nils-herrmann/clean_scirepeval huggingface dataset:
Preference-aware content dissemination in opportunistic mobile social networks

As mobile devices have become more ubiquitous, mobile users increasingly expect to utilize proximity-based connectivity, e.g., WiFi and Bluetooth, to opportunistically share multimedia content based on their personal preferences. However, many previous studies investigate content dissemination protocols that distribute a single object to as many users in an opportunistic mobile social network as possible without considering user preference. In this paper, we propose PrefCast, a preference-aware content dissemination protocol that targets on maximally satisfying user preference for content objects. Due to non-persistent connectivity between users in a mobile social network, when a user meets neighboring users for a limited contact duration, it needs to efficiently disseminate a suitable set of objects that can bring possible future contacts a high utility (the quantitative metric of preference satisfaction). We formulate such a problem as a maximum-utility forwarding model, and propose an algorithm that enables each user to predict how much utility it can contribute to future contacts and solve its optimal forwarding schedule in a distributed manner. Our trace-based evaluation shows that PrefCast can produce a 18.5% and 25.2% higher average utility than the protocols that only consider contact frequency or preference of local contacts, respectively.
Jerk-bounded manipulator trajectory planning: design for real-time applications

An online method for obtaining smooth, jerk-bounded trajectories has been developed and implemented. Jerk limitation is important in industrial robot applications, since it results in improved path tracking and reduced wear on the robot. The method described herein uses a concatenation of fifth-order polynomials to provide a smooth trajectory between two way points. The trajectory approximates a linear segment with parabolic blends trajectory. A sine wave template is used to calculate the end conditions (control points) for ramps from zero acceleration to nonzero acceleration. Joining these control points with quintic polynomials results in a controlled quintic trajectory that does not oscillate, and is near time optimal for the jerk and acceleration limits specified. The method requires only the computation of the quintic control points, up to a maximum of eight points per trajectory way point. This provides hard bounds for online motion algorithm computation time. A method for blending these straight-line trajectories over a series of way points is also discussed. Simulations and experimental results on an industrial robot are presented.
Code Review Quality: How Developers See It

In a large, long-lived project, an effective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it difficult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching.
Research on Parallel Vulnerabilities Discovery Based on Open Source Database and Text Mining

For the question of information security vulnerabilities discovery, the parallel vulnerabilities discovery method is given based on the CAPEC, CWE, CVE and other open source database and text mining. Firstly, we can extract the association vulnerability CWE under the same attack mode, then from CWE associated with CVE based on open source database. That can help us to analyze the potential parallel relationship of the multiple vulnerabilities. Secondly, the vulnerability description information will be vectorized, so that the software system is able to intelligent processing to vulnerability data. That is different from the query based on keyword matching, analyzes the similarity between the multiple vulnerabilities according to the threshold from the training set, and computes the parallel relationship between the multiple vulnerabilities and discovery the parallel vulnerabilities. Finally, this method is correct and effective by the experimental verification and in practice. According to this method, we are able to repair other parallel vulnerabilities when finding a vulnerability is exploited. An advantage of our method is that is applied to network defense.
Aging of the bony orbit: a three-dimensional computed tomographic study.

Facial aging is a dynamic process involving the aging of soft tissue and bony structures. The shape, size, and volume of the bony orbit have all been shown to change with increasing age.



In this study, we demonstrate how specific bony aspects of the orbit change with age in both male and female subjects and what impact this may have on the techniques used in facial cosmetic surgery.



Facial bone computed tomography (CT) scans were obtained from 60 white subjects (30 female, 30 male). Our study population consisted of 10 male and 10 female subjects in each of 3 age categories. Each CT scan underwent three-dimensional (3-D) reconstruction with volume rendering. Orbital aperture width was measured as a line drawn from the posterior lacrimal crest to the frontozygomatic suture. This line was then used as the x-axis from which the distance to the superior and inferior orbital rim at nine equal increments (labeled 10 to 90) was obtained. The orbital aperture area was also measured on each 3-D model. The Student t test was used to identify any trends between age groups.



The orbital aperture width and area in both our male and female subjects showed a significant increase with increasing age. There was a significant increase in height of the superior orbital rim medially in both genders, suggesting that the superior orbital rim receded with age in this region. The inferior orbital rim receded significantly laterally in our female population, while our male subjects had a recession of the entire inferior orbital rim.



These results suggest that the bony elements of the orbit change dramatically with age, and this, coupled with soft tissue changes, can lead to the appearance of the aged eye and orbit.
Information Extraction for Ontology Learning

In this chapter, we discuss how ontologies can be constructed by extracting information from Web documents. This is a challenging task, because information extraction is usually a noisy endeavor, whereas ontologies usually require clean and crisp data. This means that the extracted information has to be cleaned, disambiguated, and made logically consistent to some degree. We will discuss three approaches that extract an ontology in this spirit from Wikipedia (DBpedia, YAGO, and KOG). We will also present approaches that aim to extract an ontology from natural language documents or, by extension, from the entire Web (OntoUSP, NELL and SOFIE). We will show that information extraction and ontology construction can enter into a fruitful reinforcement loop, where more extracted information leads to a larger ontology, and a larger ontology helps extracting more information.
An Introduction to Logistic Regression Analysis and Reporting

The purpose of this article is to provide researchers, editors, and readers with a set of guidelines for what to expect in an article using logistic regression techniques. Tables, figures, and charts that should be included to comprehensively assess the results and assumptions to be verified are discussed. This article demonstrates the preferred pattern for the application of logistic methods with an illustration of logistic regression applied to a data set in testing a research hypothesis. Recommendations are also offered for appropriate reporting formats of logistic regression results and the minimum observation-to-predictor ratio. The authors evaluated the use and interpretation of logistic regression presented in 8 articles published in The Journal of Educational Research between 1990 and 2000. They found that all 8 studies met or exceeded recommended criteria.
Testing Techniques for Hardware Security

System security has emerged as a premier design requirement. While there has been an enormous body of impressive work on testing integrated circuits (ICs) desiderata such as manufacturing correctness, delay, and power, there is no reported effort to systematically test IC security in hardware. Our goal is to provide an impetus for this line of research and development by introducing techniques and methodology for rigorous testing of physically unclonable functions (PUFs). Recently, PUFs received a great deal of attention as security mechanisms due to their flexibility to form numerous security protocols and intrinsic resiliency against physical and side channels attacks. We study three classes of PUFs properties to design pertinent test methods: (i) predictability, (ii) sensitivity to component accuracy, and (iii) susceptibility to reverse engineering. As our case studies, we analyze two popular PUF structures, linear and feed-forward, and show that their security is not adequate from several points of view. The technical highlights of the paper are the first non-destructive technique for PUF reverse engineering and a new PUF structure that is capable of passing our security tests.
An Efficient Participant’s Selection Algorithm for Crowdsensing

With the advancement of mobile technology the use of Smartphone is greatly increased. Everyone has the mobile phones and it becomes the necessity of life. Today, smart devices are flooding the internet data at every time and in any form that cause the mobile crowdsensing (MCS). One of the key challenges in mobile crowd sensing system is how to effectively identify and select the well-suited participants in recruitments from a large user pool. This research work presents the concept of crowdsensing along with the selection process of participants from a large user pool. MCS provides the efficient selection process for participants that how well suited participant’s selects/recruit from a large user pool. For this, the proposed selection algorithm plays our role in which the recruitment of participants takes place with the availability status from the large user pool. At the end, the graphical result presented with the suitable location of the participants and their time slot. Mobile crowdsensing (MCS); Mobile Sensing Platform (MSP]); crowd sensing; participant; user pool; crowdsourcing
Depth camera based indoor mobile robot localization and navigation

