nils-herrmann/clean_scirepeval huggingface dataset:
Smart home system for Disabled People via Wireless Bluetooth

This paper describes the Smart Home System for Disabled People via Bluetooth Wireless. Smart home system for disable people is the system called assistive domestics focuses on making it possible for the disabled to motivate them carry out the daily activity, safe and comfortable. However in our research work, we attempt to design the smart home system including the wireless controller via Bluetooth technology. This software application adapt in mobile phone, PDA, mobile computer (Samsung Galaxy Tab) using android's operating system (OS). This software application will control the electrical appliances switches wirelessly (Bluetooth). Results from this study found that the system was successfully produced where it is able to control any of the wireless switches at a distance of approximately 25 meter radius from the main controller. The system is seen potentially be used in hospitals, home care for the elderly and facilities for disabled users.
A case study on value-based requirements tracing

Project managers aim at keeping track of interdependencies between various artifacts of the software development lifecycle, to find out potential requirements conflicts, to better understand the impact of change requests, and to fulfill process quality standards, such as CMMI requirements. While there are many methods and techniques on how to technically store requirements traces, the economic issues of dealing with requirements tracing complexity remain open. In practice tracing is typically not an explicit systematic process, but occurs rather ad hoc with considerable hidden tracing-related quality costs. This paper reports a case study on value-based requirements tracing (VBRT) that systematically supports project managers in tailoring requirements tracing precision and effort based on the parameters stakeholder value, requirements risk/volatility, and tracing costs. Main results of the case study were: (a) VBRT took around 35% effort of full requirements tracing; (b) more risky or volatile requirements warranted more detailed tracing because of their higher change probability.
The 3dSOBS+ algorithm for moving object detection

We propose the 3dSOBS+ algorithm, a newly designed approach for moving object detection based on a neural background model automatically generated by a self-organizing method. The algorithm is able to accurately handle scenes containing moving backgrounds, gradual illumination variations, and shadows cast by moving objects, and is robust against false detections for different types of videos taken with stationary cameras. Experimental results and comparisons conducted on the Background Models Challenge benchmark dataset demonstrate the improvements achieved by the proposed algorithm, that compares well with the state-of-the-art methods.
MIMO Technologies in 3GPP LTE and LTE-Advanced

3rd Generation Partnership Project (3GPP) has recently completed the specification of the Long Term Evolution (LTE) standard. Majority of the world’s operators and vendors are already committed to LTE deployments and developments, making LTE the market leader in the upcoming evolution to 4G wireless communication systems. Multiple input multiple output (MIMO) technologies introduced in LTE such as spatial multiplexing, transmit diversity, and beamforming are key components for providing higher peak rate at a better system efficiency, which are essential for supporting future broadband data service over wireless links. Further extension of LTE MIMO technologies is being studied under the 3GPP study item “LTE-Advanced” to meet the requirement of IMT-Advanced set by International Telecommunication Union Radiocommunication Sector (ITU-R). In this paper, we introduce various MIMO technologies employed in LTE and provide a brief overview on the MIMO technologies currently discussed in the LTE-Advanced forum.
Efficient Dense Scene Flow from Sparse or Dense Stereo Data

This paper presents a technique for estimating the threedimensional velocity vector field that describes the motion of each visible scene point (scene flow). The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach. We enforce the scene flow to yield consistent displacement vectors in the left and right images. The decoupling strategy has two main advantages: Firstly, we are independent in choosing a disparity estimation technique, which can yield either sparse or dense correspondences, and secondly, we can achieve frame rates of 5 fps on standard consumer hardware. The approach provides dense velocity estimates with accurate results at distances up to 50 meters.
A Large-Scale Empirical Analysis of Chinese Web Passwords

