Reviewerly/exHarmony huggingface dataset:
Measuring the Effectiveness of SAT-Based Guess-and-Determine Attacks in Algebraic Cryptanalysis

This paper studies the problem of algebraic cryptanalysis where state-of-the-art SAT solvers are used to invert some cryptographic function. We define a new metric of the hardness of CNF formulas that encode the corresponding cryptanalysis problems. The introduced metric is similar to the well-known tree-like metrics used in the theory of propositional proofs. However, unlike the latter, the new metric can be effectively estimated in application to specific cryptographic functions. The corresponding approach combines the Monte Carlo method and metaheuristic black-box optimization algorithms. The proposed algorithms require a large amount of computational resources, and for their experimental evaluation we used a supercomputer. In the experiments, we applied the proposed metrics to construct estimations of guess-and-determine attacks on the compression function of the well-known MD4 cryptographic hash algorithm.
Natural Language Processing

One of the most impressive feats a human can do is understand the verbal or written word. Many researchers and practitioners believe this feat to be a fundamental component of human intelligence. As with many aspects of AI such as image processing it is not surprising that a whole domain of research, tools and techniques have emerged which enable computers to do something similar. In the early days, a significant amount of research was undertaken using symbolic AI to construct and interoperate language using syntax and semantic representations of language. Although these early attempts were impressive for the time, symbolic Natural Language Processing (NLP) failed to deliver anything near human-level abilities.
Chainlink

Chainlink is a decentralized network of nodes that uses oracles to transfer data and information from off-blockchain sources to on-blockchain smart contracts. In this chapter, you will learn how to use the ETH/USD price feed on the Kovan testnet to access the most recent cryptocurrency price inside smart contracts.
Deploying and Hosting Machine Learning Models

This is one of the most important chapters in the book as more machine learning models transition from research labs into real-world applications. Ultimately, of course, after you have finished experimenting, you will need to consider a more production-friendly environment than your laptop. With the widespread industrial support and investment, this has been made easier through a variety of different frameworks. Tech giants such as Google, Facebook and Microsoft provide access to compressive frameworks for free. In line with industrial automation, many businesses are looking towards the use of algorithms to solve complex and repetitive problems while streamlining existing processes [1]. This has led to the widespread utilisation of machine learning algorithms that are already integrated into many distinct aspects of our lives. This is apparent in many services such as social media, home entertainment, online shopping and even our healthcare. The advanced machine learning algorithms we see today are relatively new and consequently how we deploy these algorithms has not fully matured. Until recently this was only undertaken by large tech giants but as you will see in this chapter anyone can now deploy and utilise the machine learning algorithms they develop to solve many challenges they currently face. Figure 13.1 shows the overlap between development (training) and model deployment (inferencing).
Historical Overview of Data Visualization and its Attempts and Reflections in LIS

Starting from the papyrus map of Turin in ancient Egypt, this study reviews the complete historical process of data visualization development.By taking "The Papyrus map of Turin to before the First Industrial Revolution", "The First Industrial Revolution to the eve of the birth of computers" and "After the emergence of computers" as the narrative dimension, this study lists the characters, backgrounds, design concepts and influences of different data visualization graphics development in detail, integrates the development process of data visualization tools, and summarizes the two thought development stages of data visualization and its tools in the development process.At the same time, this study make a dissicusion of the data visualization attempts in China's LIS and found problems such as single application form, lack of design, poor readability, and unfriendly publishing style when using data visualization.
A benchmarking program to support software process improvement adaptation in a developing country, a Pakistan case

Background Standardization of software development processes using the software process improvement (SPI) frameworks like capability maturity model integration (CMMI) or the International Organization for Standardization (ISO) are vital for better performance and defect-free delivery of software engineering projects. Problem The studies from developing countries show that the organizations are unable to get the desired SPI benefits during early adaptation due to lack of benchmarking initiatives. These initiative needed bridging of the gap between major software engineering fields such as project performance, productivity, and SPI measurement metrics like effort, schedule, cost, productivity, quality, and customer satisfaction. Method This research quantifies SPI benefits by bridging the existing gaps and identifying a commonly defined set of 44 significant base metrics related to ESCPQC and applies a conceptual framework over the collected data of 62 projects from three leading software organizations. Result The results have quantified the “the introduction of change effect” and showed that the adaptation of SPI has shown SPI benefits like improved quality, customer satisfaction, and effort. In contrast, schedule metrics showed a high reduction at level 3 and again increased at high maturity levels values. The last benefits are communicated following a trustable mechanism.
Comparison of document examiners’ opinions on original and photocopied signatures