The sheer volume of data generated by depth cameras provides a challenge to process in real time, in particular when used for indoor mobile robot localization and navigation. We introduce the Fast Sampling Plane Filtering (FSPF) algorithm to reduce the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (the “plane filtered” points) or points that do not correspond to planes within a specified error margin (the “outlier” points). We then introduce a localization algorithm based on an observation model that down-projects the plane filtered points on to 2D, and assigns correspondences for each point to lines in the 2D map. The full sampled point cloud (consisting of both plane filtered as well as outlier points) is processed for obstacle avoidance for autonomous navigation. All our algorithms process only the depth information, and do not require additional RGB data. The FSPF, localization and obstacle avoidance algorithms run in real time at full camera frame rates (30Hz) with low CPU requirements (16%). We provide experimental results demonstrating the effectiveness of our approach for indoor mobile robot localization and navigation. We further compare the accuracy and robustness in localization using depth cameras with FSPF vs. alternative approaches that simulate laser rangefinder scans from the 3D data.
Mind wandering and attention during focused meditation: A fine-grained temporal analysis of fluctuating cognitive states

Studies have suggested that the default mode network is active during mind wandering, which is often experienced intermittently during sustained attention tasks. Conversely, an anticorrelated task-positive network is thought to subserve various forms of attentional processing. Understanding how these two systems work together is central for understanding many forms of optimal and sub-optimal task performance. Here we present a basic model of naturalistic cognitive fluctuations between mind wandering and attentional states derived from the practice of focused attention meditation. This model proposes four intervals in a cognitive cycle: mind wandering, awareness of mind wandering, shifting of attention, and sustained attention. People who train in this style of meditation cultivate their abilities to monitor cognitive processes related to attention and distraction, making them well suited to report on these mental events. Fourteen meditation practitioners performed breath-focused meditation while undergoing fMRI scanning. When participants realized their mind had wandered, they pressed a button and returned their focus to the breath. The four intervals above were then constructed around these button presses. We hypothesized that periods of mind wandering would be associated with default mode activity, whereas cognitive processes engaged during awareness of mind wandering, shifting of attention and sustained attention would engage attentional subnetworks. Analyses revealed activity in brain regions associated with the default mode during mind wandering, and in salience network regions during awareness of mind wandering. Elements of the executive network were active during shifting and sustained attention. Furthermore, activations during these cognitive phases were modulated by lifetime meditation experience. These findings support and extend theories about cognitive correlates of distributed brain networks.
Self-compassion and reactions to unpleasant self-relevant events: the implications of treating oneself kindly.

Five studies investigated the cognitive and emotional processes by which self-compassionate people deal with unpleasant life events. In the various studies, participants reported on negative events in their daily lives, responded to hypothetical scenarios, reacted to interpersonal feedback, rated their or others' videotaped performances in an awkward situation, and reflected on negative personal experiences. Results from Study 1 showed that self-compassion predicted emotional and cognitive reactions to negative events in everyday life, and Study 2 found that self-compassion buffered people against negative self-feelings when imagining distressing social events. In Study 3, self-compassion moderated negative emotions after receiving ambivalent feedback, particularly for participants who were low in self-esteem. Study 4 found that low-self-compassionate people undervalued their videotaped performances relative to observers. Study 5 experimentally induced a self-compassionate perspective and found that self-compassion leads people to acknowledge their role in negative events without feeling overwhelmed with negative emotions. In general, these studies suggest that self-compassion attenuates people's reactions to negative events in ways that are distinct from and, in some cases, more beneficial than self-esteem.
Aspect Based Sentiment Analysis with Gated Convolutional Networks

Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.
Design Choices and Their Implications for 3D Mid-Air Manipulation Techniques

