nils-herrmann/clean_scirepeval huggingface dataset:
On the Trusted Use of Large-Scale Personal Data

Large-scale personal data has become the new oil of the Internet. However, personal data tend to be monopolized and siloed by online services which not only impedes beneficial uses but also prevents users from managing the risks associated with their data. While there is substantial legal and social policy scholarship concerning ownership and fair use of personal data, a pragmatic technical solution that allows governments and companies easy access to such data and yet protects individual rights has yet to be realized and tested. We introduce openPDS, an implementation of a Personal Data Store, which follows the recommendations of the WEF, the US NSTIC and the US Consumer Privacy Bill of Rights. openPDS allows users to collect, store, and give fine-grained access to their data in the cloud. openPDS also protects users’ privacy by only sharing anonymous answers, not raw data. Indeed, a mechanism to install third-parties applications on a user’s PDS allows for the sensitive part of the data processing to take place within the PDS. openPDS can also engage in privacy-preserving group computations to aggregate data across users without the need to share sensitive data with an intermediate entity.
Bioinformatics - an introduction for computer scientists

The article aims to introduce computer scientists to the new field of bioinformatics. This area has arisen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research---and its more recent counterparts, proteomics and functional genomics. The ultimate goal of bioinformatics is to develop in silico models that will complement in vitro and in vivo biological experiments. The article provides a bird's eye view of the basic concepts in molecular cell biology, outlines the nature of the existing data, and describes the kind of computer algorithms and techniques that are necessary to understand cell behavior. The underlying motivation for many of the bioinformatics approaches is the evolution of organisms and the complexity of working with incomplete and noisy data. The topics covered include: descriptions of the current software especially developed for biologists, computer and mathematical cell models, and areas of computer science that play an important role in bioinformatics.
Power Control System Design in Induction Heating with Resonant Voltage Inverter

This paper is concerned with the design of the power control system for a single-phase voltage source inverter feeding a parallel resonant induction heating load. The control of the inverter output current, meaning the active component of the current through the induction coil when the control frequency is equal or slightly exceeds the resonant frequency, is achieved by a Proportional-IntegralDerivative controller tuned in accordance with the Modulus Optimum criterion in Kessler variant. The response of the current loop for different work pipes and set currents has been tested by simulation under the Matlab-Simulink environment and illustrates a very good behavior of the control system.
Internet Censorship in Iran: A First Look

The Iranian government operates one of the largest and most sophisticated Internet censorship regimes in the world, but the mechanisms it employs have received little research attention, primarily due to lack of access to network connections within the country and personal risks to Iranian citizens who take part. In this paper, we examine the status of Internet censorship in Iran based on network measurements conducted from a major Iranian ISP during the lead up to the June 2013 presidential election. We measure the scope of the censorship by probing Alexa’s top 500 websites in 18 different categories. We investigate the technical mechanisms used for HTTP Host–based blocking, keyword filtering, DNS hijacking, and protocol-based throttling. Finally, we map the network topology of the censorship infrastructure and find evidence that it relies heavily on centralized equipment, a property that might be fruitfully exploited by next generation approaches to censorship circumvention.
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms A case for Extreme Gradient Boosting

Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover. turnover prediction; machine learning; extreme gradient boosting; supervised classification; regularization
What Emotions Do Novices Experience during Their First Computer Programming Learning Session?

