nils-herrmann/clean_scirepeval huggingface dataset:
A review on software development security engineering using Dynamic System Method (DSDM)

Agile methodology such as Scrum, Extreme Programming (XP), Feature Driven Development (FDD) and the Dynamic System Development Method (DSDM) have gained enough recognition as efficient development process by delivering software fast even under the time constrains. However, like other agile methods DSDM has been criticized because of unavailability of security element in its four phases. In order to have a deeper look into the matter and discover more about the reality, we conducted a literature review. Our findings highlight that, in its current form, the DSDM does not support developing secure software. Although, there are a few researches on this topic about Scrum, XP and FDD but, based on our findings, there is no research on developing secure software using DSDM. Thus, in our future work we intend to propose enhanced DSDM that will cater the security aspects in software development.
Answering enumeration queries with the crowd

1.1. 能否全部搞定?枚举查询 在本文中,我们思考了关系数据库管理系统 (RDBMS) 中一种最基本的运算,即利用筛选约束条件或断言 来扫描单一数据库表格;此查询枚举用户感兴趣的一 组特定项目。例如,请思考以下 SQL 查询,它要列 出可以忍耐低光环境的所有室内盆栽植物:SELECT DISTINCT name FROM Plants WHERE lightneeds = ‘low’。在使用传统的 RDBMS 并且给定数 据库状态时,此查询只有一个正确的答案,通过扫描 相关表格、筛选记录,再返回所有匹配的记录即可获 得该答案。这种方法对即便实际上无界的关系也奏效, 因为封闭世界假定指出:在查询执行之时数据库中没 有的任何记录都不存在。 相反,在 CrowdDB 等众包系统中,一旦耗尽已 存储表格中的记录时,我们可以向众人发送作业来索 取更多记录。然后问题演变为:查询结果集何时才算 完整?众包查询天生具有模糊性或拥有无界结果集, 其元组分散于网络或者仅存在于人脑。例如,我们可 以思考这样的查询:列出就业市场上现有的博士学位 应届毕业生,或者列出加利福尼亚州对绿色技术感兴 趣的企业。这种查询是利用众人力量的数据库系统的 主要用例,对发出要执行的查询的用户而言均属于劳 动密集型任务,但由于并不经常需要执行,所以不一 定值得去开发、调节和使用复杂的机器学习解决方案。 在本文中,我们将解决以下问题:“用户在众包 数据库系统的开放世界中应当如何思索枚举查询?” 我们开发的统计学工具可以帮助用户推导时间 / 成本 与完整度的权衡,还可用于推进查询执行和众包策略。
Review: IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature

The role of information systems in the creation and appropriation of economic value has a long tradition of research, within which falls the literature on the sustainability of IT-dependent competitive advantage. In this article, we formally define the notion of IT-dependent strategic initiative and use Jane Webster was the accepting senior editor for this paper. Anitesh Barua was the associate editor. Jeanne Ross, Anandhi Bharadwaj, and Paul Pavlou served as reviewers. it to frame a review of the literature on the sustainability of competitive advantage rooted in information systems use. We offer a framework that articulates both the dynamic approach to ITdependent strategic advantage currently receiving attention in the literature and the underlying drivers of sustainability. This framework models how and why the characteristics of the IT-dependent strategic initiative enable sustained competitive advantage, and how the determinants of sustainability are developed and strengthened over time. Such explanation facilitates the pre-implementation analysis of planned initiatives by innovators, as well as the post-implementation evaluation of existing initiatives so as to identify the basis of their sustainability. In carrying out this study, we examined the interdisciplinary literature on strategic information systems. Using a structured methodology, we reviewed the titles and abstracts of 648 articles drawn from information systems, strategic management, and marketing literature. We then examined and individually coded a relevant subset of 117 articles. The literature has identified four barriers to erosion of competitive advantage for ITdependent strategic initiatives and has surfaced the structural determinants of their magnitude. Previous work has also begun to theorize about the process by which these barriers to erosion evolve over time. Our review reveals that signifiPiccoli & Ives/IT-Dependent Strategic Initiatives 748 MIS Quarterly Vol. 29 No. 4/December 2005 cant exploratory research and theoretical development have occurred in this area, but there is a paucity of research providing rigorous tests of theoretical propositions. Our work makes three principal contributions. First, it formalizes the definition of IT-dependent strategic initiative. Second, it organizes the extant interdisciplinary research around an integrative framework that should prove useful to both research and practice. This framework offers an explanation of how and why IT-dependent strategic initiatives contribute to sustained competitive advantage, and explains the process by which they evolve over time. Finally, our review and analysis of the literature offers the basis for future research directions.
Framework of Maximum Power Extraction From Solar PV Panel Using Self Predictive Perturb and Observe Algorithm

