Reviewerly/exHarmony huggingface dataset:
A Protocol for On-Chain Tenders

Blockchains offer the possibility to describe and implement processes and interactions between actors through code that can be publicly inspected, and which can be executed in a transparent and guaranteed way, without resorting to the collaboration of third party guarantors.Although transparency, together with non-censorship and non-interruptibility, are characteristics that can endow unprecedented trustworthiness to the processes implemented, in many cases they risk clashing with the privacy requirements or, more specifically, with the need to control read data access.In this work we deal with a general category of processes and interactions of importance in different sectors such as procurement: auctions and tenders. If on the one hand it is important that the widest possible audience can be convinced of the correctness of a tender, on the other it is possible that some information must remain private, such as the value of the offersIn this work we present an auction / tender protocol which on the one hand publicly guarantees the fairness of the tender, and at the same time allows to keep bids private. We also present an implementation on Ethereum Virtual Machine (EVM) environments, with which we can make quantitative cost considerations.
An Innovative Methodology for Network Latency Detection Based on IoT Centered Blockchain Transactions

The new IoT apps will not be able to inspire people to utilize them and may ultimately lose all their potential if an interoperable and trustworthy ecosystem is not provided. IoT has its extra security difficulties such as information storage, administration, privacy concerns, and authentication. The presently deployed IoT apps have encountered various security and privacy assaults globally. Due to being less secure and low powered, the IoT devices present a simple entryway to the adversaries to obtain access to the corporate networks, leading to giving easy control over all of the data of the users. The objective of this doctorate proposed work will be to solve the security associated difficulties in multiple IoT domains like the e-commerce, vehicular ad hoc networks (VANET), mobile ad hoc networks (MANET), and Internet of Drones (IoD). The proposed study focuses on the development of a distributed framework for IoT based on blockchain. The framework includes the usage of Ethereum-based smart contracts and auction models to increase the income and QoS for both the seller and the buyer and the development of a DAG chain-based distributed framework for parking lot allocation in a network of automobiles. The suggested model includes the requirement of obtaining agreement among the nodes with probability one in such a circumstance. The suggested model demonstrates to be predictable as typical voting-based consensus protocols like Practical Byzantine Fault Tolerance (PBFT) and at the same time can accommodate a high number of nodes even in an asynchronous setting. Research on Byzantine fault-tolerant systems has been ongoing for more than four decades, and although the solutions were shown to be feasible early on, they remained unworkable for a long time. With PBFT, the first feasible solution was provided in 1999, and this sparked fresh research that has resulted in unique applications that are still being developed today employing this technology. Despite the fact that the safety and liveness properties of PBFT-type protocols have been thoroughly investigated, when it comes to practical performance, only empirical results—often obtained in artificial environments—are known, and imperfections in the communication channels are not explicitly considered. It is our goal in this paper to propose the first performance model for PBFT that takes into account the effect of unreliable channels as well as the usage of alternative transport protocols across those channels. We also performed a large number of simulations to test the model and acquire a better understanding of the influence of different deployment factors on the total transaction timeframe.
Secure Images Transmission Using a Three-Dimensional S-Box-Based Encryption Algorithm

It is known that information security and privacy revolve around three principal keys: confidentiality, integrity, and availability. Depending on the environment, application, context, or use case, one of these principles may prevail over the others. For a medical application, for example, confidentiality is essential. Therefore, any imaging transferred electronically must be encrypted to prevent unauthorized persons from seeing its contents. This paper exhibits a new efficient ciphering algorithm for the security of transmitted images in public networks such as the Internet or archived in cloud computing based on a three-dimensional S-box. The suggested method encrypts and decrypts the image block by block. Each block is 8x8 pixels. The encrypted data are obtained by replacing the original data and the key by its value in S-box. Substitution boxes (S-box) are generated using a proposed algorithm that produces a 3D S-box using the random function and permutation. The security analyses show that the presented algorithm has an important security level. The runtime of the proposed algorithms is fast compared to others image encryption algorithms.
What questions do developers ask about Julia?

