Reviewerly/exHarmony huggingface dataset:
Prognostic Competence and Socialization of Junior Schoolchildren with Health Limitations

The study concerns the problem of prognostic competence of children with health limitations, relevant for understanding the psychological content of difficulties of socialization in dysontogenesis and the emergence of deviant behavior. The aim of the article is to determine the contour of prognostic competence, relevant to the socialization space of a junior schoolchild with health limitations. We have used the comparative analysis of literary sources, represented by the unity of comparative, bibliographic, logical methods of research. We actualized A.A. Rean approach to socialization; we realized competence approach in the interpretation of Yu.V. Gromyko. We expanded and clarified the idea of socialization space of a junior schoolchild in normogenesis and dysontogenesis. The term “prognostic competence of a junior schoolchild with disabilities” is defined as a developed ability to forecast in learning, in relationship with teacher, with peers, in the family, with “alien” adults, in virtual relationship, in attitude to health. Prognostic competence is interpreted as a developed forecasting, covering significant areas of the relationship of a child with health limitations and contributing to successful socialization. The proposed understanding of socialization space, including attitude toward learning, to teacher, peers, family, “alien” adults, health and virtual relationships, clarifies the idea of the social development situation of today’s junior schoolchild. It serves as a basis for developing diagnostic tools for prognostic competence, broadens the understanding of psychological and pedagogical of support children with health limitations. The proposed understanding can be used by teachers, psychologists, social workers, parents in promotion of socialization and prevention of deviations.
Automatic control for laser-based repair preparation of composites by fiber orientation detection and short coherent interferometry

In automobile and aviation industries, composite materials are the key to lightweight efficiency. Composites containing carbon fiber reinforcements are of particular interest due to their supreme stiffness-to-weight-ratio, but the machining of this material class is still a challenge in production, rework, and repair. Laser technology presents a powerful solution to this obstacle. Laser ablation is a technique that can be utilized for repair preparation of composites. However, for large-scale industrial acceptance, laser processes must incorporate a high degree of automation. In this paper, an approach to fully automatic control of the laser-based repair preparation is presented. It features two process monitoring techniques: fiber orientation detection and short coherent interferometry. Both techniques aim to support the material ablation process by enabling homogeneous material removal to a specific material layer or specified depth. Between laser scanning cycles, a camera-based system scans the processed surface and measures the fiber orientation in each pixel with regard to an adjustable tolerance. When scarfing composite parts made from noncrimped carbon fibers, this technique can be used to detect the transition between two consecutive layers. Areas in the scarfing zone that reveal fibers of the next layer will be excluded from further laser scanning until the current fiber layer is completely ablated. Composite parts reinforced by crimped fabrics are similarly processed, but since single material layers are not easily identified, the ablation process is not controlled by fiber orientation measurements. Instead, short coherent interferometry is used to detect the ablated depth and compare it to the part's ideal layer thickness. Areas in the scarf zone that have reached the desired depth are excluded from further laser scanning. This technique is also applicable to noncrimped fabrics. The results show that when the laser-based repair preparation is controlled by these techniques, precise scarfing can be achieved.
A Study on Unofficial Geographic Location Data Acquisition Technology Path under the Background of Big Data Era

The arrival of the era of big data has made people break through the bottleneck of various technologies and resources faced by traditional social sciences, but at the same time, new problems such as data acquisition, data value mining, data storage, circulation and exchange, data privacy and security have also brought new challenges to people. In order to explore a new methodology of geographic location data acquisition, this paper proposed the technical path of unofficial geographic location data acquisition by using web crawler tools and test the feasibility of it.
Tigers, black swans, and unicorns: The need for feedback and oversight

Humans have a decision-making system which is biased to avoid costly false negatives while the criminal justice system is designed to be biased in the opposite way, avoiding costly false positives. But systems fail, people do not; a badly out of kilter system can lead even the most expert to bad outcomes. Perverse incentives, driven by the fetishizing of DNA, put pressure on an already-stressed forensic system. Every system needs feedback, both positive and negative, to correct itself and stay stable, forensic science is only one of those in a criminal justice system. Recognizing false positives, false negatives, and how they happen is critical to stabilizing and calibrating a criminal justice system. Oversight, review, and addressing wrongful convictions is a necessary form of feedback to forensic science and any balanced and fair criminal justice system.
Multiple Robust High-degree Cubature Kalman Filter for Relative Position and Attitude Estimation of Satellite Formation

