nils-herrmann/clean_scirepeval huggingface dataset:
CRITICAL SUCCESS FACTORS FOR CUSTOMER RELATIONSHIP MANAGEMENT IMPLEMENTATIONS

The growing forces of increasing global competition, continuing customer demands, and the significant revolution in Commercial Off The Shelf (COTS) solutions, especially Customer Relationship Management (CRM) applications, have together put pressure upon many organisations to implement CRM solutions and to switch their organisational processes from being product-centric to being customer-centric. A CRM initiative is not only technology; it is a business strategy supported by technology which automates and enhances the processes associated with managing customer relationships. By the end of 2010, it is predicted that companies will be spending almost $11 billion yearly on CRM solutions. However, studies have found that 70% of CRM projects have failed. Understanding the factors that enable success of CRM is vital. There is very few existing specific research into Critical Success Factors (CSFs) of CRM implementations, and there is no comprehensive view that captures all the aspects for successful CRM implementation and their inter-relationships. Therefore, the aim of this paper is to explore the current literature base of CSFs for CRM implementations and proposes a taxonomy for them. Future research work will continue to investigate in depth these factors by exploring the complex system links between CSFs using systems thinking techniques such as causal maps to investigate the complex, systemic networks of CSFs in organisations which result in emergent effects which themselves influence the failure or success of a CRM.
TASK Channels on Basal Forebrain Cholinergic Neurons Modulate Electrocortical Signatures of Arousal by Histamine.

Basal forebrain cholinergic neurons are the main source of cortical acetylcholine, and their activation by histamine elicits cortical arousal. TWIK-like acid-sensitive K(+) (TASK) channels modulate neuronal excitability and are expressed on basal forebrain cholinergic neurons, but the role of TASK channels in the histamine-basal forebrain cholinergic arousal circuit is unknown. We first expressed TASK channel subunits and histamine Type 1 receptors in HEK cells. Application of histamine in vitro inhibited the acid-sensitive K(+) current, indicating a functionally coupled signaling mechanism. We then studied the role of TASK channels in modulating electrocortical activity in vivo using freely behaving wild-type (n = 12) and ChAT-Cre:TASK(f/f) mice (n = 12), the latter lacking TASK-1/3 channels on cholinergic neurons. TASK channel deletion on cholinergic neurons significantly altered endogenous electroencephalogram oscillations in multiple frequency bands. We then identified the effect of TASK channel deletion during microperfusion of histamine into the basal forebrain. In non-rapid eye movement sleep, TASK channel deletion on cholinergic neurons significantly attenuated the histamine-induced increase in 30-50 Hz activity, consistent with TASK channels contributing to histamine action on basal forebrain cholinergic neurons. In contrast, during active wakefulness, histamine significantly increased 30-50 Hz activity in ChAT-Cre:TASK(f/f) mice but not wild-type mice, showing that the histamine response depended upon the prevailing cortical arousal state. In summary, we identify TASK channel modulation in response to histamine receptor activation in vitro, as well as a role of TASK channels on cholinergic neurons in modulating endogenous oscillations in the electroencephalogram and the electrocortical response to histamine at the basal forebrain in vivo.



Attentive states and cognitive function are associated with the generation of γ EEG activity. Basal forebrain cholinergic neurons are important modulators of cortical arousal and γ activity, and in this study we investigated the mechanism by which these neurons are activated by the wake-active neurotransmitter histamine. We found that histamine inhibited a class of K(+) leak channels called TASK channels and that deletion of TASK channels selectively on cholinergic neurons modulated baseline EEG activity as well as histamine-induced changes in γ activity. By identifying a discrete brain circuit where TASK channels can influence γ activity, these results represent new knowledge that enhances our understanding of how subcortical arousal systems may contribute to the generation of attentive states.
xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs

In this paper we show how attackers can covertly leak data (e.g., encryption keys, passwords and files) from highly secure or air-gapped networks via the row of status LEDs that exists in networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device ('side-channel'), intentionally controlling the status LEDs to carry any type of data ('covert-channel') has never studied before. A malicious code is executed on the LAN switch or router, allowing full control of the status LEDs. Sensitive data can be encoded and modulated over the blinking of the LEDs. The generated signals can then be recorded by various types of remote cameras and optical sensors. We provide the technical background on the internal architecture of switches and routers (at both the hardware and software level) which enables this type of attack. We also present amplitude and frequency based modulation and encoding schemas, along with a simple transmission protocol. We implement a prototype of an exfiltration malware and discuss its design and implementation. We evaluate this method with a few routers and different types of LEDs. In addition, we tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and also discuss different detection and prevention countermeasures. Our experiment shows that sensitive data can be covertly leaked via the status LEDs of switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per LED.  ezfiltration; air-gap; network; optical; covertchannel (key words)
Integrated non-inverting buck-boost DC-DC converter with average-current-mode control

With the increasing use of electrical portable devices, an efficient power management solution is needed to extend the battery life. Generally, basic switching regulators, such as buck and boost converters, are not capable of using the entire battery output voltage range. In this work, a non-inverting buck-boost dc-dc converter which can use the full-range output voltage (i.e., 2.5-4.7 V) of a Li-ion battery is proposed. Moreover, average-current-mode control is adopted to improve noise immunity. The die area of this chip is 1.9×1.7mm2, and it was fabricated by using TSMC 0.35μm 2P4M 5V mixed-signal polycide process. The input range of the converter is 2.5-5 V, and its output is set to 3.3V. It can supply up to 300 mA load current.
A Trainable Spaced Repetition Model for Language Learning

We present half-life regression (HLR), a novel model for spaced repetition practice with applications to second language acquisition. HLR combines psycholinguistic theory with modern machine learning techniques, indirectly estimating the “halflife” of a word or concept in a student’s long-term memory. We use data from Duolingo — a popular online language learning application — to fit HLR models, reducing error by 45%+ compared to several baselines at predicting student recall rates. HLR model weights also shed light on which linguistic concepts are systematically challenging for second language learners. Finally, HLR was able to improve Duolingo daily student engagement by 12% in an operational user study.
smartPrediction: a real-time smartphone-based fall risk prediction and prevention system

The high risk of falls and the substantial increase in the elderly population have recently stimulated scientific research on Smartphone-based fall detection systems. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to predict and prevent them from happening in the first place. To address the issue of fall prevention, in this paper, we propose a fall prediction system by integrating the sensor data of Smartphones and a Smartshoe. We designed and implemented a Smartshoe that contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data in any environment. By assimilating the Smartshoe and Smartphone sensors data, we performed an extensive set of experiments to evaluate normal and abnormal walking patterns. The system can generate an alert message in the Smartphone to warn the user about the high-risk gait patterns and potentially save them from an imminent fall. We validated our approach using a decision tree with 10-fold cross validation and found 97.2% accuracy in gait abnormality detection.
Contextual gaps: privacy issues on Facebook

Social networking sites like Facebook are rapidly gaining in popularity. At the same time, they seem to present significant privacy issues for their users. We analyze two of Facebooks’s more recent features, Applications and News Feed, from the perspective enabled by Helen Nissenbaum’s treatment of privacy as “contextual integrity.” Offline, privacy is mediated by highly granular social contexts. Online contexts, including social networking sites, lack much of this granularity. These contextual gaps are at the root of many of the sites’ privacy issues. Applications, which nearly invisibly shares not just a users’, but a user’s friends’ information with third parties, clearly violates standard norms of information flow. News Feed is a more complex case, because it involves not just questions of privacy, but also of program interface and of the meaning of “friendship” online. In both cases, many of the privacy issues on Facebook are primarily design issues, which could be ameliorated by an interface that made the flows of information more transparent to users.
A hybrid RF and vibration energy harvester for wearable devices