The programming language Julia is designed to solve the 'two language problem', where developers who write scientific software can achieve desired performance, without sacrificing productivity. Since its inception in 2012, developers who have been using other programming languages have transitioned to Julia. A systematic investigation of the questions that developers ask about Julia can help in understanding the challenges that developers face while using Julia. Such understanding can be helpful (i) for toolsmiths who can construct tools so that developers can maximize their experience of using Julia, and (ii) for Julia language maintainers with empirical evidence on areas to improve the language as well as the Julia ecosystem. We conduct an empirical study with 3,093 Stack Overflow posts where we identify 13 categories of questions related to Julia-based software development. We observe developers to ask about a diverse set of topics, such as GC, Julia's garbage collector, JuMP, a domain-specific language constructed using Julia, and symbols, a metaprogramming utility in Julia. Based on our emerging results, we recommend enhancing support for developers with Julia-based tools and techniques for cross language transfer, type-related assistance, and package resolution.
An Improved Adaptive Human Learning Optimization Algorithm with Reasoning Learning

Human Learning Optimization (HLO) is a simple yet highly efficient metaheuristic developed based on a simplified human learning model. To further extend the research of HLO, the social reasoning learning operator (SRLO) is introduced. However, the learning ability of social imitating learning operator (SILO) and SRLO is constant in the process of iterations, which is not true in a real human population as humans often adopt dynamic learning strategies to solve the problem. Inspired by this fact, an improved adaptive human learning optimization algorithm with reasoning learning (AHLORL) is proposed to enhance the global search ability, in which an adaptive ps strategy is carefully designed to sufficiently motivate the roles of SILO and SRLO and dynamically adjust the learning efficiency of the algorithm at different stages of iterations. Then, a comprehensive parameter study is performed to explain why the proposed adaptive strategy can exploit the optimization ability of SILO and SRLO effectively. Finally, the AHLORL is applied to solve the CEC 15 benchmark functions as well as multidimensional knapsack problems (MKPs), and its performance is compared with the previous HLO variants as well as the other recent metaheuristics. The experimental results show that the proposed AHLORL outperforms the other algorithms in terms of search accuracy and scalability.
CAIBAL

Abstract---Clustering is a relevant research area in Machine Learning. The purpose of clustering is to group the objects in a dataset so that each group is made up of similar ones, which have characteristics that make them groupable, and between different groups a degree of dissimilarity is essential. The process of interpretation of the groups is fundamental to the pratical applicability of clustering. Automatic labeling, as defined in this research, results in tuples composed of attributes and their respective ranges of values. Each cluster must have a number of tuples capable of providing a unique identification for all objects, so that they are distinguishable from each other by different representative attributes or ranges of different values for the same attribute. This paper presents an unsupervised clusters labeling method that employs the CAIM (Class-Attribute Interdependency Maximization) discretization algorithm in order to find representative value ranges in the attributes that will be relevant for clusters interpretation. The model in this research sought to obtain a method that mitigates the limitations observed in other works that proposed automatic labeling of clusters. The tests carried out with 03 databases - Seeds, Iris and Glass - result in an average accuracy of the suggested labels of 97.20%. The labels suggested are made up of few attributes, compared with previous labelers, and in most cases one attribute is sufficient to define it.
Vehicle Re-Identification System Based on Appearance Features

Aiming at the low recognition accuracy caused by the problems of angle, illumination, and occlusion in vehicle re-identification based on deep learning, a vehicle re-identification method based on multibranch network feature extraction and two-stage retrieval feature is proposed. The multibranch feature extraction module uses ResNet-50 as the backbone network to extract the vehicle’s attribute features and apparent features, respectively, and uses the attribute features for rough retrieval. On this basis, the attribute features and apparent features are fused for fine retrieval. Through experiments, the accuracy of vehicle re-identification on Veri-776 data set and VehicleID datasets is significantly improved. In addition, based on the improved algorithm, this paper designs and develops a vehicle re-identification system, which realizes the functions of inputting file directory, selecting target image, and querying result image, and provides a visual technical scheme for vehicle re-identification and retrieval in the real scene.
Motion Track Enhancement Method of Sports Video Image Based on OTSU Algorithm

Current sports video image tracking methods cannot get the average gray information of pixels, which leads to the low segmentation accuracy of target trajectory. Therefore, this paper proposes a method of enhancing the motion track of sports video object based on OTSU algorithm. The OTSU algorithm is introduced to segment the unsupervised image automatically based on threshold calculation. Combined with 3D histogram, the OTSU algorithm is optimized to overcome the influence of noise and nonuniform illumination and to improve the segmentation performance. Using the optimized OTSU algorithm, the preprocessing of sports video image is realized by gray level transform, median filter, and binarization. Based on this, the object motion model of sports video image is constructed, and the edge information of sports video image is extracted. Using binocular stereo vision technology, according to the image target motion model and the current space coordinates, calculate the motion track, and obtain the parameters of sports video image. Experimental results show that the proposed method can segment the target accurately in a shorter time, and the enhancement effect of target trajectory is obvious, and the error is small.
Metaheuristic Deep Learning-Driven Wireless Communication Security Adaptation Using Multivariate Analysis of Variance (MANOVA)

