Reviewerly/exHarmony huggingface dataset:
Justification of the dual-use of aerospace geomonitoring of the offshore shelf: Exploration of hydrocarbon deposits and “highlighting” the marine situation

Hydrophysical processes in the surface water layer over the hydrocarbon deposits or moving submerged objects that form its hydrothermodynamic regime are very similar. The purpose of this work is to justify the dual-use of the aerospace geomonitoring methodology of the marine shelf using the relevant informative features, in particular, by highlighting the state of the water area showing zones of temperature anomalies of various origin in the marine shelf to search for hydrocarbon deposits and to control the unauthorized moving submerged objects access. As a result of the retrospective analysis and processing of archive materials of the process of formation of temperature anomalies of natural and technogenic origin, several dozens of different Haralik parameters were checked. The five most informative of them were selected for further use: Contrast, Sum Variance, Sum Entropy, Entropy, Difference Entropy. To achieve these goals, studies were carried out in three stages. Firstly, the temperature anomalies of natural origin on the sea surface, which occur in the presence of hydrocarbon deposits, were obtained using a spacecraft. Secondly, the temperature anomalies of the technogenic origin, which are created by mobile submerged objects in the aquatic environment, were obtained. The above was carried out based on data obtained during a model experiment in the research pool. Thirdly, archived data of changes in the temperature of the water surface in the infrared region of the spectrum after the passage of moving submersible objects were processed. Data were obtained on the sea surface using recording equipment, which was placed on the platform. As a result of the studies, informative signs of temperature anomalies that are different from the background were determined and, compared with the latter, are determined from a satellite image. This confirms the possibility of dual-use of the methodology of aerospace geomonitoring of the sea shelf.
Investments in Blockchain Information Management Systems Based on Business Angels’ Criteria

Blockchain technology bears an enormous potential to revolutionize markets and economies. Previous research studies on investments in blockchain technology were carried out as part of the paradigm of investing in speculative assets without focusing on the launch of the blockchain at the company level. Currently, there is no scientific basis for properly evaluating the investments in blockchain information management systems that help managers and investors in the decision-making processs. Authors assume that the evaluation/scoring model for investment in blockchain information systems, based on business angels' investment criteria, is the solution for this market. The study's research problem is that the vast majority of the stakeholders do not look at blockchain investments through the paradigm of investing in blockchain information management systems from the company perspective. The research aims to identify the main groups of investment criteria for blockchain investments and create a theoretical framework based on the literature overview for further analysis.
Fast and Accurate Function Evaluation with LUT over Integer-Based Fully Homomorphic Encryption

Fully homomorphic encryption (FHE), which is used to evaluate arbitrary functions in addition and multiplication operations via modular arithmetic (mod q) over ciphertext, can be applied in various privacy-preserving applications. However, big data is difficult to adopt owing to its high computational cost and the challenges associated with the efficient handling of complex functions such as log(x). To address these problems, we propose a method for handling any multi-input function using a lookup table (LUT) to replace the original calculations with array indexing operations over integer-based FHE. In this study, we extend our LUT-based method to handle any input values, i.e., including non-matched element values in the LUT, to match with a near indexed value and return an approximated output over FHE. In addition, we propose a technique for splitting the table to handle large integers for improved accuracy with only a slight increase in the execution time. For the experiments, we use the Microsoft/SEAL library, and the results show that our proposed method can evaluate a 16-bit to 16-bit function in 2.110 s and a 16-bit to 32-bit function in 2.268 s, thereby outperforming previous methods implemented via bit-wise calculation over FHE.
“Let’s Talk about Gender” – Development of a Card Deck on (Gender) Sensitivity in HCI Research and Practice Based on a Contrasting Literature Review

Abstract Funding bodies in Western societies increasingly require researchers to address gender in their proposals – though often exclusively framed around binary notions. With oppressive power structures being prevalent and persuasive, these seep into current practices of Computer Science and Human-Computer Interaction (HCI) research. However, current curricula rarely provide actors in this space with grounded guidance on gender issues tied to their inquiries. Hence, developing an increased awareness of our societal responsibility towards equity can be challenging without an appropriate starting point. Drawing on a close reading of select literature discussing gender sensitivity in HCI research, we derived practical guidance in the form of recommendations for the design, proposal, conduct and presentation of research. Based on these recommendations, we then present the design of a card deck and initial tests thereof at ditact women’s IT summer university. Our analysis offers a starting point for HCI students and interested researchers to explore questions and issues around gender and to identify how gender relates to their research. This sensitisation may aid them in further reflecting on how they might better account for gendered implications of their work.
Prepare for the Worst, Hope for the Best: Active Robust Learning On Distributions

