nils-herrmann/clean_scirepeval huggingface dataset:
Data clustering - theory, algorithms, and applications

Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical dia... This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques-genetic algorithms, multiobjective optimization, soft ...
Transfer Learning in Collaborative Filtering with Uncertain Ratings

To solve the sparsity problem in collaborative filtering, researchers have introduced transfer learning as a viable approach to make use of auxiliary data. Most previous transfer learning works in collaborative filtering have focused on exploiting point-wise ratings such as numerical ratings, stars, or binary ratings of likes/dislike s. However, in many real-world recommender systems, many users may be unwilling or unlikely to rate items with precision. In contrast, practitioners can turn to various non-preference data to estimate a range or rating distribution of a user’s preference on an item. Such a range or rating distribution is called an uncertain rating since it represents a rating spectrum of uncertainty instead of an accurate point-wise score. In this paper, we propose an efficient transfer learning solution for collaborative filtering, known astransfer by integrative factorization(TIF), to leverage such auxiliary uncertain ratings to improve the performance of recommendation. In particular, we integrate auxiliary data of uncertain ratings as additional constraints in the target matrix factorization problem, and learn an expected rating value for each uncertain rating automatically. The advantages of our proposed approach include the efficiency and the improved effectiveness of collaborative filtering, showing that incorporating the auxiliary data of uncertain ratings can really bring a benefit. Experimental results on two movie recommendation tasks show that our TIF algorithm performs significantly better over a state-of-the-art non-transfer learning method.
Reducing Confusion about Grounded Theory and Qualitative Content Analysis : Similarities and Differences

Although grounded theory and qualitative content analysis are similar in some respects, they differ as well; yet the differences between the two have rarely been made clear in the literature. The purpose of this article was to clarify ambiguities and reduce confusion about grounded theory and qualitative content analysis by identifying similarities and differences in the two based on a literature review and critical reflection on the authors’ own research. Six areas of difference emerged: (a) background and philosophical base, (b) unique characteristics of each method, (c) goals and rationale of each method, (d) data analysis process, (e) outcomes of the research, and (f) evaluation of trustworthiness. This article provides knowledge that can assist researchers and students in the selection of appropriate research methods for their inquiries.
Surveying Stylometry Techniques and Applications

The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship attribution, authorship verification, authorship profiling, stylochronometry, and adversarial stylometry. Discussions on datasets, features, experimental techniques, and recent approaches are provided. Further, a current research challenge lies in the inability of authorship analysis techniques to scale to a large number of authors with few text samples. Here, we perform an extensive performance analysis on a corpus of 1,000 authors to investigate authorship attribution, verification, and clustering using 14 algorithms from the literature. Finally, several remaining research challenges are discussed, along with descriptions of various open-source and commercial software that may be useful for stylometry subtasks.
An Efficient Pseudonymous Authentication Scheme With Strong Privacy Preservation for Vehicular Communications

In this paper, we propose an efficient pseudonymous authentication scheme with strong privacy preservation (PASS), for vehicular communications. Unlike traditional pseudonymous authentication schemes, the size of the certificate revocation list (CRL) in PASS is linear with the number of revoked vehicles and unrelated to how many pseudonymous certificates are held by the revoked vehicles. PASS supports the roadside unit (RSU)-aided distributed certificate service that allows the vehicles to update certificates on road, but the service overhead is almost unrelated to the number of updated certificates. Furthermore, PASS provides strong privacy preservation to the vehicles so that the adversaries cannot trace any vehicle, even though all RSUs have been compromised. Extensive simulations demonstrate that PASS outperforms previously reported schemes in terms of the revocation cost and the certificate updating overhead.
: Formal Verification of Smart Contracts

Ethereum is a cryptocurrency framework that uses blockchain technology to provide an open distributed computing platform, called the Ethereum Virtual Machine (EVM). EVM programs are written in bytecode which operates on a simple stack machine. Programmers do not usually write EVM code; instead, they can program in a JavaScript-like language called Solidity that compiles to bytecode. Since the main application of EVM programs is as smart contracts that manage and transfer digital assets, security is of paramount importance. However, writing trustworthy smart contracts can be extremely difficult due to the intricate semantics of EVM and its openness: both programs and pseudonymous users can call into the public methods of other programs. This problem is best illustrated by the recent attack on TheDAO contract, which allowed roughly $50M USD worth of Ether to be transferred into the control of an attacker. Recovering the funds required a hard fork of the blockchain, contrary to the code is law premise of the system. In this paper, we outline a framework to analyze and verify both the runtime safety and the functional correctness of Solidity contracts in F, a functional programming language aimed at program verification.
A Comprehensive Study of Text Mining Approach

