Reviewerly/exHarmony huggingface dataset:
Local quasi-linear embedding based on kronecker product expansion of vectors

Locally Linear Embedding (LLE) is honored as the first algorithm of manifold learning. Generally speaking, the relation between a data and its nearest neighbors is nonlinear and LLE only extracts its linear part. Therefore, local nonlinear embedding is an important direction of improvement to LLE. However, any attempt in this direction may lead to a significant increase in computational complexity. In this paper, a novel algorithm called local quasi-linear embedding (LQLE) is proposed. In our LQLE, each high-dimensional data vector is first expanded by using Kronecker product. The expanded vector contains not only the components of the original vector, but also the polynomials of its components. Then, each expanded vector of high dimensional data is linearly approximated with the expanded vectors of its nearest neighbors. In this way, the proposed LQLE achieves a certain degree of local nonlinearity and learns the data dimensionality reduction results under the principle of keeping local nonlinearity unchanged. More importantly, LQLE does not increase computation complexity by only replacing the data vectors with their Kronecker product expansions in the original LLE program. Experimental results between our proposed methods and four comparison algorithms on various datasets demonstrate the well performance of the proposed methods.
Privacy-preserving Search over Encrypted Personal Health Record in Multi-Source Cloud

Abstract The biggest domain which needs a cloud platform in this smart world is the medical domain, where hospitals have a huge amount of data regarding the patient’s records. When these personal health records are stored in the cloud, the system needs some encryption mechanism to secure the records. In this paper, an enhanced Attribute-Based Encryption (ABE). The health records of the patient are decrypted with the help of private keys. In this model, the patient doesn’t need any certificate for authorization, so the model uses the details of the patients as attributes, and these details are encrypted. A threshold value is defined as t, if the number of encrypted attributes is matched with the threshold value then only the data owner can decrypt the data. A hierarchical mechanism helps the model to have smooth access to the data. The traditional algorithms are good at handling a small amount of data but this algorithm provides security as well as scalable property. The enhanced mechanism includes the unsupervised machine learning approach to label the patients based on their disease. In the cloud, a group of clusters are formed to separate the patients and allocate the doctors based on their disease treatment. The process of labeling is major drawback in case of attribute based encryption mechanism.
Soccer-Assisted Training Robot Based on Image Recognition Omnidirectional Movement

With the continuous emergence and innovation of computer technology, mobile robots are a relatively hot topic in the field of artificial intelligence. It is an important research area of more and more scholars. The core of mobile robots is to be able to realize real-time perception of the surrounding environment and self-positioning and to conduct self-navigation through this information. It is the key to the robot’s autonomous movement and has strategic research significance. Among them, the goal recognition ability of the soccer robot vision system is the basis of robot path planning, motion control, and collaborative task completion. The main recognition task in the vision system is the omnidirectional vision system. Therefore, how to improve the accuracy of target recognition and the light adaptive ability of the robot omnidirectional vision system is the key issue of this paper. Completed the system construction and program debugging of the omnidirectional mobile robot platform, and tested its omnidirectional mobile function, positioning and map construction capabilities in the corridor and indoor environment, global navigation function in the indoor environment, and local obstacle avoidance function. How to use the local visual information of the robot more perfectly to obtain more available information, so that the “eyes” of the robot can be greatly improved by relying on image recognition technology, so that the robot can obtain more accurate environmental information by itself has always been domestic and foreign one of the goals of the joint efforts of scholars. Research shows that the standard error of the experimental group’s shooting and dribbling test scores before and the experimental group’s shooting and dribbling test results after the standard error level is 0.004, which is less than 0.05, which proves the use of soccer-assisted robot-assisted training. On the one hand, we tested the positioning and navigation functions of the omnidirectional mobile robot, and on the other hand, we verified the feasibility of positioning and navigation algorithms and multisensor fusion algorithms.
LibGuides: Alma: Replacing Old Mango Links

This guide provides information about Alma ILS and Primo VE, the new library catalog, for faculty and staff. This guide includes training and information on how to replace broken links, searching the catalog, and more!
A Study on Hardware-Aware Training Techniques for Feedforward Artificial Neural Networks

