nils-herrmann/clean_scirepeval huggingface dataset:
6.2 133Mpixel 60fps CMOS image sensor with 32-column shared high-speed column-parallel SAR ADCs

To realize next-generation highly realistic sensation broadcasting systems, the research and development of 8K ultrahigh-definition television (UHDTV) systems have been promoted. To realize 8K video cameras, 33Mpixel sensors [1-2] and a full-resolution camera system that uses three 33Mpixel sensors  have been reported. However, the weight of the camera with three sensors is over 40kg because the camera requires a large-format color-separation prism. To reduce the size of the camera, single-chip imaging is a promising approach, and a compact single-chip 8K camera that weighs only 2kg has already been developed using a color 33Mpixel CMOS image sensor . However, a conventional single-chip camera has a lower image quality than a full-resolution camera because the total pixel count of the single-sensor camera is only one-third of that of the three-sensor camera, and pixel interpolation is required to configure a full-resolution image. In this paper, a 133Mpixel sensor that can be operated at 60fps to realize a full-resolution 8K single-chip camera is described. To achieve both high speed and suitable ADC resolution, 32-column multiplexing analog readout circuitry and 14b high-speed redundant successive approximation register (SAR) ADCs [5] are adopted. As a result, a full-size image with a data rate of 128.71Gb/s at 60fps has been captured.
WarningBird: Detecting Suspicious URLs in Twitter Stream

Twitter can suffer from malicious tweets containing suspicious URLs for spam, phishing, and malware distribution. Previous Twitter spam detection schemes have used account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. Malicious users, however, can easily fabricate account features. Moreover, extracting relation features from the Twitter graph is time and resource consuming. Previous suspicious URL detection schemes have classified URLs using several features including lexical features of URLs, URL redirection, HTML content, and dynamic behavior. However, evading techniques exist, such as time-based evasion and crawler evasion. In this paper, we propose WARNINGBIRD, a suspicious URL detection system for Twitter. Instead of focusing on the landing pages of individual URLs in each tweet, we consider correlated redirect chains of URLs in a number of tweets. Because attackers have limited resources and thus have to reuse them, a portion of their redirect chains will be shared. We focus on these shared resources to detect suspicious URLs. We have collected a large number of tweets from the Twitter public timeline and trained a statistical classifier with features derived from correlated URLs and tweet context information. Our classifier has high accuracy and low false-positive and falsenegative rates. We also present WARNINGBIRD as a realtime system for classifying suspicious URLs in the Twitter stream. ∗This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2011-C1090-1131-0009) and World Class University program funded by the Ministry of Education, Science and Technology through the National Research Foundation of Korea(R31-10100).
Amphetamine exposure enhances habit formation.

Performance of instrumental actions in rats is initially sensitive to postconditioning changes in reward value, but after more extended training, behavior comes to be controlled by stimulus-response (S-R) habits that are no longer goal directed. To examine whether sensitization of dopaminergic systems leads to a more rapid transition from action-outcome processes to S-R habits, we examined performance of amphetamine-sensitized rats in an instrumental devaluation task. Animals were either sensitized (7 d, 2 mg/kg/d) before training (experiment 1) or sensitized between training and testing (experiment 2). Rats were trained to press a lever for a reward (three sessions) and were then given a test of goal sensitivity by devaluation of the instrumental outcome before testing in extinction. Control animals showed selective sensitivity to devaluation of the instrumental outcome. However, amphetamine sensitization administered before training caused the animals' responding to persist despite the changed value of the reinforcer. This deficit resulted from an inability to use representations of the outcome to guide behavior, because a reacquisition test confirmed that all of the animals had acquired an aversion to the reinforcer. In experiment 2, post-training sensitization did not disrupt normal goal-directed behavior. These findings indicate that amphetamine sensitization leads to a rapid progression from goal-directed to habit-based responding but does not affect the performance of established goal-directed actions.
A Patent Analysis for Sustainable Technology Management

Technology analysis (TA) is an important issue in the management of technology. Most R&D (Research & Development) policies have depended on diverse TA results. Traditional TA results have been obtained through qualitative approaches such as the Delphi expert survey, scenario analysis, or technology road mapping. Although they are representative methods for TA, they are not stable because their results are dependent on the experts’ knowledge and subjective experience. To solve this problem, recently many studies on TA have been focused on quantitative approaches, such as patent analysis. A patent document has diverse information of developed technologies, and thus, patent is one form of objective data for TA. In addition, sustainable technology has been a big issue in the TA fields, because most companies have their technological competitiveness through the sustainable technology. Sustainable technology is a technology keeping the technological superiority of a company. So a country as well as a company should consider sustainable technology for technological competition and continuous economic growth. Also it is important to manage sustainable technology in a given technology domain. In this paper, we propose a new patent analysis approach based on statistical analysis for the management of sustainable technology (MOST). Our proposed methodology for the MOST is to extract a technological structure and relationship for knowing the sustainable technology. To do this, we develop a hierarchical diagram of technology for finding the causal relationships among technological keywords of a given domain. The aim of the paper is to select the sustainable technology and to create the hierarchical technology paths to sustainable technology for the MOST. This contributes to planning R&D strategy for the sustainability of a company. To show how the methodology can be applied to real problem, we perform a case study using retrieved patent documents related to telematics technology.
Bayesian inference for psychology. Part II: Example applications with JASP