Text mining or knowledge discovery is that sub process of data mining, which is widely being used to discover hidden patterns and significant information from the huge amount of unstructured written material. The proliferation of clouds, research and technologies are responsible for the creation of vast volumes of data. This kind of data cannot be used until or unless specific information or pattern is discovered. For this text mining uses techniques of different fields like machine learning, visualization, case-based reasoning, text analysis, database technology statistics, knowledge management, natural language processing and information retrieval. Text mining is largely growing field of computer science simultaneously to big data and artificial intelligence. This paper contains the review of text mining techniques, tools and various applications.
Cognitive functioning and acute sedative effects of risperidone and quetiapine in patients with stable bipolar I disorder: a randomized, double-blind, crossover study.

Antipsychotic medications differ in their sedative potential, which can affect cognitive performance. The primary objective of this double-blind study was to compare the effects of treatment initiation with risperidone and quetiapine on cognitive function in subjects with stable bipolar disorder.



Subjects had a DSM-IV diagnosis of bipolar I disorder in partial or full remission and a Young Mania Rating Scale score <or= 8 at screening. Subjects were randomly assigned to 1 of 2 treatment sequences: risperidone-quetiapine or quetiapine-risperidone. Subjects in the risperidone-quetiapine sequence received 2 mg of risperidone with dinner and placebo with breakfast during period 1 and 100 mg of quetiapine with dinner and 100 mg with breakfast during period 2. Subjects in the quetiapine-risperidone sequence received the same treatments in reverse order. The 2 treatment periods were separated by a 6- to 14-day washout period. Cognitive function, including attention, working memory, declarative memory, processing speed, and executive functions, was measured before and after dosing. The Visual Analog Scale for Fatigue was also completed. The primary endpoint was a neurocognitive composite score (NCS). The study was conducted from November 2004 through August 2005.



Thirty subjects were randomly assigned; 28 took all doses of study medication and completed a baseline and at least 1 postbase-line assessment in each treatment. On the NCS, significantly better overall cognitive function was seen after risperidone than after quetiapine at each time point after dosing. Subjects performed significantly better after risperidone than after quetiapine (p < .05) on 9 of the 18 individual cognitive outcome measures and significantly better after quetiapine than after risperidone on 1 measure. Sleeping or the need for sleep during the test days was reported in significantly more patients after receiving quetiapine than risperidone.



The results indicate that initiation of quetiapine treatment was associated with more immediate adverse cognitive effects and increased somnolence than risperidone treatment.
Toward Natural Language Generation by Humans

Natural language generation (NLG) has been featured in at most a handful of shipped games and interactive stories. This is certainly due to it being a very specialized practice, but another contributing factor is that the state of the art today, in terms of content quality, is simply inadequate. The major benefits of NLG are its alleviation of authorial burden and the capability it gives to a system of generating state-bespoke content, but we believe we can have these benefits without actually employing a full NLG pipeline. In this paper, we present the preliminary design of EXPRESSIONIST, an in-development mixed-initiative authoring tool that instantiates an authoring scheme residing somewhere between conventional NLG and conventional human content authoring. In this scheme, a human author plays the part of an NLG module in that she starts from a set of deep representations constructed for the game or story domain and proceeds to specify dialogic content that may express those representations. Rather than authoring static dialogue, the author defines a probabilistic context-free grammar that yields templated dialogue. This allows a human author to still harness a computer’s generativity, but in a capacity in which it can be trusted: operating over probabilities and treelike control structures. Additional features of EXPRESSIONIST’s design include arbitrary markup and realtime feedback showing currently valid derivations.
Benefits of hyperspectral remote sensing for tracking plant invasions

