nils-herrmann/clean_scirepeval huggingface dataset:
CLive: Cloud-assisted P2P live streaming

Peer-to-peer (P2P) video streaming is an emerging technology that reduces the barrier to stream live events over the Internet. Unfortunately, satisfying soft real-time constraints on the delay between the generation of the stream and its actual delivery to users is still a challenging problem. Bottlenecks in the available upload bandwidth, both at the media source and inside the overlay network, may limit the quality of service (QoS) experienced by users. A potential solution for this problem is assisting the P2P streaming network by a cloud computing infrastructure to guarantee a minimum level of QoS. In such approach, rented cloud resources (helpers) are added on demand to the overlay, to increase the amount of total available bandwidth and the probability of receiving the video on time. Hence, the problem to be solved becomes minimizing the economical cost, provided that a set of constraints on QoS is satisfied. The main contribution of this paper is CLIVE, a cloud-assisted P2P live streaming system that demonstrates the feasibility of these ideas. CLIVE estimates the available capacity in the system through a gossip-based aggregation protocol and provisions the required resources from the cloud to guarantee a given level of QoS at low cost. We perform extensive simulations and evaluate CLIVE using large-scale experiments under dynamic realistic settings.
The DRAM Latency PUF: Quickly Evaluating Physical Unclonable Functions by Exploiting the Latency-Reliability Tradeoff in Modern Commodity DRAM Devices

Physically Unclonable Functions (PUFs) are commonly used in cryptography to identify devices based on the uniqueness of their physical microstructures. DRAM-based PUFs have numerous advantages over PUF designs that exploit alternative substrates: DRAM is a major component of many modern systems, and a DRAM-based PUF can generate many unique identiers. However, none of the prior DRAM PUF proposals provide implementations suitable for runtime-accessible PUF evaluation on commodity DRAM devices. Prior DRAM PUFs exhibit unacceptably high latencies, especially at low temperatures (e.g., >125.8s on average for a 64KiB memory segment below 55C), and they cause high system interference by keeping part of DRAM unavailable during PUF evaluation. In this paper, we introduce the DRAM latency PUF, a new class of fast, reliable DRAM PUFs. The key idea is to reduce DRAM read access latency below the reliable datasheet specications using software-only system calls. Doing so results in error patterns that reect the compound eects of manufacturing variations in various DRAM structures (e.g., capacitors, wires, sense ampli- ers). Based on a rigorous experimental characterization of 223 modern LPDDR4 DRAM chips, we demonstrate that these error patterns 1) satisfy runtime-accessible PUF requirements, and 2) are quickly generated (i.e., at 88.2ms) irrespective of operating temperature using a real system with no additional hardware modications. We show that, for a constant DRAM capacity overhead of 64KiB, our implementation of the DRAM latency PUF enables an average (minimum, maximum) PUF evaluation time speedup of 152x (109x, 181x) at 70C and 1426x (868x, 1783x) at 55C when compared to a DRAM retention PUF and achieves greater speedups at even lower temperatures.
Vision-based eye-gaze tracking for human computer interface

Eye-gaze is an input mode which has the potential of an efficient computer interface. Eye movement has been the focus of research in this area. Non-intrusive eyegaze tracking that allows slight head movement is addressed in this paper. A small 2D mark is employed as a reference to compensate for this movement. The iris center has been chosen for purposes of measuring eye movement. The gaze point is estimated after acquiring the eye movement data. Preliminary experimental results are given through a screen pointing application.
Arabic Dialect Identification

The written form of the Arabic language, Modern Standard Arabic (MSA), differs in a non-trivial manner from the various spoken regional dialects of Arabic—the true “native languages” of Arabic speakers. Those dialects, in turn, differ quite a bit from each other. However, due to MSA's prevalence in written form, almost all Arabic data sets have predominantly MSA content. In this article, we describe the creation of a novel Arabic resource with dialect annotations. We have created a large monolingual data set rich in dialectal Arabic content called the Arabic On-line Commentary Data set (Zaidan and Callison-Burch 2011). We describe our annotation effort to identify the dialect level (and dialect itself) in each of more than 100,000 sentences from the data set by crowdsourcing the annotation task, and delve into interesting annotator behaviors (like over-identification of one's own dialect). Using this new annotated data set, we consider the task of Arabic dialect identification: Given the word sequence forming an Arabic sentence, determine the variety of Arabic in which it is written. We use the data to train and evaluate automatic classifiers for dialect identification, and establish that classifiers using dialectal data significantly and dramatically outperform baselines that use MSA-only data, achieving near-human classification accuracy. Finally, we apply our classifiers to discover dialectical data from a large Web crawl consisting of 3.5 million pages mined from on-line Arabic newspapers.
A probabilistic model for retrospective news event detection

Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.
Coverage in Wireless Sensor Networks: A Survey

