nils-herrmann/clean_scirepeval huggingface dataset:
Problematic Online Behaviors among Adolescents and Emerging Adults: Associations between Cyberbullying Perpetration, Problematic Social Media Use, and Psychosocial Factors

Over the past two decades, young people’s engagement in online activities has grown markedly. The aim of the present study was to examine the relationship between two specific online behaviors (i.e., cyberbullying perpetration, problematic social media use) and their relationships with social connectedness, belongingness, depression, and self-esteem among high school and university students. Data were collected from two different study groups via two questionnaires that included the Cyberbullying Offending Scale, Social Media Use Questionnaire, Social Connectedness Scale, General Belongingness Scale, Short Depression-Happiness Scale, and Single Item Self-Esteem Scale. Study 1 comprised 804 high school students (48% female; mean age 16.20 years). Study 2 comprised 760 university students (60% female; mean age 21.48 years). Results indicated that problematic social media use and cyberbullying perpetration (which was stronger among high school students) were directly associated with each other. Belongingness (directly) and social connectedness (indirectly) were both associated with cyberbullying perpetration and problematic social media use. Path analysis demonstrated that while age was a significant direct predictor of problematic social media use and cyberbullying perpetration among university students, it was not significant among high school students. In both samples, depression was a direct predictor of problematic social media use and an indirect predictor of cyberbullying perpetration. However, majority of these associations were relatively weak. The present study significantly adds to the emerging body of literature concerning the associations between problematic social media use and cyberbullying perpetration.
Document Image Binarization with Fully Convolutional Neural Networks

Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks. We formulate binarization as a pixel classification learning task and apply a novel Fully Convolutional Network (FCN) architecture that operates at multiple image scales, including full resolution. The FCN is trained to optimize a continuous version of the Pseudo F-measure metric and an ensemble of FCNs outperform the competition winners on 4 of 7 DIBCO competitions. This same binarization technique can also be applied to different domains such as Palm Leaf Manuscripts with good performance. We analyze the performance of the proposed model w.r.t. the architectural hyperparameters, size and diversity of training data, and the input features chosen.
Algorithmic Features of Eclat

Nowadays basic algorithms such as Apriori and Eclat often are conceived as mere textbook examples without much practical applicability: in practice more sophisticated algorithms with better performance have to be used. We would like to challenge that point of view by showing that a carefully assembled implementation of Eclat outperforms the best algorithms known in the field, at least for dense datasets. For that we view Eclat as a basic algorithm and a bundle of optional algorithmic features that are taken partly from other algorithms like lcm and Apriori, partly new ones. We evaluate the performance impact of these different features and report about results of experiments that support our claim of the competitiveness of Eclat.
Beyond the Balanced Scorecard : Refining the Search for Organizational Success Measures

Measuring organizational success is a continuous challenge for both managers and researchers. While financial measures were in wide use for many years, new frameworks have emerged in recent years that extend organizational perspectives beyond traditional financial measures. Among them the Balanced Scorecard is one of the most popular new frameworks. However, in spite of its wide usage, it has shown to be inadequate in various circumstances and across differing firm types. Additional studies are therefore needed to keep exploring a problem that has been relevant for at least thirty years. This study was dedicated to the question of how to assess the organizational success of commercial firms. It also tested specific measures that are relevant to different kinds of organizations, and provides managers with a useable template for assessing organizational success. We have identified twelve potential baseline measures across five major success dimensions (financial, market, process, people, and future) that can be examined as applicable to different firms and firm types. Specific firms can use this framework as a starting point from which to choose measures that would best fit their environment and strategic direction.
A Combined Color, Texture and Edge Features Based Approach for Identification and Classification of Indian Medicinal Plants

This paper presents a method for identification and classification of images of medicinal plants such as herbs, shrubs and trees based on color and texture feature using SVM and neural network classifier. The tribal people in India classify plants according to their medicinal values. In the system of medicine called Ayurveda, identification of medicinal plants is considered an important activity in the preparation of herbal medicines. Ayurveda medicines have become alternate for allopathic medicine. Hence, leveraging technology in automatic identification and classification of medicinal plants has become essential. Plant species belonging to different classes such as Papaya, Neem, Tulasi, Aloe and Garlic are considered in this work. This paper presents edge and color descriptors that have low-dimension, effective and simple. In addition, the rotation invariant texture descriptors namely, directional difference and the gradient histogram are used. These features are obtained from 900 images of medicinal plants and used
Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory

Monitoring natural environments is a challenging task on account of their hostile features. The use of wireless sensor networks (WSNs) for data collection is a feasible method since these domains lack any infrastructure. However, further studies are required to handle the data collected for a better modeling of behavior and thus make it possible to forecast impending disasters. In light of this, in this paper an analysis is conducted on the use of data gathered from urban rivers to forecast flooding with a view to reducing the damage it causes. The data were collected by means of a WSN in São Carlos, São Paulo State, Brazil, which gathered and processed data about the river level and rainfall by means of machine learning techniques and employing chaos theory to model the time series; this meant that the inputs of the machine learning technique were the time series gathered by the WSN modeled on the basis of the immersion theorem. The WSNs were deployed by our group in the city of São Carlos where there have been serious problems caused by floods. After the data interdependence had been established by the immersion theorem, the artificial neural networks were investigated to determine their degree of accuracy in the forecasting models.
Facial reconstruction--anatomical art or artistic anatomy?

Facial reconstruction is employed in the context of forensic investigation and for creating three-dimensional portraits of people from the past, from ancient Egyptian mummies and bog bodies to digital animations of J. S. Bach. This paper considers a facial reconstruction method (commonly known as the Manchester method) associated with the depiction and identification of the deceased from skeletal remains. Issues of artistic licence and scientific rigour, in relation to soft tissue reconstruction, anatomical variation and skeletal assessment, are discussed. The need for artistic interpretation is greatest where only skeletal material is available, particularly for the morphology of the ears and mouth, and with the skin for an ageing adult. The greatest accuracy is possible when information is available from preserved soft tissue, from a portrait, or from a pathological condition or healed injury.
Thinning of the cerebral cortex in aging.

The thickness of the cerebral cortex was measured in 106 non-demented participants ranging in age from 18 to 93 years. For each participant, multiple acquisitions of structural T1-weighted magnetic resonance imaging (MRI) scans were averaged to yield high-resolution, high-contrast data sets. Cortical thickness was estimated as the distance between the gray/white boundary and the outer cortical surface, resulting in a continuous estimate across the cortical mantle. Global thinning was apparent by middle age. Men and women showed a similar degree of global thinning, and did not differ in mean thickness in the younger or older groups. Age-associated differences were widespread but demonstrated a patchwork of regional atrophy and sparing. Examination of subsets of the data from independent samples produced highly similar age-associated patterns of atrophy, suggesting that the specific anatomic patterns within the maps were reliable. Certain results, including prominent atrophy of prefrontal cortex and relative sparing of temporal and parahippocampal cortex, converged with previous findings. Other results were unexpected, such as the finding of prominent atrophy in frontal cortex near primary motor cortex and calcarine cortex near primary visual cortex. These findings demonstrate that cortical thinning occurs by middle age and spans widespread cortical regions that include primary as well as association cortex.
Individual differences in judging deception: accuracy and bias.

The authors report a meta-analysis of individual differences in detecting deception, confining attention to occasions when people judge strangers' veracity in real-time with no special aids. The authors have developed a statistical technique to correct nominal individual differences for differences introduced by random measurement error. Although researchers have suggested that people differ in the ability to detect lies, psychometric analyses of 247 samples reveal that these ability differences are minute. In terms of the percentage of lies detected, measurement-corrected standard deviations in judge ability are less than 1%. In accuracy, judges range no more widely than would be expected by chance, and the best judges are no more accurate than a stochastic mechanism would produce. When judging deception, people differ less in ability than in the inclination to regard others' statements as truthful. People also differ from one another as lie- and truth-tellers. They vary in the detectability of their lies. Moreover, some people are more credible than others whether lying or truth-telling. Results reveal that the outcome of a deception judgment depends more on the liar's credibility than any other individual difference.
A Highly Efficient 200 000 RPM Permanent Magnet Motor System

This paper presents the development of an ultra-high-speed permanent magnet synchronous motor (PMSM) that produces output shaft power of 2000 W at 200 000 rpm with around 90% efficiency. Due to the guaranteed open-loop stability over the full operating speed range, the developed motor system is compact and low cost since it can avoid the design complexity of a closed-loop controller. This paper introduces the collaborative design approach of the motor system in order to ensure both performance requirements and stability over the full operating speed range. The actual implementation of the motor system is then discussed. Finally, computer simulation and experimental results are provided to validate the proposed design and its effectiveness
Dynamic priority ceilings: A concurrency control protocol for real-time systems

