nils-herrmann/clean_scirepeval huggingface dataset:
Stock Price Forecasting: New Model for Uptrend Detecting and Downtrend Anticipating Based on Long Short-Term Memory

Stock price forecasting has tremendous importance, it is one of the most challenging tasks due to the high volatility of stock market data. Share market investment is often risky, hence the need for an accurate forecasting model to minimize this risk. This article aims to present a new model for forecasting stock prices for a given horizon. The model consists of four phases: the first phase is filtering out of all the stocks those for which we cannot reject the normal distribution hypothesis of the return. Note that the returns in our case reflect the evolution of the price compared to the moving average of n previous days. Then a second filtration to keep only the stocks whose returns has a high average, low standard deviation and the error type II is low enough for the normality test. In the third phase, we will build classes by comparing returns to given limits (8 given limits). The last phase is for training and testing the LSTM classifier. The main objective of the model is to anticipate class 0 and 1, which correspond to a medium and strong downtrend, and to predict class 7 and 8 which characterize the price increase. Experience shows that our proposed model gives strong results and its accuracy is quite high.
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition

Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification. Thus far, various sentence embedding models have been proposed, and their feasibility has been demonstrated through good performances on tasks following embedding, such as sentiment analysis and sentence classification. However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks. In this paper, inspired by human language recognition, we propose the following concept of semantic coherence, which should be satisfied for a good sentence embedding method: similar sentences should be located close to each other in the embedding space. Then, we propose the ParaphraseThought (P-thought) model to pursue semantic coherence as much as possible. Experimental results on two paraphrase identification datasets (MS COCO and STS benchmark) show that the P-thought models outperform the benchmarked sentence embedding methods.
Learning Environmental Sounds with Multi-scale Convolutional Neural Network

Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.
Effects of assistance timing on metabolic cost, assistance power, and gait parameters for a hip-type exoskeleton

There are many important factors in developing an exoskeleton for assisting human locomotion. For example, the weight should be sufficiently light, the assist torque should be high enough to assist joint motion, and the assistance timing should be just right. Understanding how these design parameters affect overall performance of a complex human-machine system is critical for the development of these types of systems. The present study introduces an assistance timing controller that regulates assistance timing such that peak joint velocity and peak assistance power are offset by a reference value for our hip-type exoskeleton. This is followed by measuring the manner in which various assistance timing references affect an important metric for performance, namely metabolic cost. The results indicate that net metabolic cost exhibits a concave up pattern with the most reduction of 21%, when compared to walking without the exoskeleton, at 0% assistance timing reference. The study also examines assistance timing's effect on gait parameters; increase in assistance timing reference increases step length, decreases cadence, and increases walk ratio (i.e. step length/cadence ratio) during treadmill walking.
TG13 flowchart for the management of acute cholangitis and cholecystitis.

We propose a management strategy for acute cholangitis and cholecystitis according to the severity assessment. For Grade I (mild) acute cholangitis, initial medical treatment including the use of antimicrobial agents may be sufficient for most cases. For non-responders to initial medical treatment, biliary drainage should be considered. For Grade II (moderate) acute cholangitis, early biliary drainage should be performed along with the administration of antibiotics. For Grade III (severe) acute cholangitis, appropriate organ support is required. After hemodynamic stabilization has been achieved, urgent endoscopic or percutaneous transhepatic biliary drainage should be performed. In patients with Grade II (moderate) and Grade III (severe) acute cholangitis, treatment for the underlying etiology including endoscopic, percutaneous, or surgical treatment should be performed after the patient's general condition has been improved. In patients with Grade I (mild) acute cholangitis, treatment for etiology such as endoscopic sphincterotomy for choledocholithiasis might be performed simultaneously, if possible, with biliary drainage. Early laparoscopic cholecystectomy is the first-line treatment in patients with Grade I (mild) acute cholecystitis while in patients with Grade II (moderate) acute cholecystitis, delayed/elective laparoscopic cholecystectomy after initial medical treatment with antimicrobial agent is the first-line treatment. In non-responders to initial medical treatment, gallbladder drainage should be considered. In patients with Grade III (severe) acute cholecystitis, appropriate organ support in addition to initial medical treatment is necessary. Urgent or early gallbladder drainage is recommended. Elective cholecystectomy can be performed after the improvement of the acute inflammatory process. Free full-text articles and a mobile application of TG13 are available via http://www.jshbps.jp/en/guideline/tg13.html.
Managing Context Information for Adaptive Security in IoT Environments

Pervasive computing is becoming a reality due to the rise of the so-called Internet of Things (IoT). In this paradigm, everyday and physical objects are being equipped with capabilities to detect and communicate information they receive from their environment, turning them into smart objects. However, such entities are usually deployed on environments with changing and dynamic conditions, which can be used by them to modify their operation or behavior. Under the foundations of EU FP7 SocIoTal project, this work provides an overview about how contextual information can be taken into account by smart objects when making security decisions, by considering such information as a first-class component, in order to realize the so-called context-aware security on IoT scenarios.
Brain-computer interface systems: progress and prospects.

Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.
A cost effective estimation of depth from stereo image pairs using shallow siamese convolutional networks

We propose a cost effective depth estimation method using stereo camera and convolutional neural networks for SLAM algorithm. Convolutional neural networks outperform the traditional computer vision approaches in estimating depth of stereo image pairs. However, the performance gain of neural networks approach causes substantial increase in computation cost, which consequently decreases the operation time of mobile robots like domestic robots. To alleviate the high computation problem, this paper proposes quantized shallow Siamese convolutional neural networks which compute the similarity between patches of rectified stereo image pairs to estimate depth. Quantization of weights and reduction of layers in the neural networks can degrade the performance. To mitigate the performance degradation, this paper initially maximizes networks performance with three different methods of batch-normalization, optimal negative matching similarity training, and retraining with a global loss function. Then, the final retrained network is nonuniformly quantized. This non-uniform quantization provides efficient computation with the minimum performance loss. The final quantized shallow Siamese networks achieve 3.29% error rate for KITTI 2012.
A review of single-phase grid-connected inverters for photovoltaic modules

This review focuses on inverter technologies for connecting photovoltaic (PV) modules to a single-phase grid. The inverters are categorized into four classifications: 1) the number of power processing stages in cascade; 2) the type of power decoupling between the PV module(s) and the single-phase grid; 3) whether they utilizes a transformer (either line or high frequency) or not; and 4) the type of grid-connected power stage. Various inverter topologies are presented, compared, and evaluated against demands, lifetime, component ratings, and cost. Finally, some of the topologies are pointed out as the best candidates for either single PV module or multiple PV module applications.
Romantic Relationship Development in the Age of Facebook: An Exploratory Study of Emerging Adults' Perceptions, Motives, and Behaviors

Social networking sites are becoming a prevalent form of communication in the escalation of romantic relationships. An online survey (n=403) addressed emerging adults' experiences with Facebook and romantic relationships, particularly a unique affordance of Facebook: the ability to declare oneself as "In a Relationship" and actively link one's profile to a romantic partner's, commonly known as going Facebook official. Results identified common social perceptions of the meaning of this status (regarding commitment, intensity, and social response) and both interpersonal and social motives for posting it on Facebook. Additionally, sex differences were identified in perceptions of meaning, wherein women felt this status conveyed commitment and intensity moreso than men did. Implications of this discrepancy on heterosexual relationship satisfaction and the prevailing role of technology in romantic relationships are discussed.
Cloud Computing in Education in Developing Countries

The main advantage of cloud computing is to reduce the IT cost. By using cloud computing, organisations do not have to set up an IT infrastructure, and instead are able to rent resources and give payment only for the using services. Even with the appealing of cloud computing benefits, it is still in infancy in developing countries due to many reasons. Technology adoption has been explored to a limited degree in developing countries, particularly in relation to cloud computing in the tertiary education sector. Existing studies have examined technology adoption in developed countries and to a lesser extent in developing countries in non-education contexts such as e-government. This paper contributes to the cloud computing adoption literature in developing countries, and specifically in Saudi Arabian universities.
Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.
Rumor detection for Persian Tweets

Nowadays, striking growth of online social media has led to easier and faster spreading of rumors on cyber space, in addition to tradition ways. In this paper, rumor detection on Persian Twitter community has been addressed for the first time by exploring and analyzing the significances of two categories of rumor features: Structural and Content-based features. While applying both feature sets leads to precision more than 80%, precision around 70% using only structural features has been obtained. Moreover, we show how features of users tending to produce and spread rumors are effective in rumor detection process. Also, the experiments have led to learning of a language model of collected rumors. Finally, all features have been ranked and the most discriminative ones have been discussed.
Analysis and design for RCD clamped snubber used in output rectifier of phase-shift full-bridge ZVS converters

Detailed analysis and parameter design for a resistorcapacitor-diode (RCD) clamped snubber used in the output rectifier of phase-shift full-bridge zero-voltage-switching (PS-FB-ZVS) converters are presented. Design equations and some properties of the clamped circuit are highlighted.
Chemical-disease Relations Extraction Based on The Shortest Dependency Path Tree

Identifying chemical-disease relations (CDR) from biomedical literature could improve chemical safety and toxicity studies. This paper proposes a Shortest Dependency Path Tree (SDPT) to capture the most direct syntactic and semantic relationship between chemical and disease. Based on SDPT, structured dependency features (SDF), structured phrase features (SPF) and flattened dependency features (FDF) are proposed to represent syntactic information between two entities, which are all effective for CDR. Experiments on the CDR training and developing dataset show that our method achieves 55.05% F1-score.
Hardware system synthesis from Domain-Specific Languages

