nils-herrmann/clean_scirepeval huggingface dataset:
Convolutional Features for Correlation Filter Based Visual Tracking

Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they miti-gate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard hand-crafted features. Finally, results comparable to state-of-the-art trackers are obtained on all three benchmark datasets.
Joint Relational Embeddings for Knowledge-based Question Answering

Transforming a natural language (NL) question into a corresponding logical form (LF) is central to the knowledge-based question answering (KB-QA) task. Unlike most previous methods that achieve this goal based on mappings between lexicalized phrases and logical predicates, this paper goes one step further and proposes a novel embedding-based approach that maps NL-questions into LFs for KBQA by leveraging semantic associations between lexical representations and KBproperties in the latent space. Experimental results demonstrate that our proposed method outperforms three KB-QA baseline methods on two publicly released QA data sets.
Darknet as a Source of Cyber Intelligence: Survey, Taxonomy, and Characterization

Today, the Internet security community largely emphasizes cyberspace monitoring for the purpose of generating cyber intelligence. In this paper, we present a survey on darknet. The latter is an effective approach to observe Internet activities and cyber attacks via passive monitoring. We primarily define and characterize darknet and indicate its alternative names. We further list other trap-based monitoring systems and compare them to darknet. Moreover, in order to provide realistic measures and analysis of darknet information, we report case studies, namely, Conficker worm in 2008 and 2009, Sality SIP scan botnet in 2011, and the largest amplification attack in 2014. Finally, we provide a taxonomy in relation to darknet technologies and identify research gaps that are related to three main darknet categories: deployment, traffic analysis, and visualization. Darknet projects are found to monitor various cyber threat activities and are distributed in one third of the global Internet. We further identify that Honeyd is probably the most practical tool to implement darknet sensors, and future deployment of darknet will include mobile-based VOIP technology. In addition, as far as darknet analysis is considered, computer worms and scanning activities are found to be the most common threats that can be investigated throughout darknet; Code Red and Slammer/Sapphire are the most analyzed worms. Furthermore, our study uncovers various lacks in darknet research. For instance, less than 1% of the contributions tackled distributed reflection denial of service (DRDoS) amplification investigations, and at most 2% of research works pinpointed spoofing activities. Last but not least, our survey identifies specific darknet areas, such as IPv6 darknet, event monitoring, and game engine visualization methods that require a significantly greater amount of attention from the research community.
Seed germination and vigor.

Germination vigor is driven by the ability of the plant embryo, embedded within the seed, to resume its metabolic activity in a coordinated and sequential manner. Studies using "-omics" approaches support the finding that a main contributor of seed germination success is the quality of the messenger RNAs stored during embryo maturation on the mother plant. In addition, proteostasis and DNA integrity play a major role in the germination phenotype. Because of its pivotal role in cell metabolism and its close relationships with hormone signaling pathways regulating seed germination, the sulfur amino acid metabolism pathway represents a key biochemical determinant of the commitment of the seed to initiate its development toward germination. This review highlights that germination vigor depends on multiple biochemical and molecular variables. Their characterization is expected to deliver new markers of seed quality that can be used in breeding programs and/or in biotechnological approaches to improve crop yields.
Comprehensive Review of Medicinal Marijuana, Cannabinoids, and Therapeutic Implications in Medicine and Headache: What a Long Strange Trip It's Been ….

The use of cannabis, or marijuana, for medicinal purposes is deeply rooted though history, dating back to ancient times. It once held a prominent position in the history of medicine, recommended by many eminent physicians for numerous diseases, particularly headache and migraine. Through the decades, this plant has taken a fascinating journey from a legal and frequently prescribed status to illegal, driven by political and social factors rather than by science. However, with an abundance of growing support for its multitude of medicinal uses, the misguided stigma of cannabis is fading, and there has been a dramatic push for legalizing medicinal cannabis and research. Almost half of the United States has now legalized medicinal cannabis, several states have legalized recreational use, and others have legalized cannabidiol-only use, which is one of many therapeutic cannabinoids extracted from cannabis. Physicians need to be educated on the history, pharmacology, clinical indications, and proper clinical use of cannabis, as patients will inevitably inquire about it for many diseases, including chronic pain and headache disorders for which there is some intriguing supportive evidence.



To review the history of medicinal cannabis use, discuss the pharmacology and physiology of the endocannabinoid system and cannabis-derived cannabinoids, perform a comprehensive literature review of the clinical uses of medicinal cannabis and cannabinoids with a focus on migraine and other headache disorders, and outline general clinical practice guidelines.



