Reviewerly/exHarmony huggingface dataset:
Feature map masking based single-stage face detection

Although great progress has been made in face detection, a trade-off between speed and accuracy is still a great challenge. We propose in this paper a feature map masking based approach for single-stage face detection. As feature maps extracted from feature pyramid network might contain face unrelated features, we propose a mask generation branch to predict those significant units for face detection. The masked feature maps, where only important features are left, are then passed through the following detection process. Ground truth masks, directly generated from the training images, based on the face bounding boxes, are used to train the feature mask generation module. A mask constrained dropout module has also been proposed to drop out significant units of the shared feature maps, such that the detection performance can be further improved. The proposed approach is extensively tested using the WIDER FACE dataset. The results suggest that our detector with ResNet-152 backbone, achieves the best precision-recall performance among competing methods. As high as 95.4%, 94.0% and 86.9% accuracies have been achieved on the easy, medium and hard subsets, respectively.
An Online Prediction and Trajectory Tracking Method for Human Activity Recognition

Human activity recognition(HAR) is a focus of current research. In this study, through feature selection algorithms, we researched the online posture classification and prediction. With the best selected features and classifiers from each layer, we proposed a method of online prediction and classification, and the prediction accuracy achieved 94%. Support vector machine (SVM) was used to classify seven types of posture with an accuracy of 89.3%. For the trajectory tracking, the acceleration and angular velocity were filtered for denoising, and the gyroscope were calibrated with zero point to reduce the error in trajectory tracking. On the basis of judging posture for walking, the acceleration information in the navigation coordinate system was calculated by the posture solution of quaternion. Then, the motion trajectory was predicted by inertial navigation information. After the experiment, the proposed method could recover the walking trajectory well.
Enabling Science Fiction

Patent law promotes innovation by giving inventors 20-year-long exclusive rights to their inventions. To be patented, however, an invention must be “enabled,” meaning the inventor can actually describe it in enough detail to teach others how to make and use the invention at the time the patent is filed. When inventions are not enabled, like a perpetual motion machine or a time travel device, they are derided as “mere science fiction”—products of the human mind, or the daydreams of armchair coots, that are not suitable for the patent system.This Essay argues that, in fact, the literary genre of science fiction has its own unique—albeit far laxer—enablement requirement. Since the genre’s origins, fans have demanded that the inventions depicted in science fiction meet a minimum standard of scientific plausibility. Otherwise, the material is denigrated as lazy hand- waving or, worse, “mere fantasy.”Taking this insight further, the Essay argues that, just as patents positively affect the progress of science and technology by teaching others how to make and use real inventions, so too can science fiction, by stimulating scientists’ imagination about what sorts of technologies might one day be possible. Thus, like patents, science fiction can have real world impacts for the development of science and technology. Indeed, the Essay reveals that this trajectory—from science fiction to science reality—can be seen in the patent record itself, with several famous patents tracing their origins to works of science fiction.
Analysis of Particle Swarm Optimisation for Training Support Vector Machines

Particle swarm optimisation can easily be used to optimise a function constrained by a set of linear equalities. The converging linear particle swarm optimisation algorithm has been developed specifically for this purpose. Training of a support vector machine requires the solution of such a linearly constrained quadratic optimisation problem. Due to the combinatorial explosion experienced when solving large instances of this quadratic optimisation problem, support vector machines are notorious for poor scalability when working with large datasets. The converging linear particle swarm optimisation algorithm, in conjunction with a decomposition approach, has therefore been put forward as an alternative method for training support vector machines, with a view towards addressing the problem of scalability. In this paper, a number of problems and shortcomings that may be encountered when implementing the converging linear particle swarm optimisation algorithm for training support vector machines, such as premature convergence due to the clamping procedure employed in the original algorithmic implementations are highlighted, and an approach to overcome these shortcomings is proposed. Finally, an analysis pertaining to the scalability of the proposed approach is conducted.
Feature-Driven Method for Identifying Pathogenic Social Media Accounts