Field Programmable Gate Arrays (FPGAs) are very versatile devices, but their complicated programming model has stymied their widespread usage. While modern High-Level Synthesis (HLS) tools provide better programming models, the interface they offer is still too low-level. In order to produce good quality hardware designs with these tools, the users are forced to manually perform optimizations that demand detailed knowledge of both the application and the implementation platform. Additionally, many HLS tools only generate isolated hardware modules that the user still needs to integrate into a system design before generating the FPGA bitstream. These problems make HLS tools difficult to use for application developers who have little hardware design knowledge. To address these problems, we propose an automated methodology to generate FPGA bitstreams from high-level programs written in Domain-Specific Languages (DSLs). We leverage the domain-knowledge conveyed by the DSL and its domain-specific semantics to extract application parallelism, perform optimizations and also identify a suitable system-architecture for the implementation, thereby, relieving the user from most of the hardware-level details. We demonstrate the high productivity and high design quality this approach offers by automatically generating hardware systems from applications written in OptiML, a machine-learning DSL. To evaluate our methodology, we use four OptiML applications and show that we can easily generate different solutions which achieve different trade-offs between performance and area. More importantly, the results reveal that our generated hardware achieves much better performance compared to the one obtained from using the HLS tool without platform-specific optimizations.
Combi-Tor: Track-to-Track Association Framework for Automotive Sensor Fusion

The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided by the sensors. The current generation environment perception sensors used for automotive sensor fusion are capable of providing estimated kinematic and as well as non-kinematic information on the observed targets. This paper focuses on integrating the kinematic and non-kinematic information in a track-to-track association (T2TA) procedure. A scalable framework called Combi-Tor is introduced here that is designed to calculate the association decision using likelihood ratio tests based on the available kinematic and non-kinematic information on the targets, which are tracked and classified by different sensors. The calculation of the association decision includes the uncertainty in the sensor's local tracking and classification modules. The required sufficient statistical derivations are discussed. The performance of this T2TA framework and the traditional T2TA scheme considering only the kinematic information are evaluated using Monte-Carlo simulation. The initial results obtained using the real world sensor data is presented.
Stereo HDR Disparity Map Computation Using Structured Light

In this paper, we present work in progress towards the generation of a ground truth data set for High Dynamic Range (HDR) stereo matching. The development and evaluation of novel stereo matching algorithms that are tailored to the characteristics of HDR images would greatly benefit from the availability of such reference data. We describe our laboratory setup and processing steps for acquiring multi-exposed stereo images along with a corresponding reference disparity map computed by a structured light approach. We discuss the special requirements and challenges which HDR test scenes impose on the computation of the reference disparities and show some preliminary results.
Encryption and Fragmentation for Data Confidentiality in the Cloud

Cloud computing has emerged as a successful paradigm allowing individual users and well as companies to resort to external providers for storing/processing data or making them available to others. Together with the many benefits, cloud computing introduces however new security and privacy risks. A major issue is due to the fact that the data owner, storing data at external providers, loses control over them, leaving them potentially exposed to improper access, use, or dissemina-
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model’s sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pay attention to alternative image regions, while still attempting to make correct predictions. However, the model requires to be properly constrained during AM, otherwise, it may overfit to uncorrelated image parts and forget the valuable knowledge that it has learned from the original image classification task. To alleviate such side effects, we then design a knowledge preservation (KP) loss, which minimizes the discrepancy between responses for X-ray images from the original and the updated networks. Furthermore, we modify the CNN model to include multi-scale aggregation (MSA), improving its localization ability on small-scale disease findings, e.g., lung nodules. We experimentally validate our method on the publicly-available ChestXray14 dataset, outperforming a class activation map (CAM)-based approach, and demonstrating the value of our novel framework for mining disease locations.
Trajectory pattern mining: Exploring semantic and time information

With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. Trajectory patterns consist of hot regions and the sequential relationships among them, where hot regions refer the spatial regions with a higher density of data points. Note that some hot regions do not have any meaning for users. Moreover, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose semantic trajectory patterns which are referred to as the moving patterns with spatial, temporal, and semantic attributes. Given a user trajectory, we aim at mining frequent semantic trajectory patterns. Explicitly, we extract the three attributes from a raw trajectory, and convert it into a semantic mobility sequence. Given such a semantic mobility sequence, we propose two algorithms to discover frequent semantic trajectory patterns. The first algorithm, MB (standing for matching-based algorithm), is a naive method to find frequent semantic trajectory patterns. It generates all possible patterns and extracts the occurrence of the patterns from the semantic mobility sequence. The second algorithm, PS (standing for PrefixSpan-based algorithm), is developed to efficiently mine semantic trajectory patterns. Due to the good efficiency of PrefixSpan, algorithm PS will fully utilize the advantage of PrefixSpan. Since the semantic mobility sequence contains three attributes, we need to further transform it into a raw sequence before using algorithm PrefixSpan. Therefore, we propose the SS algorithm (standing for sequence symbolization algorithm) to achieve this purpose. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.