Reviewerly/exHarmony huggingface dataset:
Evaluation of Higher Education Development Level in Jiangsu Province Based on Factors and Clustering Algorithm

With the continuous enhancement of China’s comprehensive strength, higher education has become an essential factor in promoting economic development. Through quantitative analysis and empirical research, this paper explores the status of higher education in 13 cities in Jiangsu Province from multiple perspectives. First of all, we use factor analysis to reduce the dimensions of many indicators, quantify the overall value of higher education levels in prefecture-level cities through factor scores, and obtain corresponding education level rankings. Nanjing’s higher education level ranked first. Then, we use the principal component factor scores obtained from the factor analysis and cluster analysis of 13 cities in Jiangsu Province to find out their classification characteristics to summarize the differences between the connections between cities and the regional distribution of education levels.
GraphBench: A Benchmark Suite for Graph Computing Systems

In the Big data and IoT era, graph data processing is widely used. The graph data is a kind of structural data that defined entities as vertices and described dependencies between different entities as edges. Today, a lot of graph computing systems emerge with massive diverse graph applications deployed, evaluating graph computing systems become a challenge work. Existing graph computing benchmarks are constructed with prevalent graph computing applications. However, the graph micro-benchmark is lacking, which is a key for the system fine-grained evaluation and obtaining the upper bound performance of the system. In this paper, we take graph computing applications as the combination of basic operations and user-defined operations. Then, we build the GraphBench benchmark suite with micro-benchmarks (basic operations) and component benchmarks (graph computing applications). At last, we evaluates the current mainstream graph computing frameworks with GraphBench. We found that there is no one-size-fits-all solution for the graph computing system. Using GraphBench, we can evaluate the graph computing system at the fine-grained level and get more insights.
Computer Numerical Simulation Analysis on Construction Optimization of Double-side Wall Pit Method in Large-section Tunnel

In order to improve the schedule and reduce the risk during the construction procedure of large-section subway station tunnel, the numerical simulation software FLAC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D</sup> is used to carry out the optimization simulation analyses in this paper. The optimization analyses focus on the construction procedure and temporary vertical support of double-side wall guide pit. Through the comparison and analysis of different working conditions, the results show that the surrounding rock stress and displacement are relatively small after the excavation of the optimized double-side wall guide pit method, and the vertical temporary support has limited effect on controlling the surrounding rock stress and displacement.
ОСОБЛИВОСТІ ОРГАНІЗАЦІЙНО-ПЕДАГОГІЧНИХ УМОВ ФОРМУВАННЯ ІНДИВІДУАЛЬНОЇ ОСВІТНЬОЇ ТРАЄКТОРІЇ МАЙБУТНЬОГО ВЧИТЕЛЯ МУЗИКИ У ПРОЦЕСІ ПРОФЕСІЙНОЇ ПІДГОТОВКИ

The article defines the issues of the specificity of the structure of the individual educational trajectory of the future music teacher. The algorithm of individualization of the student learning process is considered. The definition of the terms “individual educational trajectory” and “pedagogical condition” is given. The author briefly reveals the organizational and pedagogical conditions for the formation of the individual educational trajectory of future music teachers, in which he emphasizes the improvement and revision of educational programs on the basis of partner collaboration with a student. It also points to the need to maximize the use of organizational ideas of dual education by increasing the elements of practice-oriented education. Particular attention is paid to the need to modernize the functions of the teacher-curator through updating the content and principles of the institute of the curator.
Scene Attribute Semantic Relational Regularization for Transport-Travel Scene Understanding

Attribute learning has improved the performance in scene understanding and scene recognition. However, there are many attributes described by words or short texts in a static scene and traffic crowd scene. If there are two similar scenes, the semantic relationship topology structures of corresponding attribute groups of the two scenes are also homogeneity. But it is difficult to learn a semantic relation topology projection across semantic text data and visual data. To solve the problem, we construct approximate homeomorphism mapping based on the scene attributes semantic relational regularization. Hence, we propose a novel attribute semantic topological relationship regularization based scene attribute semantic learning(ARSL) method for scene semantic understanding. We establish a transport and travel scene recognition model based on attribute semantic features which are achieved by the proposed ARSL algorithm. In order to verify the proposed method, the experiments are implemented on the static transport-travel scene dataset and dynamic transport-travel crowds scene dataset respectively. The static transport-travel scene dataset is constructed by the SUN Attribute dataset including images and texts. However, the dynamic transport-travel crowds scene dataset is constructed through the WWW Crowd dataset including videos and texts, and the dynamic transport-travel crowd scene dataset is named as the WWW Crowd-Sub dataset. The performances of the proposed method are improved by 38.48% and 17.51% on the SUN Attribute dataset and WWW Crowd-Sub dataset respectively. The experimental results on the SUN Attribute dataset and WWW Crowd-Sub dataset demonstrate that the proposed approach has superior performance compared to state of the art. It can be demonstrated that the performance of the proposed ARSL method is effective against static transport-travel scene and dynamic transport-travel crowd scene.
A Particle Filter Track-Before-Detect Method for Monopulse Radar in Space Aircraft Launch Site

