Reviewerly/exHarmony huggingface dataset:
Support vector machine and optimal parameter selection for high-dimensional imbalanced data

In this article, we consider asymptotic properties of support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. In particular, we treat high-dimensional imbalanced data. We investigate behaviors of SVM for a regularization parameter C in a framework of kernel functions. We show that SVM cannot handle imbalanced classification, and SVM is very biased in HDLSS settings. In order to overcome such difficulties, we propose robust SVM (RSVM), which gives excellent performances in HDLSS settings. We also give a pre-selection method for parameters included in a kernel function without cross-validation. Finally, we check the performance of RSVM and the optimality of the choice in numerical simulation and actual data analyses.
Blockchain-Based Grievance Management System

Since the abrupt outburst of Bitcoin, there has been a meteoric rise in the popularity of blockchain which has been scaled to various domains. Its features have changed the outlook of solutions for many problems and can be applied in various sectors, one being grievance redressal system. In this system, the grievant will submit a grievance which will pass through different levels of hierarchical authoritative screening. Each screening level will have the authority to debar, resolve, revert and forward the grievance to the higher level. Since data integrity is a built-in feature of this system, it eradicates any chances of misuse/abuse of power by the authorities. The dynamic time threshold is an additional feature that automatically transfers the grievance to the higher authority in the hierarchy, thus eliminating any chances of ignorance. Thus, the anomalies and shortcomings of the current grievance system can prevail over.
Tri-Domain pattern preserving sign prediction for signed networks

Sign prediction in signed networks is one of the essential parts in signed networks data mining area. However, in most cases, sign information of links in signed networks is insufficient, so transfer learning is necessary to be an efficient tool to help us relieve this challenge. Many methods cannot leverage the knowledge in source domain adequately. Besides, they can hardly overcome the interference of noisy instances or unrelated instances which called negative transfer phenomenon. To decrease the negative transfer phenomenon in existing methods, a sign prediction model by Tri-Domain Relationship Pattern (SP-TDRP) is proposed. TDRP selects an intermediate domain to be the bridge to transfer knowledge from source domains to the target domain. Then TDRP selects the interference instances and eliminates them to make the transferable knowledge more complete and purer. Thus, the sign classifier can be trained via transferable knowledge and predict the signs in the target domain. The considerable efficiency of SP-TDRP is evaluated on some realistic signed networks when compared with other state-of-the-art models.
An adaptive locally connected neuron model: Focusing neuron

This paper presents a new artificial neuron model capable of learning its receptive field in the topological domain of inputs. The experiments include tests of focusing neuron networks of one or two hidden layers on synthetic and well-known image recognition data sets. The results demonstrated that the focusing neurons can move their receptive fields towards more informative inputs. In the simple two-hidden layer networks, the focusing layers outperformed the dense layers in the classification of the 2D spatial data sets. Moreover, the focusing networks performed better than the dense networks even when 70% of the weights were pruned. The tests on convolutional networks revealed that using focusing layers instead of dense layers for the classification of convolutional features may work better in some data sets.
Research on power quality signals reconstruction method based on K-SVD dictionary learning

With the rapid development of modern science and continuous innovation of technology, a large number of power quality data storage and transmission has caused great burden to power system operation, so it is of great practical significance to study power quality. In order to solve the problem of the existing fixed orthogonal sparse basis is not enough to represent the unknown power quality signals flexibly when using the compression sensing theory to reconstruct power quality signals, which leads to poor signal reconstruction effect and poor applicability. K-SVD dictionary learning is introduced into the power quality signals reconstruction. Firstly, a large number of power quality signals samples are trained by K-SVD dictionary to get a super complete dictionary. Secondly, Gauss random matrix is selected as the measurement matrix. Lastly, KSVD-CoSaMP algorithm is proposed to reconstruct the power quality signals. Simulation experimental results show that, power quality signals are reconstructed using the proposed algorithm compared DCT algorithm, the SNR of all the reconstructed signals are higher than 30 dB, ERP are over 99.6%, and MSE are under 3%. The indexes of the proposed algorithm are obviously superior to the existing DCT algorithm, which further verifies the superiority and applicability of the proposed algorithm.
Short Video Datasets Show Potential for Outfits in Augmented Reality