The literature suggests that the medicinal use of cannabis may have a therapeutic role for a multitude of diseases, particularly chronic pain disorders including headache. Supporting literature suggests a role for medicinal cannabis and cannabinoids in several types of headache disorders including migraine and cluster headache, although it is primarily limited to case based, anecdotal, or laboratory-based scientific research. Cannabis contains an extensive number of pharmacological and biochemical compounds, of which only a minority are understood, so many potential therapeutic uses likely remain undiscovered. Cannabinoids appear to modulate and interact at many pathways inherent to migraine, triptan mechanisms ofaction, and opiate pathways, suggesting potential synergistic or similar benefits. Modulation of the endocannabinoid system through agonism or antagonism of its receptors, targeting its metabolic pathways, or combining cannabinoids with other analgesics for synergistic effects, may provide the foundation for many new classes of medications. Despite the limited evidence and research suggesting a role for cannabis and cannabinoids in some headache disorders, randomized clinical trials are lacking and necessary for confirmation and further evaluation.
Manufacturing Cheap, Resilient, and Stealthy Opaque Constructs

It has become common to distribute software in forms that are isomorphic to the original source code. An important example is Java bytecode. Since such codes are easy to decompile, they increase the risk of malicious reverse engineering attacks.In this paper we describe the design of a Java code obfuscator, a tool which - through the application of code transformations - converts a Java program into an equivalent one that is more difficult to reverse engineer.We describe a number of transformations which obfuscate control-flow. Transformations are evaluated with respect to potency (To what degree is a human reader confused?), resilience (How well are automatic deobfuscation attacks resisted?), cost (How much time/space overhead is added?), and stealth (How well does obfuscated code blend in with the original code?).The resilience of many control-altering transformations rely on the resilience of opaque predicates. These are boolean valued expressions whose values are known to the obfuscator but difficult to determine for an automatic deobfuscator. We show how to construct resilient, cheap, and stealthy opaque predicates based on the intractability of certain static analysis problems such as alias analysis.
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization

Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but its success can in part be attributed to the availability of large parallel data automatically acquired from the Web. In contrast, parallel data for multi-document summarization are scarce and costly to obtain. There is a pressing need to adapt an encoder-decoder model trained on single-document summarization data to work with multiple-document input. In this paper, we present an initial investigation into a novel adaptation method. It exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an abstractive summary. The adaptation method is robust and itself requires no training data. Our system compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors.
MicroRolls: expanding touch-screen input vocabulary by distinguishing rolls vs. slides of the thumb

The input vocabulary for touch-screen interaction on handhelds is dramatically limited, especially when the thumb must be used. To enrich that vocabulary we propose to discriminate, among thumb gestures, those we call MicroRolls, characterized by zero tangential velocity of the skin relative to the screen surface. Combining four categories of thumb gestures, Drags, Swipes, Rubbings and MicroRolls, with other classification dimensions, we show that at least 16 elemental gestures can be automatically recognized. We also report the results of two experiments showing that the roll vs. slide distinction facilitates thumb input in a realistic copy and paste task, relative to existing interaction techniques.
The impact of primary school teachers' educational beliefs on the classroom use of computers

For many years, researchers have searched for the factors affecting the use of computers in the classroom. In studying the antecedents of educational computer use, many studies adopt a rather limited view because only technology-related variables, such as attitudes to computers and computer experience were taken into account. The present study centres on teachers’ educational beliefs (constructivist beliefs, traditional beliefs) as antecedent of computer use, while controlling for the impact of technology-related variables (computer experience, general computer attitudes) and demographical variables (sex, age). In order to identify differences in determinants of computer use in the classroom, multilevel modelling was used (N = 525). For measuring primary teachers’ use of computers to support the leaching or learning process a modified version of the ‘Class Use of Computers’ scale of van Braak et al. [van Braak, J., Tondeur, J., & Valcke, M. (2004). Explaining different types of computer use among primary school teachers. European Journal of Psychology of Education, 19(4), 407–422] was used. The present article supports the hypothesis that teacher beliefs are significant determinants in explaining why teachers adopt computers in the classroom. Next to the impact of computer experience, general computer attitudes and gender, the results show a positive effect of constructivist beliefs on the classroom use of computers. Traditional beliefs have a negative impact on the classroom use of computers.
VANSec: Attack-Resistant VANET Security Algorithm in Terms of Trust Computation Error and Normalized Routing Overhead

VANET is an application and subclass of MANETs, a quickly maturing, promising, and emerging technology these days. VANETs establish communication among vehicles (V2V) and roadside infrastructure (V2I). As vehicles move with high speed, hence environment and topology change with time. There is no optimum routing protocol which ensures full-pledge on-time delivery of data to destination nodes, and an absolutely optimum scheme design for flawless packet exchange is still a challenging task. In VANETs, accurate and on-time delivery of fundamental safety alert messages (FSAMs) is highly important to withstand against maliciously inserted security threats affectively. In this paper, we have presented a new security-aware routing technique called VANSec. The presented scheme is more immune and resistive against different kinds of attacks and thwarts malicious node penetration attempts to the entire network. It is basically based on trust management approach. The aim of the scheme is to identify malicious data and false nodes. The simulation results of VANSec are compared with already existing techniques called trust and LT in terms of trust computation error (TCE), end-to-end delay (EED), average link duration (ALD), and normalized routing overhead (NRO). In terms of TCE, VANSec is 11.6% and 7.3% efficient than LT and trust, respectively, while from EED comparison we found VANSec to be 57.6% more efficient than trust and 5.2% more efficient than LT. Similarly, in terms of ALD, VANSec provides 29.7% and 7.8% more stable link duration than trust and LT do, respectively, and in terms of NRO, VANSec protocol has 27.5% and 14% lesser load than that of trust and LT, respectively.
Learning probabilistic networks

A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as possible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.
Detecting and investigating crime by means of data mining: a general crime matching framework

Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify crime characteristics. Actually crime analysis includes exploring and detecting crimes and their relationships with criminals. The high volume of crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of crime analysis and finally we will use the clustering results in order to perform crime matching process.
"I'd only let you down": Guilt proneness and the avoidance of harmful interdependence.

