nils-herrmann/clean_scirepeval huggingface dataset:
Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps

Despite being important, time and context have yet to be formally incorporated into the process of visually representing the temporal and contextual proximity between keywords in a concept map. In response to the context and time challenges, this study improves automated conventional concept mapping by measuring the temporal and contextual distance between pairs of co-occurring concepts. After generating a conventional concept map, it is temporally and contextually augmented in this work by applying an unsupervised temporal trend detection algorithm and a novel measure of contextual proximity. This proposed approach is demonstrated and validated without loss of generality for a spectrum of information technologies, showing that the resulting assessments of temporal and contextual proximity are highly correlated with subjective assessments of experts. The contribution of this work is emphasized and magnified against the current growing attention to big data analytics in general and to big text-data analytics in particular.
IMAGE AUTHENTICATION AND RECOVERY USING WAVELET-BASED DUAL WATERMARKING

In this paper a novel watermarking scheme for image authentication and recovery is presented. The algorithm can detect modified regions in images and is able to recover a good approximation of the original content of the tampered regions. For this purpose, two different watermarks have been used: a semi-fragile watermark for image authentication and a robust watermark for image recovery, both embedded in the Discrete Wavelet Transform domain. The proposed method achieves good image quality with mean Peak Signal-to-Noise Ratio values of the watermarked images of 42 dB and identifies image tampering of up to 20% of the original image.
Episodic Curiosity through Reachability

Rewards are sparse in the real world and most today’s reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself — thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward — making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory — which incorporates rich information about environment dynamics. This allows us to overcome the known “couch-potato” issues of prior work — when the agent finds a way to instantly gratify itself by exploiting actions which lead to unpredictable consequences. We test our approach in visually rich 3D environments in VizDoom and DMLab. In VizDoom, our agent learns to successfully navigate to a distant goal at least 2 times faster than the state-of-the-art curiosity method ICM. In DMLab, our agent generalizes well to new procedurally generated levels of the game — reaching the goal at least 2 times more frequently than ICM on test mazes with very sparse reward.
Meaning in life and mortality.

The purpose of this exploratory study was to see if meaning in life is associated with mortality in old age.



Interviews were conducted with a nationwide sample of older adults (N = 1,361). Data were collected on meaning in life, mortality, and select control measures.



Three main findings emerged from this study. First, the data suggest that older people with a strong sense of meaning in life are less likely to die over the study follow-up period than those who do not have a strong sense of meaning. Second, the findings indicate that the effect of meaning on mortality can be attributed to the potentially important indirect effect that operates through health. Third, further analysis revealed that one dimension of meaning-having a strong sense of purpose in life--has a stronger relationship with mortality than other facets of meaning. The main study findings were observed after the effects of attendance at religious services and emotional support were controlled statistically.



If the results from this study can be replicated, then interventions should be designed to help older people find a greater sense of purpose in life.
HRM in Action : Employee Engagement as a Strategic HR Tool

HR Tool Employee engagement refers to the level of commitment workers make to their employer, seen in their willingness to stay at the firm and to go beyond the call of duty.1 Firms want employees that are highly motivated and feel they have a real stake in the company’s success. Such employees are willing to finish tasks in their own time and see a strong link between the firm’s success and their own career prospects. In short, motivated, empowered employees work hand in hand with employers in an atmosphere of mutual trust. Companies with engaged workforces have also reported less absenteeism, more engagement with customers, greater employee satisfaction, less mistakes, fewer employees leaving, and naturally higher profits. Such is the power of this concept that former Secretary of State for Business, Peter Mandelson, commissioned David McLeod and Nita Clarke to investigate how much UK competitiveness could be enhanced by wider use of employee engagement. David and Nita concluded that in a world where work tasks have become increasingly similar, engaged employees could give some companies the edge over their rivals. They also identified significant barriers to engagement such as a lack of appreciation for the concept of employee engagement by some companies and managers. Full participation by line managers is particularly crucial. From the employee point of view, it is easy to view engagement as a management fad, particularly if the company fails to demonstrate the necessary commitment. Some also feel that in a recession, employee engagement becomes less of a priority when in Performance Management and Appraisal 8 CHATE R
A Face-to-Face Neural Conversation Model

Neural networks have recently become good at engaging in dialog. However, current approaches are based solely on verbal text, lacking the richness of a real face-to-face conversation. We propose a neural conversation model that aims to read and generate facial gestures alongside with text. This allows our model to adapt its response based on the "mood" of the conversation. In particular, we introduce an RNN encoder-decoder that exploits the movement of facial muscles, as well as the verbal conversation. The decoder consists of two layers, where the lower layer aims at generating the verbal response and coarse facial expressions, while the second layer fills in the subtle gestures, making the generated output more smooth and natural. We train our neural network by having it "watch" 250 movies. We showcase our joint face-text model in generating more natural conversations through automatic metrics and a human study. We demonstrate an example application with a face-to-face chatting avatar.
Effect of television advertisements for foods on food consumption in children

