Reviewerly/exHarmony huggingface dataset:
Combining experts' causal judgments

Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being compatible, and show how compatible causal models can be merged. We then use it as the basis for combining experts' causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.
YARD: A Tool for Curating Research Outputs

Repositories increasingly accept research outputs and associated artifacts that underlie reported findings, leading to potential changes in the demand for data curation and repository services. This paper describes a curation tool that responds to this challenge by economizing and optimizing curation efforts. The curation tool is implemented at Yale University’s Institution for Social and Policy Studies (ISPS) as YARD. By standardizing the curation workflow, YARD helps create high quality data packages that are findable, accessible, interoperable, and reusable (FAIR) and promotes research transparency by connecting the activities of researchers, curators, and publishers through a single pipeline.
How to improve critical thinking skills and spatial reasoning with augmented reality in mathematics learning?

Abstract Critical thinking skills and spatial reasoning are important aspect in human life. On the other hand, currently, implementing augmented reality in many fields be a trend, including in education field. This study aims to explain how to improve critical thinking skills and spatial reasoning with augmented reality in mathematics learning. The method used in this research is literature review. The data obtained from scientific journals, conference proceedings, and other relevant sources. The results show that to improve spatial reasoning can be done by utilizing its features that can develop confidence of learners, facilitate student-centered learning, create problem solving, and create interactive learning. Besides that, spatial reasoning can be improved by designing dynamic objects with AR. Dynamic objects permit learners to view objects from different sides. Therefore, active learning can be promoted.
An Automated Approach to Assessing an Application Tutorial’s Difficulty

Online step-by-step text and video tutorials play an integral role in learning feature-rich software applications. However, when searching, users can find it difficult to assess whether a tutorial is designed for their level of software expertise. Novice users can struggle when a tutorial is out of their reach, whereas more advanced users can end up wasting time with overly simple, first-principles instruction. To assist users in selecting tutorials, we investigate the feasibility of using machine-learning techniques to automatically assess a tutorial's difficulty. Using Photoshop as our primary testbed, we develop a set of distinguishable tutorial features, and use these features to train a classifier that can label a tutorial as either Beginner or Advanced with 85% accuracy. To illustrate a potential application, we developed a tutorial browsing interface called TutVis. Our initial user evaluation provides insight into TutVis's ability to support users in a range of tutorial selection scenarios.
Research on fused sorting based on logical regression in news recommendation system

Abstract A fusion sorting model based on logical regression is used to solve the problem of information overload in the news recommendation system and meet the personalized needs of users. First, the fusion sorting model is trained based on the article data and user data, and the original candidate news set is obtained by multi-channel recall method. Then the original candidate set is filtered by the fusion sorting model, and the recommendation list is generated. Finally, the experiment is carried out based on the Wuli News Recommendation System. Under the evaluation indexes of precision, recall and ILS, the fusion sorting model adopted significantly improves the recommendation ability of the system, and achieves the goal of making multiple single recommendation strategies make up for each other’s shortcomings.
Online Case Intelligent Interaction System based on Virtual Reality Technology Under the Background of Novel Coronavirus

Online case intelligent interaction system based on virtual reality technology under the background of the novel coronavirus is designed and implemented in this paper. It provides more information for the application of virtual reality technology to realize the 3D animation system. In the process of the model itself, the 3D animation model usually retains the deformation process of the object so that it can be observed. Besides, staff should use 3D scanners to scan objects and also store 3D data accurately, which requires technicians to use computers to operate very similar structures and to use 3D coordinates to enhance authenticity. With this technology support, this paper integrates the VR for constructing the system. The experiment has proven the effectiveness which will be necessary.
Automatic Keywords-based Classification of Vietnamese Texts

Text classification is a sophisticated field of research in natural language processing that deals with the problem of automatically classifying new documents into pre-defined classes. It is a complex procedure involving not only selecting the right training models, but also integrating numerous fine-tuned processes, e.g. pre-processing, transformation, and dimensionality reduction. Researchers either develop new classification models or improve the existing approaches by investigating new techniques. An ideal text classifier would mimic how humans assign text to topics. People usually categorize documents by scanning their important words rather than reading the whole text source. With this process in mind, the authors propose a framework to categorize documents and apply the idea of keyword-based classification. The authors have collected real text data from various websites and utilize the TextRank algorithm and Jaccard similarity coefficient. A wide range of experiments has been conducted to show that the proposed framework achieves good results.
FSPMTL: Flexible Self-Paced Multi-Task Learning

