Reviewerly/exHarmony huggingface dataset:
Warp Bridge Sampling: The Next Generation

Bridge sampling is an effective Monte Carlo (MC) method for estimating the ratio of normalizing constants of two probability densities, a routine computational problem in statistics, physics, chemistry, and other fields. The MC error of the bridge sampling estimator is determined by the amount of overlap between the two densities. In the case of unimodal densities, Warp-I, II, and III transformations are effective for increasing the initial overlap, but they are less so for multimodal densities. This article introduces Warp-U transformations that aim to transform multimodal densities into unimodal ones (hence “U”) without altering their normalizing constants. The construction of a Warp-U transformation starts with a normal (or other convenient) mixture distribution ϕmix that has reasonable overlap with the target density p, whose normalizing constant is unknown. The stochastic transformation that maps ϕmix back to its generating distribution N(0,1) is then applied to p yielding its Warp-U version, which we denote p˜. Typically, p˜ is unimodal and has substantially increased overlap with ϕ. Furthermore, we prove that the overlap between p˜ and N(0,1) is guaranteed to be no less than the overlap between p and ϕmix, in terms of any f-divergence. We propose a computationally efficient method to find an appropriate ϕmix, and a simple but effective approach to remove the bias which results from estimating the normalizing constant and fitting ϕmix with the same data. We illustrate our findings using 10 and 50 dimensional highly irregular multimodal densities, and demonstrate how Warp-U sampling can be used to improve the final estimation step of the Generalized Wang–Landau algorithm, a powerful sampling and estimation approach. Supplementary materials for this article are available online.
Prediction in KL Divergence

This chapter contains the main results of this volume, it is devoted to the problems of prediction with KL loss.
Development of Satellite Data-Based Multiple Regression Equations for the Estimation of Total Coliform and Petroleum Hydrocarbons Along South West Coast of India

Coastal waters are showing deteriorating trend in its quality. This leads to the damage of marine ecosystems and interferes in its normal use. In order to tackle this issue, it is important to know about the extent of pollution. Conventional method of water quality estimation includes analysis of water samples from various locations. This is a tiresome and costly process limiting its application to small scales and accessible sampling sites. In this paper, an attempt has been made to quickly estimate the concentration of Petroleum Hydrocarbons (PHC) and counts of Total Coliform (TC) which are important water quality parameters, along the south west coast of India. This study formulated satellite data-based multiple regression equations for determining the count of total coliform bacteria and concentration of petroleum hydrocarbons. The sea surface temperature and remote sensing reflectance values of different bands of MODIS sensor along with field values were used in the process. The developed algorithm is validated for future use. Maps are created using these equations, showing the distribution of these parameters along the coast using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Hence, the feasibility of VIIRS for determination of these parameters with the same algorithm is examined. Thus, based on the results, areas of high PHC and TC could be identified and necessary control measures could be adopted.
Algorithm of Maintenance Time and Maintenance Amount Based on Maintenance Degree

Given the available time and the maintenance degree, using the relationship between the maintenance degree and the maintenance time, this paper establishes an algorithm to judge whether the maintenance task can be completed within a certain time, and proposes a method for calculating the amount of equipment maintenance in the available time, which is extremely helpful for the maintenance decision making process especially with the support of computers.
High Performance Design for Redis with Fast Event-Driven RDMA RPCs

Redis is a popular key-value store built upon socket interface that remains heavy memory copy overhead within the kernel and considerable CPU overhead to maintain socket connections. The adoption of Remote Direct Memory Access (RDMA) that incorporates outstanding features such as low-latency, high-throughput, and CPU-bypass make it practical to solve the issues. However, RDMA is not readily suitable for integrating into existing key-value stores. It has a low-level programming abstraction and the original design of existing systems is a hurdle to exploit RDMA's performance benefits. RPCs can provide simple abstract programming interfaces that make it easy to be integrated into existing systems. This paper proposes a fast event-driven RDMA RPC framework named FeRR to promote the performance of Redis. First, we describe our design of FeRR that is based on one-sided RDMA verbs. Second, we propose an efficient request notification mechanism using event-driven model that can decrease the CPU consumption of polling requests. Finally, we introduce a parallel task engine to eschew the bottleneck of the single-threaded execution framework in Redis. Comprehensive experiment shows that our design achieves orders-of-magnitude better throughput than Redis - up to 2.78 million operations per second and ultra-low latency - down to 10 \(\upmu \)s per operator on a single machine.
Agility Detector Designed for Automobile Detection