This paper deals with a new version of perturb and observe tracking algorithm for maximum power extraction from the solar photovoltaic panel, which has self-predictive and decision taking ability. The working principle of self-predictive perturb and observe (SPP&O) algorithm is based on three consecutive operating points on the power-voltage characteristic. Out of three points, first two points very smartly detects the dynamic condition, as well as in normal condition, quickly searches the maximum power point (MPP) region. Moreover, by using a circular analogy, all points decide the optimal operating position for next iteration, which is responsible for quick MPP tracking as well as improved dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on hardware prototype. Moreover, comparison between SPP&O algorithm and state of art methods is made. Its satisfactory dynamic and steady-state behaviors with low algorithm complexity as well as the low computational burden of the SPP&O algorithm show the superiority over state of the art methods.
Convex Formulations for Fair Principal Component Analysis

Though there is a growing literature on fairness for supervised learning, incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first define fairness for dimensionality reduction, and our definition can be interpreted as saying a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs, and we demonstrate their effectiveness using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.
A Source Code Similarity System for Plagiarism Detection

Source code plagiarism is an easy to do task, but very difficult to detect without proper tool support. Various source code similarity detection systems have been developed to help detect source code plagiarism. Those systems need to recognize a number of lexical and structural source code modifications. For example, by some structural modifications (e.g. modification of control structures, modification of data structures or structural redesign of source code) the source code can be changed in such a way that it almost looks genuine. Most of the existing source code similarity detection systems can be confused when these structural modifications have been applied to the original source code. To be considered effective, a source code similarity detection system must address these issues. To address them, we designed and developed the source code similarity system for plagiarism detection. To demonstrate that the proposed system has the desired effectiveness, we performed a well-known conformism test. The proposed system showed promising results as compared with the JPlag system in detecting source code similarity when various lexical or structural modifications are applied to plagiarized code. As a confirmation of these results, an independent samples t-test revealed that there was a statistically significant difference between average values of F -measures for the test sets that we used and for the experiments that we have done in the practically usable range of cut-off threshold values of 35–70%.
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
Brain-computer interface using fMRI: spatial navigation by thoughts.

A brain-computer interface (BCI) is a way of conveying an individual's thoughts to control computer or electromechanical hardware. Capitalizing on the ability to characterize brain activity in a reproducible manner, we explored the possibility of using real-time fMRI to interpret the spatial distribution of brain function as BCI commands. Using a high-field (3T) MRI scanner, brain activities associated with four distinct covert functional tasks were detected and subsequently translated into predetermined computer commands for moving four directional cursors. The proposed fMRI-BCI method allowed volunteer subjects to navigate through a simple 2D maze solely through their thought processes.
Authorship Analysis of the Zeus Botnet Source Code

Authorship analysis has been used successfully to analyse the provenance of source code files in previous studies. The source code for Zeus, one of the most damaging and effective botnets to date, was leaked in 2011. In this research, we analyse the source code from the lens of authorship clustering, aiming to estimate how many people wrote this malware, and what their roles are. The research provides insight into the structure the went into creating Zeus and its evolution over time. The work has potential to be used to link the malware with other malware written by the same authors, helping investigations, classification, deterrence and detection.
Emotion Detection From Text Documents

Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.
An Approach to Source-Code Plagiarism Detection and Investigation Using Latent Semantic Analysis

Plagiarism is a growing problem in academia. Academics often use plagiarism detection tools to detect similar source-code files. Once similar files are detected, the academic proceeds with the investigation process which involves identifying the similar source-code fragments within them that could be used as evidence for proving plagiarism. This paper describes PlaGate, a novel tool that can be integrated with existing plagiarism detection tools to improve plagiarism detection performance. The tool also implements a new approach for investigating the similarity between source-code files with a view to gathering evidence for proving plagiarism. Graphical evidence is presented that allows for the investigation of source-code fragments with regards to their contribution toward evidence for proving plagiarism. The graphical evidence indicates the relative importance of the given source-code fragments across files in a corpus. This is done by using the Latent Semantic Analysis information retrieval technique to detect how important they are within the specific files under investigation in relation to other files in the corpus.
Web Search of Fashion Items with Multimodal Querying

