Reviewerly/exHarmony huggingface dataset:
An Open-Source High-Throughput, Reduced Memory Footprint, Face Detection, Pose Estimation and Landmark Localization System

Face Detection, Pose Estimation and Landmark Localization are all considered important vision processes and are very widely utilized in several applications ranging from security/safety to automotive and assisted living. In this paper we present an open-source optimized implementation of a system addressing all those processes. In particular, we optimized both the memory requirements and the performance of the very widely utilized Tree Structure Model (TSM), which is the main core in all those tasks. Several optimizations have been proposed so as to increase the performance in both uni-and multi-processor systems, while also reducing the memory footprint so as to allow for the implementation of those schemes, for the first time, in embedded systems. The proposed system is at least 100% faster (and up to 300%) and requires more than 10 time less memory than the existing implementations. Since the algorithm implemented is one of the most widely used in the area of face detection and we distribute the optimized code in an open-source manner, we believe that it can act as an important reference implementation for any similar system proposed.
The workbook “Adaptation in the labor market”: development and use in training bachelor students

The experience of development and use of the workbook “Adaptation in the labor market” as a mechanism to increase the effectiveness of independent work of bachelor students is described. The workbook used for extracurricular and classroom activities allows students to form required competencies, individualizes and differentiates the learning process, motivates the self-development and self-education, develops reflection skills and creative abilities. As a result of mastering the discipline “Adaptation in the labor market” using the workbook, students develop clearer ideas about requirements of the modern labor market, professional environment, the need for continuous self-development and self-education, which positively affects their readiness for professional activities and labor functions that meet the requirements of a modern employer
Time-lapse geochemical exploration utilizing a multi-depth sampling design

Abstract The objective of this proposed surface geochemical exploration method is to design a sampling survey in a different way than has been done conventionally. Traditionally, surface sampling is carried out at a depth ranging from 0.5-1.0 meters. A new patented method is proposed to test different depth intervals to reach the optimum depth. The survey is then repeated to verify whether the same results are consistently obtained (time-lapse). A regional east-west transect was chosen to test whether the multi-depth approach would reveal a depth-seepage relationship. The area of interest is ideal for such a study because of its relatively simple petroleum system setting. Hydrocarbons are derived from a single source and trapped in a single reservoir, thereby minimizing or eliminating the risk of confusing signals from multiple reservoirs. The transect runs through a producing well and a dry well for calibration purposes. The geochemical sensors were placed at 1, 5 and 10 meter depths using drilled cased holes along the transect. The sensors were collected after three weeks. A regional trend was established and a comparative model was created, which allowed for contrasting the different depth profiles and validating the resultant geochemical signatures with the typical signature in the area. Encouraging results were achieved from multi-depth seepage profiling in the field, but they were limited by the relatively sparse transect sample pattern. Distinct conclusions were apparent when using both light and mid-range compounds (C2-C5 and C6-C10). The geochemical anomalies suggested an optimal sampling depth for seepage detection based on this study. In some instances positive anomalies were present over undrilled leads in the area. The dataset was modelled using both neural network and linear discriminant techniques. More follow-up work is underway to validate the results by varying the depth profiles, using cased holes vs. non-cased holes, sampling air blanks and repeating the survey to confirm reproducibility of the results (time-lapse effects). The results from this study require validation and future development to improve the technology of multi-depth geochemical profiling before these techniques can be made routine in hydrocarbon exploration. The survey can be conducted concurrently with seismic acquisition to prove the presence of a petroleum charge before making more expensive decisions whether in seismic acquisition or in exploration drilling.
Default Soundness in the Old Approach: An Epistemic Analysis of Default Reasoning

By means of an epistemological analysis of the internal components of default reasoning, and revisiting Geffner’s proposal about causal asymmetries, it is given in this paper an answer to the problem of soundness in default reasoning. The kernel of that answer is an intentionalist view of certain kind of connections inside the internal structure of a default rule. This intentionalist view consists partially in assuming certain strong relationships between the prerequisite, the justifications and the conclusion in a default. Another ingredient of this perspective is the distinction between the context-dependence of a relationship and that of its relatas. It is offered a formal representation of the internal structure of default reasoning. Then it is built an intuitive characterization of the notion of default logical consequence. This notion is similar to the traditional notion in the sense that it attends to the challenge of clarifying the inferential mechanism of a schema, even when in the present paper the proposal differs from the traditional one.
Rule of Code

