nils-herrmann/clean_scirepeval huggingface dataset:
Effect on germination and early growth characteristics in sunflower (Helianthus annuus) seeds exposed to static magnetic field.

Seeds of sunflower (Helianthus annuus) were exposed in batches to static magnetic fields of strength from 0 to 250mT in steps of 50mT for 1-4h in steps of 1h. Treatment of sunflower seeds in these magnetic fields increased the speed of germination, seedling length and seedling dry weight under laboratory germination tests. Of the various treatments, 50 and 200mT for 2h yielded the peak performance. Exposure of seeds to magnetic fields improved seed coat membrane integrity and reduced the cellular leakage and electrical conductivity. Treated seeds planted in soil resulted in statistically higher seedling dry weight, root length, root surface area and root volume in 1-month-old seedlings. In germinating seeds, enzyme activities of alpha-amylase, dehydrogenase and protease were significantly higher in treated seeds in contrast to controls. The higher enzyme activity in magnetic-field-treated sunflower seeds could be triggering the fast germination and early vigor of seedlings.
On the design of millimetre-wave antennas for 5G

This paper addresses the millimeter-wave antenna design aspect of the future 5G wireless systems. The paper reviews the objectives and requirements of millimeter-wave antennas for 5G. Recent advances in mm-wave antenna are reported and design guidelines are discussed. In particular, four different designs are identified from the recent literature based on their attractive characteristics that support 5G requirements and applications. The first design employs a dual-band slotted patch antenna operating at 28 GHz and 38 GHz. The antenna has circular polarization and is excited by a single-feed microstrip line. The present design is desirable for high-gain antenna array implementation in the mm-wave band, in order to compensate for the mm-wave propagation loss. The second design that is presented employs a compact planar inverted-F antenna (PIFA) with single layer dielectric load of a superstrate to enhance the gain and achieve a wide impedance bandwidth resulting in high efficiency. The third design that operates in the mm-wave band is a T-Shaped patch antenna. The proposed antenna a wideband range from (26.5 GHz-40 GHz) of the Ka band. The PFT substrate was used as it offers some advantages; low cost, high flexibility, harmless to human body and resistive towards environmental effects. The last mm-wave antenna design presented employs two MEMO arrays each composed of 2×2 antenna elements. The two MIMO array configurations are spatially orthogonal to each other which results in polarization diversity.
Fine Motor Activities Program to Promote Fine Motor Skills in a Case Study of Down's Syndrome.

Children with Down's syndrome have developmental delays, particularly regarding cognitive and motor development. Fine motor skill problems are related to motor development. They have impact on occupational performances in school-age children with Down's syndrome because they relate to participation in school activities, such as grasping, writing, and carrying out self-care duties. This study aimed to develop a fine motor activities program and to examine the efficiency of the program that promoted fine motor skills in a case study of Down's syndrome. The case study subject was an 8 -year-old male called Kai, who had Down's syndrome. He was a first grader in a regular school that provided classrooms for students with special needs. This study used the fine motor activities program with assessment tools, which included 3 subtests of the Bruininks-Oseretsky Test of Motor Proficiency, second edition (BOT-2) that applied to Upper-limb coordination, Fine motor precision and Manual dexterity; as well as the In-hand Manipulation Checklist, and Jamar Hand Dynamometer Grip Test. The fine motor activities program was implemented separately and consisted of 3 sessions of 45 activities per week for 5 weeks, with each session taking 45 minutes. The results showed obvious improvement of fine motor skills, including bilateral hand coordination, hand prehension, manual dexterity, in-hand manipulation, and hand muscle strength. This positive result was an example of a fine motor intervention program designed and developed for therapists and related service providers in choosing activities that enhance fine motor skills in children with Down's syndrome.
Learning Sparse Task Relations in Multi-Task Learning

In multi-task learning, when the number of tasks is large, pairwise task relations exhibit sparse patterns since usually a task cannot be helpful to all of the other tasks and moreover, sparse task relations can reduce the risk of overfitting compared with the dense ones. In this paper, we focus on learning sparse task relations. Based on a regularization framework which can learn task relations among multiple tasks, we propose a SParse covAriance based mulTi-taSk (SPATS) model to learn a sparse covariance by using the 1 regularization. The resulting objective function of the SPATS method is convex, which allows us to devise an alternating method to solve it. Moreover, some theoretical properties of the proposed model are studied. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.
When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies

We show how third-party web trackers can deanonymize users of cryptocurrencies. We present two distinct but complementary attacks. On most shopping websites, third party trackers receive information about user purchases for purposes of advertising and analytics. We show that, if the user pays using a cryptocurrency, trackers typically possess enough information about the purchase to uniquely identify the transaction on the blockchain, link it to the user’s cookie, and further to the user’s real identity. Our second attack shows that if the tracker is able to link two purchases of the same user to the blockchain in this manner, it can identify the user’s entire cluster of addresses and transactions on the blockchain, even if the user employs blockchain anonymity techniques such as CoinJoin. The attacks are passive and hence can be retroactively applied to past purchases. We discuss several mitigations, but none are perfect.
Comparison of four commercial devices for RapidArc and sliding window IMRT QA

For intensity-modulated radiation therapy, evaluation of the measured dose against the treatment planning calculated dose is essential in the context of patient-specific quality assurance. The complexity of volumetric arc radiotherapy delivery attributed to its dynamic and synchronization nature require new methods and potentially new tools for the quality assurance of such techniques. In the present study, we evaluated and compared the dosimetric performance of EDR2 film and three other commercially available quality assurance devices: IBA I'MatriXX array, PTW Seven29 array and the Delta⁴ array. The evaluation of these dosimetric systems was performed for RapidArc and IMRT deliveries using a Varian NovalisTX linear accelerator. The plans were generated using the Varian Eclipse treatment planning system. Our results showed that all four QA techniques yield equivalent results. All patient QAs passed our institutional clinical criteria of gamma index based on a 3% dose difference and 3 mm distance to agreement. In addition, the Bland-Altman analysis was performed which showed that all the calculated gamma values of all three QA devices were within 5% from those of the film. The results showed that the four QA systems used in this patient-specific IMRT QA analysis are equivalent. We concluded that the dosimetric systems under investigation can be used interchangeably for routine patient specific QA.
8 Renewable Energy Opportunities in Water Desalination

Desalination is a water-treatment process that separates salts from saline water to produce potable water or water that is low in total dissolved solids (TDS). Globally, the total installed capacity of desalination plants was 61 million m3 per day in 2008 . Seawater desalination accounts for 67% of production, followed by brackish water at 19%, river water at 8%, and wastewater at 6%. Figure 1 show the worldwide feed-water percentage used in desalination. The most prolific users of desalinated water are in the Arab region, namely, Saudi Arabia, Kuwait, United Arab Emirates, Qatar, Oman, and Bahrain .
Failures of Gradient-Based Deep Learning

In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four types of simple problems, for which the gradientbased algorithms commonly used in deep learning either fail or suffer from significant difficulties. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.
Didaktische Anmerkungen zur Unterstützung der Programmierlehre durch E-Learning

Diese Arbeit verknüpft Lernziele, didaktische Methoden und Techniken zur Bearbeitung und Bewertung von Programmieraufgaben in E-Learning-Plattformen. Das Ziel ist dabei, sowohl eine Bewertungsgrundlage für den Einsatz einer Plattform für beliebige Lehrveranstaltungen der Programmierlehre zu schaffen als auch ein Gesamtkonzept für eine idealisierte E-Learning-Anwendung zu präsentieren. Dabei steht bewusst die Kompetenzbildung der Studierenden im Zentrum der Überlegungen – die dafür benötigte technische Unterstützung wird aus den didaktischen Methoden zur Vermittlung der Kompetenzen abgeleitet.
Modelling and simulation of complex sociotechnical systems: envisioning and analysing work environments

Accurate comprehension and analysis of complex sociotechnical systems is a daunting task. Empirically examining, or simply envisioning the structure and behaviour of such systems challenges traditional analytic and experimental approaches as well as our everyday cognitive capabilities. Computer-based models and simulations afford potentially useful means of accomplishing sociotechnical system design and analysis objectives. From a design perspective, they can provide a basis for a common mental model among stakeholders, thereby facilitating accurate comprehension of factors impacting system performance and potential effects of system modifications. From a research perspective, models and simulations afford the means to study aspects of sociotechnical system design and operation, including the potential impact of modifications to structural and dynamic system properties, in ways not feasible with traditional experimental approaches. This paper describes issues involved in the design and use of such models and simulations and describes a proposed path forward to their development and implementation.



