Reviewerly/exHarmony huggingface dataset:
Inter ‘Face-To-Face’: Remote Co-creation for an Artifact

In times of uncertainty regarding the viability of collaborative and face-to-face research work with the Portuguese footwear industry, due to the circumstances of physical distance such as the one we are experiencing at present, this article aims to demonstrate a two-stage methodological strategy for a co-creation process via remote, as well as its limitations and future work. In this context, two questions arise that we intend to answer: How can co-creation work be carried out remotely with participants from different organizations? What are the advantages and disadvantages of this remote co-creation work? To answer these questions, in a first phase there is a study based on the literature review, comparing studies by authors on the advantages and disadvantages of virtual meetings compared to face-to-face meetings; and, in a second phase, emphasized by the experimentation of a co-creation work for an artifact, with master’s degree students in Image Design, were used Zoom audiovisual interface and collaborative online platform Miro. It’s argued, therefore, that the virtual encounters of a co-creation process for an artifact can, on the one hand, provide geographical and temporal advantages to the participants, on the other hand, provide technical difficulties that can create expected losses of efficiency. Since this whole process is primarily motivated by the current circumstances of confinement, due to the pandemic COVID-19, it is understood that the advantages of co-creation via remote will continue in the future, even in more favorable contexts.
Dynamic Document Localization for Efficient Mining

Annotation is the process of adding notes about an entity such as documents, attributes, data repositories and data spaces to make them more visible and expressive. In probabilistic data models, it is used for generating values for attributes which could be used for generating a range of queries which are matched with a database schema. The proposed fuzzy document localization model (FDLM) lists out the top-k attributes by deriving a monotone fuzzy rank function based on query value $$Q_\mathrm{val}$$ and content value $$C_\mathrm{val}$$ . The newly arrived documents are processed with the annotated documents which are conditionally modeled with ground truth attributes in a dynamic document categorization process. The semantic matches of attributes are identified by a pre-processed conceptualization framework this in turn increases the cardinality of result set. The system is biased with a biasing parameter $$\beta $$ in order to maintain a balance with workload-based query value and database-oriented content value to set a selection bound over the range of accurate and approximate matches.Sijin, P. Champa, H. N.
The Pursuit of Usefulness: On the Value of Smart Contracts for Consumers and Merchants

The public debate about smart contracts, meant as self-help remedies grounded on distributed ledger technology, is filled with alarms and high expectations. They have been praised by the tech community as infallible software able to carry out the whole contractual cycle, from formation to enforcement. Conversely, several legal scholars have raised concerns regarding both smart contracts inability to reflect relational aspects of contract governance and the augmented complexity generated by the translation of an agreement into computer code. By building on the extant literature and avoiding any premature enthusiasm, this article focuses the discussion on the potential areas which could effectively benefit from smart contracts implementation. Firstly, it argues that smart contracts might be a viable tool to tackle effectively consumers inertia in triggering and enforcing their kinds of rights which are standardized and easily verifiable. Secondly, smart contracts have the potential to foster commercial relationships by lowering down transaction costs arising from lack of trust between merchants. Thus, smart contracts are likely to provide better alternatives to traditional tools of business practice, such as letters of credit and escrow agreements. Lastly, the article points out that, while businesses already have commercial incentives to implement smart contracts on their own, when it comes to consumer protection, regulators shall take the lead by testing, through regulatory sand-boxes, smart contracts potential.
Incorporating Complete Syntactical Knowledge for Spoken Language Understanding

Spoken Language Processing (SLU) is important in task-oriented dialog systems. Intent detection and slot filling are two significant tasks of SLU. State-of-the-art methods for SLU jointly solve these two tasks in an end-to-end fashion using pre-trained language models like BERT. However, existing methods ignore the syntax knowledge and long-range word dependencies, which are essential supplements for semantic models. In this paper, we utilize the Graph Convolutional Networks (GCNs) and dependency tree to incorporate the syntactical knowledge. Meanwhile, we propose a novel gate mechanism to model the label of the dependency arcs. Therefore, the labels and geometric connection of dependency tree are both encoded. The proposed method can adaptively attach a weight on each dependency arc based on dependency types and word contexts, which avoids encoding redundant features. Extensive experimental results show that our model outperforms strong baselines.
Operational Activities in the Field of Cybersecurity

