nils-herrmann/clean_scirepeval huggingface dataset:
Polygonal boundary approximation for a 2D general sweep based on envelope and boolean operations

This paper presents an algorithm that approximates (using polygons) the boundary of a general sweep for an arbitrary 2D curved object (possibly with holes). Based on set-theoretic properties of the general sweep, our algorithm generates the polygonal sweep boundary incrementally, where envelope approximations and union operations are repeatedly applied to intermediate boundaries of the sweep and consecutive instances of the moving object at sampled locations of the motion. For approximation, each instance of the object is polygonized along the motion, where the object may experience dynamic shape transformation with topological changes such as creating and /or destroying internal holes. The incremental nature of the proposed algorithm makes the boundary construction of a general sweep useful for applications in interactive shape design, collision detection, and mechanical part design. Our algorithm generates a precise approximation of the boundary of a general sweep with real-time performance in computing unsweeps, Minkowski sums and differences, and constant radius offsets. Some experimental results are also given in this paper.
Group awareness in distributed software development

Open-source software development projects are almost always collaborative and distributed. Despite the difficulties imposed by distance, these projects have managed to produce large, complex, and successful systems. However, there is still little known about how open-source teams manage their collaboration. In this paper we look at one aspect of this issue: how distributed developers maintain group awareness. We interviewed developers, read project communication, and looked at project artifacts from three successful open source projects. We found that distributed developers do need to maintain awareness of one another, and that they maintain both a general awareness of the entire team and more detailed knowledge of people that they plan to work with. Although there are several sources of information, this awareness is maintained primarily through text-based communication (mailing lists and chat systems). These textual channels have several characteristics that help to support the maintenance of awareness, as long as developers are committed to reading the lists and to making their project communication public.
Efficient similarity joins for near duplicate detection

With the increasing amount of data and the need to integrate data from multiple data sources, a challenging issue is to find near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. Experimental results show that our proposed algorithms can achieve up to 2.6x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem.
Nano-Power Wireless Wake-Up Receiver With Serial Peripheral Interface

We designed, implemented, tested and measured an ultra low power Wake Up Receiver (WUR), intended for use in Wireless Body Area Networks (WBAN). Gaussian On-Off Keying (GOOK) and Pulse Width Modulation (PWM) are used to modulate and encode, respectively, the preamble signal. The receiver incorporates a decoder to enable Serial Peripheral Interface (SPI). WUR was also comprehensively tested for power consumption and robustness to RF interference from wireless devices commonly found in the vicinity of persons utilising WBAN technology. Our results and comparative evaluation demonstrate that the achieved listening power of 270nW for the Wake Up Receiver is significantly lower power consumption than for the other state-of-the-art. The proposed preamble detection scheme can significantly reduce false wake ups due to other wireless devices in a WBAN. Additionally, SPI significantly reduces the overall power consumption for packet reception and decoding.
Short-term stock price prediction based on echo state networks

0957-4174/$ see front matter 2008  A doi:10.1016/j.eswa.2008.09.049 * Corresponding author.  E-mail addresses: linxw05@mails.tsinghua.edu.cn com (Z. Yang), songyixu@sohu.com (Y. Song). Neural network has been popular in time series prediction in financial areas because of their advantages in handling nonlinear systems. This paper presents a study of using a novel recurrent neural network– echo state network (ESN) to predict the next closing price in stock markets. The Hurst exponent is applied to adaptively determine initial transient and choose sub-series with greatest predictability during training. The experiment results on nearly all stocks of S&P 500 demonstrate that ESN outperforms other conventional neural networks in most cases. Experiments also indicate that if we include principle component analysis (PCA) to filter noise in data pretreatment and choose appropriate parameters, we can effectively prevent coarse prediction performance. But in most cases PCA improves the prediction accuracy only a little.
Living on the edge: The role of proactive caching in 5G wireless networks

This article explores one of the key enablers of beyond 4G wireless networks leveraging small cell network deployments, proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context awareness, and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies that exploit the spatial and social structure of the network, where proactive caching plays a crucial role. First, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to 22 and 26 percent, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge.
Vertical Subperiosteal Mid-face-lift for Treatment of Malar Festoons

Malar mounds may be accentuated by chronic lid edema, with the development from malar edema to malar mounds and finally to malar festoons. Because standard techniques do not seem effective and not specifically proposed for the treatment of malar festoons, subperiosteal vertical upper-midface lift associated with lower blepharoplasty overcomes these shortcomings. Twelve patients (3 males and 9 females, age = 47 ± 6 years) underwent video-assisted endoscopic subperiosteal vertical upper-midface lift (SUM-lift) in conjunction with a lower blepharoplasty between 2006 and 2007 for treatment of malar festoons. This includes simultaneous lower blepharoplasties and video-assisted transtemporal subperiosteal and sub-SMAS tissue release. All patients healed uneventfully without any major postoperative problems. The surgical outcome was evaluated according to the analysis of photographs obtained before and after surgery and the analysis of pre- and postoperative measurements. The technique we used (SUM-lift) achieved a significant rejuvenation of the midface and the malar festoons. Subperiosteal vertical midface lift resuspends and redrapes the facial network that originates at the level of the orbital rim. It seems to improve the permeability characteristics of the malar septum in the treatment of malar festoons and malar mounds by freeing the cheek tissue from underlying bone and redraping the malar septum. It is a reliable technique to improve malar mounds, palpebral bags, or festoons.
The neural basis of addiction: a pathology of motivation and choice.

