nils-herrmann/clean_scirepeval huggingface dataset:
T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition

Video-based action recognition with deep neural networks has shown remarkable progress. However, most of the existing approaches are too computationally expensive due to the complex network architecture. To address these problems, we propose a new real-time action recognition architecture, called Temporal Convolutional 3D Network (T-C3D), which learns video action representations in a hierarchical multi-granularity manner. Specifically, we combine a residual 3D convolutional neural network which captures complementary information on the appearance of a single frame and the motion between consecutive frames with a new temporal encoding method to explore the temporal dynamics of the whole video. Thus heavy calculations are avoided when doing the inference, which enables the method to be capable of real-time processing. On two challenging benchmark datasets, UCF101 and HMDB51, our method is significantly better than state-of-the-art real-time methods by over 5.4% in terms of accuracy and 2 times faster in terms of inference speed (969 frames per second), demonstrating comparable recognition performance to the state-of-the-art methods. The source code for the complete system as well as the pre-trained models are publicly available at https://github.com/tc3d.
Immune evasion in human papillomavirus-associated cervical cancer

Tumour-associated viruses produce antigens that, on the face of it, are ideal targets for immunotherapy. Unfortunately, these viruses are experts at avoiding or subverting the host immune response. Cervical-cancer-associated human papillomavirus (HPV) has a battery of immune-evasion mechanisms at its disposal that could confound attempts at HPV-directed immunotherapy. Other virally associated human cancers might prove similarly refractive to immuno-intervention unless we learn how to circumvent their strategies for immune evasion.
Inactivation of the ferroptosis regulator Gpx4 triggers acute renal failure in mice

Ferroptosis is a non-apoptotic form of cell death induced by small molecules in specific tumour types, and in engineered cells overexpressing oncogenic RAS. Yet, its relevance in non-transformed cells and tissues is unexplored and remains enigmatic. Here, we provide direct genetic evidence that the knockout of glutathione peroxidase 4 (Gpx4) causes cell death in a pathologically relevant form of ferroptosis. Using inducible Gpx4−/− mice, we elucidate an essential role for the glutathione/Gpx4 axis in preventing lipid-oxidation-induced acute renal failure and associated death. We furthermore systematically evaluated a library of small molecules for possible ferroptosis inhibitors, leading to the discovery of a potent spiroquinoxalinamine derivative called Liproxstatin-1, which is able to suppress ferroptosis in cells, in Gpx4−/− mice, and in a pre-clinical model of ischaemia/reperfusion-induced hepatic damage. In sum, we demonstrate that ferroptosis is a pervasive and dynamic form of cell death, which, when impeded, promises substantial cytoprotection.
Analysis of Electric DC Drive Using Matlab Simulink and SimPower Systems

In this paper model for analysis electric DC drive made in Matlab Simulink and Matlab SimPower Systems is given. Basic mathematical formulation which describes DC motor is given. Existing laboratory motor is described. Simulation model of DC motor drive and model of discontinuous load is made. Comparison of model made in Matlab Simulink and existing model in SimPower Systems is given. Essential parameters for starting simulation of used DC motor drive is given. Dynamical characteristics of DC motor drive as results of both simulation are shown. Practical use of simulation model is proposed.  analysis, DC drive, Matlab, SimPower Systems, model, simulation.
Strategies to prevent healthcare-associated infections through hand hygiene.

Previously published guidelines provide comprehensive recommendations for hand hygiene in healthcare facilities. The intent of this document is to highlight practical recommendations in a concise format, update recommendations with the most current scientific evidence, and elucidate topics that warrant clarification or more robust research. Additionally, this document is designed to assist healthcare facilities in implementing hand hygiene adherence improvement programs, including efforts to optimize hand hygiene product use, monitor and report back hand hygiene adherence data, and promote behavior change. This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA) and is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the American Hospital Association (AHA), the Association for Professionals in Infection Control and Epidemiology (APIC), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise. The list of endorsing and supporting organizations is presented in the introduction to the 2014 updates.
Internet of Things (IoT): Application systems and security vulnerabilities

IoT (Internet of Things) is a system where objects are embedded with sensor technology to interact with each other over wireless communication medium to generate, exchange and transfer data without human interaction. It provides interaction between physical world and cyber world which is known as cyber physical systems. IoT can be implemented as smart cities, smart homes, traffic management systems etc. Research has proven that, any system with the characteristics of remote management, dynamic topology, resource constraints and wireless communication medium are always susceptible to security issues. This research aims to investigate and identify the specific types of IoT applications suitable for UAE, analyse the potential security threats associated them and their impacts on these applications. A thorough exploration and review of the available literature has been done to identify the IoT application systems and related cyber vulnerabilities.
Poly(vinyl alcohol) cryogel phantoms for use in ultrasound and MR imaging.

