nils-herrmann/clean_scirepeval huggingface dataset:
Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data

With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant data in a way that captures the general state of the vehicle in a compact form. In this paper, we develop a deep learning-based method, called Drive2Vec, for embedding such sensor data in a low-dimensional yet actionable form. Our method is based on stacked gated recurrent units (GRUs). It accepts a short interval of automobile sensor data as input and computes a low-dimensional representation of that data, which can then be used to accurately solve a range of tasks. With this representation, we (1) predict the exact values of the sensors in the short term (up to three seconds in the future), (2) forecast the long-term average values of these same sensors, (3) infer additional contextual information that is not encoded in the data, including the identity of the driver behind the wheel, and (4) build a knowledge base that can be used to auto-label data and identify risky states. We evaluate our approach on a dataset collected by Audi, which equipped a fleet of test vehicles with data loggers to store all sensor readings on 2,098 hours of driving on real roads. We show in several experiments that our method outperforms other baselines by up to 90%, and we further demonstrate how these embeddings of sensor data can be used to solve a variety of real-world automotive applications.
An extension of the UTAUT model with e-quality, trust, and satisfaction constructs

This research seeks to validate a comprehensive model of quality in the context of e-business systems. It also extends the UTAUT model with e-quality, trust, and satisfaction constructs. The proposed model brings together extant research on systems and data quality, trust, and satisfaction and provides an important cluster of antecedents to eventual technology acceptance via constructs of behavioral intention to use and actual system usage.
Desert RHex Technical Report : Jornada and White Sands Trip

Researchers in a variety of fields, including aeolian science, biology, and environmental science, have already made use of stationary and mobile remote sensing equipment to increase their variety of data collection opportunities. However, due to mobility challenges, remote sensing opportunities relevant to desert environments and in particular dune fields have been limited to stationary equipment. We describe here an investigative trip to two well-studied experimental deserts in New Mexico with DRHex, a mobile remote sensing platform oriented towards desert research. D-RHex is the latest iteration of the RHex family of robots, which are six-legged, biologically inspired, small (10kg) platforms with good mobility in a variety of rough terrains, including on inclines and over obstacles of higher than robot hip height.
Classification of radiology reports using neural attention models

The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure “black-box” models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies.
Deep neural networks and mixed integer linear optimization

Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made up of layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero. In this (and other similar cases like max pooling, where the max operation involves more than one input value), for fixed parameters one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where the continuous variables correspond to the output values of each unit, and a binary variable is associated with each ReLU to model its yes/no nature. In this paper we discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution. We also present possible applications of the 0-1 MILP model arising in feature visualization and in the construction of adversarial examples. Computational results are reported, aimed at investigating (on small DNNs) the computational performance of a state-of-the-art MILP solver when applied to a known test case, namely, hand-written digit recognition.
Demystifying MMD GANs

We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramér GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training.
Clinical efficacy of treatment procedures in endodontic infection control and one year follow-up of periapical healing.

The objective was to evaluate the clinical efficacy of chemomechanical preparation of the root canals with sodium hypochlorite and interappointment medication with calcium hydroxide in the control of root canal infection and healing of periapical lesions. Fifty teeth diagnosed with chronic apical periodontitis were randomly allocated to one of three treatments: Single visit (SV group, n = 20), calcium hydroxide for one week (CH group n = 18), or leaving the canal empty but sealed for one week (EC group, n = 12). Microbiological samples were taken to monitor the infection during treatment. Periapical healing was controlled radiographically following the change in the periapical index at 52 wk and analyzed using one-way ANOVA. All cases showed microbiological growth in the beginning of the treatment. After mechanical preparation and irrigation with sodium hypochlorite in the first appointment, 20 to 33% of the cases showed growth. At the second appointment 33% of the cases in the CH group revealed bacteria, whereas the EC group showed remarkably more culture positive cases (67%). Sodium hypochlorite was effective also at the second appointment and only two teeth remained culture positive. Only minor differences in periapical healing were observed between the treatment groups. However, bacterial growth at the second appointment had a significant negative impact on healing of the periapical lesion (p < 0.01). The present study indicates good clinical efficacy of sodium hypochlorite irrigation in the control of root canal infection. Calcium hydroxide dressing between the appointments did not show the expected effect in disinfection the root canal system and treatment outcome, indicating the need to develop more efficient inter-appointment dressings.
Exploring the Use of Autoencoders for Botnets Traffic Representation

Botnets are a significant threat to cyber security. Compromised, a.k.a. malicious hosts in a network have, of late, been detected by machine learning from hand-crafted features directly sourced from different types of network logs. Our interest is in automating feature engineering while examining flow data from hosts labeled to be malicious or not. To automatically express full temporal character and dependencies of flow data requires time windowing and a very high dimensional set of features, in our case 30,000. To reduce dimensionality, we generate a lower dimensional embedding (64 dimensions) via autoencoding. This improves detection. We next increase the volume in the flows originating from hosts in our dataset known to be malicious or not by injecting noise we mix in from background traffic. The resulting lower metaphorical signal to noise ratio makes the presence of a bot even more challenging to detect so we resort to a filter encoder or an off-the-shelf denoising autoencoder. Both the filter encoding and denoising autoencoder improve upon detection compared to when hand-crafted features are used and are comparable in performance to the autoencoder.
Real-Time Computer Network Anomaly Detection Using Machine Learning Techniques