We investigate the usage of short video datasets in place of large image datasets for outfit transfer. We replicate the warp stage of prior work, SwapNet, while swapping out the 800K-image DeepFashion dataset for two 5-minute videos. By successfully training the warp stage on video, we show that the previously proposed transfer technique for images has potential to apply to subjects in motion. In addition, specifically for video applications, we show the model need not be trained on a comprehensive dataset; instead the model only needs videos for the source outfit and target subject. Our results show promise for the usage of video datasets in future work to achieve fully-textured outfit transfer. We expect the implications of convenient-to-collect video datasets to appeal to small companies that lack resources to collect and annotate big data. If combined with high performance computing, faster training times in the cloud could lead to frictionless user experiences.
One Way to Fill All the Concave Region in Grid-Based Map

SUMMARY The search space of the path planning problem can greatly affect the running time and memory consumption, for example, the concave obstacle in grid-based map usually leads to the invalid search space. In this paper, the filling container algorithm is proposed to alleviate the concave area problem in 2D map space, which is inspired from the scenario of pouring water into a cup. With this method, concave areas can be largely excluded by scanning the map repeatedly. And the effectiveness has been proved in our experiments.
Implementing Cosine Similarity Algorithm to Increase the Flexibility of Hematology Text Report Generation

The previous hematology textual summary representation system, which applies template based method of Natural Language Generation to produce hematology laboratory test results in natural language representation, was at the cutting edge to generate more detailed hematology reports. The produced reports manage to provide texts which break down the critical components and abnormal components of blood found in conventional hematology test results. The produced reports in natural language representation aimed to help patients to easily define, spot and point out which blood components are acting up. Templates provide slots to generate every single sentence to be replaced by the data that we provide. However, the previous system is only able to produce fixed unflexible slots of blood components which are defined by the system, named T-Gen System. It nearly got off the ground as it is very unflexible because the produced templates cannot hold all of both critical and abnormal components found in a produced laboratory examination result. Therefore, this research project implements cosine similarity algorithm to expand template flexibility. Testing and evaluation were carried out manually by examining given components into the system which will be added consecutively. The testing shows that every blood component which was added consecutively succesfully appeared in the produced texts.
Optimal Design of Microlens Aperture Size for Focused Plenoptic Camera

The aperture size of microlens is a key parameter of a plenoptic camera, which strongly influence the performance of the camera. Generally, the size of microlens aperture is submillimeter, which will introduce obvious diffraction effect with large f-number. Besides, optical distortions of plenoptic camera also bring degradation effect in raw plenoptic image. In this letter, based on wave optics, a more accurate imaging model with diffraction effect and optical distortion for focused plenoptic camera is proposed. The modulation transfer function (MTF) evaluation suggests that the proposed model is more close to the real one when compared to the previous models. Additionally, the raw images under different microlens aperture sizes are synthesized. Finally, a relatively optimal size of microlens aperture is obtained according the results of depth estimation.
Designing the catbus

There is a relative lack of affective technology to support young children in communicating their emotions, particularly in educational contexts. This paper is concerned with discussing prior design challenges and vision for creating an emotion-communication technology for young children. We describe a system - the Catbus - which conceptualizes a digital and interactive storytelling of children's emotions fed through their wristbands, as well as online resources to support adoption by educators and students.
Light Field Reconstruction With a Hybrid Sparse Regularization-Pseudo 4DCNN Framework

The densely-sampled light field (LF) is highly desirable in many applications, such as 3D reconstruction, post-capture refocusing and virtual/augmented reality. However, it is costly and challenging to capture them because of the high dimensional nature. Existing view synthesis methods employ depth information for the densely-sampled LF reconstruction from the undersampled LF with a large disparity range, but fail to achieve non-Lambertian performance. In this article, a novel coarse-to-fine LF reconstruction method is proposed. We will develop a hybrid reconstruction framework that fuses model-based sparse regularization with deep learning. Specifically, sparse regularization with directional filter bank is utilized to solve the large baseline problem and gives a coarse densely-sampled LF. In addition, for those that cannot be recovered by classical model-based methods due to limited angular resolution, is estimated by learning a pseudo 4D convolutional neural network, and thereby offering a refinement on the intermediate LF. Extensive experiments demonstrate the superiority of our method on both real-world and synthetic LF images when compared with state-of-the-art methods.
Machine Learning with Integrated Metasystem

We utilize machine learning algorithm to design integrated photonic meta-systems, and experimentally demonstrated the device performance.
Some problems and algorithms related to the weight order relation on the n-dimensional Boolean cube

The problem “Given a Boolean function [Formula: see text] of [Formula: see text] variables by its truth table vector. Find (if exists) a vector [Formula: see text] of maximal (or minimal) weight, such that [Formula: see text].” is considered here. It is closely related to the problem of computing the algebraic degree of Boolean functions which is an important cryptographic parameter. To solve this problem efficiently, we explore the orders of the vectors of the [Formula: see text]-dimensional Boolean cube [Formula: see text] according to their weights. The notion of “[Formula: see text]th layer” of [Formula: see text] is involved in the definition and examination of the “weight order” relation. It is compared with the known relation “precedes”. Several enumeration problems concerning these relations are solved and the relevant notes were added to three sequences in the on-line encyclopedia of integer sequences (OEIS). One special weight order is defined and examined in detail. In it, the lexicographic order is a second criterion for an ordinance of the vectors of equal weights. So a total order called weight-lexicographic order (WLO) is obtained. Two algorithms for generating the WLO sequence and two algorithms for generating the characteristic vectors of the layers are proposed. The results obtained by them were used in creating two new sequences: A294648 and A305860 in the OEIS. Two algorithms for solving the problem considered are developed — the first one works in a byte-wise manner and uses the WLO sequence, and the second one works in a bitwise manner and uses the characteristic vector as masks. The experimental results from numerous tests confirm the efficiency of these algorithms. Other applications of the obtained algorithms are also discussed — when representing, generating and ranking other combinatorial objects.
Permission and content management model based on ASP.NET technology and three-layer network architecture

In order to strengthen the management and control of personnel flow and the timeliness of information dissemination during the COVID-19 epidemic, it is necessary to improve the reliability of authority and content management. The extensive application of authority information management can closely connect the professional system with the production activities of enterprises. At present, the traditional authority management level is complex, and the management of complex data is chaotic. This paper is based on ASP.NET. This paper proposes a data authority management method. The main function of this method is to establish a standard three-tier structure of rights management level by using the open-source string template engine technology, tag analysis, SEO search engine optimization technology and information collection technology. This method provides a general process oriented and automatic management platform for users through the hierarchical management mechanism. This technology can meet people’s rights and content requirements for creating, publishing and updating websites without understanding the technology itself. The experimental results show that the rapid management mechanism based on authority management can accurately handle and manage authority and data which can improve the accuracy and timeliness of personnel flow control under the impact of the COVID-19 epidemic.
Automated Web Service Specification Generation Through a Transformation-Based Learning

Web Application Programming Interface (API) allows third-party and subscribed users to access data and functions of a software application through the network or the Internet. Web APIs expose data and functions to the public users, authorized users or enterprise users. Web API providers publish API documentations to help users to understand how to interact with web-based API services, and how to use the APIs in their integration systems. The exponential raise of the number of public web service APIs may cause a challenge for software engineers to choose an efficient API. The challenge may become more complicated when web APIs updated regularly by API providers. In this paper, we introduce a novel transformation-based approach which crawls the web to collect web API documentations (unstructured documents). It generates a web API Language model from API documentations, employs different machine learning algorithms to extract information and produces a structured web API specification that compliant to Open API Specification (OAS) format. The proposed approach improves information extraction patterns and learns the variety of structured and terminologies. In our experiment, we collect a sheer number of web API documentations. Our evaluation shows that the proposed approach find RESTful API documentations with 75% accuracy, constructs API endpoints with 84%, constructs endpoint attributes with 95%, and assigns endpoints to attributes with an accuracy 98%. The proposed approach were able to produces more than 2,311 OAS web API Specifications.
Entanglement-Enabled Communication for the Internet of Things

We consider an N-user multiple-access channel (MAC) with a varying channel state. The senders receive partial state information, but cannot communicate amongst reach other. This particular channels rate region vanishes asymptotically with a growing number of users in the sense of an exponential bound on the sum rate. However, when pre-established quantum entanglement is shared between the senders, the sum rate stays at a constant positive number. Thus a beneficial impact of entanglement-modulated coding for multi-access scenarios where many senders attempt to reach one receiver is demonstrated, a scenario with an increased likelihood in the internet of things.
Linear Lossy Identification Scheme derives Tightly-Secure Multisignature

