Reviewerly/exHarmony huggingface dataset:
Thinking on the Introduction of Mobile Phone APP into College Physical Education Teaching

Nowadays, with the continuous development of information technology, the introduction of related mobile phone APP in the current college physical education teaching can not only improve some disadvantages existing in the traditional teaching process, but also further improve students’ learning and understanding, which is of great benefit to the development of college physical education teaching. In order to make these teaching application APP software work, university physical education teachers must combine with the actual situation of university physical education teaching and carry out diversified application thinking.
A Fuzzy String Matching Scheme Resistant to Statistical Attack

The fuzzy query scheme based on vector index uses Bloom filter to construct vector index for key words. Then the statistical attack based on the deviation of frequency distribution of the vector index brings out the sensitive information disclosure. Using the noise vector, a fuzzy query scheme resistant to the statistical attack serving for encrypted database, i.e. S-BF, is introduced. With the noise vector to clear up the deviation of frequency distribution of vector index, the statistical attacks to the vector index are resolved. Demonstrated by lab experiment, S-BF scheme can achieve the secure fuzzy query with the powerful privation protection capability for encrypted cloud database without the loss of fuzzy query efficiency.
Deep Unsupervised Activity Visualization using Head and Eye Movements

We propose a method of visualizing user activities based on user's head and eye movements. Since we use an unobtrusive eyewear sensor, the measurement scene is unconstrained. In addition, due to the unsupervised end-to-end deep algorithm, users can discover unanticipated activities based on the exploratory analysis of low-dimensional representation of sensor data. We also suggest the novel regularization that makes the representation person invariant.
Abnormal Action detection in video surveillance

The growing number of anomalies happening in indoor and outdoor environments calls for accurate and robust action recognition systems. These anomalies could vary from theft, destruction of public property or even fighting innocents. The aim of this paper is to introduce a new algorithm based on machine learning paradigm to detect human actions and to label them as normal or abnormal. The algorithm starts by testing two different human detectors, cascade object detector and Faster Region Convolutional Neural Network for Human Detection (FRCNNHD). Both detectors were trained using widely available datasets. Afterwards, detected human figures are ex-tracted to form a video patch that represents human motion. For action recognition, we applied the Motion History Image to extract static features of motion. The actions are then classified using the Support Vector Machine (SVM). Finally, actions with low recognition confidence are labeled as “abnormal actions”. Experimental results on two datasets show the accuracy of our algorithm on learned actions.
Influence of Chinese English Bilingual Corpus of Traditional Chinese Medicine Prescriptions on ESP Writing Under the Background of Big Data

Objective: the construction and application of Traditional Chinese Medicine (TCM) English corpus is the new trend of TCM English research and development. The establishment and development of TCM bilingual corpus is conducive to ESP teaching, the formation of unified standards and norms of TCM English, the provision of reference standards and basis for TCM English translation, and ultimately the rapid development of TCM English subject, and the internationalization of TCM Service. Methods: the corpus used in this study is from the Chinese-English version of “Jingui Yaolue” (Synopsis of the Golden Chamber) translated by Li Zhaoguo and Liu Xiru, and the self translated version of “Tang Tou Ge Jue” (Decoctions in Rhymes). The research on English corpus of Traditional Chinese medicine based on corpus linguistics is briefly summarized, and the role and significance of the construction of English corpus of TCM prescriptions for English writing of traditional Chinese medicine are analyzed and expounded. The junior students (n = 80) of a college of TCM were randomly divided into two groups: experimental group (n = 40) and control group (n = 40). The experimental group used bilingual corpus of prescription terms as the driving force in the process of writing TCM English. The control group adopted the conventional writing mode, that is, the teacher explained the Chinese medicine vocabulary that may be involved in the writing process firstly, and then the students reviewed and wrote. In this study, a self-designed ESP writing ability scale of TCM was used to measure the writing ability of the experimental group and the control group. Results: in the five dimensions of word accuracy, sentence structure, text structure, writing efficacy and writing motivation, the post test of the experimental group and the control group were significantly better than the pre test (p < 0.05); in the post test, the performance of the experimental group was significantly better than the control group (p < 0.01). In the two dimensions of grammatical accuracy and content innovation, the post test scores of the experimental group and the control group were significantly higher than the pre test (p < 0.05), but in the post test, there was no significant difference between the experimental group and the control group (p > 0.05). Conclusion: the traditional teaching method of foreign language is mainly based on the rules taught by teachers, while the data-driven ESP teaching method, from the perspective of language acquisition, focuses on students, provides students with a language environment, enables students to discover language rules from the corpus, and greatly enhances students’ autonomous learning ability.
Global Optimization on the Sphere with Half-space Constraints: A Stochastic Hybrid Systems Approach

