nils-herrmann/clean_scirepeval huggingface dataset:
Critical Success Factors for Adoption of Electronic Health Record Systems: Literature Review and Prescriptive Analysis

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Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling

Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.
A second order accurate projection method for the incompressible Navier-Stokes equations on non-graded adaptive grids

We present an unconditionally stable second order accurate projection method for the incompressible Navier–Stokes equations on non-graded adaptive Cartesian grids. We employ quadtree and octree data structures as an efficient means to represent the grid. We use the supra-convergent Poisson solver of [C.-H. Min, F. Gibou, H. Ceniceros, A supra-convergent finite difference scheme for the variable coefficient Poisson equation on fully adaptive grids, CAM report 05-29, ], a second order accurate semi-Lagrangian method to update the momentum equation, an unconditionally stable backward difference scheme to treat the diffusion term and a new method that guarantees the stability of the projection step on highly non-graded grids. We sample all the variables at the grid nodes, producing a scheme that is straightforward to implement. We propose two and three-dimensional examples to demonstrate second order accuracy for the velocity field and the divergence free condition in the L and L1 norms.
SDN-Guard: DoS Attacks Mitigation in SDN Networks

Software Defined Networking (SDN) has recently emerged as a new networking technology offering an unprecedented programmability that allows network operators to dynamically configure and manage their infrastructures. The main idea of SDN is to move the control plane into a central controller that is in charge of taking all routing decisions in the network. However, despite all the advantages offered by this technology, Deny-of-Service (DoS) attacks are considered a major threat to such networks as they can easily overload the controller processing and communication capacity and flood switch CAM tables, resulting in a critical degradation of the overall network performance. To address this issue, we propose in this paper SDN-Guard, a novel scheme able to efficiently protect SDN networks against DoS attacks by dynamically (1) rerouting potential malicious traffic, (2) adjusting flow timeouts and (3) aggregating flow rules. Realistic experiments using Mininet show that the proposed solution succeeds in minimizing by up to 32% the impact of DoS attacks on~the controller performance, switch memory usage and control plane bandwidth and thereby maintaining acceptable network performance during such attacks.
Unsupervised Pretraining for Sequence to Sequence Learning

Sequence to sequence models are successful tools for supervised sequence learning tasks, such as machine translation. Despite their success, these models still require much labeled data and it is unclear how to improve them using unlabeled data, which is much less expensive to obtain. In this paper, we present simple changes that lead to a significant improvement in the accuracy of seq2seq models when the labeled set is small. Our method intializes the encoder and decoder of the seq2seq model with the trained weights of two language models, and then all weights are jointly fine-tuned with labeled data. An additional language modeling loss can be used to regularize the model during fine-tuning. We apply this method to low-resource tasks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main finding is that the pretraining accelerates training and improves generalization of seq2seq models, achieving state-of-the-art results on the WMT English→German task. Our model obtains an improvement of 1.3 BLEU from the previous best models on both WMT’14 and WMT’15 English→German. Our ablation study shows that pretraining helps seq2seq models in different ways depending on the nature of the task: translation benefits from the improved generalization whereas summarization benefits from the improved optimization.
The orbital angular momentum (OAM) multiplexing controversy: OAM as a subset of MIMO

Recently the orbital angular momentum (OAM) multiplexing scheme has been proposed to increase without limit the channel capacity between a transmitter and a receiver node communicating in line of sight. A controversy arose about the far-field effectiveness of this method and about it being or not a subset of MIMO communication schemes. In this contribution we first illustrate that OAM multiplexing can be completely understood as line of sight MIMO, and hence that the technique cannot bring additional advantages in far field communications. Secondly, we show that it is possible to build similar line of sight near field MIMO systems based on lenses with similar or better performance than their OAM counterparts.
Locomotion planning and performance analysis of quadruped robot based on ADAMS and MATLAB co-simulation

The robot-terrain-ground system is fundamentally a complex one caused by the complex interactions between the robot and terrain-ground due to the diversity and complexity of the terrain-ground. Firstly, the kinematics equation with controllable landing angle of quadruped robot is derived in this paper, and then a trot dynamic gait planning method of quadruped robot is proposed. The correctness and validity of the kinematics equation are verified in terms of the derived kinematics equation and dynamic gait planning method from the point of ADAMS and MATLAB co-simulation. In addition, the influence of the landing angle on the locomotion performance of the quadruped robot is studied by means of the quadruped simulation model with two different landing angles, which provides a theoretical basis for the gait planning and landing angle adjustment of real quadruped robot.
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.
Characterization of defense mechanisms against distributed denial of service attacks