There is a lack of empirical evidence concerning document examiners’ ability to perform handwriting compari sons on photocopied writings. This study aimed to compare the accuracy of examiners’ opinions on 260 original questioned signatures and on the same signatures that had been photocop ied. Six of the examiners from the Document Examination Section of the New Zealand Police participated in the study, which comprised two trials. Each trial was constructed ac cording to the accepted process of comparing a group of known (specimen) signatures with a group of questioned signatures where the writer was known to the experimenters but not to the document examiners. One trial contained originals of the specimen and questioned signatures and the other comprised photocopies of the same specimen and questioned signatures. No errors regarding authorship were made for original or pho tocopied signatures, and there were no instances where an identification/elimination opinion was reversed between a photocopy and its original. Only 2.3% of opinions relating to an original signature differed in any way from that offered for its photocopy. The high correct rates for questioned genuine signatures were similar for original (100%) and photocopied signatures (98%). The correct opinion rate regarding the proc ess of production of original and photocopied, simulated, ques tioned signatures combined was 99.7%. The results provide evidence that examiners are able to make comparisons on a complex signature with the same accuracy and similar sensi tivity when using either originals or photocopies.&#x0D; Purchase Article - $10
Utility Pole Detector for Inspection of Power Distribution Networks Using Faster R-CNN

Electricity users make power companies better monitor their services to keep distribution networks up and running. To monitor working electrical distribution systems an important tool is the visual inspection. Infrared thermography and image processing techniques are used to improve the reliability of this visual inspection to avoid several electrical failures. The target devices for visual inspections are located on poles. Therefore, the focus is on the correct detection of poles to automate the inspection from these devices. The research proposed by this work aims to build a utility pole detector as the first step of an automated diagnostic system for network inspection. The database composed of images collected from Google Street View was used to train detectors using Faster R-CNN network, YOLO, and Mask R-CNN. The Faster R-CNN detector presented 93,59% of detections when applied to a video and 14,62% of wrong detections. The research is still ongoing, and the results can still be improved.
Quantum programming paradigms: boson sampling vs qubit gates

Quantum technology and its application in quantum computing has been gaining relevance and making incredible strides in the achievement of practical implementations of this new form of hardware acceleration. However, in order to really succeed as a computational choice in the many application fields — from science to economy—, the devices need to be programmable and within reach to non-quantum users. There are multiple approaches to solve the quantum computing riddle. This paper looks at two of them, Boson sampling and qubit gate-based quantum accelerators, and compares their implementation and programmability of a common problem: the maximum clique problem.
Unsupervised demosaicking network using the recurrent renovation and the pixel-wise guidance

Demosaicking has recently been extensively studied and has achieved significant progress via deep learning. However, all the examples are trained in a supervised manner with the attendance of full-resolution polarization images, which has been compromised for practical applications. In this Letter, we propose to recover full-resolution images from a single mosaic image by combining the deep image prior with the polarization prior to capture the image-specific statistics and further guide the optimization. Specifically, we employ the pixel-wise weight on the intermediate outputs being generated by the recurrent strategy to self-supervise the learning, and the missing pixels can be iteratively and individually recovered. Experimental results on long-wave infrared (LWIR) polarization images demonstrate the effectiveness of the proposed method in terms of both quantitative measurement and visual quality.
TaskFlow

Datacenters need to become more power efficient for political and climate reasons. In this work, we introduce an idea for the community to further explore. We embed the idea in TaskFlow: a makespan conservative, energy-aware task placement policy for workflow scheduling. Using static, rough numbers and simulation, we obtain energy savings between [4.24, 47.00]% and [0.1, 13.6]%, respectively. We also present some pitfalls that should be investigated further, notably starvation of large tasks when using TaskFlow.
Neuroevolution of recurrent architectures on control tasks

