nils-herrmann/clean_scirepeval huggingface dataset:
Transfer learning for class imbalance problems with inadequate data

A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data are not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting-based instance transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.
fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment.
Varactor-Loaded Pattern Reconfigurable Array for Wide-Angle Scanning With Low Gain Fluctuation

An improved phased array using pattern reconfigurable antenna elements in this communication is proposed to realize wide-angle scanning performance with low gain fluctuation. The pattern reconfigurable element is a microstrip Yagi antenna with its parasitic strips loaded with varactors. The reconfigurability of radiation pattern from the element is enabled by tuning the capacitive reactance of the varactors. Five elements were arranged in an equally spaced linear array, and the effects of several key parameter variations on the radiation characteristics of the array are provided. The proposed array is fabricated and experimentally verified. It is seen from the measured results that the main beam of the array can scan from -70° to 70° in the H-plane with a gain fluctuation less than 2 dB. Meanwhile, the coverage of 3-dB beamwidth of the array is from about -87° to 87°. The agreement between simulated and measured results validates our design exhibiting a good performance of wide-angle scanning.
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks

Functional groups (FGs) serve as a foundation for analyzing chemical properties of organic molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry, by reducing the amount of lab experiments required for discovery or synthesis of new molecules. Here, we investigate methods based on graph convolutional neural networks (GCNNs) for localizing FGs that contribute to specific chemical properties. Molecules are modeled as undirected graphs with atoms as nodes and bonds as edges. Using this graph structure, we trained GCNNs in a supervised way on experimentally-validated molecular training sets to predict specific chemical properties, e.g., toxicity. Upon learning a GCNN, we analyzed its activation patterns to automatically identify FGs using four different methods: gradient-based saliency maps, Class Activation Mapping (CAM), gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. We evaluated the contrastive power of these methods with respect to the specificity of the identified molecular substructures and their relevance for chemical functions. GradCAM had the highest contrastive power and generated qualitatively the best FGs. This work paves the way for automatic analysis and design of new molecules.
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks

Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.
A Secure Microservice Framework for IoT

The Internet of Things (IoT) has connected an incredible diversity of devices in novel ways, which has enabled exciting new services and opportunities. Unfortunately, IoT systems also present several important challenges to developers. This paper proposes a vision for how we may build IoT systems in the future by reconceiving IoT's fundamental unit of construction not as a "thing", but rather as a widely and finely distributed "microservice" already familiar to web service engineering circles. Since IoT systems are quite different from more established uses of microservice architectures, success of the approach depends on adaptations that enable them to met the key challenges that IoT systems present. We argue that a microservice approach to building IoT systems can combine in a mutually enforcing way with patterns for microservices, API gateways, distribution of services, uniform service discovery, containers, and access control. The approach is illustrated using two case studies of IoT systems in personal health management and connected autonomous vehicles. Our hope is that the vision of a microservices approach will help focus research that can fill in current gaps preventing more effective, interoperable, and secure IoT services and solutions in a wide variety of contexts.
Development and Validation of a Surgical Workload Measure: The Surgery Task Load Index (SURG-TLX)

The purpose of the present study was to develop and validate a multidimensional, surgery-specific workload measure (the SURG-TLX), and to determine its utility in providing diagnostic information about the impact of various sources of stress on the perceived demands of trained surgical operators. As a wide range of stressors have been identified for surgeons in the operating room, the current approach of considering stress as a unidimensional construct may not only limit the degree to which underlying mechanisms may be understood but also the degree to which training interventions may be successfully matched to particular sources of stress. The dimensions of the SURG-TLX were based on two current multidimensional workload measures and developed via focus group discussion. The six dimensions were defined as mental demands, physical demands, temporal demands, task complexity, situational stress, and distractions. Thirty novices were trained on the Fundamentals of Laparoscopic Surgery (FLS) peg transfer task and then completed the task under various conditions designed to manipulate the degree and source of stress experienced: task novelty, physical fatigue, time pressure, evaluation apprehension, multitasking, and distraction. The results were supportive of the discriminant sensitivity of the SURG-TLX to different sources of stress. The sub-factors loaded on the relevant stressors as hypothesized, although the evaluation pressure manipulation was not strong enough to cause a significant rise in situational stress. The present study provides support for the validity of the SURG-TLX instrument and also highlights the importance of considering how different stressors may load surgeons. Implications for categorizing the difficulty of certain procedures, the implementation of new technology in the operating room (man–machine interface issues), and the targeting of stress training strategies to the sources of demand are discussed. Modifications to the scale to enhance clinical utility are also suggested.
Development of a robotic finger with an active dual-mode twisting actuation and a miniature tendon tension sensor

