Reviewerly/exHarmony huggingface dataset:
Aero Drums - Augmented Virtual Drums

Aero Drums system is an interactive environment. This is a virtual set-up where the drums aren’t physically present. The research utilises various image processing concepts to provide an augmented reality-based acoustic drum set. The system senses a blue-coloured stick, with the help of a video camera, which is used to strike the displayed instruments. This is done by creating a region of interest for each instrument and performing the detection and pixel calculations within them. The displayed instruments comprise a bass drum, snare drum, hi-hatt, three tom drums and crash cymbals with Hit B and Hit C which produce their respective percussions. These instruments are overlaid onto the frame for easier visualization for the user. For testing purposes, we observed that for each ten test recordings, there were around 100 metronome clicks and corresponding drum strikes. If the subject’s hand stopped moving before the mallet struck the plate, there was either too much lag time or the hit was missed. In a test, fewer than two hits were typically missed. For the matched hits, the interval between the metronome click and the drum hit was determined. Our system lets the user play the drums anytime and anywhere, allowing for a refreshing drumming experience.
Human Pose Estimation Based on Feature Fusion and Graph Encoding Optimization

Human pose estimation aims to detect the keypoints of human body in the input images. With the development of deep learning, human pose estimation has gradually matured and is widely used in human-computer interaction, motion analysis, augmented reality, and virtual reality. In this paper, we propose a novel network for human pose estimation, which contains the contextual information aggregation module, multi-level branch feature fusion, and skeleton graph encoding optimization branch. The contextual information aggregation module captures the contextual dependencies of features and aggregates them to obtain the pixel-level feature representation. The multi-level branch feature fusion mechanism fuses high-level and low-level features to gradually expand the feature map to enhance the expressiveness of the extracted features. In addition, we introduce the skeleton graph as prior knowledge to strengthen the body structure constraint and locate the keypoints of the human body more accurately. Experiments on the mainstream dataset MS COCO for human pose estimation demonstrate the effectiveness of our method.
Solving the Learning Parity with Noise Problem Using Quantum Algorithms

The Learning Parity with Noise (LPN) problem is a famous cryptographic problem consisting in recovering a secret from noised samples. This problem is usually solved via reduction techniques, that is, one reduces the original instance to a smaller one before substituting back the recovered unknowns and starting the process again. There has been an extensive amount of work where time-memory trade-offs, optimal chains of reductions or different solving techniques were considered but hardly any of them involved quantum algorithms. In this work, we are interested in studying the improvements brought by quantum computers when attacking the LPN search problem in the sparse noise regime. Our primary contribution is a novel efficient quantum algorithm based on Grover’s algorithm which searches for permutations achieving specific error patterns. This algorithm non-asymptotically outperforms the known techniques in a low-noise regime while using a low amount of memory.
Directional testing for high dimensional multivariate normal distributions

Thanks to its favorable properties, the multivariate normal distribution is still largely employed for modeling phenomena in various scientific fields. However, when the number of components p is of the same asymptotic order as the sample size n, standard inferential techniques are generally inadequate to conduct hypothesis testing on the mean vector and/or the covariance matrix. Within several prominent frameworks, we propose then to draw reliable conclusions via a directional test. We show that under the null hypothesis the directional p-value is exactly uniformly distributed even when p is of the same order of n, provided that conditions for the existence of the maximum likelihood estimate for the normal model are satisfied. Extensive simulation results confirm the theoretical findings across different values of p∕n, and show that under the null hypothesis the directional test outperforms not only the usual first and higher-order finite-p solutions but also alternative methods tailored for high dimensional settings. Simulation results also indicate that the power performance of the different tests depends on the specific alternative hypothesis.
Skeleton-based Action Recognition with Graph Involution Network

Graph convolutional networks (GCNs) have proven their effectiveness and success in skeleton-based action recognition. Regular GCNs separate spatial and temporal operations. And for spatial graph convolution, the self-attention mechanism is commonly used to enable the generation of adaptive data-driven graphs, which limits the receptive field of each vertex. In order to address such a concern, we propose the graph involution network (GIN). A novel operation called graph involution is applied to offer each joint a global reception field, while also largely improving temporal-wise and channel-wise flexibility during the process of feature aggregation. We also find that predefined graphs that came from human natural skeleton structures restrict the remote information exchanges. Therefore we also propose a novel graph topology called spiral graph topology, connecting body joints spirally, which brings valuable additional information hints and improves feature aggregation efficiency. Experiments conducted on different benchmarks on two large-scale datasets show the effectiveness of our proposed network.
Automatic Target-scoring Model based on Image Processing

