nils-herrmann/clean_scirepeval huggingface dataset:
The Insider Threat in Cloud Computing

Cloud computing is an emerging technology paradigm, enabling and facilitating the dynamic and versatile provision of computational resources and services. Even though the advantages offered by cloud computing are several, there still exist second thoughts on the security and privacy of the cloud services. Use of cloud services affects the security posture of organizations and critical infrastructures, therefore it is necessary that new threats and risks introduced by this new paradigm are clearly understood and mitigated. In this paper we focus on the insider threat in cloud computing, a topic which has not received research focus, as of now. We address the problem in a holistic way, differentiating between the two possible scenarios: a) defending against a malicious insider working for the cloud provider, and b) defending against an insider working for an organization which chooses to outsource parts or the whole IT infrastructure into the cloud. We identify the potential problems for each scenario and propose the appropriate countermeasures, in an effort to mitigate the problem.
Rhinoplasty: a practical guide for surgical planning.

A method for determining the aesthetically proportioned nasal length, tip projection, and radix projection in any given face is described. The proportioned nasal length is two-thirds (0.67) the midfacial height and exactly equal to chin vertical. Tip projection is two-thirds (0.67) the surgically planned or ideal nasal length. Radix projection, measured from the junction of the nasal bones with the orbit, is one-third (0.33) the ideal nasal length. The preferred clinical reference for measuring radix projection is the plane of the corneal surface; the radix projects 0.28 times the ideal nasal length from this surface (range: 9-14 mm). These-dimensional relationships were confirmed from direct clinical measurements taken from 87 models and subsequently applied in 126 consecutive rhinoplasties. The significance of this dimensional approach to rhinoplasty lies in the fact that planned nasal dimensions are based on facial measurements that allow the nose to vary in size directly with the face. Furthermore, it removes the dorsum as the primary focus in dimensional assessment. Rather, the dorsal prominence may be consistently described relative to a plane connecting the "ideal" radix and tip.
Predicting Natural Hair Shapes by Solving the Statics of Flexible Rods

This paper presents a new physically-based method for predicting natural hairstyles in the presence of gravity and collisions. The method is based upon a mechanically accurate model for static elastic rods (Kirchhoff model), which accounts for the natural curliness of hair, as well as for hair ellipticity. The equilibrium shape is computed in a stable and easy way by energy minimization. This yields various typical hair configurations that can be observed in the real world, such as ringlets. As our results show, the method can generate different hair types with a very few input parameters, and perform virtual hairdressing operations such as wetting, cutting and drying hair.
Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.
MARGOT: A web server for argumentation mining

Argumentation mining is a recent challenge concerning the automatic extraction of arguments from unstructured textual corpora. Argumentation mining technologies are rapidly evolving and show a clear potential for application in diverse areas such as recommender systems, policy-making and the legal domain. There is a long-recognised need for tools that enable users to browse, visualise, search, and manipulate arguments and argument structures. There is, however, a lack of widely accessible tools. In this article we describe the technology behind MARGOT, the first online argumentation mining system designed to reach out to the wider community of potential users of these new technologies. We evaluate its performance and discuss its possible application in the analysis of content from various domains.
Approach to Hand Tracking and Gesture Recognition Based on Depth-Sensing Cameras and EMG Monitoring

In this paper, a new approach for hand tracking and gesture recognition based on the Leap Motion device and surface electromyography (SEMG) is presented. The system is about to process the depth image information and the electrical activity produced by skeletal muscles on forearm. The purpose of such combination is enhancement in the gesture recognition rate. As a first we analyse the conventional approaches toward hand tracking and gesture recognition and present the results of various researches. Successive topic gives brief overview of depth-sensing cameras with focus on Leap motion device where we test its accuracy of fingers recognition. The vision-SEMG-based system is to be potentially applicable to many areas of human computer interaction.
New pre-estimation algorithm for FMCW radar systems using the Matrix Pencil Method

This paper shows a technique to enhance the resolution of a frequency modulated continuous wave (FMCW) radar system. The range resolution of an FMCW radar system is limited by the bandwidth of the transmitted signal. By using high resolution methods such as the Matrix Pencil Method (MPM) it is possible to enhance the resolution. In this paper a new method to obtain a better resolution for FMCW radar systems is used. This new method is based on the MPM and is enhanced to require less computing power. To evaluate this new technique, simulations and measurements are used. The result shows that this new method is able to improve the performance of FMCW radar systems.
An Approach for the Cooperative Control of FES With a Powered Exoskeleton During Level Walking for Persons With Paraplegia

This paper describes a hybrid system that combines a powered lower limb exoskeleton with functional electrical stimulation (FES) for gait restoration in persons with paraplegia. The general control structure consists of two control loops: a motor control loop, which utilizes joint angle feedback control to control the output of the joint motor to track the desired joint trajectories, and a muscle control loop, which utilizes joint torque profiles from previous steps to shape the muscle stimulation profile for the subsequent step in order to minimize the motor torque contribution required for joint angle trajectory tracking. The implementation described here incorporates stimulation of the hamstrings and quadriceps muscles, such that the hip joints are actuated by the combination of hip motors and the hamstrings, and the knee joints are actuated by the combination of knee motors and the quadriceps. In order to demonstrate efficacy, the control approach was implemented on three paraplegic subjects with motor complete spinal cord injuries ranging from levels T6 to T10. Experimental data indicates that the cooperative control system provided consistent and repeatable gait motions and reduced the torque and power output required from the hip and knee motors of the exoskeleton compared to walking without FES.
Media and attention, cognition, and school achievement.

