Reviewerly/exHarmony huggingface dataset:
Proposal of a Platform for Practical Work to Improve the Learning Conditions of Medical Learners in Developing Countries

The objective of this paper is to propose an interactive practical work platform for the real-time broadcasting of the audio/video flow of a surgical team in the operating room to students in a practical work room. To do this, we combine Voice Over IP (VoIP) with Internet Protocol Television (IPTV) to have an interactive television that makes the viewer active and able to ask questions. The platform is accessible through a unique web interface developed in PHP. To ensure the integrity and confidentiality of the data transferred between the hospital and the university, we used a VPN server. Thanks to the encoding technique used in the platform, the multimedia flow is fluid regardless of the available bandwidth. Thus, the problem of high-speed Internet access in rural areas of developing countries does not affect the quality of the multimedia flow.
Continuous Error Detection and Correction of Arithmetic in a Complement RRNS

A method for performing continuous error detection and correction of arithmetic is disclosed which utilizes a redundant residue number system (RRNS) and signed values encoded using the method of complements. Prior art methods for error correction in an RRNS operate on positive magnitudes only. Correction of negative values encoded using the method of complements of the full range of the RRNS representation has not been previously solved. This paper demonstrates the prior art using an example error correction of a positive result and explains why negative numbers present a problem for error correction of arithmetic. Next, two new solutions for direct correction of negative numbers encoded using the method of complements is disclosed. In the first method, two groups of trial digit sets are tested; one group for positive numbers and the other for negative numbers. A basic proof for the method is detailed which uses the Chinese Remainder Theorem. In a second method also disclosed, negative values are directly corrected along with positive numbers using a single pass hardware pipeline. The second method is developed formally and is followed with an example of a direct error correction of a negative value. Finally, details of a pipelined error detection and correction circuit is provided. Because of the carry free properties of the RRNS number system, and because of the new correction methods, an error in any digit may be corrected with equal success, even after lengthy product summation operations. The new error correction method provides continuous correction of arithmetic since the circuit designs are fully pipelined and can deliver a corrected result every clock cycle. The new error correction method is used to provide error correction to signed arithmetic using a standard method of complements encoding; this enables flexible error correction of general-purpose RNS arithmetic for advanced applications such as convolution neural network (CNN) and matrix arithmetic.
Research of Cross-domain Cloud Trust Model and its Defense Abilities Analysis

Under the open, distributed and dynamic cloud environment, entities interactions make it more complex to build up the trust relationship. Recently, trust has been recognized as an important factor to improve cloud security and becomes a research hotspot. In this paper, we proposes a new multi-level trust model to manage cloud entities' relationship from the view of 'virtual organization-sub domain-entity', and measure trust relationships in a numerical way. By keeping updating the trust information, the model can identify different entity types and defense bad entities' behaviors, optimizes resource allocation. The results show that this model is efficient to identify malicious entities and improves the ratio of service request response. So this model has good defense ability and is able to provide a better secure cloud environment.
Information Security Awareness in Tourism Enterprises

With the increase in information technology use, problems related to the security of these technologies have become significant. Businesses are required to acquire information, use information technologies, and rely on the information they collected to continue their presence. The requirement of information technology use and related security problems cannot be ignored by both individuals and businesses. One of the industries with large data pools is the tourism industry. Thus, the protection of personal or corporate information is a process that should be managed especially for businesses. In order for this management process to be successful, it is necessary for the corporations to have security policies that are supported by the senior management, possible to implement and understand, and accepted at all corporate levels. Thus, the present study aimed to explain the significance of information security, especially the security of the information in the tourism industry, which is as an important part of the service industry.
Wearable Advancements and its Implementation in Sports

In this research paper we will examine about the utilization of technological applications. This is presently far reaching crosswise over many significant sports tec. disciplines & The selection of these apparatuses to pick up an `upper hand' is an undeniably vital element of tip top sports. These developments have molded the manner in which information is gathered and handled, how data is handed-off among mentors and staff or to competitors, and has bigly affected the manner by which competitors are observed in the everyday preparing and rivalry conditions. This survey features and gives instances of the absolute most recent advances for information gathering and handling, input strategies and preparing devices. We wrap up by talking about certain contemplations for sports researchers and mentors before actualizing new technologies.
AI-NLP Analytics: A thorough Comparative Investigation on India-USA Universities Branding on the Trending Social Media Platform “Instagram”

