nils-herrmann/clean_scirepeval huggingface dataset:
Reconceptualizing functional brain connectivity in autism from a developmental perspective

While there is almost universal agreement amongst researchers that autism is associated with alterations in brain connectivity, the precise nature of these alterations continues to be debated. Theoretical and empirical work is beginning to reveal that autism is associated with a complex functional phenotype characterized by both hypo- and hyper-connectivity of large-scale brain systems. It is not yet understood why such conflicting patterns of brain connectivity are observed across different studies, and the factors contributing to these heterogeneous findings have not been identified. Developmental changes in functional connectivity have received inadequate attention to date. We propose that discrepancies between findings of autism related hypo-connectivity and hyper-connectivity might be reconciled by taking developmental changes into account. We review neuroimaging studies of autism, with an emphasis on functional magnetic resonance imaging studies of intrinsic functional connectivity in children, adolescents and adults. The consistent pattern emerging across several studies is that while intrinsic functional connectivity in adolescents and adults with autism is generally reduced compared with age-matched controls, functional connectivity in younger children with the disorder appears to be increased. We suggest that by placing recent empirical findings within a developmental framework, and explicitly characterizing age and pubertal stage in future work, it may be possible to resolve conflicting findings of hypo- and hyper-connectivity in the extant literature and arrive at a more comprehensive understanding of the neurobiology of autism.
Global versus local search: the impact of population sizes on evolutionary algorithm performance

In the field of Evolutionary Computation, a common myth that “An Evolutionary Algorithm (EA) will outperform a local search algorithm, given enough runtime and a large-enough population” exists. We believe that this is not necessarily true and challenge the statement with several simple considerations. We then investigate the population size parameter of EAs, as this is the element in the above claim that can be controlled. We conduct a related work study, which substantiates the assumption that there should be an optimal setting for the population size at which a specific EA would perform best on a given problem instance and computational budget. Subsequently, we carry out a large-scale experimental study on 68 instances of the Traveling Salesman Problem with static population sizes that are powers of two between (1+2) and (262 144 + 524 288) EAs as well as with adaptive population sizes. We find that analyzing the performance of the different setups over runtime supports our point of view and the existence of optimal finite population size settings.
A Protocol for Building Secure and Reliable Covert Channel

This paper presents a secure and lightweight protocol for reliable data transfer through moderate bandwidth covert channels. Though data transfer through covert channels is not unprecedented, existing covert channels have been restricted to covert transmission of only small amounts of data. This paper demonstrates that it is possible to transmit large amounts of data covertly with sophisticated support such as security and reliability. The proposed protocol exploits ICMP echo request as covert medium, and uses OS finger-printing techniques to simulate real TCP/IP stack behavior for further security enhancements.
Comparison of Modern Stochastic Optimization Algorithms

Gradient-based optimization methods are popular in machine learning applications. In large-scale problems, stochastic methods are preferred due to their good scaling properties. In this project, we compare the performance of four gradient-based methods; gradient descent, stochastic gradient descent, semi-stochastic gradient descent and stochastic average gradient. We consider logistic regression with synthetic data and softmax regression on the MNIST dataset of handwritten digits.
Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning

Reading text from photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been proposed to design better feature representations and models for both. In this paper, we apply methods recently developed in machine learning -- specifically, large-scale algorithms for learning the features automatically from unlabeled data -- and show that they allow us to construct highly effective classifiers for both detection and recognition to be used in a high accuracy end-to-end system.
PICCIL: Interactive Learning to Support Log File Categorization

Motivated by the real-world application of categorizing system log messages into defined situation categories, this paper describes an interactive text categorization method, PICCIL, that leverages supervised machine learning to reduce the burden of assigning categories to documents in large finite data sets but, by coupling human expertise to the machine learning, does so without sacrificing accuracy. PICCIL uses keywords and keyword rules both to preclassify documents and to assist in the manual process of grouping and reviewing documents. The reviewed documents, in turn, are used to refine the keyword rules iteratively to improve subsequent grouping and document review. We apply PICCIL to the problem of assigning semantic situation labels to the entries of a catalog of log events to support on-line labeling of log events
Impact of Using Computer Applications in Education on Teaching- Learning Process

Today’s educational policies are largely devoted to fostering the development and implementation of computer applications in education. This paper analyses the skills and competences needed for the knowledgebased society and reveals the role and impact of using computer applications to the teaching and learning processes. Also, the aim of this paper is to reveal the outcomes of a study conducted in order to determine the impact of using computer applications in teaching and learning Management and to propose new opportunities for the process improvement. The findings of this study related to the teachers’ and students’ perceptions about using computer applications for teaching and learning could open further researches on computer applications in education and their educational and economic implications.
Eight genes are required for functional reconstitution of the Caenorhabditis elegans levamisole-sensitive acetylcholine receptor.

