nils-herrmann/clean_scirepeval huggingface dataset:
Neural network identification of people hidden from view with a single-pixel, single-photon detector

Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with a deep convolutional artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N = 3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times.
Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching

Predictions concerning development, interrelations, and possible independence of working memory, inhibition, and cognitive flexibility were tested in 325 participants (roughly 30 per age from 4 to 13 years and young adults; 50% female). All were tested on the same computerized battery, designed to manipulate memory and inhibition independently and together, in steady state (single-task blocks) and during task-switching, and to be appropriate over the lifespan and for neuroimaging (fMRI). This is one of the first studies, in children or adults, to explore: (a) how memory requirements interact with spatial compatibility and (b) spatial incompatibility effects both with stimulus-specific rules (Simon task) and with higher-level, conceptual rules. Even the youngest children could hold information in mind, inhibit a dominant response, and combine those as long as the inhibition required was steady-state and the rules remained constant. Cognitive flexibility (switching between rules), even with memory demands minimized, showed a longer developmental progression, with 13-year-olds still not at adult levels. Effects elicited only in Mixed blocks with adults were found in young children even in single-task blocks; while young children could exercise inhibition in steady state it exacted a cost not seen in adults, who (unlike young children) seemed to re-set their default response when inhibition of the same tendency was required throughout a block. The costs associated with manipulations of inhibition were greater in young children while the costs associated with increasing memory demands were greater in adults. Effects seen only in RT in adults were seen primarily in accuracy in young children. Adults slowed down on difficult trials to preserve accuracy; but the youngest children were impulsive; their RT remained more constant but at an accuracy cost on difficult trials. Contrary to our predictions of independence between memory and inhibition, when matched for difficulty RT correlations between these were as high as 0.8, although accuracy correlations were less than half that. Spatial incompatibility effects and global and local switch costs were evident in children and adults, differing only in size. Other effects (e.g., asymmetric switch costs and the interaction of switching rules and switching response-sites) differed fundamentally over age.
Information security is information risk management

Information security is important in proportion to an organization's dependence on information technology. When an organization's information is exposed to risk, the use of information security technology is obviously appropriate. Current information security technology, however, deals with only a small fraction of the problem of information risk. In fact, the evidence increasingly suggests that information security technology does not reduce information risk very effectively.This paper argues that we must reconsider our approach to information security from the ground up if we are to deal effectively with the problem of information risk, and proposes a new model inspired by the history of medicine.
Protection of personal data in security alert sharing platforms

In order to ensure confidentiality, integrity and availability (so called CIA triad) of data within network infrastructure, it is necessary to be able to detect and handle cyber security incidents. For this purpose, it is vital for Computer Security Incident Response Teams (CSIRT) to have enough data on relevant security events and threats. That is why CSIRTs share security alerts and incidents data using various sharing platforms. Even though they do so primarily to protect data and privacy of users, their use also lead to additional processing of personal data, which may cause new privacy risks. European data protection law, especially with the adoption of the new General data protection regulation, sets out very strict rules on processing of personal data which on one hand leads to greater protection of individual's rights, but on the other creates great obstacles for those who need to share any personal data. This paper analyses the General Data Protection Regulation (GDPR), relevant case-law and analyses by the Article 29 Working Party to propose optimal methods and level of personal data processing necessary for effective use of security alert sharing platforms, which would be legally compliant and lead to appropriate balance between risks.
Music listening as a means of stress reduction in daily life