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.
Automatic Mood Classification of Indian Popular Music

Music has been an inherent part of human life when it comes to recreation; entertainment and much recently, even as a therapeutic medium. The way music is composed, played and listened to has witnessed an enormous transition from the age of magnetic tape recorders to the recent age of digital music players streaming music from the cloud. What has remained intact is the special relation that music shares with human emotions. We most often choose to listen to a song or music which best fits our mood at that instant. In spite of this strong correlation, most of the music softwares present today are still devoid of providing the facility of mood-aware play-list generation. This increases the time music listeners take in manually choosing a list of songs suiting a particular mood or occasion, which can be avoided by annotating songs with the relevant emotion category they convey. The problem, however, lies in the overhead of manual annotation of music with its corresponding mood and the challenge is to identify this aspect automatically and intelligently. The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques contributing considerably to analyze and identify the relation of mood with music. We take the same inspiration forward and contribute by making an effort to build a system for automatic identification of mood underlying the audio songs by mining their spectral, temporal audio features. Our focus is specifically on Indian Popular Hindi songs. We have analyzed various data classification algorithms in order to learn, train and test the model representing the moods of these audio songs and developed an open source framework for the same. We have been successful to achieve a satisfactory precision of 70% to 75% in identifying the mood underlying the Indian popular music by introducing the bagging (ensemble) of random forest approach experimented over a list of 4600 audio clips.
Comparing Business Intelligence and Big Data Skills - A Text Mining Study Using Job Advertisements

English] While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling

Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNNs have been proposed, selecting and designing the appropriate network structure for a target problem is a challenging task. In this paper, we propose a method to simultaneously optimize the network structure and weight parameters during neural network training. We consider a probability distribution that generates network structures, and optimize the parameters of the distribution instead of directly optimizing the network structure. The proposed method can apply to the various network structure optimization problems under the same framework. We apply the proposed method to several structure optimization problems such as selection of layers, selection of unit types, and selection of connections using the MNIST, CIFAR-10, and CIFAR-100 datasets. The experimental results show that the proposed method can find the appropriate and competitive network structures.
Implementation Strategies for E-Government: A Stakeholder Analysis Approach

This paper reports from a comprehensive study of e-government implementation in Ireland, conducted over the last two years. An in-depth case study is presented detailing the development of a dual strategy for implementation and provides a comparison of the results from this approach. The success and shortcomings of both approaches are identified, providing in-depth analysis into the identification and management of critical concerns in the implementation of citizen-centred e-government. Specifically, this paper highlights the importance of accounting for social and political features, unique to the public sector, which in this case have had a decisive impact on e-government implementation. Public sector organisations in particular present unique challenges to the implementation process and implementation strategies often require particular attention to the social and political elements inherent in organisational change. In e-government implementation, the main barriers are not technical but social and cultural. Implementation strategies should therefore support the process of managing stakeholder relations in order to reduce the risk of stakeholder conflict and ensure the success of e-government initiatives.
Using Quantum Computers for Quantum Simulation

Numerical simulation of quantum systems is crucial to further our understanding of natural phenomena. Many systems of key interest and importance, in areas such as superconducting materials and quantum chemistry, are thought to be described by models which we cannot solve with sufficient accuracy, neither analytically nor numerically with classical computers. Using a quantum computer to simulate such quantum systems has been viewed as a key application of quantum computation from the very beginning of the field in the 1980s. Moreover, useful results beyond the reach of classical computation are expected to be accessible with fewer than a hundred qubits, making quantum simulation potentially one of the earliest practical applications of quantum computers. In this paper we survey the theoretical and experimental development of quantum simulation using quantum computers, from the first ideas to the intense research efforts currently underway.
DLR ’ s Dynamic Actuator Modules for Robotic Space Applications

Based on our long-term goal to develop “robonauts” for space and the experiences our research department accumulated in space robotics, this article describes recent design and development results at DLR’s Robotics and Mechatronics Center. Herein, we focus on lightweight robot arms, articulated hands and highly integrated actuation modules for space applications. We show how the development process started with fully sensorized, highly dynamic joint modules with state feedback control and led to seven degree of freedom (DOF) torque-controlled robot arms that enable innovative Cartesian impedance controllers. Further, we present two space robotics examples that are based on such modular actuation units. The first one is the ROKVISS experiment which has been launched towards the ISS in 2004 and returned to earth in 2011. The second is the Dextrous Robot Hand, DEXHAND, which we developed on contract with ESA. These two projects underline the strong interest to transfer our knowledge and experience from terrestrial robotic developments to innovative space technology.
A Robust Voiced/Unvoiced Phoneme Classification from Whispered Speech Using the 'Color' of Whispered Phonemes and Deep Neural Network

speech using the ‘color’ of whispered phonemes G. NISHA MEENAKSHI, PRASANTA KUMAR GHOSH SPIRE Lab, Electrical Engineering, Indian Institute of Science (IISc), Bengaluru PROBLEM STATEMENT TO PERFORM FRAME-LEVEL CLASSIFICATION OF VOICED (V) AND UNVOICED (UV) PHONEMES FROM WHISPERED SPEECH.  Whispered speech production: Private and pathological conditions. Need for V/UV decision: Reconstruction of whispered speech.
Utilising artificial intelligence in software defined wireless sensor network

