nils-herrmann/clean_scirepeval huggingface dataset:
Privacy Impacts of Data Encryption on the Efficiency of Digital Forensics Technology

Owing to a number of reasons, the deployment of encryption solutions are beginning to be ubiquitous at both organizational and individual levels. The most emphasized reason is the necessity to ensure confidentiality of privileged information. Unfortunately, it is also popular as cyber-criminals' escape route from the grasp of digital forensic investigations. The direct encryption of data or indirect encryption of storage devices, more often than not, prevents access to such information contained therein. This consequently leaves the forensics investigation team, and subsequently the prosecution, little or no evidence to work with, in sixty percent of such cases. However, it is unthinkable to jeopardize the successes brought by encryption technology to information security, in favour of digital forensics technology. This paper examines what data encryption contributes to information security, and then highlights its contributions to digital forensics of disk drives. The paper also discusses the available ways and tools, in digital forensics, to get around the problems constituted by encryption. A particular attention is paid to the Truecrypt encryption solution to illustrate ideas being discussed. It then compares encryption's contributions in both realms, to justify the need for introduction of new technologies to forensically defeat data encryption as the only solution, whilst maintaining the privacy goal of users. Encryption; Information Security; Digital Forensics; Anti-Forensics; Cryptography; TrueCrypt
Sentiment Analysis Using Convolutional Neural Network

Sentiment analysis of text content is important for many natural language processing tasks. Especially, as the development of the social media, there is a big need in dig meaningful information from the big data on Internet through the sentiment analysis. Inspired by the successes of deep learning, we are interested in handling the sentiment analysis task using deep learning models. In this paper, we propose a framework called Word2vec + Convolutional Neural Network (CNN). Firstly, we use the word2vec proposed by Google to compute vector representations of words, which will be the input for the CNN. The purpose of using word2vec is to gain the vector representation of word and reflect the distance of words. That will lead to initialize the parameters at a good point of CNN, which can efficiently improve the performance of the nets in this problem. Secondly, we design a suitable CNN architecture for the sentiment analysis task. We use 3 pairs of convolutional layers and pooling layers in this architecture. To the best of our knowledge, this is the first time that a 7-layers architecture model is applied using word2vec and CNN to analyze sentences' sentiment. We also use the Parametric Rectified Linear Unit (PReLU), Normalization and Dropout technology to improve the accuracy and generalizability of our model. We test our framework in a public dataset which is the corpus of movie review excerpts that includes fives labels: negative, somewhat negative, neural, somewhat positive and positive. Our network achieves test accuracy of 45.4% in this dataset, which is a better performance than some other neural network model like Recursive Neural Network (RNN) and Matrix-Vector Recursive Neural Network (MV-RNN).
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

We introduce a method to train Quantized Neural Networks (QNNs) — neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At traintime the quantized weights and activations are used for computing the parameter gradients. During the forward pass, QNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations. As a result, power consumption is expected to be drastically reduced. We trained QNNs over the MNIST, CIFAR-10, SVHN and ImageNet datasets. The resulting QNNs achieve prediction accuracy comparable to their 32-bit counterparts. For example, our quantized version of AlexNet with 1-bit weights and 2-bit activations achieves 51% top-1 accuracy. Moreover, we quantize the parameter gradients to 6-bits as well which enables gradients computation using only bit-wise operation. Quantized recurrent neural networks were tested over the Penn Treebank dataset, and achieved comparable accuracy as their 32-bit counterparts using only 4-bits. Last but not least, we programmed a binary matrix multiplication GPU kernel with which it is possible to run our MNIST QNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The QNN code is available online.
Imaging cerebral atrophy: normal ageing to Alzheimer's disease

With ageing populations, the prevalence of dementia, especially Alzheimer's disease, is set to soar. Alzheimer's disease is associated with progressive cerebral atrophy, which can be seen on MRI with high resolution. Longitudinal MRI could track disease progression and detect neurodegenerative diseases earlier to allow prompt and specific treatment. Such use of MRI requires accurate understanding of how brain changes in normal ageing differ from those in dementia.


