nils-herrmann/clean_scirepeval huggingface dataset:
Flying Eyes and Hidden Controllers: A Qualitative Study of People's Privacy Perceptions of Civilian Drones in The US

Drones are unmanned aircraft controlled remotely or operated autonomously. While the extant literature suggests that drones can in principle invade people’s privacy, little is known about how people actually think about drones. Drawing from a series of in-depth interviews conducted in the United States, we provide a novel and rich account of people’s pri­ vacy perceptions of drones for civilian uses both in general and under specific usage scenarios. Our informants raised both physical and information privacy issues against government, organization and individual use of drones. Informants’ reason­ ing about the acceptance of drone use was in part based on whether the drone is operating in a public or private space. However, our informants differed significantly in their defini­ tions of public and private spaces. While our informants’ pri­ vacy concerns such as surveillance, data collection and shar­ ing have been raised for other tracking technologies such as camera phones and closed-circuit television (CCTV), our in­ terviews highlight two heightened issues of drones: (1) pow­ erful yet inconspicuous data collection, (2) hidden and inac­ cessible drone controllers. These two aspects of drones render some of people’s existing privacy practices futile (e.g., notice recording and ask controllers to stop or delete the recording). Some informants demanded notifications of drones near them and expected drone controllers asking for their explicit per­ missions before recording. We discuss implications for future privacy-enhancing drone designs.
GaitViewer: Semantic Gait Data Analysis and Visualization Tool

Clinical gait analysis studies human locomotion by characterizing movement patterns with heterogeneous acquired gait data (e.g., spatio-temporal parameters, geometry of motion, measures of force). Lack of semantic integration of these heterogeneous data slows down collaborative studies among the laboratories that have different acquisition systems. In this work we propose a semantic integration methodology for gait data, and present GaitViewer a prototype web application for semantic analysis and visualization of gait data. The proposed semantic integration methodology separates heterogeneous and mixed numerical and meta information in gait data. Ontology concepts represent the separated meta information, while numerical information is stored in a NoSQL database. Parallel coordinates visual analytics technique are used as an interface to the analytics tools proposed by the NoSQL database. We tailor GaitViewer for two common use-cases in clinical gait analysis: correlation of measured signals for different subjects, and follow-up analysis of the same subject. Finally, we discuss the potential of a large-scale adoption of frameworks such as GaitViewer for the next generation diagnosis systems for movement disorders.
Artificial Neural Networks to Forecast Air Pollution 221 Artificial Neural Networks to Forecast Air Pollution

European laws concerning urban and suburban air pollution requires the analysis and implementation of automatic operating procedures in order to prevent the risk for the principal air pollutants to be above alarm thresholds (e.g. the Directive 2002/3/EC for ozone or the Directive 99/30/CE for the particulate matter with an aerodynamic diameter of up to 10 μm, called PM10). As an example of European initiative to support the investigation of air pollution forecast, the COST Action ES0602 (Towards a European Network on Chemical Weather Forecasting and Information Systems) provides a forum for standardizing and benchmarking approaches in data exchange and multi-model capabilities for air quality forecast and (near) real-time information systems in Europe, allowing information exchange between meteorological services, environmental agencies, and international initiatives. Similar efforts are also proposed by the National Oceanic and Atmospheric Administration (NOAA) in partnership with the United States Environmental Protection Agency (EPA), which are developing an operational, nationwide Air Quality Forecasting (AQF) system. Critical air pollution events frequently occur where the geographical and meteorological conditions do not permit an easy circulation of air and a large part of the population moves frequently between distant places of a city. These events require drastic measures such as the closing of the schools and factories and the restriction of vehicular traffic. Indeed, many epidemiological studies have consistently shown an association between particulate air pollution and cardiovascular (Brook et al., 2007) and respiratory (Pope et al., 1991) diseases. The forecasting of such phenomena with up to two days in advance would allow taking more efficient countermeasures to safeguard citizens’ health. Air pollution is highly correlated with meteorological variables (Cogliani, 2001). Indeed, pollutants are usually entrapped into the planetary boundary layer (PBL), which is the lowest part of the atmosphere and has behaviour directly influenced by its contact with the ground. It responds to surface forcing in a timescale of an hour or less. In this layer, physical quantities such as flow velocity, temperature, moisture and pollutants display rapid fluctuations (turbulence) and vertical mixing is strong. Different automatic procedures have been developed to forecast the time evolution of the concentration of air pollutants, using also meteorological data. Mathematical models of the 10
Fast Global Registration