We propose a characterization of distributed denial-of-s ervice (DDOS) defenses where reaction points are network-based and attack responses are active. The purpose is to provide a framework for comparing the performance and deployment of DDOS defenses. We identify the characteristics in attack detection algorithms and a ttack responses by reviewing defenses that have appeared in the literature. We expect that this char a terization will provide practitioners and academia insights into deploying DDOS defense as network services.
Coordinating movement within swarms of UAVs through mobile networks

Unmanned Aerial Vehicles (UAV) have several uses in civilians and military applications, such as search and rescue missions, cartography and terrain exploration, industrial plant control, surveillance, public security, firefight, and others. Swarms of UAVs may further increase the effectiveness of these tasks, since they enable larger coverage, more accurate or redundant sensed data, fault tolerance, etc. Swarms of aerial robots require real-time coordination, which is just a specific case of M2M collaboration. But one of the biggest challenges of UAV swarming is that this real-time coordination has to happen in a wide-area setting where it is expensive, or even impossible, to set up a dedicated wireless infrastructure for this purpose. Instead, one has to resort to conventional 3G/4G wireless networks, where communication latencies are in the range of 50-150 ms. In this paper we tackle the problem of UAV swarm formation and maintenance in areas covered by such mobile network, and propose a bandwidth-efficient multi-robot coordination algorithm for these settings. The coordination algorithm was implemented on the top of our mobile middleware SDDL, uses its group-cast communication capability, and was tested with simulated UAVs.
Electronic travel aids for the blind based on sensory substitution

With the development of advanced human-machine interface, effective information processing algorithms, and more powerful microprocessors, it is possible to enable the blind to achieve additional perception of the environment. In 1970s, the blind-assistant facility based on sensory substitution was introduced[1]. It called as ETA (short for Electronic Travel Aids), bases on the other natural senses of the blind, such as hearing, touch, smell, feeling and etc. Then the research of vision aids has been broadly extended. After introducing the traditional methods for guiding blind, two typical input modes of travel aid are presented in this paper. One is sonar-based ETAs and the other technique is camera-based ETAs. Its principle of each modes is discussed in detail. Besides these, comparisons between the above two techniques have also been indicated in this paper.
SLING: A framework for frame semantic parsing

We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.
Machine Learning for Health Informatics

Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with “big data” in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, “little data”, or rare events, where aMLapproaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor-in-the-loop, e.g. in subspace clustering, k-Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure), and entropy (time), (3) data visualization, and last but not least (4) privacy, data protection, safety & security.
A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy

To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia.



Pre-treatment EEG data are collected in 23+14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects' EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators.



We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%.



These findings indicate that analysis of pre-treatment EEG data can predict the clinical response to clozapine in treatment resistant schizophrenia.



If replicated in a larger population, this novel approach to EEG analysis may assist the clinician in determining treatment-efficacy.
A penalty PALM method for sparse portfolio selection problems

In this paper, we propose a penalty proximal alternating linearized minimization method for the large-scale sparse portfolio problems in which a sequence of penalty subproblems are solved by utilizing proximal alternating linearized minimization frame and sparse projection techniques. For exploiting the structure of the problems and reducing the computation complexity, each penalty subproblem is solved by alternately solving two projection subproblems. The global convergence of the method to a KKT point or a local minimizer of the problem can be proved under the characteristic of the problem. The computational results with practical problems demonstrate that our method can find the suboptimal solutions of the problems efficiently and is competitive with some other local solution methods.
A Study of Automatic Speech Recognition in Noisy Classroom Environments for Automated Dialog Analysis

The development of large-scale automatic classroom dialog analysis systems requires accurate speech-to-text translation. A variety of automatic speech recognition (ASR) engines were evaluated for this purpose. Recordings of teachers in noisy classrooms were used for testing. In comparing ASR results, Google Speech and Bing Speech were more accurate with word accuracy scores of 0.56 for Google and 0.52 for Bing compared to 0.41 for AT&T Watson, 0.08 for Microsoft, 0.14 for Sphinx with the HUB4 model, and 0.00 for Sphinx with the WSJ model. Further analysis revealed both Google and Bing engines were largely unaffected by speakers, speech class sessions, and speech characteristics. Bing results were validated across speakers in a laboratory study, and a method of improving Bing results is presented. Results provide a useful understanding of the capabilities of contemporary ASR engines in noisy classroom environments. Results also highlight a list of issues to be aware of when selecting an ASR engine for difficult speech recognition tasks.
Fpga-based face detection system using Haar classifiers

This paper presents a hardware architecture for face detection based system on AdaBoost algorithm using Haar features. We describe the hardware design techniques including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the processing speed of the face detection system. Also we discuss the optimization of the proposed architecture which can be scalable for configurable devices with variable resources. The proposed architecture for face detection has been designed using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured and compared with an equivalent software implementation. We show about 35 times increase of system performance over the equivalent software implementation.
Developing an Ontology of the Cyber Security Domain