STARTING POINT
Recently, Henry Rusinek and colleagues, in a 6-year longitudinal MRI study of initially healthy elderly subjects, showed that an increased rate of atrophy in the medial temporal lobe predicted future cognitive decline with a specificity of 91% and sensitivity of 89% (Radiology 2003; 229: 691-96). WHERE NEXT? As understanding of neurodegenerative diseases increases, specific disease-modifying treatments might become available. Serial MRI could help to determine the efficacy of such treatments, which would be expected to slow the rate of atrophy towards that of normal ageing, and might also detect the onset of neurodegeneration. The amount and pattern of excess atrophy might help to predict the underlying pathological process, allowing specific therapies to be started. As the precision of imaging improves, the ability to distinguish healthy ageing from degenerative dementia should improve.
Research on the Security of Microsoft’s Two-Layer Captcha

Captcha is a security mechanism designed to differentiate between computers and humans, and is used to defend against malicious bot programs. Text-based Captchas are the most widely deployed differentiation mechanism, and almost all text-based Captchas are single layered. Numerous successful attacks on the single-layer text-based Captchas deployed by Google, Yahoo!, and Amazon have been reported. In 2015, Microsoft deployed a new two-layer Captcha scheme. This appears to be the first application of two-layer Captchas. It is, therefore, natural to ask a fundamental question: is the two-layer Captcha as secure as its designers expected? Intrigued by this question, we have for the first time systematically analyzed the security of the two-layer Captcha in this paper. We propose a simple but an effective method to attack the two-layer Captcha deployed by Microsoft, and achieve a success rate of 44.6% with an average speed of 9.05 s on a standard desktop computer (with a 3.3-GHz Intel Core i3 CPU and 2-GB RAM), thus demonstrating clear security issues. We also discuss the originality and applicability of our attack, and offer guidelines for designing Captchas with better security and usability.
Scalable Replay-Based Replication For Fast Databases

Primary-backup replication is commonly used for providing fault tolerance in databases. It is performed by replaying the database recovery log on a backup server. Such a scheme raises several challenges for modern, high-throughput multicore databases. It is hard to replay the recovery log concurrently, and so the backup can become the bottleneck. Moreover, with the high transaction rates on the primary, the log transfer can cause network bottlenecks. Both these bottlenecks can significantly slow the primary database. In this paper, we propose using record-replay for replicating fast databases. Our design enables replay to be performed scalably and concurrently, so that the backup performance scales with the primary performance. At the same time, our approach requires only 15-20% of the network bandwidth required by traditional logging, reducing network infrastructure costs significantly.
Teaching transcultural nursing through literature.

One of the biggest challenges in nursing education is to develop culturally sensitive graduates. Although theory and lecture are appropriate to introduce cultural issues, the application of those skills is limited by the kinds of clinical experiences and patient populations students may treat. Literary works are a rich source of information for nursing. This assignment was created to sensitize the students to the influence of cultural diversity. Students were assigned to read one novel from an approved list and answer the questions posed on the Cultural Discovery worksheet. The only direction that was given regarding novel selection was that the novel had to represent a culture other than the student's own. The focus was to expose students to a different culture. Classroom discussion, based on worksheet answers, followed. The assignment's good, bad, and ugly outcomes are discussed. Suggestions for adaptation of this assignment to an online format are also provided.
Kite Generator System Modeling and Grid Integration

This paper deals principally with the grid connection problem of a kite-based system, named the “Kite Generator System (KGS).” It presents a control scheme of a closed-orbit KGS, which is a wind power system with a relaxation cycle. Such a system consists of a kite with its orientation mechanism and a power transformation system that connects the previous part to the electric grid. Starting from a given closed orbit, the optimal tether's length rate variation (the kite's tether radial velocity) and the optimal orbit's period are found. The trajectory-tracking problem is not considered in this paper; only the kite's tether radial velocity is controlled via the electric machine rotation velocity. The power transformation system transforms the mechanical energy generated by the kite into electrical energy that can be transferred to the grid. A Matlab/simulink model of the KGS is employed to observe its behavior, and to insure the control of its mechanical and electrical variables. In order to improve the KGS's efficiency in case of slow changes of wind speed, a maximum power point tracking (MPPT) algorithm is proposed.
Speech scrambling based on chaotic maps and one time pad

This paper presents a chaos-based speech scrambling system. Chaotic maps have been successfully used for large-scale data encryption such as image, audio and video data, due to their good properties such as pseudo-randomness, sensitivity to changes in initial conditions and system parameters and aperiodicity. This paper uses two chaotic maps, circle map and logistic map for speech confusion and diffusion, respectively. In the confusion stage, speech samples are divided into small segments. Then, indices of ordered generated sequence of circle map are used to shuffle the positions of the speech signal segments. Then, a one-time pad generated by the logistic map is used for the diffusion stage. Several experimental results, signal-to-noise ratio, key space, key sensitivity tests, statistical analysis, chosen/known plaintext attack and time analysis show that the proposed method for speech scrambling performs efficiently and can be applied for secure real time speech communications.
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation

There have been some works that learn a lexicon together with the corpus to improve the word embeddings. However, they either model the lexicon separately but update the neural networks for both the corpus and the lexicon by the same likelihood, or minimize the distance between all of the synonym pairs in the lexicon. Such methods do not consider the relatedness and difference of the corpus and the lexicon, and may not be the best optimized. In this paper, we propose a novel method that considers the relatedness and difference of the corpus and the lexicon. It trains word embeddings by learning the corpus to predicate a word and its corresponding synonym under the context at the same time. For polysemous words, we use a word sense disambiguation filter to eliminate the synonyms that have different meanings for the context. To evaluate the proposed method, we compare the performance of the word embeddings trained by our proposed model, the control groups without the filter or the lexicon, and the prior works in the word similarity tasks and text classification task. The experimental results show that the proposed model provides better embeddings for polysemous words and improves the performance for text classification.
Personality and life satisfaction: a facet-level analysis.

At the global level of the Big Five, Extraversion and Neuroticism are the strongest predictors of life satisfaction. However, Extraversion and Neuroticism are multifaceted constructs that combine more specific traits. This article examined the contribution of facets of Extraversion and Neuroticism to life satisfaction in four studies. The depression facet of Neuroticism and the positive emotions/cheerfulness facet of Extraversion were the strongest and most consistent predictors of life satisfaction. These two facets often accounted for more variance in life satisfaction than Neuroticism and Extraversion. The findings suggest that measures of depression and positive emotions/cheerfulness are necessary and sufficient to predict life satisfaction from personality traits. The results also lead to a more refined understanding of the specific personality traits that influence life satisfaction: Depression is more important than anxiety or anger and a cheerful temperament is more important than being active or sociable.
Challenges in Teaching Russian Students to Speak English

This paper addresses the challenges brought about by the fact that the English language in modern Russia is mostly taught by non-native English speakers in the context of classic „teacher-textbook-student‟ paradigm. It also highlights the significance of shifting from this teacher-centered approach to EFL teaching and learning process, and presents the results of a teaching experiment aimed at creating a student-centered environment in a language classroom. We suggested that presenting the results of students‟ research projects in the form of Englishlanguage video films can be an effective means of expanding students‟ learning opportunities and eliminating some weaknesses of the artificial language learning environment. After five-year observations over more than 500 students learning EFL at the Petrozavodsk State University (PetrSU) and 20 non-native English-speaking teachers of the same University, we conducted a teaching experiment, which involved 22 students of the History Department and 28 students of the Law Department and resulted in creating two video films on professionally oriented topics chosen by the students and aligned with their fields of study. Our study revealed that students‟ filmmaking is an effective tool to support not only such traditional methods as computer-based learning, presentation, research, but also such innovative kinds of work as multimedia or animated presentations. Therefore, making videos in a foreign language proves to be a highly productive way of acquiring English language skills through the use of technology.
Bibliometric analysis of articles published from 1980 to 2009 in Physical Therapy, journal of the American Physical Therapy Association.

Recent evidence demonstrates growth in both the quality and quantity of evidence in physical therapy. Much of this work has focused on randomized controlled trials and systematic reviews.



The purpose of this study was to conduct a comprehensive bibliometric assessment of Physical Therapy (PTJ) over the past 30 years to examine trends for all types of studies.



This was a bibliometric analysis.



All manuscripts published in PTJ from 1980 to 2009 were reviewed. Research reports, topical reviews (including perspectives and nonsystematic reviews), and case reports were included. Articles were coded based on type, participant characteristics, physical therapy focus, research design, purpose of article, clinical condition, and intervention. Coding was performed by 2 independent reviewers, and author, institution, and citation information was obtained using bibliometric software.



Of the 4,385 publications identified, 2,519 were included in this analysis. Of these, 67.1% were research reports, 23.0% were topical reviews, and 9.9% were case reports. Percentage increases over the past 30 years were observed for research reports, inclusion of "symptomatic" participants (defined as humans with a current symptomatic condition), systematic reviews, qualitative studies, prospective studies, and articles focused on prognosis, diagnosis, or metric topics. Percentage decreases were observed for topical reviews, inclusion of only "asymptomatic" participants (defined as humans without a current symptomatic condition), education articles, nonsystematic reviews, and articles focused on anatomy/physiology.



Quality assessment of articles was not performed.



