nils-herrmann/clean_scirepeval huggingface dataset:
Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Spatiotemporal forecasting has significant implications in sustainability, transportation and health-care domain. Traffic forecasting is one canonical example of such learning task. This task is challenging due to (1) non-linear temporal dynamics with changing road conditions, (2) complex spatial dependencies on road networks topology and (3) inherent difficulty of long-term time series forecasting. To address these challenges, we propose Graph Convolutional Recurrent Neural Network to incorporate both spatial and temporal dependency in traffic flow. We further integrate the encoder-decoder framework and scheduled sampling to improve long-term forecasting. When evaluated on real-world road network traffic data, our approach can accurately capture spatiotemporal correlations and consistently outperforms state-of-the-art baselines by 12% 15%.
Towards Calm Displays: Matching Ambient Illumination in Bedrooms

We present a system for making emissive computer displays (LCDs) look like they are reflective, i.e. not emitting light but instead reflecting ambient light, an effect that we call a “calm display”. We achieve this effect by using a light sensor and a one-time calibration process to drive an algorithm which controls the display's backlight intensity and gamma correction functionality to continually match the brightness and chromaticity of the ambient light. We present an experimental evaluation of our system, showing quantitatively that the color and brightness output by our system is perceptually close to that of a piece of paper under similar lighting conditions. We argue that calm displays can more easily fade into the background, and further that they are more suitable for environments such as bedrooms where glowing displays are often out-of-place. We validate these claims and more generally explore users’ perception of calm displays, through a field study of an LCD display deployed in participants’ bedrooms.
Disentangled Representation Learning for Non-Parallel Text Style Transfer.

This paper tackles the problem of disentangling the latent variables of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for label prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning method is applied to style transfer on non-parallel corpora. We achieve substantially better results in terms of transfer accuracy, content preservation and language fluency, in comparison to previous state-of-the-art approaches.
Hardware-software codesign of accurate, multiplier-free Deep Neural Networks

While Deep Neural Networks (DNNs) push the state-of-the-art in many machine learning applications, they often require millions of expensive floating-point operations for each input classification. This computation overhead limits the applicability of DNNs to low-power, embedded platforms and incurs high cost in data centers. This motivates recent interests in designing low-power, low-latency DNNs based on fixed-point, ternary, or even binary data precision. While recent works in this area offer promising results, they often lead to large accuracy drops when compared to the floating-point networks. We propose a novel approach to map floating-point based DNNs to 8-bit dynamic fixed-point networks with integer power-of-two weights with no change in network architecture. Our dynamic fixed-point DNNs allow different radix points between layers. During inference, power-of-two weights allow multiplications to be replaced with arithmetic shifts, while the 8-bit fixed-point representation simplifies both the buffer and adder design. In addition, we propose a hardware accelerator design to achieve low-power, low-latency inference with insignificant degradation in accuracy. Using our custom accelerator design with the CIFAR-10 and ImageNet datasets, we show that our method achieves significant power and energy savings while increasing the classification accuracy.
Priming for health: gut microbiota acquired in early life regulates physiology, brain and behaviour.

The infant gut microbiome is dynamic, and radical shifts in composition occur during the first 3 years of life. Disruption of these developmental patterns, and the impact of the microbial composition of our gut on brain and behaviour, has attracted much recent attention. Integrating these observations is an important new research frontier.



Early-life perturbations of the developing gut microbiota can impact on the central nervous system and potentially lead to adverse mental health outcomes.
Deliberation for autonomous robots: A survey

Autonomous robots facing a diversity of open environments and performing a variety of tasks and interactions need explicit deliberation in order to fulfill their missions. Deliberation is meant to endow a robotic system with extended, more adaptable and robust functionalities, as well as reduce its deployment cost. The ambition of this survey is to present a global overview of deliberation functions in robotics and to discuss the state of the art in this area. The following five deliberation functions are identified and analyzed: planning, acting, monitoring, observing, and learning. The paper introduces a global perspective on these deliberation functions and discusses their main characteristics, design choices and constraints. The reviewed contributions are discussed with respect to this perspective. The survey focuses as much as possible on papers with a clear robotics content and with a concern on integrating several deliberation functions.
Importance of IoT for Smart Agriculture

This review paper deals with IoT that can be used to improve cultivation of food crops, as lots of research work is going on to monitor the effective food crop cycle, since from the start to till harvesting the famers are facing very difficult for better yielding of food crops. Although few initiatives have also been taken by the Indian Government for providing online and mobile messaging services to farmers related to agricultural queries and agro vendor’s information to farmers even such information’s are not enough for farmer so still lot of research work need to be carried out on current agricultural approaches so that continuous sensing and monitoring of crops by convergence of sensors with IoT and making farmers to aware about crops growth, harvest time periodically and in turn making high productivity of crops and also ensuring correct delivery of products to end consumers at right place and right time.
Group music therapy for patients with persistent post-traumatic stress disorder--an exploratory randomized controlled trial with mixed methods evaluation.