—Detecting a variety of anomalies in computer network, especially zero-day attacks, is one of the real challenges for both network operators and researchers. An efficient technique detecting anomalies in real time would enable network operators and administrators to expeditiously prevent serious consequences caused by such anomalies. We propose an alternative technique, which based on a combination of time series and feature spaces, for using machine learning algorithms to automatically detect anomalies in real time. Our experimental results show that the proposed technique can work well for a real network environment, and it is a feasible technique with flexible capabilities to be applied for real-time anomaly detection.
Mental State Recognition via Wearable EEG

The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical. Predicting and tracking cognitive states is possible for tasks that were previously not conducive to EEG monitoring. For instance, monitoring operators for states inappropriate to the task (e.g. drowsy drivers), tracking mental health (e.g. anxiety) and productivity (e.g. tiredness) are among possible applications for the technology. Consumer grade EEG headsets are affordable and relatively easy to use, but they lack the resolution and quality of signal that can be achieved using medical grade EEG devices. Thus, the key questions remain: to what extent are wearable EEG devices capable of mental state recognition, and what kind of mental states can be accurately recognized with these devices? In this work, we examined responses to two different types of input: instructional (‘logical’) versus recreational (‘emotional‘) videos, using a range of machine-learning methods. We tried SVMs, sparse logistic regression, and Deep Belief Networks, to discriminate between the states of mind induced by different types of video input, that can be roughly labeled as ‘logical’ vs. ‘emotional’. Our results demonstrate a significant potential of wearable EEG devices in differentiating cognitive states between situations with large contextual but subtle apparent differences.
Incorporating Environmental Issues in Product Design and Realization

In this paper, a number of options and issues are illustrated which companies and organizations seeking to incorporate environmental issues in product design and realization should consider. A brief overview and classification of a number of approaches for reducing the environmental impact is given, as well as their organizational impact. General characteristics, representative examples, and integration and information management issues of design tools supporting environmentally conscious product design are provided as well. 1 From Design for Manufacture to Design for the Life Cycle and Beyond One can argue that the “good old days” where a product was being designed, manufactured and sold to the customer with little or no subsequent concern are over. In the seventies, with the emergence of life-cycle engineering and concurrent engineering in the United States, companies became more aware of the need to include serviceability and maintenance issues in their design processes. A formal definition for Concurrent Engineering is given in (Winner, et al., 1988), as “a systematic approach to the integrated, concurrent design of products and their related processes, including manufacturing and support. This approach is intended to cause the developers, from the outset, to consider all elements of the product life cycle from conception through disposal, including quality, cost, schedule, and user requirements.” Although concurrent engineering seems to span the entire life-cycle of a product according to the preceding definition, its traditional focus has been on design, manufacturing, and maintenance. Perhaps one of the most striking areas where companies now have to be concerned is with the environment. The concern regarding environmental impact stems from the fact that, whether we want it or not, all our products affect in some way our environment during their life-span. In Figure 1, a schematic representation of a system’s life-cycle is given. Materials are mined from the earth, air and sea, processed into products, and distributed to consumers for usage, as represented by the flow from left to right in the top half of Figure 1.
Female Breast Size Attractiveness for Men as a Function of Sociosexual Orientation (Restricted vs. Unrestricted)

Mate preferences are context-dependent and may vary with different ecological conditions and raters. The present study investigated whether sociosexual orientation influenced men's rating of attractiveness of female breast size. Participants (N=128) rated female breast attractiveness as a function of size (five levels) and viewing angles (front view, oblique view, and side view). Men were divided into two groups (restricted and unrestricted), based on their responses to the Revised Sociosexual Orientation Inventory (SOI-R). As predicted, men with higher SOI-R scores (unrestricted) generally gave higher ratings than did men who scored lower on the SOI-R(restricted), but the difference was significant only at larger breast sizes. We also found that medium to large sizes were rated as the most attractive by both male groups and that viewing angle changed rating of female attractiveness and breast presented in oblique view were rated generally higher than in side view. The results of the study indicate that sociosexuality influences male perception of female breast attractiveness and confirm that accentuation of female-specific physical traits produces a stronger response in unrestricted than in restricted men.
Recommender systems in mobile apps for health a systematic review

