Reviewerly/exHarmony huggingface dataset:
Session Based Query Recommendation with Graph Neural Networks on Heterogeneous Information Trees

Many e-commerce platforms such as JD.com and Taobao have implemented personalized recommendation systems. Query recommendation or relevant search module is introduced to guide users to clarify their shopping intentions. In this paper, we present a novel Session based Query Recommendation with Graph neural networks on Heterogeneous information trees (SQRGH). We model user click behaviors (user-query-item) as a heterogeneous information tree to better present user behaviors (Fig. 2). We use graph neural networks to model interactions in user behavior, while, instead of using real queries or items as nodes, we use the click behaviors as virtual nodes to better learn about changes in the user intention, and use the identity matrix to upgrade adjacency matrices generated by traditional methods. On a real large-scale dataset in e-commerce using the JD computing platform, our proposed SQRGH method has superior performance and can recommend more reliable and reasonable results.
Work-in-Progress–—Synapse VR: Catalyzing Collaboration Through Immersive Learning

This work-in-progress paper describes a pilot of an immersive learning experience called Synapse VR in a biopsychology course on campus at Southern New Hampshire University with undergraduate students conducted in early 2020 by faculty and staff. Theories of cooperative learning and positive interdependence informed the team's coordination of the VR app's assessment framework and classroom delivery model to ensure learning design components promoting self-regulation and co-regulation of learning were accentuated. Synapse VR's first classroom pilot generated a set of suggested learning experience design practices for educators seeking to implement immersive learning experiences in college classrooms.
Development of Android Chat Application to Verify First Sender of the Image

Nowadays, people are shifting from web-based applications to android-based applications to connect people socially. It is important to identify ownership of the messages in android-based applications. Generally, the android-based social media apps (e.g., WhatsApp, etc.) provide end to end encryption to provide security. Therefore, one can never know about the origin of the forwarded information effortlessly. To know the source user, the tracking of each user who forwarded the data is required by the authorized people. But in the presented work, it is possible to know the source/origin of the information without keeping the track record of the people forwarded that data (i.e., image). It saves time and memory. In this proposed work, an image watermarking approach is implemented on the android platform to identify users and apps. The user is identified using the mobile number which is authenticated using OTP verification by Google firebase. The android-based applications (e.g., WhatsApp, etc.) is identified by using orthogonal Walsh codes as application-specific watermarks. Two different approaches are used for watermarking for performance evaluation and experimental results prove the significance of the proposed work.
Eventuality of Application of SRH-Technologies Under the Conditions of Education's Digital Transformation

The purpose of the study is to provide a methodological basis for the development and application of an innovative general pedagogical technology (called by the authors SRH-technology) of organizing the pedagogical process at a university, aimed at increasing the effectiveness of teaching bachelors through the use of innovative forms – research studies, networking with the work of students in reading groups, hackathon and foresight sessions aimed at developing hard-skills and soft-skills among undergraduate students. The scientific novelty of the research lies in the development of an innovative general pedagogical technology for teaching undergraduate students (called by the authors SRH-technology), based on a combination of personality-oriented, activity-based and systemic approaches. As a result, the effectiveness of the use of SRH technology in the educational process has been proved, and the main recommendations for the effective organization of work with students in the framework of the listed forms of classes are given.
Weight model based image compression method, part I: image analysis

In this paper, an image analysis method for future compression based on a weight model was proposed, which allows one to estimate the significance of the detailing coefficients of the orthogonal multiple-scale wavelet transform in terms of their contribution to the total image energy. The method presupposes the decomposition of the original image into a given number of levels, construction of significance maps for the detailing coefficients of each level before coding of the significant coefficients.
Some Thoughts on the Teaching of "Probability Theory and Mathematical Statistics"

At present, in today's information explosion, how to let students know more about this course through introduction courses, so as to increase students' interest in learning this course, and how to make students aware of the importance of this course, and from which perspectives the teaching content And the reform of teaching methods is a question that every teacher of Probability Theory and Mathematical Statistics must think about.
Delay distributions on encrypted real-time traffics