Blockchain involves no intermediaries. The value is between the sender and the receiver. In this chapter, readers will be exposed to the various facets of the blockchain and consensus mechanisms. Further, a specific discussion in the emergent segment of blockchain applications is also covered.
Visualization of Ensembles of Oil Reservoir Models Based on Pixelization, Small Multiples and Reservoir Similarities

Abstract Providing an overview of an ensemble of oil reservoir models could help users compare and analyze their characteristics. Approaches that show a single model at a time may hamper analysts’ understanding of the whole model set. In this paper, we propose two visualization approaches that show multiple reservoir models, simultaneously and on a single screen, with the goal of helping users to compare models and improve their understanding of ensemble characteristics. First, we calculate 2D models from the ensemble's 3D models. We then create two visualizations that represent ensembles of these 2D models. The Small Multiples approach lays out heatmaps of 2D models side-by-side on a grid. Pixelization approach shows n 2D models in a single heatmap, where each cell (i, j) contains n subcells that represent values in the coordinate (i, j) of each model. Both approaches display their elements (heatmaps and subcells) clustered by X-means, which may help analysts identify similarities and representative models in the ensemble. We used two types of distance matrices: based on Euclidean distance of models for a given property or, based on Euclidean distance of feature vectors of the 2D models. We tested our approaches within models based on Brazilian benchmark cases corresponding to a turbiditic reservoir (UNISIM-I-D/M/H) and a presalt carbonatic reservoir (UNISIM-II-D). As a result, the Small Multiples approach presented clusters of similar models for some properties of the ensembles we studied, e.g. eight clusters of porosity values in UNISIM-II-D's ensemble. This fact suggests that eight representative models can represent the ensemble, regarding porosity. Also, a Pixelization approach revealed patterns that happen in specific regions of all models of an ensemble, such as an abrupt change of porosity values in the northwest region of UNISIM-I-M's models. Both approaches have the potential to help analysts perceive situations that would be improbable to observe in a graph with only mean values for each cell. Therefore, our proposal can be helpful to users who need to deal with uncertainties and have an overview of ensembles of models for better understanding and decisionmaking, e.g. when they need to choose representative models for a process of decision analysis related to petroleum field development and management.
Some Methods of Fuzzy Conditional Inference for Application to Fuzzy Control Systems

Zadeh proposed fuzzy logic with single membership function.Two Zadeh, Mamdani and TSK proposed fuzzy conditional inference.In many applications like fuzzy control systems, the consequent part may be derived from precedent part.Zadeh, Mamdani and TSK proposed different fuzzy conditional inferences for "if … then …" for approximate reasoning.The Zadeh and Mamdani fuzzy conditional inferences are know prior information for both precedent part and consequent part.The TSK fuzzy conditional inferences need not know prior information for consequent part but it is difficult to compute.In this chapter, fuzzy conditional inference is proposed for "if…then…" This fuzzy conditional inference need not know prior information of the consequent part.The fuzzy conditional inference is discussed using the single fuzzy membership function and twofold fuzzy membership functions.The fuzzy control system is given as an application.
SPI: A Software Tool for Planning Under Uncertainty Based on Learning Factored MDPs

In this paper the SPI system is presented. SPI is a software tool for planning under uncertainty based on learning Markov Decision Processes. A brief review of some similar tools as well as the scientific basis of factored representations and some of its variants are included. Among these variants are qualitative representations and hybrid qualitative-discrete representations that are the core of the software tool. The functional structure of SPI, which is composed of four main modules, is also described. These modules are: the compiler, the policy server, a format translator and a didactic simulator. The experimental results obtained when testing SPI in a robot navigation domain using different types of representations and different state partitions demonstrated its capability to reduce state spaces.
Reform of Higher Education in the Russian Federation: Intentions and Reality

The article presents the shortcomings of the current situation in higher education. The results of the study of the Russian labour market are presented. The process of training highly qualified personnel is characterized.
Deep Reinforcement Learning Visual-Text Attention for Multimodal Video Classification

Nowadays multimedia contents including text, images, and videos have been produced and shared ubiquitously in our daily life, which has encouraged researchers to develop algorithms for multimedia search and analysis in various applications. The trend of web data becoming increasingly multimodal makes the task of multimodal classification ever more popular and pertinent. In this paper, we mainly focus on the scenario of videos for their intrinsic multimodal property, and resort to attention learning among different modalities for classification. Specifically, we formulate the multimodal attention learning as a sequential decision-making process, and propose an end-to-end, deep reinforcement learning based framework to determine the selection of modality at each time step for the final feature aggregation model. To train our policy networks, we design a supervised reward which considers the multi-label classification loss, and two unsupervised rewards which simultaneously consider inter-modality correlation for consistency and intra-modality reconstruction for representativeness. Extensive experiments have been conducted on two large-scale multimodal video datasets to evaluate the whole framework and several key components, including the parameters of policy network, the effects of different rewards, and the rationality of the learned visual-text attention. Promising results demonstrate that our approach outperforms other state-of-the-art methods of attention mechanism and multimodal fusion for video classification task.
On one applicability criterion for methods of approximate control system reachable set construction