For self-driving automobile, a prompt object detector under limited computation budget is highly desired yet indispensable for safety check. In this paper, taking restrained computation resource into consideration, we proposed a highly efficient detection backbone composed of transform shuffle units and a novel lite detection head made of depthwise convolutions to achieve real time performance for automobile detection. A set of experiments were conducted to demonstrate the customization ability of the proposed detection model and the well balanced one in terms of speed, accuracy and memory size along with several guiding rules in lite detection neural network design.
“If I’m Close with Them, It Wouldn’t Be Weird”: Social Distance and Animoji Use

Each new type of graphical icon (graphicon) in CMC has been more complex and multimodal than its predecessor. For this reason, and because of their novelty, Konrad, Herring, and Choi (2020) claim that new graphicon types are initially restricted to use in intimate relationships. We explore this proposition qualitatively by interviewing student users of Animoji – dynamic, large-scale emoji on the Apple iPhone – about who they send Animoji messages to and why. The results of a think-aloud card sort task in which participants (N = 33) matched seven Animoji with seven relationship categories at different social distances and sent a message to each one were triangulated with responses to open-ended questions before and after the task. Participants sent Animoji to close friends, significant others, and siblings, and to a lesser extent, parents and other family members. They rejected the idea of sending Animoji to more distant relationships such as a teaching assistant, a mentor, and new friends. Different Animoji were considered more or less suitable for each relationship, as well as for recipients of different genders. The reasons given by the interviewees for sending Animoji to each relationship category centered around themes of politeness, (in)formality, familiarity, and self-presentation.
The Construction and Practice of Electronic Technology Course Based on ICC

"Internet plus" education has greatly promoted the reform of teaching method and operating model in university courses. In this paper, taking the digital course of Electronic Technology published on the Digital Course Cloud platform of Higher Education Press (ICC) as an example, the construction process and experience of digital course are introduced. Based on ICC, the teaching practice is carried out. Students browse the learning materials on ICC before class, teachers focus on problem solving during class, and students complete the learning tasks on ICC after class. A teaching model integrating information technology and classroom teaching is formed, so as to provide references for the development and use of digital courses for teachers.
Vibration Analysis of Transporting Elderly Posture Behavior of Elderly-Assistant and Walking-Assistant Robot Considering Elderly Falling Angle

The modeling and simulation of transporting elderly posture of a new multifunctional elderly-assistant and walking-assistant robot (EWR) considering the elderly falling angle are investigated in this paper, which introduced by the authors lab to assist the elderly for doing the daily activities Satisfactorily. The elder was modeled as a double inverted pendulum one is the elder head and second is the elder body. The elder muscles are represented by a rotational spring and rotational damper which used to connect the double inverted pendulum. Mass-Spring-Damper system was used for robot modeling. MATLAB simulation is used to analyze the vibration characteristics when an outside disturbance is applied as input for the wholly system. Simulation results prove the stability of the elderly-robot system and show the ability of robot to prevent the elderly from falling.
Detection of the Rail Profile Wear Based on Image Processing

For the rail head wear detection, the feasibility of collecting rail images by CCD camera is analyzed. MATLAB image processing technology is used to establish the calculation model of rail wear measurement. By using the methods of edge detection and image filling, the key problems of rail image positioning are studied. The results show that the rail abrasion image detection system can get the rail abrasion accurately in real time under the condition of non-contact. The method can calculate the rail wear more accurately and provide a reference for the loss prevention of the rail and the development of new detection technology.
Time and Energy-Efficient Load Balancing Algorithm Toward Green Cloud Computing

The objectives of green cloud computing are to deal with the force and vitality effectiveness, decision of eco-neighborly equipment and programming, and reusing the material to build the item’s life. Load balancing needs to consider the heterogeneous sort of assets in cloud server farm alongside its present state while choosing the allotment of client assignments to the asset. Cloud computing spins around Web-based securing and arrival of assets from a data center. Being Web-based dynamic processing, distributed computing likewise may experience the ill effects of over-burdening of solicitations. Be that as it may, it despite everything faces a couple of difficulties, for example, asset usage in a cloud server farm and nature of administration to the end-clients because of inappropriate outstanding task at hand balances among accessible assets. In this paper, a new approach of time and energy-efficient load balancing (TELB) is introduced. This algorithm is based on time parameters and to use effective load balancing and scheduling of resources to consume more energy as well as within a minimum duration while executing the millions of tasks from various regions. This proposed algorithm has been actualized and found to give good outcomes of accurate, predictable, and reliable one.
IDEAL: An Interactive De-Anonymization Learning System