In this paper, we introduce a novel multimodal fashion search paradigm where e-commerce data is searched with a multimodal query composed of both an image and text. In this setting, the query image shows a fashion product that the user likes and the query text allows to change certain product attributes to fit the product to the user's desire. Multimodal search gives users the means to clearly express what they are looking for. This is in contrast to current e-commerce search mechanisms, which are cumbersome and often fail to grasp the customer's needs. Multimodal search requires intermodal representations of visual and textual fashion attributes which can be mixed and matched to form the user's desired product, and which have a mechanism to indicate when a visual and textual fashion attribute represent the same concept. With a neural network, we induce a common, multimodal space for visual and textual fashion attributes where their inner product measures their semantic similarity. We build a multimodal retrieval model which operates on the obtained intermodal representations and which ranks images based on their relevance to a multimodal query. We demonstrate that our model is able to retrieve images that both exhibit the necessary query image attributes and satisfy the query texts. Moreover, we show that our model substantially outperforms two state-of-the-art retrieval models adapted to multimodal fashion search.
Women's orgasm.

An orgasm in the human female is a variable, transient peak sensation of intense pleasure, creating an altered state of consciousness, usually with an initiation accompanied by involuntary, rhythmic contractions of the pelvic striated circumvaginal musculature, often with concomitant uterine and anal contractions, and myotonia that resolves the sexually induced vasocongestion and myotonia, generally with an induction of well-being and contentment. Women's orgasms can be induced by erotic stimulation of a variety of genital and nongenital sites. As of yet, no definitive explanations for what triggers orgasm have emerged. Studies of brain imaging indicate increased activation at orgasm, compared to pre-orgasm, in the paraventricular nucleus of the hypothalamus, periaqueductal gray of the midbrain, hippocampus, and the cerebellum. Psychosocial factors commonly discussed in relation to female orgasmic ability include age, education, social class, religion, personality, and relationship issues. Findings from surveys and clinical reports suggest that orgasm problems are the second most frequently reported sexual problems in women. Cognitive-behavioral therapy for anorgasmia focuses on promoting changes in attitudes and sexually relevant thoughts, decreasing anxiety, and increasing orgasmic ability and satisfaction. To date there are no pharmacological agents proven to be beneficial beyond placebo in enhancing orgasmic function in women.
United States (US) multi-center study to assess the validity and reliability of the Spinal Cord Independence Measure (SCIM III)

Multi-center, prospective, cohort study.To assess the validity and reliability of the Spinal Cord Independence Measure (SCIM III) in measuring functional ability in persons with spinal cord injury (Inpatient rehabilitation hospitals in the United States (US).Methods:Functional ability was measured with the SCIM III during the first week of admittance into inpatient acute rehabilitation and within one week of discharge from the same rehabilitation program. Motor and sensory neurologic impairment was measured with the American Spinal Injury Association Impairment Scale. The Functional Independence Measure (FIM), the default functional measure currently used in most US hospitals, was used as a comparison standard for the SCIM III. Statistical analyses were used to test the validity and reliability of the SCIM III.Results:Total agreement between raters was above 70% on most SCIM III tasks and all κ-coefficients were statistically significant (P<0.001). The coefficients of Pearson correlation between the paired raters were above 0.81 and intraclass correlation coefficients were above 0.81. Cronbach’s-α was above 0.7, with the exception of the respiration task. The coefficient of Pearson correlation between the FIM and SCIM III was 0.8 (P<0.001). For the respiration and sphincter management subscale, the SCIM III was more responsive to change, than the FIM (Overall, the SCIM III is a reliable and valid measure of functional change in SCI. However, improved scoring instructions and a few modifications to the scoring categories may reduce variability between raters and enhance clinical utility.
The Runner -- Recommender System of Workout and Nutrition for Runners