This paper describes a hybrid radio frequency (RF) and piezoelectric thin film polyvinylidene fluoride (PVDF) vibration energy harvester for wearable devices. By exploiting the impedance characteristics of parasitic capacitances and discrete inductors, the proposed harvester not only scavenges 15 Hz vibration energy but also works as a 915 MHz flexible silver-ink RF dipole antenna. In addition, an interface circuit including a 6-stage Dickson RF-to-DC converter and a diode bridge rectifier to convert the RF and vibration outputs of the hybrid harvester into DC signals to power resistive loads is evaluated. A maximum DC output power of 20.9 μ”, when using the RF to DC converter and −8 dBm input RF power, is achieved at 36 % of the open-circuit output voltage while the DC power harvested from 3 g vibration excitation reaches a maximum of 2.8 μW at 51% of open-circuit voltage. Experimental results show that the tested hybrid harvesting system simultaneously generates 7.3 μW DC power, when the distance from the harvester to a 3 W EIRP 915 MHz transmitter is 5.5 m, and 1.8 μW DC power from a 1.8 g vibration acceleration peak.
Competitive intelligence process and tools for intelligence analysis

The purpose of this survey research is twofold. First, to study and report the process that is commonly used to create and maintain a competitive intelligence (CI) program in organizations. And second, to provide an analysis of several emergent text mining, web mining and visualization-based CI tools, which are specific to collection and analysis of intelligence.  A range of recently published research literature on CI processes, applications, tools and technologies to collect and analyze competitive information within organizations is reviewed to explore their current state, issues and challenges learned from their practice.  The paper provides executive decision makers and strategic managers a better understanding of what methods are available and appropriate to the decisions they must make and the steps involved in CI undertaking.  The findings of this research provide the managers of CI programs a context for understanding which tools and techniques are better suited to their specific types of problems; and help them develop and evaluate a usable set of tools and best practices to apply to their industry.
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?

A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent robotic grasping. However, in order to fully evaluate the value of touch sensing for grasp outcome prediction, we must understand how touch sensing can influence outcome prediction accuracy when combined with other modalities. Doing so using conventional model-based techniques is exceptionally difficult. In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch. To that end, we collected more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger, and evaluated visuo-tactile deep neural network models to directly predict grasp outcomes from either modality individually, and from both modalities together. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.
Active Video/Arcade Games (Exergaming) and Energy Expenditure in College Students.

Video games have become increasingly popular among young adults. The purpose of this pilot study was to determine if interactive video/arcade games, requiring physical activity to play, increase the energy expenditure (EE) and heart rate (HR) of young adults enough to elicit a training response. Thirteen male and female participants 26.6 ± 5.7 years of age were in the study. Participants were familiarized with equipment and allowed to practice with three games: (1) moving and striking lighted pads, (2) riding a bike to increase the pace of a race car, and (3) boxing against a video simulated opponent. A portable metabolic cart and HR monitor were attached to participants to measure baseline and exercise values. Participants could play any of the three games for 30 minutes while metabolic and HR data were collected. Exercise data were compared to baseline measures, and the 3 games were compared for EE. Paired sample t-tests showed baseline and exercise values differed for HR (t(12) = -18.91, p < 0.01), and EE (t(12) = -15.62, p < 0.01). The boxing game provided the highest VO2 (17.47 ± 4.79 ml·kg(·-1)min(-1)). Participants achieved 60% or better of their HR reserve (162.82 ± 10.78 beats·min(-1),) well within the ACSM guidelines for a training HR. Caloric expenditure during the 30-minute exercise session (226. 07 ± 48.68) is also within the ACSM recommendations for daily physical activity. Thus, interactive video/arcade games that require physical activity to play can be utilized as part of an overall aerobic exercise program.
Cable Suspended Robots: Design, Planning and Control

Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-eflector but not push it. This feature makes design, planning, and control of cable-suspended robots a lot more challenging compared to their counterparts parallel-actuated robots. This paper describes kinematic and dynamic models, workspace, trajectory planning, and feedback controllers f o r these robots. These results are demonstrated through simulation and experiments on a six degree-of-freedom cable suspended robot.
The Case Against Statistical Significance Testing

In recent years the use of traditional statistical methods in educational research has increasingly come under attack. In this article, Ronald P Carver exposes the fantasies often entertained by researchers about the meaning of statistical significance. The author recommends abandoning all statistical significance testing and suggests other ways of evaluating research results. Carver concludes that we should return to the scientific method of examining data and replicating results rather than relying on statistical significance testing to provide equivalent information.
Integrating disability studies concepts into occupational therapy education using service learning.

