nils-herrmann/clean_scirepeval huggingface dataset:
Using Smart Sensor Strings for Continuous Monitoring of Temperature Stratification in Large Water Bodies

A "smart" thermistor string for continuous long-term temperature profiling in large water bodies is described allowing highly matched yet low-cost spatial and temporal temperature measurements. The sensor uses the three-wire SDI-12 communications standard to enable a low-powered radio or data logger on supporting buoys to command measurements and retrieve high-resolution temperature data in digital form. Each "smart" temperature sensor integrates a thermistor element, measurement circuitry, power control, calibration coefficient storage, temperature computation, and data communications. Multiple addressable sensors at discrete vertical depths are deployed along a three-wire cable that provides power and allows data transfer at regular intervals. Circuit, manufacturing, and automated calibration techniques allow temperature measurements with a resolution of plusmn0.003degC, and with intersensor matching of plusmn0.006degC. The low cost of each sensor is achieved by using poor tolerance thermistor and circuit components in conjunction with a 15-bit charge-balance analog-to-digital converter. Sensor inaccuracies and temperature coefficients are corrected by a two-point calibration procedure made possible by a standard-curve generator within the sensor, based upon the method of finite differences. This two-point calibration process allows in-field sensor string calibration in stratified water bodies and provides a means to correct for long-term calibration drift without having to return the string to a laboratory
Hand sign language recognition for Bangla alphabet using Support Vector Machine

The sign language considered as the main language for deaf and dumb people. So, a translator is needed when a normal person wants to talk with a deaf or dumb person. In this paper, we present a framework for recognizing Bangla Sign Language (BSL) using Support Vector Machine. The Bangla hand sign alphabets for both vowels and consonants have been used to train and test the recognition system. Bangla sign alphabets are recognized by analyzing its shape and comparing its features that differentiates each sign. In proposed system, hand signs are first converted to HSV color space from RGB image. Then Gabor filters are used to acquire desired hand sign features. Since feature vector obtained using Gabor filter is in a high dimension, to reduce the dimensionality a nonlinear dimensionality reduction technique that is Kernel PCA has been used. Lastly, Support Vector Machine (SVM) is employed for classification of candidate features. The experimental results show that our proposed method outperforms the existing work on Bengali hand sign recognition.
On Higher-order Singular Value Decomposition from Incomplete Data

Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values , one can first impute the missing entries through a certain tensor completion method and then perform HOSVD to the reconstructed data. However, the two-step procedure can be inefficient and does not make reliable decomposition. In this paper, we formulate an incomplete HOSVD problem and combine the two steps into solving a single optimization problem, which simultaneously achieves imputation of missing values and also tensor decomposition. We also present two algorithms for solving the problem based on block coordinate update. Global convergence of both algorithms is shown under mild assumptions. The convergence of the second algorithm implies that of the popular higher-order orthogonality iteration (HOOI) method, and thus we, for the first time, give global convergence of HOOI. In addition, we compare the proposed methods to state-of-the-art ones for solving incomplete HOSVD and also low-rank tensor completion problems and demonstrate the superior performance of our methods over other compared ones. Furthermore, we apply them to face recognition and MRI image reconstruction to show their practical performance.
Provably Fair Representations

Machine learning systems are increasingly used to make decisions about people’s lives, such as whether to give someone a loan or whether to interview someone for a job. This has led to considerable interest in making such machine learning systems fair. One approach is to transform the input data used by the algorithm. This can be achieved by passing each input data point through a representation function prior to its use in training or testing. Techniques for learning such representation functions from data have been successful empirically, but typically lack theoretical fairness guarantees. We show that it is possible to prove that a representation function is fair according to common measures of both group and individual fairness, as well as useful with respect to a target task. These provable properties can be used in a governance model involving a data producer, a data user and a data regulator, where there is a separation of concerns between fairness and target task utility to ensure transparency and prevent perverse incentives. We formally define the ‘cost of mistrust’ of using this model compared to the setting where there is a single trusted party, and provide bounds on this cost in particular cases. We present a practical approach to learning fair representation functions and apply it to financial and criminal justice datasets. We evaluate the fairness and utility of these representation functions using measures motivated by our theoretical results.
Stock Price Cycles and Business Cycles

