nils-herrmann/clean_scirepeval huggingface dataset:
Laplacian based structure-aware error diffusion

In this paper, we propose a new halftoning scheme that preserves the structure and tone similarities of images while maintaining the simplicity of Floyd-Steinberg error diffusion. Our algorithm is based on the Floyd-Steinberg error diffusion algorithm, but the threshold modulation part is modified to improve the over-blurring issue of the Floyd-Steinberg error diffusion algorithm. By adding some structural information on images obtained using the Laplacian operator to the quantizer thresholds, the structural details in the textured region can be preserved. The visual artifacts of the original error diffusion that is usually visible in the uniform region is greatly reduced by adding noise to the thresholds. This is especially true for the low contrast region because most existing error diffusion algorithms cannot preserve structural details but our algorithm preserves them clearly using threshold modulation. Our algorithm has been evaluated using various types of images including some with the low contrast region and assessed numerically using the MSSIM measure with other existing state-of-art halftoning algorithms. The results show that our method performs better than existing approaches both in the textured region and in the uniform region with the faster computation speed.
A Machine Learning Approach to SPARQL Query Performance Prediction

In this paper we address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. Traditional approaches for estimating SPARQL query cost are based on statistics about the underlying data. However, in many use-cases involving querying Linked Data, statistics about the underlying data are often missing. Our approach does not require any statistics about the underlying RDF data, which makes it ideal for the Linked Data scenario. We show how to model SPARQL queries as feature vectors, and use k-nearest neighbors regression and Support Vector Machine with the nu-SVR kernel to accurately predict SPARQL query execution time.
Data Mining Algorithms and Techniques in Mental Health: A Systematic Review

Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as ‘techniques’ AND ‘Data Mining’ AND ‘Mental Health’, ‘algorithms’ AND ‘Data Mining’ AND ‘dementia’ AND ‘schizophrenia’ AND ‘depression’, etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer’s, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient’s quality of life.
Game-based Analysis of the Network Attack-Defense Interaction

The interactive behavior between the attacker and the defender in a network environment is similar to information warfare where both attacker and defender may have several available strategies to achieve maximum gratification. The process of positioning security within a network environment is synonymous to a decision-making process. Security decisionmaking involves the allocation of scarce network security resources to counter or mitigate security attacks. To ensure effective security, security decision-makers must ensure that the resources are allocated and deployed in the most optimum manner. Game theory provides a quantitative framework for the analysis and modeling of such network security cases. Gametheoretic models view network security scenarios as an optimization game comprising of multiple players notably the attackers (malicious users) and the defenders (system administrators) and has become a major source of attraction in security research. These types of games are referred to as security games. Security games and their solutions are potential tools for security decision making and algorithm development as well as for predicting attacker behavior. In this paper, we first explore the fundamentals of game-theory with respect to security, and then presents a two-player zero-sum game model of the interaction between malicious users and network administrators. A description of the major components of such game is presented and a solution technique for solving such game scenario is proposed. We then describe how expected results can be analyzed to show the optimality of resulting strategies and how they may be employed by system administrators to better protect the network. Index Terms security games, strategies, attackers, defenders, stochastic games, deterministic games, game theory.
Mechanisms of N-acetylcysteine in the prevention of DNA damage and cancer, with special reference to smoking-related end-points.

Although smoking cessation is the primary goal for the control of cancer and other smoking-related diseases, chemoprevention provides a complementary approach applicable to high risk individuals such as current smokers and ex-smokers. The thiol N-acetylcysteine (NAC) works per se in the extracellular environment, and is a precursor of intracellular cysteine and glutathione (GSH). Almost 40 years of experience in the prophylaxis and therapy of a variety of clinical conditions, mostly involving GSH depletion and alterations of the redox status, have established the safety of this drug, even at very high doses and for long-term treatments. A number of studies performed since 1984 have indicated that NAC has the potential to prevent cancer and other mutation-related diseases. N-Acetylcysteine has an impressive array of mechanisms and protective effects towards DNA damage and carcinogenesis, which are related to its nucleophilicity, antioxidant activity, modulation of metabolism, effects in mitochondria, decrease of the biologically effective dose of carcinogens, modulation of DNA repair, inhibition of genotoxicity and cell transformation, modulation of gene expression and signal transduction pathways, regulation of cell survival and apoptosis, anti-inflammatory activity, anti-angiogenetic activity, immunological effects, inhibition of progression to malignancy, influence on cell cycle progression, inhibition of pre-neoplastic and neoplastic lesions, inhibition of invasion and metastasis, and protection towards adverse effects of other chemopreventive agents or chemotherapeutical agents. These mechanisms are herein reviewed and commented on with special reference to smoking-related end-points, as evaluated in in vitro test systems, experimental animals and clinical trials. It is important that all protective effects of NAC were observed under a range of conditions produced by a variety of treatments or imbalances of homeostasis. However, our recent data show that, at least in mouse lung, under physiological conditions NAC does not alter per se the expression of multiple genes detected by cDNA array technology. On the whole, there is overwhelming evidence that NAC has the ability to modulate a variety of DNA damage- and cancer-related end-points.
Dissociation between Awareness and Spatial Coding: Evidence from Unilateral Neglect