Multi-Task Learning (MTL) is a method to simultaneously utilize commonalities and differences across tasks to improve the learning performances with limited data. However, in most real-world problems, there are many sample noises which might decline the performance of MTL significantly. To address this challenge, Self-Paced Learning (SPL) method is introduced to improve its performance by increasing the numbers of instances gradually from the simplest samples to the most difficult samples. In the current self-paced multi-task learning methods, SPL is introduced as a term in the optimization process, which causes significant limitations in the combination of SPL and MTL. In this paper, we propose a new flexible framework, which combines MTL with SPL and has two stages in the learning process to make it more suitable for learning difficult samples and tasks. With this framework, we are able to take advantages of both of the existing MTL models and SPL models. Further experiments with the synthetic and real-world datasets demonstrate the higher efficiency of our approach when compared with other state-of-the-art models.
A Study on Recent Trends of Consensus Algorithms for Private Blockchain Network

Blockchain is expected to play a seminal role in the future of finance and cybersecurity. Blockchain has the traits of decentralization, stability, safety, and non-modifiability. The consensus algorithm performs an important role in retaining the order of transaction and performance of blockchain networks. Using the right algorithm might also deliver a great boom to the performance of the blockchain applications. In this paper, we reviewed the ideas and characteristics of some of the different consensus algorithms and analyzed the performance and application eventualities of various consensus mechanisms with an example of an electronic voting system. The electronic voting system needs the ability to address the following constraints: Performance, Fault Tolerance, Byzantine Fault Tolerant to make it a scalable application. We have also studied different algorithms, in particular, the ones in distributed systems and came up with a novel way to solve the problem mentioned above.
Two constructions of asymptotically optimal codebooks via the trace functions

In this paper, we present two new constructions of complex codebooks with multiplicative characters, additive characters and trace functions over finite fields, and determin the maximal cross-correlation amplitude of these codebooks. We prove that the codebooks we constructed are asymptotically optimal with respect to the Welch bound. Moreover, in the first construction, we generalize the result in Zhang and Feng (IEEE Trans. Inform. Theory 58(4), 2507–2511, 2012). In the second construction, we generalize the results in Hong et al. (IEEE Trans. Inf. Theory 60(6), 3698–3705, 2014), we can asymptotically achieve Welch bound for any odd prime p, we also derive the whole distribution of their inner products. The parameters of these codebooks are new.
Reinforced Zero-Shot Cross-Lingual Neural Headline Generation

Cross-lingual neural headline generation (CNHG), which aims at training a single, large neural network that directly generates a target language headline given a source language news document, has received considerable attention in recent years. Unlike conventional neural headline generation, CNHG faces the problem that there are no large-scale parallel corpora of source language articles and target language headlines. Consequently, CNHG is a zero-shot scenario. To solve this problem, we propose zero resource CNHG with reinforcement learning. We develop a reinforcement learning framework that is composed of two modules: a neural machine translation (NMT) module and a CNHG module. The translation module translates an input document into a source language document, and the headline generation module takes the previous output as input to generate a target language headline. Then, both modules receive a reward for joint training. The experimental results reveal that our method significantly outperforms baseline models.
Using Artificial Neural Networks to Predict Solar Radiation for Duhok City, Iraq

The amount of solar radiation received at the Earth's surface is influenced by local weather conditions. This paper investigates the effects of meteorological parameters on daily average solar radiation (DASR) in Duhok city, Iraq. Artificial Neural Networks (ANNs) based on multilayer preceptor feed-forward (MLP-FF) techniques are used to predict daily average solar radiation (DASR). The input variables used are a daily average of the relative humidity (RH), minimum temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> ), maximum temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> ), wind speed (WS), cloud layer (CL), atmospheric pressure (AP) and ultraviolet (UV) levels to estimate DASR. To identify and evaluate the effects of various input parameters on solar radiation, eight ANN-based models have been developed. To obtain the best estimation results, the number of neurons in the hidden layer has been varied. The best values of the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R) have been calculated. For some models, the results obtained show good and better predictive accuracy than others. The present study indicates that various of the meteorological parameters can have a significant effect on the forecasting of solar radiation.
In Datacenter Performance, The Only Constant Is Change