This paper presents hardware-aware training techniques for efficient hardware implementation of feedforward artificial neural networks (ANNs). Firstly, an investigation is done on the effect of the weight initialization on the hardware implementation of the trained ANN on a chip. We show that our unorthodox initialization technique can result in better area efficiency in comparison to the state-of-art weight initialization techniques. Secondly, we propose training based on large floating-point values. This means the training algorithm at the end finds a weight-set consisting of integer numbers by just ceiling/flooring of the large floating-point values. Thirdly, the large floating-point training algorithm is integrated with a weight and bias value approximation module to approximate a weight-set while optimizing an ANN for accuracy, to find an efficient weight-set for hardware realization. This integrated module at the end of training generates a weight-set that has a minimum hardware cost for that specific initialized weight-set. All the introduced algorithms are included in our toolbox called ZAAL. Then, the trained ANNs are realized on hardware under constant multiplication design using parallel and time-multiplexed architectures using TSMC 40nm technology in Cadence.
A Fast and Lightweight Speech Synthesis Model based on FastSpeech2

In this paper, we present a fast and lightweight speech synthesis model that is suitable for on-device applications. By leveraging the techniques of long-short range attention, depth-wise separable convolution, and linear attention, we significantly reduce the model size and complexity of the baseline FastSpeech2-based Transformer framework. Unlike the baseline model that requires O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to compute attention and convolution operations because of nested-loop computations, our proposed model only requires O(N) computations due to the modification of a nested-loop into two cascaded single loops. Experimental results show that our proposed model is able to generate speech with a real-time factor of 0.26 and requires only 10.4 million parameters. Despite the reduction in model size and complexity, still, the generated speech quality of our model is nearly close to the baseline.
Who owns TikTok and why is the app considered a national security threat

TikTok is owned by Beijing-based technology company ByteDance, which was founded by Chinese billionaire Zhang Yiming in 2012.
A Novel Approach in Requirement Engineering during Software Build-up

Requirement engineering is a tough task for System Analysts because requirements keep changing or varying throughout the process. It seemed to become more torrid when a pandemic hit globally. The issue now lies on how to cope up with the situations and keep all the stakeholders and clienteles in the same loop. The aim of this paper is to identify how the procedures are being carried out from remote locations. The paper finds out that there have been a lot of breakthroughs that have proved to be efficient and timesaving than the traditional methods. The Objective of this work is to find out a new approach to elicit data, information and to schedule a regular meeting with stakeholders through online platforms by using MS Team, Webex.com, Zoom, and Google meet. It helps the developers to reduce cost, timing, and errors at early phase of software engineering development life cycle.
Research on Steganalysis of Digital Image Based on Deep Learning

Steganography is a technique and science of information hiding. Corresponding to the development of image steganography, steganalysis has made substantial progress. In recent years, deep learning is widely used in the fields of computer vision and natural language processing. With people's in-depth understanding of deep learning, convolutional neural network technology has been introduced into the field of steganography and steganalysis, and has made a series of breakthroughs and development in methods and performance. Under this background, this paper summarizes and discusses the main methods and the most representative work of how to apply convolutional neural network model of deep learning to steganalysis of digital images. Finally, it discusses the problems and future development of steganalysis and steganalysis of digital image based on deep learning.
The Application of Blue Ink Cloud Class in the Teaching of College Students’ Innovation and Entrepreneurship Basic Courses

This article introduces the characteristics of timeliness, accuracy, interactivity and sharing of the Blue Ink Cloud Class. Teaching with the help of the Blue Ink Cloud Class platform can change the poor learning status of college students in the basic courses of innovation and entrepreneurship, stimulate college students’ interest in learning, enhance college students’ autonomous learning ability, and improve classroom teaching efficiency and teaching quality.
Prime-Based Encoding Algorithm to Ensure Integrity of Electronic Health Records

The development in the field of medical of Internet things leading the world to access patients EHR globally, resulting in speedy diagnosis of patients health. At the same time, as Internet information is digital and reversible, it is easily accessed by third-party agencies to make money by exploiting law. Although many initiatives are taken by the organizations, we are unable to stop the fraud cases. Medical images (MI) also play an important role in diagnosis which embodies many details and is also in threat. This paper also discusses the benefits and importance of using EHR and its pros and cons. Medical image watermarking (MIW) plays an important role to safeguard patient details along with MI. This paper proposes a prime-based multiple watermark MIW technique to ensure safety to PHR and MIs. The proposed system is evaluated for three data compression encoding algorithms and compared against traditional systems.
The Development of Conceptual Approaches and Principles for Monitoring the Activities of Universities, Taking into Account the Opinions of Interested Groups (Stakeholders)

The adaptation of the concept of sustainable development and the stakeholder concept in relation to the activities of universities will be justified in this article. Also, conceptual approaches to the organization of the activity control system have been developed. The system-forming principles of the organization of external and internal control of university activities in Russia, taking into account the views of interested groups (stakeholders) are highlighted.
Situational Awareness Platform Based on Multi-source Vulnerability Fusion