In recent years, many learning systems have been developed for higher level forms of data, such as learning on distributions in which each example itself is a distribution. This article proposes active robust learning on distributions. In learning on distributions, there is no access to distributions themselves but rather access is through a sample drawn from a distribution. Therefore, similar to robust learning, any estimates of examples are inexact. In order to address these difficulties, we provide an upper bound on the risk of the classifier in the next stage of active learning, where the size of the labeled dataset increases. Based on this upper bound, we propose probabilistic minimax active learning (PMAL) as a general multiclass active learning method that is easy to use in many Bayesian settings, which provably selects an example with knowledge of its label minimizing the expected risk. We present an efficient approximation of the objective with a known error bound to deal with the intractability of the proposed method for active robust learning. Here, we face a nonconvex problem, which we solve by means of a related convex problem with a bound on the norm of the difference between their solutions. To utilize the information about the estimates of distributions, we propose active robust learning on the distributions method based on learning the kernel embedding of distributions by a recent Bayesian method. The experiments demonstrate the effectiveness of the resulting method on a set of synthetic and real-world distributional datasets.
Fault Aware Dynamic Resource Manager for Fault Recognition and Avoidance in Cloud

Fault tolerance (FT) schemes are intended to work on a minimized and static amount of physical resources. When a host failure occurs, the conventional FT frequently proceeds with the execution on the accessible working hosts.... | Find, read and cite all the research you need on Tech Science Press
Instrumental Digital Didactics of Physics Study in the Aspect of M-learning

The experience of distance and combined learning during the pandemic COVID-19 has demonstrated the complexity of organizing experimental research in natural sciences, especially physics. In conditions of limited access to full-sized laboratory equipment, smartphones have significant potential: they contain sensitive elements (distance, lighting, magnetic field sensors, accelerometer, GPS and others). Their use in the educational process of teaching physics is effective for the formation of key and subject skills of students in terms of the digital age. The use of digital means of obtaining and processing experimental data during the educational process in natural sciences enriches pedagogical science with methods that can be described by the general concept of «instrumental digital didactics» (IDD). The authors explain a definition of IDD, identify the types and functions of IDD tools. It is shown that the use of smartphone sensors for educational research in physics demonstrates the intersection of the conceptual fields of IDD and m-learning. Foreign and Ukrainian experience of the use of information technology capabilities of smartphones at the physics study is analyzed; in this context, the classification of mobile applications, in particular, designed to process data from sensitive elements is considered. An anonymous survey in June and December 2020 concerning the distance learning experience revealed, on the one hand, the presence of earlier experience and interest of students in use of smartphone sensors for physics research, on the other hand, the need for clear instructions. The methods proposed by the authors, which are demonstrated on the resource STEM-laboratory MANLab (stemua.science), do not require an experiment in a laboratory and are based on the use of only sensitive elements of a smartphone and PC. The design features of sensors can also be studied during physics lessons. The author’s technological map of use of the mobile application Phyphox for laboratory work on the topic «Mechanics» is proposed.
Coping with Diversity - A System for End-users to Customize Web User Interfaces

To address diverse interaction needs of heterogeneous users' groups, user interfaces must be flexible to accommodate for customization that are specific to each user profile. Although, existing web interfaces provide some flexibility, some problems still remain: a) manual adjustments carried out by end users are required for each web application; b) the flexibility provided by current web interfaces is insufficient to address diverse interaction needs of various users' profiles and c) few users are aware about such options to customize the presentation of web interfaces. To contribute to the customization of user interface according to the needs of diverse users, in this work we asses the suitability of a tool that customize web interfaces based on the needs and preferences of end users. UIFlex is a web-based browser plugin that enables users to define their interaction profile. In this task, users are supported by fifteen web-based design rules that were extracted from the literature and the knowledge of authorities. To customize the presentation of web interfaces, UIFlex relies on a set of rules defined for each individual user and "injects" JavaScript codes, Cascading Style Sheets (CSS) and in some cases HyperText Markup Language (HTML) codes in any page that follows W3C standards. UIFlex was evaluated by 104 users of diverse interaction profiles. The results obtained are promising and suggest that the solution improves the perception that the interactive system performs as desired by users.
A Survey on Image Captioning datasets and Evaluation Metrics