In this chapter, we present a feature-driven approach to detect PSM accounts in social media. Inspired by the literature, we set out to assess PSMs from three broad perspectives: (1) user-related information (e.g., user activity, profile characteristics), (2) source-related information (i.e., information linked via URLs shared by users) and (3) content-related information (e.g., tweets characteristics). For the user-related information, we investigate malicious signals using causality analysis (i.e., if user is frequently a cause of viral cascades) and profile characteristics (e.g., number of followers, etc.). For the source-related information, we explore various malicious properties linked to URLs (e.g., URL address, content of the associated website, etc.). Finally, for the content-related information, we examine attributes (e.g., number of hashtags, suspicious hashtags, etc.) from tweets posted by users. Experiments on real-world Twitter data from different countries demonstrate the effectiveness of the proposed approach in identifying PSM users.
Review in Smart Oculus Lenses

Smart systems and applications play a important role in everyday life. And this feature makes it more valued and even more attractive to the users. In the communication access to information on demand, regarding place and time are almost a common practice. This ongoing advancement in innovation prompts the new developing wearable gadgets, for example, brilliant focal points. Normally, gadgets are intended for the advantages and functionalities that they can give well-being and the security angle. Brilliant glass depends on segments that are accessible in present-day cell phones. This incorporates the CPU, sensors, and working framework. This manner would have comparative security dangers. This paper recognizes the future innovation with well-being and security. The oculus focal points are outfitted with sensor, Wi-Fi, and Bluetooth to give alternatives, and openness includes directly before your eyes. This innovation makes it conceivable to understand message, surf the web without causing any interruption, gathering data of environment, and that is only the tip of the iceberg. The fate of IOT is boundless. The oculus focal point is like the Google’s most yearning venture—The glass. Smart oculus lenses technology protects your private and confidential documents from prying eyes. It can review and send your private and confidential documents with confidence. It provides continuous authentication across all your devices. This smart oculus lens is advanced feature spotlight sign documents with facial recognition. It also prevents cyber fraud.
Simulation of Modular Exponentiation Circuit for Shor's Algorithm in Qiskit

This paper discusses and demonstrates the construction of a quantum modular exponentiation circuit in the Qiskit simulator for use in Shor's Algorithm for integer factorization problem (IFP), which is deemed to be able to crack RSA cryptosystems when a large-qubit quantum computer exists. We base our implementation on Vedral, Barenco, and Ekert (VBE) proposal of quantum modular exponentiation, one of the firsts to explicitly provide the aforementioned circuit. Furthermore, we present an example simulation of how to construct a 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> mod 15 circuit in a step-by-step manner, giving clear and detailed information and consideration that currently not provided in the existing literature, and present the whole circuit for use in Shor's Algorithm. Our present simulation shows that the 4-bit VBE quantum modular exponentiation circuit can be constructed, simulated, and measured in Qiskit, while the Shor's Algorithm incorporating this VBE approach itself can be constructed but not yet simulated due to an overly large number of QASM instructions.
Solving time-dependent heat conduction problems using metaheuristic algorithms extended with a novel local search strategy

Abstract This study proposes a novel and dexterous local search scheme for improving the exploitation phase of a generic metaheuristic algorithm. The proposed local search considers a twofold probing mechanism, which takes advantage of a chaotic number generated by the hybrid chaotic map composed of Logistic map and Kent map to move around the so-far-obtained global best solutions to reach feasible candidate solutions. Also, an iterative local search scheme inspired by a variant of the differential evolution algorithm is incorporated into the proposed manipulation scheme to enhance intensification on the promising regions. The proposed scheme is included in the well-reputed metaheuristics of differential evolution, crow search, whale optimization, and sine–cosine algorithms to assess the resulting improvements made on the optimization accuracy. Forty optimization benchmark functions composed of unimodal and multimodal test problems have been solved by the local search improved and basic forms of these optimizers to identify the amelioration in terms of solution accuracy and robustness. Two different real-world constrained optimization problems have been solved by these algorithms to analyze the improvement in solution qualities maintained by the utilization of the proposed local search method. Furthermore, these mentioned optimization algorithms along with their improved forms have been applied to one-dimensional transient heat conduction problems to obtain accurate temperature distribution across the heat transfer medium. Optimization results reveal that utilizing local search enhanced metaheuristic algorithms can be considered a favorable alternative to conventional solution methods for solving transient heat conduction problems.
Autonomous decision making by the self-generated priority under multi-task