All computing infrastructure suffers from performance variability, be it bare-metal or virtualized. This phenomenon originates from many sources: some transient, such as noisy neighbors, and others more permanent but sudden, such as changes or wear in hardware, changes in the underlying hypervisor stack, or even undocumented interactions between the policies of the computing resource provider and the active workloads. Thus, performance measurements obtained on clouds, HPC facilities, and, more generally, datacenter environments are almost guaranteed to exhibit performance regimes that evolve over time, which leads to undesirable nonstationarities in application performance. In this paper, we present our analysis of performance of the bare-metal hardware available on the CloudLab testbed where we focus on quantifying the evolving performance regimes using changepoint detection. We describe our findings, backed by a dataset with nearly 6.9M benchmark results collected from over 1600 machines over a period of 2 years and 9 months. These findings yield a comprehensive characterization of real-world performance variability patterns in one computing facility, a methodology for studying such patterns on other infrastructures, and contribute to a better understanding of performance variability in general.
Unveiling Practices and Challenges of Machine Teachers of Customer Service Conversational Systems

This paper describes a set of qualitative interventions which aimed to unveil the challenges of teaching conversational machines used in the automatic customer service. The study aimed to understand the activity of mapping information into conversational systems platforms to create chatbots (text or voice-based) to attend end-users in conjunction with call centers. We interviewed eleven domain experts with non-machine learning skills responsible for curating the content of the chatbots. Additionally, we conducted four design workshops with experienced curators to understand deeper their challenges when teaching novice curators. We list a group of challenges and opportunities for improving the machine teacher's practices and supporting decision making
Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach

The goal of automatic algorithm configuration is to recommend good parameter settings for an algorithm or solver on a per-instance basis, i.e., for the specific problem instance being solved. Realtime algorithm configuration is a practically motivated variant of algorithm configuration, in which the problem instances arrive in a sequential manner and high-quality configurations must be chosen during runtime. We model the realtime algorithm configuration problem as an extended version of the recently introduced contextual preselection bandit problem. Our approach combines a method for selecting configurations from a pool of candidates with a surrogate configuration generation procedure based on a genetic crossover procedure. In contrast to existing methods for realtime algorithm configuration, the approach based on contextual preselection bandits allows for the incorporation of problem instance features as well as parameterizations of algorithms. We test our algorithm on different realtime algorithm configuration scenarios and find that it outperforms the state of the art.
Similarity Image Retrieval Model based on Local Feature Fusion and Deep Metric Learning

With the development of deep learning, this technology has unique advantages in feature extraction and feature nonlinear mapping, and it has become the primary solution to computer vision problems. Research on similarity image retrieval has turned machine learning into deep learning. Generally, similarity image retrieval systems based on deep learning first train a model for image classification, and then extract bottleneck layer features as image representation features for storage and retrieval. The problems with such a design are: the bottleneck layer features lose image detail information, and the features of similar images are not compact enough. This article aims to improve the method for the above problems. Adding metric learning to constrain the image features to solve the problem of inadequate compact image features; using local feature fusion features instead of bottleneck layer features as image representation features can express more image content. Finally, the effectiveness of the improvement proposed in this paper is verified by experiments.
A Robust Multiclass Vehicle Detection and Classification Algorithm for Traffic Surveillance System

The main goal of traffic surveillance systems (TSSs) is to extract useful traffic information by analyzing signals from cameras. This paper presents a system for vehicle detection and classification from static pole-mounted roadside surveillance cameras on busy streets in the presence of different kinds of vehicles. There has been considerable research to accommodate this subject since the 90s; but most studies have been only carried out in developed countries where traffic infrastructures are built around automobiles, whereas in developing countries, motorbikes are dominant. This paper proposes a method that robustly detects, classifies and counts vehicles into three classes: light (motorbikes, bikes, tricycles), medium (cars, sedans, SUV), heavy vehicle (trucks, buses), and a novel tracking algorithm designed to enable classification by majority voting to cope with motorbikes' sudden changes in direction. Extensive experiments with real-world data to evaluate the system's performance have shown promising results: a detection rate of 95.3% in daytime scenes.
A brief analysis of gesture recognition in VR