In the era of digital communities, a massive volume of data is created from people's online activities on a daily basis. Such data is sometimes shared with third-parties for commercial benefits, which has caused people's concerns about privacy disclosure. Privacy preserving technologies have been developed to protect people's sensitive information in data publishing. However, due to the availability of data from other sources, e.g., blogging, it is still possible to de-anonymize users even from anonymized data sets. This paper presents the design and implementation of an Interactive De-Anonymization Learning system-IDEAL. The system can help students learn about de-anonymization through engaging hands-on activities, such as tuning different parameters to evaluate their impact on the accuracy of de-anonymization, and observing the affect of data anonymization on de-anonymization. A pilot lab session to evaluate the system was conducted among thirty-five students at Prairie View A&M University and the feedback was very positive.
Deep supervised multimodal semantic autoencoder for cross‐modal retrieval

Abstract Cross‐modal retrieval aims to do flexible retrieval among different modals, whose main issue is how to measure the semantic similarities among multimodal data. Though many existing methods have been proposed to enable cross‐modal retrieval, they rarely consider the preservation of content information among multimodal data. In this paper, we present a three‐stage cross‐modal retrieval method, named MMCA‐CMR . To reduce the discrepancy among multimodal data, we first attempt to embed multimodal data into a common representation space. We then combine the feature vectors with the content information into the semantic‐aware feature vectors. We finally obtain the feature‐aware and content‐aware projections via multimodal semantic autoencoders. With semantic deep autoencoders, MMCA‐CMR promotes a more reliable cross‐modal retrieval by learning feature vectors from different modalities and content information simultaneously. Extensive experiments demonstrate that the proposed method is valid in cross‐modal retrieval, which significantly outperforms state‐of‐the‐art on four widely‐used benchmark datasets.
S2AP: Sequential Senti-Weibo Analysis Platform

Microblogging sentiment analysis aims at exploring people's opinion on social networks such as Twitter and Weibo. Existing work mainly focus on the English corpus based on Distant Supervision, which ignores the noise data in corpus and internationalization. The field of Weibo sentiment analysis lacks a large-scale and complete corpus for application and evaluation. In this work, we formulate the problem of corpus construction into an Information Retrieval problem and construct a Weibo sentiment analysis corpus called Senti-weibo. We also release a weibo pre-processing toolkit in order to unify the pre-processing rules of Weibo text. Eventually, we apply these works to implement a real-time Weibo sentiment analysis platform: S2AP, which serves to analyze and track the sequential sentiment of Weibo topics.
Chatbot for the Improvement of Conversational Skills of Japanese Language Learners

Conversation practice is essential for second-language acquisition and necessary for learners to reach an acceptable communicative level. In an ideal scenario, students should regularly hold conversations with native speakers of the target language, but this is often not possible. Although the teachers use the target language during the classes, they cannot offer a continuous conversation with each student, and they are usually not available wherever and whenever the student requires them to practice. This chapter presents the development and use of a mobile application to hold conversations in the Japanese language. The objective is to provide a software tool to improve the level of communicative competence (both inside and outside a classroom environment). So, the authors created and used a conversational agent (chatbot) using Dialogflow (a Google API), which is connected to the application's interface through the Internet using a client access token to give responses to user inputs in real time.
Research and Design of Computer Main Course Standard

Course standard is an important part of teaching quality guarantee system. Strengthening the construction of teaching quality standard system is of great significance for strengthening course teaching and promoting teaching construction. Based on the existing literature research results of teaching quality standards and teaching quality evaluation, combined with the training objectives of computer talents under the background of new engineering, this paper selects some representative computer main courses for qualitative research, so as to investigate the brewing, generation and practice process of computer core curriculum teaching quality standards in the realistic university environment, To explore the rationality and value of its existence. Furthermore, by refining the indicators of classroom teaching evaluation system, this paper constructs a new evaluation system of teachers' classroom teaching quality, including different evaluation subjects, evaluation standards and weights of different evaluation subjects, calculation of comprehensive evaluation results and feedback of opinions.
Extending Answer Set Programs with Neural Networks

