Reviewerly/exHarmony huggingface dataset:
A Trialist Perspective of the Labor Inclusion of Indigenous People in Ecuador through PESTEL and Neutrosophic Cognitive Maps

The purpose of this research was to determine from a trialist perspective what factors currently affect the labor inclusion of people from indigenous cultures in Ecuadorian society and to propose strategies for their mitigation. A broad and generic analysis is carried out to assess whether the norms and the social reality evident in the country effectively adhere to each other. We used a fusion of PESTEL method and Neutrosophic Cognitive Maps, since during the mathematical exercise that endorses the analysis, indeterminacy is incorporated into the modeling of the causal relationships between the factors analyzed. Finally, the figure of indigenous labor inclusion is declared as an unfinished topic. Therefore, the priority factor to consider is the establishment of policies favoring the employment of indigenous people. As a solution, this paper ends with the proposal of compliant strategies to promote improvements in this area.
Artificial Intelligence and Market Manipulations: Ex-ante Evaluation in the Regulator's Arsenal

The digital economy's development poses questions unprecedented in their magnitude in potential market manipulations and manipulations of consumer choices. Deceptive and unfair strategies in consumer law may coexist and mutually reinforce each other with infringements in the field of competition, whether it be algorithmic collusion or abuse of a dominant position. Faced with the difficulty of detecting and sanctioning these practices ex-post, questions are raised about the sanction's dissuasive effect and its capacity to prevent possibly irreversible damage. To this end, this article considers the available supervision tools for the authorities in charge of market surveillance, the consumers or the stakeholders of the companies concerned.
Face Recognition Based on Multi-scale and Double-Layer MB-LBP Feature Fusion

Aiming at the reason that traditional face recognition algorithms are greatly affected by factors such as illumination and noise, a feature extraction method based on multi-scale and double-layer MB-LBP operator is proposed. First, extract three MB-LBP features with scales of 1*1, 2*2, and 3*3, respectively. Based on three different scale MB-LBP feature pictures, the MB-LBP features of the second layer are extracted, and then the respective histogram features are counted and all features are combined into a higher dimensional feature. Principal component analysis (PCA) is used to reduce the dimensionality of the fused feature data. Finally, support vector machine (SVM) is used for classification. By testing on ORL and AR face databases respectively, this method has significantly improved the accuracy of face recognition compared to traditional methods, reaching 99.5% and 99.2% respectively. In addition, in order to verify the adaptability of the algorithm to light and noise, through the brightness adjustment and noise addition to the face data set, and then test and verify. while the recognition accuracy of the traditional method has dropped significantly, the recognition accuracy of the algorithm is still more than 98%.
Experimental Sensitivity Analysis of Grid-Based Parameter Adaptation Method

Grid-based parameter adaptation method has been recently proposed as a general-purpose approach for online parameter adaptation in metaheuristics. The method is independent of the specific algorithm technicalities. It operates directly in the parameter domain, which is properly discretized forming multiple grids. Short runs of the algorithm are conducted to estimate its behavior under different parameter configurations. Thus, it differs from relevant methods that usually incorporate ad hoc procedures designed for specific metaheuristics. The method has been demonstrated on two popular population-based metaheuristics with promising results. Similarly to other parameter tuning and control methods, the grid-based approach has three decision parameters that control granularity of the grids and length of algorithm runs. The present study extends a preliminary analysis on the impact of each parameter, based on experimental statistical analysis. The differential evolution algorithm is used as the targeted metaheuristic, and the established CEC 2013 test suite offers the experimental testbed. The obtained results and analysis verify previous evidence on the method’s parameter tolerance, offering also an insightful view on the parameters interplay.
An Investigation on the Impact of Non-Uniform Random Sampling Techniques for t-SNE

t-Distributed Statistical Neighbor Embedding (t-SNE) is a dimensionality reduction technique that has gained much popularity for its increased capability of creating low-dimensional embeddings that preserve well-separated clusters from high-dimensional spaces. Despite its strengths, the running times for t-SNE are usually high and do not scale well with the size of datasets, which limits its applicability to scenarios that involve, for example, Big Data and interactive visualization. Downsampling the dataset into more manageable sizes is a possible straightforward workaround, but it is not clear from the literature how much the quality of the embedding suffers from the downsampling, and whether uniform random sampling is indeed the best possible solution. In this paper, we report on a thorough series of experiments performed to draw conclusions about the quality of embeddings from running t-SNE on samples of data using different sampling techniques: uniform random sampling, random walk sampling, our proposed affinity-based random walk sampling, and the so-called hubness sampling. Throughout our testing, the affinity-based variant of random walk sampling distinguished itself as a promising alternative to uniform random sampling.
Second Order Enhanced Multi-glimpse Attention in Visual Question Answering