We present a business cycle model in which Bayesian learning about stock price behavior gives rise to boom and bust cycles in stock prices. Stock price cycles transmit into the real economy by generating inefficient price signals for the desirability of new investment and act as a ‘financial accelerator’ that operates even in the absence of financial frictions. The model successfully replicates the quantitative behavior of U.S. stock prices and business cycles, despite assuming time-separable consumption preferences with low risk aversion, a perfectly flexible labor market, and featuring productivity shocks as the only exogenous driving force. Under rational stock price expectations, the model performs poorly: it generates only small amounts of stock price volatility and fails to match the volatility of investment and hours worked in the absence of investment-specific technology shocks.
OpenSky: A swiss army knife for air traffic security research

The Automatic Dependent Surveillance - Broadcast (ADS-B) protocol is one of the key components of the next generation air transportation system. Since ADS-B will become mandatory by 2017 in the European airspace, it is crucial that aspects such as its security and privacy are promptly investigated by the research community. However, as expensive specialized equipment was previously necessary to collect real-world data on a large scale, such data has not been freely accessible until now. To enable researchers around the world to conduct experimental studies based on real air traffic data, we have created OpenSky, a participatory sensor network for air traffic research. In this paper, we describe the setup and capabilities of OpenSky, and detail some of the research into air traffic security that we have conducted using OpenSky.
Adaptive Population Sizing Schemes in Genetic Algorithms

This chapter presents a review of adaptive population sizing schemes used in genetic algorithms. We start by briefly revisiting theoretical models which rely on a facetwise design decomposition, and then move on to various self-adjusting population sizing schemes that have been proposed in the literature. For each method, the major advantages and disadvantages are discussed. The chapter ends with recommendations for those who design and compare self-adjusting population sizing mechanisms for genetic and evolutionary algorithms.
Comparative Analysis of 6 Bit Thermometer-to-Binary Decoders for Flash Analog-to-Digital Converter

In the design of high speed Flash ADC selection of Thermometer to Binary decoder plays an important role. This paper describes different decoder topologies suitable for Flash ADCs. Comparative analysis between them is presented in terms of hardware required, propagation delay & power consumption. Result shows that fat tree & Mux based topologies are suitable for high speed conversions, but Mux based topology is effective in terms of power consumption. Presence of bubble error reduces output correction capability. In this paper advance scheme is proposed for correction of bubble error in Mux based decoder.
An Iterative Algorithm for Camera Pose Estimation with Lie Group Representation

For solving camera pose problem, a novel accurate and fast pose estimation algorithm based on Lie group representation is proposed. Firstly, object function based on total space error of all feature points is built. Secondly, object function is linearized based on Lie group representation. The Jacobian matrix of camera pose is deduced and pose parameters are updated. Finally, the computation efficiency of algorithm is promoted by using Kronecker product to optimize the iteration process. Simulation and real experiments show that the proposed algorithm is able to obtain camera pose with high precision, but consumes less time compared with the existing algorithms. The proposed algorithm is capable of estimating camera pose with high precision and efficiency.
The influence of color on emotional perception of natural scenes.

Is color a critical factor when processing the emotional content of natural scenes? Under challenging perceptual conditions, such as when pictures are briefly presented, color might facilitate scene segmentation and/or function as a semantic cue via association with scene-relevant concepts (e.g., red and blood/injury). To clarify the influence of color on affective picture perception, we compared the late positive potentials (LPP) to color versus grayscale pictures, presented for very brief (24 ms) and longer (6 s) exposure durations. Results indicated that removing color information had no effect on the affective modulation of the LPP, regardless of exposure duration. These findings imply that the recognition of the emotional content of scenes, even when presented very briefly, does not critically rely on color information.
The impact of data replication on job scheduling performance in the Data Grid

In the Data Grid environment, the primary goal of data replication is to shorten the data access time experienced by the job and consequently reduce the job turnaround time. After introducing a Data Grid architecture that supports efficient data access for the Grid job, the dynamic data replication algorithms are put forward. Combined with different Grid scheduling heuristics, the performances of the data replication algorithms are evaluated with various simulations. The simulation results demonstrate f shortest lacement data e ists, d the ten-
Development of HILS simulator for steering control in hot strip finishing mill

In this paper, hardware-in-the loop-simulation(HILS) simulator for steering control is developed. The simulator describes lateral movement of the strip in hot strip finishing mill. The simulator include strip dynamics, mill model, mill motor and AGC controllers. The factors considered for strip dynamics are initial off-center and wedge difference. The wedge is realized using the expansion of deformation process. To test validation of a simulator, PID controller is tested.
Using Program Analysis to Improve Database Applications

