nils-herrmann/clean_scirepeval huggingface dataset:
ESPAC: Enabling Security and Patient-centric Access Control for eHealth in cloud computing

We consider the problem of patient self-controlled access privilege to highly sensitive Personal Health Information (PHI), where PHI is expected to be securely stored in cloud storage for uninterrupted anytime, anywhere remote access. In order to assure the privacy of PHI, we propose Efficient and Secure Patient-centric Access Control (ESPAC) scheme which allows data requesters to have different access privileges based on their roles, and then assigns different attribute sets to them. Extensive security and performance analyses demonstrate that the ESPAC scheme is able to achieve desired security requirements with acceptable communication delay.
Comprehensible reasoning and automated reporting of medical examinations based on deep learning analysis

In the future, medical doctors will to an increasing degree be assisted by deep learning neural networks for disease detection during examinations of patients. In order to make qualified decisions, the black box of deep learning must be opened to increase the understanding of the reasoning behind the decision of the machine learning system. Furthermore, preparing reports after the examinations is a significant part of a doctors work-day, but if we already have a system dissecting the neural network for understanding, the same tool can be used for automatic report generation. In this demo, we describe a system that analyses medical videos from the gastrointestinal tract. Our system dissects the Tensorflow-based neural network to provide insights into the analysis and uses the resulting classification and rationale behind the classification to automatically generate an examination report for the patient's medical journal.
A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

This paper presents a novel online-capable interaction-aware intention and maneuver prediction framework for dynamic environments. The main contribution is the combination of model-based interaction-aware intention estimation with maneuver-based motion prediction based on supervised learning. The advantages of this framework are twofold. On one hand, expert knowledge in the form of heuristics is integrated, which simplifies the modeling of the interaction. On the other hand, the difficulties associated with the scalability and data sparsity of the algorithm due to the so-called curse of dimensionality can be reduced, as a reduced feature space is sufficient for supervised learning. The proposed algorithm can be used for highly automated driving or as a prediction module for advanced driver assistance systems without the need of intervehicle communication. At the start of the algorithm, the motion intention of each driver in a traffic scene is predicted in an iterative manner using the game-theoretic idea of stochastic multiagent simulation. This approach provides an interpretation of what other drivers intend to do and how they interact with surrounding traffic. By incorporating this information into a Bayesian network classifier, the developed framework achieves a significant improvement in terms of reliable prediction time and precision compared with other state-of-the-art approaches. By means of experimental results in real traffic on highways, the validity of the proposed concept and its online capability is demonstrated. Furthermore, its performance is quantitatively evaluated using appropriate statistical measures.
A new exact method for the two-dimensional orthogonal packing problem

The two-dimensional orthogonal packing problem (2OPP ) consists of determining if a set of rectangles (items) can be packed into one rectangle of fixed size (bin). In this paper we propose two exact algorithms for solving this problem. The first algorithm is an improvement on a classical branch&bound method, whereas the second algorithm is based on a two-step enumerative method. We also describe reduction procedures and lower bounds which can be used within the branch&bound method. We report computational experiments for randomly generated benchmarks, which demonstrate the efficiency of both methods.
Deep Learning With Spiking Neurons: Opportunities and Challenges

Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing. This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. In this review, we address the opportunities that deep spiking networks offer and investigate in detail the challenges associated with training SNNs in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware. A wide range of training methods for SNNs is presented, ranging from the conversion of conventional deep networks into SNNs, constrained training before conversion, spiking variants of backpropagation, and biologically motivated variants of STDP. The goal of our review is to define a categorization of SNN training methods, and summarize their advantages and drawbacks. We further discuss relationships between SNNs and binary networks, which are becoming popular for efficient digital hardware implementation. Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications. We compare the suitability of various neuromorphic systems that have been developed over the past years, and investigate potential use cases. Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific advantages. Deep SNNs offer great opportunities to work with new types of event-based sensors, exploit temporal codes and local on-chip learning, and we have so far just scratched the surface of realizing these advantages in practical applications.
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Model selection strategies for machine learning algorithm s typically involve the numerical optimisation of an appropriate model selection criterion, ofte n based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can b e broken down into bias and variance components. While unbiasedness is oft en cited as a beneficial quality of a model selection criterion, we demonstrate that a low varian ce is at least as important, as a nonnegligible variance introduces the potential for over-fitt ing in model selection as well as in training the model. While this observation is in hindsight perhaps rat her obvious, the degradation in performance due to over-fitting the model selection criterion can b e surprisingly large, an observation that appears to have received little attention in the machine lea rning literature to date. In this paper, we show that the effects of this form of over-fitting are often of c mparable magnitude to differences in performance between learning algorithms, and thus canno t be ignored in empirical evaluation. Furthermore, we show that some common performance evaluati on practices are susceptible to a form of selection bias as a result of this form of over-fitting and hence are unreliable. We discuss methods to avoid over-fitting in model selection and sub sequent selection bias in performance evaluation, which we hope will be incorporated into best pra ctice. While this study concentrates on cross-validation based model selection, the findings are quit general and apply to any model selection practice involving the optimisation of a model se lection criterion evaluated over a finite sample of data, including maximisation of the Bayesian evid nce and optimisation of performance bounds.
Analysis and Application of an On-Package Planar Inverted-F Antenna