A robot finger is developed with enhanced grasping force and speed using active dual-mode twisting actuation, which is a type of twisted string actuation. This actuation system has two twisting modes (Speed Mode and Force Mode) with different radii of the twisted string, and the twisting mode can be changed automatically. Therefore, the actuator operates like an automatic transmission having two transmission ratios, and the robot finger can generate a large grasping force or fast motion depending on the twisting mode. In addition, a miniature tendon tension sensor is developed and embedded in the fingertip for automatic mode change and grasping force control. A relation between the fingertip force and the tension of the tendon is derived by kinematic and kinetic analyses of the robot finger. The performance of the robotic finger and the tension sensor are verified by experimental results.
Multimodal Question Answering over Structured Data with Ambiguous Entities

In recent years, we have witnessed profound changes in the way people satisfy their information needs. For instance, with the ubiquitous 24/7 availability of mobile devices, the number of search engine queries on mobile devices has reportedly overtaken that of queries on regular personal computers. In this paper, we consider the task of multimodal question answering over structured data, in which a user supplies not just a natural language query but also an image. Our system addresses this by optimizing a non-convex objective function capturing multimodal constraints. Our experiments show that this enables it to answer even very challenging ambiguous entity queries with high accuracy.
Flicker-Free Electrolytic Capacitor-Less Universal Input Offline LED Driver With PFC

Recent developments in improving lighting efficiency and cost reduction of LEDs have made them suitable alternatives to the current lighting systems. In this paper, a novel offline structure is proposed to drive LEDs. The proposed circuit has a high-input power factor, high efficiency, a long lifetime, and it produces no flicker. To increase the lifetime of the converter, the proposed circuit does not include any electrolytic capacitors in the power stage. The proposed circuit consists of a transition mode flyback converter in order to improve power factor. Additionally, a buck converter is added to the third winding of the flyback transformer in order to create two parallel paths for the electrical power to feed the output load. DC power reaches the load through one stage (flyback) and ac power reaches the load through two stages of conversion (flyback + buck). Therefore, in the proposed one-and-a-half stage circuit, the efficiency is improved compared to a regular two-stage circuit. Although the proposed structure has some output current ripple, it is low enough (less than 8%) that the structure can be rendered flicker free, as shall be discussed. Principles of operation and design equations are presented as well as experimental results for a 700 mA/20 W universal input prototype.
A Framework for Smart Servicescape : A Case of Smart Home Service Experience

The rapid development of IoT technology has accelerated the growth of smart services. Despite the proliferation of smart services, academic research is still in its early stage particularly in terms of service experience and service design. Concerning a service experience viewpoint, it is essential to consider the context and environment of smart services, namely “smart servicescape,” as this can influence users’ entire experience. Moreover, the smart servicescape will have different characteristics due to the convergence of online and offline connected environments. With this background, this study aimed to propose a framework for the smart servicescape by identifying new dimensions that reflect the characteristics of smart services. Accordingly, an initial analytic framework of service experience blueprint was established on the basis of the conventional servicescape and service blueprinting. Twenty movie clips on smart home services officially produced by ICT corporations were collected, were analyzed through grounded theory, and were classified according to the analytic framework. Through a series of qualitative analysis, the framework structure was improved to make it more suitable for the smart servicescape. Finally, this study proposed a framework for the smart servicescape derived from the smart home service experience blueprint. The values of this framework can be identified in two aspects: (1) by identifying new dimensions to reflect the characteristics of smart services such as Smart device, Datascape, and Connected scape; and (2) by suggesting the structure of the service experience blueprint infused with the perspective of service experience, which consists of service encounters and the servicescape.
High performance Hi-K + metal gate strain enhanced transistors on (110) silicon

For the first time, the performance impact of (110) silicon substrates on high-k + metal gate strained 45 nm node NMOS and PMOS devices is presented. Record PMOS drive currents of 1.2 mA/um at 1.0 V and 100 nA/um Ioff are reported. It will be demonstrated that 2D short channel effects strongly mitigate the negative impact of (110) substrates on NMOS performance. Narrow width (110) device performance is shown and compared to (100) for the first time. Device reliability is also reported showing no fundamental issue for (110) substrates.
Robot task planning using semantic maps

Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, like labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation, localization, and obstacle avoidance), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowlege, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrate hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in three ways: enriching the planning domain, relaxing unachievable goals, and improving the efficiency of the planner in large domains. Finally, we show several exepriments that demonstrate the effectiveness of our solutions in a doman involving robot navigation in a domestic environment.
Cannabis for Pain and Headaches: Primer.

Marijuana has been used both medicinally and recreationally since ancient times and interest in its compounds for pain relief has increased in recent years. The identification of our own intrinsic, endocannabinoid system has laid the foundation for further research.