Software Defined Wireless Sensor Network (SDWSN) is realised by infusing Software Defined Network (SDN) model in Wireless Sensor Network (WSN), Reason for that is to overcome the challenges of WSN. Artificial Intelligence (AI) and machine learning play an important role in our society, give rise to systems that can manage themselves. WSNs have been used in various industrial applications, where reliability and network performance are critical success factors. Many advanced AI techniques can be utilised to improve the performance and reliability of these applications. Investigating the AI algorithms applied to SDN may bring improved network management, security or routing in SDWSN which may result in a more reliable network. We look at machine learning algorithms applied in SDN and discuss the possibility of using these AI in SDWSN to address the WSN challenges and improve its performance and reliability.
Effective Nearest-Neighbor Music Recommendations

Automated recommendations for next tracks to listen to or to include in a playlist are a common feature on modern music platforms. Correspondingly, a variety of algorithmic approaches for determining tracks to recommend have been proposed in academic research. The most sophisticated among them are often based on conceptually complex learning techniques which can also require substantial computational resources or special-purpose hardware like GPUs. Recent research, however, showed that conceptually more simple techniques, e.g., based on nearest-neighbor schemes, can represent a viable alternative to such techniques in practice.
 In this paper, we describe a hybrid technique for next-track recommendation, which was evaluated in the context of the ACM RecSys 2018 Challenge. A combination of nearest-neighbor techniques, a standard matrix factorization algorithm, and a small set of heuristics led our team KAENEN to the 3rd place in the "creative" track and the 7th one in the "main" track, with accuracy results only a few percent below the winning teams. Given that offline prediction accuracy is only one of several possible quality factors in music recommendation, practitioners have to validate if slight accuracy improvements truly justify the use of highly complex algorithms in real-world applications.
Wireless water quality cloud monitoring system with self-healing algorithm

The need of a water quality monitoring system is crucial for aquaculture and environmental control evaluation. This paper focuses on the development of the Water Quality (WQ) monitoring module that consists of hardware and software components. It highlights the details of the hardware components and the algorithm as well as the software that is connected to the cloud. There are many works on storing environmental data in cloud storage in Malaysia. The new platform to date for the Internet of Things (IoT) and cloud database is Favoriot. Favoriot is a platform for IoT and machine-to-machine (M2M) development. For this project, Favoriot platform is used for real time data. The self-healing algorithm is design to reduce human intervention and continuous data collected in the remote areas. The result shows that the self-healing algorithm is able to recover itself without physical reseting, in case during distruption of wireless service connection failure.
Analysis, Modeling and Control of Doubly-Fed Induction Generators for Wind Turbines

This thesis deals with the analysis, modeling, and control of the doubly-fed induction generator (DFIG) for wind turbines. Different rotor current control methods are investigated with the objective of eliminating the influence of the back electromotive force (EMF), which is that of, in control terminology, a load disturbance, on the rotor current. It is found that the method that utilizes both feed forward of the back EMF and so-called “active resistance” manages best to suppress the influence of the back EMF on the rotor current, particularly when voltage sags occur, of the investigated methods. This method also has the best stability properties. In addition it is found that this method also has the best robustness to parameter deviations. The response of the DFIG wind turbine system to grid disturbances is simulated and verified experimentally. A voltage sag to 80% (80% remaining voltage) is handled very well. Moreover, a second-order model for prediction of the response of small voltage sags of the DFIG wind turbines is derived, and its simulated performance is successfully verified experimentally. The energy production of the DFIG wind turbine is investigated and compared to that of other wind turbine systems. The result found is that the energy capture of the DFIG wind turbine is almost the same as for an active stall-controlled fixed-speed (using two fixed speeds) wind turbine. Compared to a full-power-converter wind turbine the DFIG wind turbine can deliver a couple of percentage units more energy to the grid. Voltage sag ride-through capabilities of some different variable-speed wind turbines has been investigated. It has been found that the energy production cost of the investigated wind turbines with voltage sag ride-through capabilities is between 1–3 percentage units higher than that of the ordinary DFIG wind turbine without the ride-through capability. Finally, a flicker reduction control law for stall-controlled wind turbines with induction generators, using variable rotor resistance, is derived. The finding is that it is possible to reduce the flicker contribution by utilizing the derived rotor resistance control law with 40– 80% depending on the operating condition.