At present, in the field of military shooting, most of the target-scoring methods are artificial, acoustics-based, optics-based, and so on. These kinds of methods have their own disadvantages respectively such as high cost, time-consuming and low accuracy. To solve these problems, starting from the field of computer vision, combined with the image processing algorithms, in this paper, we propose an automatic target-scoring model. Firstly, image correction and subtraction operations are performed on the two images before and after shooting. Then, image processing operations such as binarization, opening and closing operations, and Canny edge detection are performed on the images that have eliminated most of the background interference, and a clear image of a single bullet hole is obtained. Finally, the Hough transform algorithm is used to identify the bullet holes, the Euclidean distance between the center of the bullet hole and the target surface is calculated, so the final target score is obtained. The testing images are all taken at a fixed angle of view 50 meters away from the targets. The experimental results show that the algorithm can accurately identify the location of bullet holes, with fast processing and more low cost than other algorithms.
Simulating Deterministic Dynamics by Drawing Coloured Balls at Random in Urns

In this work, we illustrate by detailed examples an equivalence relation established in a previous publication between deterministic nonlinear differential dynamical systems and random Markovian processes.
Detecting Pregnancy Risk Type Using LSTM Algorithm

Pregnancy is the most important yet vulnerable phase for all mothers-to-be. Approximately nine months of pregnancy requires special attention from medical workers to monitor the health of the womb. Specifically early detection of risks and diseases that may happen during pregnancy. Risk detection requires understanding, experience, and precise calculations from available dataset. Current methodology of pregnancy risk is manual calculation using KSPR (Poedji Rochyati Score Card). However, manual calculation opens a lot of human error possibilities. Therefore, there is a need to develop a more accurate system using the available data. This study aims to classify the risk of pregnant women using multi-class classification using the LSTM method. Data used in this research are primarily collected dataset from Dinas Kesehatan Kabupaten Boyolali. To create an accurate model, we pre-processed dataset into trainable data for a deep learning model. These processes include balancing data and feature selection. Pre-processed data are then trained and tested. Model hyperparameter are then tuned to provide the best evaluation metric. Final prediction model evaluation metrics collected from the model are 94.63% accuracy, sensitivity 94.57%, precision 94.88%, and F1-Score 94.60%.
Spatial-Attention Location-Aware Multi-Object Tracking

Most existing one-shot multi-object tracking (MOT) methods have already made great progress in jointly accomplishing detection and re-identification tasks with a single network. However, they often ignore detection misalignments and may heavily rely on tightly enclosed image patches, which aggravates the dependence of tracking and re-identification performance on detection accuracy. We evaluate the confidence of appearance embeddings with predicted location precision, which alleviates this heavy dependence. To deal with misalignments, person search is introduced to jointly train the detection and re-identification using proposals via multi-task learning. In addition, we equip our network with feature fusion strategy at different scales and spatial-channel attention module to narrow the semantic gaps and focus on informative regions. The designed network serves as an online multi-object tracker and can be easily trained end-to-end. Extensive experiments show that our proposed method achieves the competitive performance against most state-of-the-art methods on several MOTChallenge benchmarks while running at over 12 FPS.
Measurement Module Polling Algorithm and Indirect Measurement Evaluation in a Personalized Distributed Information and Measurement System with Standalone Measurement Modules

The report seeks to develop an algorithm for polling measurement modules and estimating the error of indirect measurements in a personalized distributed information and measurement system with autonomous measurement modules and transmission of the results to the central computer. Such values as the pace and rhythm of walking are measured indirectly in a personalized distributed information and measurement system, and an error estimate is carried out for these parameters.
Efficient Description of some Classes of Codes using Group Algebras

Circulant matrices are an important tool widely used in coding theory and cryptography.A circulant matrix is a square matrix whose rows are the cyclic shifts of the first row.Such a matrix can be efficiently stored in memory because it is fully specified by its first row.The ring of n x n circulant matrices can be identified with the quotient ring F[x]/(x(n) -1).In consequence, the strong algebraic structure of the ring F[x]/(x(n) -1) can be used to study properties of the collection of all n x n circulant matrices.The ring F[x]/(x(n) -1) is a special case of a group algebra and elements of any finite dimensional group algebra can be represented with square matrices which are specified by a single column.In this paper we study this representation and prove that it is an injective Hamming weight preserving homomorphism of F-algebras and classify it in the case where the underlying group is abelian.
Build Gaussian Distribution Under Deep Features for Anomaly Detection and Localization