Synthetic cannabinoids are being developed and synthesized from the marijuana plant such as dronabinol and nabilone. The US Food and Drug Administration approved the use of dronabinol and nabilone for chemotherapy-associated nausea and vomiting and HIV (Human Immunodeficiency Virus) wasting. Nabiximols is a cannabis extract that is approved for the treatment of spasticity and intractable pain in Canada and the UK. Further clinical trials are studying the effect of marijuana extracts for seizure disorders. Phytocannabinoids have been identified as key compounds involved in analgesia and anti-inflammatory effects. Other compounds found in cannabis such as flavonoids and terpenes are also being investigated as to their individual or synergistic effects. This article will review relevant literature regarding medical use of marijuana and cannabinoid pharmaceuticals with an emphasis on pain and headaches.
A Multi-Theoretical Literature Review on Information Security Investments using the Resource-Based View and the Organizational Learning Theory

The protection of information technology (IT) has become and is predicted to remain a key economic challenge for organizations. While research on IT security investment is fast growing, it lacks a theoretical basis for structuring research, explaining economictechnological phenomena and guide future research. We address this shortcoming by suggesting a new theoretical model emerging from a multi-theoretical perspective adopting the Resource-Based View and the Organizational Learning Theory. The joint application of these theories allows to conceptualize in one theoretical model the organizational learning effects that occur when the protection of organizational resources through IT security countermeasures develops over time. We use this model of IT security investments to synthesize findings of a large body of literature and to derive research gaps. We also discuss managerial implications of (closing) these gaps by providing practical examples.
face2faceVR: using AR to assist VR in ubiquitous environment usage

As virtual reality (VR) usage becomes more popular, one of the issues, among others, which still prevents VR from being used in a more ubiquitous manner is spatial awareness, unlike augmented reality (AR). Generally, there are two forms of such an awareness; recognizing the environment and recognizing other people around us. We propose face2faceVR; an easy to use implementation of AR tracking to assist VR towards recognizing other nearby VR users. The contribution of this work are the following; 1) it is compatible with mobile VR technology that already caters towards a wider adoption, 2) it does not require a networked or shared virtual environment, and 3) it is an inexpensive implementation without any additional peripherals or hardware.
Drug delivery and nanoparticles: Applications and hazards

The use of nanotechnology in medicine and more specifically drug delivery is set to spread rapidly. Currently many substances are under investigation for drug delivery and more specifically for cancer therapy. Interestingly pharmaceutical sciences are using nanoparticles to reduce toxicity and side effects of drugs and up to recently did not realize that carrier systems themselves may impose risks to the patient. The kind of hazards that are introduced by using nanoparticles for drug delivery are beyond that posed by conventional hazards imposed by chemicals in classical delivery matrices. For nanoparticles the knowledge on particle toxicity as obtained in inhalation toxicity shows the way how to investigate the potential hazards of nanoparticles. The toxicology of particulate matter differs from toxicology of substances as the composing chemical(s) may or may not be soluble in biological matrices, thus influencing greatly the potential exposure of various internal organs. This may vary from a rather high local exposure in the lungs and a low or neglectable exposure for other organ systems after inhalation. However, absorbed species may also influence the potential toxicity of the inhaled particles. For nanoparticles the situation is different as their size opens the potential for crossing the various biological barriers within the body. From a positive viewpoint, especially the potential to cross the blood brain barrier may open new ways for drug delivery into the brain. In addition, the nanosize also allows for access into the cell and various cellular compartments including the nucleus. A multitude of substances are currently under investigation for the preparation of nanoparticles for drug delivery, varying from biological substances like albumin, gelatine and phospholipids for liposomes, and more substances of a chemical nature like various polymers and solid metal containing nanoparticles. It is obvious that the potential interaction with tissues and cells, and the potential toxicity, greatly depends on the actual composition of the nanoparticle formulation. This paper provides an overview on some of the currently used systems for drug delivery. Besides the potential beneficial use also attention is drawn to the questions how we should proceed with the safety evaluation of the nanoparticle formulations for drug delivery. For such testing the lessons learned from particle toxicity as applied in inhalation toxicology may be of use. Although for pharmaceutical use the current requirements seem to be adequate to detect most of the adverse effects of nanoparticle formulations, it can not be expected that all aspects of nanoparticle toxicology will be detected. So, probably additional more specific testing would be needed.
Speakers optimize information density through syntactic reduction

If language users are rational, they might choose to structu e their utterances so as to optimize communicative properties. In particular, in formation-theoretic and psycholinguistic considerations suggest that this may inc lude maximizing the uniformity of information density in an utterance. We investig a e this possibility in the context ofsyntactic reduction , where the speaker has the option of either marking a higher-order unit (a phrase) with an extra word, or leaving it unmarked. We demonstrate that speakers are more likely to reduce less i nformation-dense phrases. In a second step, we combine a stochastic model of st ructured utterance production with a logistic-regression model of syntactic r eduction to study which types of cues speakers employ when estimating the predictab il ty of upcoming elements. We demonstrate that the trend toward predictabil ity-sensitive syntactic reduction (Jaeger, 2006) is robust in the face of a wide varie ty of control variables, and present evidence that speakers use both surface a nd structural cues for predictability estimation.