The impact of television (TV) advertisements (commercials) on children's eating behaviour and health is of critical interest. In a preliminary study we examined lean, over weight and obese children's ability to recognise eight food and eight non-food related adverts in a repeated measures design. Their consumption of sweet and savoury, high and low fat snack foods were measured after both sessions. Whilst there was no significant difference in the number of non-food adverts recognised between the lean and obese children, the obese children did recognise significantly more of the food adverts. The ability to recognise the food adverts significantly correlated with the amount of food eaten after exposure to them. The overall snack food intake of the obese and overweight children was significantly higher than the lean children in the control (non-food advert) condition. The consumption of all the food offered increased post food advert with the exception of the low-fat savoury snack. These data demonstrate obese children's heightened alertness to food related cues. Moreover, exposure to such cues induce increased food intake in all children. As suggested the relationship between TV viewing and childhood obesity appears not merely a matter of excessive sedentary activity. Exposure to food adverts promotes consumption.
FSMRank: Feature Selection Algorithm for Learning to Rank

In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T2). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.
Vitrectomy fluidics.

The goal of all vitreous surgery is to perform the desired intraoperative intervention with minimum collateral damage in the most efficient way possible. An understanding of the principles of fluidics is of importance to all vitreoretinal surgeons to achieve these aims. Advances in technology mean that surgeons are being given increasing choice in the settings they are able to select for surgery. Manufacturers are marketing systems with aspiration driven by peristaltic, Venturi and hybrid pumps. Increasingly fast cut rates are offered with optimised, and in some cases surgeon-controlled, duty cycles. Function-specific cutters are becoming available and narrow-gauge instrumentation is evolving to meet surgeon demands with higher achievable flow rates. In parallel with the developments in outflow technology, infusion systems are advancing with lowering flow resistance and intraocular pressure control to improve fluidic stability during surgery. This review discusses the important aspects of fluidic technology so that surgeons can select the optimum machine parameters to carry out safe and effective surgery.
A novel microstrip antenna with pre-controllable resonant frequency and impedance matching

A novel minimized microstrip planar antenna with pre-controllable resonant frequency and impedance matching is presented for microwave engineering applications. Double-sided planar structure is adopted to build the resonant loop of the antenna. The tuning of the impedance matching and the resonant frequency is studied, and the impedance matching and resonant frequency can be adjusted independently. These characteristics are validated by full wave electromagnetic simulation. A 2.4 GHz antenna for ZigBee application is designed based on the proposed structure. The simulation shows that the -10 dB bandwidth is from 2.21 GHz to 2.74 GHz with an omnibearing radiation, 2.48 dBi gain, and 35 dB return loss at the central frequency, respectively. The antenna is fabricated with a volume of 0.285λ0×0.325λ0×0.0065λ0. The measurement shows that the -10 dB bandwidth is from 2.205 GHz to 2.756 GHz, the VSWR is less than 1.04 and the return loss is better than 34 dB at 2.46 GHz.
DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission

Deep neural networks (DNNs) are key computational building blocks for emerging classes of web services that interact in real time with users via voice, images and video inputs. Although GPUs have gained popularity as a key accelerator platform for deep learning workloads, the increasing demand for DNN computation leaves a significant gap between the compute capabilities of GPU-enabled datacenters and the compute needed to service demand.
 The state-of-the-art techniques to improve DNN performance have significant limitations in bridging the gap on real systems. Current network pruning techniques remove computation, but the resulting networks map poorly to GPU architectures, yielding no performance benefit or even slowdowns. Meanwhile, current bandwidth optimization techniques focus on reducing off-chip bandwidth while overlooking on-chip bandwidth, a key DNN bottleneck.
 To address these limitations, this work introduces DeftNN, a GPU DNN execution framework that targets the key architectural bottlenecks of DNNs on GPUs to automatically and transparently improve execution performance. DeftNN is composed of two novel optimization techniques - (1) synapse vector elimination, a technique that identifies non-contributing synapses in the DNN and carefully transforms data and removes the computation and data movement of these synapses while fully utilizing the GPU to improve performance, and (2) near-compute data fission, a mechanism for scaling down the on-chip data movement requirements within DNN computations. Our evaluation of DeftNN spans 6 state-of-the-art DNNs. By applying both optimizations in concert, DeftNN is able to achieve an average speedup of 2.1X on real GPU hardware. We also introduce a small additional hardware unit per GPU core to facilitate efficient data fission operations, increasing the speedup achieved by DeftNN to 2.6X.
Targeted transcutaneous electrical nerve stimulation for phantom limb sensory feedback

In this work, we investigated the use of noninvasive, targeted transcutaneous electrical nerve stimulation (TENS) of peripheral nerves to provide sensory feedback to two amputees, one with targeted sensory reinnervation (TSR) and one without TSR. A major step in developing a closed-loop prosthesis is providing the sense of touch back to the amputee user. We investigated the effect of targeted nerve stimulation amplitude, pulse width, and frequency on stimulation perception. We discovered that both subjects were able to reliably detect stimulation patterns with pulses less than 1 ms. We utilized the psychophysical results to produce a subject specific stimulation pattern using a leaky integrate and fire (LIF) neuron model from force sensors on a prosthetic hand during a grasping task. For the first time, we show that TENS is able to provide graded sensory feedback at multiple sites in both TSR and non-TSR amputees while using behavioral results to tune a neuromorphic stimulation pattern driven by a force sensor output from a prosthetic hand.
Learning Topic Models by Neighborhood Aggregation

Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of both held-out document classification tasks and topic coherence.
Dermoscopy findings in tinea capitis: case report and literature review.