Abstract In the task of image captioning, a natural language explanation is generated for a given image. It uses the subfields of artificial intelligence: computer vision and language generation. Convolutional Neural Network (CNN) is generally applied to capture image features and language processing models such as Recurrent Neural Network for sentence generation. In this paper, various datasets and evaluation metrics which are useful for image captioning task are discussed. Also, the datasets and evaluation metrics applied by the state-of-the-art image captioning models is summarized.
THINK: A novel conversation model for generating grammatically correct and coherent responses

Many existing conversation models that are based on the encoder–decoder framework incorporate complex encoders. These powerful encoders serve to enrich the context vectors, so that the generated responses are more diverse and informative. However, these approaches face two potential challenges. First, the high complexity of the encoder means relative simplicity of the decoder. There is a danger that the decoder becomes too simple to effectively capture previously generated information. As a result, the decoder may produce duplicated and self-contradicting responses. Second, by having a complex encoder, the model may generate incoherent responses because the complex context vectors may deviate from the true semantics of context. In this work, we propose a conversation model named “THINK” (Teamwork generation Hover around Impressive Noticeable Keywords) that is equipped with a complex decoder to avoid generating duplicated and self-contradicting responses. The model also simplifies the context vectors and increases the coherence of generated responses in a reasonable way. For this model, we propose Teamwork generation framework and Semantics extractor. Compared with other baselines, both automatic and human evaluation showed the advantages of our model.
Abstractive Text Summarization Approaches with Analysis of Evaluation Techniques

In today’s world where all the information is available at our fingertips, it is becoming more and more difficult to retrieve vital information from large documents without reading the whole text. Large textual documents require a great deal of time and energy to understand and extract the key components from the text. In such a case, summarized versions of these documents provide a great deal of flexibility in understanding the context and important points of the text. In our work, we have attempted to prepare a baseline machine learning model to summarize textual documents, and have worked with various methodologies. The summarization system takes the raw text as an input and produces the predicted summary as an output. We have also used various evaluation metrics for the analysis of the predicted summary. Both extractive and abstractive based text summarization have been described and experimented with. We have also verified this baseline system on three different evaluation metrics i.e. BLEU, ROUGE, and a textual entailment method. We have also done an in-depth discussion of the three evaluation techniques used, and have systematically proved the advantages of using a semantic-based evaluation technique to calculate the overall summarization score of a text document.
Fragile Complexity of Adaptive Algorithms

The fragile complexity of a comparison-based algorithm is f(n) if each input element participates in O(f(n)) comparisons. In this paper, we explore the fragile complexity of algorithms adaptive to various restrictions on the input, i.e., algorithms with a fragile complexity parameterized by a quantity other than the input size n. We show that searching for the predecessor in a sorted array has fragile complexity \(\varTheta (\log k)\), where k is the rank of the query element, both in a randomized and a deterministic setting. For predecessor searches, we also show how to optimally reduce the amortized fragile complexity of the elements in the array. We also prove the following results: Selecting the kth smallest element has expected fragile complexity \(O(\log \log k)\) for the element selected. Deterministically finding the minimum element has fragile complexity \(\varTheta (\log (\mathrm {Inv}))\) and \(\varTheta (\log (\mathrm {Runs}))\), where \(\mathrm {Inv}\) is the number of inversions in a sequence and \(\mathrm {Runs}\) is the number of increasing runs in a sequence. Deterministically finding the median has fragile complexity \(O(\log (\mathrm {Runs}) + \log \log n)\) and \(\varTheta (\log (\mathrm {Inv}))\). Deterministic sorting has fragile complexity \(\varTheta (\log (\mathrm {Inv}))\) but it has fragile complexity \(\varTheta (\log n)\) regardless of the number of runs.
Open–source–based Environment for Network Traffic Anomaly Detection

The paper presents an open–source–based environment for network traffic anomaly detection. The system complements the well known network security platforms as it tries to detect unexplained descriptions of the traffic. For this purpose several anomaly detection algorithms were applied. To assure better system performance, the moving history approach is also applied.
Development and evaluation of computational applications for teaching kinematics in engineering

The development of two computational applications for the teaching of kinematics in Mechanical Engineering and related academic programs is presented. An evaluation of the applications is included. The evaluation consists of tests that allow to assess the impact of the interaction of the students with the simulations of the corresponding mechanical system and the grasp of kinematics concepts. In addition, through an analysis of the results, it was possible to identify concepts that represent greater learning difficulties and help guide the instructor to provide the needed feedback to the group. Given the current circumstances imposed by the Covid-19 pandemic, these apps are a contribution for the teaching of kinematics in engineering, that can be freely used as a resource to accompany the remote instruction and to make up for the laboratory activities that cannot be carried out, yet.
An Optimization Model for Transaction Placement in Blockchain Shards