In recent years, a robot is required to perform multitask autonomously in human living space. It needs to take actions according to situations. We proposed a method which does decision making on a robot with multi-task according to a situation by using an importance of each task. To respond to changes in importance of task, the robot learned each task independently by using reinforcement learning. An action is selected uniquely using action values and importance of each task in this system. The parameters are designed according to the value that represents the status of each task as an index for evaluating the importance. Therefore, it is necessary to design parameters suitable for the environment for each task. If the environment changes, the parameters must be designed accordingly. Therefore, in this research, we propose an autonomous decision-making method based on priority self-generation. The robot self-generates the priority of each task based on the experience gained by the robot, and realizes an action selection system with the priority matching the environment. We carried out an experiment which set three tasks to the robot applied proposal method. From experimental results, we confirmed the usefulness of proposed method.
HisRec: Bridging Heterogeneous Information Spaces for Recommendation via Attentive Embedding

A large volume of knowledge has been accumulated with the prevalence of social networks. Although extra knowledge can mitigate the problem of data sparsity, it is still a challenging task to integrate data across different information spaces for recommendation. In this paper, we propose to address recommendation that involves heterogeneous information spaces, namely interactive space (i.e. user-item), structural space (user-user) and semantic space (user-attribute). Instead of modeling each information space independently, we propose to seamlessly integrate information across heterogeneous spaces. To do this, we propose an attention mechanism in which the users/items attend differently to their structural neighbors (structural space) for learning compact representations. The attentions are parameterized by the interactions between user/item attributes (semantic space), and they are collaboratively learned for the recommendation task (interactive space). In this way, information across different spaces can be complementary to each other for boosting recommendation performance. We also prove that the proposed attentive embedding method is a generalization of traditional social regularization and network embedding methods. We validate the effectiveness of the proposed model with two real world datasets, and show that the proposed model is able to outperform state-of-the-art recommender models significantly.
Multimedia Educational Presentations for Independent (Autonomous) Work of Younger Students

The article reveals the possibilities of using multimedia educational presentations as a means of methodological support of the distance&#x0D; educational process in primary school. It is emphasized that multimedia educational presentations are considered by the authors only as an&#x0D; emergency alternative to traditional education, in conditions when there is a need for a general temporary transition to its distance form. The&#x0D; problems of organizing distance learning, which are especially relevant for primary school, and the ways of their solution with the help of&#x0D; multimedia educational presentations, which are able to stimulate independent (autonomous) work of primary schoolchildren with minimal&#x0D; involvement of adult family members, are identified. The requirements for a multimedia educational presentation are given, which correspond&#x0D; to the recommendations of the Ministry of Education of the Russian Federation for mastering the Basic educational program of an elementary&#x0D; school, as well as its characteristics and an algorithm for creating. The description of the multimedia educational presentation on the subject&#x0D; “Literary reading” for the lesson in the distance mode is given.
Improving Software Reuse Prediction Using Feature Selection Algorithms

Software engineering is witnessing a market struggle in delivering functional products due to time constraints. Software developers are reusing already existing components instead of developing the whole software from scratch. Predicting successful reuse can help before any reuse attempt. In this paper, feature selection was utilized to extract the most relevant attributes from a public dataset. Results showed that feature selection improved software reuse experience prediction.
Analisis Kesulitan Belajar Siswa Kelas III Sekolah Dasar Dalam Menyelesaikan Soal Cerita Matematika Berdasarkan Teori Newman di SDK HANOWAI ATAMBUA

Abstrack: The porpuse of this study is to describe student learning diffilcuties and factors the cause of students having difficulty learning third grade elementary school students in solving math story questions based on Newman’s theory. This method is descriptive qualitative. Collecting data used in this study was a structured interview and test method, anf the subjects in this study were 12 students of class III SDK Hanowai Atambua. The result showed thst the learning difficulties of grade III students in solving math strory problems related to the standard unit material for length, weight, and time were based on Newman’s theory namely (1) reading difficulties faced by students, there were 2 subject, (2) there were 3 students’ understanding difficulties, (3) difficulties in transformation, (4) difficulties in process skill, and (5) difficulties in writing answer were experienced by all research subjects. The factors that cause it are because students are not fluet in reading question, students are not able to write things that are known and asked, students find it difficult to determine what formulas will be used for the completion process and students are unable to draw coclusions on the final prosess. So it can be concluded that grade III SDK Hanowai students need to improve the learning process to fix the problems in material learning.
The role of research culture in publications on social sciences