Anomaly detection in images has attracted a lot of attention in the field of computer vision.It aims at identifying images that deviate from the norm and segmenting the defect within images.However, anomalous samples are difficult to collect comprehensively, and labeled data is costly to obtain in many practical scenarios.We proposes a simple framework for unsupervised anomaly detection.Specifically, the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis (PCA), then build the distribution of normal features via the multivariate Gaussian (MVG), and determine whether the test image is an abnormal image according to Mahalanobis distance.We further investigate which features are most effective in detecting anomalies.Extensive experiments on the MVTec anomaly detection dataset show that the proposed method achieves 98.6% AUROC in image-level anomaly detection and outperforms previous methods by a large margin.
A Survey on Data Security in Network Storage Systems

The development of mobile Internet produces massive data and causes rapid evolution and improvement of storage systems. The new generation of widely applied storage systems is represented by storage area networks, distributed storage systems, and cloud storage. Current development of network storage often pays much attention to performance and capacity improvement while ignoring system security. Consequently, data leakage frequently happened in the storage system in recent years. Therefore, as the main carrier of data, the security of the storage system must be taken into serious consideration. By investigating the existing literature, we find that there still lacks a comprehensive review of the data security in network area storage. To this end, we survey existing research on data security in storage systems and conduct a comprehensive evaluation of their security status. We propose a set of evaluation criteria including confidentiality, access control, integrity, availability, auditability, deduplication, scalability, and three types of cost. Through the assessment of existing works based on these criteria, we conclude the shortcomings of the current storage systems and point out several research directions for future research.
POLYPARADIGMAL APPROACH IN THE PROFESSIONAL TRAINING OF A FUTURE TEACHER

The article presents a psychological and pedagogical analysis of scientific approaches to the process of training future professionals. The content of promising scientific approaches in professional training of future specialists, in particular, integration, activity, system, which are systemic formations that organically complement, interact, integrate and thus form a system-synergistic integrity and unite in a polyparadigmatic approach. Polyparadigmatic approach, integrating complementary and interconnected approaches based on a common synergetic methodology, in which internal factors, approaches, principles, conditions, forms, methods are of paramount importance, ensures the effectiveness of the educational process, aims to implement various forms of dialogue, development professional qualities of future teachers. These approaches are based on the perception of individuality, self-worth of the individual as a carrier of subjective experience, due to the peculiarities of professional activities based on subject-subject relations, pedagogical dialogue and provides an organization of learning that integrates and reveals universal individual creativity quality of the student, there is their transformation and adaptation to professional activity in various educational and cultural institutions. Systematic analysis of scientific approaches allows to define the professional training of future teachers as a multilevel and multifaceted system, which is the unity of pedagogical values, technologies, approaches, essential strengths of the individual and aimed at creative self-expression and self-realization of future professionals. The polyparadigmatic approach is not limited to these approaches, but also contains competency, synergetic, personality-oriented, culturological, axiological, interpretive approaches that affirm the idea of social, activity, creative essence of man as a person, as a product of historical development, as a carrier of culture, spirituality.
Bibliotherapy in the Pandemic Time: A Bibliometric Analysis

This research aims to obtaining the general parametric insights on the productivity and influence of the studies on that fields as the attempt to deal with the impact of the spread of the corona virus particularly to students and their language learning. The inclusion of the language learning is connected with the notion of language as the main ingredient of text(s) selected for bibliotherapy. By relying upon Publish or Perish (PoP) metrics, the analysed data were taken from the articles published within the range from 2020-2022 which are thought to be the period of the emergence and highly spread of Corona Virus. To guide the exact and comprehensive search, seven keywords such as covid, pandemic, students, text, literature, language learning and Indonesia are combined with bibliotherapy as the main topic. The results indicated that globally 148 articles mark the productivity of the research on the bibliotherapy, yet the high impact gain by the combination of bibliotherapy with ‘student, pandemic, and literature’ as shown by the metrics of citation and index. Unfortunately, this global tendency is far different from the Indonesian context which reveals no inquiry yet conducted on the bibliotherapy issue despite the big number of victims and affected covid-19 in this country.
LiFUSO: A Tool for Library Feature Unveiling based on Stack Overflow Posts