This paper develops a stochastic, hybrid optimization algorithm for globally minimizing an arbitrary continuously differentiable (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) function on the unit sphere intersected with an arbitrary half-space in ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Hysteresis switching between coordinate charts is used to enable the algorithm to fully explore the sphere by flowing. During flows, the optimization algorithm uses (projected) gradient descent when near the boundary of the half space. It may use an update rule inspired by accelerated gradient methods away from the boundary of the half space. It uses hysteresis switching between the two continuous-time update methods. Periodically, stochastic probing on the sphere is used to attempt to improve the value of the cost function. This stochastic step prevents the algorithm from getting stuck at singularities of the cost function's gradient that do not correspond to global minima. A stability analysis of the algorithm is provided and the algorithm is demonstrated on a numerical example.
Binary Satin Bowerbird Optimizer for the Set Covering Problem

The set covering problem (SCP) is one of the most studied NP-hard problems in the literature. To solve the SCP efficiently, this study considers a recently proposed bio-inspired meta-heuristic algorithm, called satin bowerbird optimizer (SBO). Since the SBO was first introduced for the global optimization problem, it works on a continuous solution space. To adapt the algorithm to the SCP, this study introduces a binary version of the SBO (BSBO). The BSBO simply converts real value coded solution vector of the SBO to binary coded solution vector by applying a binarization procedure. In addition to binarization procedures, a solution improving operator is employed in the BSBO to transform infeasible solutions into feasible solutions and remove redundant columns to reduce solution cost. The performance of the proposed BSBO is tested on a well-known benchmark problem set consists of 65 instances. With regards to the best-known solutions of the instances, efficient results are obtained by the proposed BSBO by finding near-optimal solutions. Furthermore, standard deviations of the runs demonstrate the robustness of the algorithm. As a consequence, it should be noted that the proposed solution approach is capable of finding efficient results for the SCP.
Understanding Convolutional Networks Using Linear Interpreters - Extended Abstract

We are interested in the general problem of understanding the decisions made by convolutional neural networks. We focus on three popular and successful architectures. First, the VGG architecture used for image classification, that takes images into labels. Second, the EDSR architecture that upscales small resolution images into high resolution images with 16 times more pixels. And finally, the CycleGAN architecture that translates images of same size from one domain to another, for example: photos to paintings of different styles, or facades to segmentation maps. In these three cases image pixels are either reduced to scalars (classification), or increased to form more complex images (super-resolution), or kept in the same vector space but in different style domains (image-to-image translation). These three architectures are largely made of linear systems. In fact, all of their learnable parameters are contained in convolutional layers, which are linear. Furthermore, we note that the action of all the non-linear layers involved are motivated by linear operations. For example, an ReLU is easily interpreted as a 1/0-mask that multiplies the input (linear) with a mask determined by the sign of each feature pixel (non-linear). A max-pooling layer selects one among a group of pixels and allows a similar interpretation by using selection masks. An instance-normalization layer subtracts a mean and divides by a standard deviation. If we fix the mean and standard deviation, it becomes linear. Now we consider a fixed input image, determine the 1/0-mask for each ReLU and max-pooling, mean and variances of instance-normalizations, and fix them. The network then becomes a large linear system, that we call the Linear Interpreter. Our purpose is to study these systems arising from popular deep learning architectures. We can use well known methods and theory of linear systems to get important insights on how these networks work.
Images’ Partially Blurred Part Location and Restoration based on the Calculation Model and GAN Algorithm

Digital images would be blurred due to defocus and motion of objects. A lot of researches have been done on the restoration of motion blurred images. Unlike the global motion blurred image, the local blurred image needs different methods to recover as there is both clear and blurred part in an image. In this paper, we proposed a method combining calculation model and Generative Adversarial Network (GAN), which can automatically identify the local blurred region and deblurring. Also, the blurred part can be filled back correctly. Firstly, the gradient image of the complete image is calculated based on the Sobel operator. The boundary information of the fuzzy region and the Gauss function variance of the blurred and clear region are obtained. And the power spectral gradient of the whole image and each local pixel block are further compared with the variance of the Gauss function. After the analysis is accomplished, a more accurate fixed position is achieved. After finishing extracting with clipping pixels, the extracted fuzzy region is input into the pre-trained Generative Adversarial Network to restore the local image. Finally, the alpha channel algorithm is used to calculate the RGB component without the alpha channel of the two images. The simulation results show the feasibility of this method.
Forensic Analysis of Residual Artifacts on Bitnami Hadoop