With the rapid development of artificial intelligence technology, human‐computer interaction, as the core of virtual reality system, has become an increasingly important part of modern science and technology products. At present, human‐computer interaction is mainly based on contact, which must be completed through direct contact between hand and receiving equipment, rather than through indirect contact between hand and equipment. Direct interaction can not give full play to the advantages of flexible human hand, so gesture recognition becomes extremely important in interaction. The development of gesture recognition technology based on vision is a process from 2D to 3D. Early gesture recognition is based on the recognition technology of two‐dimensional color image, which means to get two‐dimensional static image after taking the scene with ordinary camera, and then recognize the content of the image through the computer graphic algorithm. With the development of camera and sensor technology, the depth information of gesture can be captured, and 3D gesture recognition technology can recognize various hand shapes, gestures and actions. Virtual reality is a kind of simulated virtual environment generated by computer according to reality and imagination space, which makes users have immersive feeling in the real world. Through various interactive behaviors, it stimulates users’ perception systems such as vision, touch and hearing, so as to establish a comprehensive sensory experience. Nowadays, virtual reality interaction is more based on the handle of virtual reality device or operating gloves, but using these devices is too complex, so gesture interaction based on bare hands will have a broader prospect. The virtual reality interaction technology based on gesture recognition is to recognize human hand movements with cameras and other devices and interact with the virtual scene. Users can control the virtual scene and interact through simple gestures. Gesture recognition is now applied in many industries, but the interaction of gesture in virtual reality is not so popular. This paper will analyze the development status of gesture recognition in virtual reality technology, and prospect the development trend of virtual reality technology based on gesture recognition.
Teaching and Learning Virtual Strategy for the Navigation of Multiple-UAV

This paper presents a virtual application based on the engineering area teaching-learning process for the mobile robots cooperative control. The application development is basically focus on advanced control algorithms implementation in a 3D virtual environment shown in the Unity 3D graphic engine, which exchanges information in real time using mathematical software improving the control strategy. This strategy considers the multiple UAVs autonomous navigation in order to perform cooperative tasks. Finally, experimental tests that show users' interaction and immersion in the virtual environment are presented while doing cooperative navigation tasks.
The security of vulnerable senior citizens through dynamically sensed signal acquisition

Abstract Traditional signal recognition methods generally use biosensors for signal acquisition. With senior citizens, sensor signal acquisition will be affected by their movements. These signal fluctuations are large, and if the signal area cannot be fixed, it may result in problems such as data loss. The most important issue caused by data loss is the safety for vulnerable seniors. Therefore, here we study abnormal behavior recognition based on dynamic sensing. In this paper, we look to improve the problems that exist in traditional methods. Using the SW‐520D sensor, activity signals of the elderly are first collected. By comparing the received signal strength sets, dynamic sensor data flow of the abnormal behavior for senior citizens can be determined. A multiple linear regression estimation method is used to solve the problem of data loss in dynamic sensor data flow environments. We obtain system parameter thresholds in both area isolation and segmentation using the stochastic resonance method. From this, a direct notch is constructed that enters the dynamic sensor data stream, and the interference component filtering of abnormal behavior signals is processed. The amplitude‐frequency response feature extraction method is used for high‐precision isolation and segmentation of abnormal behavior signal areas such as falls, improving the accuracy of senior behavior signal recognition, and realizing safety monitoring for the elderly. The improved method was used to identify the signals of abnormal behaviors of young people. The minimum recognition error rate was only 2%, the recognition accuracy rate was as high as 98%, and the calculation time was only 19 ms.
On the Design and Performance of Chinese OSCCA-approved Cryptographic Algorithms

SM2, SM3, and SM4 are cryptographic standards authorized to be used in China. To comply with Chinese cryptography laws, standard cryptographic algorithms in products targeting the Chinese market may need to be replaced with the algorithms mentioned above. It is important to know beforehand if the replaced algorithms impact performance. Bad performance may degrade user experience and increase future system costs.We present a performance study of the standard cryptographic algorithms (RSA, ECDSA, SHA-256, and AES-128) and corresponding Chinese cryptographic algorithms.Our results indicate that the digital signature algorithms SM2 and ECDSA have similar design and also similar performance. SM2 and RSA have fundamentally different designs. SM2 performs better than RSA when generating keys and signatures. Hash algorithms SM3 and SHA-256 have many design similarities, but SHA-256 performs slightly better than SM3. AES-128 and SM4 share some similarities in the design. In the controlled experiment, AES-128 outperforms SM4 with a significant margin.