Marie Evans Schmidt and Elizabeth Vandewater review research on links between various types of electronic media and the cognitive skills of school-aged children and adolescents. One central finding of studies to date, they say, is that the content delivered by electronic media is far more influential than the media themselves. Most studies, they point out, find a small negative link between the total hours a child spends viewing TV and that child's academic achievement. But when researchers take into account characteristics of the child, such as IQ or socioeconomic status, this link typically disappears. Content appears to be crucial. Viewing educational TV is linked positively with academic achievement; viewing entertainment TV is linked negatively with achievement. When it comes to particular cognitive skills, say the authors, researchers have found that electronic media, particularly video games, can enhance visual spatial skills, such as visual tracking, mental rotation, and target localization. Gaming may also improve problem-solving skills. Researchers have yet to understand fully the issue of transfer of learning from electronic media. Studies suggest that, under some circumstances, young people are able to transfer what they learn from electronic media to other applications, but analysts are uncertain how such transfer occurs. In response to growing public concern about possible links between electronic media use and attention problems in children and adolescents, say the authors, researchers have found evidence for small positive links between heavy electronic media use and mild attention problems among young people but have found only inconsistent evidence so far for a link between attention deficit hyperactivity disorder and media use. The authors point out that although video games, interactive websites, and multimedia software programs appear to offer a variety of possible benefits for learning, there is as yet little empirical evidence to suggest that such media are more effective than other forms of instruction.
Introduction to behavioral addictions.

Several behaviors, besides psychoactive substance ingestion, produce short-term reward that may engender persistent behavior, despite knowledge of adverse consequences, i.e., diminished control over the behavior. These disorders have historically been conceptualized in several ways. One view posits these disorders as lying along an impulsive-compulsive spectrum, with some classified as impulse control disorders. An alternate, but not mutually exclusive, conceptualization considers the disorders as non-substance or "behavioral" addictions.



Inform the discussion on the relationship between psychoactive substance and behavioral addictions.



We review data illustrating similarities and differences between impulse control disorders or behavioral addictions and substance addictions. This topic is particularly relevant to the optimal classification of these disorders in the forthcoming fifth edition of the American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders (DSM-V).



Growing evidence suggests that behavioral addictions resemble substance addictions in many domains, including natural history, phenomenology, tolerance, comorbidity, overlapping genetic contribution, neurobiological mechanisms, and response to treatment, supporting the DSM-V Task Force proposed new category of Addiction and Related Disorders encompassing both substance use disorders and non-substance addictions. Current data suggest that this combined category may be appropriate for pathological gambling and a few other better studied behavioral addictions, e.g., Internet addiction. There is currently insufficient data to justify any classification of other proposed behavioral addictions.



Proper categorization of behavioral addictions or impulse control disorders has substantial implications for the development of improved prevention and treatment strategies.
IoT-Based Smart Rehabilitation System

Internet of Things (IoT) makes all objects become interconnected and smart, which has been recognized as the next technological revolution. As its typical case, IoT-based smart rehabilitation systems are becoming a better way to mitigate problems associated with aging populations and shortage of health professionals. Although it has come into reality, critical problems still exist in automating design and reconfiguration of such a system enabling it to respond to the patient's requirements rapidly. This paper presents an ontology-based automating design methodology (ADM) for smart rehabilitation systems in IoT. Ontology aids computers in further understanding the symptoms and medical resources, which helps to create a rehabilitation strategy and reconfigure medical resources according to patients' specific requirements quickly and automatically. Meanwhile, IoT provides an effective platform to interconnect all the resources and provides immediate information interaction. Preliminary experiments and clinical trials demonstrate valuable information on the feasibility, rapidity, and effectiveness of the proposed methodology.
Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity

Different word embedding models capture different aspects of linguistic properties. This inspired us to propose a model (MMaxLSTM-CNN) for employing multiple sets of word embeddings for evaluating sentence similarity/relation. Representing each word by multiple word embeddings, the MaxLSTM-CNN encoder generates a novel sentence embedding. We then learn the similarity/relation between our sentence embeddings via Multi-level comparison. Our method M-MaxLSTMCNN consistently shows strong performances in several tasks (i.e., measure textual similarity, identify paraphrase, recognize textual entailment). According to the experimental results on STS Benchmark dataset and SICK dataset from SemEval, M-MaxLSTM-CNN outperforms the state-of-the-art methods for textual similarity tasks. Our model does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pretrained word embeddings to have the same dimension.
Metamorphic Testing and Testing with Special Values