For the multisignature schemes, the tight security in the plain public key model is considered as one of the desirable features. In this paper, we first give a generic construction of multisignature schemes which is tightly secure in the plain public key model. Our construction is based on the ID schemes which have two properties called the lossiness and the linearity. Moreover, we then find that the lossiness of most of the schemes is proven from the linearity. In this sense, we propose a new property of ID schemes and show that the combination of the linearity of ID schemes and the new property implies the lossiness. This result implies the relationship between these properties to shrink the conditions required for our generic construction.
Learning Diaries—A Valuable Companion of Mobile Learning for Higher Education in Software Engineering

Over the past two decades, mobile learning has become a popular part of educational technology in higher education. Mobile learning has the potential to individualize, personalize, and optimize learning processes. It enables students to learn, collaborate, and share ideas among each other anywhere and anytime with the aid of mobile devices. This requires a high degree of students' autonomy and self-organization and may lead to a significant fragmentation of the learning process. Learning diaries can serve as a didactic tool to support, structure, and consolidate students' learning progress in distance learning phases. In this paper, we present our experiences with integrating reflective learning diaries into eight Bachelor courses taught in mobile learning settings. We introduce different course designs combining mobile learning and learning diaries, and conclude with our lessons learnt on how to effectively integrate learning diaries into mobile learning settings. Especially in software engineering education, learning diaries offer an effective learning tool to develop students' autonomy and reflection capabilities.
srcClone

Detecting code clones is an established method for comprehending and maintaining systems. One important but challenging form of code clone detection involves detecting semantic clones, which are those that are semantically similar code segments that differ syntactically. Existing approaches to semantic clone detection do not scale well to large code bases and have room for improvement in their precision and recall. In this paper, we present a scalable slicing-based approach for detecting code clones, including semantic clones. We determine code segment similarity based on their corresponding program slices. We take advantage of a lightweight, publicly available, and scalable program slicing approach to compute the necessary information. Our approach uses dependency analysis to find and measure cloned elements, and provides insights into elements of the code that are affected by an entire clone set/class. We have implemented our approach as a tool called srcClone. We evaluate it by comparing it to two semantic clone detectors in terms of clones, performance, and scalability; and perform recall and precision analysis using established benchmark scenarios. In our evaluation, we illustrate our approach is both relatively scalable and accurate. srcClone can also be used by program analysts to run on non-compilable and incomplete source code, which serves comprehension and maintenance tasks very well. We believe our approach is an important advancement in program comprehension that can help improve clone detection practices and provide developers greater insights into their software.
Estimating Correlated Angles Using the Hypertoroidal Grid Filter

Estimation for multiple correlated quantities generally requires considering a domain whose dimension is equal to the sum of the dimensions of the individual quantities. For multiple correlated angular quantities, considering a hyper-toroidal manifold may be required. Based on a Cartesian product of d equidistant one-dimensional grids for the unit circle, a grid for the d-dimensional hypertorus can be constructed. This grid is used for a novel filter. For n grid points, the update step is in O(n) for arbitrary likelihoods and the prediction step is in O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for arbitrary transition densities. The run time of the latter can be reduced to O(n log n) for identity models with additive noise. In an evaluation scenario, the novel filter shows faster convergence than a particle filter for hypertoroidal domains and is on par with the recently proposed Fourier filters.
Hierarchical classification using ML/DL for sussex-huawei locomotion-transportation (SHL) recognition challenge

In this paper, our team, SensingGO, presents a hierarchical classifier for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge. We first separate the original data into motorized activities and non-motorized activities in the first layer of the classifier by using accelerometer data. For the non-motorized activities, we calculate auto-correlation values with accelerometer data as input features. For the motorized activities, we take magnetometer and barometer with mean, maximum, standard deviation values as input features. Finally, we integrate the recognition results of each layer of the classifier, and the average F1-score is 50% to the validation data.
Autonomous staircase climbing robot for rescue application

Abstract This paper discusses the construction and application of an autonomous staircase climbing robot. Such a robot has several applications in real life. It can be used in dangerous regions for surveillance, rescue, and exploration with the help of a camera placed on it or by carrying resources. After going through various research papers a concept of such a robot was visioned. Following which design was formulated and simulations were carried out. A prototype was fabricated with mechanical and electrical parts. It is a simple climbing robot with two rods as support at the front. A simple rack and pinion gear are used to lift the robot while climbing. An ultrasonic sensor is placed at the front to sense the width and height of the stairs. Various testing and analysis have been done on the prototype to eliminate all drawbacks. The robot has shown to successfully climb the stairs.