Reviewerly/exHarmony huggingface dataset:
Research on Topology Identification of Distribution Network Under the Background of Big Data

Traditional identification of distribution network topology needs to rely on manual identification and verification, which has the problems of low identification rate and poor accuracy. Under the current background of power big data, effective results can be obtained by fully mining the potential effective information of big data and applying it to the identification of distribution network topology structure. This paper presents a line-to-line relationship identification method based on the maximum correlation minimum redundancy method and a line-to-transformer relationship identification method based on the conversion of three-phase unbalanced outlet voltage. The voltage acquisition system is fully utilized, and the maximum correlation minimum redundancy method (mRMR) is used to eliminate the redundancy of feature variables in line-to-line relationship identification to obtain the most accurate correlation results. In the identification of line-transformer relationship, the conversion method of three-phase unbalanced outlet voltage is adopted to eliminate the influence of misjudgment caused by three-phase unbalanced voltage and improve the identification accuracy. The identification method proposed in this paper is applied to the topology identification of a regional distribution network. By comparison with the actual topology, the identification accuracy is up to 99.78% and the effect is remarkable.
Opposite and Chaos Searching Genetic Algorithm Based for UAV Path Planning

One of the most challenges in Unmanned Aerial Vehicles (UAV) system is path planning, as it provides basic navigation for UAVs to accomplish various missions under different backgrounds. Genetic algorithm (GA) is an efficient swarm intelligence algorithm in solving combinational optimization problems. To realize more rapid and efficient path planning under the limitation of multiple obstacles, this paper presents a novel genetic algorithm, namely Opposite and Chaos searching Genetic Algorithm (OCGA). At initial stage, population are generated by opposite and chaos searching for better optimization ability. At iteration stage, the algorithm updates population with a crossover operator improved by teaching-learning based optimization's (TLBO) learning strategy to increase convergence and prevent local optima. Simulation results validate the performance of proposed algorithm is superior to Triple-colony Ant Optimization (TP-ACO) in terms of optimization ability and convergence speed.
A New Methodology to Find Private Key of RSA Based on Euler Totient Function

The aim of this paper is to present a new methodology to find the private key of RSA. A new initial value which is generated from a new equation is selected to speed up the process. In fact, after this value is found, brute force attack is chosen to discover the private key. In addition, for a proposed equation, the multiplier of Euler totient function to find both of the public key and the private key is assigned as 1. Then, it implies that an equation that estimates a new initial value is suitable for the small multiplier. The experimental results show that if all prime factors of the modulus are assigned larger than 3 and the multiplier is 1, the distance between an initial value and the private key is decreased about 66%. On the other hand, the distance is decreased less than 1% when the multiplier is larger than 66. Therefore, to avoid attacking by using the proposed method, the multiplier which is larger than 66 should be chosen. Furthermore, it is shown that if the public key equals 3, the multiplier always equals 2.
Librarian Contributions to a Regional Response in the COVID-19 Pandemic in Western North Carolina

At the start of the COVID-19 pandemic, the Mountain Area Health Education Center formed a response team with the goal of building capacity across Western North Carolina health systems to collectively identify needs, coordinate resources to fill gaps, and strategically manage the regional threats of the pandemic. The library team collaborated on interprofessional projects by gathering information and using LibGuides to quickly and easily organize and share resources. The team met challenges, including moving to telecommuting, balancing a growing workload, and navigating a changing information landscape, and in doing so, strengthened relationships across the organization and the region.
Suspicious Action Detection in Intelligent Surveillance System Using Action Attribute Modelling

Research in the field of image processing and computer vision for recognition of suspicious activity is growing actively. Surveillance systems play a key role in monitoring of sensitive places such as airports, railway stations, shopping complexes, roads, parking areas, roads, banks. For a human it is very difficult to monitor surveillance videos continually, therefore a smart and intelligent system is required that can do real time monitoring of all activities and can categories between usual and some abnormal activities. In this paper many different abnormal activities has been discussed. More focuses is given to violence activity like hitting, slapping, punching etc. For this large human action dataset like UCF101, Kaggel is required. This paper proposes a method to model violence actions using Gaussian Mixture Model with Universal Attribute Model. In this action vector is used to remove redundant attributes and get a low dimensional relevant action vectors.
Construction practice of student evaluation system based on JFinal + webix integrated framework and Baidu AI platform