Users speaking different languages may prefer different patterns in creating their passwords, and thus knowledge on English passwords cannot help to guess passwords from other languages well. Research has already shown Chinese passwords are one of the most difficult ones to guess. We believe that the conclusion is biased because, to the best of our knowledge, little empirical study has examined regional differences of passwords on a large scale, especially on Chinese passwords. In this paper, we study the differences between passwords from Chinese and English speaking users, leveraging over 100 million leaked and publicly available passwords from Chinese and international websites in recent years. We found that Chinese prefer digits when composing their passwords while English users prefer letters, especially lowercase letters. However, their strength against password guessing is similar. Second, we observe that both users prefer to use the patterns that they are familiar with, e.g., Chinese Pinyins for Chinese and English words for English users. Third, we observe that both Chinese and English users prefer their conventional format when they use dates to construct passwords. Based on these observations, we improve a PCFG (Probabilistic Context-Free Grammar) based password guessing method by inserting Pinyins (about 2.3% more entries) into the attack dictionary and insert our observed composition rules into the guessing rule set. As a result, our experiments show that the efficiency of password guessing increases by 34%.
The current prevalence of child sexual abuse worldwide: a systematic review and meta-analysis

Systematic reviews on prevalence estimates of child sexual abuse (CSA) worldwide included studies with adult participants referring on a period of abuse of about 50 years. Therefore we aimed to describe the current prevalence of CSA, taking into account geographical region, type of abuse, level of country development and research methods. We included studies published between 2002 and 2009 that reported CSA in children below 18 years. We performed a random effects meta-analysis and analyzed moderator variables by meta-regression. Fifty-five studies from 24 countries were included. According to four predefined types of sexual abuse, prevalence estimates ranged from 8 to 31 % for girls and 3 to 17 % for boys. Nine girls and 3 boys out of 100 are victims of forced intercourse. Heterogeneity between primary studies was high in all analyses. Our results based on most recent data confirm results from previous reviews with adults. Surveys in children offer most recent estimates of CSA. Reducing heterogeneity between studies might be possible by standardized measures to make data more meaningful in international comparisons.
Ultrafast imaging in biomedical ultrasound

Although the use of ultrasonic plane-wave transmissions rather than line-per-line focused beam transmissions has been long studied in research, clinical application of this technology was only recently made possible through developments in graphical processing unit (GPU)-based platforms. Far beyond a technological breakthrough, the use of plane or diverging wave transmissions enables attainment of ultrafast frame rates (typically faster than 1000 frames per second) over a large field of view. This concept has also inspired the emergence of completely novel imaging modes which are valuable for ultrasound-based screening, diagnosis, and therapeutic monitoring. In this review article, we present the basic principles and implementation of ultrafast imaging. In particular, present and future applications of ultrafast imaging in biomedical ultrasound are illustrated and discussed.
Combining supervised and unsupervised learning for zero-day malware detection

Malware is one of the most damaging security threats facing the Internet today. Despite the burgeoning literature, accurate detection of malware remains an elusive and challenging endeavor due to the increasing usage of payload encryption and sophisticated obfuscation methods. Also, the large variety of malware classes coupled with their rapid proliferation and polymorphic capabilities and imperfections of real-world data (noise, missing values, etc) continue to hinder the use of more sophisticated detection algorithms. This paper presents a novel machine learning based framework to detect known and newly emerging malware at a high precision using layer 3 and layer 4 network traffic features. The framework leverages the accuracy of supervised classification in detecting known classes with the adaptability of unsupervised learning in detecting new classes. It also introduces a tree-based feature transformation to overcome issues due to imperfections of the data and to construct more informative features for the malware detection task. We demonstrate the effectiveness of the framework using real network data from a large Internet service provider.
Improving Neural Networks with Dropout

Improving Neural Networks with Dropout Nitish Srivastava Master of Science Graduate Department of Computer Science University of Toronto 2013 Deep neural nets with a huge number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from a neural network during training. This prevents the units from co-adapting too much. Dropping units creates thinned networks during training. The number of possible thinned networks is exponential in the number of units in the network. At test time all possible thinned networks are combined using an approximate model averaging procedure. Dropout training followed by this approximate model combination significantly reduces overfitting and gives major improvements over other regularization methods. In this work, we describe models that improve the performance of neural networks using dropout, often obtaining state-of-the-art results on benchmark datasets.
Loophole-free Bell inequality violation using electron spins separated by 1.3 kilometres