To improve the non-cooperative weak targets detecting and tracking for monopulse radar in space aircraft launch site, a particle filter track-before-detect method with CFAR based particle weighting is proposed in this paper. The Monte Carlo simulation experiments show that the method can provide 95% tracking with range error less than 6m and velocity error less than 15m/s. And the real data experiments of Tiangong-2 space station show that the method can track non-cooperative weak target stably, and make the tracking range doubled at least.
OPEN SOURCING EVIDENCE FROM THE INTERNET – THE PROTECTION OF PRIVACY IN CIVILIAN CRIMINAL INVESTIGATIONS USING OSINT (OPEN-SOURCE INTELLIGENCE)

Amsterdam Law Forum (ALF) is the student-run 'International Law Journal' of VU University. Every year ALF publishes a winter, spring, and summer issue. The journal consists of three sections; scientific articles, opinion articles, and commentaries. As of this year, ALF also creates a section for inaugural speeches. In addition, ALF hosts a conference in spring with a relevant legal theme, where renowned speakers are invited to share their perspectives. Overall, ALF is a topical journal that provides a platform for established scholars and young academics to share knowledge, opinions and experiences and to make contributions to the international law discourse. Staff, PhD students and master students who have written a very good thesis are invited to submit an article to ALF. What is learned in the cradle is carried to the tomb: we are looking forward to sharing your articles on our website!
Topology Discriminative Pooling via Graph Kernel Derived Nodes and Edges Co-Embedding

As a vital component of graph neural networks, graph pooling remains largely an open problem. Most existing methods are induced by empirical insights, while ignore the effect of topology information for graph coarsening guidance. In this paper, we propose a Topology Discriminative Pooling (TD-Pool) approach, which derives graph pooling architectures from graph kernels for semantic and structural feature co-embedding. Concretely, TD-Pool contains two graph modules, namely recurrent edge module and convolutional node module, for respectively learning topological/semantic embeddings on edges/nodes. The edge module combines Weisfeiler-Lehman graph kernel with neural networks, which captures topology features in a data-driven manner. It is deployed on the line graphs, whose nodes correspond to the original graph edges for semantic-topology decoupling. The node module learns semantic features with graph convolution in the spectral domain, which acts on the original graphs nodes. Extensive experiments demonstrate the effectiveness of our TD-Pool in graph-level tasks.
Improving Deep Metric Learning with Virtual Classes and Examples Mining

In deep metric learning, the training procedure relies on sampling informative tuples. However, as the training procedure progresses, it becomes nearly impossible to sample relevant hard negative examples without proper mining strategies or generation-based methods. Recent work on hard negative generation have shown great promises to solve the mining problem. However, this generation process is difficult to tune and often leads to incorrectly labeled examples. To tackle this issue, we introduce MIRAGE, a generation-based method that relies on virtual classes entirely composed of generated examples that act as buffer areas between the training classes. We empirically show that virtual classes significantly improve the results on popular datasets (Cub-200-2011 and Cars-196) compared to other generation methods.
Who’s Behind That Website? Classifying Websites by the Degree of Commercial Intent

Web hosting companies strive to provide customised customer services and want to know the commercial intent of a website. Whether a website is run by an individual person, a company, a non-profit organisation, or a public institution constitutes a great challenge in website classification as website content might be sparse. In this paper, we present a novel approach for determining the commercial intent of websites by using both supervised and unsupervised machine learning algorithms. Based on a large real-world data set, we evaluate our model with respect to its effectiveness and efficiency and observe the best performance with a multilayer perceptron.
Association Rules Hiding via Multi-Objective Differential Evolution Algorithm