Students' evaluation of teaching is a key link to realize teaching quality monitoring and promote teachers' teaching level. Based on the practice of student evaluation in our university, this paper constructs a multi-level student evaluation system, and develops an online student evaluation system by using JFinal+webix integration framework. The new Internet plus evaluation model is established to improve the efficiency of student evaluation and the enthusiasm of students to evaluate teaching. Meanwhile, based on the analysis of students' comments on teaching by Baidu AI platform, It provides data support for the improvement of learning level and the optimization of teaching evaluation.
Symmetric distinguishability as a quantum resource

We develop a resource theory of symmetric distinguishability, the fundamental objects of which are elementary quantum information sources, i.e., sources that emit one of two possible quantum states with given prior probabilities. Such a source can be represented by a classical-quantum state of a composite system $XA$, corresponding to an ensemble of two quantum states, with $X$ being classical and $A$ being quantum. We study the resource theory for two different classes of free operations: $(i)$ ${\rm{CPTP}}_A$, which consists of quantum channels acting only on $A$, and $(ii)$ conditional doubly stochastic (CDS) maps acting on $XA$. We introduce the notion of symmetric distinguishability of an elementary source and prove that it is a monotone under both these classes of free operations. We study the tasks of distillation and dilution of symmetric distinguishability, both in the one-shot and asymptotic regimes. We prove that in the asymptotic regime, the optimal rate of converting one elementary source to another is equal to the ratio of their quantum Chernoff divergences, under both these classes of free operations. This imparts a new operational interpretation to the quantum Chernoff divergence. We also obtain interesting operational interpretations of the Thompson metric, in the context of the dilution of symmetric distinguishability.
Small Object Detection from Remote Sensing Images with the Help of Object-Focused Super-Resolution Using Wasserstein GANs

In this paper, we investigate and improve the use of a super-resolution approach to benefit the detection of small objects from aerial and satellite remote sensing images. The main idea is to focus the super-resolution on target objects within the training phase. Such a technique requires a reduced number of network layers depending on the desired scale factor and the reduced size of the target objects. The learning of our super-resolution network is performed using deep residual blocks integrated in a Wasserstein Generative adversarial network. Then, detection task is performed by exploiting two state-of-the-art detectors including Faster-RCNN and YOLOv3. Experiments were conducted on small vehicle detection from both aerial and satellite images from the VEDAI and xView data sets. Results showed that object-focused super-resolution improves the detection performance and facilitates the transfer learning from one data set to another.
Hedging in interpreted and spontaneous speeches: a comparative study of Chinese and American political press briefings

Abstract The investigation of potential stylistic differences between translated and non-translated texts has been proven to be a promising line in corpus-based translation studies, yet similar research on the product of interpreting seems both smaller in scale and slower in development. This study compares the use of hedges in interpreted and spontaneous English speeches collected in similar settings. Specifically, we gathered the two types of speech data from Chinese and American political press briefings respectively and analyzed their differences in the employment of various hedging devices. The results show that interpreted speeches not only contain significantly fewer hedges but they are also dependent on a narrower range of such devices than spontaneous speeches, which is consistent with the tendency towards simplification in translations. Also, our findings suggest that interpreted and spontaneous speeches tend to follow distinct hedging patterns in terms of preferred linguistic choices. Potential factors triggering such differences are discussed.
Making Theory Reasoning Simpler

Abstract Reasoning with quantifiers and theories is at the core of many applications in program analysis and verification. Whilst the problem is undecidable in general and hard in practice, we have been making large pragmatic steps forward. Our previous work proposed an instantiation rule for theory reasoning that produced pragmatically useful instances. Whilst this led to an increase in performance, it had its limitations as the rule produces ground instances which (i) can be overly specific, thus not useful in proof search, and (ii) contribute to the already problematic search space explosion as many new instances are introduced. This paper begins by introducing that specifically addresses these two concerns as it produces general solutions and it is a simplification rule, i.e. it replaces an existing clause by a ‘simpler’ one. Encouraged by initial success with this new rule, we performed an experiment to identify further common cases where the complex structure of theory terms blocked existing methods. This resulted in four further simplification rules for theory reasoning. The resulting extensions are implemented in the Vampire theorem prover and evaluated on SMT-LIB, showing that the new extensions result in a considerable increase in the number of problems solved, including 90 problems unsolved by state-of-the-art SMT solvers.
CTAB-GAN: Effective Table Data Synthesizing