We conducted a study to track the emotions, their behavioral correlates, and relationship with performance when novice programmers learned the basics of computer programming in the Python language. Twenty-nine participants without prior programming experience completed the study, which consisted of a 25 minute scaffolding phase (with explanations and hints) and a 15 minute fadeout phase (no explanations or hints) with a computerized learning environment. Emotional states were tracked via retrospective self-reports in which learners viewed videos of their faces and computer screens recorded during the learning session and made judgments about their emotions at approximately 100 points. The results indicated that flow/engaged (23%), confusion (22%), frustration (14%), and boredom (12%) were the major emotions students experienced, while curiosity, happiness, anxiety, surprise, anger, disgust, fear, and sadness were comparatively rare. The emotions varied as a function of instructional scaffolds and were systematically linked to different student behaviors (idling, constructing code, running code). Boredom, flow/engaged, and confusion were also correlated with performance outcomes. Implications of our findings for affect-sensitive learning interventions are discussed.
A Dual-Wideband and High Gain Magneto-Electric Dipole Antenna and Its 3D MIMO System With Metasurface for 5G/WiMAX/WLAN/X-Band Applications

A substrate integrated magneto-electric (ME) dipole antenna with metasurface is proposed for the 5G/WiMAX/WLAN/X-band MIMO applications. In order to provide a low profile, the radiated electric dipoles integrated with shorted wall are used in the multi-layer substrates at different heights. Owing to the coordination of the metasurface and the ME dipole, dual wideband and high gain have been obtained. As a result of the 3-D hexagonal structure, good envelope correlation coefficient and mean effective gain performance can be achieved by the MIMO antenna system. The antenna element can provide an impedance bandwidth of 66.7% (3.1–6.2 GHz) with a stable gain of 7.6±1.5 dBi and an impedance bandwidth of 20.3% (7.1–8.7 GHz) with a gain of 7.4±1.8 dBi for the lower and upper bands, respectively. The overall size of the element is <inline-formula> <tex-math notation="LaTeX">$60\times 60\times 7.92$ </tex-math></inline-formula> mm<sup>3</sup>. Hence, it is well-suited for the future 5G/WiMAX/WLAN/X-band MIMO communications.
Rapid, Reliable Shape Setting of Superelastic Nitinol for Prototyping Robots

Shape setting Nitinol tubes and wires in a typical laboratory setting for use in superelastic robots is challenging. Obtaining samples that remain superelastic and exhibit desired precurvatures currently requires many iterations, which is time consuming and consumes a substantial amount of Nitinol. To provide a more accurate and reliable method of shape setting, in this paper we propose an electrical technique that uses Joule heating to attain the necessary shape setting temperatures. The resulting high power heating prevents unintended aging of the material and yields consistent and accurate results for the rapid creation of prototypes. We present a complete algorithm and system together with an experimental analysis of temperature regulation. We experimentally validate the approach on Nitinol tubes that are shape set into planar curves. We also demonstrate the feasibility of creating general space curves by shape setting a helical tube. The system demonstrates a mean absolute temperature error of 10 °C.
The development of face expertise

Recent neuroimaging studies in adults indicate that visual areas selective for recognition of faces can be recruited through expertise for nonface objects. This reflects a new emphasis on experience in theories of visual specialization. In addition, novel work infers differences between categories of nonface objects, allowing a re-interpretation of differences seen between recognition of faces and objects. Whether there are experience-independent precursors of face expertise remains unclear; indeed, parallels between literature for infants and adults suggest that methodological issues need to be addressed before strong conclusions can be drawn regarding the origins of face recognition.
Consumer adoption of mobile technologies: a literature review

Although the total number of mobile subscriptions reached 6.8 billion worldwide (ITU, 2013) confirming in the process that mobile technology is widespread, academic literature has so far focused only on explaining adoption of mobile technology and services. This literature review analyses 67 studies from the period 2005–2013 and finds the following: a) most studies are quantitative in nature and come from Asia, b) the Technology Acceptance Model is the most used theory, and c) future research should also utilize qualitative methods and examine the behavioural outcomes of mobile adoption instead of simply adoption in consumer markets.
Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images

Methods based on kernel density estimation have been successfully applied for various data mining tasks. Their natural interpretation together with suitable properties make them an attractive tool among others in clustering problems. In this paper, the Complete Gradient Clustering Algorithm has been used to investigate a real data set of grains. The wheat varieties, Kama, Rosa and Canadian, characterized by measurements of main grain geometric features obtained by X-ray technique, have been analyzed. The proposed algorithm is expected to be an effective tool for recognizing wheat varieties. A comparison between the clustering results obtained from this method and the classical k-means clustering algorithm shows positive practical features of the Complete Gradient Clustering Algorithm.
Target detection in synthetic aperture radar imagery : a state-ofthe-art survey

Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidean distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.
Location Systems: An Introduction to the Technology Behind Location Awareness

Advances in electronic location technology and the coming of age of mobile computing have opened the door for location-aware applications to permeate all aspects of everyday life. Location is at the core of a large number of high-value applications ranging from the life-and-death context of emergency response to serendipitous social meet-ups. For example, the market for GPS products and services alone is expected to grow to US$200 billion by 2015. Unfortunately, there is no single location technology that is good for every situation and exhibits high accuracy, low cost, and universal coverage. In fact, high accuracy and good coverage seldom coexist, and when they do, it comes at an extreme cost. Instead, the modern localization landscape is a kaleidoscope of location systems based on a multitude of different technologies including satellite, mobile telephony, 802.11, ultrasound, and infrared among others. This lecture introduces researchers and developers to the most popular technologies and systems for location estimation and the challenges and opportunities that accompany their use. For each technology, we discuss the history of its development, the various systems that are based on it, and their trade-offs and their effects on cost and performance. We also describe technology-independent algorithms that are commonly used to smooth streams of location estimates and improve the accuracy of object tracking. Finally, we provide an overview of the wide variety of application domains where location plays a key role, and discuss opportunities and new technologies on the horizon. KEyWoRDS localization, location systems, location tracking, context awareness, navigation, location sensing, tracking, Global Positioning System, GPS, infrared location, ultrasonic location, 802.11 location, cellular location, Bayesian filters, RFID, RSSI, triangulation
Intelligent Intrusion Detection in Low-Power IoTs

Security and privacy of data are one of the prime concerns in today’s Internet of Things (IoT). Conventional security techniques like signature-based detection of malware and regular updates of a signature database are not feasible solutions as they cannot secure such systems effectively, having limited resources. Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. Furthermore, energy efficient IoT devices running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate IoT in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e., sensor nodes and base station) is destabilised by the attackers. In this article, we have presented an intrusion detection and prevention mechanism by implementing an intelligent security architecture using random neural networks (RNNs). The application’s source code is also instrumented at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. To validate the feasibility of the proposed security solution, it is implemented for an existing IoT system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node within the system operating range and anomalous activity in the base station with an accuracy of 97.23%. Overall, the proposed security solution has presented a minimal performance overhead.
Integrating end-to-end learned steering into probabilistic autonomous driving

We propose an integrated approach of combining end-to-end learned trajectory proposals with a probabilistic sampling based planning algorithm for autonomous driving. A convolutional neural network is trained based on monocular image data to predict prospective steering angles. By using a local history of image data, we achieve an implicit spatial representation of parked cars or other obstacles commonly found in urban and residential areas. Through this local history, calculated using the vehicle's velocity data, the trajectory proposals are not only capable of lane following, but also comfortably circumnavigate obstacles. Training data is collected by recording video data and the vehicles CAN bus during human driving, thus imitating human behavior. The integration of end-to-end learning into a modularized architecture allows for additional safety constraints and complementary sensor information to be combined with intuitive steering. Our first results take a promising step towards general architectures for autonomous vehicles that combine deep learning with factorized probabilistic modeling.
Organizational impact of system quality, information quality, and service quality