Abstract Real-time streams are common for nowadays communication for both data and video. Real-time data communication such as chat application and telemetry, as well as real-time video communication such as video call, video conference, or monitoring video are prone to the third party attacked. End to end encryption has been considered as the most realistic solution such as applied by WhatsApp so that the transmitted data is difficult to be revealed by the attacker. Since encryption and decryption processes injected additional delay, the algorithm should be fast enough to avoid inconvenience to users. This paper examines delay distribution for encrypted real-time traffics. The simulation shows that the decryption contributed the most to the end to end delay. Decryption delay is about 4.58 times higher than encryption and transmission delay. Although assessment involves only two links, the complex network has relatively lower than the obtained decryption delay. Meanwhile, encryption contributed only about 0.2% to the overall end to end delay.
Search Problems in Contemporary Power Girds

In this article, we present a collection of significant (computationally-hard) search problems, encountered in contemporary decentralized power grids, such as complex smart grids. Against this background, we show how Answer Set Programming (ASP) can be utilized as a powerful tool for efficiently addressing these problems. The overall demonstration provides the ability to assess potential (harmful) issues that a power electrical system may face, due to a variety of topological failures, a fact which, in turn, results in augmented reliability and robustness of energy distribution and management.
An Improved Virtual Sample Generation Method Based on Quadrat Density Method and Quantile Regression for Small Sample Size Problem

The gradual realization of automation has caused explosive growth of data and increased the amount of researchable data. However, due to the low probability of occurrence and high difficulty in obtaining, representative data is lacking. One of the effective ways to solve this problem is virtual sample generation (VSG). In this study, a novel VSG method is put forward. The sample squares are divided in the input space according to Dominance Analysis, and the virtual inputs are generated by using the Quadrat Density Method in reverse. The corresponding virtual output is predicted by Gaussian Process Regression. Through Quantile Regression, analyze the correlation between input variables and output variables. The generated virtual samples are screened, and the virtual samples that do not meet the correlation relationship are eliminated. In order to verify the effectiveness of the proposed method, experiments are carried out on two numerical simulations and a real-world application from a cascade reaction process for high-density polyethylene. The results show that the method proposed in this paper is superior to other methods.
Exploration Algorithm for a Mobile Robot. An Experiment

Exploring an unknown space it is one of the most well-known tasks to be performed by mobile robots. Among the most used sensors for obstacle detection there are ultrasonic sensors that have the advantage of simplicity in implementation as well as the possibility of obtaining a fast response time from the robot control system.
COPS: A complete oblivious processing system

The communication link between the processor and the off-chip memory in a modern computing system is vulnerable to hardware-level security attacks. An adversary can snoop the memory access behavior over the communication link to retrieve secret information even when the data transferred over the link are encrypted. The Oblivious RAM (ORAM) mechanism is a promising solution for such a problem. ORAM hides a true memory access with a group of random memory read and write operations. To reliably estimate the cost and performance overhead incurred by ORAM in real scenarios, a complete oblivious processing system where a fully functional ORAM is integrated into a real processor system that can run an Operating System (OS) and execute real-world application workloads is required. In this paper, we present a generic design for ORAM integration into a general-purpose processing system that can be used for the integration of any ORAM design. Our design targets the ORAM integration into high-performance memory bus (AXI). The AXI-compatibility of our design makes it usable with any processor system, thus, making the ORAM integration independent of processor architecture. As a case study, we develop a complete oblivious processing system (we call it COPS) by integrating an ORAM into a general-purpose RISC-V based processing system. We evaluate ORAM performance in real-world execution environment using realistic workloads with full Operating System support. Our elevation demonstrates that our ORAM prototype, on average, can return a 64 bytes requested block to the processor in 1.46μs, which is ∼ 13x faster than the prior-art implementation.
Proposing a Perceived Expertise Tool in Business Data Analytics

The business data analytics domain exhibits a particularly diversified and demanding field of interaction for the end-users. It entails complex tasks and actions expressed by multidimensional data visualization and exploration contents that users with different business roles, skills and experiences need to understand and make decisions so to meet their goals. Many times this engagement is proven to be overwhelming for professionals, highlighting the need for adaptive and personalized solutions that would consider their level of expertise towards an enhanced user experience and quality of outcomes. However, measuring adequately the perceived expertise of individuals using standardized means is still an open challenge in the community. As most of the current approaches employ participatory research design practices that are time consuming, costly, difficult to replicate or to produce comparable, unbiased, results for informed interpretations. Hence, this paper proposes a systematic alternative for capturing expertise through a Perceived Expertise Tool (PET) that is devised based on grounded theoretical perspectives and psychometric properties. Preliminary evaluation with 54 professionals in the data analytics domain showed the accepted internal consistency and validity of PET as well as its significant correlation with other affiliated theoretical and domain-specific concepts. Such findings may suggest a good basis for the standardized modeling of users’ perceived expertise that could lead to effective adaptation and personalization.
A Study on the Differences in Speech Interaction Behavior between Teacher and Student in High School Classroom Based on improved Flanders Interaction Analysis System