Association rules (ARs) mining has been widely used in discovering interesting patterns from massive data as a key data mining technique. However, this mining technique may lead to the disclosure of sensitive ARs, and thus, the ARs hiding strategy is very important and necessary. In this paper, a novel ARs hiding method (DEvoArH) is proposed for hiding the ARs. Noting that there are three side-effects in ARs hiding (ARH), i.e., the hiding failure rate, the lost rules rate, and the ghost rules rate, a multi-objective differential evolution algorithm is carefully designed. Specifically, the Pareto-optimal solutions are defined as the elite set, and then each solution is capable of learning from the elite set with a certain probability. Considering the convergence speed and search ability, a directional mutation and random mutate round is introduced into the mutation operator. Besides, to further decrease the data change rate, a database pre-processing mechanism is proposed to filter the unrelated data before the hiding process. Finally, the results of the comparison experiments also demonstrate the superiority of the proposed ARH method in terms of the three side-effects and efficiency.
Lesion tracking method using CNN for non-invasive ultrasound theranostic system

Our laboratory has developed a system to treat kidney, liver, and other tumors and also stones produced by these organs with high-intensity focused ultrasound while tracking lesions that move due to respiration, heart contractions, and body movement. This system estimates the movement of organs by analyzing the ultrasonic image obtained from the probe and compensates for this motion with robotic controls. In recent years, various medical imaging analysis techniques using deep learning have been proposed and examined, but they have not been sufficiently examined as a technique for tracking a specific organ with a robot system. The purpose of this study was to investigate and verify the performance of Faster R-CNN, which is commonly used for general image recognition, for detecting kidneys in ultrasound images. RegNet, a newly proposed neural network that simultaneously predicts the presence and location of kidneys, has shown superior results in detecting and tracking kidneys in comparison to Faster R-CNN. However, further improvement is necessary because the existence prediction and position detection functions produce independent results.
A Hybrid Screen Size Independent Authentication Scheme for Smart Devices

Abstract Although, several authentication schemes have already been proposed for smart devices; however, most of these schemes does not consider the fact that smart devices come in different sizes. Hence, they are not screen size independent — which is the point of interest in this paper. Again, alongside screen size independence, a secure scheme also must defend the aforementioned attacks. Taking these concerns into account, in this paper, a hybrid screen size independent authentication scheme is proposed for smart devices that integrates Vibration Code or VC and Press Touch Code or PTC using a juggling-based approach. Here, VC ensures resilience against the shoulder surfing attack since it is a sense based technique and hybridization of both these schemes contribute to attaining resilience against the smudge and brute force attacks up to some extent. In addition, the proposed scheme does not require a space more than the placement of a finger on the screen; and thus, it is screen size independent. The proposed scheme is evaluated in terms of security and functionality; and compared with other similar schemes where it outperforms the others.
FPGA Based High-Throughput Real-Time Feature Extraction for Modulation Classification

The spectral correlation density (SCD) function is a feature extraction method used in signal classification systems. Due to its computational complexity, SCD has not been a desirable method for systems under power and real-time constraints. In this study, we present results for a hardware implementation of key kernels of the SCD function on a Field Programmable Gate Array (FPGA). By analyzing profiling results for a state of the art GPU implementation, we developed a preliminary architecture that is able to accelerate the most computationally demanding aspects of the SCD algorithm. We find that this FPGA architecture is able to achieve a 2.03X speedup relative to state of the art GPU-based SCD implementations by coupling SCD's large-scale data-parallel nature with an architecture well suited for fine-grained control flow and data access patterns.
Privacy-preserving polynomial interpolation and its applications on predictive analysis

Privacy-preserving polynomial interpolation refers to a process which requires two parties to jointly finding out a polynomial over their private coordinate pairs. Unfortunately, the existing general approach remains impractical. To date, no practical solution to privacy-preserving polynomial interpolation exists. In this paper, we aim to fill this gap by presenting an efficient solution to enable this process. To this end, we first transform the privacy-preserving polynomial interpolation into privacy-preserving calculation of function values, and design a succinct privacy-preserving scalar product protocol. Then, we tackle the original problem by employing Lagrange interpolation in combination with our privacy-preserving scalar product protocol. Finally, we offer some application examples of how our protocol can be used to conduct privacy-preserving predictive analysis.
Quantum Computing

Quantum computingQuantum computing systems with increasing numbers of quantum bits are becoming available for intermediate-scale quantum computingQuantum computing applications. Among several physical implementations, superconductingSuperconducting circuits are one of the promising base technologies, as the manufacturing and integration of Josephson-junction-based qubitsQubit has made significant advances over the last fifteen years. Full-stack system development is key for the breakthrough of quantum computingQuantum computing. In this chapter, we provide an overview of current superconductingSuperconducting qubitQubit implementations, system performance metrics, and the open-source quantum computingQuantum computing software ecosystem QiskitQiskit.
Entangled coherent states for quantum radar applications