Real-time systems have stringent deadline requirements for their tasks. To meet the requirements, a real-time system must use scheduling algorithms that ensure a predictable response even in the face of mutually exclusive accesses to critical sections. We present a concurrency control protocol for systems using the earliest deadline first scheduling algorithm. The protocol specifies a dynamic priority ceiling for each critical section which is the earliest deadline of jobs which are currently in or will enter the critical section. Jobs trying to enter a critical section will be blocked if they do not have a priority higher than the priority ceiling of any critical section which is in use. We show that the protocol prevents both deadlock and chained blocking. The schedulability condition and implementation issues of the protocol are also discussed.
ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files

Email communication carrying malicious attachments or links is often used as an attack vector for initial penetration of the targeted organization. Existing defense solutions prevent executables from entering organizational networks via emails, therefore recent attacks tend to use non-executable files such as PDF. Machine learning algorithms have recently been applied for detecting malicious PDF files. These techniques, however, lack an essential element - they cannot be updated daily. In this study we present ALPD, a framework that is based on active learning methods that are specially designed to efficiently assist anti-virus vendors to focus their analytical efforts. This is done by identifying and acquiring new PDF files that are most likely malicious, as well as informative benign PDF documents. These files are used for retraining and enhancing the knowledge stores. Evaluation results show that in the final day of the experiment, Combination, one of our AL methods, outperformed all the others, enriching the anti-virus's signature repository with almost seven times more new PDF malware while also improving the detection model's performance on a daily basis.
3-D Ultrasonic Fingerprint Sensor-on-a-Chip

A fully integrated 3-D ultrasonic fingerprint sensor-on-a-chip is presented. The device consists of a 110× 56 piezoelectric micromachined ultrasonic transducer (PMUT) array bonded at the wafer level to custom readout electronics fabricated in a 180-nm CMOS process with a HV (24 V) transistor option. With the 24 V driving signal strength, the sensor consumes 280 μJ to image a 4.73 mm × 3.24 mm section of a fingerprint at a rate of 380 fps. A wakeup mode that detects the presence of a finger at 4 fps and dissipates 10 μW allows the proposed sensor to double as a power switch. The sensor is capable of imaging both the surface epidermal and subsurface dermal fingerprints and is insensitive to contaminations, including perspiration or oil. The 3-D imaging capability combined with the sensor's sensitivity to the acoustic properties of the tissue translates into excellent robustness against spoofing attacks.
Fingertip skin–inspired microstructured ferroelectric skins discriminate static/dynamic pressure and temperature stimuli

In human fingertips, the fingerprint patterns and interlocked epidermal-dermal microridges play a critical role in amplifying and transferring tactile signals to various mechanoreceptors, enabling spatiotemporal perception of various static and dynamic tactile signals. Inspired by the structure and functions of the human fingertip, we fabricated fingerprint-like patterns and interlocked microstructures in ferroelectric films, which can enhance the piezoelectric, pyroelectric, and piezoresistive sensing of static and dynamic mechanothermal signals. Our flexible and microstructured ferroelectric skins can detect and discriminate between multiple spatiotemporal tactile stimuli including static and dynamic pressure, vibration, and temperature with high sensitivities. As proof-of-concept demonstration, the sensors have been used for the simultaneous monitoring of pulse pressure and temperature of artery vessels, precise detection of acoustic sounds, and discrimination of various surface textures. Our microstructured ferroelectric skins may find applications in robotic skins, wearable sensors, and medical diagnostic devices.
A 90-W Peak Power GaN Outphasing Amplifier With Optimum Input Signal Conditioning

A 90-W peak-power 2.14-GHz improved GaN outphasing amplifier with 50.5% average efficiency for wideband code division multiple access (W-CDMA) signals is presented. Independent control of the branch amplifiers by two in-phase/quadrature modulators enables optimum outphasing and input power leveling, yielding significant improvements in gain, efficiency, and linearity. In deep-power backoff operation, the outphasing angle of the branch amplifiers is kept constant below a certain power level. This results in class-B operation for the very low output power levels, yielding less reactive loading of the output stages, and therefore, improved efficiency in power backoff operation compared to the classical outphasing amplifiers. Based on these principles, the optimum design parameters and input signal conditioning are discussed. The resulting theoretical maximum achievable average efficiency for W-CDMA signals is presented. Experimental results support the foregoing theory and show high efficiency over a large bandwidth, while meeting the linearity specifications using low-cost low-complexity memoryless pre-distortion. These properties make this amplifier concept an interesting candidate for future multiband base-station implementations.
Contour Model-Based Hand-Gesture Recognition Using the Kinect Sensor

