nils-herrmann/clean_scirepeval huggingface dataset:
Fast and Accurate OOV Decoder on High-Level Features

This work proposes a novel approach to out-of-vocabulary (OOV) keyword search (KWS) task. The proposed approach is based on using high-level features from an automatic speech recognition (ASR) system, so called phoneme posterior based (PPB) features, for decoding. These features are obtained by calculating time-dependent phoneme posterior probabilities from word lattices, followed by their smoothing. For the PPB features we developed a special novel very fast, simple and efficient OOV decoder. Experimental results are presented on the Georgian language from the IARPA Babel Program, which was the test language in the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum term weighted value (MTWV) metric and computational speed, for single ASR systems, the proposed approach significantly outperforms the state-of-the-art approach based on using in-vocabulary proxies for OOV keywords in the indexed database. The comparison of the two OOV KWS approaches on the fusion results of the nine different ASR systems demonstrates that the proposed OOV decoder outperforms the proxy-based approach in terms of MTWV metric given the comparable processing speed. Other important advantages of the OOV decoder include extremely low memory consumption and simplicity of its implementation and parameter optimization.
Targeting EEG/LFP Synchrony with Neural Nets

We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are “big” in terms of the size of recorded data but rarely have sufficient labels required to train complex models (e.g., conventional deep learning methods). Furthermore, in many scientific applications, the goal is to be able to understand the underlying features related to the classification, which prohibits the blind application of deep networks. This motivates the development of a new model based on parameterized convolutional filters guided by previous neuroscience research; the filters learn relevant frequency bands while targeting synchrony, which are frequency-specific power and phase correlations between electrodes. This results in a highly expressive convolutional neural network with only a few hundred parameters, applicable to smaller datasets. The proposed approach is demonstrated to yield competitive (often state-of-the-art) predictive performance during our empirical tests while yielding interpretable features. Furthermore, a Gaussian process adapter is developed to combine analysis over distinct electrode layouts, allowing the joint processing of multiple datasets to address overfitting and improve generalizability. Finally, it is demonstrated that the proposed framework effectively tracks neural dynamics on children in a clinical trial on Autism Spectrum Disorder.
A framework for secure and decentralized sharing of medical imaging data via blockchain consensus.

The electronic sharing of medical imaging data is an important element of modern healthcare systems, but current infrastructure for cross-site image transfer depends on trust in third-party intermediaries. In this work, we examine the blockchain concept, which enables parties to establish consensus without relying on a central authority. We develop a framework for cross-domain image sharing that uses a blockchain as a distributed data store to establish a ledger of radiological studies and patient-defined access permissions. The blockchain framework is shown to eliminate third-party access to protected health information, satisfy many criteria of an interoperable health system, and readily generalize to domains beyond medical imaging. Relative drawbacks of the framework include the complexity of the privacy and security models and an unclear regulatory environment. Ultimately, the large-scale feasibility of such an approach remains to be demonstrated and will depend on a number of factors which we discuss in detail.
Photovoltaic-Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting

Due to the increasing proportion of distributed photovoltaic (PV) production in the generation mix, the knowledge of the PV generation capacity has become a key factor. In this paper, we propose to compute the PV plant maximum power starting from the indirectly estimated irradiance. Three estimators are compared in terms of ability to compute the PV plant maximum power, bandwidth, and robustness against measurements noise. The approaches rely on measurements of the DC voltage, current, and cell temperature, and on a model of the PV array. We show that the considered methods can accurately reconstruct the PV maximum generation even during curtailment periods, i.e., when the measured PV power is not representative of the maximum potential of the PV array. Performance evaluation is carried out by using a dedicated experimental setup on a 14.3 kWp rooftop PV installation. Results also proved that the analyzed methods can outperform pyranometer-based estimations with a simple sensing system. We show how the obtained PV maximum power values can be applied to train time series based solar maximum power forecasting techniques. This is beneficial when the measured power values, commonly used as training, are not representative of the maximum PV potential.
Efficacy of Yamamoto new scalp acupuncture versus Traditional Chinese acupuncture for migraine treatment.

Traditional Chinese acupuncture (TCA) is an effective alternative treatment in migraine headache. The aim of this study was to compare the therapeutic effect of Yamamoto new scalp acupuncture (YNSA), a recently developed microcupuncture system, with TCA for the prophylaxis and treatment of migraine headache.



In a randomized clinical trial, 80 patients with migraine headache were assigned to receive YNSA or TCA. A pain visual analogue scale (VAS) and migraine therapy assessment questionnaire (MTAQ) were completed before treatment, after 6 and 18 sections of treatment, and 1 month after completion of therapy.