The size and complexity of real-world sociotechnical systems can present significant barriers to their design, comprehension and empirical analysis. This article describes the potential advantages of computer-based models and simulations for understanding factors that impact sociotechnical system design and operation, particularly with respect to process and occupational safety.
Linguistic Relativity

The linguistic relativity hypothesis, the proposal that the particular language we speak influences the way we think about reality, forms one part of the broader question of how language influences thought. Despite long-standing historical interest in the hypothesis, there is relatively little empirical research directly addressing it. Existing empirical approaches are classified into three types. 1. Structure-centered approaches begin with language differences and ask about their implications for thought. 2. Domain-centered approaches begin with experienced reality and ask how different languages encode it. 3. Behavior-centered approaches begin with some practical concern and seek an explanation in language. These approaches are compared, and recent methodological improvements highlighted. Despite empirical advances, a theoretical account needs to articulate exactly how languages interpret experiences and how those interpretations influence thought. This will entail integrating theory and data concerning both the general relation of language and thought and the shaping influence of specific discursive structures and practices.
A REVIEW ON DATA MINING METHODS FOR IDENTITY CRIME DETECTION

Identity Crime is well known, established, and costly. Identity Crime is the term used to refer to all types of crime in which someone wrongfully obtains and uses another person’s personal data in some way that involves fraud or deception, typically for economic gain. Forgery and use of fraudulent identity documents are major enablers of Identity Fraud. It has affected the e-commerce. It is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of lots of money worldwide each year. Also along with transaction the application domain such as credit application is hit by this crime. These are growing concerns for not only governmental bodies but business organizations also all over the world. This paper gives a brief summary of the identity fraud. Also it discusses various data mining techniques used to overcome it.
Gender equity & human development.

The welfare of both women and men constitutes the human welfare. At the turn of the century amidst the glory of unprecedented growth in national income, India is experiencing the spread of rural distress. It is mainly due to the collapse of agricultural economy. Structural adjustments and competition from large-scale enterprises result in loss of rural livelihoods. Poor delivery of public services and safety nets, deepen the distress. The adverse impact is more on women than on men. This review examines the adverse impact of the events in terms of endowments, livelihood opportunities and nutritional outcomes on women in detail with the help of chosen indicators at two time-periods roughly representing mid nineties and early 2000. The gender equality index computed and the major indicators of welfare show that the gender gap is increasing in many aspects. All the aspects of livelihoods, such as literacy, unemployment and wages now have larger gender gaps than before. Survival indicators such as juvenile sex ratio, infant mortality, child labour have deteriorated for women, compared to men, though there has been a narrowing of gender gaps in life expectancy and literacy. The overall gender gap has widened due to larger gaps in some indicators, which are not compensated by the smaller narrowing in other indicators both in the rural and urban context.
DOLIRUQLD 0 DQDJHPHQW 5 HYLHZ 6 XPPHU 9 RO 1 R

The success of Internet based businesses in the Business to Customer segment in recent years is an indication of the events to unfold at the dawn of the new millennium. It is widely projected that the Business to Business segment is poised for a spectacular growth. However, a consistent definition and a framework for a business model for the Internet based business is still non-existent. This paper is an effort to fill in this gap. We propose a three dimensional framework for defining a business model and apply it to the emerging market structure. Furthermore, we also identify certain factors that would guide organizations in their choice of an appropriate business model. We also identify possibilities for further theory building in this area.
Quantitative Authorship Attribution: An Evaluation of Techniques

The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author’s writing sample. Over the past three centuries, many types of textual measurements have been proposed, but never before have the majority of these measurements been tested on the same dataset. A large-scale comparison of textual measurements is crucial if current techniques are to be used effectively and if new and more powerful techniques are to be developed. This article presents the results of a comparison of thirty-nine different types of textual measurements commonly used in attribution studies, in order to determine which are the best indicators of authorship. Based on the results of these tests, a more accurate approach to quantitative authorship attribution is proposed, which involves the analysis of many different textual measurements.
A Statistical Analysis of the Relationship between Harmonic Surprise and Preference in Popular Music