This paper reports on a trade study we performed to support the development of a Cyber ontology from an initial malware ontology. The goals of the Cyber ontology effort are first described, followed by a discussion of the ontology development methodology used. The main body of the paper then follows, which is a description of the potential ontologies and standards that could be utilized to extend the Cyber ontology from its initially constrained malware focus. These resources include, in particular, Cyber and malware standards, schemas, and terminologies that directly contributed to the initial malware ontology effort. Other resources are upper (sometimes called 'foundational') ontologies. Core concepts that any Cyber ontology will extend have already been identified and rigorously defined in these foundational ontologies. However, for lack of space, this section is profoundly reduced. In addition, utility ontologies that are focused on time, geospatial, person, events, and network operations are briefly described. These utility ontologies can be viewed as specialized super-domain or even mid-level ontologies, since they span many, if not most, ontologies -including any Cyber ontology. An overall view of the ontological architecture used by the trade study is also given. The report on the trade study concludes with some proposed next steps in the iterative evolution of the
Virgin soil in irony research : Personality , humor , and the “ sense of irony ”

he aim of the paper is fourfold: (a) show why humor scholars should study irony, (b) explore the need for considering interindividual differences in healthy adults’ irony performance, (c) stress the necessity for developing tools assessing habitual differences in irony performance, and (d) indicate future directions for joint irony and humor research and outline possible applications. Verbal irony is often employed with a benevolent humorous intent by speakers, but can also serve as a means of disparagement humor. In both cases, encoding and decoding activities entailing irony need to be considered in the context of the psychology of humor. We argue that verbal irony performance can be considered a phenomenon native to the realm of humor and individual differences. We point out that research has widely neglected the meaningfulness of variance in irony performance within experimental groups when looking at determinants of irony detection and production. Based on theoretical considerations and previous empirical findings we show that this variance can be easily related to individual-differences variables such as the sense of humor, dispositions toward laughter and ridicule (e.g., gelotophobia), and general mental ability. Furthermore, we hypothesize that there is an enduring trait determining irony performance we will label the sense of irony. The sense of irony possibly goes along with inclinations toward specific affective and cognitive processing patterns when dealing with verbal irony. As an application, novel irony performance tests can help to study psychological and neurophysiological correlates of irony performance more feasibly, that is, in nonclinical groups.  https://doi.org/10.1037/tps0000054 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-124094 Accepted Version Originally published at: Bruntsch, Richard; Hofmann, Jennifer; Ruch, Willibald (2016). Virgin soil in irony research: Personality, humor, and the “sense of irony”. Translational Issues in Psychological Science, 2(1):25-34.  https://doi.org/10.1037/tps0000054 VIRGIN SOIL IN IRONY RESEARCH 1 Running head: VIRGIN SOIL IN IRONY RESEARCH Virgin soil in irony research: Personality, humor, and the “sense of irony”
Fault tree analysis for the communication of a fleet formation flight of UAVs

Fleet flight of drones has been an interesting topic in the last years due to the advantages of the cooperative fleet of UAVs in both civilians and military applications. Fault Tree Analysis (FTA) is a well-known technique used to identify the causes that lead to the failure of a top event. In this paper, we aim to analyze the reliability of communications between drones or between drones and ground station control using FTA. Improving the reliability of the communication system would guarantee the success of the mission. Data for basic events has been gathered from the Nonelectronic Parts Reliability Data Publication (NPRD). The probability of occurrence of communication failure between the drones is calculated taking into consideration the presence or absence of a crash.
Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation

In this paper we describe a novel decentralized control strategy to realize formations of mobile robots. We first describe how to design artificial potential fields to obtain a formation with the shape of a regular polygon. We provide a formal proof of the asymptotic stability of the sys tem, based on the definition of a proper Lyapunov function. We also prove that our control strategy is not affected by the problem of local minima. Then, we exploit a bijective coordinate transformation to deform the polygonal formation, thus obtaining a completely arbitrarily shaped forma tion. Simulations and experimental tests are provided to va lidate the control strategy.
Information Security Threats Classification Pyramid

Threat classification is extremely important for organizations, as it is an important step towards implementation of information security. Most of the existing threat classifications listed threats in static ways without linking threats to information system areas. The aim of this paper is to design a methodology that can classify deliberate threats in a dynamic way to represent each threat in different areas of the information system. This technique is based on the following factors: the attacker's prior knowledge (i. e. the knowledge hold by the source of the threat) about the system, loss of security information and the criticality of the area that might be affected by that threat.