Selecting a library from a vast ecosystem can be a daunting task. The libraries are not only numerous, but they also lack an enumeration of the features they offer. A feature enumeration for each library in an ecosystem would help developers select the most appropriate library for the task at hand. Within this enumeration, a library feature could take the form of a brief description together with the API references through which the feature can be reused. This paper presents LiFUSO, a tool that leverages Stack Overflow posts to compute a list of such features for a given library. Each feature corresponds to a cluster of related API references based on the similarity of the Stack Overflow posts in which they occur. Once LiFUSO has extracted such a cluster of posts, it applies natural language processing to describe the corresponding feature. We describe the engineering aspects of the tool, and illustrate its usage through a preliminary case study in which we compare the features uncovered for two competing libraries within the same domain. An executable version of the tool is available at https://github.com/softwarelanguageslab/lifuso and its demonstration video is accessible at https://youtu.be/tDE1LWa86cA.
Learning Technology and Child–Computer Interaction

Abstract Learning technology research focuses on the design, development, and/or use of technologies that support learning, whereas CCI research focuses on the design, development, and/or use of technologies that support children’s lives (with a heavy emphasis on learning). Therefore, learning technology and CCI research can be described as research that focuses on the design, development, and/or use of technologies that support learning and/or children’s lives. In this chapter, we provide an introduction to CCI and learning technologies as interdisciplinary fields of research, provide good working definitions and discuss their commonalities, synergies, and complementarities.
Dimensions in Measuring Performance of Agile Software Development Projects: A Literature Review

Adopting and implementing Agile principles and practices in a software development project is not new these days. With the flexibility to manage changes in requirements and the rapid delivery of work products, many organisations transition from a plan-driven approach to an agile-driven one, ultimately promising high-quality work products. Some studies reported that it is a struggle for some development teams to measure the performance in agile-based projects. The purpose of this paper is to review the current literature on performance measurement frameworks or models, especially in an agile context. By conducting the literature searches and reviews, this paper provides information on the implementation of measurement metrics in agile software development projects. There are many different dimensions in measuring the performance of agile-based projects: organisational, process, project/team and individual. Each dimension is not an independent entity but intertwined in many ways. For organisations working on agile software development projects, further research is required to gain insight into the metrics of each dimension, the relationships between dimensions, and the possible integration of multiple dimensions.
Joint Global and Local Dual-GCN for Document-level Relation Extraction

Document level relation extraction is a challenging task because different entities in a document are often distributed in different sentences, where the same entity may appear several times in the document. Most importantly, two entities that are far away from each other in the document may also have a latent relationship which makes it difficult to extract the relationship at the document level. However, most of the previous works focus on either only the relationship between local and global semantics or only the representation based on local context. They lack the unity of the relationship between the parts and the whole, which has greatly hampered efficient document-level relation extraction. In this paper, a joint global and local Dual-GCN (JDGCN) model is proposed to solve the above problems. By using two global anchor nodes, the anchor node-based mention graph convolution module and entity graph convolution module are combined. The local and global perception representation of entities is realized through the graph convolution of mention graph, and the relational inference is carried out through entity graph for relation extraction. At last, we have made experiments to validate the model proposed in this paper on the DocRED datasets, and the experiment results show that the proposed method is effective and efficient to document-level relation extraction against the state-of-the-art baseline methods.
Light-Weight Branch-Shared Multi-View Convolutional Neural Networks Crowd Counting

Crowd counting plays an important role in event planning, video surveillance, and other fields. At present, the development of single-view crowd counting is relatively mature, but due to the limitation of a single field of view, it is not suitable in some dense occluded scenes. Multi-view crowd counting uses images from multiple views to estimate the number of crowds in the current scene. Most multi-view crowd counting methods based on the deep convolutional neural networks use independent and identical view branches, which bring massive redundant features and increase the model's complexity. This paper proposes a light-weight and branch-shared convolutional neural networks method, which decreases the number of learnable parameters. This method uses the same view branch to extract multi-scale feature maps with images from different views. The camera-view feature maps will be projected into the same plane in world space to fuse, then the scene-level feature maps extracted at different scales are regressed to a scene-level density map. Extensive experiments are conducted on two public datasets (PETS2009, CityStreet), and compared with five existing methods, this method can achieve better performance.