The branding of the universities through social media has enormous influence among individuals. Social media has prospered through different networking sites such as Twitter, Facebook, Instagram, etc. However, in recent times, Instagram has become popular due to its unique features, which includes posting images, writing description alongside these images, and many more. People can even comment on these posts. The emotions expressed by people through their posts represent their real feelings and can be used for the analysis of public opinion regarding any topic. In our paper, we are applying the concepts of NLP on these textual descriptions and comments to perform a comparative investigation on the branding of the colleges/universities of India and USA. Our method provides a completely innovative way to view social media data. Our analysis can help the colleges/universities of both the country in providing an insight into their recognition among students through Instagram considering that the younger generation is largely influenced by Instagram. It can also help them in their rapid development and future goals.
The Effect of the Design of the Number of Poles on the Communication-field

In this paper, in order to clarify the effect of the number of poles in debate-like communication-field mechanisms, we conducted experiments on the subject of “Kudan-no-Sengen,” which is the activity using drama method to experience direct democratic decision-making. Although “Kudan-no-Sengen” is similar to the debate, which divides participants into two teams, the activity divides participants into four teams, i.e., four poles. In this study, we reformulated “Kudan-no-Sengen” and conducted experiments comparing “Kudan-no-Sengen” with two poles and that with four poles. As a result of experiments, it is revealed that the participants felt more fun and felt that they were able to generate more opinions in the condition with four poles than with two poles. In addition, it is revealed that the participants changed their opinions more in the condition with two poles than with four poles. The design of the number of poles in the discussion is an essential design element in the mechanism design of the communication-field. Discussion in this paper gives important suggestions in all communication-field mechanisms.
Sensor Scheduling Under Action Dependent Decision-Making Epochs

In this paper, we consider the problem of optimally allocating sensing resources for maximizing information gained in a multi-target tracking scenario. In particular, we examine the optimal allocation of a single ground-based sensor to multiple space-based targets to maximize the information gained in the space situational awareness problem. The optimization problem is solved in a receding horizon fashion at action dependent decision epochs that are not assumed to occur at regular intervals. We use a parallel Markov Chain Monte Carlo algorithm to compute the optimal target assignment sequence under constraints posed by the dynamics of the sensor. Information gain is quantified in terms of the differential entropy of the state probability density function. The effectiveness of the approach is demonstrated through a simulation study.
Discrimination of Probability Distribution and Low Frequency Anomaly of Seismic Wave Data

In order to find the distribution characteristics of low frequency signals and probability density before the Alashan M5.8 earthquake on April 15, 2015 in Inner Mongolia, the waveform data of three stations (from 00 points on April 1, 2015 to 23 points on April 15, 2015) were analyzed by using the continuous digital seismic waveform data of Wuhai, Shizuishan and Dongshengmiao stations. At the same time, the frequency and probability density distributions of the spectrum anomaly period are also tracked. The results show that: (1) The probability density distributions of seismic waveforms recorded by the three stations with and without seismic time periods are completely different, and the distribution of probability density with time change has the character of generalized Gaussian distribution. (2) Before the Azuoqi M5.8 Earthquake, the seismic waveforms recorded at Wuhai, Shizuishan and Dongshengmiao seismic stations near the epicenter not only exhibited the phenomenon of frequency shift to low frequency, but also changed the distribution of frequency and probability density in the corresponding period of abnormal occurrence. However, in the same period, the change patterns of the three stations were similar, and the probability density was divided into two parts. The distribution of probability density varies greatly in different time periods.
Soft-Clustering Driven Flip-flop Placement Targeting Clock-induced OCV

On-Chip Variation (OCV) in advanced technology nodes introduces delay uncertainties that may cause timing violations. This problem drastically affects the clock tree that, besides the growing design complexity, needs to be appropriately synthesized to tackle the increased variability effects. To reduce the magnitude of the clock-induced OCV, we incrementally relocate the flip-flops and the clock gaters in a bottom-up manner to implicitly guide the clock tree synthesis engine to produce clock trees with increased common clock tree paths. The relocation of the clock elements is performed using a soft clustering approach that is orthogonal to the clock tree synthesis method used. The clock elements are repeatedly relocated and incrementally re-clustered, thus gradually forming better clusters and settling to more appropriate positions to increase the common paths of the clock tree. This behavior is verified by applying the proposed method in industrial designs, resulting in clock trees which are more resilient to process variations, while exhibiting improved overall timing.
Fully Autonomous First Order Mathematical Optimizer