Prevalent theories about consciousness propose a causal relation between lack of spatial coding and absence of conscious experience: The failure to code the position of an object is assumed to prevent this object from entering consciousness. This is consistent with influential theories of unilateral neglect following brain damage, according to which spatial coding of neglected stimuli is defective, and this would keep their processing at the nonconscious level. Contrary to this view, we report evidence showing that spatial coding and consciousness can dissociate. A patient with left neglect, who was not aware of contralesional stimuli, was able to process their color and position. However, in contrast to (ipsilesional) consciously perceived stimuli, color and position of neglected stimuli were processed separately. We propose that individual object features, including position, can be processed without attention and consciousness and that conscious perception of an object depends on the binding of its features into an integrated percept.
Salsify: Low-Latency Network Video through Tighter Integration between a Video Codec and a Transport Protocol

Salsify is a new architecture for real-time Internet video that tightly integrates a video codec and a network transport protocol, allowing it to respond quickly to changing network conditions and avoid provoking packet drops and queueing delays. To do this, Salsify optimizes the compressed length and transmission time of each frame, based on a current estimate of the network’s capacity; in contrast, existing systems generally control longer-term metrics like frame rate or bit rate. Salsify’s per-frame optimization strategy relies on a purely functional video codec, which Salsify uses to explore alternative encodings of each frame at different quality levels. We developed a testbed for evaluating real-time video systems end-to-end with reproducible video content and network conditions. Salsify achieves lower video delay and, over variable network paths, higher visual quality than five existing systems: FaceTime, Hangouts, Skype, and WebRTC’s reference implementation with and without scalable video coding.
Frequency-Shift Keying Demodulation and Manchester-Bit Decoding Using a Digital Radio and Digital Signal Processing Techniques

This paper discusses a method of frequency-shift keying (FSK) demodulation and Manchester-bit decoding using a digital signal processing (DSP) approach. The demodulator is implemented on a single-channel high-speed digital radio board. The board architecture contains a high-speed A/D converter, a digital receiver chip, a host DSP processing chip, and a back-end D/A converter . The demodulator software is booted off an on-board EPROM and run on the DSP chip . The algorithm accepts complex digital baseband data available from the front-end digital receiver chip . The target FSK modulation is assumed to be in the RF range (VHF or UHF signals). A block diagram of the single-channel digital radio is shown in Figure 1 .
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real

Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making. Although there exist a lot of works dealing with modeling driver behavior of a single object, it remains a challenge to make predictions for multiple highly interactive agents that react to each other simultaneously. In this work, we propose a generic probabilistic hierarchical recognition and prediction framework which employs a two-layer Hidden Markov Model (TLHMM) to obtain the distribution of potential situations and a learning-based dynamic scene evolution model to sample a group of future trajectories. Instead of predicting motions of a single entity, we propose to get the joint distribution by modeling multiple interactive agents as a whole system. Moreover, due to the decoupling property of the layered structure, our model is suitable for knowledge transfer from simulation to real world applications as well as among different traffic scenarios, which can reduce the computational efforts of training and the demand for a large data amount. A case study of highway ramp merging scenario is demonstrated to verify the effectiveness and accuracy of the proposed framework.
Functional movement screening: the use of fundamental movements as an assessment of function-part 2.

Part 1 of this two-part series (presented in the June issue of IJSPT) provided an introduction to functional movement screening, as well as the history, background, and a summary of the evidence regarding the reliability of the Functional Movement Screen (FMS™). Part 1 presented three of the seven fundamental movement patterns that comprise the FMS™, and the specific ordinal grading system from 0-3, used in the their scoring. Specifics for scoring each test are presented. Part 2 of this series provides a review of the concepts associated with the analysis of fundamental movement as a screening system for functional movement competency. In addition, the four remaining movements of the FMS™, which complement those described in Part 1, will be presented (to complete the total of seven fundamental movements): Shoulder Mobility, the Active Straight Leg Raise, the Trunk Stability Push-up, and Rotary Stability. The final four patterns are described in detail, and the specifics for scoring each test are presented, as well as the proposed clinical implications for receiving a grade less than a perfect "3". The intent of this two part series is to present the concepts associated with screening of fundamental movements, whether it is the FMS™ system or a different system devised by another clinician. Such a fundamental screen of the movement system should be incorporated into pre-participation screening and return to sport testing in order to determine whether an athlete has the essential movements needed to participate in sports activities at a level of minimum competency. Part 2 concludes with a discussion of the evidence related to functional movement screening, myths related to the FMS™, the future of functional movement screening, and the concept of movement as a system.
[Unilateral gigantomastia of pregnancy: case-report of a giant breast hamartoma].