Abstract The increase in cybercrime and the importance of cyberspace for the preparatory stage of crimes led to the redefinition of operational and exploratory activities. It has become desirable to move away from the concept of targeted surveillance and towards prevention systems used on a massive scale. This trend is manifested in using technological achievements like hacking software for legal purposes. The goal of this article is to define the concept of state-run on-line remote search of information systems, including the use of breakthrough security software like spyware, in the context of procedural- and pre-trial guarantees, the presumption of innocence and protection of privacy. The article attempts to verify the following research hypothesis: that the creation of new appropriate guarantees (institutional, substantive and procedural) for the digital environment is required since the existing safeguards appropriate to the actions in the real world cannot be applied by analogy in the digital environment without influencing the effectiveness of the measures. The legal framework for operational activities in the digital world must take into account the requirement of subsidiarity and necessity of maintaining a balance between effective criminal law and respect for privacy.
Pedestrian fusion tracking method based on multimodal information com-plementation

A pedestrian fusion tracking method with complementary multimodal information is proposed, and a fusion decision tracking model with detection followed by fusion and then tracking is established. The detection module uses a modified CenterNet network with a richer feature information backbone network and a lightweight prediction module, and the scene data is collected to train multiple detectors for multiple modalities. A decision process based on the confidence of detection results and feature similarity is proposed to achieve the fusion of multimodal detection results, and the fused results are fed to the tracker to achieve continuous pedestrian tracking. The results show that the proposed fusion tracking model can complement each other’s multi-modal information and provide better and more robust tracking results than the single-modal tracker for continuous tracking in multiple scenes.
Hybrid Recommender System for Detection of Rare Cases Applied to Pulsar Candidate Selection

Detection of extremely rare cases is a challenging problem for most machine learning algorithms, especially if class overlapping is present. In this paper we propose a hybrid recommender system that uses a target rare case to state users&#x0027; requirements and ranks the candidates using a similarity function which is calculated as a weighted sum of individual feature similarities. Specifically, the weight of each feature is computed as a product of its association with the class label and the outlyingness of its value. We apply this hybrid recommender system on the radio pulsar candidate selection problem, for detection of two different types of rare cases: low signal-to-noise (S/N) pulsars and Fast Radio Bursts (FRBs). Our results show that the proposed approach successfully detects both low S/N pulsars and FRBs. When there is class overlapping, as in case of low S/N pulsars, treating rare feature values as outliers and increasing their weights in the similarity function improve the detection performance. For FRBs, which compared to the low S/N pulsars are relatively more distinguishable from the non-astrophysical signals, uniform weighting outperformed the feature-weighting methods. The proposed hybrid recommender system can be used in other application domains that share similar requirements such as high recall and face similar challenges such as class imbalance and class overlapping.
The application of meta-learning in the field of disease prediction and detection

In the field of disease prediction and detection, due to the lack of medical data, the amount of data stored in the public data set is small and the data is very complex. It is difficult to collect a large number of fully labeled medical data, and there is currently no based meta-learning Articles on disease prediction and detection, Therefore, in response to this problem, this article summarizes the difference between meta-learning and traditional machine learning, the main processes and methods of disease prediction and detection based on meta-learning methods, and the analysis of the mainstream meta-learning framework MAML, as well as the existing meta-learning framework based on MAML. Comparison of learning research methods; as far as current research is concerned, there are few related articles in the field of disease prediction based on machine learning. I hope that the methods and comparisons summarized in this article can make relevant researchers familiar with the progress and technology of existing research.
The Application of Neural Networks in Cryptography

In this paper, it will be shown cooperation between Petri nets and neural networks. The approach to the formation of the structure of a neural network with the help of Petri nets model , which you can describe the algorithms and, in particular, encryption algorithms Built model in networks Petri, according to the proposed approach, is the basis for further construction neural network. The idea of an informal transformation, which makes sense from because the structure of the Petri net provides a justification for the structure of the neural network, which leads to a decrease in the number of parameters for training in the neural network (in the considered). At the same time, training is only a fine adjustment of the parameter values. Also the transformation can be explained by the fact that both Petri nets and neural networks are function description languages, with the difference that in the case of neural networks, the function being set must first be trained (or find the values of the parameters). This is discussed by the example of a simulator for encryption algorithms nets. Also, in the article Probabilistic Petri Nets and their properties are described, except for the technique of their use for system's research. 5 theorems are proved and 5 generalizing conclusions about application of languages of to a problem of Probabilistic Petri nets resolvability are made.
Most Significant Bit-Plane Based Local Ternary Pattern for Biomedical Image Retrieval