This research paper describes a technique for performing mathematical optimization, which is free from any hyperparameters. The optimization requires only first order derivatives of the objective function with respect to the learning parameters in the parameter update equation. The step size, more commonly known as the learning rate, which is usually manually set for training is replaced by a heuristic which calculates an optimal step size relative to the instantaneous value of the objective function. This replacing heuristic doesn't consume any statistical information and is based purely on relative geometric approximations; thereby, making the algorithm fully autonomous. Finally the results obtained on the MNIST dataset by using this technique are presented and discussed in brief while comparing to other optimization algorithms. More importantly, the algorithm proves its stability on typical machine learning problems and achieves performance comparable to Adam optimizer which is the current standard.
Multi-Document Summarization Based on Keyword Fusion

In this paper, we focus on two extractive multi document summarization approaches based on keyword extraction: FuseKeywords (FK) which extracts keywords for each document separately then fuses the two keyword lists, and FuseDocsFirst (FDF) which fuses the documents first and extracts a master keyword list from the fused documents. We are interested in determining which the order of keyword extraction and fusion will have a significant impact on the quality of the summarization. The performance of each tool is evaluated using the ROUGE metrics, and the performance for similar and dissimilar documents are compared.
Research and Analysis of Target Location Method Based on Improved Jacobi Algorithm

In the three-dimensional coordinate system, there are four omnidirectional antenna radar stations with fixed coordinates. These radar stations form a network radar and observe the target. In this paper, the beat signal with white Gaussian noise obtained by mixing the FMCW(Frequency Modulated Continuous Wave) transmission signal and the echo signal is processed by FFT to obtain the frequency value of the beat signal. Finally, the state information including the target distance obtained by the improved Jacobi iterative algorithm is compared and compared with the traditional Jacobi iterative algorithm. The simulation results were analyzed, and the superiority of the improved algorithm was verified, which provided help for accurate and timely meteorological observations.
Derived Heading Using Lagrange Five Point Difference Method and Fusion (poster)

In general, bearing measurements combined with the derived heading increases the accuracy of target state estimate in bearing only tracking (BOT). Here, Lagrange five point difference (LFPD) method is proposed for deriving the heading measurements. This method is compared with Lagrange three point difference (LTPD) and centered difference (CD) method by considering the scenario of two sensors tracking a single target. The bearing and derived heading measurements from two sensors are fused using measurement fusion (MF) technique. The nonlinear Extended Kalman filter (EKF) is implemented using fused measurement to obtain the optimized target state estimate. Performance analyses are made between LFPD compared with LTPD and CD by considering without and with fusion technique. Simulation results indicate MF-LFPD performs comparatively better.
Chinese Wrong Words Detection and Correction Algorithm Based on Sentence Perplexity and Iterative Correction

With the rapid development of the field of artificial intelligence, the traditional way to check whether there is a typo in the text is very inefficient. People have been looking for a way to automatically detect and correct wrong words used in texts. The use of artificial intelligence technology for detection and correction of typography came into being. The defects and causes of the existing Chinese wrong words detection and correction technology is analyzed in this paper. The algorithm is designed to detect and correct the wrong words twice and judge the interval between the first result and the second result. The sentence perplexity is calculated by combining the deep neural network language model, forming a double filter to reduce the misjudgment in the process of wrong words detection. Experiments in various text styles indicate that the improved algorithm can reduce more than 90% of misjudgments in the process of detection and correction.
Fast Scale Invariant Tracker and Re-identification for First-Person Social Videos

We address the problem of pedestrian tracking in videos of crowded scene which are captured by first-person viewpoint. The constant motion of camera and pedestrian makes this task challenging. The prime challenges are natural head motion of wearer and target loss and reappearance in a later frame, due to frequent changes in field of view. We propose that the use of first-person vision specific optical flow information and also the modification in the update process along with search region of trackers are useful to identify a lost target in a later frame. This process is termed re-identification in this paper. The specific trackers modified are MEEM and STRUCK. In addition to re-identification we achieve scale invariant tracking (upto 50% scale variation) and speed up by a factor of 2. We name our tracker as EgoTracker, since it utilizes the information which is specific to egocentric vision.
Carrier Sentence Selection with Word and Context Embeddings

