nils-herrmann/clean_scirepeval huggingface dataset:
Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications

Parallel control and management have been proposed as a new mechanism for conducting operations of complex systems, especially those that involved complexity issues of both engineering and social dimensions, such as transportation systems. This paper presents an overview of the background, concepts, basic methods, major issues, and current applications of Parallel transportation Management Systems (PtMS). In essence, parallel control and management is a data-driven approach for modeling, analysis, and decision-making that considers both the engineering and social complexity in its processes. The developments and applications described here clearly indicate that PtMS is effective for use in networked complex traffic systems and is closely related to emerging technologies in cloud computing, social computing, and cyberphysical-social systems. A description of PtMS system architectures, processes, and components, including OTSt, Dyna CAS, aDAPTS, iTOP, and TransWorld is presented and discussed. Finally, the experiments and examples of real-world applications are illustrated and analyzed.
Asymptotic Loss in Privacy due to Dependency in Gaussian Traces

The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users’ sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.
A model of force and impedance in human arm movements

This paper describes a simple computational model of joint torque and impedance in human arm movements that can be used to simulate three-dimensional movements of the (redundant) arm or leg and to design the control of robots and human-machine interfaces. This model, based on recent physiological findings, assumes that (1) the central nervous system learns the force and impedance to perform a task successfully in a given stable or unstable dynamic environment and (2) stiffness is linearly related to the magnitude of the joint torque and increased to compensate for environment instability. Comparison with existing data shows that this simple model is able to predict impedance geometry well.
Q-MAC: QoS and mobility aware optimal resource allocation for dynamic application offloading in mobile cloud computing

Smartphone applications are getting more multi-farious and demanding of increased energy and computing resources. Mobile Cloud Computing (MCC) made a novel platform which allows personal Smartphones to execute heavy computing tasks with the assistance of powerful cloudlet servers attached to numerous wireless access points (APs). Furthermore, due to users' mobility in anywhere, ensuring the continuous connectivity of mobile devices in given wireless network access point is quite difficult because the signal strength becomes sporadic at that time. In this paper, we develop a QoS and mobility aware optimal resource allocation architecture, namely Q-MAC, for remote code execution in MCC that offers higher efficiency in timeliness and reliability domains. By carrying continuous track of user, our proposed architecture performs the offloading process. Our test-bed implementation results show that the Q-MAC outperforms the state-of-the-art methods in terms of success percentage, execution time and workload distribution.
Truly balanced step recovery diode pulse generator with single power supply

A space efficient and simple circuit for ultra wideband (UWB) balanced pulse generation is presented. The pulse generator uses a single step recovery diode to provide a truly balanced output. The diode biasing is integrated with the switching circuitry to improve the compactness of the design. Two versions of the circuit with lumped and distributed pulse forming networks have been tested. The pulse parameters for distributed pulse shaping network were: rise/fall time (10-90%) 183 ps, pulse width (50-50%) 340 ps, pulse peak to peak voltage 896 mV (12.05 dBm peak power) and for the lumped case: rise time (10-90%) 272 ps, fall time (90-10%) 566 ps pulse width (50-50%) 511 ps, pulse amplitude /spl plusmn/1.6V (17 dBm peak power). In both cases excellent balance of the two pulses at the output ports can be observed. It should be noted that above parameters were obtained with typical inexpensive RF components. The circuit reduces the complexity of the design because of the lack of broadband baluns required for UWB balanced antennas. The circuit may be used as part of a UWB transmitter.
Alternating least squares for personalized ranking

Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user-item preference relationship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the system has to infer user preferences from indirect information: presence or absence of events, such as a user viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional prediction mean squared error. The naive minimization of a ranking objective function is typically expensive. This difficulty is usually overcome by a trade-off: sacrificing the accuracy to some extent for computational efficiency by sampling the objective function. In this paper, we present a computationally effective approach for the direct minimization of a ranking objective function, without sampling. We demonstrate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.
Relationship between total quality management ( TQM ) and continuous improvement of international project management ( CIIPM )

The purpose of this study is to investigate the relationship between the total quality management (TQM) practice and the continuous improvement of international project management (CIIPM) practice. Based on a literature review and qualitative interviews with TQM and project management experts, four hypotheses are posed on how TQM elements affect CIIPM. A cross-sectional survey collected from over 100 mid to senior level international managers is used to validate these hypotheses. The study suggests that the relationship between ‘soft’ TQM elements and CIIPM is more significant than the relationship between ‘hard’ TQM elements and CIIPM.
Fuzzy logic based traffic light controller

Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In a conventional traffic light controller, the traffic lights change at constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers implemented in current practice, are based on the 'time-of-the-day' scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. A traffic light controller based on fuzzy logic can be used for optimum control of fluctuating traffic volumes such as over saturated or unusual load conditions. The objective is to improve the vehicular throughput and minimize delays. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. The length of the current green phase is extended or terminated depending upon the 'arrival' i.e. the number of vehicles approaching the green phase and the 'queue' that corresponds to the number of queuing vehicles in red phases. A prototype system for controlling traffic at an intersection is designed using VB6 and Matlab tool. The traffic intersection is simulated in VB6 and the data regarding the traffic parameters is collected in VB6 environment. The decision on the duration of the extension is taken using the Matlab tool. This decision is based on the Arrival and Queue of vehicles, which is imported in Matlab from VB6 environment. The time delay experienced by the vehicles using the fixed as well as fuzzy traffic controller is then compared to observe the effectiveness of the fuzzy traffic controller.
Can virtual reality improve anatomy education? A randomised controlled study of a computer-generated three-dimensional anatomical ear model.