All the recruited patients completed the study. Baseline characteristics were similar between the two groups. Frequency and severity of migraine attacks, nausea, the need for rescue treatment, and work absence rate decreased similarly in both groups. Recovery from headache and ability to continue daily activities 2 hours after medical treatment showed similar improvement in both groups (p>0.05).



Classic acupuncture and YNSA are similarly effective in the prophylaxis and treatment of migraine headache and may be considered as alternatives to pharmacotherapy.
User requirements of mobile technology: results from a content analysis of user reviews

Advanced mobile technology continues to shape professional environments. Smart cell phones, pocket computers and laptop computers reduce the need of users to remain close to a wired information system infrastructure and allow for task performance in many different contexts. Among the consequences are changes in technology requirements, such as the need to limit weight and size of the devices. In the current paper, we focus on the factors that users find important in mobile devices. Based on a content analysis of online user reviews that was followed by structural equation modeling, we found four factors to be significantly related with overall user evaluation, namely functionality, portability, performance, and usability. Besides the practical relevance for technology developers and managers, our research results contribute to the discussion about the extent to which previously established theories of technology adoption and use are applicable to mobile technology. We also discuss the methodological suitability of online user reviews for the assessment of user requirements, and the complementarity of automated and non-automated forms of content analysis.
Sensor for Measuring Strain in Textile

In this paper a stain sensor to measure large strain (80%) in textiles is presented. It consists of a mixture of 50wt-% thermoplastic elastomer (TPE) and 50wt-% carbon black particles and is fiber-shaped with a diameter of 0.315mm. The attachment of the sensor to the textile is realized using a silicone film. This sensor configuration was characterized using a strain tester and measuring the resistance (extension-retraction cycles): It showed a linear resistance response to strain, a small hysteresis, no ageing effects and a small dependance on the strain velocity. The total mean error caused by all these effects was +/-5.5% in strain. Washing several times in a conventional washing machine did not influence the sensor properties. The paper finishes by showing an example application where 21 strain sensors were integrated into a catsuit. With this garment, 27 upper body postures could be recognized with an accuracy of 97%.
A Crowd Monitoring Framework using Emotion Analysis of Social Media for Emergency Management in Mass Gatherings

In emergency management for mass gatherings, the knowledge about crowd types can highly assist with providing timely response and effective resource allocation. Crowd monitoring can be achieved using computer vision based approaches and sensory data analysis. The emergence of social media platforms presents an opportunity to capture valuable information about how people feel and think. However, the literature shows that there are a limited number of studies that use social media in crowd monitoring and/or incorporate a unified crowd model for consistency and interoperability. This paper presents a novel framework for crowd monitoring using social media. It includes a standard crowd model to represent different types of crowds. The proposed framework considers the effect of emotion on crowd behaviour and uses the emotion analysis of social media to identify the crowd types in an event. An experiment using historical data to validate our framework is described.
DIDACache: An Integration of Device and Application for Flash-based Key-value Caching

caching is crucial to today’s low-latency Internet services. Conventional key-value cache systems, such as Memcached, heavily rely on expensive DRAM memory. To lower Total Cost of Ownership, the industry recently is moving toward more cost-efficient flash-based solutions, such as Facebook’s McDipper [14] and Twitter’s Fatcache [56]. These cache systems typically take commercial SSDs and adopt a Memcached-like scheme to store and manage key-value cache data in flash. Such a practice, though simple, is inefficient due to the huge semantic gap between the key-value cache manager and the underlying flash devices.
 In this article, we advocate to reconsider the cache system design and directly open device-level details of the underlying flash storage for key-value caching. We propose an enhanced flash-aware key-value cache manager, which consists of a novel unified address mapping module, an integrated garbage collection policy, a dynamic over-provisioning space management, and a customized wear-leveling policy, to directly drive the flash management. A thin intermediate library layer provides a slab-based abstraction of low-level flash memory space and an API interface for directly and easily operating flash devices. A special flash memory SSD hardware that exposes flash physical details is adopted to store key-value items. This co-design approach bridges the semantic gap and well connects the two layers together, which allows us to leverage both the domain knowledge of key-value caches and the unique device properties. In this way, we can maximize the efficiency of key-value caching on flash devices while minimizing its weakness. We implemented a prototype, called DIDACache, based on the Open-Channel SSD platform. Our experiments on real hardware show that we can significantly increase the throughput by 35.5%, reduce the latency by 23.6%, and remove unnecessary erase operations by 28%.
Gamifying Collaborative Prioritization: Does Pointsification Work?

