nils-herrmann/clean_scirepeval huggingface dataset:
A FPGA-Based Neuromorphic Locomotion System for Multi-Legged Robots

The paper develops a neuromorphic system on a Spartan 6 field programmable gate array (FPGA) board to generate locomotion patterns (gaits) for three different legged robots (biped, quadruped, and hexapod). The neuromorphic system consists of a reconfigurable FPGA-based architecture for a 3G artificial neural network (spiking neural network), which acts as a Central Pattern Generator (CPG). The locomotion patterns, are then generated by the CPG through a general neural architecture, which parameters are offline estimated by means of grammatical evolution and Victor-Purpura distance-based fitness function. The neuromorphic system is fully validated on real biped, quadruped, and hexapod robots.
DSH: data sensitive hashing for high-dimensional k-nnsearch

The need to locate the k-nearest data points with respect to a given query point in a multi- and high-dimensional space is common in many applications. Therefore, it is essential to provide efficient support for such a search. Locality Sensitive Hashing (LSH) has been widely accepted as an effective hash method for high-dimensional similarity search. However, data sets are typically not distributed uniformly over the space, and as a result, the buckets of LSH are unbalanced, causing the performance of LSH to degrade. In this paper, we propose a new and efficient method called Data Sensitive Hashing (DSH) to address this drawback. DSH improves the hashing functions and hashing family, and is orthogonal to most of the recent state-of-the-art approaches which mainly focus on indexing and querying strategies. DSH leverages data distributions and is capable of directly preserving the nearest neighbor relations. We show the theoretical guarantee of DSH, and demonstrate its efficiency experimentally.
Chip to wafer temporary bonding with self-alignment by patterned FDTS layer for size-free MEMS integration

In this paper, we present a low-cost and rapid self-alignment process for temporary bonding of MEMS chip onto carrier wafer for size-free MEMS-IC integration. For the first time, a hydrophobic self-assembled monolayer (SAM), FDTS (CF<inf>3</inf>(CF<inf>2</inf>)<inf>7</inf>(CH<inf>2</inf>)<inf>2</inf>SiCl<inf>3</inf>), was successfully patterned by lift-off process on an oxidized silicon carrier wafer. Small volume of H<inf>2</inf>O (∼µl/cm<sup>2</sup>) was then dropped and spread on the non-coated hydrophilic SiO<inf>2</inf> surface for temporary bonding of MEMS chip. Our results demonstrated that the hydrophobic FDTS pattern on carrier wafer enables rapid and precise self-alignment of MEMS chip onto SiO<inf>2</inf> binding-site by capillary force. After transfer the MEMS chips to target wafer, FDTS can be removed by O<inf>2</inf> plasma treatment or UV irradiation.
Harmonic current control for Interior Permanent Magnet Synchronous Machines applying current controller design method by using complex vectors

This paper proposes a cross-coupling decoupling PI controller based on a complex vector notation for the harmonic current control system of Interior Permanent Magnet Synchronous Machines. The proposed method could achieve decoupling control in synchronous reference frame rotated with the harmonic order, and could improve a performance of harmonic current control system, e.g. transient response. In addition, the proposed current controller is designed by using only controller bandwidth and fundamental parameters such as armature resistance and d-q axis inductances, therefore the proposed method is not effected by identification errors of spatial harmonics and has low parameter sensitivity. Effectiveness of the proposed method is revealed by simulation results and experimental results.
Organizational ambidexterity: exploring the knowledge base

The present research work shows the results of an analysis about the existing literature on one of the ‘topics’ which is currently raising greater interest among scholars and researchers in the fields of strategic management and organization science, namely: organizational ambidexterity. More precisely, and seeking to identify and visualize the intellectual structure or knowledge base of the research developed in relation to this construct, a decision was made to analyze a total of 283 research papers which appeared after the publication in the journal California Management Review in the summer of 1996 of the seminal work by Tushman and O’Reilly III entitled ‘Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change,’ where these authors suggested that organizations need to explore and exploit simultaneously if they want to be ambidextrous. As for the methodology applied, it was based on the utilization of bibliometric techniques—particularly citation analyses and author co-citation analyses and social networks analysis.
Exploiting the PI3K/AKT Pathway for Cancer Drug Discovery

Evolving studies with several different targeted therapeutic agents are demonstrating that patients with genomic alterations of the target, including amplification, translocation and mutation, are more likely to respond to the therapy. Recent studies indicate that numerous components of the phosphatidylinositol-3-kinase (PI3K)/AKT pathway are targeted by amplification, mutation and translocation more frequently than any other pathway in cancer patients, with resultant activation of the pathway. This warrants exploiting the PI3K/AKT pathway for cancer drug discovery.
Online handwriting recognition: the NPen++ recognizer

This paper presents the online handwriting recognition system NPen++ developed at the University of Karlsruhe and Carnegie Mellon University. The NPen++ recognition engine is based on a multi-state time delay neural network and yields recognition rates from 96% for a 5,000 word dictionary to 93.4% on a 20,000 word dictionary and 91.2% for a 50,000 word dictionary. The proposed tree search and pruning technique reduces the search space considerably without losing too much recognition performance compared to an exhaustive search. This enables the NPen++ recognizer to be run in real-time with large dictionaries. Initial recognition rates for whole sentences are promising and show that the MS-TDNN architecture is suited to recognizing handwritten data ranging from single characters to whole sentences.
Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data

Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.
Projection Based Weight Normalization for Deep Neural Networks

Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.
Diffusion of innovations in service organizations: systematic review and recommendations.

