nils-herrmann/clean_scirepeval huggingface dataset:
Objectives, criteria and methods for the design of the SmartHand transradial prosthesis

This paper presents the requirements, design criteria and methodology used to develop the design of a new selfcontained prosthetic hand to be used by transradial amputees. The design is based on users’ needs, on authors background and knowledge of the state of the art, and feasible fabrication technology with the aim of replicating as much as possible the functionality of the human hand. The paper focuses on the design approach and methodology which is divided into three steps: (i) the mechanical actuation units, design and actuation distribution; (ii) the mechatronic development and finally (iii) the controller architecture design. The design is presented here and compared with significant commercial devices and research prototypes.
Deficits of organizational strategy and visual memory in obsessive-compulsive disorder.

This study was conducted to investigate the deficits of organizational strategy and visual memory in obsessive-compulsive disorder (OCD). Thirty OCD patients and 30 healthy controls aged 20-35 years participated. The Maudsley Obsessive-Compulsive Inventory, Beck Anxiety Inventory, Wechsler Adult Intelligence Scale, and Rey-Osterrieth Complex Figure (ROCF) test were administered to participants. The authors scored ROCF performances using the Boston Qualitative Scoring System. The OCD patients showed poorer planning ability and higher fragmentation than did healthy controls when copying the ROCF, and they showed even poorer performances in the immediate and delayed recall conditions. The authors found that the Organization score in the copy condition mediated the difference between the OCD group and the healthy group in immediate recall. The direct effect of diagnosis (OCD or healthy) on the immediate recall condition of the ROCF was also significant. This study indicates that people with OCD have poor memory function and organizational deficits.
Neural Network Classification Algorithm with M- Learning Reviews to Improve the Classification Accuracy

The development of communication technology has led to easy access of information through the internet. Nowadays, the use of mobile devices is increasing rapidly which in turn has popularized the pedagogical methods such as learning through mobile devices. Several mobile learning systems are available and also the user opinions about these systems are aired in the social blogs or review websites. Neural networks have high acceptance ability for noisy data, high accuracy and are preferable in data mining. In Knowledge Discovery in Databases (KDD), Neural Networks are employed in classification process. This research paper develops an opinion mining system for M-Learning reviews, the goal of this system is to extract the opinions and reviews, and determine whether these reviews and opinions are positive or negative. This research works proposes to score the words in the opinion using Singular Value Decomposition; select information gain based attributes, additionally feed forward from input layer to the output layer and presents a novel neural network classification algorithm to improve the classification accuracy.
Evolutionary foundations for cancer biology

New applications of evolutionary biology are transforming our understanding of cancer. The articles in this special issue provide many specific examples, such as microorganisms inducing cancers, the significance of within-tumor heterogeneity, and the possibility that lower dose chemotherapy may sometimes promote longer survival. Underlying these specific advances is a large-scale transformation, as cancer research incorporates evolutionary methods into its toolkit, and asks new evolutionary questions about why we are vulnerable to cancer. Evolution explains why cancer exists at all, how neoplasms grow, why cancer is remarkably rare, and why it occurs despite powerful cancer suppression mechanisms. Cancer exists because of somatic selection; mutations in somatic cells result in some dividing faster than others, in some cases generating neoplasms. Neoplasms grow, or do not, in complex cellular ecosystems. Cancer is relatively rare because of natural selection; our genomes were derived disproportionally from individuals with effective mechanisms for suppressing cancer. Cancer occurs nonetheless for the same six evolutionary reasons that explain why we remain vulnerable to other diseases. These four principles-cancers evolve by somatic selection, neoplasms grow in complex ecosystems, natural selection has shaped powerful cancer defenses, and the limitations of those defenses have evolutionary explanations-provide a foundation for understanding, preventing, and treating cancer.
Intrusion Detection and Countermeasure of Virtual Cloud Systems - State of the Art and Current Challenges