Due to various reasons, the classroom teaching of junior high school and senior high school will be different. Improved Flanders Interaction Analysis (iFIAS) is an important analysis method for quantitative analysis of classroom teaching. Through the application of this method, the process of teacher-student speech interaction in classroom teaching can be recorded and analyzed objectively. This article uses iFIAS to analyze a junior high school history class and a senior high school history class, find out the difference between the teacher-student speech interaction behavior in the junior high school class and the senior high school class, and guess the reasons for it.
Research on Target Detection Method Based on Deep Learning

Deep learning is an important research field of machine learning. In recent years, many breakthroughs have been made in the field of target detection, which has been applied to specific target detection tasks. This paper first introduces the representative traditional detection methods and discusses their limitations, and then introduces in detail the target detection models based on candidate regions and regression. At the same time, their advantages are summarized and compared. Through the comprehensive test of PASCAL VOC2007 and VOC 2012 test data, the results show that the DSSD detection method is the best, and the YOLOv3 detection method is the best for vehicle detection. In addition, the self-produced data set of field construction vehicles was used to detect Yolov3 and test the detection accuracy. The results showed that the detection accuracy was above 0.9. Yolov3 can be well used for vehicle detection. Finally, the detection methods based on deep learning are summarized.
MoveFeel

We propose MoveFeel, a movement computing framework that leverages vision-based analysis to compute meaningful metrics for assessing expressive dance movement. Our system is a multi-component workflow which extracts and collects dance movement images, processes and quantifies human pose skeletons, and computes attributes pertaining to dance movement that adhere to the movement methodologies based on "The Dynamics of Movement" by Rudolph Laban. We conduct a feasibility study to classify the expressive intentions of dance phrases encapsulated within a dance routine. We detail the interacting components of our system and discuss the interpretation and conversion of subjective dance principles into quantified metrics. MoveFeel demonstrates promise in generating metrics that can effectively distinguish dance phrases that are associated with positively or negatively expressed emotion. Our goal is to build upon these techniques and apply them to the movement arts (dance, theatre), medicine (therapy), and education.
The Scott model of PCF in univalent type theory

Abstract We develop the Scott model of the programming language PCF in univalent type theory. Moreover, we work constructively and predicatively. To account for the non-termination in PCF, we use the lifting monad (also known as the partial map classifier monad) from topos theory, which has been extended to univalent type theory by Escardó and Knapp. Our results show that lifting is a viable approach to partiality in univalent type theory. Moreover, we show that the Scott model can be constructed in a predicative and constructive setting. Other approaches to partiality either require some form of choice or quotient inductive-inductive types. We show that one can do without these extensions.
Unified End-to-End Sentence Denoising

It takes more than correct grammar to speak good English. In this paper, we describe the sentence denoising task that reduces the vagueness, redundancy, and irrationality of a grammatical sentence. We define a rich, linguistics-inspired noise taxonomy and establish the formal definition of the problem. A unified end-to-end model based on Transformer is proposed and an efficient algorithm for constructing the training data is given, together with a separate fine-tuning step to get the ideal model. Our method outperforms previous results and keeps good accuracy as the noise composition gets more complicated.
Unsupervised Domain Adaptation based on the Geography Structural Information

Traditional domain adaptation aligns the marginal and conditional distribution of the source and target domain. While the representation after projection is not discriminative enough for the final classification. In this paper, we propose a new method, the conditional distribution of both domains is aligned, the class information is employed to further enhance the discriminability of the samples. After aligning the class information, samples can be classified easier. Meanwhile, the geographic structure information in the samples is well preserved to train the classifier of the model. Several experiments are done to prove the effectiveness of the model and demonstrated good performance on three frequently used data sets: Office-Home, Amazon-Review, PIE.
The Where of Mineral Names: Anglesite, Parys Mountain, Anglesey, Wales

ANGLESITE, PbSO4, is a simple sulfate of lead, named for the island of Anglesey, in northwest Wales, United Kingdom (fig. 1). It crystallizes in the orthorhombic system and is a member of the celes...
Design of UML Diagrams for Intervention for Autism Children (IAC System)