Recommender systems have been gaining popularity and appreciation over the past few years and they kept growing towards a semantic web. Internet users search for more and more facilities to get information and recommendations based on their preferences, experience and expectations. Nowadays, there are many recommender systems on the web for music, movies, diets, products, etc. Some of them use very efficient recommending techniques (ex. Amazon), while others are very simple, based on algorithms that do not always provide relevant or interesting recommendations. The solution we propose is a recommender system for running professionals and amateurs, which is able to provide information to users regarding the workout and the diet that best suits them, based on their profile information, preferences and declared purpose. The solution mixes a social dimension derived from an expanding community with expert knowledge defined within an ontology. Moreover, our model addresses adaptability in terms of personal profile, professional results and unfortunate events that might occur during workouts.
Finding unusual review patterns using unexpected rules

In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them . This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.
A comparative study of K-Means, DBSCAN and OPTICS

In view of today's information available, recent progress in data mining research has lead to the development of various efficient methods for mining interesting patterns in large databases. It plays a vital role in knowledge discovery process by analyzing the huge data from various sources and summarizing it into useful information. It is helpful for analyzing the volumes of data in different domains like Marketing, Health, Science and Technology. Cluster analysis is widely used approach to notice the trends in the volumes of data. In this paper, we evaluated the performance of the different clustering approaches like as K-Means, DBSCAN, and OPTICS in terms of accuracy, outlier's formation, and cluster size prediction.
Improved Radiation Performance of Low Profile Printed Slot Antenna Using Wideband Planar AMC Surface

In this paper, a novel design of low profile printed slot antenna using wideband artificial magnetic conductor (AMC) is presented with improved radiation performance. For this purpose, the radiating slots with three unequal arms fed by coplanar waveguide are employed for broadening the impedance bandwidth with three resonances. It achieves a wide impedance bandwidth in the measured frequency range of 7.96-12.56 GHz (S11 ≤ -10 dB) for X-band applications. By loading a novel wideband planar AMC surface as the antenna ground plane, the radiation properties of this printed slot antenna are enhanced. This design includes the measured -10 dB impedance bandwidth from 5.75 to 14.51 GHz (86.48%). The proposed wideband planar AMC design operates at the frequency of 10.15 GHz with ±90° reflection phase bandwidth of 8-12.38 GHz (43.15%). The AMC surface is designed with the 5 × 8 array of periodic patches which are developed underneath the broadband printed slot antenna. In comparison with the proposed design without AMC, the printed slot antenna loaded with AMC introduces 62.82% size reduction, a bandwidth enhancement of 41.65%, and better impedance matching. It also contributes considerable features like a low profile antenna with unidirectional radiation patterns and an increased gain of more than 10.6 dBi.
Colocation Data Center Demand Response Using Nash Bargaining Theory

The huge yet flexible power consumption of data centers makes them promising resources for demand response, particularly for emergency demand response (EDR) which requires a certain amount of load curtailment during emergencies. However, current data centers often participate in EDR by starting up their backup diesel generators, resulting in both high costs and large carbon emissions. In this paper, we focus on cost-effective and eco-friendly demand response in colocation data centers by designing economic incentives for tenants to reduce their loads during emergency periods for EDR. In particular, we model and analyze the interaction among the data center operator and tenants by using Nash bargaining theory, and derive the optimal solutions for the load reduction and reimbursement for each tenant under two different bargaining protocols (i.e., sequential bargaining and concurrent bargaining). We prove that the derived solutions are Pareto-efficient and fair, and therefore self-enforcing and satisfactory for all entities. Numerical results based on trace-driven simulations show that the proposed bargaining approach is beneficial to both the data center operator and tenants, while also reducing the carbon emissions to the environment from data center demand response.
Automatic Curriculum Graph Generation for Reinforcement Learning Agents

In recent years, research has shown that transfer learning methods can be leveraged to construct curricula that sequence a series of simpler tasks such that performance on a final target task is improved. A major limitation of existing approaches is that such curricula are handcrafted by humans that are typically domain experts. To address this limitation, we introduce a method to generate a curriculum based on task descriptors and a novel metric of transfer potential. Our method automatically generates a curriculum as a directed acyclic graph (as opposed to a linear sequence as done in existing work). Experiments in both discrete and continuous domains show that our method produces curricula that improve the agent’s learning performance when compared to the baseline condition of learning on the target task from scratch.