The paper is devoted to the development of criterion for the applicability of methods for the approximate reachable sets construction for control systems. This criterion allows to indirectly assess the quality of the sets computed by such methods. The proposed criterion checks the fulfillment of the main property of the reachable set, namely, the possibility of leading the system to the points of such a set. The paper provides a description of two grid methods of reachable sets construction, as well as the results of numerical experiments.
Multimodal person discovery using label propagation over speaking faces graphs

The indexing of large datasets is a task of great importance, since it directly impacts on the quality of information that can be retrieved from these sets. Unfortunately, some datasets are growing in size so fast that manually indexing becomes unfeasible. Automatic indexing techniques can be applied to overcome this issue, and in this study, a unsupervised technique for multimodal person discovery is proposed, which consists in detecting persons that are appearing and speaking simultaneously on a video and associating names to them. To achieve that, the data is modeled as a graph of speaking-faces, and names are extracted via OCR and propagated through the graph based on audiovisual relations between speaking faces. To propagate labels, two graph based methods are proposed, one based on random walks and the other based on a hierarchical approach. In order to assess the proposed approach, we use two graph clustering baselines, and different modality fusion approaches. On the MediaEval MPD 2017 dataset, the proposed label propagation methods outperform all literature methods except one, which uses a different approach on the pre-processing step. Even though the Kappa coefficient indicates that the random walk and the hierarchical label propagation produce highly equivalent results, the hierarchical propagation is more than 6 times faster than the random walk under same configurations.
Critical consciousness against Armageddon: The end of capitalism vs. the end of time

In The Muqaddimah Muhammad Ibn Khaldun (2015) [1337] explains the rise and fall of ancient civilisations such as Persian and Byzantine empires. According to Khaldoun, empires are founded when nomad...
Weighted Frequent Itemset Mining Using OWA on Uncertain Transactional Database

The technology of data mining has a broad scope in banking, manufacturing, medical science, and business decision-making. In these applications, the most commonly used term is Frequent Itemset Mining Algorithms which is one of the vital parts of Association Rule Mining. The evolutionary improvement in FIM is Weighted Frequent Itemset Mining, and these algorithms can be executed on Certain/Probabilistic Uncertain databases for calculating important frequent itemsets, having weight and expected support equal to or greater than user-specified minimum weight or minimum probability, respectively. The weight for an itemset is calculated by taking an average of weights of all items in the itemset. In this case, if the weights of one or two items are very high compared to others than the average can be decided by only higher weights and ignore low values. In this research paper, OWA operator is used in place of the traditional mean for calculating the weight of an itemset. A new algorithm is developed in this research and executed on example database. The results show that the generated itemsets are less in the count but hold more importance.
Augmented Reality and Mixed Reality Technologies

This chapter discusses some of the most popular technologies available for augmented and mixed reality (AR/MR). It further explores current applications of AR in both research and industry and offers considerations for AR-based serious game development. Finally, it discusses some of the current issues around AR including ethical implications.
Tribal Welfare Application: A System to Connect Local Artisans &amp; Agencies of MoTA

Rural development signifies both the economic amelioration of mankind as well as greater social amendment. In order to furnish a better prospect of economic and social expansion of the Tribes, a digital platform is needed for accessing their merits. It entitles the artisan to commerce their products to different locale in and around India. Our propound project work is to develop a mobile application that permit them to upload their craft work as an image that could assist the agencies of Ministry of Tribal Affairs (MoTA) to perceive their work. Furthermore, it proffers a chat section for productive interrelation among the agencies, tribes and common people (Commoners). It render the Agencies of MoTA to merge with the artisans in reference to skill up-gradation training, branding and marketing support. The artisans can moreover post their queries concerning the training session and other queries.
A Variety of Contributions to Argumentation Theory

In From argument schemesArgument scheme /argumentative scheme to argumentative relations in the wild we have brought together a variety of contributions to argumentation theory that we want to bring to the attention of the international communityCommunity of argumentation scholars and students of argumentation.
Multi-Resolution Analysis of Edge-Texture Features for Mammographic Mass Classification