Increased organizational dependence on information systems drives management attention towards improving information systems’ quality. A recent survey shows that ‘‘Improve IT quality” is one of the top concerns facing IT executives. As IT quality is a multidimensional measure, it is important to determine what aspects of IT quality are critical to organizations to help Chief Information Officers (CIOs) to devise effective IT quality improvement strategies. In this research, we model the relationship between information systems’ (IS) quality and organizational impact. We hypothesize greater organizational impact in situations in which system quality, information quality and service quality are high. We also hypothesize a positive relationship between system quality and information quality. We test our hypotheses using survey data. Our structural equation model exhibits a good fit with the observed data. Our results show that IS service quality is the most influential variable in this model (followed by information quality and system quality), thus highlighting the importance of IS service quality for organizational performance. This paper contributes theoretically to IS success models through the system quality-to-information quality and IS quality-to-organizational impact links. Implications of our results for practice and research are discussed.
Password Strength: An Empirical Analysis

It is a well known fact that user-chosen passwords are somewhat predictable: by using tools such as dictionaries or probabilistic models, attackers and password recovery tools can drastically reduce the number of attempts needed to guess a password. Quite surprisingly, however, existing literature does not provide a satisfying answer to the following question: given a number of guesses, what is the probability that a state-of-the-art attacker will be able to break a password? To answer the former question, we compare and evaluate the effectiveness of currently known attacks using various datasets of known passwords. We find that a "diminishing returns" principle applies: in the absence of an enforced password strength policy, weak passwords are common; on the other hand, as the attack goes on, the probability that a guess will succeed decreases by orders of magnitude. Even extremely powerful attackers won't be able to guess a substantial percentage of the passwords. The result of this work will help in evaluating the security of authentication means based on user- chosen passwords, and our methodology for estimating password strength can be used as a basis for creating more effective proactive password checkers for users and security auditing tools for administrators.
Cutty: Aggregate Sharing for User-Defined Windows

Aggregation queries on data streams are evaluated over evolving and often overlapping logical views called windows. While the aggregation of periodic windows were extensively studied in the past through the use of aggregate sharing techniques such as Panes and Pairs, little to no work has been put in optimizing the aggregation of very common, non-periodic windows. Typical examples of non-periodic windows are punctuations and sessions which can implement complex business logic and are often expressed as user-defined operators on platforms such as Google Dataflow or Apache Storm. The aggregation of such non-periodic or user-defined windows either falls back to expensive, best-effort aggregate sharing methods, or is not optimized at all.
 In this paper we present a technique to perform efficient aggregate sharing for data stream windows, which are declared as user-defined functions (UDFs) and can contain arbitrary business logic. To this end, we first introduce the concept of User-Defined Windows (UDWs), a simple, UDF-based programming abstraction that allows users to programmatically define custom windows. We then define semantics for UDWs, based on which we design Cutty, a low-cost aggregate sharing technique. Cutty improves and outperforms the state of the art for aggregate sharing on single and multiple queries. Moreover, it enables aggregate sharing for a broad class of non-periodic UDWs. We implemented our techniques on Apache Flink, an open source stream processing system, and performed experiments demonstrating orders of magnitude of reduction in aggregation costs compared to the state of the art.
A review of spatial reasoning and interaction for real-world robotics

Truly universal helper robots capable of coping with unknown, unstructured environments must be capable of spatial reasoning, i.e., establishing geometric relations between objects and locations, expressing those in terms understandable by humans. It is therefore desirable that spatial and semantic environment representations are tightly interlinked. 3D robotic mapping and the generation of consistent metric representations of space is highly useful for navigation and exploration, but they do not capture symbol-level information about the environment. This is, however, essential for reasoning, and enables interaction via natural language, which is arguably the most common and natural communication channel used and understood by humans. This article presents a review of research in three major fields relevant for this discussion of spatial reasoning and interaction. Firstly, dialogue systems are an integral part of modern approaches to situated human-robot interaction. Secondly, interactive robots must be equipped with environment representations and reasoning methods that is suitable for both navigation and task fulfillment, as well as for interaction with human partners. Thirdly, at the interface between these domains are systems that ground language in systemic environment representation and which allow the integration of information from natural language descriptions into robotic maps. For each of these areas, important approaches are outlined and relations between the fields are highlighted, and challenging applications as well as open problems are discussed.