Modern artificial intelligence works typically train the parameters of fixed-sized deep neural networks using gradient-based optimization techniques. Simple evolutionary algorithms have recently been shown to also be capable of optimizing deep neural network parameters, at times matching the performance of gradient-based techniques, e.g. in reinforcement learning settings. In addition to optimizing network parameters, many evolutionary computation techniques are also capable of progressively constructing network architectures. However, constructing network architectures from elementary evolution rules has not yet been shown to scale to modern reinforcement learning benchmarks. In this paper we therefore propose a new approach in which the architectures of recurrent neural networks dynamically evolve according to a small set of mutation rules. We implement a massively parallel evolutionary algorithm and run experiments on all 19 OpenAI Gym state-based reinforcement learning control tasks. We find that in the majority of cases, dynamic agents match or exceed the performance of gradient-based agents while utilizing orders of magnitude fewer parameters. We believe our work to open avenues for real-life applications where network compactness and autonomous design are of critical importance. We provide our source code, final model checkpoints and full results at github.com/MaximilienLC/nra.
On the fitness landscapes of interdependency models in the travelling thief problem

Since its inception in 2013, the Travelling Thief Problem (TTP) has been widely studied as an example of problems with multiple interconnected sub-problems. The dependency in this model arises when tying the travelling time of the "thief" to the weight of the knapsack. However, other forms of dependency as well as combinations of dependencies should be considered for investigation, as they are often found in complex real-world problems. Our goal is to study the impact of different forms of dependency in the TTP using a simple local search algorithm. To achieve this, we use Local Optima Networks, a technique for analysing the fitness landscape.
AVERT: An Automatic Verilog Testbench Generation Tool for Grammatical Evolution

With the ever-growing complexity of digital circuits, the appeal of using Machine Learning in digital circuit design has grown significantly. Over the last 25 years, Evolutionary Algorithms have been used to create digital circuits in a field known as Evolutionary Hardware (EH). Using Hardware Description Languages (HDLs) in EH is an attractive prospect as the evolved circuits would be easy to understand and simple to integrate with existing IP designs. However, one limitation of this method is that a testbench must be created by hand to check the Device Under Testing's (DUT) functionality, increasing the preparatory work required to evolve a new design. This paper presents AVERT, an Automatic Verilog Testbench Generation Tool for Grammatical Evolution. By specifying the connection ports of the DUT and the test vectors (test cases) in a standard test vector file, we describe a system that allows a testbench to be generated for sequential and combinational designs written in both structural and behavioural modelling styles. We take three problems from the literature and successfully evolve candidate solutions for each problem. Experimental results show a significant improvement in simulation performance by evaluating multiple DUTs per testbench compared to a single DUT per testbench.
AI Aero Science Model To Predict Security And To Improve The Fault Space System

Over the past two years, information sharing and collaboration between pilots, supervisors, developers and maintenance workers has led to significant improvements in aviation safety. As the volume of information sharing increases, it has become necessary to integrate intelligence (AI) principles and to learn in-depth processes, data-driven structures to predict behavior, search patterns, and discover hidden secrets. In-depth learning systems can be used in the near future. For example, face security systems currently only use surveillance data to predict and report inconsistencies and conflicts, which may result in alarm delays or malfunction. AI, especially machine learning (ML), still had a long way to go before it was widely used in the application field, but was already integrated into new technologies. One area that Al explores is in the field of satellite operations, particularly to support the sale of large satellite stars, which include relative positions, communications, life management, etc.
Open Relation Extraction via Query-Based Span Prediction

Open relation extraction (ORE) aims to assign semantic relationships between arguments, essential to the automatic construction of knowledge graphs. The previous methods either depend on external NLP tools (e.g., PoS-taggers) and language-specific relation formations, or suffer from inherent problems in sequence representations, thus leading to unsatisfactory extraction in diverse languages and domains. To address the above problems, we propose a Query-based Open Relation Extractor (QORE). QORE utilizes a Transformers-based language model to derive a representation of the interaction between arguments and context, and can process multilingual texts effectively. Extensive experiments are conducted on seven datasets covering four languages, showing that QORE models significantly outperform conventional rule-based systems and the state-of-the-art method LOREM [6]. Regarding the practical challenges [1] of Corpus Heterogeneity and Automation, our evaluations illustrate that QORE models show excellent zero-shot domain transferability and few-shot learning ability.
Extraction of Characteristic Breast Dimensions of North Vietnamese Girl-Students Using Random Forest Algorithm