This work is dedicated to the role of research culture in promotion of scientific articles. Analysis is conducted on the most common errors or inaccuracies in writing the scientific articles. Definition is given to the research culture; the variants of derogation from research culture are described. At the same time, in comprehension of the role of research culture in scientific publications on social sciences, of particular importance is the experience of researchers, peculiarities of their social mentality, and engagement in the scientific context on the subject matter, as well as consonance of their scientific approach to one or another research school or direction. Maintenance of gnoseological status of the research is also of holds much significance. The following conclusions were formulated: 1) research culture is associated with the type of social mentality, which is of prime importance for the works on social sciences, thus, the articles dedicated to the topical social problems should reflect the specificity of social mentality; 2) command of the norms and rules of the Russian literary language also indicates the research culture, the authors of the article often do not pay due attention to this factor, and thus it affects the fate of their publication, necessitating revision; 3) research culture correlates with the questions of ethics in the research.
Forensic Human Image Identification Using Medical Indicators

Diseases not only bring troubles to people's body functions and mind but also influence the appearances and behaviours of human beings. Similarly, we can analyse the diseases from people's appearances and behaviours and use the personal medical history for human identification. In this article, medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced, and were applied in the field of forensic identification of human images, which we called medical forensic identification of human images (mFIHI). The proposed method analysed the people's medical signs by studying the appearance and behaviour characteristics depicted in images or videos, and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects. Through a conformity and difference analysis on medical indicators and their indicated diseases, it would provide an important information for human identification from images or videos. A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI, and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.
A Comprehensive Study on Load Balancing Algorithms in Cloud

In cloud computing, the allocation of tasks is one of the major NP-hard optimization problems. This problem attracts the researchers to provide solutions by designing and applying various optimization techniques. Depending on the infrastructure of cloud, number of tasks or user requests, the allocation is planned. The task allocation leads to two different scenarios: underloaded or overloaded. Hence, a load balancing is required for efficient use of the cloud structure. The load balancing of task (dependent/independent) in a virtual machine is one among of the major objectives in cloud structure. Load distribution in cloud computing has many dimensions like memory and computational load. Load balancing deals with the assessment of load between the nodes and doing normalization across nodes. Researchers have suggested different load balancing strategies in cloud environment. This research paper has discussed different load balancing strategies and presents a taxonomy of the load balancing algorithms based on various parameters.
Finding the Blank with Sequence Labeling for English Learning

Previous approaches to generate fill in the blanks (FITB) questions for English learning mostly are limited to corpus-based approaches that generate Cloze collocates or most frequent co-occurring words. In this study, we propose a Natural Language Processing based approach to generate Fill In The Blank English learning exercises. First, we classify the nature of the problem, and second, we look into the generation of learning exercises. We limited the scope of this research to verb conjugation exercises but it can be extended to other types of learning exercises as well. We looked into the generation of English learning exercises as two types of problems, a sequence labeling problem where we label each token from a sentence whether it could be a potential blank or not and as a sequence to sequence generation problem where we measure its effectiveness in generating such English learning exercises. Generation of FITB for English grammar through these approaches can be useful in the field of Education and can act as a baseline for future work on this problem.
Bayesian MSTBurr mixture model in the construction of 3D-MRI brain tumor images

Abstract Detection of a brain tumor could be done with the serial of MRI images. The location and size of the tumor should be determined by viewing the 2D images individually. This kind of analysis is inefficient and error-prone. For better visualization, this study reconstructs a 3D structure from 2D MRI images. In recognizing the brain tumors, image segmentation is performed using the clustering analysis via Bayesian MSTBurr Mixture Model. The optimum cluster is selected by calculating the Correct Classification Ratio. The segmentation results for each image slice are performed in 3D rendering with the Matlab Volume Viewer. This study succeeded in creating a 3D model with a segmentation accuracy of 93.66% and an estimation of the tumor volume of about 33,556 mm 3 .
Cross-Domain Latent Modulation for Variational Transfer Learning

We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep representations from one data domain and use it as perturbation to the reparameterization of the latent variable in another domain. Specifically, deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal. Second, the learned deep representations are cross-modulated to the latent encoding of the alternate domain. The consistency between the reconstruction from the modulated latent encoding and the generation using deep representation samples is then enforced in order to produce inter-class alignment in the latent space. We apply the proposed model to a number of transfer learning tasks including unsupervised domain adaptation and image-to-image translation. Experimental results show that our model gives competitive performance.