Applications that interact with database management systems (DBMSs) are ubiquitous. Such database applications are usually hosted on an application server and perform many small accesses over the network to a DBMS hosted on the database server to retrieve data for processing. For decades, the database and programming systems research communities have worked on optimizing such applications from different perspectives: database researchers have built highly efficient DBMSs, and programming systems researchers have developed specialized compilers and runtime systems for hosting applications. However, there has been relatively little work that optimizes database applications by considering these specialized systems in combination and looking for optimization opportunities that span across them. In this article, we highlight three projects that optimize database applications by looking at both the programming system and the DBMS in a holistic manner. By carefully revisiting the interface between the DBMS and the application, and by applying a mix of declarative database optimization and modern program analysis techniques, we show that a speedup of multiple orders of magnitude is possible in real-world applications.
Mutual-coupling based phased-array calibration: A robust and versatile approach

The transmit and receive modules of a large phased array are often calibrated for amplitude and phase variations by an internal calibration network and an offline characterization of the complete array in an anechoic chamber. Such a solution is less obvious in view of current trends towards integration and modularity. An alternative to surpass network and offline characterization is mutual-coupling based calibration. However, existing stepwise procedures for resolving the module transfer characteristics from (active) coupling measurements can be subject to propagating errors originating from e.g. measurement noise, manufacturing differences, and defective modules. In this paper we propose an alternative approach, which is more versatile and robust than a stepwise procedure, and we test this approach for two different array architectures with simulated coupling data.
Neuronal Circuits for Fear Expression and Recovery: Recent Advances and Potential Therapeutic Strategies

Recent technological developments, such as single unit recordings coupled to optogenetic approaches, have provided unprecedented knowledge about the precise neuronal circuits contributing to the expression and recovery of conditioned fear behavior. These data have provided an understanding of the contributions of distinct brain regions such as the amygdala, prefrontal cortex, hippocampus, and periaqueductal gray matter to the control of conditioned fear behavior. Notably, the precise manipulation and identification of specific cell types by optogenetic techniques have provided novel avenues to establish causal links between changes in neuronal activity that develop in dedicated neuronal structures and the short and long-lasting expression of conditioned fear memories. In this review, we provide an update on the key neuronal circuits and cell types mediating conditioned fear expression and recovery and how these new discoveries might refine therapeutic approaches for psychiatric conditions such as anxiety disorders and posttraumatic stress disorder.
Evolution of fashion brands on Twitter and Instagram

Social media has become a popular platform for marketing and brand advertisement especially for fashion brands. To promote their products and gain popularity, different brands post their latest products and updates as photos on different social networks. Little has been explored on how these fashion brands use different social media to reach out to their customers and obtain feedback on different products. Understanding this can help future consumers to focus on their interested brands on specific social media for better information and also can help the marketing staff of different brands to understand how the other brands are utilizing the social media. In this article we focus on the top-20 fashion brands and comparatively analyze how they target their current and potential future customers on Twitter and Instagram. Using both linguistic and deep image features, our work reveals an increasing diversification of trends accompanied by a simultaneous concentration towards a few selected trends. It provides insights about brand marketing strategies and their respective competencies. Our investigations show that the brands are using Twitter and Instagram in a distinctive manner.
AODVSEC: A Novel Approach to Secure Ad Hoc on-Demand Distance Vector (AODV) Routing Protocol from Insider Attacks in MANETs

Mobile Ad hoc Network (MANET) is a collection of mobile nodes that can communicate with each other using multihop wireless links without requiring any fixed based-station infrastructure and centralized management. Each node in the network acts as both a host and a router. In such scenario, designing of an efficient, reliable and secure routing protocol has been a major challenging issue over the last many years. Numerous schemes have been proposed for secure routing protocols and most of the research work has so far focused on providing security for routing using cryptography. In this paper, we propose a novel approach to secure Ad hoc On-demand Distance Vector (AODV) routing protocol from the insider attacks launched through active forging of its Route Reply (RREP) control message. AODV routing protocol does not have any security provision that makes it less reliable in publicly open ad hoc network. To deal with the concerned security attacks, we have proposed AODV Security Extension (AODVSEC) which enhances the scope of AODV for the security provision. We have compared AODVSEC with AODV and Secure AODV (SAODV) in normal situation as well as in presence of the three concerned attacks viz. Resource Consumption (RC) attack, Route Disturb (RD) attack, Route Invasion (RI) attack and Blackhole (BH) attack. To evaluate the performances, we have considered Packet Delivery Fraction (PDF), Average End-to-End Delay (AED), Average Throughput (AT), Normalized Routing Load (NRL) and Average Jitter and Accumulated Average Processing Time.
DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing

Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been reported, which dramatically reduce the time complexity than iterative reconstruction algorithms. In this paper, we propose a novel Deep Residual Reconstruction Network (DRNet) to reconstruct the image from its Compressively Sensed (CS) measurement. The DR-Net is proposed based on two observations: 1) linear mapping could reconstruct a high-quality preliminary image, and 2) residual learning could further improve the reconstruction quality. Accordingly, DR-Net consists of two components, i.e., linear mapping network and residual network, respectively. Specifically, the fully-connected layer in neural network implements the linear mapping network. We then expand the linear mapping network to DR-Net by adding several residual learning blocks to enhance the preliminary image. Extensive experiments demonstrate that the DR-Net outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1, and 0.25, respectively. The code of DR-Net has been released on: https://github.com/coldrainyht/caffe dr2
Ping to Win?: Non-Verbal Communication and Team Performance in Competitive Online Multiplayer Games

Non-verbal communication plays a large role in online competitive multiplayer games, as team members attempt to coordinate with each other without distraction to achieve victory. Some games enable this communication through "pings," alerts that are easy to activate and provide auditory and visual cues for teammates. In this paper, we review the literature on gestures and non-verbal communication and, through an empirical analysis of 84,489 players across 10,293 matches in the popular game, League of Legends, illustrate ping use in multiplayer games and test the impact of ping actions on performance in teams. We show that the amount of pings depends on player role and in-game activity and that pings by players have a positive but concave relationship with player performance. These findings demonstrate the importance of non-verbal communication and interruption on the performance of virtual team members. We conclude by discussing the implications of these results for theorizing and designing sociotechnical systems that rely on users to engage in synchronous, collaborative work in shared visual spaces.
A meta-analysis of the relationship between organizational citizenship behavior and counterproductive work behavior.

Job performance is increasingly being seen to encompass constructs such as organizational citizenship behavior (OCB) and counterproductive work behavior (CWB). To clarify the OCB-CWB relationship, a meta-analysis was conducted. Results indicate a modest negative relationship (p = -0.32). The relationship strength did not increase appreciably when the target of the behavior (the organization vs. other employees) was the same. Moreover, OCB and CWB exhibited somewhat distinct patterns of relationships with antecedents. The OCB-CWB relationship was moderated by the source of the ratings, the presence of antithetical items, and the type of response options. An employee-centric perspective is proposed whereby both OCB and CWB are perceived as adaptive behavior. Implications for organizations are discussed.
Training of Airport Security Screeners

scientifically based training system that is effective and efficient and allows achieving an excellent level of detection performance. Object recognition is a very complex process but essentially it means to compare visual information to object representations stored in visual memory. The ability to recognize an object class depends on whether itself or similar instance has been stored previously in visual memory. In other words, you can only recognize what you have learned. This explains why training is so important. Identifying the threat items in Fig. 1a and 1b is difficult without training because the objects are depicted in a view that is rather unusual in everyday life. Detecting a bomb such as in Fig. 1c is difficult for untrained people because usually we do not encounter bombs in everyday life. Therefore, a good training system must contain many forbidden objects in many viewpoints in order to train screeners to detect them reliably. Indeed, several studies from our lab and many others worldwide have found that object recognition is often dependent on viewpoint. Moreover, there are numerous studies from neuroscience suggesting that objects are stored in a view-based format in the brain. As you can see  1a and 1b are indeed much easier to recognize if they are shown in a view that is more often encountered in real life. Because you never know how terrorists place their threat items in a bag, airport security screeners should be trained to detect prohibited items from all kinds of different viewpoints. In a close collaboration with Zurich State Police, Airport division we have Current x-ray machines provide high resolution images, many image processing features and even automatic explosive detection. But the machine is only one half of the whole system. The last and most important decision is always taken by the human operator. In fact, the best and most expensive equipment is of limited use, if a screener finally fails to recognize a threat in the x-ray image. This is of special importance because according to several aviation security experts the human operator is currently the weakest link in airport security. This matter is being realized more and more and several authorities as well as airports are planning to increase investments into a very important element of aviation security: Effective and efficient training of screeners. Indeed, …