An on-package planar inverted-F antenna (PIFA) is described. The on-package PIFA consists of a single folded metal plate and has several advantages, including small size, light weight, low cost, and ease of fabrication. The antenna radiation pattern is omnidirectional in the H-plane. The coupling effect between the on-package PIFA and the radio-frequency (RF) components in the shielding package was studied. The antenna performance rarely changes and the isolation between the antenna and the RF components can be maximized when the locations of the components are appropriately arranged in the package. Finally, a wireless local-area network (WLAN) front-end module (FEM) including the switch, low-pass filter (LPF), bandpass filter (BPF), and power amplifier (PA), is embedded into the shielding package of the antenna. The error vector magnitude (EVM) of the resultant antenna integrated FEM, together with a WLAN card containing the baseband/medium-access control (MAC) circuitry, is tested. Good performance is obtained, showing the usability of the proposed antenna configuration.
IoT Mashup as a Service: Cloud-Based Mashup Service for the Internet of Things

Mashup, a way to compose a new service from existing services, is expected to play a great role in internet of things (IoT) environment. Many recent researches suggest that mashup in IoT environment is possible with existing web mashup technology if each thing exposes its functionalities as a web service. However, this approach may have limitations in dealing with many heterogeneous devices and computation scalability in the presence of large number of things involved. In this paper, we propose a cloud-based IoT mashup service model, called IoT Mashup as a Service (IoTMaaS), to overcome heterogeneity of devices by following the model driven architecture principles and computational scalability based on cloud computing paradigm. We also design a cloud platform on which IoTMaaS be executed in harmony with stakeholders such as end users, device manufacturers, and cloud computing providers. We proved the concept of the architecture by implementing a prototype platform with an vacant room detection application.
Novotel World Trade Centre , Dubai , UAE , ISBN : 978-1-922069-41-2 Using “ Data Mining ” to Detect Frauds of Internal Audits

Fraud detection has become a critical component of financial audits. Iran as a developing country usually faces too many frauds in financial reports. The researches on important factories such as Iran Docharkh, Mazda Yadak and Bahman Group indicate that the production systems in Iran usually face problems which have kept the production system away from the right management techniques, Just in Time production (J.I.T) and Lean production. As a result accounting system will be far away from Lean Accounting and also from using the standard costs. On the other hand the most effective factor of finished value is inventory. The inventory contains raw materials inventory, work in process inventory (W.I.P) and finished products (F.P) inventory. These factors are the reasons which will increase or decrease the company profit and are able to indicate frauds in financial reports. Showing extra profit causes some problems for shareholders and lack of profit causes taxes problems. This paper will present a new model (3TFP model) to help auditors for remote controlling of data and solve the problems mentioned. In this model first we will convert financial year to 12 months, after updating the bill of material we will physically count B.O.M, Work in Process and finished products in the end of each month and we will control them. Then the inventory of beginning and ending periods and consuming rate will be computed. After computing the factors mentioned, the cumulative consumption in each month for finished products, B.O.M and work in process products should be computed. Drawing diagrams for data gained and identifying critical regions are the next phases. Then we will use Fish-Bone method to analyze information. And finally we will use Pareto method to detect and clear the most suspicious factors causing frauds. It should be mentioned that implementing this model needs computer programming because of high volume of data.
Quasi-online reinforcement learning for robots

This paper describes quasi-online reinforcement learning: while a robot is exploring its environment, in the background a probabilistic model of the environment is built on the fly as new experiences arrive; the policy is trained concurrently based on this model using an anytime algorithm. Prioritized sweeping, directed exploration, and transformed reward functions provide additional speed-ups. The robot quickly learns goal-directed policies from scratch, requiring few interactions with the environment and making efficient use of available computation time. From an outside perspective it learns the behavior online and in real time. We describe comparisons with standard methods and show the individual utility of each of the proposed techniques
Using behavior models for anomaly detection in hybrid systems

The importance of safety and reliability in today's real-world complex hybrid systems, such as process plants, led to the development of various anomaly detection and diagnosis techniques. Model-based approaches established themselves among the most successful ones in the field. However, they depend on a model of a system, which usually needs to be derived manually. Manual modeling requires a lot of efforts and resources. This paper gives a procedure for anomaly detection in hybrid systems that uses automatically generated behavior models. The model is learned from logged system's measurements in a hybrid automaton framework. The presented anomaly detection algorithm utilizes the model to predict the system behavior, and to compare it with the observed behavior in an online manner. Alarms are raised whenever a discrepancy is found between these two. The effectiveness of this approach is demonstrated in detecting several types of anomalies in a real-world running production system.
Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Accurate and early diagnosis of Alzheimer’s disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.
Bigdata Oriented Multimedia Mobile Health Applications