Wireless sensor networks are a rapidly growing area for research and commercial development. Wireless sensor networks are used to monitor a given field of interest for changes in the environment. They are very useful for military, environmental, and scientific applications to name a few. One of the most active areas of research in wireless sensor networks is that of coverage. Coverage in wireless sensor networks is usually defined as a measure of how well and for how long the sensors are able to observe the physical space. In this paper, we take a representative survey of the current work that has been done in this area. We define several terms and concepts and then show how they are being utilized in various research works.
Guided-wave and leakage characteristics of substrate integrated waveguide

The substrate integrated waveguide (SIW) technique makes it possible that a complete circuit including planar circuitry, transitions, and rectangular waveguides are fabricated in planar form using a standard printed circuit board or other planar processing techniques. In this paper, guided wave and modes characteristics of such an SIW periodic structure are studied in detail for the first time. A numerical multimode calibration procedure is proposed and developed with a commercial software package on the basis of a full-wave finite-element method for the accurate extraction of complex propagation constants of the SIW structure. Two different lengths of the SIW are numerically simulated under multimode excitation. By means of our proposed technique, the complex propagation constant of each SIW mode can accurately be extracted and the electromagnetic bandstop phenomena of periodic structures are also investigated. Experiments are made to validate our proposed technique. Simple design rules are provided and discussed.
Breast Cancer Classification in Histopathological Images using Convolutional Neural Network

Computer based analysis is one of the suggested means that can assist oncologists in the detection and diagnosis of breast cancer. On the other hand, deep learning has been promoted as one of the hottest research directions very recently in the general imaging literature, thanks to its high capability in detection and recognition tasks. Yet, it has not been adequately suited to the problem of breast cancer so far. In this context, I propose in this paper an approach for breast cancer detection and classification in histopathological images. This approach relies on a deep convolutional neural networks (CNN), which is pretrained on an auxiliary domain with very large labelled images, and coupled with an additional network composed of fully connected layers. The network is trained separately with respect to various image magnifications (40x, 100x, 200x and 400x). The results presented in the patient level achieved promising scores compared to the state of the art methods. Convolutional neural network (CNN); histopathological images; imagenet; classification
An Overview of Inter-Vehicular Communication Systems, Protocols and Middleware

Inter-vehicular communication is an important research area that is rapidly growing due to considerable advances in mobile and wireless communication technologies, as well as the growth of microprocessing capabilities inside today’s cars, and other moving vehicles. A good amount of research has been done to exploit the different services that can be provided to enhance the safety and comfort of the driver. Additional functions provide the car electronics and the passengers with access to the Internet and other core network resources. This paper provides a survey of the latest advances in the area of inter-vehicular communication (IVC) including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) functions and services. In addition, the paper presents the most important projects and protocols that are involved in IVC systems as well as the different issues and challenges that exist at each layer of the networking model.
A Hybrid Cryptosystem Based On Vigenere Cipher and Columnar Transposition Cipher

Privacy is one of the key issues addressed by information Security. Through cryptographic encryption methods, one can prevent a third party from understanding transmitted raw data over unsecured channel during signal transmission. The cryptographic methods for enhancing the security of digital contents have gained high significance in the current era. Breach of security and misuse of confidential information that has been intercepted by unauthorized parties are key problems that information security tries to solve. This paper sets out to contribute to the general body of knowledge in the area of classical cryptography by developing a new hybrid way of encryption of plaintext. The cryptosystem performs its encryption by encrypting the plaintext using columnar transposition cipher and further using the ciphertext to encrypt the plaintext again using Vigenère cipher. At the end, cryptanalysis was performed on the ciphertext. The implementation will be done using java programming.
Personalized Multitask Learning for Predicting Tomorrow ’ s Mood , Stress , and Health

While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.
Comparability of cause of death between ICD-9 and ICD-10: preliminary estimates.

This report presents preliminary results describing the effects of implementing the Tenth Revision of the International Classification of Diseases (ICD-10) on mortality statistics for selected causes of death effective with deaths occurring in the United States in 1999. The report also describes major features of the Tenth Revision (ICD-10), including changes from the Ninth Revision (ICD-9) in classification and rules for selecting underlying causes of death. Application of comparability ratios is also discussed.



The report is based on cause-of-death information from a large sample of 1996 death certificates filed in the 50 States and the District of Columbia. Cause-of-death information in the sample includes underlying cause of death classified by both ICD-9 and ICD-10. Because the data file on which comparability information is derived is incomplete, results are preliminary.



Preliminary comparability ratios by cause of death presented in this report indicate the extent of discontinuities in cause-of-death trends from 1998 through 1999 resulting from implementing ICD-10. For some leading causes (e.g., Septicemia, Influenza and pneumonia, Alzheimer's disease, and Nephritis, nephrotic syndrome and nephrosis), the discontinuity in trend is substantial. The ranking of leading causes of death is also substantially affected for some causes of death.