Visual Question Answering (VQA) is formulated as predicting the answer given an image and question pair. A successful VQA model relies on the information from both visual and textual modalities. Previous endeavours of VQA are made on the good attention mechanism, and multi-modal fusion strategies. For example, most models, till date, are proposed to fuse the multi-modal features based on implicit neural network through cross-modal interactions. To better explore and exploit the information of different modalities, the idea of second order interactions of different modalities, which is prevalent in recommendation system, is re-purposed to VQA in efficiently and explicitly modeling the second order interaction on both the visual and textual features, learned in a shared embedding space. To implement this idea, we propose a novel Second Order enhanced Multi-glimpse Attention model (SOMA) where each glimpse denotes an attention map. SOMA adopts multi-glimpse attention to focus on different contents in the image. With projected the multi-glimpse outputs and question feature into a shared embedding space, an explicit second order feature is constructed to model the interaction on both the intra-modality and cross-modality of features. Furthermore, we advocate a semantic deformation method as data augmentation to generate more training examples in Visual Question Answering. Experimental results on VQA v2.0 and VQA-CP v2.0 have demonstrated the effectiveness of our method. Extensive ablation studies are studied to evaluate the components of the proposed model.
Some algorithms under non-parametric framework versus an unsupervised approach

In this communication some algorithms for polygonal approximation of closed digital curve under a non-parametric framework are compared with an unsupervised approach to polygonal approximation. The two approaches are compared with respect to number of vertices, maximum error, measures WE∞ and WE2, Rosin’s measure and relative execution time. The algorithms are tested using images from MPEG7 dataset and it is observed that the algorithms under non-parametric framework produce too many vertices many of which are redundant resulting in rough approximations whereas the unsupervised approach produces less number of vertices with low value of WE∞ and WE2 leading to smooth approximations and high value of Rosin’s measure, but its relative execution time is high.
Using Sentence Embedding for Cross-Language Plagiarism Detection

The growth of textual content in various languages and the advancement of automatic translation systems has led to an increase of cases of translated plagiarism. When a text is translated into another language, word order will change and words may be substituted by synonyms, and as a result detection will be more challenging. The purpose of this paper is to introduce a new technique for English-Arabic cross-language plagiarism detection. This method combines word embedding, term weighting techniques, and universal sentence encoder models, in order to improve detection of sentence similarity. The proposed model has been evaluated based on English-Arabic cross-lingual datasets, and experimental results show improved performance when compared with other Arabic-English cross-lingual evaluation methods presented at SemEval-2017.
Athlete 3D pose estimation from a monocular TV sports video using pre-trained temporal convolutional networks

Our goal is to estimate athlete 3D pose from monocular TV sports video with a lower cost of collecting training data. To achieve this goal, we utilize a pre-trained deep neural network as a 3D pose estimator for estimating human 3D pose from 2D joint locations of the person in each image. Each image in popular datasets used for training such 3D pose estimator is obtained from a camera whose axis is parallel to the ground. On the other hand, since an image in TV sports video is generally taken from a bird's eye view, joint locations of a human is distorted in the lower part of the image. Therefore, it is not appropriate to give 2D joint locations of the person directly to the pre-trained 3D pose estimator. To resolve this problem, we propose to correct 2D joint locations in an image of TV sports video by a homography transformation that maps the points in the image of TV sports video to the corresponding points in the image taken by the camera that captures training data for the 3D pose estimator. Experimental results show that the proposed method can estimate athlete 3D pose from monocular TV sports video.
Intelligent Fog Computing for Industrial Wireless Sensor Networks

The intelligent Fogs are designed based on Adaptive Cognitive Rule Principles, which process the data and compare them with the reference data for diagnosis of faults, if any. Hundreds of machines and equipment are present in the industries. Failure in the individual machine or equipment can make self-damage and also cause economic loss. The access point, the base station, is there for proper data transmission. The data that is sent from the sensors is given to the cloud platform; after that it is only given to the monitoring units. The chemical reaction optimization (CRO) depends on swarm intelligence meta-heuristics. The molecular structure in the CRO gives the complete solution. It is considered that normally two types of energies are there in molecules, that means, kinetic energy and potential energy. Standardized protocols are required for establishing the communication protocol among Fog gateways and sensor nodes.
Graph-Ontology Model of Cognitive-Similar Information Retrieval (on the Requirements Tracing Task Example)