Poly(vinyl alcohol) cryogel, PVA-C, is presented as a tissue-mimicking material, suitable for application in magnetic resonance (MR) imaging and ultrasound imaging. A 10% by weight poly(vinyl alcohol) in water solution was used to form PVA-C, which is solidified through a freeze-thaw process. The number of freeze-thaw cycles affects the properties of the material. The ultrasound and MR imaging characteristics were investigated using cylindrical samples of PVA-C. The speed of sound was found to range from 1520 to 1540 m s(-1), and the attenuation coefficients were in the range of 0.075-0.28 dB (cm MHz)(-1). T1 and T2 relaxation values were found to be 718-1034 ms and 108-175 ms, respectively. We also present applications of this material in an anthropomorphic brain phantom, a multi-volume stenosed vessel phantom and breast biopsy phantoms. Some suggestions are made for how best to handle this material in the phantom design and development process.
Attribute Based Encryption for Secure Access to Cloud Based EHR Systems

Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients' end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR's access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.
Situated Engineering Learning: Bridging Engineering Education Research and the Learning Sciences

This article summarizes prior work in the learning sciences and discusses one perspective—situative learning—in depth. Situativity refers to the central role of context, including the physical and social aspects of the environment, on learning. Furthermore, it emphasizes the socially and culturally negotiated nature of thought and action of persons in interaction. The aim of the article is to provide a foundation for future work on engineering learning and to suggest ways in which the learning sciences and engineering education research communities might work to their mutual benefit.
60-GHz LTCC antenna array with microstrip to CPW transition

We present an antenna array design at 60 GHz on low-temperature co-fired ceramic (LTCC) with a microstrip to coplanar waveguide (CPW) transition. The design consists of aperture-coupled microstrip patch antennas on LTCC Heraeus HL2000 with small embedded air holes to synthesize a lower dielectric substrate. The arrays are excited through a microstrip-line feed network with quarter-wave matching via a microstrip to CPW transition. The transition is designed so that the antenna can be measured with the patch facing upwards while reducing the effect of the probe station. An open cavity is made to ensure the microstrip-line is also placed away from the probe station plate. Good agreement between simulated and measured return loss with simulated gain of 14dB is achieved.
Design and Motion Planning of Indoor Pipeline Inspection Robot

41   This project deals with a design and motion planning algorithm of a caterpillar-based pipeline robot that can be used for inspection of 80–100-mm pipelines in an indoor pipeline environment. The robot system consists of a Robot body, a control system, a CMOS camera, an accelerometer, a temperature sensor, a ZigBee module. The robot module will be designed with the help of CAD tool. The control system consists of Atmega16 micro controller and Atmel studio IDE. The robot system uses a differential drive to steer the robot and spring loaded four-bar mechanisms to assure that the robot expands to have grip of the pipe walls. Unique features of this robot are the caterpillar wheel, the four-bar mechanism supports the well grip of wall, a simple and easy user interface.
Yoga for Low-Income Older Adults: Silver Age Yoga

In the next few decades, the proportion of Americans age 65 or older is expected to increase from 12% (36 million) to 20% (80 million) of the total US population . As life expectancy increases, an even greater need arises for cost-effective interventions to improve function and quality of life among older adults [2-4]. All older adults face numerous health problems that can reduce or limit both the quality and quantity of life they will experience. Some of the main problems faced by older adults include reduced physical function and well-being, challenges with mental and emotional functioning and well-being, and more limited social functioning. Not surprisingly, these factors comprise the primary components of comprehensive health-related quality of life [5,6].
New online ecology of adversarial aggregates: ISIS and beyond

Support for an extremist entity such as Islamic State (ISIS) somehow manages to survive globally online despite considerable external pressure and may ultimately inspire acts by individuals having no history of extremism, membership in a terrorist faction, or direct links to leadership. Examining longitudinal records of online activity, we uncovered an ecology evolving on a daily time scale that drives online support, and we provide a mathematical theory that describes it. The ecology features self-organized aggregates (ad hoc groups formed via linkage to a Facebook page or analog) that proliferate preceding the onset of recent real-world campaigns and adopt novel adaptive mechanisms to enhance their survival. One of the predictions is that development of large, potentially potent pro-ISIS aggregates can be thwarted by targeting smaller ones.
Antioxidant and Associated Capacities of Camu Camu (Myrciaria dubia): A Systematic Review

An aging population in the United States presents important challenges for patients and physicians. The presence of inflammation can contribute to an accelerated aging process, the increasing presence of comorbidities, oxidative stress, and an increased prevalence of chronic pain. As patient-centered care is embracing a multimodal, integrative approach to the management of disease, patients and physicians are increasingly looking to the potential contribution of natural products. Camu camu, a well-researched and innovative natural product, has the potential to contribute, possibly substantially, to this management paradigm. The key issue is to raise camu camu's visibility through increased emphasis on its robust evidentiary base and its various formulations, as well as making consumers, patients, and physicians more aware of its potential. A program to increase the visibility of camu camu can contribute substantially not only to the management of inflammatory conditions and its positive contribution to overall good health but also to its potential role in many disease states.
Design and Implementation of an Intra-domain routing module for an SDN controller for Traffic Engineering in SDN environment