The High-degree Cubature Kalman Filter (HCKF) is proposed as a novel methodology based on the arbitrary degree spherical rule, which can achieve better performance than the traditional Kalman filter. However, it also has a large calculation burden when used in a high-dimension and high-degree of accuracy estimation system. The number of sampling points of an HCKF increases polynomially with increasing state-space dimensions, which further increases the calculation burden. The reduction of the number of the state-space dimensions is the main contribution of this study. A strategy for HCKF based on the partitioning of the state-space and orthogonal principle is introduced, referred to as the Multiple Robust HCKF (MRHCKF). It is shown that this technique can effectively reduce the calculation burden for the high-dimension system with robust performance. Numerical simulations are performed for the example of high-dimension relative position and attitude estimation to show that the proposed method can obtain nearly the same performance as the HCKF, while drastically reducing computational complexity.
Part-based correlation filter tracking by exploiting the similarity and contribution of reliable parts

Challenges like occlusion, background clutter, illumination and scale variation make visual object tracking a tough problem in computer vision. Part-based tracking methods have been widely used to solve the partial occlusion issue, different from various holistic appearance model based trackers, it combines the local appearances of the target to build a robust global appearance. In this paper, we propose a novel object tracking method based on multi-part scheme and Bayesian framework, using the Kernelized Correlation Filter (KCF) as the base tracker. Additionally, two robust reliability metrics are proposed: the first one utilizes the similarity between the corresponding parts in consecutive frames to measure the reliability; while the second one measures the contribution that each part made for the global target object. Besides, adaptive updating strategy and scale estimation which employ the relationships among parts are proposed to deal with appearance modeling. Extensive experiments have been conducted on latest benchmark to demonstrate that our method outperforms state-of-the-art trackers.
A Comparative Study of Maintainability versus availability Index of Open Source Software

Background/Objectives: The OSS is generally unique in relation to the proprietary programming. In the OSS improvement situation an absolute designer alternately aggregation for developers composes that source book to the primary rendition of the product and makes it uninhibitedly accessible over those webs. At that point other developers would welcome to help the existing code to its next arrival. Settling on the source book of the programming accessible on the web permits developers around the universe to help code, include new functionality, change of the existing source book and submitting bug fixes of the current arrival. Previously, such a programming improvement situation the upkeep of the open hotspot product is a culprit assignment. Creating an OSS framework infers an arrangement from claiming incessant upkeep deliberations to debugging existing purpose what's more including new purpose of the programming framework. The procedure for making those adjustments to product frameworks then afterward their initially arrival may be known as upkeep procedure. The expression maintainability may be nearly identified with that product support. Portability, adaptability and issue tolerance are the opposite essential part that ensures that seamlessness of the programming item. Those generally utilized programming metric to quantify the maintainability is known as Maintainability list (MI). Methods: In this investigation the MI from claiming four A large portion mainstream OSS to be specific Apache, Mozilla Firefox, MySql and FileZilla for fifty progressive discharges might have been observationally investigated. The MI as far as programming measurements in particular Lines about code (LOC), Cyclomatic unpredictability (CC), and Halstead Volume (V) might have been registered for every last one of fifty progressive variants about four OSS. Findings: Proposed agent based approach are cost effective, efficient and accurate solutions to evaluate the software (web application) RMA metrics including the monitoring in cloud computing services that means "Testing/Debugging as a Service Evaluation". Proposed system has performs faster and produce more accurate results to assure the quality of the software related to the non-functional. Keywords: Agent, Availability, Cloud Computing, Cyclomatic intricacy, Lines Code, Maintenance, Maintainability, Maintainability Index, Reliability, RMA, Software of Open Source, T/DaaS, Volume of Halstead
Stabilization of All Bell States in a Lossy Coupled-Cavity Array

A scheme is proposed to generate maximally entangled states of two Λ -type atoms trapped in separate overdamped optical cavities using quantum-jump-based feedback. This proposal can stabilize not only the singlet state, but also the other three triplet states by alternating the detuning parameter and relative phase of the classical fields. Meanwhile it is convenient to manipulate atoms, and much more robust against spontaneous emission of atoms. The parameters related to the potential experiment are analyzed comprehensively and it is confirmed that the quantum feedback technology is a significant tool for entanglement production with a high fidelity.
Talking Avatar