While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from generative Adversarial Networks (GAN) and address two main data types in the industry, i.e., continuous and categorical. In this paper, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types, including a mix of continuous and categorical variables. Moreover, we address data imbalance and long-tail issues, i.e., certain variables have drastic frequency differences across large values. To achieve those aims, we first introduce the information loss and classification loss to the conditional GAN. Secondly, we design a novel conditional vector, which efficiently encodes the mixed data type and skewed distribution of data variable. We extensively evaluate CTAB-GAN with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results into higher accuracy for five machine learning algorithms, by up to 17%.
Intelligent colocation of HPC workloads

Many HPC applications suffer from a bottleneck in the shared caches, instruction execution units, I/O or memory bandwidth, even though the remaining resources may be underutilized. It is hard for developers and runtime systems to ensure that all critical resources are fully exploited by a single application, so an attractive technique for increasing HPC system utilization is to colocate multiple applications on the same server. When applications share critical resources, however, contention on shared resources may lead to reduced application performance. In this paper, we show that server efficiency can be improved by first modeling the expected performance degradation of colocated applications based on measured hardware performance counters, and then exploiting the model to determine an optimized mix of colocated applications. This paper presents a new intelligent resource manager and makes the following contributions: (1) a new machine learning model to predict the performance degradation of colocated applications based on hardware counters and (2) an intelligent scheduling scheme deployed on an existing resource manager to enable application co-scheduling with minimum performance degradation. Our results show that our approach achieves performance improvements of 7 % (avg) and 12 % (max) compared to the standard policy commonly used by existing job managers.
SISTEM INFORMASI PENGOLAHAN DATA TERNAK UNIT PELAKSANA TEKNIS PRODUKSI DAN KESEHATAN HEWAN BERBASIS WEB

The processing data is process of input and output data become another form that very needed which is the special information of livestock processing data. The unit of implementation technical production and health of animal (Prokeswan) Lape sudistrict and Lopok subdistrict as one of a government service that have various type of affairs, like like the processing data of livestock that cover the result of registration of livestock data and a mutation data of livestock. A livestock data collection is activities that carried out by subdistrict officials to know the number of livestock in every village. Research items to design and build information system processing of livestock in information of livestock processing data. The unit of implementation technical production and health of animal (Prokeswan) lape subdistrict and Lopok subdistrict in Web-based. The system was developed using the PHP language programming and using MySql as a database. The method of the developed of this software using waterfall method. The testing of this software through Black Box testing that is testing that do with testing the software in functional terms. The instruments Technic Collecting data in this research using interview and literature review. The final result of research is design and build an information system processing of livestock data Web-based can facilitate officer and employer to processing livestock data, this system also can speed up saving data, processing, collecting and display data to produce the quality information and also processing the data become a report that able to present the complete livestock data of UPT Prokeswan Lape subdistrict and Lopok subdistrict.
Quantum metrology with precision reaching beyond <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math> -scaling through <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -probe entanglement-generating interactions

Nonlinear interactions are recognized as potential resources for quantum metrology, facilitating parameter estimation precisions that scale as the exponential Heisenberg limit of ${2}^{\ensuremath{-}N}$. We explore such nonlinearity and propose an associated quantum measurement scenario based on the nonlinear interaction of $N$-probe entanglement-generating form. This scenario provides an enhanced precision scaling of ${D}^{\ensuremath{-}N}/(N\ensuremath{-}1)!$ with $D&gt;2$ a tunable parameter. In addition, it can be readily implemented in a variety of experimental platforms and applied to measurements of a wide range of quantities, including local gravitational acceleration $g$, magnetic field, and its higher-order gradients.
Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard

Leaderboards are a ubiquitous part of modern research in applied machine learning. By design, they sort entries into some linear order, where the top-scoring entry is recognized as the "state of the art" (SOTA). Due to the rapid progress being made in information retrieval today, particularly with neural models, the top entry in a leaderboard is replaced with some regularity. These are touted as improvements in the state of the art. Such pronouncements, however, are almost never qualified with significance testing. In the context of the MS MARCO document ranking leaderboard, we pose a specific question: How do we know if a run is significantly better than the current SOTA? We ask this question against the backdrop of recent IR debates on scale types: in particular, whether commonly used significance tests are even mathematically permissible. Recognizing these potential pitfalls in evaluation methodology, our study proposes an evaluation framework that explicitly treats certain outcomes as distinct and avoids aggregating them into a single-point metric. Empirical analysis of SOTA runs from the MS MARCO document ranking leaderboard reveals insights about how one run can be "significantly better" than another that are obscured by the current official evaluation metric (MRR@100).
DeepInterpolation: fusion of multiple interpolations and CNN to obtain super‐resolution