With the increasingly severe cyberspace security situation, situational awareness has become a focus in the field of Cyberspace Security. The effect of situational awareness decision-making depends on the breadth and depth of data, the rationality of data processing, and the clarity and intuition of the presentation method. This paper designs a situational awareness platform, introduces vulnerability identification and fusion, situational awareness and data visualization. In particular, the data fusion method of online and offline multi-source vulnerability is proposed to enrich the data sources, and the soft splicing technology of display arrays is used to improve the visual compatibility under high resolution. In this paper, the data processing method is continuously optimized through experiments, which improves the overall processing efficiency and prediction accuracy of the platform.
Application of Blue Ink Cloud Class in College English Cooperative Learning

Abstract The use of Blue ink cloud class is convenient for college English online and offline blended college English teaching model. This paper constructs a new cooperative learning strategy for college English group based on Blue ink cloud class teaching platform, which improves the effectiveness of College English teaching effectively. The study provides a new perspective for the reform of College English Teaching under the “the Internet +”.background.
Time-dependent Side Channels in Quantum Key Distribution

Time-dependent side channels pose a security risk for quantum key distribution (QKD). We apply a numerical security proof technique to study such a side-channel, which we identify in a common source implementation for measurement-device-independent QKD employing Faraday mirrors.
Maneuvering target tracking based on improved particle filter

UAVs(unmanned aerial vehicle) have been widely used in various industries. In order to solve the problem of tracking the maneuvering target by the photoelectric payload of the UAV, this paper proposes a tracking algorithm based on improved particle filter. It uses the direction finding cross positioning method to obtain the suggested distribution of particle filter initialization, and designs a simplified the particle filter sampling strategy.The tracking algorithm not only effectively improves the tracking accuracy of maneuvering targets, but also improves the real-time computing performance of the particle filter algorithm.
Multi-party quantum secret sharing based on logical GHZ-type states against collective noise

In this paper, we discussed the local preparation methods of two types of multi-qubit logical GHZ-type states using controlled quantum gates, and drew the corresponding quantum circuits. Subsequently, we investigated the measurement-related properties of logical GHZ-type state and thus proposed two multi-party quantum secret sharing schemes against collective-dephasing and collective-rotation noise, respectively. Further, we demonstrated that the schemes can effectively resist some familiar attack strategies. Finally, we analyzed the quantum efficiency of our schemes and made a comprehensive comparison with previous similar schemes.
Sensor-based image manipulation localization with Discriminative Random fields and Graph Cut

We present PRNU-based image manipulation localization as a probabilistic labeling task in a flexible discriminative random field (DRF) setup. Instead of reaching local decisions independent of each other, discriminative random fields incorporate local inter-label dependencies while keeping the formulation general enough to make label assignments depend on both local and non-local image characteristics. With an improved form of association potential combining normalized correlation and the deviation of the measured correlation from the expected correlation and an interaction potential defined as the weighted L2 norm squared between intensities of neighboring pixels, we were able to localize even considerably small manipulations on realistic tampered images. We experimented with different combinations of window sizes to capture features to predict the correlation more accurately than already existing algorithms. Experimental results indicate that our algorithm outperforms recent state of the art methods based on multiscale analysis strategies. We also found that for inspecting manipulated images which are JPEG compressed, it helps to train the predictor with JPEG images rather than with uncompressed images and for all quality factors, it is possible to work with two predictors, one trained for images with lower quality factors and another for higher quality factors.
Spmpg: Robust Person Image Generation With Semantic Parsing Map

Person image generation is an interesting research topic that attracts many attention. In this paper, a robust body semantic parsing map guided automatic people pose synthesizing approach is proposed. Compared with commonly used skeleton based generation, our approach takes full advantage of available human body information, and hence successfully achieves improved performance. In the proposed method, extensive attention and multiscale discrimination mechanisms are enrolled to further advance generation performance. Verification experiments are extensively conducted on the Market-1501 and Deepfashion dataset to systematically evaluate the effectiveness of our approach.
An acceleration method for pairwise constraints cross-entropy algorithm