Levamisole-sensitive acetylcholine receptors (L-AChRs) are ligand-gated ion channels that mediate excitatory neurotransmission at the neuromuscular junctions of nematodes. They constitute a major drug target for anthelminthic treatments because they can be activated by nematode-specific cholinergic agonists such as levamisole. Genetic screens conducted in Caenorhabditis elegans for resistance to levamisole toxicity identified genes that are indispensable for the biosynthesis of L-AChRs. These include 5 genes encoding distinct AChR subunits and 3 genes coding for ancillary proteins involved in assembly and trafficking of the receptors. Despite extensive analysis of L-AChRs in vivo, pharmacological and biophysical characterization of these receptors has been greatly hampered by the absence of a heterologous expression system. Using Xenopus laevis oocytes, we were able to reconstitute functional L-AChRs by coexpressing the 5 distinct receptor subunits and the 3 ancillary proteins. Strikingly, this system recapitulates the genetic requirements for receptor expression in vivo because omission of any of these 8 genes dramatically impairs L-AChR expression. We demonstrate that 3 alpha- and 2 non-alpha-subunits assemble into the same receptor. Pharmacological analysis reveals that the prototypical cholinergic agonist nicotine is unable to activate L-AChRs but rather acts as a potent allosteric inhibitor. These results emphasize the role of ancillary proteins for efficient expression of recombinant neurotransmitter receptors and open the way for in vitro screening of novel anthelminthic agents.
The current evidence on stapled versus hand-sewn anastomoses in the digestive tract.

The innovation process and developments in technology have given surgeons new products which can improve their performance and benefit our patients. Before the era of laparoscopic surgery one of the most important applications in surgical practice was the introduction of staplers. In this article, the evidence supporting the decision whether to use a mechanical device (stapler) or to make a hand-sewn anastomosis is presented. A sytematic review of the literature was performed. The search included published meta-anaylses, randomized clinical trials and comparative studies. Key words for the initial search were: surgical anastomosis, stapler, hand-sewn. There was no language restriction. The reference lists from the selected articles were also checked by the author. Literature data on main outcomes concerning the application of one or the other surgical technique have been analysed. The literature search yielded published data on various procedures in digestive surgery. Most of the available high-quality evidence was for gastric and colorectal resections. Resection of the esophagus, use of staplers in emergency procedures and some initial reports on pancreatic surgery were also retrieved. The evidence from the literature shows that stapler anastomoses take less operative time and are more costly than hand-sewn anastomoses. Regarding the morbidity and leaks rate the staplers give equal or better results when compared with the hand-sewn technique. Nevertheless, proper handling of staplers and experience remain crucial issues if one wants to gain benefits when using these devices.
A Compact Design of High-Power Spurious-Free Low-Pass Waveguide Filter

In this letter, a new technique is proposed for the design of a compact high-power low-pass rectangular waveguide filter with a wide spurious-free frequency behavior. Specifically, the new filter is intended for the suppression of the fundamental mode over a wide band in much higher power applications than the classical corrugated filter with the same frequency specifications. Moreover, the filter length is dramatically reduced when compared to alternative techniques previously considered.
Autotuning Sparse Matrix-Vector Multiplication for Multicore

Sparse matrix-vector multiplication (SpMV) is an important kernel in scientific and engineering computing. Straightforward parallel implementations of SpMV often perform poorly, and with the increasing variety of architectural features in multicore processors, it is getting more difficult to determine the sparse matrix data structure and corresponding SpMV implementation that optimize performance. In this paper we present pOSKI, an autotuning system for SpMV that automatically searches over a large set of possible data structures and implementations to optimize SpMV performance on multicore platforms. pOSKI explores a design space that depends on both the nonzero pattern of the sparse matrix, typically not known until run-time, and the architecture, which is explored off-line as much as possible, in order to reduce tuning time. We demonstrate significant performance improvements compared to previous serial and parallel implementations, and compare performance to upper bounds based on architectural models. General Terms: Design, Experimentation,
Quality data extraction methodology based on the labeling of coffee leaves with nutritional deficiencies