Sharding technology can be used to improve the scalability of a blockchain system. The suitable placement of transactions into shards can reduce cross-shard ones, so as to improve system throughput and reduce delay. An optimization model for transaction placement to blockchain shards is proposed. Based on time analysis and a transaction flow graph, the fitness is computed to decide which shard is the most suitable one to place a transaction. The experimental results show that the proposed model can effectively reduce cross-shard transactions and reduce transaction delay.
Real-Time Barcode Detection from Streamlined Video from a Drone

Barcode is the main tagging system for millions of products sold each day. Many efficient solutions already exist but those require a lot of human effort. In this paper, we detect barcodes from a live video stream recorded using a camera attached to a drone. The recorded video is processed using different coding techniques using Raspberry Pi as an embedded platform. Our technique helps to avoid human effort and to give satisfactory results.
Self-Attention-Based Real-Time Signal Detector for Communication Systems With Unknown Channel Models

In this letter, a deep-learning-assisted signal detector is developed for communication systems with unknown channel models. By embedding domain knowledge into a self-attention model, a novel detection unit is devised that enables both reliable estimation and fast training. Furthermore, a sliding-window structure is in combined use with the detection unit to realize real-time signal recovery. We evaluate the performance of the proposed detector using a chemical communication experimental platform, and show the superiority of our design in terms of detection accuracy as well as implementation complexity.
Cryptocurrency &amp;amp; Its Impact on Environment

Cryptocurrencies have gone a long way since their inception. While the conventional financial sector initially dismissed digital currencies as tools for crooks and speculators, the sector has made considerable progress in establishing itself as a genuine and (possibly) world-changing arena. There are still concerns about the long-term effects of widespread cryptocurrency usage. Many skeptics and environmentalists, in particular, have expressed worry about the energy consumption of cryptocurrency mining, which may result in increasing carbon emissions and climate change. The mainstreaming of cryptocurrency, as it has been dubbed, is clearly a significant event in the world of finance. It's also a significant event in the world of, well, the globe. Whether you like cryptocurrencies or oppose them, there's no denying that bitcoin and other proof-of-work blockchains use massive amounts of energy.
Research on image saliency target detection in fog and haze weather based on improved FT algorithm

Abstract The increase of air turbidity in fog and haze weather is not conducive to the accurate extraction of image feature information of important salient targets such as vehicles and pedestrians, nor is it conducive for drivers to make correct judgment on the road environment ahead of their vision. Therefore, this paper proposes an improved FT algorithm in fog and haze weather. This algorithm firstly uses the Retinex algorithm to remove fog and haze and restore color of fog and haze weather images, then uses FT algorithm to detect the saliency of the target, and finally completes the simulation based on MATLAB platform. It can be seen from the simulation results that the improved FT algorithm is significantly better than the effect of using the traditional FT algorithm, which means that the improved FT algorithm has a better role in helping drivers to detect road salient targets in fog and haze weather. And this improvement has a strong practical value for reducing the incidence of traffic accidents in fog and haze weather.
Lazy Lifelong Planning for Efficient Replanning in Graphs with Expensive Edge Evaluation

We present an incremental search algorithm, called Lifelong-GLS, which combines the vertex efficiency of Lifelong Planning A* (LPA*) and the edge efficiency of Generalized Lazy Search (GLS) for efficient replanning on dynamic graphs where edge evaluation is expensive. We use a lazily evaluated LPA* to repair the cost-to-come inconsistencies of the relevant region of the current search tree based on the previous search results, and then we restrict the expensive edge evaluations only to the current shortest subpath as in the GLS framework. The proposed algorithm is complete and correct in finding the optimal solution in the current graph, if one exists. We also show the efficiency of the proposed algorithm compared to the standard LPA* and the GLS algorithms over consecutive search episodes in a dynamic environment.
3D Face Reconstruction System From A Single Photo Based On Regression Neural Network

3D human face modeling is one of the most popular research directions in the field of computer stereo vision. On the one hand, the complex physiological structure of human face as well as various expressions and attitude changes bring great challenges to three-dimensional modeling, which makes the modeling method of human face have high research value and reference significance. On the other hand, 3D face reconstruction has a broad application prospect, and the application demand attracts more researchers to invest in it. Based on the 3DMM and 3DDFA method, this paper utilizes regression neural network to realize end-to-end face reconstruction. After that, we build a visualization program with the training model to realize face modeling based on a single face image of any pose or expression. It's a 3D face reconstruction system which has functions such as face alignment, face rotation and so on.