This article considers graph-ontology tools that provide construction, visualization and analysis of an ontology graph using functions for selecting vertices and arcs, set-theoretic operations on graphs, and aspect projection operations. An aspect is specified in terms of general system theory. Aspect projection operations for graph representations of ontologies reduce the dimension of graphs to a level affordable for displaying and human perception. As applied for information retrieval process, it makes possible to move from the task of classical information retrieval to the implementation of cognitive-similar information retrieval task, represented as a search for a path or neighborhood on a multi-meta-hypergraph of an ontology, dynamically formed on the base of ontological images of founded documents or their fragments. The ontology graph is formed via auto-extracting entities and relationships from natural language texts. This article considers the application of the developed tools in the process of analysis and synthesis of knowledge on the example of technical requirements tracing.
Development of a System for Static Analysis of C ++ Language Code

The main goal of the system is to make it easier to standardize the style of program code written in C++. Based on the results of the review of existing static analyzers, in addition to the main requirements, requirements for the structure of stylistic rules were identified. Based on the results obtained, a system for static analysis of the C++ language has been developed, consisting of a set of modules. The system is implemented using the Python 3.7 programming language. HTML and CSS markup languages were used to generate html reports. To ensure that rules can be stored in the database, the MongoDB database management system and the pymongo driver module were used.
Recognizing Abstract Classes of Text Based on Discourse

The problem of classifying shorter and longer texts into abstract classes is formulated and its application areas for CRM are proposed. These classes include reasoning patterns expressed in text such as object-level versus metalanguage, document styles such as public versus containing sensitive information, containing a description of a problem versus a solution for a problem, instructions on how to solve it. The commonality between the tasks of classification into such classes is that keyword analysis is insufficient and an accent to higher-level (discourse) representation is necessary. To do that, we define thicket kernels as an extension of parse tree kernels from the level of individual sentences toward the level of paragraphs to classify texts at a high level of abstraction (Galitsky et al. in Graph structures for knowledge representation and reasoning, pp 39–57, 2014). We build a set of extended trees for a paragraph of text from the individual parse trees for sentences. It is performed based on anaphora and rhetorical structure relations between the phrases in different sentences. Tree kernel learning is applied to extended trees to take advantage of additional discourse-related information. We evaluate our approach in the security-related domain of the design documents. These are the documents which contain a formal well-structured presentation on how a system is built. Design documents need to be differentiated from product requirements, architectural, general design notes, templates, research results and other types of documents, which can share the same keywords. We also evaluate classification in the literature domain, classifying text in Kafka’s novel “The Trial” as metalanguage versus novel’s description in scholarly studies (a mixture of metalanguage and language-object).
Online RPG Environment for Reinforcement Learning

Researchers have achieved significant success in controlling various board, arena and strategy games with Deep Neural Networks in recent years [13], [15]. Nevertheless, there have been relatively few attempts to control Role-Playing Games (RPGs), which are perhaps closest to real-life environments conceptually. In this paper, a lightweight, easy-to-use online RRG is introduced to test and train deep reinforcement learning algorithms in a structured environment. Several different models were trained on our environment, and analysis on their results and behaviour is provided. The project is open-source and available on: https://github.com/szemenyeim/AIRPGEnv.
Automated Driving: Liability of the Software Producer and the Producer of the Automated Vehicle

Even though automated vehicles are expected to bring great safety benefits, accidents will still occur with automated vehicles. An automated vehicle could be equipped with self-learning software (reinforcement learning), which learns whilst driving. This software could make a ‘learning error’ and, for instance, not recognize worn-down lane markings as such. Is the producer of the automated vehicle liable for the damage caused by this ‘learning error’? And what if the damage is not caused by a ‘learning error’, but by a glitch in a software update, can the producer of the automated vehicle be held liable for the damage caused? And is such a software update a product? In this paper, the challenges posed by automated driving to the European Product Liability Directive will be discussed. The legal position of the software producer as well as the legal position of the producer of the automated vehicle are explored in order to find answers to the questions mentioned.
Combinator-as-a-Process for Representing the Information Structure of Deep Computing

In the present work, the main attention is paid to elucidating the technological features of computations with individual generators, or objects. Their interaction is considered in an applicative environment, which allows us to elucidate the internal structure of ordinary operations, the knowledge of which allows us to understand their properties. The choice of initial constant generators, considered initial and expressed by combinators, is discussed. These initial generators are used as the main “building blocks” that enter into the applicative medium in interaction with each other. As a result of the interaction, constructions arise that give representative sets of ordinary operators and immersed computing systems.
Presence Sensors: A Comparison Between the Ultrasonic and the Infrared in the Detection of People and Vehicles