In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs). The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception. Several HMDs have been tested and compared. The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists. The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process.
A 90–100-GHz 4  $\times$ 4 SiGe BiCMOS Polarimetric Transmit/Receive Phased Array With Simultaneous Receive-Beams Capabilities

This paper presents a 4 × 4 transmit/receive (T/R) SiGe BiCMOS phased-array chip at 90-100 GHz with vertical and horizontal polarization capabilities, 3-bit gain control (9 dB), and 4-bit phase control. The 4 × 4 phased array fits into a 1.6×1.5 mm2 grid, which is required at 94 GHz for wide scan-angle designs. The chip has simultaneous receive (Rx) beam capabilities (V and H) and this is accomplished using dual-nested 16:1 Wilkinson combiners/divider with high isolation. The phase shifter is based on a vector modulator with optimized design between circuit level and electromagnetic simulation and results in 1 dB and gain and phase error, respectively, at 85-110 GHz. The behavior of the vector modulator phase distortion versus input power level is investigated and measured, and design guidelines are given for proper operation in a transmit (Tx) chain. The V and H Rx paths result in a gain of 22 and 25 dB, respectively, a noise figure of 9-9.5 (max. gain), and 11 dB (min. gain) measured without the T/R switch, and an input P1 dB of -31 to -26 dBm over the gain control range. The measured output Psat is ~ -5 dBm per channel, limited by the T/R switch loss. Measurements show ±0.6- and ±0.75-dB variation between the 4 × 4 array elements in the Tx mode (Psat) and Rx mode, respectively, and 40-dB coupling between the different channels on the chip. The chip consumes 1100 mA from a 2-V supply in both the Tx and Rx modes. The design can be scaled to >10 000 elements using polyimide redistribution layers on top of the chip and the application areas are in W-band radars for landing systems.
A Comparison of the Elastic Properties of Graphene- and Fullerene-Reinforced Polymer Composites: The Role of Filler Morphology and Size

Nanoscale carbon-based fillers are known to significantly alter the mechanical and electrical properties of polymers even at relatively low loadings. We report results from extensive molecular-dynamics simulations of mechanical testing of model polymer (high-density polyethylene) nanocomposites reinforced by nanocarbon fillers consisting of graphene flakes and fullerenes. By systematically varying filler concentration, morphology, and size, we identify clear trends in composite stiffness with reinforcement. To within statistical error, spherical fullerenes provide a nearly size-independent level of reinforcement. In contrast, two-dimensional graphene flakes induce a strongly size-dependent response: we find that flakes with radii in the 2-4 nm range provide appreciable enhancement in stiffness, which scales linearly with flake radius. Thus, with flakes approaching typical experimental sizes (~0.1-1 μm), we expect graphene fillers to provide substantial reinforcement, which also is much greater than what could be achieved with fullerene fillers. We identify the atomic-scale features responsible for this size- and morphology-dependent response, notably, ordering and densification of polymer chains at the filler-matrix interface, thereby providing insights into avenues for further control and enhancement of the mechanical properties of polymer nanocomposites.
Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies.

Muscular sound is a mechanical phenomenon detectable at the surface of an active muscle, which has been known and described since 1800. Only recently, because of the availability of reliable transducers and sophisticated analysis techniques, has this signal become attractive for monitoring the mechanical aspects of muscle contraction. The muscular sound characteristics were investigated both during electrically elicited and voluntary contractions. In the first case, the influence of the biophysical and mechanical properties of the muscle on this signal was studied. During voluntary efforts the summation of the mechanical activity of the recruited motor units was analyzed. The results indicate that the muscular sound may be an adjunct tool to the force, the physiological force tremor, and electromyogram to obtain information on the muscle mechanical model as well as on muscle motor control. This review focuses on the following aspects of the signal: (1) recording problems; (2) muscle sound properties during stimulation of isolated and in vivo muscle; (3) time and frequency domain analysis during non fatiguing and fatiguing contractions; (4) comparison with other signals related to muscle activity; (5) discussion on the origin; and (6) possible practical applications.
Measuring User Productivity in Machine Translation Enhanced Computer Assisted Translation

This paper addresses the problem of reliably measuring productivity gains by professional translators working with a machine translation enhanced computer assisted translation tool. In particular, we report on a field test we carried out with a commercial CAT tool in which translation memory matches were supplemented with suggestions from a commercial machine translation engine. The field test was conducted with 12 professional translators working on real translation projects. Productivity of translators were measured with two indicators, post-editing speed and post-editing effort, on two translation directions, English–Italian and English–German, and two linguistic domains, legal and information technology. Besides a detailed statistical analysis of the experimental results, we also discuss issues encountered in running the test.