Breast hamartoma is a benign tumour consisting of fat, fibrous and glandular tissue. A young woman in her 19th week of pregnancy underwent exceptional surgery for a unilateral gigantomastia secondary to a rapid-growth giant hamartoma during her second pregnancy. Rigourous clinical and ultrasonographic examinations were performed followed by multiple biopsies. The decision to perform surgery was guided by the risk to skin integrity and of tumour infarct. Our report provides detailed information on gestational benign breast tumours, on the specificities of medical imaging and breast surgery in pregnant patients.
User Clustering in Online Advertising via Topic Models

In the domain of online advertising, our aim is to serve the best ad to a user who visits a certain webpage, to maximize the chance of a desired action to be performed by this user after seeing the ad. While it is possible to generate a different prediction model for each user to tell if he/she will act on a given ad, the prediction result typically will be quite unreliable with huge variance, since the desired actions are extremely sparse, and the set of users is huge (hundreds of millions) and extremely volatile, i.e., a lot of new users are introduced everyday, or are no longer valid. In this paper we aim to improve the accuracy in finding users who will perform the desired action, by assigning each user to a cluster, where the number of clusters is much smaller than the number of users (in the order of hundreds). Each user will fall into the same cluster with another user if their event history are similar. For this purpose, we modify the probabilistic latent semantic analysis (pLSA) model by assuming the independence of the user and the cluster id, given the history of events. This assumption helps us to identify a cluster of a new user without re-clustering all the users. We present the details of the algorithm we employed as well as the distributed implementation on Hadoop, and some initial results on the clusters that were generated by the algorithm.
Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.
Multi-scale mechanical improvement produced in carbon nanotube fibers by irradiation cross-linking

Fibers and yarns based on carbon nanotubes (CNT) are emerging as a possible improvement over more traditional high strength carbon fibers used as reinforcement elements in composite materials. This is driven by a desire to translate the exceptional mechanical properties of individual CNT shells to achieve high performance macroscopic fibers and yarns. One of the central limitations in this approach is the weak shear interactions between adjacent CNT shells and tubes within macroscopic fibers and yarns. Furthermore, the multiple levels of interaction, e.g., between tubes within a multi-walled CNTor between bundles within a fiber, compound the problem. One promising direction to overcome this limitation is the introduction of strong and stiff cross-linking bonds between adjacent carbon shells. A great deal of research has been devoted to studying such cross-linking by the irradiation of CNT based materials using either high energy particles, such as electrons, to directly covalently cross-link CNTs, or electromagnetic irradiation, such as gamma rays to strengthen polymer cross-links between CNT shells and tubes. Here we review recent progress in the field of irradiation-induced cross-linking at multiple levels in CNT based fibers with a focus on mechanical property improvements.
A microcrack-based continuous damage model for brittle geomaterials

A new microcrack-based continuous damage model is developed to describe the behavior of brittle geomaterials under compression dominated stress ®elds. The induced damage is represented by a second rank tensor, which re ̄ects density and orientation of microcracks. The damage evolution law is related to the propagation condition of microcracks. Based on micromechanical analyses of sliding wing cracks, the actual microcrack distributions are replaced by an equivalent set of cracks subjected to a macroscopic local tensile stress. The principles of the linear fracture mechanics are used to develop a suitable macroscopic propagation criterion. The onset of microcrack coalescence leading to localization phenomenon and softening behavior is included by using a critical crack length. The constitutive equations are developed by considering that microcrack growth induces an added material  ̄exibility. The eective elastic compliance of damaged material is obtained from the de®nition of a particular Gibbs free energy function. Irreversible damage-related strains due to residual opening of microcracks after unloading are also taken into account. The resulting constitutive equations can be arranged to reveal the physical meaning of each model parameter and to determine its value from standard laboratory tests. An explicit expression for the macroscopic eective constitutive tensor (compliance or stiness) makes it possible, in principal, to determine the critical damage intensity at which the localization condition is satis®ed. The proposed model is applied to two typical brittle rocks (a French granite and Tennessee marble). Comparison between test data and numerical simulations show that the proposed model is able to describe main features of mechanical behaviors observed in brittle geomaterials under compressive stresses.
Online Learning for Offloading and Autoscaling in Renewable-Powered Mobile Edge Computing

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run- time performance when compared to standard reinforcement learning algorithms such as Q- learning.
Algorithmic Transparency in the News Media

2167-0811 (Print) 2167-082X (Online) Journal homepage: http://www.tandfonline.com/loi/rdij20 Algorithmic Transparency in the News Media Nicholas Diakopoulos & Michael Koliska To cite this article: Nicholas Diakopoulos & Michael Koliska (2016): Algorithmic Transparency in the News Media, Digital Journalism,  To link to this article: http://dx.doi.org/10.1080/21670811.2016.1208053
A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets

Linear time series models, such as the autoregressive integrated moving average (ARIMA) model, are among the most popular statistical models used to forecast time series. In recent years non-linear computational models, such as artificial neural networks (ANN), have been shown to outperform traditional linear models when dealing with complex data, like financial time series. This paper proposes a novel hybrid forecasting model which exploits the linear modelling strengths of the ARIMA model, and the flexibility of a self-organising fuzzy neural network (SOFNN). The system's performance is evaluated using several datasets, and our results indicate that a hybrid system is an effective tool for time series forecasting.