Sensors have become fundamental elements in automation and industrial processes, for updating in a precise and fast way. Among the various types of sensors, there are presence sensors. They are able to perform simple detection tasks more accurately and efficiently than people. This work presented the comparison between two types of sensors: the ultrasonic and the infrared. It is also proposed a method of estimating the length of the vehicle by presence detection. The preliminary results show that the technique is interesting and cheap to distinguish and detect vehicles types.
A Practical Explanation of Backward Sign Propagation in Quantum Computing

In this paper, we perform a practical explanation of backward sign propagation in quantum computing. In our work, a 2-qubit quantum state is applied on the CNOT gate. The first qubit acts as a target qubit and the second qubit behaves as a control qubit. In this 2-qubit system, a phase error occurs on the target qubit. Our work is in the form of mathematical calculations, i.e. the calculation of the state, the density operator, the trace, and the Bloch vector of our quantum system. We also design its quantum circuit. The circuit is executed on IBM Qiskit and IBM Q Experience. As a result, we show the backward sign propagation occurs in separable and pure quantum state with the Hadamard basis state of the target qubit for any state of the control qubit.
Immediate gestalt: shapes, typography and (quite irregular) shape packing

Instantaneously understanding the gestalt of thousands of words is achieved through the programmatic placement of the words and control of their presentation characteristics, such as size, repetition, and font. As early as the fourteenth century, words were used as building blocks for images. Hundreds of years later, this typographic experiment continues with the addition of raw computational power. The ability to place thousands of words in interesting forms gives rise to a quantitatively different form of expression. The resulting procedures are expressive enough to represent shapes, textures, and shading automatically. Though based on approaches for addressing the classic problem of algorithmic two-dimensional bin-packing, aesthetically pleasing results are achieved through the incorporation of a small set of rules to guide the layout.
Pattern Recognition of Batak Script Using Habbian Method

The introduction of patterns is an analytical work, which is to process raw data (not loading explicit structures) to find the various elements of the attributes/information associated with it. This analytical work is commonplace for human beings. By using the five senses, human beings can see, hear, and feel the condition of its environment to then identify the things that are in it, digest and understand it, so that he can do the activities or interactions that suit his wishes. Facial recognition, voice, signposts and writings, the computer performs pattern recognition by following several stages: sensing, segmentation, feature extraction, and classification/inference. One of the methods used to recognize the pattern is with the help of neural networks. In recognizing the Batak alphabet pattern, it uses one of the methods that exist in the neural network, using the Habb method. So, it is hoped that the Habb method can recognize pattern patterns or features of the Batak script.
An Investigation on Seashells Powder as Filter For Drinking Water

In order to implement economical way of managing unwanted substance, seashell can be reused instead of abundant it at landfill. This research was implemented to determine the characteristics and effects of seashell as main constituent with addition of clay as binder and sawdust as combustion material to determine whether the seashell can be used as water filter for drinking water technology. Four different filter are tested with different composition of seashells, clay and sawdust to determine the efficiency of filter in filtration. Flow rate had been tested by taking average flow rate in one hour. Water sources had been tested before and after filtration to determine pH value. Turbidity test and microbial test was conducted to determine the effectiveness of filters in removing microbial contamination such as total coliforms and E.coli presence in water. With average flow rate between 0.237 l/h to 1.448 l/h, filter had turbidity removal efficiency from 71.9% to 34.82%. High composition of seashell in filter can reduced large amount of total coliform and E.coli. Total coliform from unfiltered water reduced from 105 (cfu /100 ml) to 24 (cfu /100 ml) and E.coli reduced from 56 (cfu /100 ml) to 15 (cfu /100 ml). High amount of seashell in filter able to remove physical contaminant and microorganism presence in water because in provide suitable porosity to trap unwanted particles in water.
Using AES and DES Cryptography for System Development File Submission Security Mobile-Based

The purpose of this study is to develop a system for the security of submission word, excel, and pdf files implementation to LPPPTK KPTK Gowa. The method used is AES and DES cryptography, AES cryptography is a symmetry algorithm that uses the same key for the encryption and description process, and DES cryptography is included in the symmetry and cryptographic systems classified as a type of block cipher. The previous study has been conducted to secure file submission in word form and excel using the blowfish algorithm and AES cryptography. The results of this study indicate that by developing a mobile-based file delivery security system using AES and DES Cryptography, the data in a file can be encrypted so as to avoid interference from unauthorized parties and to keep the contents of the data from a file sent safely. The encrypted file types are not the only word and excel files, but in this study can also be used for pdf files. Based on functional testing, valid results are obtained, the file transfer system has been running in accordance with the results of the testing factor and can function properly and also to test the file transfer logic using AES cryptography and DES error-free logic.