This article describes an occupational therapy educational program's experience with service-learning courses that has fostered student learning about service to the community and disability as a multidimensional construct. Faculty-reflective perspectives about disability and ways to enhance learning about disability as a human experience are presented as an important consideration for health care education curriculum design and course development. Through review of educational evaluation described in research on service learning, the authors used a multi-method assessment matrix to capture students' perspectives on their service learning. The community project investigated accessibility issues that persons with disabilities encountered in social participation at community arts venues. Results from student surveys, interviews, focus groups, and journal entries indicated that service learning contributed to occupational therapy students' appreciation and understanding about disability as an individual, environmental, and societal construct.
Design and Analysis of 239 GHz CMOS Push-Push Transformer-Based VCO With High Efficiency and Wide Tuning Range

A push-push transformer-based voltage-controlled oscillator (VCO) is proposed and analyzed to achieve high efficiency and a wide tuning range at sub-terahertz (THz) frequencies. Analyses show that the coupling factor of the transformer to obtain high output power (POUT) has to be carefully chosen by consideration of gate-to-drain voltage gain as well as matching impedances seen from the drain and to the gate of a VCO transistor at the 2nd harmonic frequency (2fo). Analysis also shows that the transformer-based resonator allows a wide tuning range. In addition, it has been shown that the introduction of a parallel inductor to a varactor leads to high POUT and low phase noise. The proposed 239 GHz VCO with a 65 nm CMOS process demonstrates the high efficiency of 1.45%, the POUT of -4.8 dBm, and the wide tuning range of 12.5% with a supply voltage of 1.2 V.
Emotion AI , Real-Time Emotion Detection using CNN

In this paper, we describe a Convolutional Neural Network (CNN) approach to realtime emotion detection. We utilize data from the Extended Cohn-Kanade dataset, Japanese Female Facial Expression data set, and our own custom images in order to train this model, and apply preprocessing steps to improve performance. We re-train a LeNet and AlexNet implementation, both of which perform with above 97% accuracy. Qualitative analysis of real-time images shows that the above models perform reasonably well at classifying facial expressions, but not as well as the quantitative results would indicate.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio, the reconstructed images are often blurry and lack sharp details, especially for higher undersampling rates. Recently, adversarial and perceptual loss functions have been shown to achieve more visually appealing results. However, it remains an open question how to (1) optimally combine these loss functions with the MSE loss function and (2) evaluate such a perceptual enhancement. In this work, we propose a hybrid method, in which a visual refinement component is learnt on top of an MSE loss-based reconstruction network. In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis. Applied on a large cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate significant improvements (p < 0.01) over the state-of-the-art in both a human observer study and the semantic interpretability score.
Rectenna performance under power-optimized waveform excitation

Power-optimized waveforms (POWs) have been shown to increase the power-conversion efficiency in energy harvesting circuits. This paper is the first attempt to study the effects of a POW on these circuits and determine how its design can affect circuit performance. As an example, a 5.8 GHz single-shunt rectenna was designed to investigate the variation of POW parameters. It was shown that there is an optimal range of subcarrier spacing to maximize POW gain and minimize voltage ripple. Furthermore, a relationship between the maximum number of equal energy subcarriers, ripple voltage, and circuit parameters has been determined.
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies

Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer’s Disease diagnostics. Then we propose our own algorithm for Alzheimer’s Disease diagnostics based on a convolutional neural network and sMRI and DTI modalities fusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (http://adni. loni.usc.edu). Comparison with a single modality approach shows promising results. We also propose our own method of data augmentation for balancing classes of different size and analyze the impact of the ROI size on the classification results as well.