With the booming of the mobile computing and web technology, virtual and intelligent mobile applications become increasingly popular, e.g. web computing and web-based information retrieval. However, under contemporary network conditions and web application environment, it remains a challenging problem to achieve a trade-off between algorithm complexity and hardware performance. In this article, a Talking Avatar architecture is presented based on third-party cloud services. First, the authors propose a cloud service based multi-level layered software framework, which consists of user interface layer, business logic layer and data layer. Second, human face synthesis, speech conversion and social sharing schemes are introduced to integrate third-party cloud services. Third, experimental results on Android platforms indicate that the proposed Talking Avatar can be served efficiently in terms of memory consumption as well as average response time. In addition, stronger functions are provided compared with existing methods.
Incorporate User Representation for Personal Question Answer Selection Using Siamese Network

Many natural language questions are inherently subjective. They can not be answered properly if we do not know the personal preferences of the answerer. For example, "Do you like cats?" There is no "the only correct answer" to this question. To answer it, the model has to be able to capture the persona of the answerers. However, the users usually do not answer different questions with equal chance. Instead, while some are answered with a high frequency, others are hardly answered by anyone. To deal with this imbalanced sparsity in data, we first introduce a Siamese Network to capture the preferences patterns of the users. Then the model is ensembled with an additional dense layer to predict the answers of the users. Applying to an online dating dataset, our approach achieves a high accuracy of 78.7%.
Optimizing the C Index Using a Canonical Genetic Algorithm

Clustering is an important family of unsupervised machine learning methods. Cluster validity indices are widely used to assess the quality of obtained clustering results. The C index is one of the most popular cluster validity indices. This paper shows that the C index can be used not only to validate but also to actually find clusters. This leads to difficult discrete optimization problems which can be approximately solved by a canonical genetic algorithm. Numerical experiments compare this novel approach to the well-known c-means and single linkage clustering algorithms. For all five considered popular real-world benchmark data sets the proposed method yields a better C index than any of the other (pure) clustering methods.
Petri Net Approach for Estimation the Information Systems Security Level in Any Logic Environment

At present, the security of information system is becoming one of the biggest challenges for all organizations. Information security is the protection of information and systems from unauthorized access, use, disclosure, modification, destruction in order to provide confidentiality, integrity, and availability. In this article, we discuss the methodology for estimating the security of information systems based on hybrid modeling in the Any Logic environment. A conceptual model of the information protection system is described in this paper. Moreover, a data structure that allows the behavior of agents to access the information system is presented here.
Research load balancing technology of distributed database based on consistent hash

Consistent hash algorithm is applied to build a distributed database load balancing model, but this model should deal with thousands of user requests, handle 10 billion data information, along with low-latency response scenarios, all of this is a grim challenge. When load balancing control mechanism is built by the consistency hash, some nodes in the database cluster are overloaded, but some are idle. These load imbalances can seriously do great damage to the overall performance of distributed database system. This paper proposes a detailed description of variance mathematics model about dynamic load balancing, the core is to track system load, evaluation, classification and storage of each node in distributed cluster. This algorithm controls mutual feedback between node load states, idle data node allots the item of overload node, overall suppression of single point overload. After by experimental simulation, compared with the auxiliary loop hash model, this algorithm improves load balancing efficiency by 30% and settles disputes about distributed database load imbalance based on consistent hashing.
Cross-lingual Speech-based Tobi Label Generation Using Bidirectional Lstm

In this paper we investigate the automatic generation of ToBI-style prosody labels. The work is motivated by the idea of using prosodic information to facilitate the automatic lexicon discovery for unseen and under-resourced languages for which sufficient training data is not available. Specifically, the prosodic boundaries are meant to serve as additional top-down information in the word segmentation step. To this end we attempt to apply the trained Japanese models cross-lingually on a language not seen in training (English). We generate break index labels, using only the speech signal as input, with no additional information given at test time in the form of transcripts or prior word segmentations. The labels are generated using bidirectional LSTMs trained on spontaneous Japanese speech. We evaluate the quality of these labels using established metrics, with an F1 score of 0.55 for cross-lingual prosodic break detection (given a tolerance of 80 ms).
Person re-identification based on linear classification margin

The core of the person re-identification task is to find a discriminative feature map or a good measure to measure the similarity between two pedestrians. Under the traditional softmax loss function constraint, the characteristics of the FC layer output of the convolutional neural network are only separable and the discriminative ability is insufficient. Our proposes a person re-ID method based on decision margin. By adding the traditional softmax loss function to the angle decision margin, the characteristics of strong discriminative power in network learning are enhanced. This paper provides a clear geometric interpretation by normalizing the last fully connected layer parameters and pedestrian characteristics of the network and projecting the embedded features into the geometric space for person re-identification. Combining two different classification margins, a linear classification margin is proposed and good performance is achieved. This paper validates the effectiveness of the algorithm on the mainstream databases of Market1501 and DukeMTMC-reID. The results show that our method can get more discriminative features, and its experimental performance is significantly improved compared to the existing advanced algorithms.
Social Privacy Score Through Vulnerability Contagion Process

