nils-herrmann/clean_scirepeval huggingface dataset:
Community detection using a neighborhood strength driven Label Propagation Algorithm

Studies of community structure and evolution in large social networks require a fast and accurate algorithm for community detection. As the size of analyzed communities grows, complexity of the community detection algorithm needs to be kept close to linear. The Label Propagation Algorithm (LPA) has the benefits of nearly-linear running time and easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we propose new update rule and label propagation criterion in LPA to improve both its computational efficiency and the quality of communities that it detects. The speed is optimized by avoiding unnecessary updates performed by the original algorithm. This change reduces significantly (by order of magnitude for large networks) the number of iterations that the algorithm executes. We also evaluate our generalization of the LPA update rule that takes into account, with varying strength, connections to the neighborhood of a node considering a new label. Experiments on computer generated networks and a wide range of social networks show that our new rule improves the quality of the detected communities compared to those found by the original LPA. The benefit of considering positive neighborhood strength is pronounced especially on real-world networks containing sufficiently large fraction of nodes with high clustering coefficient.
Clinical Implications and Methodological Challenges in the Study of the Neuropsychological Correlates of Cannabis, Stimulant, and Opioid Abuse

Chronic consumption of several drugs of abuse (cannabis, stimulants, opioids) has been associated with the presence of neuropsychological impairments in a broad range of functions. Nevertheless, in recent years neuropsychological research on substance abuse has focused on the study of impairments in the executive functions linked to the prefrontal cortex and their influence on the personality, cognitions, and behaviors of the substance abusers. The aim of our review is, first, to summarize the main neuropsychological impairments shown by classic studies, as well as these new discoveries in executive functioning; second, to consider the mediating role of neuropsychological status on treatment outcomes and analyze the impact of these impairments in clinical practice with drug addicts; and third, to review the principal methodological challenges associated with research in the field of the neuropsychology of substance abuse. We also highlight the convenience of intervening in those functions most relevant to the abusers' persistence in consumption and risk of relapse.
Common regions of the human frontal lobe recruited by diverse cognitive demands

Though many neuroscientific methods have been brought to bear in the search for functional specializations within prefrontal cortex, little consensus has emerged. To assess the contribution of functional neuroimaging, this article reviews patterns of frontal-lobe activation associated with a broad range of different cognitive demands, including aspects of perception, response selection, executive control, working memory, episodic memory and problem solving. The results show a striking regularity: for many demands, there is a similar recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate cortex. Much of the remainder of frontal cortex, including most of the medial and orbital surfaces, is largely insensitive to these demands. Undoubtedly, these results provide strong evidence for regional specialization of function within prefrontal cortex. This specialization, however, takes an unexpected form: a specific frontal-lobe network that is consistently recruited for solution of diverse cognitive problems.
An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data

The number of Non-Performing Loans has increased in recent years, paralleling the current financial crisis, thus increasing the importance of credit scoring models. This study proposes a three stage hybrid Adaptive Neuro Fuzzy Inference System credit scoring model, which is based on statistical techniques and Neuro Fuzzy. The proposed model’s performance was compared with conventional and commonly utilized models. The credit scoring models are tested using a 10-fold cross-validation process with the credit card data of an international bank operating in Turkey. Results demonstrate that the proposed model consistently performs better than the Linear Discriminant Analysis, Logistic Regression Analysis, and Artificial Neural Network (ANN) approaches, in terms of average correct classification rate and estimated misclassification cost. As with ANN, the proposed model has learning ability; unlike ANN, the model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in the explanation of why credit applications
Why it is time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS

GPU acceleration is a promising approach to speed up query processing of database systems by using low cost graphic processors as coprocessors. Two major trends have emerged in this area: (1) The development of frameworks for scheduling tasks in heterogeneous CPU/GPU platforms, which is mainly in the context of coprocessing for applications and does not consider specifics of database-query processing and optimization. (2) The acceleration of database operations using efficient GPU algorithms, which typically cannot be applied easily on other database systems, because of their analytical–algorithm-specific cost models. One major challenge is how to combine traditional database query processing with GPU coprocessing techniques and efficient database operation scheduling in a GPU-aware query optimizer. In this thesis, we develop a hybrid query processing engine, which extends the traditional physical optimization process to generate hybrid query plans and to perform a cost-based optimization in a way that the advantages of CPUs and GPUs are combined. Furthermore, we aim at a portable solution between different GPU-accelerated database management systems to maximize applicability. Preliminary results indicate great potential.
Beyond PageRank: machine learning for static ranking

Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web. We gain a further boost in accuracy by using data on the frequency at which users visit Web pages. We use RankNet, a ranking machine learning algorithm, to combine these and other static features based on anchor text and domain characteristics. The resulting model achieves a static ranking pairwise accuracy of 67.3% (vs. 56.7% for PageRank or 50% for random).
A survey of autonomic computing - degrees, models, and applications

Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed ‘hype’ and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering.
Captology: A Critical Review

This critical review of B.J. Fogg’s book Persuasive Technology regards captology as an eclectic and formative work. It summarises two other reviewers’ work and identifies several new strengths. It scrutinises Fogg’s functional triad computers functioning as tools, media and social actors and some categorical changes are recommended. It investigates further Johnson’s concerns about specific ethical omissions, nominating a new term, compusuasion, for the resultant but unintended, exogenous behaviour/attitude change effects of captological design. The review commences to more carefully define what constitutes persuasion and draws attention to the distinction between persuasion techniques in general and the behavioural changes that result from advocacy and education. The reviewer concludes that a fundamental ethic be that the designer’s intent be exposed at the commencement of the user’s engagement with the program and proffers the idea of persuasion resulting in a new conviction, induced by others, as a helpful definition of
Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks

Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon’s Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation. For all five, we show high agreement between Mechanical Turk non-expert annotations and existing gold standard labels provided by expert labelers. For the task of affect recognition, we also show that using non-expert labels for training machine learning algorithms can be as effective as using gold standard annotations from experts. We propose a technique for bias correction that significantly improves annotation quality on two tasks. We conclude that many large labeling tasks can be effectively designed and carried out in this method at a fraction of the usual expense.
Software Testing - Goals, Principles, and Limitations

Software testing is an activity which is aimed for evaluating quality of a program and also for improving it, by identifying defects and problems. Software testing strives for achieving its goals (both implicit and explicit) but it does have certain limitations, still testing can be done more effectively if certain established principles are be followed. In spite of having limitations, software testing continues to dominate other verification techniques like static analysis, model checking, and proofs. So it is indispensable to understand the goals, principles and limitations of software testing so that the effectiveness of software testing could be maximized.
Spatially Sparse Precoding in Millimeter Wave MIMO Systems

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.
Broadcasting Intermediate Blocks as a Defense Mechanism Against Selfish-Mine in Bitcoin

Although adopted by many cryptocurrencies, the Bitcoin mining protocol is not incentive-compatible, as the selfish mining strategy enables a miner to gain unfair mining rewards. Existing defenses either demand fundamental changes to block validity rules or have little effect on an attacker with more than one third of the total mining power. This paper proposes an effective defense mechanism against resourceful selfish miners. Our defense requires miners to publish intermediate blocks, the by-products of the mining process, then build on each other’s work. By adding transparency to the mining process, block forks are resolved by comparing the amount of work of each branch. Moreover, this mechanism has the advantages of backward compatibility, low communication and computational costs, accelerating block propagation, and mitigating double-spending attacks on fast payments. To evaluate our design, we computed the most profitable mining strategy within our defense with analysis and simulation. Our simulation showed that within our defense, a selfish miner with almost half of the total mining power can only gain marginal unfair block rewards.
Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability

Due to enormous progress in the areas of auto-stereoscopic 3D displays, digital video broadcast and computer vision algorithms, 3D television (3DTV) has reached a high technical maturity and many people now believe in its readiness for marketing. Experimental prototypes of entire 3DTV processing chains have been demonstrated successfully during the last few years, and the motion picture experts group (MPEG) of ISO/IEC has launched related ad hoc groups and standardization efforts envisaging the emerging market segment of 3DTV. In this context the paper discusses an advanced approach for a 3DTV service, which is based on the concept of video-plus-depth data representations. It particularly considers aspects of interoperability and multi-view adaptation for the case that different multi-baseline geometries are used for multi-view capturing and 3D display. Furthermore it presents algorithmic solutions for the creation of depth maps and depth image-based rendering related to this framework of multi-view adaptation. In contrast to other proposals, which are more focused on specialized configurations, the underlying approach provides a modular and flexible system architecture supporting a wide range of multi-view structures. r 2007 Elsevier B.V. All rights reserved.
Weighting Finite-State Transductions With Neural Context

How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the output string, using recurrent neural networks. In contrast, we propose to keep the traditional architecture, which uses a finite-state transducer to score all possible output strings, but to augment the scoring function with the help of recurrent networks. A stack of bidirectional LSTMs reads the input string from leftto-right and right-to-left, in order to summarize the input context in which a transducer arc is applied. We combine these learned features with the transducer to define a probability distribution over aligned output strings, in the form of a weighted finite-state automaton. This reduces hand-engineering of features, allows learned features to examine unbounded context in the input string, and still permits exact inference through dynamic programming. We illustrate our method on the tasks of morphological reinflection and lemmatization.
PARS: A Uniform and Open-source Password Analysis and Research System

In this paper, we introduce an open-source and modular password analysis and research system, PARS, which provides a uniform, comprehensive and scalable research platform for password security. To the best of our knowledge, PARS is the first such system that enables researchers to conduct fair and comparable password security research. PARS contains 12 state-of-the-art cracking algorithms, 15 intra-site and cross-site password strength metrics, 8 academic password meters, and 15 of the 24 commercial password meters from the top-150 websites ranked by Alexa. Also, detailed taxonomies and large-scale evaluations of the PARS modules are presented in the paper.
Trends in Fan-out wafer and panel level packaging

The constant drive to further miniaturization and heterogeneous system integration leads to a need for new packaging technologies that also allow large area processing and 3D integration with strong potential for low cost applications. Here, Fan-Out Wafer Level Packaging [FOWLP] is one of the latest packaging trends in microelectronics. Besides developments to higher and heterogeneous integration the movement to larger formats and panel level packaging to lower cost is noticeable.
Evaluating STT-RAM as an energy-efficient main memory alternative

In this paper, we explore the possibility of using STT-RAM technology to completely replace DRAM in main memory. Our goal is to make STT-RAM performance comparable to DRAM while providing substantial power savings. Towards this goal, we first analyze the performance and energy of STT-RAM, and then identify key optimizations that can be employed to improve its characteristics. Specifically, using partial write and row buffer write bypass, we show that STT-RAM main memory performance and energy can be significantly improved. Our experiments indicate that an optimized, equal capacity STT-RAM main memory can provide performance comparable to DRAM main memory, with an average 60% reduction in main memory energy.
The REVIVE (REal Women's VIews of Treatment Options for Menopausal Vaginal ChangEs) survey in Europe: Country-specific comparisons of postmenopausal women's perceptions, experiences and needs.

To achieve a better comprehension of the variability of perceptions, experiences and needs in terms of sexual and vaginal health in postmenopausal women (PMW) from four different European countries.



An internet-based survey was conducted in Italy, Germany, Spain and the United Kingdom with a total surveyed population of 3768 PMW aged between 45 and 75 years.



The UK sample was significantly older, with almost a quarter of participants over 65 years of age, and had the highest proportion of women experiencing recent vulvar and vaginal atrophy (52.8%). The majority of Italian and Spanish participants were receiving VVA treatment, whereas in the UK only 28% of PMW were on medication. The most common menopausal symptom was vaginal/vulvar dryness, with almost 80% of participants reporting it in all the countries except the UK (48%). On the other hand, vaginal/vulvar irritation was more frequently reported in the UK (41%). The percentage of participants with a partner was lower in the UK (71%), as was the monthly rate of sexual activity (49%). In the UK, the proportion of participants who had seen a healthcare professional for gynaecological reasons in the last year was lower than in other countries (27% vs. ≥50%), as was the proportion who has discussed their VVA symptoms with them (45% vs. ∼67%). In this sense, UK PMW waited for a longer before asking for help (especially for pain with intercourse and dryness). The main issues relating to VVA treatment difficulties expressed by participants were administration route in the UK, efficacy in Germany, and side-effects in Italy.



Although all European women shared the same expectation of improving the quality of their sex lives, the opportunity for that varied among different countries in relation to the healthcare system and to the effective communication achieved with healthcare professionals when managing VVA.
Mercury BLASTN: Faster DNA Sequence Comparison using a Streaming Hardware Architecture

Large-scale DNA sequence comparison, as implemented by BLAST and related algorithms, is one of the pillars of modern genomic analysis. One way to accelerate these computations is with a streaming architecture, in which processors are arranged in a pipeline that replicates the multistage structure of the algorithm. To achieve high performance, the processor hardware implementing the critical seed matching and ungapped extension stages of BLAST should be specialized to execute these stages as quickly as possible. However, accelerating these stages requires solving two key problems: first, the seed matching stage is not of a form which has traditionally been amenable to hardware acceleration; and second, the accelerated implementation of BLAST should retain sensitivity at least comparable to that of the original software.  We describe Mercury BLASTN, an FPGA-based implementation of BLAST for DNA. Mercury BLASTN combines a Bloom filtering approach to seed matching with a modified ungapped extension algorithm to overcome barriers to placing the early stages of BLAST onto hardware. On a previous-generation FPGA hardware platform, Mercury BLASTN runs 5 to 11 times faster than NCBI BLASTN current-generation general-purpose CPUs, with the prospect of a further eight-fold speedup on current-generation FPGAs. Moreover, its sensitivity to significant DNA sequence alignments is 99% of that observed with software NCBI BLASTN.  Academic users should contact the authors for information on acquiring a prototype of the Mercury BLASTN system.
Real-Time Dynamic Object Detection for Autonomous Driving Using Prior 3D-Maps

Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.