A primary behavioral pathology in drug addiction is the overpowering motivational strength and decreased ability to control the desire to obtain drugs. In this review the authors explore how advances in neurobiology are approaching an understanding of the cellular and circuitry underpinnings of addiction, and they describe the novel pharmacotherapeutic targets emerging from this understanding.



Findings from neuroimaging of addicts are integrated with cellular studies in animal models of drug seeking.



While dopamine is critical for acute reward and initiation of addiction, end-stage addiction results primarily from cellular adaptations in anterior cingulate and orbitofrontal glutamatergic projections to the nucleus accumbens. Pathophysiological plasticity in excitatory transmission reduces the capacity of the prefrontal cortex to initiate behaviors in response to biological rewards and to provide executive control over drug seeking. Simultaneously, the prefrontal cortex is hyperresponsive to stimuli predicting drug availability, resulting in supraphysiological glutamatergic drive in the nucleus accumbens, where excitatory synapses have a reduced capacity to regulate neurotransmission.



Cellular adaptations in prefrontal glutamatergic innervation of the accumbens promote the compulsive character of drug seeking in addicts by decreasing the value of natural rewards, diminishing cognitive control (choice), and enhancing glutamatergic drive in response to drug-associated stimuli.
Configuring Value for Competitive Advantage : On Chains , Shops , and Networks

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Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling

This paper presents a fault classification method which makes use of a Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the vibration signals of cylindrical shells. The calculation of Pseudomodal Energies, for the purposes of condition monitoring, has previously been found to be an accurate method of extracting features from vibration signals. This calculation is therefore used to extract features from vibration signals obtained from a diverse population of cylindrical shells. Some of the cylinders in the population have faults in different substructures. The pseudomodal energies calculated from the vibration signals are then used as inputs to a neuro-fuzzy model. A leave-one-out cross-validation process is used to test the performance of the model. It is found that the neuro-fuzzy model is able to classify faults with an accuracy of 91.62%, which is higher than the previously used multilayer perceptron.
Intrusion detection by machine learning: A review

The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.
Type II-C CRISPR-Cas9 Biology, Mechanism, and Application.

Genome editing technologies have been revolutionized by the discovery of prokaryotic RNA-guided defense system called CRISPR-Cas. Cas9, a single effector protein found in type II CRISPR systems, has been at the heart of this genome editing revolution. Nearly half of the Cas9s discovered so far belong to the type II-C subtype but have not been explored extensively. Type II-C CRISPR-Cas systems are the simplest of the type II systems, employing only three Cas proteins. Cas9s are central players in type II-C systems since they function in multiple steps of the CRISPR pathway, including adaptation and interference. Type II-C CRISPR systems are found in bacteria and archaea from very diverse environments, resulting in Cas9s with unique and potentially useful properties. Certain type II-C Cas9s possess unusually long PAMs, function in unique conditions (e.g., elevated temperature), and tend to be smaller in size. Here, we review the biology, mechanism, and applications of the type II-C CRISPR systems with particular emphasis on their Cas9s.
Exploiting deep neural networks for detection-based speech recognition

In recent years deep neural networks (DNNs) – multilayer perceptrons (MLPs) with many hidden layers – have been successfully applied to several speech tasks, i.e., phoneme recognition, out of vocabulary word detection, confidence measure, etc. In this paper, we show that DNNs can be used to boost the classification accuracy of basic speech units, such as phonetic attributes (phonological features) and phonemes. This boosting leads to higher flexibility and has the potential to integrate both top-down and bottom-up knowledge into the Automatic Speech Attribute Transcription (ASAT) framework. ASAT is a new family of lattice-based speech recognition systems grounded on accurate detection of speech attributes. In this paper we compare DNNs and shallow MLPs within the ASAT framework to classify phonetic attributes and phonemes. Several DNN architectures ranging from five to seven hidden layers and up to 2048 hidden units per hidden layer will be presented and evaluated. Experimental evidence on the speaker-independent Wall Street Journal corpus clearly demonstrates that DNNs can achieve significant improvements over the shallow MLPs with a single hidden layer, producing greater than 90% frame-level attribute estimation accuracies for all 21 phonetic features tested. Similar improvement is also observed on the phoneme classification task with excellent frame-level accuracy of 86.6% by using DNNs. This improved phoneme prediction accuracy, when integrated into a standard large vocabulary continuous speech recognition (LVCSR) system through a word lattice rescoring framework, results in improved word recognition accuracy, which is better than previously reported word lattice rescoring
Alternating Optimisation and Quadrature for Robust Reinforcement Learning

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables – state features that are randomly determined by the environment in a physical setting but are controllable in a simulator. This paper considers the problem of finding an optimal policy while taking into account the impact of environment variables. We present alternating optimisation and quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings. ALOQ is robust to the presence of significant rare events, which may not be observable under random sampling, but have a considerable impact on determining the optimal policy. We provide experimental results demonstrating our approach learning more efficiently than existing methods.
DeepMind Lab

DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.
Closeness discrepancies in romantic relationships: implications for relational well-being, stability, and mental health.