Autism Spectrum Disorder (ASD) or Autism is a complex developmental disability that typically appears during early childhood and can impact a person’s social skills, communication, relationships, and self-regulation. Early recognition, as well as behavioural, educational and family therapies may reduce symptoms and support development and learning. Parent as well as the affected child faces severe challenges. IAC system enables the autistic child to overcome such challenges. The main functionalities of this system includes Training parent, Communicator, ABA, Schools nearby, Apprisal Provision, Financial Resources and Emotional Analyser. For this system, StarUML an open source software modelling tool is used to develop UML diagrams and provide users with ready-to-use, expressive modelling. The interesting quality of IAC is that it provides services both to the parent as well as the child affected by ADS.
Learning from learning: detecting account takeovers by identifying forgetful users

Credential-stuffing attacks are increasing in frequency, allowing threat actors to use data breaches from one source to perpetrate another. While multi-factor authentication remains a crucial preventative measure to protect against credential stuffing, the availability of credential data sets with contact information and the correlation with demographic data can allow threat actors to overcome it through interactive social engineering. Concurrently, alternative defence mechanisms such as network source profiling and device fingerprinting lose effectiveness as privacy-protecting technologies reduce the observable variability between legitimate and fraudulent user sessions. Sean A McElroy of Lumin Digital presents original research which suggests that by measuring a user's increasing familiarity with a web application over time, outliers in use may indicate account takeover fraud. Credential-stuffing attacks are increasing in frequency, allowing threat actors to use data breaches from one source to perpetrate another. While multi-factor authentication remains a crucial preventative measure to protect against credential stuffing, the availability of credential data sets with contact information and the correlation with demographic data can allow threat actors to overcome it through interactive social engineering. Concurrently, alternative defence mechanisms such as network source profiling and device fingerprinting lose effectiveness as privacy-protecting technologies reduce the observable variability between legitimate and fraudulent user sessions.
Visualization of Multi-Fidelity Approximations of Stochastic Economic Dispatch

As renewable energy generation deployment increases, the operation of electrical grids becomes more complex. Economic dispatch is part of a grid operator's regular decision process where the amount of energy to generate is determined based on the number of available generators and the actual level of energy demand. Renewable generators are inherently stochastic due to the chaotic nature of weather patterns, and thus, real-time decisions of economic dispatch become increasingly complex. Modeling efforts to assist in these decisions in the highest fidelity typically take hours to days to solve on leadership-class computers; too long for the 5-minute operational time-frame demanded of operators. Alternatively, multi-fidelity approximations can be used to predict generation levels quickly and with sufficient accuracy to be used for real-time operations. We have developed a visualization tool to demonstrate the utility of multi-fidelity approximations by displaying contextual results of economic dispatch approximations, comparisons across fidelity levels of generation levels and possible failures to meet demand, and meta-data on the modeling setup.
SMOTE Based Weighted Kernel Extreme Learning Machine for Imbalanced Classification Problems

Most of the classification problems in the real-world suffer from class imbalance. The performance of traditional classification algorithms is biased towards the majority class while handling a class imbalance problem. Several methods have been used to handle the class imbalance problems. These methods are based on three main approaches which are data level, algorithmic, and ensemble. Weighted Kernel-based Synthetic Minority Oversampling Technique (WK-SMOTE) is a recently proposed method that utilizes the benefits of both the data level approach and algorithmic approach. Inspired by the idea and performance of WK-SMOTE this work proposes a novel Synthetic Minority Oversampling based Weighted Kernelized Extreme Learning Machine (SMOTE-WKELM). SMOTE-WKELM is a variant of Weighted Kernelized Extreme Learning Machine (WKELM), which uses SMOTE for oversampling of the minority class instances. Experiments are performed on 15 datasets with varying imbalance ratios, downloaded from keel dataset repository for performance evaluation. The results on these datasets show that the proposed method performs better than the other state of the art methods in consideration.
An Improvised Particle Swarm Optimization Using Balanced Local Best

With the increasing growth of data and model complexity, optimization has become an important part of machine learning and is facing more and more challenges in various fields. Among various optimization techniques, particle swarm optimization is the simplest algorithm and falls under the swarm intelligence domain. It is a meta-heuristic population-based stochastic search algorithm, but the standard particle swarm optimization has a slow rate of convergence, and moreover, it can be trapped in a local optimum value, especially with complex multimodal problems. An improvised particle swarm optimization is proposed based on the modification of the inertial weights, random values and the cognitive component. Some benchmark minimization functions are used to validate the proposed model with the standard particle swarm optimization and velocity clamping model.