Gamification has been applied in software engineering contexts, and more recently in requirements engineering with the purpose of improving the motivation and engagement of people performing specific engineering tasks. But often an objective evaluation that the resulting gamified tasks successfully meet the intended goal is missing. On the other hand, current practices in designing gamified processes seem to rest on a try, test and learn approach, rather than on first principles design methods. Thus empirical evaluation should play an even more important role.We combined gamification and automated reasoning techniques to support collaborative requirements prioritization in software evolution. A first prototype has been evaluated in the context of three industrial use cases. To further investigate the impact of specific game elements, namely point-based elements, we performed a quasi-experiment comparing two versions of the tool, with and without pointsification. We present the results from these two empirical evaluations, and discuss lessons learned.
Cybercriminals, cyberattacks and cybercrime

Nowadays, cybercrime is growing rapidly around the world, as new technologies, applications and networks emerge. In addition, the Deep Web has contributed to the growth of illegal activities in cyberspace. As a result, cybercriminals are taking advantage of system vulnerabilities for their own benefit. This article presents the history and conceptualization of cybercrime, explores different categorizations of cybercriminals and cyberattacks, and sets forth our exhaustive cyberattack typology, or taxonomy. Common categories include where the computer is the target to commit the crime, where the computer is used as a tool to perpetrate the felony, or where a digital device is an incidental condition to the execution of a crime. We conclude our study by analyzing lessons learned and future actions that can be undertaken to tackle cybercrime and harden cybersecurity at all levels.
El Farol Bar problem, Potluck problem and electric energy balancing - on the importance of communication

Power balancing is an important issue when micro-grids in island mode are considered. It requires active, real-time decision making to minimise the imbalances. El Farol Bar and Potluck problems are artificial problems designed to challenge the decision making process in a situation of limited information. They are theoretical, ill-defined problems designed to show that rational decision making in a situation of scarce information can give worse results than non-rational behavior. Potluck problem can be compared to electric power balancing in microgrids. But due to reduced complexity and constraints of theoretical problems and differences in goal functions the approach has to be in each case different. The study of these differences in this paper shows the importance of exchange of information between participants (or agents); it also suggest what type of information are necessary in what conditions. The amount of information that needs to be exchanged depends on the relation between participants (agents): the less cooperating their relation is, the less information they are willing to exchange.
Integrated GPS / INS System for Pedestrian Navigation in a Signal Degraded Environment

This paper evaluates the performance of a shoe/foot mounted inertial system for pedestrian navigation application. Two different grades of inertial sensors are used, namely a medium cost tactical grade Honeywell HG1700 inertial measurement unit (IMU) and a low-cost MEMS-based Crista IMU (Cloud Cap Technology). The inertial sensors are used in two different ways for computing the navigation solution. The first method is a conventional integration algorithm where IMU measurements are processed through a set of mechanization equation to compute a six degree-offreedom (DOF) navigation solution. Such a system is referred to as an Inertial Navigation System (INS). The integration of this system with GPS is performed using a tightly coupled integration scheme. Since the sensor is placed on the foot, the designed integrated system exploits the small period for which foot comes to rest at each step (stance-phase of the gait cycle) and uses Zero Velocity Update (ZUPT) to keep the INS errors bounded in the absence of GPS. An algorithm for detecting the stance-phase using the pattern of three-dimensional acceleration is discussed. In the second method, the navigation solutions is computed using the fact that a pedestrian takes one step at a time, and thus positions can be computed by propagating the step-length in the direction of pedestrian motion. This algorithm is termed as pedestrian dead-reckoning (PDR) algorithm. The IMU measurement in this algorithm is used to detect the step, estimate the step-length, and determine the heading for solution propagation. Different algorithms for stridelength estimation and step-detection are discussed in this paper. The PDR system is also integrated with GPS through a tightly coupled integration scheme. The performance of both the systems is evaluated through field tests conducted in challenging GPS environments using both inertial sensors. The specific focus is on the system performance under long GPS outages of duration upto 30 minutes.
Interactive Game Based Learning: Advantages and Disadvantages