Results of this study, although preliminary, are essential to analyzing trends in mortality between ICD-9 and ICD-10. In particular, the results provide a means for interpreting changes between 1998, which is the last year in which ICD-9 was used, and 1999, the year in which ICD-10 was implemented for mortality in the United States.
Novel Unobtrusive Approach for Sleep Monitoring Using Fiber Optics in an Ambient Assisted Living Platform

Sleep plays a vital role in a person’s health and well-being. Unfortunately, most people suffering from sleep disorders remain without diagnosis and treatment since the current sleep assessment systems are cumbersome and expensive. As a result, there is an increasing demand for cheaper and more affordable sleep monitoring systems in real-life environments. In this paper, we propose a novel non-intrusive system for sleep quality monitoring using a microbend fiber optic mat placed under the bed mattress. The sleep quality is assessed based on different parameters. Moreover, the sensor has been integrated into an existing Ambient Assisted Living framework to be validated in real scenarios. Three senior female residents participated in our study and the sleep data was collected over a one-month period in a home-living situation. The proposed system shows accurate and consistent results with a survey collected from each participant showing their sleep patterns and other in-home activities.
Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism

In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space.
3D image reconstruction: Techniques, applications and challenges

Image Reconstruction is generally an inverse problem, which helps to recover the original ideal image from its given bad version, such as one that is snowed by noise, blurred by atmospheric turbulence or that has some regions damaged. Image reconstruction techniques are used to create 3D images from sets of various projections. The task of generating fast and accurate 3D image reconstruction has found its application in the field of computer vision like robotics, entertainment, reverse engineering, augmented reality, human computer interaction and animation. This paper deals with detailed study on different methods and evaluation technique used in 3D image reconstructions like visualization technique, triangulation method, multiple kinect capturing system, handling photometric stereo and shadowing information in handling patches. Finally several recommendations on future study have been recommended based on recent issues like reconstructing geometrical shadow region, handling self shadow, shiny object, non-Iambatian surface, occlusion, reconstruction without the use of sensors like Global Positioning System (GPS) and Inertial Navigation System (INS).
Semantic-Based Text Document Clustering Using Cognitive Semantic Learning and Graph Theory

Semantic-based text document clustering aims to group documents into a set of topic clusters. We propose a new approach for semantically clustering of text documents based on cognitive science and graph theory. We apply a computational cognitive model of semantic association for human semantic memory, known as Incremental Construction of an Associative Network (ICAN). The vector-based model of Latent Semantic Analysis (LSA), has been a leading computational cognitive model for semantic learning and topic modeling, but it has well-known limitations including not considering the original word-order and doing semantic reduction with neither a linguistic nor cognitive basis. These issues have been overcome by the ICAN model. ICAN model is used to generate document-level semantic-graphs. Cognitive and graph-based topic and context identification methods are used for semantic reduction of ICAN graphs. A corpus-graph is generated from ICAN graphs, and then a community-detection graph algorithm is applied for the final step of document clustering. Experiments are conducted on three commonly used datasets. Using the purity and entropy criteria for clustering quality, our results show a notable outperformance over the LSA-based approach.
Throwable tetrahedral robot with transformation capability

In this paper, a tetrahedral mobile robot with central axis for transformation to the flat vehicle is presented. The throwable robot with the function of going into narrow spaces when its in the flat-vehicle mode is explained in detail. A prototype has been developed to illustrate the concept. Motion experiments confirm the novel properties of this mechanism: mode changing function and omnidirectional motion. Basic Motion experiments, with a test vehicle are also presented.
Examination findings in legally confirmed child sexual abuse: it's normal to be normal.

Studies of alleged victims of child sexual abuse vary greatly in the reported frequency of physical findings based on differences in definition of abuse and of "findings." This study was designed to determine the frequency of abnormal findings in a population of children with legal confirmation of sexual abuse, using a standardized classification system for colposcopic photographic findings.



Case files and colposcopic photographs of 236 children with perpetrator conviction for sexual abuse, were reviewed. The photos were reviewed blindly by a team member other than the examiner, and specific anatomical findings were noted and classified as normal to abnormal on a scale of 1 to 5. Historical and behavioral information, as well as legal outcome was recorded, and all data entered into a dBase III program. Correlations were sought between abnormal findings and other variables.



The mean age of the patients was 9.0 years (range 8 months to 17 years, 11 months), with 63% reporting penile-genital contact. Genital examination findings in girls were normal in 28%, nonspecific in 49%, suspicious in 9%, and abnormal in 14% of cases. Abnormal anal findings were found in only 1% of patients. Using discriminant analysis, the two factors which significantly correlated with the presence of abnormal genital findings in girls were the time since the last incident, and a history of blood being reported at the time of the molest.



Abnormal genital findings are not common in sexually abused girls, based on a standardized classification system. More emphasis should be placed on documenting the child's description of the molestation, and educating prosecutors that, for children alleging abuse: "It's normal to be normal."
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific features on a large unlabeled corpus. This allows us to explicitly incorporate relationspecific information into the word embeddings. The learned word embeddings are then used to construct feature vectors for a relation classification model. On a well-established semantic relation classification task, our method significantly outperforms a baseline based on a previously introduced word embedding method, and compares favorably to previous state-ofthe-art models without syntactic information or manually constructed external resources. Furthermore, when incorporating external resources, our method outperforms the previous state of the art.