This paper aims to identify and analyze recommender systems developed for the health area, available in mobile applications. Therefore, literature was reviewed systematically from ACM, IEEE, Springer and Science Direct databases. 1006 studies were found, eight of which met the eligibility criteria. From the chosen studies, only one was not applied to the areas of nutrition and physical activity. The recommender techniques adopted were Collaborative Filtering and Content-based Filtering. The main mobile device identified was the Smartphone and the Operating System was Android. After analyzing the results, it was possible to realize that, although they are widely used in e­commerce, recommendation systems do not have many mobile healthcare applications, and the existing ones are recent.
Treating equine paraphimosis.

the inability to retract the penis fully into the preputial cavity—in stallions is an emergency demanding immediate, aggressive medical treatment. Failure to treat paraphimosis can have catastrophic consequences for a breeding stallion. FIGURE 1 shows the normal penile and preputial anatomy. Paraphimosis commonly begins as penile prolapse followed by development of excessive preputial edema that prevents retraction of the penis into the preputial cavity. If this condition is untreated, a vicious cycle ensues, compounding the severity of the condition. The longer the penis remains prolapsed, the more lymphatic and venous drainage are compromised, resulting in edema of the internal preputial lamina (FIGURE 2). This edema can become severe enough that the preputial ring constricts, exacerbating distal penile swelling. The exposed penile and preputial tissues are fragile and, if unprotected, can easily be damaged by pressure necrosis and exposure to environmental elements. Excoriation, infection, and/or sloughing of the associated tissue may follow . In cases of prolonged penile and preputial prolapse, fibrosis and damage to the internal pudendal nerves may result in permanent penile prolapse or paralysis if treatment is not successful.1–3 Etiology The most common etiology precipitating paraphimosis is traumatic injury to the prepuce and/or penis during breeding activities. These injuries can result in or be associated with lacerations and/or hematoma formation. Other potential etiologies include the use of phenothiazine tranquilizers, severe debilitation, neurologic conditions such as those associated with equine herpesvirus-1 infection, and systemic diseases such as purpura hemorrhagica and dourine. Paraphimosis can also be a sequela of priapism (persistent erection) or penile paralysis.1–3
Wireless sensor-based control of mobile robots motion

The paper deals with the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Bluetooth wireless transmission and Sun SPOT technology. The Sun SPOT is designed to be a flexible development platform, capable of hosting widely differing application modules. Web technologies are changing the education in robotics. A feature of remote control laboratories is that users can interact with real mobile robot motion processes through the Internet. Motion control of mobile robots is very important research field today, because mobile robots are a interesting subject both in scientific research and practical applications. In this paper the object of the remote control is the Boe-Bot mobile robot from Parallax. Bot mobile robot is the simplest, low-cost platform and the most suitable for the small-sized, light, battery-driven autonomous vehicle. The vehicle has two driving wheels and the angular velocities of the two wheels are independently controlled. When the vehicle is moving towards the target in an unknown environment with obstacles, an avoiding strategy is necessary. A remote control program has been implemented.
Surface Edge Explorer (see): Planning Next Best Views Directly from 3D Observations

Surveying 3D scenes is a common task in robotics. Systems can do so autonomously by iteratively obtaining measurements. This process of planning observations to improve the model of a scene is called Next Best View (NBV) planning. NBV planning approaches often use either volumetric (e.g., voxel grids) or surface (e.g., triangulated meshes) representations. Volumetric approaches generalise well between scenes as they do not depend on surface geometry but do not scale to high-resolution models of large scenes. Surface representations can obtain high-resolution models at any scale but often require tuning of unintuitive parameters or multiple survey stages. This paper presents a scene-model-free NBV planning approach with a density representation. The Surface Edge Explorer (SEE) uses the density of current measurements to detect and explore observed surface boundaries. This approach is shown experimentally to provide better surface coverage in lower computation time than the evaluated state-of-the-art volumetric approaches while moving equivalent distances.
Decentralized Cloud-SDN Architecture in Smart Grid: A Dynamic Pricing Model

Smart grids (SG) energy management system and electric vehicle (EV) have gained considerable reputation in recent years. This has been enabled by the high growth of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand and to manage building energy during the day, it is important to perform an intelligent scheduling for EVs charging and discharging service and buildings areas by including different metrics, such as real-time price and demand–supply curve. In this paper, we propose a real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Our proposed approach uses a decentralized cloud computing architecture based on software define networking (SDN) technology and network function virtualization (NFV). We aim to schedule user's requests in a real-time way and to supervise communications between microgrids controllers, SG and user entities (i.e., EVs, electric vehicles public supply stations, advance metering infrastructure, smart meters, etc.). We formulate the problem as a linear optimization problem for EV and a global optimization problem for all microgrids. We solve the problems by using different decentralized decision algorithms. To the best of our knowledge, this is the first paper that proposes a pricing model based on decentralized Cloud-SDN architecture in order to solve all the aforementioned issues. The extensive simulations and comparisons with related works proved that our proposed pricing model optimizes the energy load during peak hours, maximizes EVs utility, and maintains the microgrid stability. The simulation is based on real electric load of the city of Toronto.