In this paper, Most Significant Bit-plane using Local Ternary Pattern is proposed for retrieval of biomedical images. Biomedical images have been evolved in hospitals and medical institutions for disease diagnosis of patients and to detect and record the patient’s history. Algorithm uses transformation scheme to calculate transformed value of each neighboring pixel present in a local bit plane. The proposed method is generated by calculating two binary pattern from a ternary pattern using difference of local biplane transformed values with the intensity value of center pixel. The retrieval efficiency of proposed method is tested on two standard bio-medical databases OASIS-MRI and MESSIDOR database by using Average Retrieval Precision (ARP). We get 66.17% result on OASIS database and 56.27% on MESSIDOR dataset. The MSBPLTP is evaluated using ARP and compared with existing image retrieval methods.
FROM DEATH AND DECAY TO DATABASE AND DISPLAY: A REMOTE LEARNING EXPLORATION OF TAPHONOMIC PROCESSES IN THE ASTORIA FORMATION, OREGON

In the Earth sciences, the need for virtual field experiences (VFEs) is rapidly expanding. Not only has the pandemic increased the need for VFEs for use in Earth science courses that had to be adapted for remote teaching, but ongoing accessibility challenges also necessitate the development of alternative options to in-person field experiences. Towards that end, the EPICC (Eastern Pacific Invertebrate Communities of the Cenozoic) TCN (Thematic Collections Network) produced a series of virtual field experiences that explore various aspects of the Astoria Formation, a Miocene-aged unit located on the scenic central Oregon Coast. The Astoria Formation VFE can help educators and their students explore the ecological, geological, and scientific forces that shape the fossil record along the Oregon Coast. As one module of the project, the EPICC VFE outreach team created a taphonomy-focused VFE, From death and decay to database and display: the journey of a fossil through time and space, designed to explore the process by which dead organisms become fossils. Place-based learning features heavily in this module through extensive use of photographs of modern faunal assemblages from Oregon Coast tidepools and beaches as well as in situ fossils of similar ecosystems preserved in the Astoria Formation. To help instructors implement this activity, a student guide was developed with active learning activities that can be modified for either classroom or lab activities. This learning tool is appropriate for a wide range of educational levels, from instructors of grades 7-12 to college instruction, in which ready-made VFEs can be integrated into physical and historical geology courses.
Zimbabwe's central government: Framework for use and management of email

This research on use and management of email in Zimbabwe's central government revealed a number of shortfalls in the manner official email were used and managed. Lapses in the application of the legal, policy and procedural frameworks coupled with email skills deficiencies and use of inappropriate information and communication technologies resulted in poor management of email. A lot of official emails were lost, fragmented, inappropriately disposed and difficult to access. This led to the development, by the authors, of a framework to improve the use and management of email which bordered on the controlled use of email, management of email in line with set guidelines and the pursuance of a high degree of professionalism and integrity. The framework is set to improve the use and management of email in order to make email an authentic, reliable, useful and useable official record. This mixed methods research involved 240 questionnaire respondents from 12 out of 22 head offices of government ministries in Zimbabwe, as well as interviewees who comprised of the Administration and Information Technology Directors from central government, the Director of the National Archives of Zimbabwe and seven National Archives of Zimbabwe archivists. Data were also collected through personal observation and document reviews.
Implementation of Encryption Technique for analysis of Image Transmission during COVID-19 Pandemic

The necessity of rapid and appropriate diagnosis is emphasized by the COVID 19 as a serious danger to world health. The interchange of patient data across health centers addresses the growth of the number of patients and minimizes needless trials. The sharing of medical information helps patient care to accelerate; thus the sharing of health data is a need nowadays. Due to the sharing of information online among healthcare professionals, safety remains a key concern. In a diagnostic method during the COVID-19 pandemic, computer tomography (CT) and X-ray photos play an essential role in the transmission of information to organizations. Steganography techniques of encryption and imaging can be used in order to secure transmission of COVID-19 pictures. This work proposes a novel encryption for COVID-19 diagnostic using the image steganography (EIS-SDT) paradigm for safe data transfer. For the breakdown of images, a discrete multiple-level transformer for EIS-SDT employs the technique for optimizing the pixel selection for MANTA RY Foraging. For secrete image encryption, the EIS-SDT approach employs a dual logistic chaotic map. Additional security for the steganography process is ensured by the implementation of the DLCM-based encryption mechanism. The EIS-SDT model is efficiently performed by thorough simulation results analysis, and the results are analyzed using many assessment parameters. The analysis showed that the EIS-SDT model exceeds existing technology considerably.
Feature Selection with Particle Swarm Optimization for Human Activity Recognition Using Learning Vector Quantization