The growth of Hadoop usages is estimated to become increasingly larger over the coming years. This also makes it to become a target of criminals. Forensic researchers are moving towards the investigation researches of locating and documenting the residual artifacts to trace the criminal activities on Hadoop Platform. Bitnami is one of the popular Hadoop Platforms, providing the Big Data solution to run own Hadoop server in the cloud. The retrieval of digital evidence to embody the crime scenes on Bitnami is a challenge in forensic investigation, due to its complex infrastructure and, lack of knowledge on location of digital evidence. This paper proposes a forensic investigation framework for locating and discovering the residual artifacts that remain on the Bitnami Hadoop Stack. The remained artifacts can provide the potential evidences for forensic examiners to extract the evidences, and reconstruct the crime scene.
Mining Spatio-Temporal Features from mmW Radar echoes for Hand Gesture Recognition

Human gesture recognition is a new way of interaction and a new application direction of millimeter wave radar. Compared with Doppler radar, FMCW radar can eliminate Doppler frequency interference of moving targets at different distances and accurately obtain the velocity-range information during gesture motion. In this paper, we use the 77GHz millimeter wave radar to extract the time variation characteristics of the Doppler frequency of the gesture. The convolutional neural network was selected to classify the gesture mining spatiotemporal features of the five volunteers. The experimental results show that the feature can describe the gesture velocity change information well and can significantly improve the versatility of the network by adding small amount data of more volunteers data to establish a personal dataset.
A Public Key Encryption System Derived from the Pascal Parallelogram

The problem faced by all symmetric or private key cryptosystems is the difficulty of communicating the private key to all participants in the network. The degree of key sharing is on the order of the square of the number of nodes in the network. The concept of asymmetric or public key cryptology effectively solved this problem by dividing the key into two parts, one of which is made public. The remaining part need never be transmitted beyond the creator of the key. At the beginning of the era of public-key cryptology, many proposed cryptosystems were based on the classic subset sum knapsack problem. However most of these were shown to be insecure by the research of Lagarias and Odlyzko, and also Brickell. In this paper, we modify a previous method that we originally called the Pascal Knapsack Method. The current approach improves on security and computation time and provides a reasonable alternative to the most common public-key cryptosystem, the Rivest-Shamir-Adleman PKC.
Accelerating weight spectrum calculation of linear block codes defined over GF(q) using GPU

Recently a new family of almost Maximum Distance Separable (MSD) five-times extended Reed Solomon codes was introduced. The properties of these codes were proven analytically and their weight spectra were calculated by computer for selected fields up to GF(512). Given the constrained time and processing power available, in order to make the computation feasible, several important optimizations to the process of code weight spectrum calculation had to be implemented. This paper focuses on describing techniques in Matlab and C, to be used in order to achieve a substantial (several orders of magnitude) improvement in weight spectrum calculation runtime for general linear block codes defined over GF(q). Furthermore, a GPU implementation is described in detail and compared quantitatively with previously published results.
Constantin Silvestri, the problematic musician. New press documents

Abstract Constantin Silvestri was a man, an artist who reached the peaks of glory as well as the depths of despair. He was a composer whose modern visions were too complex for his peers to undestand and accept, but which nevertheless stood the test of time. He was an improvisational pianist with amazing technique and inventive skills, and was obsessed with the score in the best sense of the word. He was a musician well liked and supported by George Enescu and Mihail Jora. He was a conductor whose interpretations of any opus, particularly Romantic, captivate from the very first notes; the movements of the baton, the expression of his face, even one single look successfully brought to life the oeuvres of various composers, endowing them with expressiveness, suppleness and a modern character that few other composers have ever managed to achieve. Regarded as a very promising conductor, a favourite with the audiences, wanted by the orchestras in Bucharest in the hope of creating new repertoires, Constantin Silvestri was nevertheless quite the problematic musician for the Romanian press. Newly researched documents reveal fragments from this musician’s life as well as the features of a particular time period in the modern history of Romanian music.
Open Your Mind to Become an Effective Safety Leader

“Progress is impossible without change, and those who cannot change their minds cannot change anything.” - George Bernard Shaw There are many great leaders in electrical safety, but how many of them have changed their minds on effectively communicating the challenges and needs for electrical safety? How many leaders in today's multi-generational workplace have been effective at accepting this challenge and driving change? Under the IEEE IAS Electrical Safety Workshop mission statement of “Advancing the Electrical Safety Culture,” safety leaders are being tasked with finding new ways to change and advance the culture in a period when many companies are looking to technology to help them gain the innovative advantage. Clear and effective communication amongst all generations is the key to advancing electrical safety. Safety professionals and workers alike must rise to this challenge by looking first at their own styles of communication, communication of their coworkers and peers, and then by working closely together to effectively and clearly communicate electrical safety.
Context-Enhanced Vehicle Tracking Method Under the Connected Environment (Poster)