More than 50 years ago, John Bell proved that no theory of nature that obeys locality and realism can reproduce all the predictions of quantum theory: in any local-realist theory, the correlations between outcomes of measurements on distant particles satisfy an inequality that can be violated if the particles are entangled. Numerous Bell inequality tests have been reported; however, all experiments reported so far required additional assumptions to obtain a contradiction with local realism, resulting in ‘loopholes’. Here we report a Bell experiment that is free of any such additional assumption and thus directly tests the principles underlying Bell’s inequality. We use an event-ready scheme that enables the generation of robust entanglement between distant electron spins (estimated state fidelity of 0.92 ± 0.03). Efficient spin read-out avoids the fair-sampling assumption (detection loophole), while the use of fast random-basis selection and spin read-out combined with a spatial separation of 1.3 kilometres ensure the required locality conditions. We performed 245 trials that tested the CHSH–Bell inequality S ≤ 2 and found S = 2.42 ± 0.20 (where S quantifies the correlation between measurement outcomes). A null-hypothesis test yields a probability of at most P = 0.039 that a local-realist model for space-like separated sites could produce data with a violation at least as large as we observe, even when allowing for memory in the devices. Our data hence imply statistically significant rejection of the local-realist null hypothesis. This conclusion may be further consolidated in future experiments; for instance, reaching a value of P = 0.001 would require approximately 700 trials for an observed S = 2.4. With improvements, our experiment could be used for testing less-conventional theories, and for implementing device-independent quantum-secure communication and randomness certification.
CHANNEL ESTIMATION FOR OFDM SYSTEMS BASED ON COMB-TYPE PILOT ARRANGEMENT IN FREQUENCY SELECTIVE FADING CHANNELS

In this paper, the channel estimation methods for OFDM systems based on comb-type pilot sub-carrier arrangement are investigated. The channel estimation algorithm based on combtype pilots is divided into pilot signal estimation and channel interpolation. The pilot signal estimation based on LS or MMSE criteria, together with channel interpolation based on piecewise-linear interpolation or piecewise second-order polynomial interpolation is studied. Owing to the MMSE estimate of pilot signals, the inter-carrier interference and additive white Gaussian noise are reduced considerably. The computational complexity of pilot signal estimation based on MMSE criterion can be reduced by using a simplified LMMSE estimator with low-rank approximation using singular value decomposition. Phase compensators before and after interpolation are also presented to combat the phase changes of subchannel symbols arising from the frame synchronization errors. Comparing to the transform-domain processing based channel estimation algorithm, the MMSE estimate of pilot signals together with phase compensated linear interpolation algorithm provides better performance and requires less computations.
Capacity limits of information processing in the brain

Despite the impressive complexity and processing power of the human brain, it is severely capacity limited. Behavioral research has highlighted three major bottlenecks of information processing that can cripple our ability to consciously perceive, hold in mind, and act upon the visual world, illustrated by the attentional blink (AB), visual short-term memory (VSTM), and psychological refractory period (PRP) phenomena, respectively. A review of the neurobiological literature suggests that the capacity limit of VSTM storage is primarily localized to the posterior parietal and occipital cortex, whereas the AB and PRP are associated with partly overlapping fronto-parietal networks. The convergence of these two networks in the lateral frontal cortex points to this brain region as a putative neural locus of a common processing bottleneck for perception and action.
Discriminative Neural Sentence Modeling by Tree-Based Convolution

This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling. Our model leverages either constituency trees or dependency trees of sentences. The tree-based convolution process extracts sentences structural features, which are then aggregated by max pooling. Such architecture allows short propagation paths between the output layer and underlying feature detectors, enabling effective structural feature learning and extraction. We evaluate our models on two tasks: sentiment analysis and question classification. In both experiments, TBCNN outperforms previous state-of-the-art results, including existing neural networks and dedicated feature/rule engineering. We also make efforts to visualize the tree-based convolution process, shedding light on how our models work.
Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks

Over the last few years, C-RAN is proposed as a transformative architecture for 5G cellular networks that brings the flexibility and agility of cloud computing to wireless communications. At the same time, content caching in wireless networks has become an essential solution to lower the content- access latency and backhaul traffic loading, leading to user QoE improvement and network cost reduction. In this article, a novel cooperative hierarchical caching (CHC) framework in C-RAN is introduced where contents are jointly cached at the BBU and at the RRHs. Unlike in traditional approaches, the cache at the BBU, cloud cache, presents a new layer in the cache hierarchy, bridging the latency/capacity gap between the traditional edge-based and core-based caching schemes. Trace-driven simulations reveal that CHC yields up to 51 percent improvement in cache hit ratio, 11 percent decrease in average content access latency, and 18 percent reduction in backhaul traffic load compared to the edge-only caching scheme with the same total cache capacity. Before closing the article, we discuss the key challenges and promising opportunities for deploying content caching in C-RAN in order to make it an enabler technology in 5G ultra-dense systems.
Summary of Distributed Resources Impact on Power Delivery Systems

Because traditional electric power distribution systems have been designed assuming the primary substation is the sole source of power and short-circuit capacity, DR interconnection results in operating situations that do not occur in a conventional system. This paper discusses several system issues which may be encountered as DR penetrates into distribution systems. The voltage issues covered are the DR impact on system voltage, interaction of DR and capacitor operations, and interaction of DR and voltage regulator and LTC operations. Protection issues include fuse coordination, feeding faults after utility protection opens, impact of DR on interrupting rating of devices, faults on adjacent feeders, fault detection, ground source impacts, single phase interruption on three phase line, recloser coordination and conductor burndown. Loss of power grid is also discussed, including vulnerability and overvoltages due to islanding and coordination with reclosing. Also covered separately are system restoration and network issues.
Mutual-information-based registration of medical images: a survey

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.
Efficient Large-Scale Approximate Nearest Neighbor Search on the GPU

We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a novel highly parallelizable re-ranking method for candidate vectors by efficiently reusing already computed intermediate values. Due to its small memory footprint during traversal the method lends itself to an efficient, parallel GPU implementation. This Product Quantization Tree (PQT) approach significantly outperforms recent state of the art methods for high dimensional nearest neighbor queries on standard reference datasets. Ours is the first work that demonstrates GPU performance superior to CPU performance on high dimensional, large scale ANN problems in time-critical real-world applications, like loop-closing in videos.
ObliviSync: Practical Oblivious File Backup and Synchronization

Oblivious RAM (ORAM) protocols are powerful techniques that hide a client’s data as well as access patterns from untrusted service providers. We present an oblivious cloud storage system, ObliviSync, that specifically targets one of the most widely-used personal cloud storage paradigms: synchronization and backup services, popular examples of which are Dropbox, iCloud Drive, and Google Drive. This setting provides a unique opportunity because the above privacy properties can be achieved with a simpler form of ORAM called write-only ORAM, which allows for dramatically increased efficiency compared to related work. Our solution is asymptotically optimal and practically efficient, with a small constant overhead of approximately 4x compared with non-private file storage, depending only on the total data size and parameters chosen according to the usage rate, and not on the number or size of individual files. Our construction also offers protection against timing-channel attacks, which has not been previously considered in ORAM protocols. We built and evaluated a full implementation of ObliviSync that supports multiple simultaneous read-only clients and a single concurrent read/write client whose edits automatically and seamlessly propagate to the readers. We show that our system functions under high work loads, with realistic file size distributions, and with small additional latency (as compared to a baseline encrypted file system) when paired with Dropbox as the synchronization service.
Path Planning for Planetary Exploration Rovers and Its Evaluation based on Wheel Slip Dynamics

In this paper, a path planning and its evaluation method is described with taking into account wheel slip dynamics of lunar/planetary exploration rovers. The surface of the planetary body is largely covered with powdery soil. On such loose soil, the wheel slippage which will make the rover get stuck must be concerned. Since the slippage dynamically depends on the posture/velocity of vehicle, soil characteristics, and wheel-soil interactions, it becomes difficult issues to incorporate the wheel slip dynamics as a criterion into path-planning algorithms. To tackle the slippage problem, the authors develop the path-planning algorithm and the path-evaluation method based on the following approach. First, a path on a rough terrain is generated with the terrain-based criteria function. Subsequently, the dynamics simulation of a rover is carried out in which the rover is controlled to follow the candidate path. Finally, the path is properly evaluated based on the slip motion profiles calculated by the simulation. Demonstrations for the proposed technique are addressed along with a discussion on characteristics of the candidate path and the slip motion profile of the rover