Interactive Game-Based Learning might be used to raise the awareness of students concerning questions of sustainability. Sustainability is a very complex topic. By interacting with a simulation game, students can get a more detailed and holistic conception of how sustainability can be achieved in everyday purchasing situations. The SuLi (Sustainable Living) game was developed to achieve this goal. In an evaluation study we found evidence that SuLi is an interesting alternative to more traditional approaches to learning. Nevertheless, there are still many open questions, as, e.g., whether one should combine simulation games with other forms of teaching and learning or how to design simulation games so that students really acquire detailed concepts of the domain.
Online Job Scheduling in Distributed Machine Learning Clusters

Nowadays large-scale distributed machine learning systems have been deployed to support various analytics and intelligence services in IT firms. To train a large dataset and derive the prediction/inference model, e.g., a deep neural network, multiple workers are run in parallel to train partitions of the input dataset, and update shared model parameters. In a shared cluster handling multiple training jobs, a fundamental issue is how to efficiently schedule jobs and set the number of concurrent workers to run for each job, such that server resources are maximally utilized and model training can be completed in time. Targeting a distributed machine learning system using the parameter server framework, $w$ e design an online algorithm for scheduling the arriving jobs and deciding the adjusted numbers of concurrent workers and parameter servers for each job over its course, to maximize overall utility of all jobs, contingent on their completion times. Our online algorithm design utilizes a primal-dual framework coupled with efficient dual subroutines, achieving good long-term performance guarantees with polynomial time complexity. Practical effectiveness of the online algorithm is evaluated using trace-driven simulation and testbed experiments, which demonstrate its outperformance as compared to commonly adopted scheduling algorithms in today's cloud systems.
From Spin Glasses to Hard Satisfiable Formulas

We introduce a highly structured family of hard satisfiable 3-SAT formulas corresponding to an ordered spin-glass model from statistical physics. This model has provably “glassy” behavior; that is, it has many local optima with large energy barriers between them, so that local search algorithms get stuck and have difficulty finding the true ground state, i.e., the unique satisfying assignment. We test the hardness of our formulas with two complete Davis-Putnam solvers, Satz and zChaff, and two incomplete solvers, WalkSAT and the recently introduced Survey Propagation algorithm SP. We compare our formulas to random XOR-SAT formulas and to two other generators of hard satisfiable instances, the minimum disagreement parity formulas of Crawford et al., and Hirsch’s hgen2. For the complete solvers the running time of our formulas grows exponentially in √ n, and exceeds that of random XOR-SAT formulas for small problem sizes. More interestingly, our formulas appear to be harder for WalkSAT than any other known generator of satisfiable instances.
Towards an automation of the mutation analysis dedicated to model transformation

A benefit of model-driven engineering relies on the automatic generation of artefacts from high-level models through intermediary levels using model transformations. In such a process, the input must be well designed, and the model transformations should be trustworthy. Because of the specificities of models and transformations, classical software test techniques have to be adapted. Among these techniques, mutation analysis has been ported, and a set of mutation operators has been defined. However, it currently requires considerable manual work and suffers from the test data set improvement activity. This activity is a difficult and time-consuming job and reduces the benefits of the mutation analysis. This paper addresses the test data set improvement activity. Model transformation traceability in conjunction with a model of mutation operators and a dedicated algorithm allow to automatically or semi-automatically produce improved test models. The approach is validated and illustrated in two case studies written in Kermeta.
A Secure Data Deduplication Scheme Based on Differential Privacy

In cloud computing environment, especially in big data era, adversary may use data deduplication service supported by the cloud service provider as a side channel to eavesdrop users' privacy or sensitive information. In order to tackle this serious issue, in this paper, we propose a secure data deduplication scheme based on differential privacy. The highlights of the proposed scheme lie in constructing a hybrid cloud framework, using convergent encryption algorithm to encrypt original files, and introducing differential privacy mechanism to resist against the side channel attack. Performance evaluation shows that our scheme is able to effectively save network bandwidth and disk storage space during the processes of data deduplication. Meanwhile, security analysis indicates that our scheme can resist against the side channel attack and related files attack, and prevent the disclosure of privacy information.
Neuron-intrinsic inhibitors of axon regeneration: PTEN and SOCS3.

Our understanding of how axon regeneration is controlled in both the peripheral and central nervous systems remains fragmentary. Research into the regenerative capacity of adult neurons has elucidated PTEN and SOCS3 as distinctive but complementary arms of the regenerative program. These molecules act as negative regulators of major signaling pathways and impact the processes occurring in the cell body, such as protein translation and transcription, and in the axons, such as cytoskeleton assembly. In this review, we summarize the role of PTEN and SOCS3 in limiting axon regeneration and discuss the molecular and cellular mechanisms underlying their growth-inhibitory effects.