Software Defined Networking (SDN) provides an environment to test and use custom ideas in networking. One of the areas that needs this flexibility is routing in networking. In this study we design and implement a custom intra-domain routing approach in an SDN environment. In SDN routing can be implemented as part of a controller or as an application on top of a controller. In this study we implemented a module in Floodlight controller v1.1 with OpenFlow 1.3 support. This module interacts with another custom module that monitors active bandwidth use of inter-switch links inside a network. Using the information provided by monitoring module, routing module uses available capacity in inter-switch links to determine widest path between any given two points. We tested and evaluated the developed system to show its efficiency. Newly developed module can be used in traffic engineering with additional control options.
Physician Acceptance Behavior of the Electronic Medical Records Exchange: An Extended Decomposed Theory of Planned Behavior

In this study, we examined physician acceptance behavior of the electronic medical record (EMR) exchange. Although several prior studies have focused on factors that affect the adoption or use of EMRs, empirical study that captures the success factors that encourage physicians to adopt the EMR exchange is limited. Therefore, drawing on institutional trust integrated with the decomposed theory of planned behavior (TPB) model, we propose a theoretical model to examine physician intentions of using the EMR exchange. A field survey was conducted in Taiwan to collect data from physicians. Structural equation modeling (SEM) using the partial least squares (PLS) method was employed to test the research model. The results showed that the usage intention of physicians is significantly influenced by 4 factors (i.e., attitude, subjective norm, perceived behavior control, and institutional trust). These 4 factors were assessed by their perceived usefulness and compatibility, facilitating conditions and self-efficacy, situational normality, and structural assurance, respectively. The results also indicated that institutional trust integrated with the decomposed TPB model provides an improved method for predicting physician intentions to use the EMR exchange. Finally, the implications of this study are discussed.
EEG-based decoding of error-related brain activity in a real-world driving task.

Recent studies have started to explore the implementation of brain-computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections.



We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests.



An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car.



The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search

Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the index that focuses on the most relevant regions. However, due to the lack of bounding-box datasets for objects of interest among retrieval benchmarks, most recent work on regional representations has focused on either uniform or class-agnostic region selection. In this paper, we first fill the void by providing a new dataset of landmark bounding boxes, based on the Google Landmarks dataset, that includes 94k images with manually curated boxes from 15k unique landmarks. Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods. In addition, we further introduce a novel regional aggregated selective match kernel (R-ASMK) to effectively combine information from detected regions into an improved holistic image representation. R-ASMK boosts image retrieval accuracy substantially at no additional memory cost, while even outperforming systems that index image regions independently. Our complete image retrieval system improves upon the previous state-of-the-art by significant margins on the Revisited Oxford and Paris datasets. Code and data will be released.
Terrorism Event Classification Using Fuzzy Inference Systems

Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decisionmaking model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluation is the comparison between unstructured and structured events using the same FIS setting. The second comparison is the model settings between FIS and ANFIS for classifying structured events. The data set consists of news articles related to terrosim events in three southern provinces of Thailand. The experimental results show that the classification performance of the FIS resulting from structured events achieves satisfactory accuracy and is better than the unstructured events. In addition, the classification of structured events using ANFIS gives higher performance than the events using only FIS in the prediction of terrorism events. KeywordsEvent classification; terrorism domain; fuzzy inference system (FIS); adaptive neuro-fuzzy inference system (ANFIS); membership function (MF)
How Linguistic Metaphor Scaffolds Reasoning

Language helps people communicate and think. Precise and accurate language would seem best suited to achieve these goals. But a close look at the way people actually talk reveals an abundance of apparent imprecision in the form of metaphor: ideas are 'light bulbs', crime is a 'virus', and cancer is an 'enemy' in a 'war'. In this article, we review recent evidence that metaphoric language can facilitate communication and shape thinking even though it is literally false. We first discuss recent experiments showing that linguistic metaphor can guide thought and behavior. Then we explore the conditions under which metaphors are most influential. Throughout, we highlight theoretical and practical implications, as well as key challenges and opportunities for future research.
UvA-DARE ( Digital Academic Repository ) Imaging studies in pathological gambling : similarities and differences with alcohol dependence

The purpose of this review is to gain more insight in the neuropathology of pathological gambling and problem gambling, and to discuss challenges in this research area. Results from the reviewed PG studies show that PG is more than just an impulse control disorder. PG seems to fit very well with recent theoretical models of addiction, which stress the involvement of the ventral tegmental-orbito frontal cortex. Differentiating types of PG on game preferences (slot machines vs. casino games) seems to be useful because different PG groups show divergent results, suggesting different neurobiological pathways to PG. A framework for future studies is suggested, indicating the need for hypothesis driven pharmacological and functional imaging studies in PG and integration of knowledge from different research areas to further elucidate the neurobiological underpinnings of this disorder. Cognitive and neuroimaging findings in pathological gambling
A Case Study for Mobile Device Forensics Tools

Smartphones have become a prominent part of our technology driven world. When it comes to uncovering, analyzing and submitting evidence in today's criminal investigations, mobile phones play a more critical role. Thus, there is a strong need for software tools that can help investigators in the digital forensics field effectively analyze smart phone data to solve crimes.
 This paper will accentuate how digital forensic tools assist investigators in getting data acquisition, particularly messages, from applications on iOS smartphones. In addition, we will lay out the framework how to build a tool for verifying data integrity for any digital forensics tool.