We propose a new device that can be used to produce entangled states between coherent states, acting like Schrödinger's cat states, useful for a quantum radar technology. The reason to use such states is to get an arbitrary number of photons entangled produced at will. The aim is to improve performance with respect to a common limitation of quantum radar. Such a device, dubbed Cooper-pair box (or charge qubit) in literature, is a well-known system that can be operated through a superconductive Josephson junction. Such states were recently observed in cavity quantum electrodynamics. Here we exploit their experimental implementation in strongly coupled cavities. The reference quantum radar is the one recently studied in experiments compared to a noise radar.
Strategies for school policies and procedures, induction and new students’ arrivals

The policies will cover a number of different areas, including statutory regulations and guidelines, health and safety, school improvement, special educational needs, community, curriculum and early years. Often, rigid adherence to policy and procedure associated with behaviour can have a negative effect not only upon the person on the receiving end of it but also on the one implementing it. The issue of the use of mobile phones within schools is one area in which schools have different policies and procedures. The important thing is for you to follow what you have been told. Because phones have cameras as well as recording devices, there are a number of safeguarding aspects that have to be considered, along with the usual issues relating to the fact that phones are in effect mobile computers accessing the internet.
THE STUDY OF INTERTEXTUALITY ON NOVEL ENTITLED A VERY YUPPY WEDDING AND DIVORTIARE BY IKA NATASSA

This research aimed to describe intertextual in the novel A Very Yuppy Wedding and Divortiare by Ika Natassa. This research used a descriptive qualitative approach in the form of structural. The result of the data analysis is that theme has similarity. The themes of these novels are love, friendship, and family. Figure and characterization in the novels has less different, the figures describe as a work hard figure not only for the job but also on their relationship. In both of the novel, both of the main characters are women and use the first person as the point of view. Whereas the plot of the novels has different, that is novel Divortiare has a flash-back plot and A Verry Yuppy Wedding has a progressive plot. Both of the novel have differences in the story but have the same happy ending. The background of the stories are not different. Ika Natassa takes both of the settings in Jakarta. The conclusion is both of the novels have no significant differences.
An Intelligent Skin Color Detection Method Based on FCM with Big Data

Aiming at the problems of purchasing cosmetics, female always rely on their intuition or follow the fashion trend for color selection. They never understand how to select the right color to match with them. A systematic method for detecting the facial skin colors of female for the application for make-up is proposed in this study. In this method, the female face recognition with considerable images was first discussed. Next, the innovative method of capturing color with points was discussed along with making the complexion-aggregated analysis. In the study, RGB was conversed to be Lab space for collected data. The data were analyzed based on fuzzy clustering to get grouping. The results were presented in the final grouping Lab value and RGB index. Besides, a FaceRGB program was constructed to capture color automatically from 13000 images. 17 clustering results were obtained by the ColorFCM program constructed in this study. The results can be used to select the customized color of cosmetics to satisfy female and an expert system can further be made in the future.
Convolutional Neural Network based Review System for Automatic Past Event Search using Visual Question Answering

A consumer may additionally take hundreds of pics from mobile phones, Collecting the no of photos, videos that seize cherish capable moments from one's past, such as workshops, STTP's, FDP's or Conferences. The users may use personal photos/videos as a imply of getting higher portions from their recollections about these events. To plan and advance question grasp community model, content material primarily based and textual content primarily based snap shots or movies event search engine, textual content based totally reply technology engine, engine for generating snap shots or movies evidences corresponding to generated answers. The proposed gadget will be evaluated on the basis of a number overall performance assessment parameters. The proposed system can be implemented in Python through using VQA and other QA datasets.
Predictive maintenance magnetic sensor using random forest method

Abstract BMKG has observations of earth’s magnets which are scattered in Indonesia. The BMKG earth magnetic sensor produces real-time data output. This research focuses on predictive maintenance models on earth magnetic sensors based on data output. The resulting data output is in the form of a delimited format in the form of spaces so that it is easy to process. The magnetic component used is the total component data (F) from the earth’s magnetic sensor. Data processing using python scripts with the algorithm used is the random forest method by comparing the resulting value difference to find out whether the data generated is still in tolerance or not. The results of the prediction model with the number of estimators 10 produce RF Score = 0.98 and MAE = 0.83.