Women’s breasts are measured with many different dimensions, which are quite diverse and complicated depending on the purpose of use. The aim of this study is to select the typical dimensions of girl-student breasts to obtain important dimensions for breast classification, thereby reducing noise, reducing costs, and contributing to building a basis for improving the sizing system, designing, and choosing the right woman’s bras. 460 girl students in Northern Vietnam between the ages of 18 and 24 with a body mass index of 14.5–24.3 kg/m2 are selected in this study. Their breasts were 3D scanned to identify 3 parameters of the body (height, weight and body mass index) and 18 breast parameters. The Principal Component Analysis method, Random Forest, and Learning Vector Quantization algorithms are applied to extract the characteristic breast dimensions. The results show that the accuracy reaches 0.9615, the sensitivity reaches 0.989 and the specificity reaches 0.977 with the Random Forest method. The most important dimensions for breast classification are breast volume, the difference between bust and underbust circumference, outer breast, bust, upper bust, inner breast, and bust circumference. Height, weight, body mass index, prolapse, and thoracic arch are the least important parameters when classifying girl-student breasts.
WikidataComplete – An Easy-to-Use Method for Rapid Validation of Text-Extracted New Facts Applied to the Wikidata Knowledge Graph

Wikidata is one of the most used knowledge graphs (KG) and it plays a vital role in the Semantic Web community. Many industries have integrated Wikidata into solutions dedicated to intelligent assistance, information retrieval, or knowledge integration. As one of the biggest KGs, Wikidata receives millions of edits every year. However, it is still far from complete. Generally, a natural workflow to ingest a new fact into Wikidata starts by searching the relative information in a free-text document collection (e.g., Wikipedia articles). This information is used to create a new fact (or update a fact) on Wikidata. The entire process is labor-intensive. In this paper, we present WikidataComplete, a plugin that facilitates Wikidata editors to contribute to the completion of the Wikidata KG. For the implementation of WikidataComplete, we integrated the latest question-answering (QA) technologies in order to extract the new facts. We embed our fact-ingestion workflow directly on the Wikidata entity page to make the insertion of facts smooth and efficient. Ultimately, WikidataComplete can be a handy tool for Wikidata contributors, and it has the potential to complete millions of missing facts in the Wikidata KG.
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes

This work proposes a real-time algorithm for reconstructing 3D human poses in crowded scenes from multiple calibrated views. The key challenge of this problem is to efficiently match 2D observations across multiple views. Previous methods perform multi-view matching either at the full-body level, which is sensitive to 2D pose estimation error, or at the part level, which ignores 2D constraints between different types of body parts in the same view. Instead, our approach reasons about all plausible skeleton proposals during multi-view matching, where each skeleton may consist of an arbitrary number of parts instead of being a whole body or a single part. To this end, we formulate the multi-view matching problem as mode seeking in the space of skeleton proposals and develop an efficient algorithm named QuickPose to solve the problem, which enables real-time motion capture in crowded scenes. Experiments show that the proposed algorithm achieves the state-of-the-art performance in terms of both speed and accuracy on public datasets.
Immersive virtual reality

This chapter intends to be a guide to practitioners and researchers interested in the use of immersive virtual technology in the context of language teaching and, more specifically, virtual exchange. To that end, three main aspects will be addressed. Firstly, the relationship between virtual reality and language education will be explored, with a focus on research studies conducted in non-immersive environments, particularly in virtual worlds. Secondly, Immersive virtual reality (IVR) will be presented as a technology linked to complex theoretical underpinnings and offering an array of different devices. The final section provides a detailed review of the research conducted to date addressing IVR and language learning, as well as its affordances for virtual exchange.
Emergency Remote Education: satisfaction and competences of teachers

ABSTRACT The study offers an early vision of an incidental non-probability sample of 333 teachers from twelve Ibero-American countries, in connection with the satisfaction with Emergency Remote Education and competences perceived to address it. A validated questionnaire comprised of 28 items was applied. The nonparametric Pearson Chi-Square test is used to determine the association between variables and Cramer’s V to establish their intensity. The competences perceived by teachers are highly statistically associated with the competences observed in their students for their work online, as well as teacher satisfaction with the ERE associated with student response. In conclusion, initial concerns of teachers are connected more to students than to tools or resources.
PROSPECTS FOR IMPROVING ORGANIZATIONAL BEHAVIOR IN HIGHER EDUCATION

The teaching staff of institutions of higher education performs an important task of training relevant personnel in various fields, which always makes it important to improve working conditions for these purposes and to minimize obstacles. In higher education institutions, deficiencies often arise, both in its management as a whole, and in the organization of work and relationships in faculties. To correct these shortcomings, an appropriate correction of the legal framework and norms of organizational behavior is required. Ultimately, this will contribute to improving labor efficiency.