As a social being, humans must do a variety of activities ranging from waking up in the morning to going back to sleep at night. Of the many activities, this research wants to find out how many activities humans do, for example how many times people walk, lie down, and so on in a day. To find out the number of these activities, this study uses Learning Vector Quantization (LVQ) method for recognizing human activities. This study uses 561 features from 1000 data to be used for the training process and 500 data to be used in the testing process. Data with a large number of features can affect the length of the calculation process. Because LVQ uses features as input data, we need a method that can reduce the number of features. One method that can be used is the Particle Swarm Optimization (PSO) method. The training process is carried out using the PSO selection feature with the parameters of the number of particles 30, maximum iterations 30, the value of ω 0.3, Φp of 0.3 and Φg of 0.8. From the training process, gBest and LVQ parameters are obtained with a value of α 0.1, a reduction of α (dec α) 0.5, minimum α 0,0000001 and a maximum of epoch or iteration 5 with a final accuracy of 17%.
Simple Java Program Structure

In our first program, we displayed a single message on the computer. We did this using the System.out.println() method. This is a command in Java that we can use to perform certain actions.
A Multisource Situation Information Fusion Method Based on Dynamic Evidence Combination

To address the problems of fusion efficiency, detection rate (DR), and false detection rate (FDR) that are associated with existing information fusion methods, a multisource information fusion method featuring dynamic evidence combination based on layer clustering and improved evidence theory is proposed in this study. First, the original alerts are hierarchically clustered and conflicting evidence is eliminated. Then, dynamic evidence combination is applied to fuse the condensed alerts, thereby improving the efficiency and accuracy of the fusion. The experimental results show that the proposed method is superior to current fusion methods in terms of fusion efficiency, DR, and FDR.
Adaptive Prediction of Hip Joint Center from X-ray Images Using Generalized Regularized Extreme Learning Machine and Globalized Bounded Nelder-Mead Strategy

The prediction of hip joint center (HJC) is an important step in hip dysplasia screening. Existing state-of-the-art identification methods focus on the development of Mose circle, functional and predictive methods. Those approaches extract few factors and ignore the adaptive HJC prediction, and their applications are not universally applicable. This paper proposes an adaptive HJC prediction model from X-ray images. The proposed network is based on generalized regularized extreme learning machine (GRELM) with three improvements: a multivariable feature extraction module, obtaining comprehensive predictive factors; an attribute optimization module based on Pearson correlation method and entropy weights, guiding the network to focus on useful information at variables; And appending a globalized bounded Nelder-Mead (GBNM) strategy to the framework to automatically and efficiently determine optimal model parameters. By integrating the above improvements in series, the models’ performances are gradually enhanced. Experimental results demonstrate the effectiveness of our method. Our method can be easily connected in series with an automatic landmark detection module, and the HJC can be quickly determined based on these anatomical landmarks using the proposed model.
Integrating Manifold Knowledge for Global Entity Linking with Heterogeneous Graphs

Entity Linking (EL) aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base (KB), which has recently been dominated by global models. Although many global EL methods attempt to model the topical coherence among all linked entities, most of them failed in exploiting the correlations among various linking clues, such as the semantics of mentions and their candidates, the neighborhood information of candidate entities in KB and the fine-grained type information of entities. As we will show in the paper, interactions among these types of information are very useful for better characterizing the topic features of entities and more accurately estimating the topical coherence among all the referred entities within the same document. In this paper, we present a novel HEterogeneous Graph-based Entity Linker (HEGEL) for global entity linking, which builds an informative heterogeneous graph for every document to collect various linking clues. Then HEGEL utilizes a novel heterogeneous graph neural network (HGNN) to integrate the different types of information and model the interactions among them. Experiments on the standard benchmark datasets demonstrate that HEGEL can well capture the global coherence and outperforms the prior state-of-the-art EL methods.
A Hybrid Approach for Metaheuristic Algorithms Using Island Model

This paper introduces a novel parallel way to combine different meta-heuristic algorithms by using the island model, which is called Hybrid Island Metaheuristic Algorithm (HIMA). This parallel hybridization structure can improve the diversity of the whole algorithm and combine the features of different algorithms together. In this paper, three traditional algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Fireworks Algorithm (FWA) have been used to build three different HIMA algorithms, PSO-GAs (HIMA-PGA), FWA-GAs (HIMA-FGA) and FWA-PSO-GAs (HIMA-FPGA). The performance of the proposed algorithms is compared to that of the traditional Island GA and to that of the others as well. All three HIMA algorithms show a better result quality compared to the island GA. Moreover, the experiment results comfirm that the sub-PSO improves the convergence speed of the algorithm while the sub-FWA can improve the result quality on some proposed functions.