The integration of low-level perception with high-level reasoning is one of the oldest problems in Artificial Intelligence. Recently, several proposals were made to implement the reasoning process in complex neural network architectures. While these works aim at extending neural networks with the capability of reasoning, a natural question that we consider is: can we extend answer set programs with neural networks to allow complex and high-level reasoning on neural network outputs? As a preliminary result, we propose NeurASP -- a simple extension of answer set programs by embracing neural networks where neural network outputs are treated as probability distributions over atomic facts in answer set programs. We show that NeurASP can not only improve the perception accuracy of a pre-trained neural network, but also help to train a neural network better by giving restrictions through logic rules. However, training with NeurASP would take much more time than pure neural network training due to the internal use of a symbolic reasoning engine. For future work, we plan to investigate the potential ways to solve the scalability issue of NeurASP. One potential way is to embed logic programs directly in neural networks. On this route, we plan to first design a SAT solver using neural networks, then extend such a solver to allow logic programs.
Preventing Attacks on the Easiest Applications with Vulnerabilities by Verification of Their Committed System Calls

The issue of detecting and preventing attacks on applications has been and remains one of the urgent tasks of information security. Flaws in the program code lead to disruption of the normal operation of the software. Data integrity, availability and confidentiality of the data, interruption of the execution of running processes or even the system as a whole may occur due to design flaws. The aim of this work is to prevent attacks on the application by overflowing the buffer using the developed complex to prevent attacks. To achieve this goal, the shortcomings of modern systems for preventing attacks on applications are briefly reviewed, the structure of the developed software package, the operation algorithms of each module of the software package, the mechanism for buffer overflows are examined, and the developed software package is tested on a simple buffer overflow.
The Model of Big Data Cloud Computing Based on Extended Subjective Logic

This paper has firstly introduced big data services and cloud computing model based on different process forms, and analyzed the authentication technology and security services of the existing big data to understand their processing characteristics. Operation principles and complexity of the big data services and cloud computing have also been studied, and summary about their suitable environment and pros and cons have been made. Based on the Cloud Computing, the author has put forward the Model of Big Data Cloud Computing based on Extended Subjective Logic (MBDCC-ESL), which has introduced Jφsang's subjective logic to test the data credibility and expanded it to solve the problem of the trustworthiness of big data in the cloud computing environment. Simulation results show that the model works pretty well.
Video object detection based on the spatial-temporal convolution feature memory model

Video object detection technology can improve the battlefield object search capability of tank fire control system. However, complex battlefield environment and faster speeds of tank and objects bring great challenge to video object detection. A video object detection method is proposed in this paper for the tank fire control system. Given the rich spatial-temporal information in the video and the large position deviation of the target in the adjacent video frames, a spatial-temporal convolutional feature memory model consisted of spatial-temporal convolution feature alignment mechanism and convolution gated recurrent unit is proposed to transmit and fuse the information of adjacent frames. Moreover, the feature extraction network and the detection sub-network are improved by the deformable convolution networks to increase the detection accuracy of deformed objects. To evaluate the proposed method, a database named TFCS VID including 1396 videos labelled for seven types of typical objects in the battlefield was developed. Compared to several other video object detection methods, the proposed method achieved excellent detection results on TFCS VID and could better meet the actual application requirements of equipment.
Usable Everlasting Encryption using the Pornography Infrastructure (Fast Abstract)

Nine years before Snapchat and its ephemeral messages, Aumann, Ding, and Rabin introduced the idea of everlasting security: an encryption that could not be decrypted after a certain date, no matter the adversary's computing power. Their method is efficient but not adapted to real-life constraints and cannot effectively be used today. In this paper we look at potential entropy sources available today, and propose a new solution that makes use of the already existing communications from pornography distribution networks, and look at its social implications. The method proposed has multiple advantages stemming from the fact that pornography is shameful in most societies, and it is usable off-the-shelf by individuals with limited technical skills, although it still requires some effort.
Existence results for some classes of differential systems with "maxima"

Local existence of solutions of initial value problems associated with a new type of systems of differential equations with “maxima” are investigated. Keywords: Systems of differential equations with "maxima", initial value problems, local existence of solutions.
The Naturalistic Tradition

Traditional experimental studies of analogical retrieval converged in documenting the centrality of superficial similarities during the retrieval of analogous situations from long-term memory. The present Chapter reviews a more recent generation of observational studies which began to analyze the spontaneous analogies produced by professionals of several disciplines as they work in their areas of expertise. The analogies generated within scientific domains such as Biology, Psychology, or Geology revealed a close thematic connection between the target analogs and the retrieved sources, with distant analogizing being mostly restricted to the goal of illustrating ideas to others. However, the productive analogies generated by journalists, economists, educators, and developers of industrial products were drawn to the same domain as the target in a small proportion of the cases, thus suggesting that the retrieval of analogous cases is not invariably constrained by superficial similarity. After reviewing the use of analogies within this varied array of professional activities, we succinctly describe the computer models that were engineered to simulate this pattern of analogical retrieval.