The use of computer-generated 3-dimensional (3-D) anatomical models to teach anatomy has proliferated. However, there is little evidence that these models are educationally effective. The purpose of this study was to test the educational effectiveness of a computer-generated 3-D model of the middle and inner ear.



We reconstructed a fully interactive model of the middle and inner ear from a magnetic resonance imaging scan of a human cadaver ear. To test the model's educational usefulness, we conducted a randomised controlled study in which 28 medical students completed a Web-based tutorial on ear anatomy that included the interactive model, while a control group of 29 students took the tutorial without exposure to the model. At the end of the tutorials, both groups were asked a series of 15 quiz questions to evaluate their knowledge of 3-D relationships within the ear.



The intervention group's mean score on the quiz was 83%, while that of the control group was 65%. This difference in means was highly significant (P < 0.001).



Our findings stand in contrast to the handful of previous randomised controlled trials that evaluated the effects of computer-generated 3-D anatomical models on learning. The equivocal and negative results of these previous studies may be due to the limitations of these studies (such as small sample size) as well as the limitations of the models that were studied (such as a lack of full interactivity). Given our positive results, we believe that further research is warranted concerning the educational effectiveness of computer-generated anatomical models.
About malicious software in smartphones

Phones with some of the capabilities of modern computers also have the same kind of drawbacks. These phones are commonly referred to as smartphones. They have both phone and personal digital assistant (PDA) functionality. Typical to these devices is to have a wide selection of different connectivity options from general packet radio service (GPRS) data transfer to multi media messages (MMS) and wireless local area network (WLAN) capabilities. They also have standardized operating systems, which makes smartphones a viable platform for malware writers. Since the design of the operating systems is recent, many common security holes and vulnerabilities have been taken into account during the design. However, these precautions have not fully protected these devices. Even now, when smartphones are not that common, there is a handful of viruses for them. In this paper we will discuss some of the most typical viruses in the mobile environment and propose guidelines and predictions for the future.
Methods for artifact detection and removal from scalp EEG: A review

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.
Social Loafing : A Meta-Analytic Review and Theoretical Integration

Social loafing is the tendency for individuals to expend less effort when working collectively than when working individually. A meta-analysis of 78 studies demonstrates that social loafing is robust and generalizes across tasks and S populations. A large number of variables were found to moderate social loafing. Evaluation potential, expectations of co-worker performance, task meaningfulness, and culture had especially strong influence. These findings are interpreted in the light of a Collective Effort Model that integrates elements of expectancy-value, social identity, and self-validation theories.
Induction of metastasis, cancer stem cell phenotype, and oncogenic metabolism in cancer cells by ionizing radiation

Radiation therapy is one of the major tools of cancer treatment, and is widely used for a variety of malignant tumours. Radiotherapy causes DNA damage directly by ionization or indirectly via the generation of reactive oxygen species (ROS), thereby destroying cancer cells. However, ionizing radiation (IR) paradoxically promotes metastasis and invasion of cancer cells by inducing the epithelial-mesenchymal transition (EMT). Metastasis is a major obstacle to successful cancer therapy, and is closely linked to the rates of morbidity and mortality of many cancers. ROS have been shown to play important roles in mediating the biological effects of IR. ROS have been implicated in IR-induced EMT, via activation of several EMT transcription factors—including Snail, HIF-1, ZEB1, and STAT3—that are activated by signalling pathways, including those of TGF-β, Wnt, Hedgehog, Notch, G-CSF, EGFR/PI3K/Akt, and MAPK. Cancer cells that undergo EMT have been shown to acquire stemness and undergo metabolic changes, although these points are debated. IR is known to induce cancer stem cell (CSC) properties, including dedifferentiation and self-renewal, and to promote oncogenic metabolism by activating these EMT-inducing pathways. Much accumulated evidence has shown that metabolic alterations in cancer cells are closely associated with the EMT and CSC phenotypes; specifically, the IR-induced oncogenic metabolism seems to be required for acquisition of the EMT and CSC phenotypes. IR can also elicit various changes in the tumour microenvironment (TME) that may affect invasion and metastasis. EMT, CSC, and oncogenic metabolism are involved in radioresistance; targeting them may improve the efficacy of radiotherapy, preventing tumour recurrence and metastasis. This study focuses on the molecular mechanisms of IR-induced EMT, CSCs, oncogenic metabolism, and alterations in the TME. We discuss how IR-induced EMT/CSC/oncogenic metabolism may promote resistance to radiotherapy; we also review efforts to develop therapeutic approaches to eliminate these IR-induced adverse effects.
Towards unsupervised weed scouting for agricultural robotics

Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems able to classify a set group of species known a priori. This greatly limits deployability as classification systems must be retrained for any field with a different set of weed species present within them. In order to overcome this limitation, this paper works towards developing a clustering approach to weed scouting which can be utilized in any field without the need for prior species knowledge. We demonstrate our system using challenging data collected in the field from an agricultural robotics platform. We show that considerable improvements can be made by (i) learning low-dimensional (bottleneck) features using a deep convolutional neural network to represent plants in general and (ii) tying views of the same area (plant) together. Deploying this algorithm on in-field data collected by AgBotII, we are able to successfully cluster cotton plants from grasses without prior knowledge or training for the specific plants in the field.
A preliminary study of emotional intelligence , empathy and exam performance in first year medical students I

A group of 156 first year medical students completed measures of emotional intelligence (EI) and physician empathy, and a scale assessing their feelings about a communications skills course component. Females scored significantly higher than males on EI. Exam performance in the autumn term on a course component (Health and Society) covering general issues in medicine was positively and significantly related to EI score but there was no association between EI and exam performance later in the year. High EI students reported more positive feelings about the communication skills exercise. Females scored higher than males on the Health and Society component in autumn, spring and summer exams. Structural equation modelling showed direct effects of gender and EI on autumn term exam performance, but no direct effects other than previous exam performance on spring and summer term performance. EI also partially mediated the effect of gender on autumn term exam performance. These findings provide limited evidence for a link between EI and academic performance for this student group. More extensive work on associations between EI, academic success and adjustment throughout medical training would clearly be of interest.  q Ethical approval from the College of Medicine and Veterinary Medicine was sought and received for this investigation. Student information was gathered and used in accordance with the Data Protection Act.  Corresponding author.
A Survey of 3D Face Recognition Methods

Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction,facial expression, and aging. The main purpose of this overview is to describe the recent 3D face recognition algorithms. The last few years more and more 2D face recognition algorithms are improved and tested on less than perfect images. However, 3D models hold more information of the face, like surface information, that can be used for face recognition or subject discrimination. Another major advantage is that 3D face recognition is pose invariant. A disadvantage of most presented 3D face recognition methods is that they still treat the human face as a rigid object. This means that the methods aren’t capable of handling facial expressions. Although 2D face recognition still seems to outperform the 3D face recognition methods, it is expected that this will change in the near future.
Misleading Online Content: Recognizing Clickbait as "False News"

Tabloid journalism is often criticized for its propensity for exaggeration, sensationalization, scare-mongering, and otherwise producing misleading and low quality news. As the news has moved online, a new form of tabloidization has emerged: ?clickbaiting.? ?Clickbait? refers to ?content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page? [?clickbait,? n.d.] and has been implicated in the rapid spread of rumor and misinformation online. This paper examines potential methods for the automatic detection of clickbait as a form of deception. Methods for recognizing both textual and non-textual clickbaiting cues are surveyed, leading to the suggestion that a hybrid approach may yield best results.
A compact 12-watt high-efficiency 2.1-2.7 GHz class-E GaN HEMT power amplifier for base stations

A compact broadband class-E power amplifier design is presented. High broadband power efficiency is observed from 2.0–2.5 GHz, where drain efficiency ≫74% and PAE ≫71%, when using 2nd-harmonic input tuning. The highest in-band efficiency performance is observed at 2.14 GHz from a 40V supply with peak drain-efficiency of 77.3% and peak PAE of 74.0% at 12W output power and 14dB gain. The best broadband output power performance is observed from 2.1–2.7 GHz without 2nd-harmonic input tuning, where the output power variation is within 1.5dB and power efficiency is between 53% and 66%.
Offline Handwritten Character Recognition Using Neural Network

This paper is aimed at recognition of offline handwritten characters in a given scanned text document with the help of neural networks. Image preprocessing, segmentation and feature extraction are various phases involved in character recognition. The first step is image acquisition followed by noise filtering, smoothing and image normalization of scanned image. Segmentation decomposes image into sub images and feature extraction extracts features from input image. Neural Network is created and trained to classify and recognize handwritten characters.
High Performance Layout Analysis of Arabic and Urdu Document Images

Text-lines extraction and their reading order determination is an important step in optical character recognition (OCR) systems. Research in OCR of Arabic script documents has primarily focused on character recognition and therefore most of researchers use primitive methods like projection profile analysis for text-line extraction. Although projection methods achieve good accuracy on clean, skew corrected documents, their performance drops under challenging situations (border noise, skew, complex layouts). This paper presents a robust layout analysis system for extracting text-lines in reading order from scanned Arabic script document images written in different languages (Arabic, Urdu, Persian) and styles (Naskh, Nastaliq). The presented system is based on a suitable combination of different well established techniques for analyzing Latin script documents that have proven to be robust against different types of document image degradations. Evaluation of the presented system on Arabic and Urdu document image datasets consisting of a variety of complex single- and multi-column layouts achieves high accuracies for text and non-text segmentation, text-line extraction, and reading order determination.