In RGB-D sensor-based pose estimation, training data collection is often a challenging task. In this paper, we propose a new hand motion capture procedure for establishing the real gesture data set. A 14-patch hand partition scheme is designed for color-based semiautomatic labeling. This method is integrated into a vision-based hand gesture recognition framework for developing desktop applications. We use the Kinect sensor to achieve more reliable and accurate tracking under unconstrained conditions. Moreover, a hand contour model is proposed to simplify the gesture matching process, which can reduce the computational complexity of gesture matching. This framework allows tracking hand gestures in 3-D space and matching gestures with simple contour model, and thus supports complex real-time interactions. The experimental evaluations and a real-world demo of hand gesture interaction demonstrate the effectiveness of this framework.
Behaviour oriented design for real-time-strategy games

Design is an essential part of all games and narratives, yet designing and implementing believable game strategies can be time consuming and error-prone. This motivates the development and application of systems AI methodologies. Here we demonstrate for the first time the iterative development of agent behaviour for a real-time strategy game (here StarCraft) utilising Behaviour Oriented Design (BOD) . BOD provides focus on the robust creation and easy adjustment of modular and hierarchical cognitive agents. We demonstrate BOD’s usage in creating an AI capable of playing the StarCraft character the Zerg hive mind, and document its performance against a variety of opponent AI systems. In describing our tool-driven development process, we also describe the new Abode IDE, provide a brief literature review situating BOD in the AI game literature, and propose possible future work.
Personal Verification Using Palmprint and Hand Geometry Biometric

A new approach for the personal identification using hand images is presented. This paper attempts to improve the performance of palmprint-based verification system by integrating hand geometry features. Unlike other bimodal biometric systems, the users does not have to undergo the inconvenience of passing through two sensors since the palmprint and hand geometry features can be are acquired from the same image, using a digital camera, at the same time. Each of these gray level images are aligned and then used to extract palmprint and hand geometry features. These features are then examined for their individual and combined performance. The image acquisition setup used in this work was inherently simple and it does not employ any special illumination nor does it use any pegs to cause any inconvenience to the users. Our experimental results on the image dataset from 100 users confirm the utility of hand geometry features with those from palmprints and achieve promising results with a simple image acquisition setup.
A 7.3 THz Cut-Off Frequency, Inline, Chalcogenide Phase-Change RF Switch Using an Independent Resistive Heater for Thermal Actuation

An inline chalcogenide phase change RF switch utilizing germanium telluride (GeTe) and driven by an integrated, electrically isolated thin film heater for thermal actuation has been fabricated. A voltage or current pulse applied to the heater terminals was used to transition the phase change material between the crystalline and amorphous states. An on-state resistance of 1.2 Ω (0.036 Ω-mm), with an off-state capacitance and resistance of 18.1 fF and 112 kΩ respectively were measured. This results in an RF switch cut-off frequency (Fco) of 7.3 THz, and an off/on DC resistance ratio of 9 × 104. The heater pulse power required to switch the GeTe between the two states was as low as 0.5W, with zero power consumption during steady state operation, making it a non-volatile RF switch. To the authors' knowledge, this is the first reported implementation of an RF phase change switch in a 4-terminal, inline configuration.
Effective PPG sensor placement for reflected red and green light, and infrared wristband-type photoplethysmography

Using a wristband-type Photoplethymography (PPG) sensor, useful biomedical information such as heart rate and oxygen saturation can be acquired. Most of commercially-used wrist-type PPG sensors use green light reflections for its greater absorptivity of hemoglobin compared to other lights; this is important because wrists have comparably low concentration of blood flow. For reliable biomedical signal processing, we propose measurement sites for reflected red, green, infrared light PPG sensors on wrist. Amplitude, detection rate, and accuracy of heart rate are compared to determine the signal quality on measurement sites. Traditionally, wrist-type PPG sensors are implemented in measurement site 2, 3 or between 2 and 3 (between the distal Radius and the head of Ulna). Experiments show that all three reflected light PPG sensors generate good quality of PPG signals on measurement sites 4 and 11 (around the distal of Radius of left hand) in test subjects.
An administration concept for the enterprise role-based access control model

Using an underlying role-based model for the administration of roles has proved itself to be a successful approach. This paper sets out to describe the enterprise role-based access control model (ERBAC) in the context of SAM Jupiter, a commercial enterprise security management software.We provide an overview of the role-based conceptual model underlying SAM Jupiter. Having established this basis, we describe how the model is used to facilitate a role-based administration approach. In particular, we discuss our notion of 'scopes', which describe the objects over which an administrator has authority. The second part provides a case study based on our real-world experiences in the implementation of role-based administrative infrastructures. Finally, a critical evaluation and comparison with current approaches to administrative role-based access control is provided.