The exponential usage of messaging services for communication raises many questions in privacy fields. Privacy issues in such services strongly depend on the graph-theoretical properties of users' interactions representing the real friendships between users. One of the most important issues of privacy is that users may disclose information of other users beyond the scope of the interaction, without realizing that such information could be aggregated to reveal sensitive information. Determining vulnerable interactions from non-vulnerable ones is difficult due to the lack of awareness mechanisms. To address this problem, we analyze the topological relationships with the level of trust between users to notify each of them about their vulnerable social interactions. Particularly, we analyze the impact of trusting vulnerable friends in infecting other users' privacy concerns by modeling a new vulnerability contagion process. Simulation results show that over-trusting vulnerable users speeds the vulnerability diffusion process through the network. Furthermore, vulnerable users with high reputation level lead to a high convergence level of infection, this means that the vulnerability contagion process infects the biggest number of users when vulnerable users get a high level of trust from their interlocutors. This work contributes to the development of privacy awareness framework that can alert users of the potential private information leakages in their communications.
Distributional Reinforcement Learning with Quantum Neural Networks

Traditional reinforcement learning (RL) uses the return, also known as the expected value of cumulative random rewards, for training an agent to learn an optimal policy.However, recent research indicates that learning the distribution over returns has distinct advantages over learning their expected value as seen in different RL tasks.The shift from using the expectation of returns in traditional RL to the distribution over returns in distributional RL has provided new insights into the dynamics of RL.This paper builds on our recent work investigating the quantum approach towards RL.Our work implements the quantile regression (QR) distributional Q learning with a quantum neural network.This quantum network is evaluated in a grid world environment with a different number of quantiles, illustrating its detailed influence on the learning of the algorithm.It is also compared to the standard quantum Q learning in a Markov Decision Process (MDP) chain, which demonstrates that the quantum QR distributional Q learning can explore the environment more efficiently than the standard quantum Q learning.Efficient exploration and balancing of exploitation and exploration are major challenges in RL.Previous work has shown that more informative actions can be taken with a distributional perspective.Our findings suggest another cause for its success: the enhanced performance of distributional RL can be partially attributed to its superior ability to efficiently explore the environment.
Multifeature fusion for robust human detection in thermal infrared imagery

Despite all the significant advances in human detection in various environmental conditions, it is still a challenging task. Most of the human detection algorithms mainly use color information, which is not robust to lighting changes and varying colors under which such a detector should operate namely day and nighttime. This problem is further amplified with infrared (IR) imagery, which only contains grayscale information. The proposed algorithm for human detection uses intensity distribution, gradient, and texture features for effective detection of humans in IR imagery. For the detection of intensity, histogram information is obtained in the grayscale channel. For extracting gradients, we utilize the histogram of oriented gradients for better information in the various lighting scenarios. For extracting texture information, center-symmetric local binary pattern gives rotational invariance as well as lighting invariance for robust features under these conditions. Various binning strategies help keep the inherent structure embedded in the features, which provide enough information for robust detection of the humans in the scene. The features are then classified using an AdaBoost classifier to provide a tree-like structure for detection in multiple scales. The algorithm has been trained and tested on IR imagery and has been found to be fairly robust to viewpoint changes and lighting changes in dynamic backgrounds and visual scenes.
Quantitative Cytologic Descriptors to Differentiate CLL, Sézary, Granular, and Villous Lymphocytes Through Image Analysis

We aimed to find descriptors to identify chronic lymphocytic leukemia (CLL), Sézary, granular, and villous lymphocytes among normal and abnormal lymphocytes in peripheral blood.Image analysis was applied to 768 images from 15 different types of lymphoid cells and monocytes to determine four discriminant descriptors. For each descriptor, numerical scales were obtained using 627 images from 79 patients. An assessment of the four descriptors was performed using smears from 209 new patients.Cyan correlation of the nucleus identified clumped chromatin, and standard deviation of the granulometric curve of the cyan of the nucleus was specific for cerebriform chromatin. Skewness of the histogram of the u component of the cytoplasm identified cytoplasmic granulation. Hairiness showed specificity for cytoplasmic villi. In the assessment, 96% of the smears were correctly classified.The quantitative descriptors obtained through image analysis may contribute to the morphologic identification of the abnormal lymphoid cells considered in this article.