Clouds are distributed Internet-based platforms that provide highly resilient and scalable environments to be used by enterprises in a multitude of ways. Cloud computing offers enterprises technology innovation that business leaders and IT infrastructure managers can choose to apply based on how and to what extent it helps them fulfil their business requirements. It is crucial that all technical consultants have a rigorous understanding of the ramifications of cloud computing as its influence is likely to spread the complete IT landscape. Security is one of the major concerns that is of practical interest to decision makers when they are making critical strategic operational decisions. Distributed Denial of Service (DDoS) attacks are becoming more frequent and effective over the past few years, since the widely publicised DDoS attacks on the financial services industry that came to light in September and October 2012 and resurfaced in the past two years. In this paper, we introduce advanced cloud security technologies and practices as a series of concepts and technology architectures, from an industry-centric point of view. This is followed by classification of intrusion detection and prevention mechanisms that can be part of an overall strategy to help understand identify and mitigate potential DDoS attacks on business networks. The paper establishes solid coverage of security issues related to DDoS and virtualisation with a focus on structure, clarity, and well-defined blocks for mainstream cloud computing security solutions and platforms. In doing so, we aim to provide industry technologists, who may not be necessarily cloud or security experts, with an effective tool to help them understand the security implications associated with cloud adoption in their transition towards more knowledge-based systems. Cloud Computing Security; Distributed Denial of Service; Intrusion Detection; Intrusion Prevention; Virtualisation
An Attribute-Based Access Control Extension for OpenStack and Its Enforcement Utilizing the Policy Machine

Role-Based Access Control (RBAC) has been the dominant access control model in industry since the 1990s. It is widely implemented in many applications, including major cloud platforms such as OpenStack, AWS, and Microsoft Azure. However, due to limitations of RBAC, there is a shift towards Attribute-Based Access Control (ABAC) models to enhance flexibility by using attributes beyond roles and groups. In practice, this shift has to be gradual since it is unrealistic for existing systems to abruptly adopt ABAC models, completely eliminating current RBAC implementations.In this paper, we propose an ABAC extension with user attributes for the OpenStack Access Control (OSAC) model and demonstrate its enforcement utilizing the Policy Machine (PM) developed by the National Institute of Standards and Technology. We utilize some of the PM's components along with a proof-of-concept implementation to enforce this ABAC extension for OpenStack, while keeping OpenStack's current RBAC architecture in place. This provides the benefits of enhancing access control flexibility with support of user attributes, while minimizing the overhead of altering the existing OpenStack access control framework. We present use cases to depict added benefits of our model and show enforcement results. We then evaluate the performance of our proposed ABAC extension, and discuss its applicability and possible performance enhancements.
A novel FMCW waveform for multi-target detection and the corresponding algorithm

Frequency Modulation Continuous Wave (FMCW) as a quite practical radar modulation technique, is increasingly applied in the intelligent transportation field. A classical FMCW radar system has good performance of short-range applications. However, in multi-target detection situations, so-called ghost targets usually occur because of the Doppler effect in the echo signal, leading to the false alarm rate. For solving this problem, a novel FMCW waveform is proposed, which consists of two parts: a trapezoid wave and a triangle wave. In accordance to a trapezoid wave, the ambiguous ranges are calculated by the slow ramp and the unambiguous relative velocities are detected by the flat frequencies. Nevertheless, some ghost targets still remain owing to the mismatch of intersections in Velocity-Range (V-R) map. Thus, another triangular pairing algorithm is added. It helps that the rest of ghost targets are completely removed. The simulated results demonstrate that the proposed waveform of multiple chirp signals with different slopes behaves well.
[Transgender voice and communication treatment: Review of the literature].