We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective is defined densely over the surfaces and the optimization achieves tight alignment with no initialization. No correspondence updates or closest-point queries are performed in the inner loop. An extension of the algorithm can perform joint global registration of many partially overlapping surfaces. Extensive experiments demonstrate that the presented approach matches or exceeds the accuracy of state-of-the-art global registration pipelines, while being at least an order of magnitude faster. Remarkably, the presented approach is also faster than local refinement algorithms such as ICP. It provides the accuracy achieved by well-initialized local refinement algorithms, without requiring an initialization and at lower computational cost.
Organic Photodiodes: The Future of Full Color Detection and Image Sensing.

Major growth in the image sensor market is largely as a result of the expansion of digital imaging into cameras, whether stand-alone or integrated within smart cellular phones or automotive vehicles. Applications in biomedicine, education, environmental monitoring, optical communications, pharmaceutics and machine vision are also driving the development of imaging technologies. Organic photodiodes (OPDs) are now being investigated for existing imaging technologies, as their properties make them interesting candidates for these applications. OPDs offer cheaper processing methods, devices that are light, flexible and compatible with large (or small) areas, and the ability to tune the photophysical and optoelectronic properties - both at a material and device level. Although the concept of OPDs has been around for some time, it is only relatively recently that significant progress has been made, with their performance now reaching the point that they are beginning to rival their inorganic counterparts in a number of performance criteria including the linear dynamic range, detectivity, and color selectivity. This review covers the progress made in the OPD field, describing their development as well as the challenges and opportunities.
Exploring the role of robotic kissing “Kissenger” in digital communication through alan turing's imitation game

With advances in robotics and artificial intelligence, and increasing prevalence of commercial social robots and chatbots, humans are communicating with artificially intelligent machines for various applications in a wide range of settings. Intimate touch is crucial to relationships forming and emotional bonding, however it remains as a missing element in digital communication between humans, as well as humans and machines. A kissing machine, Kissenger, is built to transmit haptic kissing sensations in a communication network. In order to investigate the role of robotic kissing using the Kissenger device in digital communication, we conducted a modified version of the Imitation Game described by Alan Turing by including the use of the kissing machine. The study consists of 4 rounds, in which participants communicate with two players, a male and a female, using online chatrooms with the goal to identify the female player. In the last two rounds, the male player is replaced with a chatbot with female personas. Participants are told to remotely kiss the players using the kissing device at least once during the chat in the second and fourth round, but not in the first and third round. Results show that robotic kissing has no effect on the winning rates of the male and female players during human-human communication, but it increases the winning rate of the female player when a chatbot is involved in the game.
A Summary Review of Wireless Sensors and Sensor Networks for Structural Health Monitoring

In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered monitoring systems. A benefit of wireless structural monitoring systems is that they are inexpensive to install because extensive wiring is no longer required between sensors and the data acquisition system. Researchers are discovering that wireless sensors are an exciting technology that should not be viewed as simply a substitute for traditional tethered monitoring systems. Rather, wireless sensors can play greater roles in the processing of structural response data; this feature can be utilized to screen data for signs of structural damage. Also, wireless sensors have limitations that require novel system architectures and modes of operation.
A systematic literature review of green software metrics