Studies have shown that some musical pieces may preferentially activate reward centers in the brain. Less is known, however, about the structural aspects of music that are associated with this activation. Based on the music cognition literature, we propose two hypotheses for why some musical pieces are preferred over others. The first, the Absolute-Surprise Hypothesis, states that unexpected events in music directly lead to pleasure. The second, the Contrastive-Surprise Hypothesis, proposes that the juxtaposition of unexpected events and subsequent expected events leads to an overall rewarding response. We tested these hypotheses within the framework of information theory, using the measure of "surprise." This information-theoretic variable mathematically describes how improbable an event is given a known distribution. We performed a statistical investigation of surprise in the harmonic structure of songs within a representative corpus of Western popular music, namely, the McGill Billboard Project corpus. We found that chords of songs in the top quartile of the Billboard chart showed greater average surprise than those in the bottom quartile. We also found that the different sections within top-quartile songs varied more in their average surprise than the sections within bottom-quartile songs. The results of this study are consistent with both the Absolute- and Contrastive-Surprise Hypotheses. Although these hypotheses seem contradictory to one another, we cannot yet discard the possibility that both absolute and contrastive types of surprise play roles in the enjoyment of popular music. We call this possibility the Hybrid-Surprise Hypothesis. The results of this statistical investigation have implications for both music cognition and the human neural mechanisms of esthetic judgments.
Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet, more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution.
A hybrid symbolic execution assisted fuzzing method

We present a new automated method for efficient detection of security vulnerabilities in binary programs. This method starts with a bounded symbolic execution of the target program so as to explore as many paths as possible. Constraints of the explored paths are collected and solved for inputs. The inputs will then be fed to the following interleaved coverage-based fuzzing and concolic execution. As the paths explored by the bounded symbolic execution may cover some unique paths that can be rarely reached by random testing featured fuzzing and locality featured concolic execution, the efficiency and effectiveness of the overall exploration can be greatly enhanced. In particular, the bounded symbolic execution can effectively prevent the fuzzing guided exploration from converging to the less interesting but easy-to-fuzz branches.
How do Service-oriented Architectures Influence Organizational Agility?

Managing unanticipated changes in turbulent and dynamic market environments requires organizations to reach an extended level of flexibility, which is known as agility. Agility can be defined as ability to sense environmental changes and to readily respond to those. While information systems are alleged to have a major influence on organizational agility, service-oriented architecture (SOA) poses an opportunity to shape agile information systems and ultimately organizational agility. However, related research studies predominantly comprise theoretical claims only. Seeking a detailed picture and in-depth insights, we conduct a qualitative exploratory case study. The objective of our research-in-progress is therefore to provide first-hand empirical data to contribute insights into SOA’s influence on organizational agility. We contribute to the two related research fields of SOA and organizational agility by addressing lack of empirical research on SOA’s organizational implications.
Obsidian: A Safer Blockchain Programming Language

Blockchain platforms, such as Ethereum, promise to facilitate transactions on a decentralized computing platform among parties that have not established trust. Recognition of the unique challenges of blockchain programming has inspired developers to create domain-specific languages, such as Solidity, for programming blockchain systems. Unfortunately, bugs in Solidity programs have recently been exploited to steal money. We propose a new programming language, Obsidian, to make it easier for programmers to write correct programs.
Outliers Detection on Educational Data using Fuzzy Association Rule Mining

Most of the mining techniques have only concerned with interesting patterns. However, in the recent years, there is an increasing demand in mining Unexpected Items or Outliers or Rare Items. Several application domains have realized the direct mapping between outliers in data and real world anomalies that are of great interest to an analyst. Outliers represents semantically correct but infrequent situationin a database. Detecting outliers allows extracting useful and actionable knowledge to the domain experts. In Educational Data, outliers are those students who have secured scores deviated so much from the average scores of other students. The educational data are Quantitative in nature. Any mining technique on quantitative data will partition the quantitative attributes with unnatural boundaries which lead to overestimate or underestimate the boundary values. Fuzzy logic handles this in a more realistic way. Knowing the threshold values apriori is not possible, hence our method uses dynamically calculated Support and Rank measures rather than predefined values. Our method uses a modified Fuzzy Apriori Rare Item sets Mining (FARIM) algorithm to detect the outliers (weak student). This will help the teachers in giving extra coaching for the weak students.