The relation between music listening and stress is inconsistently reported across studies, with the major part of studies being set in experimental settings. Furthermore, the psychobiological mechanisms for a potential stress-reducing effect remain unclear. We examined the potential stress-reducing effect of music listening in everyday life using both subjective and objective indicators of stress. Fifty-five healthy university students were examined in an ambulatory assessment study, both during a regular term week (five days) and during an examination week (five days). Participants rated their current music-listening behavior and perceived stress levels four times per day, and a sub-sample (n = 25) additionally provided saliva samples for the later analysis of cortisol and alpha-amylase on two consecutive days during both weeks. Results revealed that mere music listening was effective in reducing subjective stress levels (p = 0.010). The most profound effects were found when 'relaxation' was stated as the reason for music listening, with subsequent decreases in subjective stress levels (p ≤ 0.001) and lower cortisol concentrations (p ≤ 0.001). Alpha-amylase varied as a function of the arousal of the selected music, with energizing music increasing and relaxing music decreasing alpha-amylase activity (p = 0.025). These findings suggest that music listening can be considered a means of stress reduction in daily life, especially if it is listened to for the reason of relaxation. Furthermore, these results shed light on the physiological mechanisms underlying the stress-reducing effect of music, with music listening differentially affecting the physiological stress systems.
Haptics : State of the Art

This paper presents a novel approach to the understanding of Haptic and its related fields where haptics is used extensively like in display systems, communication, different types of haptic devices, and interconnection of haptic displays where virtual environment should feel like equivalent physical systems. There have been escalating research interests on areas relating to haptic modality in recent years, towards multiple fields. However, there seems to be limited studies in determining the various subfields and interfacing and related information on haptic user interfaces and its influence on the fields mentioned. This paper aims to bring forth the theory behind the essence of Haptics and its Subfields like haptic interfaces and its applications.
A G-Band (140–220 GHz) planar stubbed branch-line balun in BCB technology

A G-Band planar stubbed branch-line balun is designed and fabricated in 3μm thick BCB technology. This topology of the balun does not need thru-substrate via hole or thin-film resistor which makes it extremely suitable for realization on single-layer high-resistivity substrates commonly used at millimeter-wave or post-processed BCB layers on top of standard semi-insulating wafers. The design is simulated and validated by measurements. Measurement results on two fabricated back-to-back baluns show better than 10 dB input and output return loss and 3.2 dB insertion loss from 140 to 220 GHz.
Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Novel Approach

In this paper, a novel approach for Kannada, Telugu and Devanagari handwritten numerals recognition based on global and local structural features is proposed. Probabilistic Neural Network (PNN) Classifier is used to classify the Kannada, Telugu and Devanagari numerals separately. Algorithm is validated with Kannada, Telugu and Devanagari numerals dataset by setting various radial values of PNN classifier under different experimental setup. The experimental results obtained are encouraging and comparable with other methods found in literature survey. The novelty of the proposed method is free from thinning and size
An Applied Method for Selecting a Business Process Management Tool in Enterprise Software Project

Business Process Management (BPM) tool, which will be used in the management, improvement and maintenance of business processes, continues to increase its importance in the enterprise software projects where business processes are involved. In enterprise software projects, many criteria are in the foreground in the selection of BPM tool. These criteria are shaped by the requirements and the technical architecture of the enterprise software project. In this study, BPM selection criteria were determined for the enterprise software project and a method was proposed considering these criteria. In order to make BPM selection correctly, the scoring method was used for criteria. Among the criteria, the criteria related to usability also played an important role. At the end of the study, the lessons learned while selecting the BPM tool were included.
Combinatorial Optimization on Gate Model Quantum Computers: A Survey

The advent of quantum computing processors with possibility to scale beyond experimental capacities magnifies the importance of studying their applications. Combinatorial optimization problems can be one of the promising applications of these new devices. These problems are recurrent in industrial applications and they are in general difficult for classical computing hardware. In this work, we provide a survey of the approaches to solving different types of combinatorial optimization problems, in particular quadratic unconstrained binary optimization (QUBO) problems on a gate model quantum computer. We focus mainly on four different approaches including digitizing the adiabatic quantum computing, global quantum optimization algorithms, the quantum algorithms that approximate the ground state of a general QUBO problem, and quantum sampling. We also discuss the quantum algorithms that are custom designed to solve certain types of QUBO problems.
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?

Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations. However, contemporary BNNs whose weights and activations are both single bits suffer from severe accuracy degradation. To understand why, we investigate the representation ability, speed and bias/variance of BNNs through extensive experiments. We conclude that the error of BNNs are predominantly caused by the intrinsic instability (training time) and non-robustness (train & test time). Inspired by this investigation, we propose the Binary Ensemble Neural Network (BENN) which leverages ensemble methods to improve the performance of BNNs with limited efficiency cost. While ensemble techniques have been broadly believed to be only marginally helpful for strong classifiers such as deep neural networks, our analysis and experiments show that they are naturally a perfect fit to boost BNNs. We find that our BENN, which is faster and more robust than state-of-the-art binary networks, can even surpass the accuracy of the full-precision floating number network with the same architecture.
Autonomic Management of Cloud Service Centers with Availability Guarantees

Modern cloud infrastructures live in an open world, characterized by continuous changes in the environment and in the requirements they have to meet. Continuous changes occur autonomously and unpredictably, and they are out of control of the cloud provider. Therefore, advanced solutions have to be developed able to dynamically adapt the cloud infrastructure, while providing continuous service and performance guarantees. A number of autonomic computing solutions have been developed such that resources are dynamically allocated among running applications on the basis of short-term demand estimates. However, only performance and energy trade-off have been considered so far with a lower emphasis on the infrastructure dependability/availability which has been demonstrated to be the weakest link in the chain for early cloud providers. The aim of this paper is to fill this literature gap devising resource allocation policies for cloud virtualized environments able to identify performance and energy trade-offs, providing a priori availability guarantees for cloud end-users.
Proliferative verrucous leukoplakia: an aggressive form of oral leukoplakia.

Proliferative verrucous leukoplakia (PVL) is an aggressive form of oral leukoplakia that is persistent, often multifocal, and refractory to treatment with a high risk of recurrence and malignant transformation. This article describes the clinical aspects and histologic features of a case that demonstrated the typical behavior pattern in a long-standing, persistent lesion of PVL of the mandibular gingiva and that ultimately developed into squamous cell carcinoma. Prognosis is poor for this seemingly harmless-appearing white lesion of the oral mucosa.
Design of a lower limb exoskeleton for experimental research on gait control

This paper presents the design of a modular lower limb exoskeleton intended for gait control research. The design requirements for this project will be presented as well as the solutions proposed to fulfill the purpose of the exoskeleton. More precisely, the exoskeleton will be used to measure the pseudo-impedance of the knee and ankle at specific instants during gait, such as heel strike and toe-off. It is intended to use these parameters in the control of powered prostheses, robotic exoskeletons or other devices working in parallel with the human body. In this work the details of the design of an innovative exoskeleton based on the modular concept is presented. The exoskeleton has two modes of operation (active and passive) that can be selectively applied to the knee or the ankle joints. The mechanical modularity can be easily extended to incorporate a hip joint. The design of the control strategy to measure the human pseudo-impedance will also be introduced. Finally, the preliminary tests using the exoskeleton in the passive mode including EMG will be presented.
Eye-Tracking Database for a Set of Standard Video Sequences

This correspondence describes a publicly available database of eye-tracking data, collected on a set of standard video sequences that are frequently used in video compression, processing, and transmission simulations. A unique feature of this database is that it contains eye-tracking data for both the first and second viewings of the sequence. We have made available the uncompressed video sequences and the raw eye-tracking data for each sequence, along with different visualizations of the data and a preliminary analysis based on two well-known visual attention models.
Backside TSV protrusion induced by thermal shock and thermal cycling

This paper reports on thermal-mechanical failures of through-silicon-vias (TSVs), in particular, for the first time, the protrusions at the TSV backside, which is exposed after wafer bonding, thinning and TSV revealing. Temperature dependence of TSV protrusion is investigated based on wide-range thermal shock and thermal cycling tests. While TSV protrusion on the TSV frontside is not visible after any of the tests, protrusions on the backside are found after both thermal shock tests and thermal cycling tests at temperatures above 250°C. The average TSV protrusion height increases from ~0.1 μm at 250°C to ~0.5 μm at 400°C and can be fitted to an exponential function with an activation energy of ~0.6eV, suggesting a Cu grain boundary diffusion mechanism.
A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making in the microgrid. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. This approach will be referred in the following as fuzzy-HGA. Results are compared with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67% in the considered energy trading problem yielding at the same time a simpler RB.  Microgrid, Energy Management System, Battery Energy Storage, Power Flow Optimization, Storage System Management, Fuzzy Systems, Evolutionary Computation, Hierarchical Genetic Algorithms.
Compact UWB Bandpass Filter Using Stub-Loaded Multiple-Mode Resonator