Five studies demonstrated that highly guilt-prone people may avoid forming interdependent partnerships with others whom they perceive to be more competent than themselves, as benefitting a partner less than the partner benefits one's self could trigger feelings of guilt. Highly guilt-prone people who lacked expertise in a domain were less willing than were those low in guilt proneness who lacked expertise in that domain to create outcome-interdependent relationships with people who possessed domain-specific expertise. These highly guilt-prone people were more likely than others both to opt to be paid on their performance alone (Studies 1, 3, 4, and 5) and to opt to be paid on the basis of the average of their performance and that of others whose competence was more similar to their own (Studies 2 and 5). Guilt proneness did not predict people's willingness to form outcome-interdependent relationships with potential partners who lacked domain-specific expertise (Studies 4 and 5). It also did not predict people's willingness to form relationships when poor individual performance would not negatively affect partner outcomes (Study 4). Guilt proneness therefore predicts whether, and with whom, people develop interdependent relationships. The findings also demonstrate that highly guilt-prone people sacrifice financial gain out of concern about how their actions would influence others' welfare. As such, the findings demonstrate a novel way in which guilt proneness limits free-riding and therefore reduces the incidence of potentially unethical behavior. Lastly, the findings demonstrate that people who lack competence may not always seek out competence in others when choosing partners.
The pair correlation of zeros of the Riemann zeta function and distribution of primes

Assuming a special version of the Montgomery-Odlyzko law on the pair correlation of zeros of the Riemann zeta function conjectured by Rudnick and Sarnak and assuming the Riemann Hypothesis, we prove new results on the prime number theorem, difference of consecutive primes, and the twin prime conjecture. 1.  Assuming the Riemann Hypothesis (RH), let us denote by  1=2 ig a nontrivial zero of a primitive L-function Ls;p attached to an irreducible cuspidal automorphic representation of GLm; m ^ 1, over Q. When m  1, this L-function is the Riemann zeta function zs or the Dirichlet L-function Ls; c for a primitive character c. Rudnick and Sarnak [13] examined the n-level correlation for these zeros and made a far reaching conjecture which is called the Montgomery [  Law by Katz and Sarnak . Rudnick and Sarnak also proved a case of their conjecture when a test function f has its Fourier transform b f supported in a restricted region. In this article, we will show that a version of the above conjecture for the pair correlation of zeros of the zeta function zs implies interesting arithmetical results on prime distribution (Theorems 2, 3, and 4). These results can give us deep insight on possible ultimate bounds of these prime distribution problems. One can also see that the pair (and nlevel) correlation of zeros of zeta and L-functions is a powerful method in number theory. Our computation shows that the test function f and the support of its Fourier transform b f play a crucial role in the conjecture. To see the conjecture in Rudnick and Sarnak [13] in the case of the zeta function zs and n  2, the pair correlation, we use a test function f x; y which satisfies the following three conditions: (i) f x; y  f y; x for any x; y 2 R, (ii) f x t; y t  f x; y for any t 2 R, and (iii) f x; y tends to 0 rapidly as jx; yj ! 1 on the hyperplane x y  0.  (2001) 41±50 0003-889X/01/010041-10 $ 3.50/0 Birkhäuser Verlag, Basel, 2001 Archiv der Mathematik  Supported in part by China NNSF Grant # 19701019. 2) Supported in part by USA NSF Grant # DMS 97-01225. Define the function W2x; y  1ÿ sin pxÿ y pxÿ y : Denote the Dirac function by dx which satisfies  R dxdx  1 and defines a distribution f 7! f 0. We then define the pair correlation sum of zeros gj of the zeta function: R2T; f ; h  P g1;g2 distinct h g1 T ; g2 T f Lg1 2p ; Lg2 2p ; where T ^ 2, L  log T, and hx; y is a localized cutoff function which tends to zero rapidly when jx; yj tends to infinity. The conjecture proposed by Rudnick and Sarnak [13] is that R2T; f ; h 1 2p TL
CLOSING THE SKILLS AND TECHNOLOGY GAP IN SOUTH AFRICA BACKGROUND PAPER 3 : THE ROLE OF HIGHER EDUCATION IN CLOSING THE SKILLS GAP IN SOUTH AFRICA 1

The paper is part of a series of background papers which seeks to identify and analyze key constraints in higher education, skills development, and technology absorption in accelerating labor absorption and shared growth in South Africa. The background papers form part of the ‘Closing the Skills and Technology Gaps in South Africa’ project which was financed by the Australian Agency for International Development.
Distributed Spatial Data Clustering as a New Approach for Big Data Analysis

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. Distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic Distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has super-linear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communications.