Transgender persons constitute a small but growing population in ENT department: as a matter of fact, many voice parameters significantly contribute to the perception of gender (fundamental frequency, supraglottic resonance patterns, etc.). The persons involved in transition processes may therefore aim at changing their own voice properties, either by means of speech therapy or by medical intervention (hormonotherapy and/or surgery). The current voice assessment and outcome measures for this population before and after treatment are nevertheless still lacking validity. A well-accepted general framework including self-perception, subjective assessment of the practitioner and objective measures is not well documented. This review is therefore meant as a contribution to the development of a state of the art in the field.
A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams

In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular time-sampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.
CULTURAL CONTINUITY AS A MODERATOR OF SUICIDE RISK AMONG CANADA ’ S FIRST NATIONS

Eight years ago the journal Transcultural Psychiatry published the results of an epidemiological study (Chandler and Lalonde 1998) in which the highly variable rates of youth suicide among British Columbia’s First Nations were related to six markers of “cultural continuity” – community-level variables meant to document the extent to which each of the province’s almost 200 Aboriginal “bands” had taken steps to preserve their cultural past and to secure future control of their civic lives. Two key findings emerged from these earlier efforts. The first was that, although the province-wide rate of Aboriginal youth suicide was sharply elevated (more than 5 times the national average), this commonly reported summary statistic was labelled an “actuarial fiction” that failed to capture the local reality of even one of the province’s First Nations communities. Counting up all of the deaths by suicide and then simply dividing through by the total number of available Aboriginal youth obscures what is really interesting – the dramatic differences in the incidence of youth suicide that actually distinguish one band or tribal council from the next. In fact, more than half of the province’s bands reported no youth suicides during the 6-year period (1987-1992) covered by this study, while more than 90% of the suicides occurred in less than 10% of the bands. Clearly, our data demonstrated, youth suicide is not an “Aboriginal” problem per se but a problem confined to only some Aboriginal communities. Second, all six of the “cultural continuity” factors originally identified – measures intended to mark the degree to which individual Aboriginal communities had successfully taken steps to secure their cultural past in light of an imagined future – proved to be strongly related to the presence or absence of youth suicide. Every community characterized by all six of these protective factors experienced no youth suicides during the 6-year reporting period, whereas those bands in which none of these factors were present suffered suicide rates more than 10 times the national average. Because these findings were seen by us, and have come to be seen by others,1 not only as clarifying the link between cultural continuity and reduced suicide risk but also as having important policy implications, we have undertaken to replicate and broaden our earlier research efforts. We have done this in three ways. First, we have extended our earlier examination of the community-by-community incidence of Aboriginal youth suicides to include also the additional
3D Reconstruction by Shadow Carving: Theory and Practical Evaluation

Cast shadows are an informative cue to the shape of objects. They are particularly valuable for discovering object’s concavities which are not available from other cues such as occluding boundaries. We propose a new method for recovering shape from shadows which we call shadow carving. Given a conservative estimate of the volume occupied by an object, it is possible to identify and carve away regions of this volume that are inconsistent with the observed pattern of shadows. We prove a theorem that guarantees that when these regions are carved away from the shape, the shape still remains conservative. Shadow carving overcomes limitations of previous studies on shape from shadows because it is robust with respect to errors in shadows detection and it allows the reconstruction of objects in the round, rather than just bas-reliefs. We propose a reconstruction system to recover shape from silhouettes and shadow carving. The silhouettes are used to reconstruct the initial conservative estimate of the object’s shape and shadow carving is used to carve out the concavities. We have simulated our reconstruction system with a commercial rendering package to explore the design parameters and assess the accuracy of the reconstruction. We have also implemented our reconstruction scheme in a table-top system and present the results of scanning of several objects.
BRE large compartment fire tests – characterising post-flashover fires for model validation