Nutritional deficiencies detection for coffee leaves is a task which is often undertaken manually by experts on the field known as agronomists. The process they follow to carry this task is based on observation of the different characteristics of the coffee leaves while relying on their own experience. Visual fatigue and human error in this empiric approach cause leaves to be incorrectly labeled and thus affecting the quality of the data obtained. In this context, different crowdsourcing approaches can be applied to enhance the quality of the data extracted. These approaches separately propose the use of voting systems, association rule filters and evolutive learning. In this paper, we extend the use of association rule filters and evolutive approach by combining them in a methodology to enhance the quality of the data while guiding the users during the main stages of data extraction tasks. Moreover, our methodology proposes a reward component to engage users and keep them motivated during the crowdsourcing tasks. The extracted dataset by applying our proposed methodology in a case study on Peruvian coffee leaves resulted in 93.33% accuracy with 30 instances collected by 8 experts and evaluated by 2 agronomic engineers with background on coffee leaves. The accuracy of the dataset was higher than independently implementing the evolutive feedback strategy and an empiric approach which resulted in 86.67% and 70% accuracy respectively under the same conditions.
Profile of minocycline and its potential in the treatment of schizophrenia

Accumulating evidence suggests that neuroinflammation affecting microglia plays an important role in the etiology of schizophrenia, and appropriate control of microglial activation may be a promising therapeutic strategy for schizophrenia. Minocycline, a second-generation tetracycline that inhibits microglial activation, has been shown to have a neuroprotective effect in various models of neurodegenerative disease, including anti-inflammatory, antioxidant, and antiapoptotic properties, and an ability to modulate glutamate-induced excitotoxicity. Given that these mechanisms overlap with neuropathologic pathways, minocycline may have a potential role in the adjuvant treatment of schizophrenia, and improve its negative symptoms. Here, we review the relevant studies of minocycline, ranging from preclinical research to human clinical trials.
Providing SCADA Network Data Sets for Intrusion Detection Research

High profile attacks such as Stuxnet and the cyber attack on the Ukrainian power grid have increased research in Industrial Control System (ICS) and Supervisory Control and Data Acquisition (SCADA) network security. However, due to the sensitive nature of these networks, there is little publicly available data for researchers to evaluate the effectiveness of the proposed solution. The lack of representative data sets makes evaluation and independent validation of emerging security solutions difficult and slows down progress towards effective and reusable solutions. This paper presents our work to generate representative labeled data sets for SCADA networks that security researcher can use freely. The data sets include packet captures including both malicious and non-malicious Modbus traffic and accompanying CSV files that contain labels to provide the ground truth for supervised machine learning. To provide representative data at the network level, the data sets were generated in a SCADA sandbox, where electrical network simulators were used to introduce realism in the physical component. Also, real attack tools, some of them custom built for Modbus networks, were used to generate the malicious traffic. Even though they do not fully replicate a production network, these data sets represent a good baseline to validate detection tools for SCADA systems.
Using the Student ’ s t-test with extremely small sample sizes

is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms. Researchers occasionally have to work with an extremely small sample size, defined herein as N ≤ 5. Some methodologists have cautioned against using the t-test when the sample size is extremely small, whereas others have suggested that using the t-test is feasible in such a case. The present simulation study estimated the Type I error rate and statistical power of the one-and two-sample t-tests for normally distributed populations and for various distortions such as unequal sample sizes, unequal variances, the combination of unequal sample sizes and unequal variances, and a lognormal population distribution. Ns per group were varied between 2 and 5. Results show that the t-test provides Type I error rates close to the 5% nominal value in most of the cases, and that acceptable power (i.e., 80%) is reached only if the effect size is very large. This study also investigated the behavior of the Welch test and a rank-transformation prior to conducting the t-test (t-testR). Compared to the regular t-test, the Welch test tends to reduce statistical power and the t-testR yields false positive rates that deviate from 5%. This study further shows that a paired t-test is feasible with extremely small Ns if the within-pair correlation is high. It is concluded that there are no principal objections to using a t-test with Ns as small as 2. A final cautionary note is made on the credibility of research findings when sample sizes are small. The dictum " more is better " certainly applies to statistical inference. According to the law of large numbers, a larger sample size implies that confidence intervals are narrower and that more reliable conclusions can be reached. The reality is that researchers are usually far from the ideal " mega-trial " performed with 10,000 subjects (cf. Ioannidis, 2013) and will have to work with much smaller samples instead. For a variety of reasons, such as budget, time, or ethical constraints, it may not be possible to gather a large sample. In some fields of science, such as research on rare animal species, persons having a rare illness, or prodigies scoring at the extreme of an ability distribution (e.g., Ruthsatz & Urbach, 2012), …
Diverse Personalization with Determinantal Point Process