In this paper, multi-resolution analysis of two edge-texture based descriptors, Discriminative Robust Local Binary Pattern (DRlbp) and Discriminative Robust Local Ternary Pattern (DRltp), are proposed for the determination of mammographic masses as benign or malignant. As an extension of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP), DRlbp and LTP-based features overcome the drawbacks of these features preserving the edge information along with texture. With the hypothesis that multi-resolution analysis of these features for different regions related to mammaographic masses with wavelet transform will capture more discriminating patterns and thus can help in characterizing masses. In order to evaluate the efficiency of the proposed approach, several experiments are carried out using the mini-MIAS database where a 5-fold cross validation technique is incorporated with Support Vector Machine (SVM) on the optimal set of features obtained via stepwise logistic regression method. An area under the receiver operating characteristic (ROC) curve ([Formula: see text] value) of 0.96 is achieved with DRlbp attributes as the best performance. The superiority of the proposed scheme is established by comparing the obtained results with recently developed other competing schemes.
Provisioning of UE Behavior Prognostics at the Network Edge

Data analytics will play a key role in fifth generation (5G) networks. Multi-access edge computing (MEC) which distributes cloud intelligence at the network edge can provide analytics data that optimize user quality of experience and network performance. In this paper, we propose an extension to existing MEC service which provides actual radio network information. The proposed extension enables applications deployed at the network edge to send prognostic user behavior information to the network.
Lexical Function Identification Using Word Embeddings and Deep Learning

In this work, we report the results of our experiments on the task of distinguishing the semantics of verb-noun collocations in a Spanish corpus. This semantics was represented by four lexical functions of the Meaning-Text Theory. Each lexical function specifies a certain universal semantic concept found in any natural language. Knowledge of collocation and its semantic content is important for natural language processing, as collocation comprises the restrictions on how words can be used together. We experimented with a combination of GloVe word embeddings as a recent and extended algorithm for vector representation of words and a deep neural architecture, in order to recover most of the context of verb-noun collocations in a meaningful way which could discriminate among lexical functions. Our corpus was a collection of 1,131 Excelsior newspaper issues. As our results showed, the proposed deep neural architecture outperformed state-of-the-art supervised learning methods.
Social Algorithms and Optimization

Social algorithms have become popular and effective for solving problems in optimization and computational intelligence. They are population-based algorithms using multiple, interacting, and coevolving agents. We will review the brief history and introduce some of the commonly used social algorithms. We will also analyze these algorithms and then highlight some open problems so as to inspire further research.
Image Watermarking Algorithm Based on Complex Wavelet Domain and Chaotic Encryption

In this paper, we design a digital watermarking algorithm based on dual-tree complex wavelet transform and chaotic system pixel scrambling. In the scrambling of watermarking information, the generalized two-dimensional cat mapping beused to scramble the watermarking twice. In watermarking algorithm, the selection of embedding area will play an important role in the effect of watermarking algorithm. According to the human visual system, we embedded the watermarking information into the complex wavelet domain (±45°) high frequency domain of the image. Experiments show that our algorithm not only ensures the invisibility of the watermarking, but also has good robustness under the attack of noise, rotation and shearing.
Visual Power Line Inspection Via Multi-direction Transform

Aiming at the quality degradation problem of image captured on foggy day in visual power line inspection, which will affect the accurate analysis of power line operating state, an image defogging algorithm based on dark channel prior hypothesis and multi-directional transform is proposed. The traditional dark channel prior algorithm can achieve good results, but it will cause a certain degree of image color distortion of the highlight component. In order to improve the performance of dark channel defogging, this paper utilizes multi-directional transform to extract low-frequency images for transmission estimation, which can effectively alleviate the color distortion caused by traditional dark channel processing. The experimental results show that the overall performance of the proposed algorithm is better than other algorithms in objective evaluation indicators, which can provide support for further obtaining accurate information of power line operation.
Competitive Evolution Multi-Agent Deep Reinforcement Learning

As an effective method to solve the optimal policy in multi-agent systems, multi-agent deep reinforcement learning (MADRL) has achieved impressive results in many applications. However, the training of a deep neural network used in MADRL is time-consuming and laborious. In order to improve the training efficiency and accelerate the training speed of the neural network, this paper proposed a sharing learning framework to support the effectively training of multi-agent state-action value function neural network through making full use of sharing diverse experience from different agents to train a single shared network. Besides, a novel learning algorithm combined with competition and evolution idea was developed to accelerate the training process and improve the performance of the trained neural network. Finally, the experiment results prove the effectiveness of the proposed framework and the fact that the proposed algorithm with an appropriate update rate can indeed accelerate the training process and improve the performance of the trained network by providing a well-matched opponent.