This paper presents the first data-driven model for selecting carrier sentences with word and context embeddings. In computer-assisted language learning systems, fill-in-the-blank items help users review or learn new vocabulary. A crucial step in automatic generation of fill-in-the-blank items is the selection of carrier sentences that illustrate the usage and meaning of the target word. Previous approaches for carrier sentence selection have mostly relied on features related to sentence length, vocabulary difficulty and word association strength. We train a statistical classifier on a large-scale, automatically constructed corpus of sample carrier sentences for learning Chinese as a foreign language, and use it to predict the suitability of a candidate carrier sentence for a target word. Human evaluation shows that our approach leads to substantial improvement over a word co-occurrence heuristic, and that context embeddings further enhance selection performance.
Reducing Dimensionality of Variables for a Classification Problem: Fraud Detection

Fraud detection can be considered as a classification task since we can use datasets with labelled instances as fraud cases and legal cases. Although, many classifiers were applied to this problem, the data pre-processing related to the reduction of values of each variable is an uncommon approach. We explore a method to reduce the cardinality of the variables in a dataset of fraud transaction to identify improvement in this classification problem. Our best result indicated an improvement of + 31.8% in terms of F1-measure when we reduce the cardinality to detect fraud cases.
RDoCer: Information Retrieval and Sentence Extraction for Mental Health using Research Domain Criteria

Research Domain Criteria (RDoC) is a classification framework for mental illness, recently introduced by the National Institute of Mental Health. The RDoC Task at this years' BioNLP Open Shared Tasks 2019 workshop is a competition for inviting text mining groups around the world for developing informatics models for two subtasks: information retrieval and sentence extraction for mental health using RDoC. For competing in the RDoC task, we developed RDoCer, which uses a Term Frequency Inverse Document Frequency-based similarity measure for information retrieval and supervised machine learning models for sentence extraction. In comparison to the other models competed at the RDoC task, RDoCer performs competitively across the two subtasks while notably ranking first for the Sustained Threat RDoC construct in the sentence extraction subtask. The findings of this study have implications for mental health informaticians as well as researchers, curators, clinicians working in this domain.
Design Hypothesis: Knowledge-Relevance Model

This chapter asks how constructive design researchers construe hypotheses. What guides the construal process, is it theory, research program, or design reasoning? We will argue that this question involves two sub-questions: one concerns the hypothesis that guides research, another the hypothesis that guides design. We argue that constructive design research has to balance both of these to be effective, and we also point out that many of the controversies we have discussed in the previous chapters are in fact efforts to grapple with the Janus-faced character of the discipline. It is this character that has inspired us to suggest the Knowledge-Relevance model that help the constructive design researcher to balance the core research activities when the process is driven by design.
A Novel Topic Number Selecting Algorithm for Topic Model

A novel algorithm named the MTN (Multiple-Topic-Number) algorithm is introduced to deal with the problem of topic number selecting in topic model issue. The purpose of our algorithm is to build the LDA (Latent Dirichlet Allocation) matrices of different topic numbers to make the LDA matrices and machine learning algorithm combined better. So it can be used to solve the traditional problem of selecting topic number: under-size or over-size. The method here is to use different levels of machine learning tree structure to complete the combination. Experimental results show the efficiency of our proposed algorithm.
Energy Efficient Container Consolidation Method in Cloud Environment Based on Heuristic Algorithm

While container-based clouds are increasingly gaining popularity, minimizing the power consumption of the data center is still one of the major challenges for cloud providers. Dynamic consolidation of containers presents a significant opportunity to save energy in data centers and there are researches use optimization metaheuristic algorithm to find a near-optimal solution for this problem. However, the computation time and complexity of such algorithms increase exponentially with the number of containers. In this paper, a heuristic dynamic container consolidation method which consists of four algorithms that solve the problems in different stages of container consolidation has been proposed. We migrate redundant containers from the hosts before they overload and place them to other host to guarantee QoS requirements. An adaptive reserved resources to prevent re-overload of hosts has also been applied. Experimental results demonstrate that our proposed approach can lead to further energy saving with QoS guarantees compared with some existing approaches.
Simplified Model for Recognition Facial Emotions

There is a suggested approach for getting a simplified model to enable the realization of fast facial emotion recognition. The model realizes the hyperplane classification of mimic expressions of the main facial emotional states. The main advantage of this approach is small computational complexity, which will allow realizing the recognition of the changes in people's emotional state without any special equipment (for low-resolution or long-distance video cameras). The sphere of application is control over the drivers in the process of driving the vehicle, complex production operators, etc.