The problem of testing programs without test oracles is well known. A commonly used approach is to use special values in testing but this is often insufficient to ensure program correctness. This paper demonstrates the use of metamorphic testing to uncover faults in programs, which could not be detected by special test values. Metamorphic testing can be used as a complementary test method to special value testing. In this paper, the sine function and a search function are used as examples to demonstrate the usefulness of metamorphic testing. This paper also examines metamorphic relationships and the extent of their usefulness in program testing.
ZVS phase shift full bridge converter with controlled leakage inductance of transformer

Generally additional leakage inductance and two clamp diodes are adopted into the conventional phase shift full bridge (PSFB) converter for reducing the voltage stress of secondary rectifier diodes and extending the range of zero voltage switching (ZVS) operation. However, the core and copper loss caused by additional leakage inductor can be high enough to decrease the whole efficiency of DC/DC converter. Therefore, a new ZVS PSFB converter with controlled leakage inductance of transformer is proposed. The proposed converter makes both the transformer and additional leakage inductor with same ferrite core by separated primary winding (SPW). Using this method, leakage inductance is controlled by the winding ratio of SPW. Moreover, by using this integrated magnetic component with single core, size and core loss can be greatly reduced and it results in the improvement of efficiency and power density of DC/DC converter. The operational principle and analysis of proposed converter are presented and verified by the 1.2kW prototype.
Deep Learning for Single-View Instance Recognition

Deep learning methods have typically been trained on large datasets in which many training examples are available. However, many real-world product datasets have only a small number of images available for each product. We explore the use of deep learning methods for recognizing object instances when we have only a single training example per class. We show that feedforward neural networks outperform state-of-the-art methods for recognizing objects from novel viewpoints even when trained from just a single image per object. To further improve our performance on this task, we propose to take advantage of a supplementary dataset in which we observe a separate set of objects from multiple viewpoints. We introduce a new approach for training deep learning methods for instance recognition with limited training data, in which we use an auxiliary multi-view dataset to train our network to be robust to viewpoint changes. We find that this approach leads to a more robust classifier for recognizing objects from novel viewpoints, outperforming previous state-of-the-art approaches including keypoint-matching, template-based techniques, and sparse coding.
Varieties of repetitive behavior in autism: comparisons to mental retardation.

Systematic study of abnormal repetitive behaviors in autism has been lacking despite the diagnostic significance of such behavior. The occurrence of specific topographies of repetitive behaviors as well as their severity was assessed in individuals with mental retardation with and without autism. The occurrence of each behavior category, except dyskinesias, was higher in the autism group and autistic subjects exhibited a significantly greater number of topographies of stereotypy and compulsions. Both groups had significant patterns of repetitive behavior co-occurrence. Autistic subjects had significantly greater severity ratings for compulsions, stereotypy, and self-injury. Repetitive behavior severity also predicted severity of autism. Although abnormal repetition is not specific to autism, an elevated pattern of occurrence and severity appears to characterize the disorder.
Analysis of multi-phase permanent-magnet synchronous machines

Large AC machines are sometimes fed by multiple inverters. This paper presents the complete steady-state analysis of the PM synchronous machine with multiplex windings, suitable for driving by multiple independent inverters. Machines with 4, 6 and 9 phases are covered in detail. Particular attention is given to the magnetic interactions not only between individual phases, but between channels or groups of phases. This is of interest not only for determining performance and designing control systems, but also for analysing fault tolerance. It is shown how to calculate the necessary self- and mutual inductances and how to reduce them to a compact dq-axis model without loss of detail.
Building virtual cities, inspiring intelligent citizens: Digital games for developing students' problem solving and learning motivation

This study investigates the effectiveness digital game-based learning (DGBL) on students’ problem solving, learning motivation, and academic achievement. In order to provide substantive empirical evidence, a quasi-experimental design was implemented over the course of a full semester (23 weeks). Two ninth-grade Civics and Society classes, with a total of 44 students (15–16 years old), were randomly assigned to one of two conditions: an experimental group (incorporating DGBL) and a comparison group (taught using traditional instruction). Two-way mixed ANOVA was employed to evaluate changes in problem solving ability and compare the effectiveness the two strategies, while ANCOVA was used to analyze the effects on learning motivation and academic achievement. The results of this study are summarized as follows: (1) The DGBL strategy was clearly effective in promoting students’ problem solving skills, while the control group showed no improvement. Additionally, data from the mid-test and post-test demonstrate that, as a higher order thinking skill, problem-solving requires a full semester to develop. (2). DGBL resulted in better learning motivation for students in the experimental group as compared to learners receiving TI. (3) Contrary to some suggestions that digital games could inhibit academic achievement, no statistically significant difference was found between the two groups. Most importantly, the quantitative improvement in problem-solving and learning motivation suggest that DGBL can be exploited as a useful and productive tool to support students in effective learning while enhancing the classroom atmosphere. Future research in DGBL should emphasize the evaluation of other higher order elements of the cognitive domain in terms of academic achievement outcomes and skills, such as critical and creative thinking.
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information

Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks.