nils-herrmann/clean_scirepeval huggingface dataset:
Lossless and lossy memory I/O link compression for improving performance of GPGPU workloads

State-of-the-art graphic processing units (GPUs) provide very high memory bandwidth, but the performance of many general-purpose GPU (GPGPU) workloads is still bounded by memory bandwidth. Although compression techniques have been adopted by commercial GPUs, they are only used for compressing texture and color data, not data for GPGPU workloads. Furthermore, the microarchitectural details of GPU compression are proprietary and its performance benefits have not been previously published. In this paper, we first investigate required microarchitectural changes to support lossless compression techniques for data transferred between the GPU and its off-chip memory to provide higher effective bandwidth. Second, by exploiting some characteristics of floating-point numbers in many GPGPU workloads, we propose to apply lossless compression to floating-point numbers after truncating their least-significant bits (i.e., lossy compression). This can reduce the bandwidth usage even further with very little impact on overall computational accuracy. Finally, we demonstrate that a GPU with our lossless and lossy compression techniques can improve the performance of memory-bound GPGPU workloads by 26% and 41% on average.
Examination of the fetal brain by transabdominal three-dimensional ultrasound: potential for routine neurosonographic studies.

To evaluate the role of transabdominal three-dimensional (3D) ultrasound in the assessment of the fetal brain and its potential for routine neurosonographic studies.



We studied prospectively 202 consecutive fetuses between 16 and 24 weeks' gestation. A 3D ultrasound volume of the fetal head was acquired transabdominally. The entire brain anatomy was later analyzed using the multiplanar images by a sonologist who was expert in neonatal cranial sonography. The quality of the conventional planes obtained (coronal, sagittal and axial, at different levels) and the ability of the 3D multiplanar neuroscan to visualize properly the major anatomical structures of the brain were evaluated.



Acceptable cerebral multiplanar images were obtained in 92% of the cases. The corpus callosum could be seen in 84% of the patients, the fourth ventricle in 78%, the lateral sulcus (Sylvian fissure) in 86%, the cingulate sulcus in 75%, the cerebellar hemispheres in 98%, the cerebellar vermis in 92%, the medulla oblongata in 97% and the cavum vergae in 9% of them. The thalami and the cerebellopontine cistern (cisterna magna) were identified in all cases. At or beyond 20 weeks, superior visualization (in > 90% of cases) was achieved of the cerebral fissures, the corpus callosum (97%), the supracerebellar cisterns (92%) and the third ventricle (93%). Some cerebral fissures were seen initially at 16-17 weeks.



Multiplanar images obtained by transabdominal 3D ultrasound provide a simple and effective approach for detailed evaluation of the fetal brain anatomy. This technique has the potential to be used in the routine fetal anomaly scan.
Detection of Unauthorized IoT Devices Using Machine Learning Techniques

Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption of such devices, their diversity, standardization obstacles, and inherent mobility, organizations require an intelligent mechanism capable of automatically detecting suspicious IoT devices connected to their networks. In particular, devices not included in a white list of trustworthy IoT device types (allowed to be used within the organizational premises) should be detected. In this research, Random Forest, a supervised machine learning algorithm, was applied to features extracted from network traffic data with the aim of accurately identifying IoT device types from the white list. To train and evaluate multi-class classifiers, we collected and manually labeled network traffic data from 17 distinct IoT devices, representing nine types of IoT devices. Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases. Some IoT device types were identified quicker than others (e.g., sockets and thermostats were successfully detected within five TCP sessions of connecting to the network). Perfect detection of unauthorized IoT device types was achieved upon analyzing 110 consecutive sessions; perfect classification of white listed types required 346 consecutive sessions, 110 of which resulted in 99.49% accuracy. Further experiments demonstrated the successful applicability of classifiers trained in one location and tested on another. In addition, a discussion is provided regarding the resilience of our machine learning-based IoT white listing method to adversarial attacks.
An Empirical Study of the Reliability of UNIX Utilities

The following section describes the tools we built to test the utilities. These tools include the fuzz (random character) generator, ptyjig (to test interactive utilities), and scripts to automate the testing process. Next, we will describe the tests we performed, giving the types of input we presented to the utilities. Results from the tests will follow along with an analysis of the results, including identification and classification of the program bugs that caused the crashes. The final section presents concluding remarks, including suggestions for avoiding the types of problems detected by our study and some commentary on the bugs we found. We include an Appendix with the user manual pages for fuzz and ptyjig.
Data Driven Energy Efficiency in Buildings

Buildings across the world contribute significantly to the overall energy consumption and are thus stakeholders in grid operations. Towards the development of a smart grid, utilities and governments across the world are encouraging smart meter deployments. High resolution (often at every 15 minutes) data from these smart meters can be used to understand and optimize energy consumptions in buildings. In addition to smart meters, buildings are also increasingly managed with Building Management Systems (BMS) which control different sub-systems such as lighting and heating, ventilation, and air conditioning (HVAC). With the advent of these smart meters, increased usage of BMS and easy availability and widespread installation of ambient sensors, there is a deluge of building energy data. This data has been leveraged for a variety of applications such as demand response, appliance fault detection and optimizing HVAC schedules. Beyond the traditional use of such data sets, they can be put to effective use towards making buildings smarter and hence driving every possible bit of energy efficiency. Effective use of this data entails several critical areas from sensing to decision making and participatory involvement of occupants. Picking from wide literature in building energy efficiency, we identify five crust areas (also referred to as 5 Is) for realizing data driven energy efficiency in buildings : i) instrument optimally; ii) interconnect sub-systems; iii) inferred decision making; iv) involve occupants and v) intelligent operations. We classify prior work as per these 5 Is and discuss challenges, opportunities and applications across them. Building upon these 5 Is we discuss a well studied problem in building energy efficiency non-intrusive load monitoring (NILM) and how research in this area spans across the 5 Is.
Gender differences in personality traits across cultures: robust and surprising findings.

Secondary analyses of Revised NEO Personality Inventory data from 26 cultures (N = 23,031) suggest that gender differences are small relative to individual variation within genders; differences are replicated across cultures for both college-age and adult samples, and differences are broadly consistent with gender stereotypes: Women reported themselves to be higher in Neuroticism, Agreeableness, Warmth, and Openness to Feelings, whereas men were higher in Assertiveness and Openness to Ideas. Contrary to predictions from evolutionary theory, the magnitude of gender differences varied across cultures. Contrary to predictions from the social role model, gender differences were most pronounced in European and American cultures in which traditional sex roles are minimized. Possible explanations for this surprising finding are discussed, including the attribution of masculine and feminine behaviors to roles rather than traits in traditional cultures.
Factor in the neighbors: Scalable and accurate collaborative filtering

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on collaborating filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The most common approach to CF is based on neighborhood models, which originate from similarities between products or users. In this work we introduce a new neighborhood model with an improved prediction accuracy. Unlike previous approaches that are based on heuristic similarities, we model neighborhood relations by minimizing a global cost function. Further accuracy improvements are achieved by extending the model to exploit both explicit and implicit feedback by the users. Past models were limited by the need to compute all pairwise similarities between items or users, which grow quadratically with input size. In particular, this limitation vastly complicates adopting user similarity models, due to the typical large number of users. Our new model solves these limitations by factoring the neighborhood model, thus making both item-item and user-user implementations scale linearly with the size of the data. The methods are tested on the Netflix data, with encouraging results.
IoTPOT: A Novel Honeypot for Revealing Current IoT Threats

We analyze the increasing threats against IoT devices. We show that Telnet-based attacks that target IoT devices have rocketed since 2014. Based on this observation, we propose an IoT honeypot and sandbox, which attracts and analyzes Telnet-based attacks against various IoT devices running on different CPU architectures such as ARM, MIPS, and PPC. By analyzing the observation results of our honeypot and captured malware samples, we show that there are currently at least 5 distinct DDoS malware families targeting Telnet-enabled IoT devices and one of the families has quickly evolved to target more devices with as many as 9 different CPU architectures.
A fully-adaptive wideband 0.5–32.75Gb/s FPGA transceiver in 16nm FinFET CMOS technology

This paper describes the design of a low power fully-adaptive wideband, flexible reach transceiver in 16nm FinFET CMOS embedded within FPGA. The receiver utilizes a 3-stage CTLE with a segmented AGC to minimize parasitic peaking and 15-tap DFE to operate over both short and long channels. The transmitter uses a swing boosted CML driver architecture. Low noise wideband fractional N LC PLLs combined with linear active inductor based phase interpolators and high speed clocking are utilized for low jitter clock generation. The transceiver achieves >1200mVdpp TX swing with <;190 fs RJ and 5.39 ps TJ to achieve BER <; 10-15 over a 30 dB loss backplane at 32.75 Gb/s, while consuming 577 mW.
Adaptive and scalable Android malware detection through online learning

It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) do not change over time. In this work, we address the problem of malware population drift and propose a novel online machine learning based framework, named DroidOL to handle it and effectively detect malware. In order to perform accurate detection, security-sensitive behavior are captured from apps in form of inter-procedural control-flow sub-graph features using a state-of-the-art graph kernel. In order to perform scalable detection and to adapt to the drift and evolution in malware population, an online passive-aggressive classifier is used. In a large-scale comparative analysis with more than 87,000 apps, DroidOL achieves 84.29% accuracy outperforming two state-of-the-art malware techniques by more than 20% in their typical batch learning setting and more than 3% when they are continuously re-trained. Our experimental findings strongly indicate that online learning based approaches are highly suitable for real-world malware detection.
Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls

Large-scale databases of human activity in social media have captured scientific and policy attention, producing a flood of research and discussion. This paper considers methodological and conceptual challenges for this emergent field, with special attention to the validity and representativeness of social media big data analyses. Persistent issues include the over-emphasis of a single platform, Twitter, sampling biases arising from selection by hashtags, and vague and unrepresentative sampling frames. The sociocultural complexity of user behavior aimed at algorithmic invisibility (such as subtweeting, mock-retweeting, use of “screen captures” for text, etc.) further complicate interpretation of big data social media. Other challenges include accounting for field effects, i.e. broadly consequential events that do not diffuse only through the network under study but affect the whole society. The application of network methods from other fields to the study of human social activity may not always be appropriate. The paper concludes with a call to action on practical steps to improve our analytic capacity in this promising, rapidly-growing field.
Information Complexity in Bandit Subset Selection

We consider the problem of efficiently exploring the arms of a stochastic bandit to identify the best subset of a specified size. Under the PAC and the fixed-budget formulations, we derive improved bounds by using KL-divergence-based confidence intervals. Whereas the application of a similar idea in the regret setting has yielded bounds in terms of the KL-divergence between the arms, our bounds in the pure-exploration setting involve the “Chernoff information” between the arms. In addition to introducing this novel quantity to the bandits literature, we contribute a comparison between strategies based on uniform and adaptive sampling for pure-exploration problems, finding evidence in favor of the latter.
Multiclass Boosting: Theory and Algorithms

The problem of multi-class boosting is considered. A new fra mework, based on multi-dimensional codewords and predictors is introduced . The optimal set of codewords is derived, and a margin enforcing loss proposed. The resulting risk is minimized by gradient descent on a multidimensional functi onal space. Two algorithms are proposed: 1) CD-MCBoost, based on coordinate des cent, updates one predictor component at a time, 2) GD-MCBoost, based on gradi ent descent, updates all components jointly. The algorithms differ in the w ak learners that they support but are both shown to be 1) Bayes consistent, 2) margi n enforcing, and 3) convergent to the global minimum of the risk. They also red uce to AdaBoost when there are only two classes. Experiments show that both m et ods outperform previous multiclass boosting approaches on a number of data sets.
Towards a Modular Recommender System for Research Papers written in Albanian

In the recent years there has been an increase in scientific papers publications in Albania and its neighboring countries that have large communities of Albanian speaking researchers. Many of these papers are written in Albanian. It is a very time consuming task to find papers related to the researchers’ work, because there is no concrete system that facilitates this process. In this paper we present the design of a modular intelligent search system for articles written in Albanian. The main part of it is the recommender module that facilitates searching by providing relevant articles to the users (in comparison with a given one). We used a cosine similarity based heuristics that differentiates the importance of term frequencies based on their location in the article. We did not notice big differences on the recommendation results when using different combinations of the importance factors of the keywords, title, abstract and body. We got similar results when using only theand body. We got similar results when using only the title and abstract in comparison with the other combinations. Because we got fairly good results in this initial approach, we believe that similar recommender systems for documents written in Albanian can be built also in contexts not related to scientific publishing. recommender system; Albanian; information retrieval; intelligent search; digital library
Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake

The US response to the 2010 Haiti Earthquake was a large effort coordinated by three major agencies that worked in tandem with the Government of Haiti, the United Nations, and many countries from around the globe. Managing this response effort was a complex undertaking that relied extensively on knowledge management systems (KMS). For the first time, however, US government agencies employed social media technologies such as wikis and collaborative workspaces as the main knowledge sharing mechanisms. In this research we present a case study developed through action research of how these social media technologies were used, what influences they made on knowledge sharing, reuse, and decision-making, and how knowledge was effectively (and at times ineffectively) maintained in these systems. First-hand knowledge of the response is used, offering strategies for future deployment of social media and important
Real-Time Active Tampering Detection of Surveillance Camera and Implementation on Digital Signal Processor

Active and automatic camera tampering detection is more and more important, especially for wide area surveillance systems with large amount of video cameras installed. Generally, a camera functions for video recording and lacks the ability to detect whether tampering has occurred. Most tampering solutions are passive, as evidenced by many surveillance systems frequently left unattended. Tampering detection of real-time automated cameras is thus of priority concern important for timely operator warning. This work describes a novel algorithm for camera tamper detection scheme. Developed for video surveillance and security applications, the pro-posed approach identifies camera tampering by detecting large edge differences and grayscale histogram comparisons between current and previous frames. The optimized algorithm is implemented on PC and TI DAVINCI DM6437 DSP as well. Extensive experiments were performed. Experimental results indicate that the proposed approach can detect various tampering cases. The overall recognition performance was 94%.
Wild patterns: Ten years after the rise of adversarial machine learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.
Matching Results of Latent Dirichlet Allocation for Text

Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be updated in an online manner. This includes inferring new documents into the model, passing parameter priors to the inference algorithm or a mixture of both, leading to more complicated and computationally expensive models. We present a method to match and compare the resulting LDA topics of different models with light weight easy to use similarity measures. We address the on-line problem by keeping the model inference simple and matching topics solely by their high probability word lists.
Microplastics in the marine environment: a review of the methods used for identification and quantification.

This review of 68 studies compares the methodologies used for the identification and quantification of microplastics from the marine environment. Three main sampling strategies were identified: selective, volume-reduced, and bulk sampling. Most sediment samples came from sandy beaches at the high tide line, and most seawater samples were taken at the sea surface using neuston nets. Four steps were distinguished during sample processing: density separation, filtration, sieving, and visual sorting of microplastics. Visual sorting was one of the most commonly used methods for the identification of microplastics (using type, shape, degradation stage, and color as criteria). Chemical and physical characteristics (e.g., specific density) were also used. The most reliable method to identify the chemical composition of microplastics is by infrared spectroscopy. Most studies reported that plastic fragments were polyethylene and polypropylene polymers. Units commonly used for abundance estimates are "items per m(2)" for sediment and sea surface studies and "items per m(3)" for water column studies. Mesh size of sieves and filters used during sampling or sample processing influence abundance estimates. Most studies reported two main size ranges of microplastics: (i) 500 μm-5 mm, which are retained by a 500 μm sieve/net, and (ii) 1-500 μm, or fractions thereof that are retained on filters. We recommend that future programs of monitoring continue to distinguish these size fractions, but we suggest standardized sampling procedures which allow the spatiotemporal comparison of microplastic abundance across marine environments.
Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals

The objective of this paper is to provide a review of recent advances in automatic vibration- and audio-based fault diagnosis in machinery using condition monitoring strategies. It presents the most valuable techniques and results in this field and highlights the most profitable directions of research to present. Automatic fault diagnosis systems provide greater security in surveillance of strategic infrastructures, such as electrical substations and industrial scenarios, reduce downtime of machines, decrease maintenance costs, and avoid accidents which may have devastating consequences. Automatic fault diagnosis systems include signal acquisition, signal processing, decision support, and fault diagnosis. The paper includes a comprehensive bibliography of more than 100 selected references which can be used by researchers working in this field.