Manipulation is one of the most important tasks required in virtual environments and thus it has been thoroughly studied for widespread input devices such as mice or multi-touch screens. Nowadays, the Kinect sensor has turned mid-air interaction into another affordable and popular way of interacting. Mid-air interaction enables the possibility of interacting remotely without any physical contact and in a more natural manner. Nonetheless, although some scattered manipulation techniques have been proposed for mid-air interaction, there is a lack of evaluations and comparisons that hinders the selection and development of these techniques. To solve this issue, we gathered four design choices that can be used to classify mid-air manipulation techniques. Namely, choices are based on the required number of hands, separation of translation–rotation, decomposition of rotation, and interaction metaphors. Furthermore, we developed, adapted, and compared three manipulation techniques selected for studying the implications of the design choices. These implications are useful to select among already existing techniques as well as to inform technique developers.
Critical factors that affect the success of business intelligence systems BIS implementation in an organization

As more organizations move towards intelligent IT infrastructure, nowadays, Business Intelligence Systems (BIS) become a more widely used IT solution. Even though it is a widely used IT solution, many BIS implementations are not successful because they are time consuming and expensive. The benefits of BIS can be achieved if the system is implemented successfully. This paper explores the critical success factors (CSFs) that effect BIS implementation both from the perspectives of management and technology. Also, based on literature and interviews, a conceptual framework of successful implementation of BIS is proposed. The framework summarizes CSFs of BIS based on project implementation life cycle (pre implementation stage, implementation stage and post-implementation stage) from managerial and technological perspectives. Finally, results, suggestions and directions for future researches are discussed.
Design of cavity-backed spiral antennas

In this paper, the design procedure of the 2-18 GHz and the 18-40 GHz cavity-backed spiral antenna is described. By varying the antenna parameters the VSWR and the axial ratio characteristics of the antenna are investigated. For the 2-18 GHz spiral antenna, the solid absorber and the Marchand balun are applied. The measured results of the fabricated antenna are compared with the simulated ones. For the 18-40 GHz antenna, the honeycomb absorber having variable loss tangent is considered in simulation to investigate for improvement of the VSWR and the axial ratio characteristics.
Latent factor models with additive and hierarchically-smoothed user preferences

Items in recommender systems are usually associated with annotated attributes: for e.g., brand and price for products; agency for news articles, etc. Such attributes are highly informative and must be exploited for accurate recommendation. While learning a user preference model over these attributes can result in an interpretable recommender system and can hands the cold start problem, it suffers from two major drawbacks: data sparsity and the inability to model random effects. On the other hand, latent-factor collaborative filtering models have shown great promise in recommender systems; however, its performance on rare items is poor. In this paper we propose a novel model LFUM, which provides the advantages of both of the above models. We learn user preferences (over the attributes) using a personalized Bayesian hierarchical model that uses a combination(additive model) of a globally learned preference model along with user-specific preferences. To combat data-sparsity, we smooth these preferences over the item-taxonomy using an efficient forward-filtering and backward-smoothing inference algorithm. Our inference algorithms can handle both discrete attributes (e.g., item brands) and continuous attributes (e.g., item prices). We combine the user preferences with the latent-factor models and train the resulting collaborative filtering system end-to-end using the successful BPR ranking algorithm. In our extensive experimental analysis, we show that our proposed model outperforms several commonly used baselines and we carry out an ablation study showing the benefits of each component of our model.
Soft-Switching Solid-State Transformer (S4T)

This paper presents a new topology for a fully bidirectional soft-switching solid-state transformer (S4T). The minimal topology, featuring 12 main devices and a high-frequency transformer, does not use an intermediate dc voltage link, and provides sinusoidal input and output voltages. The S4T can be configured to interface with two- or multiterminal dc, single- or multiphase ac systems. An auxiliary resonant circuit creates zero-voltage-switching conditions for main devices from no-load to full-load, and helps manage interactions with circuit parasitic elements. The modularized structure allows series and/or parallel stacking of converter cells for high-voltage and high-power applications.