Experiences of closeness in romantic relationships are associated with heightened levels of relational well-being and mental health. However, individuals differ in the degree of closeness they desire in their relationships. This study used the construct of inclusion of other in self (IOS) to analyze discrepancies between individuals' actual and ideal levels of closeness in their relationships and the implications of these discrepancies for relational well-being and mental health. A longitudinal survey of partnered individuals demonstrated that optimal levels of relational well-being and mental health existed when individuals had minimal discrepancies between actual and ideal IOS, regardless of their actual levels of IOS. Individuals whose actual levels of IOS moved closer to their ideal levels over a 2-year period reported improved relational well-being and mental health. Individuals with little to no discrepancies between actual and ideal IOS were also less likely to break-up with their partners over time.
An Optimization Model for Outlier Detection in Categorical Data

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most existing methods are designed for numeric data. They will encounter problems with real-life applications that contain categorical data. In this paper, we formally define the problem of outlier detection in categorical data as an optimization problem from a global viewpoint. Moreover, we present a local-search heuristic based algorithm for efficiently finding feasible solutions. Experimental results on real datasets and large synthetic datasets demonstrate the superiority of our model and algorithm.
Exploring hacker assets in underground forums

Many large companies today face the risk of data breaches via malicious software, compromising their business. These types of attacks are usually executed using hacker assets. Researching hacker assets within underground communities can help identify the tools which may be used in a cyberattack, provide knowledge on how to implement and use such assets and assist in organizing tools in a manner conducive to ethical reuse and education. This study aims to understand the functions and characteristics of assets in hacker forums by applying classification and topic modeling techniques. This research contributes to hacker literature by gaining a deeper understanding of hacker assets in well-known forums and organizing them in a fashion conducive to educational reuse. Additionally, companies can apply our framework to forums of their choosing to extract their assets and appropriate functions.
A 92GHz bandwidth SiGe BiCMOS HBT TIA with less than 6dB noise figure

A low-noise, broadband amplifier with resistive degeneration and transimpedance feedback is reported with 200 mVpp input linearity and less than 6 dB noise figure up to 88 GHz. The measured gain of 13 dB, noise figure, linearity, and group delay variation of ±1.5 ps are in excellent agreement with simulation. Eye diagram measurements were conducted up to 120 Gb/s and a dynamic range larger than 36 dB was obtained from eye diagram measurements at 40 Gb/s. The chip, which includes a 50Ω output buffer, occupies 0.138 mm2 and consumes 21 mA from a 2.3V supply.
Customizing Computational Methods for Visual Analytics with Big Data

The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.
Automatic Creation of a Sentence Aligned Sinhala-Tamil Parallel Corpus

A sentence aligned parallel corpus is an important prerequisite in statistical machine translation. However, manual creation of such a parallel corpus is time consuming, and requires experts fluent in both languages. Automatic creation of a sentence aligned parallel corpus using parallel text is the solution to this problem. In this paper, we present the first ever empirical evaluation carried out to identify the best method to automatically create a sentence aligned Sinhala-Tamil parallel corpus. Annual reports from Sri Lankan government institutions were used as the parallel text for aligning. Despite both Sinhala and Tamil being under-resourced languages, we were able to achieve an F-score value of 0.791 using a hybrid approach that makes use of a bilingual dictionary.
A 0 . 3-μ m CMOS 8Gb / s 4-PAM Serial Link Transceiver

An 8-Gb/s 0.3-µm CMOS transceiver uses multilevel signaling (4-PAM) and transmit pre-shaping in combination with receive equalization to reduce ISI due to channel low-pass effects. High on-chip frequencies are avoided by multi-plexing and demultiplexing the data directly at the pads. Timing recovery takes advantage of a novel frequency acquisition scheme and a linear PLL with a loop bandwidth >30MHz, phase margin >48 and capture range of 20MHz without a frequency acquisition aid. The transmitted 8-Gbps data is successfully detected by the receiver after a 10-m coaxial cable. The 2mm x 2mm chip consumes 1.1W at 8Gbps with a 3-V supply.
Predicting the Evolution of Scientific Output

Various efforts have been made to quantify scientific impact and identify the mechanisms that influence its future evolution. The first step is the identification of what constitutes scholarly impact and how it is measured. In this direction, various approaches focus on future citation count or h-index prediction at author or publication level, on fitting the distribution of citation accumulation or accurately identifying award winners, upcoming hot research topics or academic rising stars. A plethora of features have been contemplated as possible influential factors and assorted machine-learning methodologies have been adopted to ensure timely and accurate estimations. Here, we provide an overview of the field challenges, as well as a taxonomy of the existing approaches to identify the open issues that are yet to be addressed.