Reliable and comprehensive measurement data from large-scale fire tests is needed for validation of computer fire models, but is subject to various uncertainties, including radiation errors in temperature measurement. Here, a simple method for post-processing thermocouple data is demonstrated, within the scope of a series of large-scale fire tests, in order to establish a well characterised dataset of physical parameter values which can be used with confidence in model validation. Sensitivity analyses reveal the relationship of the correction uncertainty to the assumed optical properties and the thermocouple distribution. The analysis also facilitates the generation of maps of an equivalent radiative flux within the fire compartment, a quantity which usefully characterises the thermal exposures of structural components. Large spatial and temporal variations are found, with regions of most severe exposures not being collocated with the peak gas temperatures; this picture is at variance with the assumption of uniform heating conditions often adopted for post-flashover fires.
Collaborative manufacturing with physical human – robot interaction

Although the concept of industrial cobots dates back to 1999, most present day hybrid human-machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human-robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.
Load hiding of household's power demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy.
Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow

This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers' active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid.
An overview of in vitro dissolution/release methods for novel mucosal drug delivery systems.

In vitro dissolution/release tests are an important tool in the drug product development phase as well as in its quality control and the regulatory approval process. Mucosal drug delivery systems are aimed to provide both local and systemic drug action via mucosal surfaces of the body and exhibit significant differences in formulation design, as well as in their physicochemical and release characteristics. Therefore it is not possible to devise a single test system which would be suitable for release testing of such complex dosage forms. This article is aimed to provide a comprehensive review of both compendial and noncompendial methods used for in vitro dissolution/release testing of novel mucosal drug delivery systems aimed for ocular, nasal, oromucosal, vaginal and rectal administration.
Microfluidics-Based Biochips: Technology Issues, Implementation Platforms, and Design-Automation Challenges

Microfluidics-based biochips are soon expected to revolutionize clinical diagnosis, deoxyribonucleic acid (DNA) sequencing, and other laboratory procedures involving molecular biology. In contrast to continuous-flow systems that rely on permanently etched microchannels, micropumps, and microvalves, digital microfluidics offers a scalable system architecture and dynamic reconfigurability; groups of unit cells in a microfluidics array can be reconfigured to change their functionality during the concurrent execution of a set of bioassays. As more bioassays are executed concurrently on a biochip, system integration and design complexity are expected to increase dramatically. This paper presents an overview of an integrated system-level design methodology that attempts to address key issues in the synthesis, testing and reconfiguration of digital microfluidics-based biochips. Different actuation mechanisms for microfluidics-based biochips, and associated design-automation trends and challenges are also discussed. The proposed top-down design-automation approach is expected to relieve biochip users from the burden of manual optimization of bioassays, time-consuming hardware design, and costly testing and maintenance procedures, and it will facilitate the integration of fluidic components with a microelectronic component in next-generation systems-on-chips (SOCs).
Face detection: A deep convolutional network method based on grouped facial part

In this paper, a novel method is proposed for face detection, which is of simple structure but robust to severe occlusion. In detail, the size-free images are firstly segmented to a series of candidate windows. Then these candidate windows are further filtered by grouped facial part networks to generate a set of windows which may contain faces. Finally, the set of face proposals are input to a multi-task deep convolutional network (DCN) for further classification and calibration. Importantly, we take the spatial position relations of local facial parts into consideration and find it helpful to handle the severe occlusion. Our method achieves outstanding performance on the widely used datasets FDDB and AFW, compared to the other proposed face detectors. Especially on FDDB, our method achieves a high recall rate of 90.13%.
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes

The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that can handle multiple views in an order-agnostic manner. The architecture is fully convolutional, and for training we use a proxy task of silhouette prediction, rather than directly learning a mapping from 2D images to 3D shape as has been the target in most recent work. We demonstrate that with the SilNet architecture there is generalisation over the number of views – for example, SilNet trained on 2 views can be used with 3 or 4 views at test-time; and performance improves with more views. We introduce two new synthetics datasets: a blobby object dataset useful for pretraining, and a challenging and realistic sculpture dataset; and demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally, we show that SilNet exceeds the state of the art on the ShapeNet benchmark dataset [6] at generating silhouettes in new viewpoints, and we use SilNet to generate novel views of the sculpture dataset.