The implementation of accurate models to improve access technologies, communication transmission, and network slicing is anticipated to play a big part in the edge computing approach, as the demands and needs of individuals are quickly evolving. Deep learning models have tended to deliver more benefits in a wide range of applications; it also assists data providers in demonstrating considerable improvements in tackling complex real-world challenges. While the integration of connected wireless networks and deep learning is still in its infancy, wireless communication networks are increasingly focused on sophisticated technologies to meet the current and future needs of end users. Based on the theoretical and practical aspect that ranges from the basic aspect to future applications of wireless communication, this study is intended in addressing the opportunities of adopting metaheuristic deep learning-driven wireless communication. The researchers intend to apply descriptive design to the study as this enables in understanding the critical aspects of the study in an elaborate manner. The authors use both a primary data sources and a secondary data sources for performing the analysis. The secondary data source is sourced to understand the application of deep learning in the wireless communication area, and primary research is used to gather the data from the respondents to test the hypothesis and provide conclusions based on the analyses. The scope of this work is to utilize a quantitative model to undertake an analysis on evaluating the key parameters in the application of deep learning in wireless communication, which will allow for critical analysis and interpretation based on the findings.
DIC measurement for large-scale structures based on adaptive warping image stitching

As a representative method of optical non-interference measurement, digital image correlation (DIC) technology is a non-contact optical mechanics method that can measure the displacement and deformation of the whole field. However, when the measurement range of the field is too large, the existing DIC method cannot measure the full-field strain accurately, which limits the application of the DIC measurement in the case of a large size and wide-field view. To address this issue, a DIC measurement method for large-scale structures based on adaptive warping image stitching is proposed in this paper. First, multiple adjacent high-resolution images are collected at different locations of large-scale structures. Secondly, the collected images are stitched by applying the adaptive warping image stitching algorithm to obtain a panoramic image. Finally, the DIC algorithm is applied to solve the whole deformation field. In the experiments, we first verify the feasibility of the proposed method for image matching and fusion through the numerical simulation of a rigid body translation experiment. Then the accuracy and robustness of the proposed method in practical application are verified by rigid body translation and a three-point bending experiment. The experimental results demonstrate that the measurement range of DIC is improved significantly with the adaptive warping image stitching algorithm.
Mobile User Interaction Design Patterns: A Systematic Mapping Study

Interaction design patterns have evolved as a resource that facilitates documentation and the reuse of proven solutions. They provide a structured and understandable mechanism for what to do in the design. Mobile devices have characteristics, configurations, and restrictions that make the construction of their interfaces full of particularities to this environment, and problems that are often common to designers and developers. This study presented a systematic mapping of the state-of-the-art regarding interaction design patterns for mobile devices. A total of 23 studies that include articles and books met the selection criteria in this mapping, examining relevant scientific databases and books that were cited in relevant articles. As a main result, 336 patterns were compiled, with 261 of these problems and solutions being dissimilar from each other. The paper describes patterns in 18 categories covering different interaction aspects. Pattern structural elements with mentions in more than five papers included: Name, Solution, Problem, Context, Examples, Related Patterns, Forces, Consequences and Figure. Four studies reported empirical evaluation of the patterns with a limited number of users. The paper contributed with a categorization of existing patterns and the challenges for uniformization of structure and empirical evidence with user evaluation.
Plan-Similarity Based Heuristics for Goal Recognition

Plan and goal recognition are important tasks in constructing smart assistants and can be used to support activity recognition. However, all current approaches have the disadvantage of either being slow and inefficient regarding both online computation time and prior manual modelling effort tackled by human experts due to using domains which makes them infeasible for real-life settings; or requiring large amounts of annotated observation sequences which is not realistic as data annotation is expensive. This paper introduces a new approach requiring neither annotated observation data nor domains but only exemplary plans for each possible goal. It is based on plan-similarity based heuristics which makes it fast, yet is still able to achieve good results. This leads to new possibilities regarding the applicability in real-life settings and increases the usefulness for supporting activity recognition.
Impact of Sentence Representation Matching in Neural Machine Translation

Most neural machine translation models are implemented as a conditional language model framework composed of encoder and decoder models. This framework learns complex and long-distant dependencies, but its deep structure causes inefficiency in training. Matching vector representations of source and target sentences improves the inefficiency by shortening the depth from parameters to costs and generalizes NMTs with a different perspective to cross-entropy loss. In this paper, we propose matching methods to derive the cost based on constant word-embedding vectors of source and target sentences. To find the best method, we analyze the impact of the methods with varying structures, distance metrics, and model capacity in a French to English translation task. An optimally configured method is applied to English translation tasks from and to French, Spanish, and German. In the tasks, the method showed performance improvement by 3.23 BLEU at maximum, with an improvement of 0.71 on average. We evaluated the robustness of this method to various embedding distributions and models, such as conventional gated structures and transformer networks, and empirical results showed that it has a higher chance to improve performance in those models.
Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomes

Vast quantities of electronic patient medical data are currently being collated and processed in large federated data repositories. For instance, TriNetX, Inc., a global health research network, has access to more than 300 million patients, sourced from healthcare organizations, biopharmaceutical companies, and contract research organizations. As such, pipelines that are able to algorithmically extract huge quantities of patient data from multiple modalities present opportunities to leverage machine learning and deep learning approaches with the possibility of generating actionable insight. In this work, we present a modular, semi-automated end-to-end machine and deep learning pipeline designed to interface with a federated network of structured patient data. This proof-of-concept pipeline is disease-agnostic, scalable, and requires little domain expertise and manual feature engineering in order to quickly produce results for the case of a user-defined binary outcome event. We demonstrate the pipeline's efficacy with three different disease workflows, with high discriminatory power achieved in all cases.
Practical and efficient out-of-domain detection with adversarial learning

Detecting Out-of-Domain (OOD) questions is important in real-world applications and is the subject of active research. Traditional In-Domain (IND) classifier-based methods can easily distinguish OOD questions if they are very different from IND ones, but not otherwise. Although large-scale network-based approaches have been used to solve this problem and obtained better results, the computational costs associated with the training and prediction processes are usually very high. In addition, these methods did not seek to leverage any unlabeled data. To address these issues, adversarial learning methods have been widely used in the field of image processing. At the same time, there are currently only a few practices in the domain of text classification, and their performance remains suboptimal. In this paper, we propose a novel practical and efficient OOD question detection framework using adversarial learning. We conducted a range of experiments on three open datasets. The experimental results demonstrate the advantages of our framework compared to baseline methods.
FAirWrite - Movement Reconstruction and Recognition Using a Low-cost IMU

We present a system for detecting air-writing motions conducted with a finger worn IMU. The vision is that a person could casually leave virtual notes in the environment by air-writing short notes when passing by a location. The challenge that we address is that such casual finger writing, in particular without a visual reference feedback (the user doing the air writing does not see the result), tends to produce extremely noisy trajectories which are inherently challenging to decode. The main contribution of this work is the development and systematic evaluation of a system for reconstructing such noisy trajectories and mapping them onto digits and characters. We describe the FAirWrite system&#x2019;s architecture, a Graphical User Interface (GUI) to interact with it, and the recognition and classification process to achieve the state-of-the-art results.
Template-Based Question Answering System Over the Semantic Web

A question answering system is the most promising way of retrieving data from the available knowledge base to the end-users to get the appropriate result for their questions. Many question answering systems convert the questions into triples which are mapped to the knowledge base from which the answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed that classifies the question types and finds appropriate SPARQL query template for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD 8 dataset presents good performance and can help users to find answers to their questions.
An Examination of Special Education Teachers’ Digital Practices

The aim of this study is to understand how mobile devices are being used to support students’ learning (i.e., mobile learning) in specialist schools, and in specialist support units within mainstream schools. A validated survey instrument is used to examine these practices through the lens of a sociocultural digital framework that highlights distinctive mobile learning approaches. One hundred and twenty-six teachers responded to the survey. The findings provide a nuanced understanding of teachers’ current digital pedagogical approaches, and show potential benefits for students, including increased agency. Possible directions for the development of special education teachers’ digital practices are also provided.