Green IT is getting increasing attention in software engineering research. Nevertheless energy efficiency studies have mostly focused on the hardware side of IT, the software role still requires deepening in terms of methods and techniques. Furthermore, it is necessary to understand how to assess the software“greenness” for stimulating the energy efficiency awareness, since early phases of the software lifecycle. The main goal of this study is to describe and to classify metrics related to software “greenness” present in the software engineering literature. Furthermore, this study analyzes the evolution of those metrics, in terms of type, context, and evaluation methods. To achieve this goal, a systematic literature review has been performed surveying the metrics claimed in the last decade. After examined 960 publications, we selected 23 of them as primary studies, from which we isolated extracting 96 different green metrics. Therefore, we analyzed search results in order to show what is the trend of research about green software metrics, how metrics perform measurement on resources, and what type of metrics are more appealing for defined contexts.
Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions

In this paper, we present a survey of recent works in developing neuromorphic or neuro-inspired hardware systems. In particular, we focus on those systems which can either learn from data in an unsupervised or online supervised manner. We present algorithms and architectures developed specially to support on-chip learning. Emphasis is placed on hardware friendly modifications of standard algorithms, such as backpropagation, as well as novel algorithms, such as structural plasticity, developed specially for low-resolution synapses. We cover works related to both spike-based and more traditional non-spike-based algorithms. This is followed by developments in novel devices, such as floating-gate MOS, memristors, and spintronic devices. CMOS circuit innovations for on-chip learning and CMOS interface circuits for post-CMOS devices, such as memristors, are presented. Common architectures, such as crossbar or island style arrays, are discussed, along with their relative merits and demerits. Finally, we present some possible applications of neuromorphic hardware, such as brain–machine interfaces, robotics, etc., and identify future research trends in the field.
Trimming and Improving Skip-thought Vectors

The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to trim and improve it. First, we validate a hypothesis that, given a current sentence, inferring the previous and inferring the next sentence provide similar supervision power, therefore only one decoder for predicting the next sentence is preserved in our trimmed skip-thought model. Second, we present a connection layer between encoder and decoder to help the model to generalize better on semantic relatedness tasks. Third, we found that a good word embedding initialization is also essential for learning better sentence representations. We train our model unsupervised on a large corpus with contiguous sentences, and then evaluate the trained model on 7 supervised tasks, which includes semantic relatedness, paraphrase detection, and text classification benchmarks. We empirically show that, our proposed model is a faster, lighter-weight and equally powerful alternative to the original skip-thought model.
Computer literacy: essential in today's computer-centric world

Computer literacy is a fundamental part of undergraduate curriculum today. Computer literacy is as basic to undergraduate students as the course work in core curriculum in today's computer-centric information age . The number of computers-in-use worldwide is growing, especially in the developing countries . Computers affect every facet of our lives and every sector of the global society. Employers prefer workers who are computer literate because they are more productive and efficient at work than those who are not computer literate. This paper focuses on computer literacy and its benefits to people of all ages -- children, teenagers, adults and seniors. I also describe the implementation of computer competency requirement at Belmont Abbey College and the process students follow to satisfy this requirement in order to graduate.
Critical Analysis on Advanced Persistent Threats

Since the birth of Internet, cyber securities have always been an area full of unsolved problems for researchers. Particularly in the age of information, every corporate and government site needs to keep their sensitive data secure from hackers or intruders. With rapid advancement in improved security measures, there always comes along a threat which forces researchers to be on alert. In recent times “Advanced Persistent Threat” (APT) has been among the most highlighted threat for security experts. At early stages such attacks were dedicated to government or financial organizations, but recent studies based on security breaches indicate that such attacks are now carried out on a much wider domain. In this paper crucial attack stages with the most common methods and tools use by intruders to initiate APTs are discussed, along with recommendation on how a model can be defined to perceive an APT attack being conducted on a network.
Comparing the performance of different x86 SIMD instruction sets for a medical imaging application on modern multi- and manycore chips

Single Instruction, Multiple Data (SIMD) vectorization is a major driver of performance in current architectures, and is mandatory for achieving good performance with codes that are limited by instruction throughput. We investigate the efficiency of different SIMD-vectorized implementations of the RabbitCT benchmark. RabbitCT performs 3D image reconstruction by back projection, a vital operation in computed tomography applications. The underlying algorithm is a challenge for vectorization because it consists, apart from a streaming part, also of a bilinear interpolation requiring scattered access to image data. We analyze the performance of SSE (128 bit), AVX (256 bit), AVX2 (256 bit), and IMCI (512 bit) implementations on recent Intel x86 systems. A special emphasis is put on the vector gather implementation on Intel Haswell and Knights Corner microarchitectures. Finally we discuss why GPU implementations perform much better for this specific algorithm.
RNA modifications and structures cooperate to guide RNA–protein interactions