This article summarizes an extensive literature review addressing the question, How can we spread and sustain innovations in health service delivery and organization? It considers both content (defining and measuring the diffusion of innovation in organizations) and process (reviewing the literature in a systematic and reproducible way). This article discusses (1) a parsimonious and evidence-based model for considering the diffusion of innovations in health service organizations, (2) clear knowledge gaps where further research should be focused, and (3) a robust and transferable methodology for systematically reviewing health service policy and management. Both the model and the method should be tested more widely in a range of contexts.
Intervenable factors associated with suicide risk in transgender persons: a respondent driven sampling study in Ontario, Canada

Across Europe, Canada, and the United States, 22-43 % of transgender (trans) people report a history of suicide attempts. We aimed to identify intervenable factors (related to social inclusion, transphobia, or sex/gender transition) associated with reduced risk of past-year suicide ideation or attempt, and to quantify the potential population health impact.



The Trans PULSE respondent-driven sampling (RDS) survey collected data from trans people age 16+ in Ontario, Canada, including 380 who reported on suicide outcomes. Descriptive statistics and multivariable logistic regression models were weighted using RDS II methods. Counterfactual risk ratios and population attributable risks were estimated using model-standardized risks.



Among trans Ontarians, 35.1 % (95 % CI: 27.6, 42.5) seriously considered, and 11.2 % (95 % CI: 6.0, 16.4) attempted, suicide in the past year. Social support, reduced transphobia, and having any personal identification documents changed to an appropriate sex designation were associated with large relative and absolute reductions in suicide risk, as was completing a medical transition through hormones and/or surgeries (when needed). Parental support for gender identity was associated with reduced ideation. Lower self-reported transphobia (10(th) versus 90(th) percentile) was associated with a 66 % reduction in ideation (RR = 0.34, 95 % CI: 0.17, 0.67), and an additional 76 % reduction in attempts among those with ideation (RR = 0.24; 95 % CI: 0.07, 0.82). This corresponds to potential prevention of 160 ideations per 1000 trans persons, and 200 attempts per 1,000 with ideation, based on a hypothetical reduction of transphobia from current levels to the 10(th) percentile.



Large effect sizes were observed for this controlled analysis of intervenable factors, suggesting that interventions to increase social inclusion and access to medical transition, and to reduce transphobia, have the potential to contribute to substantial reductions in the extremely high prevalences of suicide ideation and attempts within trans populations. Such interventions at the population level may require policy change.
A trust framework model for situational contexts

Trust is a widely studied and acknowledged concept including a diversity of operationalizations. A trust framework model for situational contexts is presented to communicate a clearer and better understanding of the trust situation studied. The framework model includes the situational trust model, the trust transaction, and the trust equation. The situational trust model relates the trustor, the trustee, the trust object, and the trust environment. The application of the trust model is exemplified to define the trust situation of consumer trust in e-commerce.
Information overload: why some people seem to suffer more than others

We studied information overload among senior managers in an industrial company. We used the critical incident collection technique to gather specific examples of information overload and coping strategies. We then used textual interpretation and the affinity diagram technique to interpret the interviews and to categorize our respondents, the critical incidents they described, and the coping strategies they mentioned. Our results show that the extent to which people suffer from information overload is closely related to the strategies they use to deal with it.
Finding time period-based most frequent path in big trajectory data

The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. In this paper, we study a new path finding query which finds the most frequent path (MFP) during user-specified time periods in large-scale historical trajectory data. We refer to this query as time period-based MFP (TPMFP). Specifically, given a time period T, a source v_s and a destination v_d, TPMFP searches the MFP from v_s to v_d during T. Though there exist several proposals on defining MFP, they only consider a fixed time period. Most importantly, we find that none of them can well reflect people's common sense notion which can be described by three key properties, namely suffix-optimal (i.e., any suffix of an MFP is also an MFP), length-insensitive (i.e., MFP should not favor shorter or longer paths), and bottleneck-free (i.e., MFP should not contain infrequent edges). The TPMFP with the above properties will reveal not only common routing preferences of the past travelers, but also take the time effectiveness into consideration. Therefore, our first task is to give a TPMFP definition that satisfies the above three properties. Then, given the comprehensive TPMFP definition, our next task is to find TPMFP over huge amount of trajectory data efficiently. Particularly, we propose efficient search algorithms together with novel indexes to speed up the processing of TPMFP. To demonstrate both the effectiveness and the efficiency of our approach, we conduct extensive experiments using a real dataset containing over 11 million trajectories.
Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.
Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence

This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning. We examined the literature on experimental case studies conducted in the domain during the past six years (2008-2013). Search terms identified 209 mature pieces of research work, but inclusion criteria limited the key studies to 40. We analyzed the research questions, methodology and findings of these published papers and categorized them accordingly. We used non-statistical methods to evaluate and interpret findings of the collected studies. The results have highlighted four distinct major directions of the LA/EDM empirical research. We discuss on the emerged added value of LA/EDM research and highlight the significance of further implications. Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.
Adaptive Video Content Manipulation for OLED Display Power Management

Emerged as a choice of display for vivid colours and power efficiency in expensive Smartphones, in recent days Organic Light Emitting Diode (OLED) displays are increasingly replacing Liquid Crystal Displays (LCD) and Plasma in Smartphones and large screen Televisions. Yet, OLED displays are one of the most energy consuming modules in modern Smartphones. Power consumption of OLED displays have strong correlation with the colours illuminated by its pixels. Improvements in pixel density by manufacturers makes the displays more power hungry. In this paper, we present Human Visual System (HVS) aware adaptive colour transformation and darkening methods to create power-efficient videos for OLED displays while preserving visual fidelity of the videos. Our measurements and subjective evaluations show that we can save up to 60% of energy consumption with minimum distortion to visual quality of the video. As our colour transformation process uses pre-computed colour-map and the darkening process uses the gamma-correction function which is already implemented in most of the display hardware, runtime computation overheads are minimum and hence, our method can be used to manipulate realtime/live video streams as well.
Perioperative Nursing Leaders Implement Clinical Practice Guidelines Using the Iowa Model of Evidence-Based Practice.

Many health care organizations, nursing leaders, and individual clinicians are not providing care consistently based on evidence and many are not aware of the evidence that is available. Preventable complications have an adverse effect on hospital reimbursement and the burden is placed on hospital personnel and nursing leaders to use current evidence to improve care and prevent complications, such as surgical site infections. Using AORN resources, leadership involvement and ownership, and implementing a theoretical model will contribute to implementing daily evidence-based practice and help to decrease the chasm between research and practice.
Deep Directed Generative Models with Energy-Based Probability Estimation

Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training. This can be approximately achieved by Markov chain Monte Carlo methods, but may still face a formidable obstacle that is the difficulty of mixing between modes with sharp concentrations of probability. Whereas an MCMC process is usually derived from a given energy function based on mathematical considerations and requires an arbitrarily long time to obtain good and varied samples, we propose to train a deep directed generative model (not a Markov chain) so that its sampling distribution approximately matches the energy function that is being trained. Inspired by generative adversarial networks, the proposed framework involves training of two models that represent dual views of the estimated probability distribution: the energy function (mapping an input configuration to a scalar energy value) and the generator (mapping a noise vector to a generated configuration), both represented by deep neural networks.
NR - The New 5G Radio-Access Technology

This paper provides a detailed overview of the key technology features of the new 5G/NR radio-access technology, the first release of which has recently been published by 3GPP release 15, of the NR specifications finalized by the end of 2017. This first release is limited to non-standalone NR operation, implying that NR devices rely on LTE for initial access and mobility. The final release-15 specifications, to be available in June 2018, will also support stand-alone NR operation. The difference between stand-alone and non-standalone operation is primarily affecting higher layers and the interface to the core network; the basic radio technology is the same in both cases. This paper will give a detailed overview of the NR radioaccess technology with focus on the key features that distinguish it from 4G LTE.
When and why do retrieval attempts enhance subsequent encoding?

Unsuccessful retrieval attempts can enhance subsequent encoding and learning. In three experiments, subjects either attempted to retrieve word pairs prior to studying them (e.g., attempting to recall tide-? before studying tide-beach) or did not attempt retrieval and retention of the studied targets was assessed on a subsequent cued recall test. Experiment 1 showed that attempting retrieval enhanced subsequent encoding and recall relative to not attempting retrieval when the word pairs were semantically related, but not when the pairs were unrelated. In Experiment 2, studying a different word pair prior to the correct pair (e.g., studying tide-wave prior to tide-beach) did not produce the same effect as attempting retrieval prior to studying. Constraining retrieval to a particular candidate word prior to study (e.g., recalling tide-wa__ before studying tide-beach) produced a negative effect on subsequent recall. Experiment 3 showed that attempting retrieval did not enhance encoding when a brief delay occurred between the retrieval attempt and the subsequent study trial. The results support the idea that a search set of candidates related to the retrieval cue is activated during retrieval and that this retrieval-specific activation can enhance subsequent encoding of those candidates.