Not all patients with post-traumatic stress disorder (PTSD) respond to cognitive behavioural therapy (CBT). Literature suggests group music therapy might be beneficial in treating PTSD. However, feasibility and effectiveness have not been assessed. The study objectives were to assess whether group music therapy was feasible for patients who did not respond to CBT, and whether it has an effect on PTSD symptoms and depression.



The study employed mixed methods comprising of an exploratory randomized controlled trial, qualitative content analysis of therapy, and patient interviews.



Patients with significant PTSD symptoms (n = 17) following completion of CBT were randomly assigned to treatment (n = 9) or control groups (n = 8). The treatment group received 10 weeks of group music therapy after which exit interviews were conducted. Control group patients were offered the intervention at the end of the study. Symptoms were assessed on the Impact of Events Scale-Revised and Beck Depression Inventory II at the beginning and end of treatment.



Treatment-group patients experienced a significant reduction in severity of PTSD symptoms (-20.18; 95% confidence interval [CI]: [-31.23, -9.12]) and a marginally significant reduction in depression (-11.92; 95%CI: [-24.05, 0.21]) at 10 weeks from baseline compared to the control. Patients viewed music therapy as helpful and reported experiences concur with current literature.



Group music therapy appears feasible and effective for PTSD patients who have not sufficiently responded to CBT. Limitations include the small sample size and lack of blinding. Further research should address these limitations, test sustainability, and identify specific factors that address symptoms in treatment.
Streaming video over the Internet: approaches and directions

Due to the explosive growth of the Internet and increasing demand for multimedia information on the web, streaming video over the Internet has received tremendous attention from academia and industry. Transmission of real-time video typically has bandwidth, delay, and loss requirements. However, the current best-effort Internet does not offer any quality of service (QoS) guarantees to streaming video. Furthermore, for video multicast, it is difficult to achieve both efficiency and flexibility. Thus, Internet streaming video poses many challenges. To address these challenges, extensive research has been conducted. This special issue is aimed at dissemination of the contributions in the field of streaming video over the Internet. To introduce this special issue with the necessary background and provide an integral view on this field, we cover six key areas of streaming video. Specifically, we cover video compression, application-layer QoS control, continuous media distribution services, streaming servers, media synchronization mechanisms, and protocols for streaming media. For each area, we address the particular issues and review major approaches and mechanisms. We also discuss the tradeoffs of the approaches and point out future research directions.
Optimal Control of a Double Inverted Pendulum on a Cart

In this report a number of algorithms for optimal control of a double inverted pendulum on a cart (DIPC) are investigated and compared. Modeling is based on Euler-Lagrange equations derived by specifying a Lagrangian, difference between kinetic and potential energy of the DIPC system. This results in a system of nonlinear differential equations consisting of three 2-nd order equations. This system of equations is then transformed into a usual form of six 1-st order ordinary differential equations (ODE) for control design purposes. Control of a DIPC poses a certain challenge, since unlike a robot, the system is underactuated: one controlling force per three degrees of freedom (DOF). In this report, problem of optimal control minimizing a quadratic cost functional is addressed. Several approaches are tested: linear quadratic regulator (LQR), state-dependent Riccati equation (SDRE), optimal neural network (NN) control, and combinations of the NN with the LQR and the SDRE. Simulations reveal superior performance of the SDRE over the LQR and improvements provided by the NN, which compensates for model inadequacies in the LQR. Limited capabilities of the NN to approximate functions over the wide range of arguments prevent it from significantly improving the SDRE performance, providing only marginal benefits at larger pendulum deflections.
A high-sensitive ultra-thin MEMS capacitive pressure sensor

This paper reports an ultra-thin MEMS capacitive pressure sensor with high pressure sensitivity of better than 150aF/Pa, and small die size of 1.0mm × 1.0mm × 60µm. It is able to detect ambient pressure change with a resolution of 0.025% in a pressure range +/−3.5KPa. This capacitive pressure sensor decouples the pressure sensing from its capacitance sensing by using a hermetically sealed capacitor that is electrically isolated but mechanically coupled with a pressure sensing diaphragm such that a large dynamic range and high pressure sensitivity can be readily achieved. Because the capacitor is hermetically sealed in a cavity, this capacitive pressure sensor is also immune to measurement media and EMI (Electromagnetic Interference) effects.
Detecting Small Signs from Large Images

In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of memory available on GPUs. Moreover, performance degrades when detecting small objects. To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small traffic signs from large images under real world conditions. In particular, large images are broken into small patches as input to a Small-Object-Sensitive-CNN (SOS-CNN) modified from a Single Shot Multibox Detector (SSD) framework with a VGG-16 network as the base network to produce patch-level object detection results. Scale invariance is achieved by applying the SOS-CNN on an image pyramid. Then, image-level object detection is obtained by projecting all the patch-level detection results to the image at the original scale. Experimental results on a real-world conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment

Using emotion detection technologies from biophysical signals, this study explored how emotion evolves during learning process and how emotion feedback could be used to improve learning experiences. This article also described a cutting-edge pervasive e-Learning platform used in a Shanghai online college and proposed an affective e-Learning model, which combined learners’ emotions with the Shanghai e-Learning platform. The study was guided by Russell’s circumplex model of affect and Kort’s learning spiral model. The results about emotion recognition from physiological signals achieved a best-case accuracy (86.3%) for four types of learning emotions. And results from emotion revolution study showed that engagement and confusion were the most important and frequently occurred emotions in learning, which is consistent with the findings from AutoTutor project. No evidence from this study validated Kort’s learning spiral model. An experimental prototype of the affective e-Learning model was built to help improve students’ learning experience by customizing learning material delivery based on students’ emotional state. Experiments indicated the superiority of emotion aware over non-emotion-aware with a performance increase of 91%.
DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps

Ad networks for mobile apps require inspection of the visual layout of their ads to detect certain types of placement frauds. Doing this manually is error prone, and does not scale to the sizes of today’s app stores. In this paper, we design a system called DECAF to automatically discover various placement frauds scalably and effectively. DECAF uses automated app navigation, together with optimizations to scan through a large number of visual elements within a limited time. It also includes a framework for efficiently detecting whether ads within an app violate an extensible set of rules that govern ad placement and display. We have implemented DECAF for Windows-based mobile platforms, and applied it to 1,150 tablet apps and 50,000 phone apps in order to characterize the prevalence of ad frauds. DECAF has been used by the ad fraud team in Microsoft and has helped find many instances of ad frauds.
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using highfrequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine— on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.
Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where

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Nematicidal activity of essential oils: a review

Plant parasitic nematodes are the most destructive group of plant pathogens worldwide and their control is extremely challenging. Plant Essential oils (EOs) and their constituents have a great potential in nematode control since they can be developed for use as nematicides themselves or can serve as model compounds for the development of derivatives with enhanced activity. This study reviews the plant EOs evaluated as potential nematicides and their toxic effects against pinewood nematode (Bursaphelenchus xylophilus) and root-knot nematodes (Meloidogyne spp.). Additionally, the nematicidal activity to M. javanica of several EOs from Spanish aromatic plants and their components is described.
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks

Multi-scale deep CNNs have been used successfully for problems mapping each pixel to a label, such as depth estimation and semantic segmentation. It has also been shown that such architectures are reusable and can be used for multiple tasks. These networks are typically trained independently for each task by varying the output layer(s) and training objective. In this work we present a new model for simultaneous depth estimation and semantic segmentation from a single RGB image. Our approach demonstrates the feasibility of training parts of the model for each task and then fine tuning the full, combined model on both tasks simultaneously using a single loss function. Furthermore we couple the deep CNN with fully connected CRF, which captures the contextual relationships and interactions between the semantic and depth cues improving the accuracy of the final results. The proposed model is trained and evaluated on NYUDepth V2 dataset [23] outperforming the state of the art methods on semantic segmentation and achieving comparable results on the task of depth estimation.
Finding your next core business.

How do you know when your core needs to change? And how do you determine what should replace it? From an in-depth study of 25 companies, the author, a strategy consultant, has discovered that it's possible to measure the vitality of a business's core. If it needs reinvention, he says, the best course is to mine hidden assets. Some of the 25 companies were in deep crisis when they began the process of redefining themselves. But, says Zook, management teams can learn to recognize early signs of erosion. He offers five diagnostic questions with which to evaluate the customers, key sources of differentiation, profit pools, capabilities, and organizational culture of your core business. The next step is strategic regeneration. In four-fifths of the companies Zook examined, a hidden asset was the centerpiece of the new strategy. He provides a map for identifying the hidden assets in your midst, which tend to fall into three categories: undervalued business platforms, untapped insights into customers, and underexploited capabilities. The Swedish company Dometic, for example, was manufacturing small absorption refrigerators for boats and RVs when it discovered a hidden asset: its understanding of, and access to, customers in the RV market. The company took advantage of a boom in that market to refocus on complete systems for live-in vehicles. The Danish company Novozymes, which produced relatively low-tech commodity enzymes such as those used in detergents, realized that its underutilized biochemical capability in genetic and protein engineering was a hidden asset and successfully refocused on creating bioengineered specialty enzymes. Your next core business is not likely to announce itself with fanfare. Use the author's tools to conduct an internal audit of possibilities and pinpoint your new focus.
Predicting overall survivability in comorbidity of cancers: A data mining approach

Cancer and other chronic diseases have constituted (and will do so at an increasing pace) a significant portion of healthcare costs in the United States in recent years. Although prior research has shown that diagnostic and treatment recommendations might be altered based on the severity of comorbidities, chronic diseases are still being investigated in isolation from one another in most cases. To illustrate the significance of concurrent chronic diseases in the course of treatment, this study uses SEER’s cancer data to create two comorbid data sets: one for breast and female genital cancers and another for prostate and urinal cancers. Several popular machine learning techniques are then applied to the resultant data sets to build predictive models. Comparison of the results shows that having more information about comorbid conditions of patients can improve models’ predictive power, which in turn, can help practitioners make better diagnostic and treatment decisions. Therefore, proper identification, recording, and use of patients’ comorbidity status can potentially lower treatment costs and ease the healthcare related economic challenges.