Personalization has become an important part of recommendation systems for online products including news, search, media and advertising. Real world recommender systems need to also take into account the diversity and serendipity of the set of recommended items so as to not overwhelm the user with too similar items and to discover user interests that were previously unknown to the system (Szpektor et al., 2013). Recently developed methods that include diversity as a part of the model have achieved promising results (Yue & Joachims, 2008; Kulesza & Taskar; Raman et al., 2012).
Implementation of ECG signal processing and analysis techniques in digital signal processor based system

This work describes the implementation of wavelet-based de noising algorithm on electrocardiogram (ECG) signal and detection of important parameter such as heart rate, amplitude, timings of the ECG, etc. The algorithm is implemented in DSP (TI TMS320C67x) based starter kit (DSK) with a two-electrode ECG preamplifier. The signal from the ECG preamplifier is acquired through the Codec input of the DSP starter kit. The acquired data is subjected to signal processing techniques such as removal of power line frequencies and high frequency component removal using wavelet-denoising technique. ECG component analysis such as QRS peak detection, heart rate calculation, etc is performed using nonlinear filter technique called first order derivative and moving average filter. The performance of the algorithm (such as speed, calculation efficiency, accuracy, etc) is studied in the DSP environment as well as MATLAB environment for comparison. The results of this study reveal the potentiality of the DSP system for routine clinical use.
Min-Hash Fingerprints for Graph Kernels: A Trade-off among Accuracy, Efficiency, and Compression

Graph databases that emerge from several relevant scenarios (e.g., social networks, the Web) require powerful data management algorithms and techniques. A fundamental operation in graph data management is computing the similarity between two graphs. However, due to the large scale and high dimensionality of real graph databases, computing graph similarity becomes a challenging problem in real settings. Many graph data management tasks, such as graph mining, classi cation, and retrieval, can be contextualized in the framework of graph kernels. A graph kernel is, roughly speaking, a function that computes the similarity between graph structures as means to enable the application of linear methods to graph data. Nevertheless, large databases usually require the use of compact representations of graphs known as graph ngerprints (or signatures). Graph ngerprinting techniques provide a solution that is a trade-o among accuracy, e ciency, and compression in graph kernels. In this article, we study the problem of generating ngerprints for graph kernels. We introduce a graph ngerprinting technique based on the min-hashing scheme, which is a powerful strategy for computing the similarity between large sets of items using a small amount of data. An algorithm for the generation of graph ngerprints as vectors of min-hash values is presented and integrated into the framework of graph kernels. Results show that graph ngerprinting achieves e ciency gains of up to one order of magnitude with up to 97% space savings when compared against the complete set of graph substructures. Moreover, the proposed technique is up to 9 times more accurate than a baseline method.
Basic level scene understanding: categories, attributes and structures

A longstanding goal of computer vision is to build a system that can automatically understand a 3D scene from a single image. This requires extracting semantic concepts and 3D information from 2D images which can depict an enormous variety of environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the richly annotated SUN database which is a collection of annotated images spanning 908 different scene categories with object, attribute, and geometric labels for many scenes. This database allows us to systematically study the space of scenes and to establish a benchmark for scene and object recognition. We augment the categorical SUN database with 102 scene attributes for every image and explore attribute recognition. Finally, we present an integrated system to extract the 3D structure of the scene and objects depicted in an image.
Concept-based interactive query expansion

Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for Web ranking algorithms. In fact, there are still many situations in which the users are presented with imprecise or very poor results. One of the key difficulties is the fact that users usually submit very short and ambiguous queries, and they do not fully specify their information needs. That is, it is necessary to improve the query formation process if better answers are to be provided. In this work we propose a novel concept-based query expansion technique, which allows disambiguating queries submitted to search engines. The concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from query relations mined using association rules. The concepts related to the current query are then shown to the user who selects the one concept that he interprets is most related to his query. This concept is used to expand the original query and the expanded query is processed instead. Using a Web test collection, we show that our approach leads to gains in average precision figures of roughly 32%. Further, if the user also provides information on the type of relation between his query and the selected concept, the gains in average precision go up to roughly 52%.