nils-herrmann/clean_scirepeval huggingface dataset:
The Stock Sonar - Sentiment Analysis of Stocks Based on a Hybrid Approach

The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in-text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accuracy of an existing document-level sentiment classifier.
Directional Clustering Through Matrix Factorization

This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction. This directional distance measure arises in several applications, including document classification and human brain imaging. Using ideas from the field of constrained low-rank matrix factorization and sparse approximation, a novel approach is presented that differs from classical clustering methods, such as seminonnegative matrix factorization, K-EVD, or k-means clustering, yet combines some aspects of all these. As in nonnegative matrix factorization and K-EVD, the matrix decomposition is iteratively refined to optimize a data fidelity term; however, no positivity constraint is enforced directly nor do we need to explicitly compute eigenvectors. As in k-means and K-EVD, each optimization step is followed by a hard cluster assignment. This leads to an efficient algorithm that is shown here to outperform common competitors in terms of clustering performance and/or computation speed. In addition to a detailed theoretical analysis of some of the algorithm's main properties, the approach is empirically evaluated on a range of toy problems, several standard text clustering data sets, and a high-dimensional problem in brain imaging, where functional magnetic resonance imaging data are used to partition the human cerebral cortex into distinct functional regions.
An i-vector Extractor Suitable for Speaker Recognition with both Microphone and Telephone Speech

It is widely believed that speaker verification systems perform better when there is sufficient background training data to deal with nuisance effects of transmission channels. It is also known that these systems perform at their best when the sound environment of the training data is similar to that of the context of use (test context). For some applications however, training data from the same type of sound environment is scarce, whereas a considerable amount of data from a different type of environment is available. In this paper, we propose a new architecture for text-independent speaker verification systems that are satisfactorily trained by virtue of a limited amount of application-specific data, supplemented with a sufficient amount of training data from some other context. This architecture is based on the extraction of parameters (i-vectors) from a low-dimensional space (total variability space) proposed by Dehak . Our aim is to extend Dehak’s work to speaker recognition on sparse data, namely microphone speech. The main challenge is to overcome the fact that insufficient application-specific data is available to accurately estimate the total variability covariance matrix. We propose a method based on Joint Factor Analysis (JFA) to estimate microphone eigenchannels (sparse data) with telephone eigenchannels (sufficient data). For classification, we experimented with the following two approaches: Support Vector Machines (SVM) and Cosine Distance Scoring (CDS) classifier, based on cosine distances. We present recognition results for the part of female voices in the interview data of the NIST 2008 SRE. The best performance is obtained when our system is fused with the state-of-the-art JFA. We achieve 13% relative improvement on equal error rate and the minimum value of detection cost function decreases from 0.0219 to 0.0164.
Embedded Trench Redistribution Layers (RDL) by Excimer Laser Ablation and Surface Planer Processes

This paper reports the demonstration of 2-5 µm embedded trench formation in dry film polymer dielectrics such as Ajinomoto build-up film (ABF) and Polyimide without using chemical mechanical polishing (CMP) process. The trenches in these dielectrics were formed by excimer laser ablation, followed by metallization of trenches by copper plating processes and overburden removal with surface planer tool. The materials and processes integrated in this work are scalable to large panel fabrication at much higher throughput, for interposers and high density fan-out packaging at lower cost and higher performance than silicon interposers.
A Graph Rewriting Approach for Converting Asynchronous ROMs into Synchronous Ones

Most of FPGAs have Configurable Logic Blocks (CLBs) to implement combinational and sequential circuits and block RAMs to implement Random Access Memories (RAMs) and Read Only Memories (ROMs). Circuit design that minimizes the number of clock cycles is easy if we use asynchronous read operations. However, most of FPGAs support synchronous read operations, but do not support asynchronous read operations. The main contribution of this paper is to provide one of the potent approaches to resolve this problem. We assume that a circuit using asynchronous ROMs designed by a non-expert or quickly designed by an expert is given. Our goal is to convert this circuit with asynchronous ROMs into an equivalent circuit with synchronous ones. The resulting circuit with synchronous ROMs can be embedded into FPGAs. We also discuss several techniques to decrease the latency and increase the clock frequency of the resulting circuits. key words: FPGA, block RAMs, asynchronous read operations, rewriting algorithm
Real-time obstacle detection using dense stereo vision and dense optical flow