The authors propose architectures that learn end-to-end mapping functions to improve the spatial resolution of the input natural images. The models are unique in forming non-linear combinations of three image interpolation techniques using the convolutional neural network. Another proposed architecture uses a skip connection with nearest-neighbour interpolation, achieving almost similar results. The architectures have been carefully designed to ensure that the reconstructed images lie precisely in the manifold of high-resolution images, thereby preserving the high-frequency components with fine details. They have compared with the state-of-the-art and recent deep learning-based natural image super-resolution techniques and found that their methods can preserve the sharp details in the image, while also obtaining comparable or better peak-signal-to-noise ratio values than them. Since their methods use image interpolations and a shallow convolutional neural network (CNN) with a fewer number of smaller filters, the computational cost is kept low. They have reported the results of the best two proposed architectures on five standard data sets for an upscale factor of 2. Their methods generalise well in most cases, which is evident from the better results obtained with increasingly complex data sets. For four times upscaling, they have designed similar architectures for comparing with other methods.
Deep Learning for Subtyping and Prediction of Diseases: Long-Short Term Memory

The long short-term memory neural network (LSTM) is a type of recurrent neural network (RNN). During the training of RNN architecture, sequential information is used and travels through the neural network from input vector to the output neurons, while the error is calculated and propagated back through the network to update the network parameters. Information in these networks incorporates loops into the hidden layer. Loops allow information to flow multi-directionally so that the hidden state signifies past information held at a given time step. Consequently, the output is dependent on the previous predictions which are already known. However, RNNs have limited capacity to bridge more than a certain number of steps. Mainly this is due to the vanishing of gradients which causes the predictions to capture the short-term dependencies as information from earlier steps decays. As more layers in RNN containing activation functions are added, the gradient of the loss function approaches zero. The LSTM neural networks (LSTM-ANNs) enable learning long-term dependencies. LSTM introduces a memory unit and gate mechanism to enable capture of the long dependencies in a sequence. Therefore, LSTM networks can selectively remember or forget information and are capable of learn thousands timesteps by structures called cell states and three gates.
Design of Effective Smart Communication System for Impaired People

In communication medium, sharing a conversation dialogue between the normal person and deaf and dumb person is one of the challenging tasks still. The dumb person can practice hand gesture language in their community but not to others. This research article focuses to minimize the difficulty level between these two communities with smart glove devices. Besides, the author believes that result of the proposed model provides a good impact on the dump community. The smart glove contains input, control, and output module to get, process, and display the data respectively. Our proposed model is used to help these communities to interact with each other continuously without any error. The proposed model is constructed with good specification flex sensors. Little change of resistance in flex sensor is providing changes in their gesture language. So this orientation direction is calculated well and gives better results over existing methods. The wireless set can be made with Bluetooth technologies here. Here the gestures are assigned based on the alphabet letter. The sign language performs and gives audible output in the display section of the proposed model. It gives good results in our experimental setup. This research work focuses on good recognition rate, accuracy, and efficiency. The good recognition rate shows the continuous conversation between the two persons. Moreover, this research article compares the recognition rate, accuracy, and efficiency of the proposed model with an existing model.
Enhancing Prosodic Features by Adopting Pre-trained Language Model in Bahasa Indonesia Speech Synthesis

Deep neural network text-to-speech (TTS) systems can produce high-quality audio. However, modern TTS systems usually need a sizable of studio-quality <text, audio> pairs as input. In view of the insufficient research on Bahasa Indonesia, available data are usually worse in term of both quality and size. The End-to-End(E2E) TTS systems trained on those corpora are difficult to generate satisfactory speech, especially the prosodic features are not obvious. Therefore, we propose a method to enhance the prosodic features of synthesized speech based on GST-Tacotron2 model, and pre-trained language model with the BERT (Bidirectional Encoder Representation from Transformers) model. The BERT learned from large number of unlabeled text data contains rich linguistic information, which can help TTS systems produce the more obvious prosodic features. The subjective evaluation of our experimental results shows that the proposed method can indeed enhance the rhythm of synthesized speech.