognition Human Interaction Proof Schemes

Image-recognition Human Interaction Proof (HIP) schemes are widely used security defense mechanisms that are utilized by service providers to determine whether a human user is interacting with their system and not malicious software. Inspired by recent research, which underpins the necessity for designing user-centered HIPs, this paper examines, in the frame of an accredited cognitive style theory (Field Dependence-Independence – FD-I), whether human cognitive differences in visual information processing affect users’ visual behavior when interacting with an image-recognition HIP challenge. For doing so, we conducted an eye tracking study (n = 46) in which users solved an image-recognition HIP challenge. Analysis of users’ interactions and eye gaze data revealed differences in users’ visual behavior and interactions between Holistic and Analytic users within image-recognition HIP tasks. Findings underpin the added value of considering users’ cognitive processing differences in the design of adaptive and adaptable HIP security schemes.
Security of IoT-Based E-Healthcare System: A Blockchain Solution

In recent days a new class of devices called IoT devices is used in e-healthcare system for the collection and processing of medical data. An IoT-based e-healthcare system consists of several wearable devices, wearable smart gadgets, and low-cost sensors that are capable of transfer of data from the patient body to the healthcare data center. However, security is a big challenge in this area as these IoT devices are vulnerable to different network attacks such as eavesdropping, interference and jamming attack, etc. This paper aims to provide a secure solution based on the blockchain technology. The data collected from the body sensors are chained in the blockchain so that data leaking and unauthorized modification will not be possible in the whole communication. Discussion of the security analysis and results prove that the model will resist different types of attacks.
Fast Matrix Multiplication Algorithm for a Bank of Digital Filters

A method of implementing matrix multiplication for use in digital filter banks (sets) is presented. This method allows to obtain noticeable savings of computational costs compared to standard methods. Reduction of computational complexity of digital filter banks (sets) is achieved without any performance loss. The method proposed in the report is based on the use of a combination of the known method of 3M multiplication of complex matrices and the Strassen method for fast multiplication of matrices. A feature of the application of the Strassen method in this case is that it is recursively applied, starting with some dimension of blocks. Multiplication of lower dimension blocks is carried out by the traditional method. In order to reduce computational complexity, the minimum dimension of blocks was selected, starting from which it is advisable to use the Strassen method. An analysis of the computational complexity of the proposed method has shown that its use in implementing a bank (set) of digital filters allows to obtain a gain in complexity compared to a traditional algorithm by about 1.8-1.9 times with large dimensions of matrices. An approximate analysis of the sensitivity of the method proposed in the article to rounding errors that occur during digital processing has been carried out. As a result of the analysis, it was found that the proposed method has approximately the same sensitivity to rounding errors as the traditional method.
FFT-Based Robust Video Steganography over Non-dynamic Region in Compressed Domain

The proposed research work presents a novel data hiding method for video steganography in the compressed domain. In this method, the random numbered secret frames are selected from the RGB cover video sequence. This method increases the complexity level of video steganography by considering the specific host to conceal confidential data. It extracts the specific non-dynamic region from the secret frame and transforms the pixel value to the frequency domain using Fast Fourier Transform (FFT). The usage of random Least Significant Bit (LSB) of the real part of FFT as a carrier object leads to good video quality and secret data-carrying capacity. Furthermore, the secure compressed stego video is reconstructed using the H.264 video compression technique. The proposed method is experimented on some well-known video datasets by considering RGB images with different resolutions as a secret message. Performance evaluation parameters evaluate the proposed method’s efficiency, imperceptibility, robustness, and embedding capacity, and the improved results are compared with reported methodologies. The results show a significant improvement in the imperceptibility as a Peak Signal-to-Noise Ratio (PSNR) value is reached up to infinity (Inf) in some cases. At the same time, the similarity between embedded and extracted message is achieved nearer to 1 with the negligible Bit Error Rate (BER) less than 0.1%, and the embedding capacity greater than 0.5% in all cases indicates an excellent sign of carrying a big amount of confidential data.
Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models

When dealing with complex models (i.e., models with many variables, a high degree of dependency between variables, or many states per variable), the efficient representation of quantitative information in probabilistic graphical models (PGMs) is a challenging task. To address this problem, this study introduces several new structures, aptly named value-based potentials (VBPs), which are based exclusively on the values. VBPs leverage repeated values to reduce memory requirements. In the present paper, they are compared with some common structures, like standard tables or unidimensional arrays, and probability trees (PT). Like VBPs, PTs are designed to reduce the memory space, but this is achieved only if value repetitions correspond to context-specific independence patterns (i.e., repeated values are related to consecutive indices or configurations). VBPs are devised to overcome this limitation. The goal of this study is to analyze the properties of VBPs. We provide a theoretical analysis of VBPs and use them to encode the quantitative information of a set of well-known Bayesian networks, measuring the access time to their content and the computational time required to perform some inference tasks.
Exploiting Multilingual Neural Linguistic Representation for Sentiment Classification of Political Tweets in Code-mix Language

Social media is more and more utilized by people around the world to express their feelings and opinions in the kind of short text messages. Twitter has been a rapidly growing microblogging social networking website where people express their opinions in a precise and simple manner of expressions. It has also become a platform for governmental, non-governmental and individual opinions and policy announcements. Detecting sentiments from tweets has a wide range of applications including identifying the anxiety or depression of individuals and measuring the well-being or mood of a community. In addition, the sentiment classification becomes complex when the tweet is written in codemix language which is a mix of two different languages. The main objective of this paper is to classify tweets written in mix of Tamil and English language into positive, negative, or neutral. This is achieved by fine tuning a pretrained multilingual text representation model as well as deep learning transformers. The proposed approach is experimented with large scale of tweets collected for societal issues in India. We also provide a comparative study of different machine learning and deep learning models. The proposed architecture based on neural linguistic representation provides significant accuracy in classifying both Tamil and codemix tweets.
Real-time Private Membership Test using Homomorphic Encryption

With the ever increasing volume of private data residing on the cloud, privacy is becoming a major concern. Often times, sensitive information is leaked during a querying process between a client and an online server hosting a database; The query may leak information about the element the client is looking up, while sensitive details about the contents of its database can leak on the server side. The ability to check if an element is included in a database while maintaining both the client's and the server's privacy is known as the Private Membership Test. In this context, we propose a method to privately query a database with computational complexity O(1) using Bloom filters and Homomorphic Encryption. The proposed methodology also enables post-encryption insertions and deletions without requiring a new setup. Experimental results show that our proposed solution has practical setup, insertion and deletion times for databases of up to a few million entries, with constant query time less than 0.3 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> , considering a false positive rate lower than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . We instantiate our methodology for a URL denylisting service, and demonstrate that it can provide solid security guarantees without affecting the user experience.
Machine Learning Based Network Censorship

Network censorship is to actively modify network users' experience in order to restrict behaviors to some extent. One of the most popular network censorship actions is to add several IP addresses or domain names into the blacklist and prevent users from visiting those websites. However, this blacklist should not be static since most web developers, after finding their websites are in the blacklist, would like to transplant their content to other IP addresses or domain names and redirect network users to new addresses. In response to these concerns, in this paper, we propose a machine learning based network censorship design which can update the blacklist by analyzing network users' behavior. By leveraging data analysis for the network users' behaviors when facing network censorship, we collect a lot of useful information and utilize it to add or delete members from the blacklist. We think this network censorship design can be more effective than static censorship with respect to filtering forbidden information and retaining qualified candidates.