Accurate detection of moving obstacles from a moving vehicle is at the core of safe autonomous driving research. Stereo vision based sensors have been extensively used for this task as they are passive and provide a large amount 3D and 2D data. However, since no motion information is revealed, in intersections or crowded urban areas, static and dynamic objects immediately next to each other, or closely positioned obstacles moving in different directions are often merged into a single obstacle leading to dangerous misinterpretations. In this paper we address these problems through a powerful fusion between dense stereo vision and dense optical flow in a depth-adaptive occupancy grid framework. The proposed fusion model is presented and then applied for obstacle detection in an intersection assistance system.
Simulating cartoon style animation

Traditional hand animation is in many cases superior to simulated motion for conveying information about character and events. Much of this superiority comes from an animator's ability to abstract motion and play to human perceptual effects. However, experienced animators are difficult to come by and the resulting motion is typically not interactive. On the other hand, procedural models for generating motion, such as physical simulation, can create motion on the fly but are poor at stylizing movement. We start to bridge this gap with a technique that creates cartoon style deformations automatically while preserving desirable qualities of the object's appearance and motion. Our method is focused on squash-and-stretch deformations based on the velocity and collision parameters of the object, making it suitable for procedural animation systems. The user has direct control of the object's motion through a set of simple parameters that drive specific features of the motion, such as the degree of squash and stretch. We demonstrate our approach with examples from our prototype system.
Suicidal Decapitation by Hanging-A Population-based Study.

A prospective study was undertaken at Forensic Science SA over a 15-year period from July 2002 to June 2017 for all cases of adult (>18 years) suicidal hangings with decapitation. A total of 1446 cases of suicidal hangings were identified from a general population of approximately 1.5 million (1206 males-age range 18-97 years, average 42.6; and 240 females-age range 18-96 years, average 40.1). Only three cases of decapitation were found, all from long-drop hangings; these consisted of three males (ages 32-55 years; average 45 years). Spinal transections had occurred between the first and second, second and third, and third and fourth cervical vertebrae, respectively. In this study, the number of suicidal hangings with decapitation represented only 0.2% of the total number of hangings. These events are therefore extremely rare, most likely due to most suicidal hangings occurring from relatively low levels in a domestic environment.
The Role of ICT to Achieve the UN Sustainable Development Goals (SDG)

This paper is aiming at illustrating the potential of ICT for achieving the Sustainable Development Goals which were declared by the United Nations in 2015 as binding for all nations of our planet addressing both developing and developed countries. ICT must play a significant role if the SDGs should be achieved as projected in 2030. The paper gives an overview of some of the existing efforts in this area and is written as an appeal to all professionals, scientists and IT-professional and their organization to take a holistic approach for all ICT-activities and projects to always include and monitor the effects of their work on the SDGs. The impacts of ICT on sustainability are twofold: On the one hand there might be negative effects on sustainability such as the generation of electronic waste, on the other hand ICT is definitely an enabler to more efficient resource usage, education and business operations which is critical success factor for achieving the SDGs.
A Comprehensive Study of StaQC for Deep Code Summarization

Learning the mapping between natural language (NL) and programming language, such as retrieving or generating code snippets based on NL queries and annotating code snippets using NL, has been explored by lots of research works [2, 19, 21]. At the core of these works are machine learning and deep learning models, which usually demand for large datasets of <NL, code> pairs for training. This paper describes an experimental study of StaQC [50], a large-scale and high-quality dataset of <NL, code> pairs in Python and SQL domain, systematically mined from the Stack Overflow forum (SO). We compare StaQC with two other popular datasets mined from SO on the code summarization task, showing that StaQC helps achieve substantially better results, improving the current state-of-the-art model by an order of 8% ∼ 9% in BLEU metric.
Can Rumour Stance Alone Predict Veracity?

Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets’ times as the only features for modelling true and false rumours achieve F1 scores in the range of 80%, outperforming those approaches where stance is used jointly with content and user based features.
Iterative Hough Forest with Histogram of Control Points for 6 DoF object registration from depth images

State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we introduce a novel architecture called Iterative Hough Forest with Histogram of Control Points that is capable of estimating occluded and cluttered objects' 6D pose given a candidate 2D bounding box. Our Iterative Hough Forest is learnt using patches extracted only from the positive samples. These patches are represented with Histogram of Control Points (HoCP), a “scale-variant” implicit volumetric description, which we derive from recently introduced Implicit B-Splines (IBS). The rich discriminative information provided by this scale-variance is leveraged during inference, where the initial pose estimation of the object is iteratively refined based on more discriminative control points by using our Iterative Hough Forest. We conduct experiments on several test objects of a publicly available dataset to test our architecture and to compare with the state-of-the-art.
Cyber military strategy for cyberspace superiority in cyber warfare

In this paper, we proposed robust and operational cyber military strategy for cyberspace superiority in cyber warfare. We considered cyber forces manpower, cyber intelligence capability, and an organization of cyber forces for improving cyber military strategy. In cyber forces power field, we should cultivated personals that can perform network computer operations and hold cyber security technology such as cyber intelligence collection, cyber-attack, cyber defence, and cyber forensics. Cyber intelligence capability includes cyber surveillance/reconnaissance, cyber order of battle, pre-CTO, and cyber damage assessment. An organization of cyber forces has to change tree structure to network structure and has to organize task or functional centric organizations. Our proposed cyber military strategy provides prior decision of action to operate desired effects in cyberspace.
Learning management systems and cloud file hosting services: A study on students' acceptance

The aim of this paper is to investigate the motivations that lead higher education students to replace several Learning Management Systems (LMS) services with cloud file hosting services for information sharing and collaboration among them. The research approach is based on the Technology Acceptance Model (TAM). More specifically, the model is devoted to identifying barriers and enablers to the acceptance of these technologies. A questionnaire comprising three factors (Attitude toward using technology, Perceived ease of use and Perceived usefulness) was applied to a sample consisting of 121 higher education students. Results show that the perceived ease of use of cloud file hosting services is above that of LMS tools and services and that cloud file hosting services presented higher levels of perceived usefulness than standard learning management tools. In addition, attitude toward using cloud file hosting services is well above that of using LMS tools.
A fuzzy classification system for prediction of the results of the basketball games

Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of the KEEL system. Feature selection algorithms are applied and a majority voting is used in order to select the most representative features.
IoT-based occupancy monitoring techniques for energy-efficient smart buildings

With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption of buildings via smart HVAC control. In this respect, the major challenges we envision are 1) to achieve occupancy monitoring in a minimally intrusive way, e.g., using the existing infrastructure in the buildings and not requiring installation of any apps in the users' smart devices, and 2) to develop effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources. This paper surveys the existing works on occupancy monitoring and multi-modal data fusion techniques for smart commercial buildings. The goal is to lay down a framework for future research to exploit the spatio-temporal data obtained from one or more of various IoT devices such as temperature sensors, surveillance cameras, and RFID tags that may be already in use in the buildings. A comparative analysis of existing approaches and future predictions for research challenges are also provided.
Religious coping and health status in medically ill hospitalized older adults.