Human activities such as international trade and travel promote biological invasions by accidentally or deliberately dispersing species outside their native biogeographical ranges (Lockwood, 2005; Alpert, 2006). Invasive species are now viewed as a significant component of global change and have become a serious threat to natural communities (Mack et al., 2000; Pyšek & Richardson, 2010). The ecological impact of invasive species has been observed in all types of ecosystems. Typically, invaders can change the niches of co-occurring species, alter the structure and function of ecosystems by degrading native communities and disrupt evolutionary processes through anthropogenic movement of species across physical and geographical barriers (D’Antonio & Vitousek, 1992; Mack et al., 2000; Richardson et al., 2000; Levine et al., 2003; Vitousek et al., 2011). Concerns for the implications and consequences of successful invasions have stimulated a considerable amount of research. Recent invasion research ranges from the developing testable hypotheses aimed at understanding the mechanisms of invasion to providing guidelines for control and management of invasive species. Several recent studies have used hyperspectral remote sensing (Underwood et al., 2003; Lass et al., 2005; Underwood Department of Biological Sciences, Murray State University, Murray, KY 42071, USA, Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele all’Adige, TN, Italy, Center for the Study of Institutions, Population, and Environmental Change, Indiana University, 408 N. Indiana Avenue, Bloomington, IN 47408, USA, Ashoka Trust for Research in Ecology and the Environment (ATREE), Royal Enclave, Srirampura, Jakkur Post, Bangalore 560064, India
Comparison of miniscalpel-needle release, acupuncture needling, and stretching exercise to trigger point in myofascial pain syndrome.

Myofascial pain syndrome (MPS) is one of the most common causes of chronic musculoskeletal pain. Several methods have been recommended for the inactivation of trigger points (TrPs). We carried out this study to investigate the effectiveness of miniscalpel-needle (MSN) release and acupuncture needling and self neck-stretching exercises on myofascial TrPs of the upper trapezius muscle.



Eighty-three TrPs in 43 patients with MPS were treated and randomly assigned to 3 groups: group 1 received MSN release in conjunction with self neck-stretching exercises; group 2 received acupuncture needling treatment and performed self neck-stretching exercises; and group 3, the control group, was assigned self neck-stretching exercises only. The therapeutic effectiveness was evaluated using subjective pain intensity (PI) with a visual analog scale, pressure pain threshold (PPT), and contralateral bending range of motion (ROM) of cervical spine at pretreatment, 2 weeks, and 3 months after treatment.



The improvement of PI, PPT, and contralateral bending ROM of cervical spine was significantly greater in group 1 and 2 than that in control group at 2 weeks and 3 months follow-up. Compared with group 2, patients in group 1 had a statistically significant reduction in PI, an increase in PPT, contralateral bending ROM of cervical spine at 3 months follow-up.



The effectiveness of MSN release for MPS is superior to that of acupuncture needling treatment or self neck-stretching exercises alone. The MSN release is also safe, without severe side effects in treatment of MPS.
Comparative Analysis of DES , AES , RSA Encryption Algorithms

As the increasing growth of the computing technology and network technology, it also increases data storage demands.  Security has become a crucial issue in electronic communication. Secret writing has come up as a solution, and plays a vital role in data security system. It uses some algorithms to scramble data into unreadable text which might be only being decrypted by party those having the associated key. These algorithms consume a major amount of computing resources such as memory and battery power and computation time. This paper accomplishes comparative analysis of encryption standards DES, AES and RSA considering various parameters such as computation time, memory usages. A cryptographic tool is used for performing experiments. Experiments results are given to analyses the effectiveness of symmetric and asymmetric algorithms.  Encryption, secret key encryption, public key encryption, DES, AES, RSA encryption, Symmetric
An introduction to case-based reasoning

Case-based reasoning means using old experiences to understand and solve new problems. In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem. Case-based reasoning can mean adapting old solutions to meet new demands; using old cases to explain new situations; using old cases to critique new solutions; or reasoning from precedents to interpret a new situation (much like lawyers do) or create an equitable solution to a new problem (much like labor mediators do). This paper discusses the processes involved in case-based reasoning and the tasks for which case-based reasoning is useful.
Factoid Question Answering with Distant Supervision