These trends provide an indirect indication of the evolution of the physical therapy profession through the publication record in PTJ. Collectively, the data indicated an increased emphasis on publishing articles consistent with evidence-based practice and clinically based research. Bibliometric analyses indicated the most frequent citations were metric studies and references in PTJ were from journals from a variety of disciplines.
Interval Estimation for a Binomial Proportion

We revisit the problem of interval estimation of a binomial proportion. The erratic behavior of the coverage probability of the standard Wald confidence interval has previously been remarked on in the literature (Blyth and Still, Agresti and Coull, Santner and others). We begin by showing that the chaotic coverage properties of the Wald interval are far more persistent than is appreciated. Furthermore, common textbook prescriptions regarding its safety are misleading and defective in several respects and cannot be trusted. This leads us to consideration of alternative intervals. A number of natural alternatives are presented, each with its motivation and context. Each interval is examined for its coverage probability and its length. Based on this analysis, we recommend the Wilson interval or the equal-tailed Jeffreys prior interval for small n and the interval suggested in Agresti and Coull for larger n. We also provide an additional frequentist justification for use of the Jeffreys interval.
Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

Accurate detection and tracking of objects is vital for effective video understanding. In previous work, the two tasks have been combined in a way that tracking is based heavily on detection, but the detection benefits marginally from the tracking. To increase synergy, we propose to more tightly integrate the tasks by conditioning the object detection in the current frame on tracklets computed in prior frames. With this approach, the object detection results not only have high detection responses, but also improved coherence with the existing tracklets. This greater coherence leads to estimated object trajectories that are smoother and more stable than the jittered paths obtained without tracklet-conditioned detection. Over extensive experiments, this approach is shown to achieve state-of-the-art performance in terms of both detection and tracking accuracy, as well as noticeable improvements in tracking stability.
General Symptom Extraction from VA Electronic Medical Notes

There is need for cataloging signs and symptoms, but not all are documented in structured data. The text from clinical records are an additional source of signs and symptoms. We describe a Natural Language Processing (NLP) technique to identify symptoms from text. Using a human-annotated reference corpus from VA electronic medical notes we trained and tested an NLP pipeline to identify and categorize symptoms. The technique includes a model created from an automatic machine learning model selection tool. Tested on a hold-out set, its precision at the mention level was 0.80, recall 0.74 and an overall f-score of 0.80. The tool was scaled-up to process a large corpus of 964,105 patient records.
Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit

Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.



EMR data from 7256 survivor PICU episodes (5632 patients) collected between 2009 and 2017 at Children's Hospital Los Angeles was analyzed. Each episode contained 375 variables representing physiology, labs, interventions, and drugs. Between medical and physical discharge, when clinicians determined the patient was ready for ICU discharge, they were assumed to be in a physiologically acceptable state space (PASS) for discharge. Each patient's heart rate, systolic blood pressure, diastolic blood pressure in the PASS window were measured and compared to age-normal values, regression-quantified PASS predictions, and recurrent neural network (RNN) PASS predictions made 12 hours after PICU admission.



Mean absolute errors (MAEs) between individual PASS values and age-normal values (HR: 21.0 bpm; SBP: 10.8 mm Hg; DBP: 10.6 mm Hg) were greater (p < .05) than regression prediction MAEs (HR: 15.4 bpm; SBP: 9.9 mm Hg; DBP: 8.6 mm Hg). The RNN models best approximated individual PASS values (HR: 12.3 bpm; SBP: 7.6 mm Hg; DBP: 7.0 mm Hg).



The RNN model predictions better approximate patient-specific PASS values than regression and age-normal values.
Brain Tumor Detection and Identification Using K-Means Clustering Technique

Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Imager(MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. During the past few years, brain tumor segmentation in Magnetic Resonance Imaging(MRI) has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. Image processing is an active research area in which medical image processing is a highly challenging field. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. In this paper an efficient algorithm is proposed for tumor detection based on segmentation of brain MRI images using K-Means clustering.
The Sound of Pixels

We introduce PixelPlayer, a system that, by leveraging large amounts of unlabeled videos, learns to locate image regions which produce sounds and separate the input sounds into a set of components that represents the sound from each pixel. Our approach capitalizes on the natural synchronization of the visual and audio modalities to learn models that jointly parse sounds and images, without requiring additional manual supervision. Experimental results on a newly collected MUSIC dataset show that our proposed Mix-and-Separate framework outperforms several baselines on source separation. Qualitative results suggest our model learns to ground sounds in vision, enabling applications such as independently adjusting the volume of sound sources.