Associations between specific religious coping (RC) behaviors and health status in medically ill hospitalized older patients were examined and compared with associations between nonreligious coping (NRC) behaviors and health status. The sample consisted of 577 patients age 55 or over consecutively admitted to the general medical inpatient services of Duke University Medical Center (78%) or the Durham VA Medical Center (22%). Information was gathered on 21 types of RC, 11 types of NRC, and 3 global indicators of religious activity (GIRA). Health measures included multiple domains of physical health, depressive symptoms, quality of life, stress-related growth, cooperativeness, and spiritual growth. Demographic factors, education, and admitting hospital were control variables. "Negative" and "positive" types of religious coping were identified. Negative RC behaviors related to poorer physical health, worse quality of life, and greater depression were reappraisals of God as punishing, reappraisals involving demonic forces, pleading for direct intercession, and expression of spiritual discontent. Coping that was self-directed (excluding God's help) or involved expressions reflecting negative attitudes toward God, clergy, or church members were also related to greater depression and poorer quality of life. Positive RC behaviors related to better mental health were reappraisal of God as benevolent, collaboration with God, seeking a connection with God, seeking support from clergy/church members, and giving religious help to others. Of 21 RC behaviors, 16 were positively related to stress-related growth, 15 were related to greater cooperativeness, and 16 were related to greater spiritual growth. These relationships were both more frequent and stronger than those found for NRC behaviors. Certain types of RC are more strongly related to better health status than other RC types. Associations between RC behaviors and mental health status are at least as strong, if not stronger, than those observed with NRC behaviors.
Mechanical Design of a Rover for Mobile Manipulation in Uneven Terrain in the Context of the SpaceBot Cup

This paper describes the development and test of a mobile manipulation platform intended for a terrestrial robotic competition. While current space missions are planned to minimize complex manipulation tasks, plans for future space missions go beyond these restrictions. Infrastructure deployment, human-robot cooperative missions and complex sample collection require increasingly complex manipulation capabilities. To meet this need the Spacebot Cup consists of several complex manipulation tasks in unstructured terrain. These requirements were the main design driver for the presented system. The presented rover consists of a 3-Boogie-Chasis designed to increase the maximum stepping size, flexible rubber wheels to increase the maximal climbing inclination on loose surfaces and a small six degree of freedom manipulator to handle objects within the competition. The iterative simulation and experiment process used to develop the flexible rubber wheels is presented. Furthermore experiments are presented which allow a performance comparison between flexible and rigid wheels on loose surfaces.
Using the h-index to rank influential information scientists

We apply a new bibliometric measure, the h-index (Hirsch, 2005), to the literature of information science. Faculty rankings based on raw citation counts are compared with those based on h-counts. There is a strong positive correlation between the two sets of rankings. We show how the h-index can be used to express the broad impact of a scholar’s research output over time in more nuanced fashion than straight citation counts.
Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer

Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club’s performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season’s economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.
An empirical study on detecting fake reviews using machine learning techniques

Reputation systems in E-commerce (EC) play a substantial role that allows various parties to achieve mutual benefits by establishing relationships. The reputation systems aim at helping consumers in deciding whether to negotiate with a given party. Many factors negatively influence the sight of the customers and the vendors in terms of the reputation system. For instance, lack of honesty or effort in providing the feedback reviews, by which users might create phantom feedback from fake reviews to support their reputation. Moreover, the opinions obtained from users can be classified into positive or negative which can be used by a consumer to select a product. In this paper, we study online movie reviews using Sentiment Analysis (SA) methods in order to detect fake reviews. Text classification and SA methods are applied on a real conducted dataset of movie reviews. Specifically, we compare four supervised machine learning algorithms: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN-IBK), and Decision Tree (DT-J48) for sentiment classification of reviews in two different situations without stopwords and with stopwords methods are employed. The measured results show that for both methods the SVM algorithm outperforms other algorithms, and it reaches the highest accuracy not only in text classification but also to detect fake reviews.
VHDL Implementation of 8-Bit ALU

In this paper VHDL implementation of 8-bit arithmetic logic unit (ALU) is presented. The design was implemented using VHDL Xilinx Synthesis tool ISE 13.1 and targeted for Spartan device. ALU was designed to perform arithmetic operations such as addition and subtraction using 8-bit fast adder, logical operations such as AND, OR, XOR and NOT operations, 1’s and 2’s complement operations and compare. ALU consist of two input registers to hold the data during operation, one output register to hold the result of operation, 8-bit fast adder with 2’s complement circuit to perform subtraction and logic gates to perform logical operation. The maximum propagation delay is 13.588ns and power dissipation is 38mW. The ALU was designed for controller used in network interface card.