Vehicle tracking is one of the key technologies for intelligent vehicle, which can prevent collision and save lives. The on-board sensors are commonly used for vehicle tracking while they always contain some noises and clutter measurements. The context-based information can bring several advantages for refining estimations, which thereby improves the performance of tracking. This paper proposes a novel context-enhanced vehicle tracking method with contextual information for modeling interaction behaviors between the vehicles and the environment, while the traditional algorithms assume that vehicles move independently. The approach combines interaction force with dynamic Bayesian networks by using context about the environment, object and traffic. In order to handle complex driving behavior of correlated context random variables, the dynamic Bayesian networks are used for the reasoning and implementation of the impact of contextual interaction. The simulation confirms the proposed approach improves the tracking accuracy and gets a better prediction of the uncertainties motion.
Multi-objective Optimization of Composing Tasks from Distributed Workflows in Cloud Computing Networks

This manuscript proposed and explored a novel strategy for optimizing the composition of tasks from distributed workflows to achieve parallel execution, optimality toward resource utilization. The critical objective of the proposal is to optimize the task sequences from the workflows initiated to execute parallel in distributed RDF environment, which is unique regard to the earlier contributions related to parallel query planning and execution strategies found in contemporary literature. All of these existing models are aimed to notify the tasks from the given workflow, which are less significant to optimize the parallel execution of the multiple tasks located in distributed workflows. In order to this, the multi-objective optimization of composing task (MOCT) sequences from distributed workflows is proposed. The MOCT optimizes the execution of tasks from multiple workflows initiated in parallel. A novel scale called “task sequence consistency score” uses the order of other metric task sequence coverages, dependency scope, and lifespan as input. The experiments are conducted on the proposed model, and other benchmark models are found in contemporary literature. The results are obtained from the experimental study evincing that the MOCT is significant and robust to optimize the task sequences in order to execute distribute workflows in parallel. The comparative analysis of the results is obtained from MOCT, and other contemporary models are performed using ANOVA standards like t-test and Wilcoxon signed-rank test.
A Novel Method for Segmentation of Leukocyte Nuclei Based on Color Transformation

Acute lymphoblastic leukemia is one of the most common hematologic malignancies among children, caused by uncontrolled growth of leukocytes. Since the main hallmarks of the disease is not specific, a considerable number of patients have been being misdiagnosed. Early diagnosis of the disease is usually made by morphological investigation of leukocytes under microscope. In light of the facts that decrease in cytoplasm-to-nucleus ratio is one of the main indicators of cancerous cells, and an accurate segmentation phase will lead to extraction of representative features, segmentation step is acknowledged as being crucial in design of a computer aided diagnosis (CAD). Previous researches have utilized standard, pre-defined color spaces, such as CMYK, for the segmentation of leukocyte nucleus. However, since these color spaces were not designed specifically for the segmentation task, they cannot extract nuclei efficiently. Thus, in this paper, by using feed forward neural networks, we propose a color transformation method, which maps RGB to a special 2D space. Design parameters of the neural network are tuned by using genetic algorithm. In the new space, nuclei of the leukocytes have the highest discrimination against background, so by using Otsu thresholding, one can extract the nuclei easily. Efficacy of the proposed method is evaluated by using ALL-IDB2 dataset, which is publicly available. Our obtained Dice similarity coefficient is higher than that of the other newly devised algorithms, showing its great performance.
Development of a Methodology of 3D Modeling of Heads for Their Application in the Design of Customized Sports Helmets

This works deals with the development of a methodology for 3D modeling of heads, so that, with the digital model that is obtained, the interior of a sports helmet can be customized to improve comfort and protection against impacts during its subsequent use. In order to define the final methodology, according to each one of the business lines (face-to-face and online), different tools have been studied and tested; all intended for the same purpose, which is 3D modeling, but through different techniques. The techniques used have been the one of scanning by means of structured light technology, time-of-flight and photogrammetry with different digital cameras. The three techniques have been tested throughout the study on a dummy bust and have subsequently been applied to real users. This study aimed to determine the influence of certain factors, such as luminosity or the presence of hair among others, in each of the different techniques of modelling. The error between the obtained digital models and the real head has been determined through different control measurements. So establish what conditions are most appropriate for each of the techniques and select the optimal methodology for each of the future lines of business.