A compact microstrip-line ultra-wideband (UWB) bandpass filter (BPF) using the proposed stub-loaded multiple-mode resonator (MMR) is presented. This MMR is formed by loading three open-ended stubs in shunt to a simple stepped-impedance resonator in center and two symmetrical locations, respectively. By properly adjusting the lengths of these stubs, the first four resonant modes of this MMR can be evenly allocated within the 3.1-to-10.6 GHz UWB band while the fifth resonant frequency is raised above 15.0GHz. It results in the formulation of a novel UWB BPF with compact-size and widened upper-stopband by incorporating this MMR with two interdigital parallel-coupled feed lines. Simulated and measured results are found in good agreement with each other, showing improved UWB bandpass behaviors with the insertion loss lower than 0.8dB, return loss higher than 14.3dB, and maximum group delay variation less than 0.64ns in the realized UWB passband
High Step-Up Forward Flyback Converter with Nondissipative Snubber for Solar Energy Application

A high step-up forward flyback converter with nondissipative snubber for solar energy application is introduced here. High gain DC/DC converters are the key part of renewable energy systems .The designing of high gain DC/DC converters is imposed by severe demands. It produces high step-up voltage gain by using a forward flyback converter. The energy in the coupled inductor leakage inductance can be recycled via a nondissipative snubber on the primary side. It consists of a combination of forward and flyback converter on the secondary side. It is a hybrid type of forward and flyback converter, sharing the transformer for increasing the utilization factor. By stacking the outputs of them, extremely high voltage gain can be obtained with small volume and high efficiency even with a galvanic isolation. The separated secondary windings in low turn-ratio reduce the voltage stress of the secondary rectifiers, contributing to achievement of high efficiency. Here presents a high step-up topology employing a series connected forward flyback converter, which has a series connected output for high boosting voltage-transfer gain. A MATLAB/Simulink model of the Photo Voltaic (PV) system using Maximum Power Point Tracking (MPPT) has been implimented along with a DC/DC hardware prototype.
Waalbot: An Agile Small-Scale Wall Climbing Robot Utilizing Pressure Sensitive Adhesives

This paper proposes a small-scale agile wall climbing robot able to navigate on smooth surfaces of any orientation, including vertical and inverted surfaces, which uses adhesive elastomer materials for attachment. Using two actuated legs with rotary motion and two passive revolute joints at each foot the robot can climb and steer in any orientation. Due to its compact design, a high degree of miniaturization is possible. It has onboard power, sensing, computing, and wireless communication which allow for semi-autonomous operation. Various aspects of a functioning prototype design and performance are discussed in detail, including leg and feet design and gait control. The current prototype can climb 90deg slopes at a speed of 6 cm/s and steer to any angle. This robot is intended for inspection and surveillance applications and, ultimately, space missions
High performance and fast object detection in road environments

In this paper, we present a high performance and fast object detection method based on a fully convolutional network (FCN) for advanced driver assistance systems (ADAS). Object detection methods based on deep learning have high performance but they require high computational complexity. Even if a method works on the high-performance graphics processing unit (GPU) hardware platform, it is hard to guarantee real-time processing. General object detectors based on deep learning try to localize too many classes of objects in various dynamic environments. The proposed detection method based on FCN improves detection performance and maintains real-time processing in road environments through various schemes related to the limitation of object class type, data augmentation, network architecture, and multi-ratio default boxes. Our experimental results show that the proposed method outperforms a previous method both in terms of performance and speed.