Dermoscopy is a method of increasing importance in the diagnoses of cutaneous diseases. On the scalp, dermoscopic aspects have been described in psoriasis, lichen planus, seborrheic dermatitis and discoid lupus. We describe the "comma" and "corkscrew hair" dermoscopic aspects found in a child of skin type 4, with tinea capitis.
A Self-Calibrating Radar Sensor System for Measuring Vital Signs

Vital signs (i.e., heartbeat and respiration) are crucial physiological signals that are useful in numerous medical applications. The process of measuring these signals should be simple, reliable, and comfortable for patients. In this paper, a noncontact self-calibrating vital signs monitoring system based on the Doppler radar is presented. The system hardware and software were designed with a four-tiered layer structure. To enable accurate vital signs measurement, baseband signals in the radar sensor were modeled and a framework for signal demodulation was proposed. Specifically, a signal model identification method was formulated into a quadratically constrained l1 minimization problem and solved using the upper bound and linear matrix inequality (LMI) relaxations. The performance of the proposed system was comprehensively evaluated using three experimental sets, and the results indicated that this system can be used to effectively measure human vital signs.
Toward a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit

Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography—distance decay, structured variation in population density, mental maps, and “the Big Sort” (spatial homophily)—manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.
A model for assessing the impact of e-learning systems on employees' satisfaction

In a knowledge and information society, e-learning has built on the extensive use of advanced information and communication technologies to deliver learning and instruction. In addition, employees who need the training do not have to gather in a place at the same time, and thus it is not necessary for them to travel far away for attending training courses. Furthermore, the flexibility allows employees who perform different jobs or tasks for training courses according to their own scheduling. Since many studies have discussed learning and training of employees and most of them are focused on the learning emotion, learning style, educational content, and technology, there is limited research exploring the relationship between the e-learning and employee’s satisfaction. Therefore, this study aims to explore how to enhance employee’s satisfaction by means of e-learning systems, and what kinds of training or teaching activities are effective to increase their learning satisfaction. We provide a model and framework for assessing the impact of e-learning on employee’s satisfaction which improve learning and teaching outcomes. Findings from the study confirmed the validity of the proposed model for e-learning satisfaction assessment. In addition, the results showed that the four variables technology, educational content, motivation, and attitude significantly influenced employee’s learning satisfaction.
Topic oriented community detection through social objects and link analysis in social networks

0950-7051/$ see front matter 2011  ⇑ Corresponding author at: Shenzhen Institutes of A Academy of Sciences, Shenzhen 518055, China. E-mail addresses: zy.zhao@siat.ac.cn, zy.zhao10@ @siat.ac.cn (S. Feng), qiang.wang1@siat.ac.cn (Q. (J.Z. Huang), Graham.Williams@togaware.com (G.J. W Fan) . Community detection is an important issue in social network analysis. Most existing methods detect communities through analyzing the linkage of the network. The drawback is that each community identified by those methods can only reflect the strength of connections, but it cannot reflect the semantics such as the interesting topics shared by people. To address this problem, we propose a topic oriented community detection approach which combines both social objects clustering and link analysis. We first use a subspace clustering algorithm to group all the social objects into topics. Then we divide the members that are involved in those social objects into topical clusters, each corresponding to a distinct topic. In order to differentiate the strength of connections, we perform a link analysis on each topical cluster to detect the topical communities. Experiments on real data sets have shown that our approach was able to identify more meaningful communities. The quantitative evaluation indicated that our approach can achieve a better performance when the topics are at least as important as the links to the analysis.
The Network Data Repository with Interactive Graph Analytics and Visualization

(NR) is the first interactive data repository with a web-based platform for visual interactive analytics. Unlike other data repositories (e.g., UCI ML Data Repository, and SNAP), the network data repository (networkrepository.com) allows users to not only download, but to interactively analyze and visualize such data using our web-based interactive graph analytics platform. Users can in real-time analyze, visualize, compare, and explore data along many different dimensions. The aim of NR is to make it easy to discover key insights into the data extremely fast with little effort while also providing a medium for users to share data, visualizations, and insights. Other key factors that differentiate NR from the current data repositories is the number of graph datasets, their size, and variety. While other data repositories are static, they also lack a means for users to collaboratively discuss a particular dataset, corrections, or challenges with using the data for certain applications. In contrast, NR incorporates many social and collaborative aspects that facilitate scientific research, e.g., users can discuss each graph, post observations, and visualizations.