Cross-entropy Semi-supervised Clustering Based on Pairwise Constraints (CE-sSC) can make full use of pairwise constraints by introducing cross-entropy as the pairwise constraints measure, which significantly boost the performance of clustering. However, CE-sSC suffers from the problems of slow convergence and local optimum, which impacts the running velocity and the searching space. The swarm intelligence clustering algorithms represented by Particle Swarm Optimization K-means algorithm (PSOKmeans) addresses these problems by parallelly computing the particles during the iteration. But in the multi-mode problem, some particles might deteriorate during the iteration, which impact the performance of these particles and impacts the searching space of the whole swarm. Therefore, in order to solve the above issues, we propose an acceleration method for pairwise constraints cross-entropy algorithm (SSACE-sSC). The algorithm takes Salp Swarm Algorithm(SSA) and the CE-sSC into account, uses the characteristics of salp swarm to optimize the clustering solution. Further experiments conducted in UCI data show that the proposed clustering algorithm can derive a better performance and velocity.
Transferable Discriminative Feature Mining For Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to seek an effective model for unlabeled target domain by leveraging knowledge from a labeled source domain with a related but different distribution. Many existing approaches ignore the underlying discriminative features of the target data and the discrepancy of conditional distributions. To address these two issues simultaneously, the paper presents a Transferable Discriminative Feature Mining (TDFM) approach for UDA, which can naturally unify the mining of domain-invariant discriminative features and the alignment of class-wise features into one single framework. To be specific, to achieve the domain-invariant discriminative features, TDFM jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data. It then conducts the class-wise alignment by decreasing intra-class variations and increasing inter-class differences across domains, encouraging the emergence of transferable discriminative features. When combined, these two procedures are mutually beneficial. Comprehensive experiments verify that TDFM can obtain remarkable margins over state-of-the-art domain adaptation methods.
Lifelogging meets alternate and cross-realities: an investigation into broadcasting personal visual realities to remote audiences

We address in this work the topic of broadcasting one’s visual reality, captured by the video cameras embedded in mobile and wearable devices, to a remote audience. We discuss several designs of such life broadcasting systems, which we position at the intersection of lifelogging, Alternate Reality, and Cross-Reality technology. To this end, we introduce the “Alternate Reality Broadcast-Time” matrix for the broadcasting and consumption of alternate realities, in which designs of systems that implement sharing and consumption of personal visual realities can be positioned, characterized, and compared. These design options range from simple video streaming over the web using conventional video protocols to mediated and augmented reality, to audio narration and vibrotactile rendering of concepts automatically detected from video captured by wearable cameras. To demonstrate the usefulness of our broadcast-time matrix, we describe three prototypes implementing lifelogging, concept recognition from video, augmented and mediated vision, and vibrotactile feedback. Our contributions open the way toward new applications that blend lifelogging, consumption of multimedia alternate realities, and XR technology to empower users with richer opportunities for self-expression and new means to connect with the followers of events in their lives.
Multiple Sequence Alignment of Libation Formulae Suggest Linear a is Minoan-Greek

Here, we align multiple texts of various Libation formulae in Linear A. The alignment delineates words, and highlights synonymous syllables, of the Libation formulae. Remarkably, two vessels and their contents are specified and verify the proto-Greek origin of Linear A. The two vessels are: (1) “receptacle of grain” SU-TE-DA-KE (σιτοδόκος) followed by its content “grains” SE-TO-I-JA (σιτος,-oο) and (2) “receptacle of oil” I-RI-DE-KA (ελαιοδόκος) followed by its content “olive oil” A-RE-PI (ἄλειφαρ, oil) RE-NA (ἔλαιον, olive). We hypothesize that *301 is pronounced RI, and translate I-RI-WA-E (ἐλαιόω) into “olive-oil”, and I-RI-TI (ἀλείατα) into “wheat-meal”, both common in libation. Furthermore, the multiple sequence alignment juxtaposes the Goddess, I-DA-A (θεά or δεά in Tyrrhenian) and I-DA-MI (δαίμων) with JA-SA-SA-RA-ME, the Ashera poles ( אשרים ) and with JA-SA-RA-A-NA-NE, a luxuriant tree ( רענן ). In turn, the labrys inscription I-DA-MA-TE could refer to the God mother (θεά μήτηρ), Demeter (Δημήτηρ). Finally, we hypothesize that KA-NA-SI is derived from the noun “empty” (κενός), and the verb “to pour” (Εγκανασσειν). Interestingly, KA-NA-SI is aligned with the Hebrew synonymous translation “I emptied” RU-KA-NA-TI ( רוקנתי ) and RU-KA-JA-SI ( ריקיתי ). As such, we conclude Minoan is mainly a proto-Greek language, yet permeated with proto-Semitic influence.