An emerging body of evidence indicates that post-transcriptional gene regulation relies not only on the sequence of mRNAs but also on their folding into intricate secondary structures and on the chemical modifications of the RNA bases. These features, which are highly dynamic and interdependent, exert direct control over the transcriptome and thereby influence many aspects of cell function. Here, we consider how the coupling of RNA modifications and structures shapes RNA–protein interactions at different steps of the gene expression process.
Fingerprint Model Based on Fingerprint Image Topology and Ridge Count Values

In this paper the authors propose a mathematical model of fingerprint images based on the vectors of ridge count values. The model takes into account the instability of the values of the ridge count in the region of the lines curvature and possible mutations of minutiae. This increases the stability of fingerprint template and reliability of identification. The vectors of ridge count are formed using topological descriptors, which are constructed for the neighborhood of all fingerprint minutiae. In the proposed model, a template of a fingerprint image keeps the list of minutiae, the list of topological vectors and the list of the ridge count vectors. The value of the ridge count can be represented as a fractional number.
Fully Convolutional Network for Depth Estimation and Semantic Segmentation

Scene understanding is an active area of research in computer vision that encompasses several different problems. This paper addresses two of those problems: we use a fully convolutional network architecture to perform, in a single pass, both depth prediction of a scene from a single monocular image, and pixel-wise semantic labeling using the same image input and its depth information. We optimize the first task on L2 and berHu loss, and the latter on negative log likelihood loss per pixel. Our model encompasses residual blocks and efficient up-sampling units to provide high-resolution outputs, thus removing the need for post-processing steps. We achieve reasonable validation accuracies of 49% and 66% in the semantic labeling task, when using 38 and 6 classes respectively.
Development of an Online Biometric-enabled Class Attendance Register System

This study presents an automated Online Biometric-enabled Class Attendance Register System (OBCARS) prototype. The system design and development is aimed at addressing the challenges of misplaced and/or torn attendance register paper sheets in various classrooms in Higher Educational Institutions. Also, the system is aimed at providing an efficient and effective class attendance tracking method that prevents attendance marking impersonation among students, and eases students' attendance record computation. The system adopted the use of biometric fingerprint reader for an individual student to input his/her attendance record for lectures attended in the system's back-end database. The system was tested with 50 students in the Department of Computer Science, University of Fort Hare Alice, South Africa for usability and fingerprint recognition accuracy. The test result indicated 97% accuracy at system level. The average execution time for student attendance tracking is 8.7 seconds against 22.6 seconds using paper attendance.
Normal vulvovaginal, perineal, and pelvic anatomy with reconstructive considerations.

A thorough insight into the female genital anatomy is crucial for understanding and performing pelvic reconstructive procedures. The intimate relationship between the genitalia and the muscles, ligaments, and fascia that provide support is complex, but critical to restore during surgery for correction of prolapse or aesthetic reasons. The external female genitalia include the mons pubis, labia majora and minora, clitoris, vestibule with glands, perineal body, and the muscles and fascia surrounding these structures. Through the perineal membrane and the perineal body, these superficial vulvar structures are structurally related to the deep pelvic muscle levator ani with its fascia. The levator ani forms the pelvic floor with the coccygeus muscle and provides vital support to all the pelvic organs and stability to the perineum. The internal female genital organs include the vagina, cervix, uterus, tubes, and ovaries with their visceral fascia. The visceral fascia also called the endopelvic fascia, surrounds the pelvic organs and connects them to the pelvic walls. It is continuous with the paraurethral and paravaginal fascia, which is attached to the perineal membrane. Thus, the internal and external genitalia are closely related to the muscles and fascia, and work as one functioning unit.
The use of computational intelligence in intrusion detection systems: A review

Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will be on core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.