Automatic question answering (QA), which can greatly facilitate the access to information, is an important task in artificial intelligence. Recent years have witnessed the development of QA methods based on deep learning. However, a great amount of data is needed to train deep neural networks, and it is laborious to annotate training data for factoid QA of new domains or languages. In this paper, a distantly supervised method is proposed to automatically generate QA pairs. Additional efforts are paid to let the generated questions reflect the query interests and expression styles of users by exploring the community QA. Specifically, the generated questions are selected according to the estimated probabilities they are asked. Diverse paraphrases of questions are mined from community QA data, considering that the model trained on monotonous synthetic questions is very sensitive to variants of question expressions. Experimental results show that the model solely trained on generated data via the distant supervision and mined paraphrases could answer real-world questions with the accuracy of 49.34%. When limited annotated training data is available, significant improvements could be achieved by incorporating the generated data. An improvement of 1.35 absolute points is still observed on WebQA, a dataset with large-scale annotated training samples.
DeepWeak: Reasoning common software weaknesses via knowledge graph embedding

Common software weaknesses, such as improper input validation, integer overflow, can harm system security directly or indirectly, causing adverse effects such as denial-of-service, execution of unauthorized code. Common Weakness Enumeration (CWE) maintains a standard list and classification of common software weakness. Although CWE contains rich information about software weaknesses, including textual descriptions, common sequences and relations between software weaknesses, the current data representation, i.e., hyperlined documents, does not support advanced reasoning tasks on software weaknesses, such as prediction of missing relations and common consequences of CWEs. Such reasoning tasks become critical to managing and analyzing large numbers of common software weaknesses and their relations. In this paper, we propose to represent common software weaknesses and their relations as a knowledge graph, and develop a translation-based, description-embodied knowledge representation learning method to embed both software weaknesses and their relations in the knowledge graph into a semantic vector space. The vector representations (i.e., embeddings) of software weaknesses and their relations can be exploited for knowledge acquisition and inference. We conduct extensive experiments to evaluate the performance of software weakness and relation embeddings in three reasoning tasks, including CWE link prediction, CWE triple classification, and common consequence prediction. Our knowledge graph embedding approach outperforms other description- and/or structure-based representation learning methods.
AprilTag 2: Efficient and robust fiducial detection

AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag system into this improved system. This work describes AprilTag 2, a completely redesigned tag detector that improves robustness and efficiency compared to the original AprilTag system. The tag coding scheme is unchanged, retaining the same robustness to false positives inherent to the coding system. The new detector improves performance with higher detection rates, fewer false positives, and lower computational time. Improved performance on small images allows the use of decimated input images, resulting in dramatic gains in detection speed.
Using the Bitcoin Transaction Graph to Predict the Price of Bitcoin

Bitcoin is the world’s leading cryptocurrency, allowing users to make transactions securely and anonymously over the Internet. In recent years, The Bitcoin the ecosystem has gained the attention of consumers, businesses, investors and speculators alike. While there has been significant research done to analyze the network topology of the Bitcoin network, limited research has been performed to analyze the network’s influence on overall Bitcoin price. In this paper, we investigate the predictive power of blockchain network-based features on the future price of Bitcoin. As a result of blockchain-networkbased feature engineering and machine learning optimization, we obtain up-down Bitcoin price movement classification accuracy of roughly 55%.
Case study on technology and distance in education at the Harvard Business School

For almost a century, the Harvard Business School has used case studies as the basis for experiential learning in both MBA and Executive Education courses. This article presents a case on the school itself: its learning objectives and environment and the challenges of integrating technology into programs that have traditionally relied upon face-to-face, highly interactive classes. The authors summarize findings from interviews with faculty about the use of various types of technology and describe in detail two courses that are a hybrid of online and in-class sessions. The research highlights both the opportunities for technology enhanced education at HBS and the challenges to conducting such programs.
Making choices impairs subsequent self-control: a limited-resource account of decision making, self-regulation, and active initiative.

The current research tested the hypothesis that making many choices impairs subsequent self-control. Drawing from a limited-resource model of self-regulation and executive function, the authors hypothesized that decision making depletes the same resource used for self-control and active responding. In 4 laboratory studies, some participants made choices among consumer goods or college course options, whereas others thought about the same options without making choices. Making choices led to reduced self-control (i.e., less physical stamina, reduced persistence in the face of failure, more procrastination, and less quality and quantity of arithmetic calculations). A field study then found that reduced self-control was predicted by shoppers' self-reported degree of previous active decision making. Further studies suggested that choosing is more depleting than merely deliberating and forming preferences about options and more depleting than implementing choices made by someone else and that anticipating the choice task as enjoyable can reduce the depleting effect for the first choices but not for many choices.