nils-herrmann/clean_scirepeval huggingface dataset:
Multiport UHF RFID-Tag Antenna for Enhanced Energy Harvesting of Self-Powered Wireless Sensors

This paper presents the design and experimental evaluation of a long-range solar powered sensor-enhanced radio frequency identification (RFID) tag. The tag antenna is a multiport microstrip patch with an overlay of thin-film solar cells for energy harvesting. A second port is allocated on the patch antenna for supplementary energy harvesting from the RF signal transmitted by the reader. An $I^{2}C$-RFID chip along with a microcontroller unit (MCU) and temperature and humidity sensor are incorporated into the tag design to implement a low-cost wireless sensor using a commercial RFID reader. The measurements of the fabricated RFID-tag sensor demonstrate that a maximum sensing/reading range of 27 m is achieved when all the circuits are powered using solar cells, while it is 7.48 m with only the secondary option of energy harvesting. The proposed sensor with dual energy harvesting achieves both a longer range and a lifetime compared with similar battery-powered sensor-enhanced RFID tags. The RFID sensor is also evaluated in a climate chamber and the sensor data (temperature/humidity) were remotely recorded with an excellent accuracy using a commercial ultra high frequency (UHF) RFID reader. In addition, the sensor can be programmed for the temperature/humidity surveillance of sensitive items, such as those found in various supply chain and transportation applications.
Using Maximum Entropy For Sentence Extraction

A maximum entropy classi er can be used to extract sentences from documents. Experiments using technical documents show that such a classi er tends to treat features in a categorical manner. This results in performance that is worse than when extracting sentences using a naive Bayes classi er. Addition of an optimised prior to the maximum entropy classi er improves performance over and above that of naive Bayes (even when naive Bayes is also extended with a similar prior). Further experiments show that, should we have at our disposal extremely informative features, then maximum entropy is able to yield excellent results. Naive Bayes, in contrast, cannot exploit these features and so fundamentally limits sentence extraction performance.
Memristive devices for computing: Beyond CMOS and beyond von Neumann

Traditional CMOS technology and its continuous down-scaling have been the driving force to improve performance of existing computer architectures. Today, however, both technology and computer architectures are facing challenges that make them incapable of delivering the growing computing performance requirement at pre-defined constraints. This forces the exploration of both novel architectures and technologies; not only to maintain the economic profit of technology scaling, but also to enable the computing architecture solutions for big-data and data-intensive applications. This paper discusses the emerging memristive device as a complement (or an alternative) to CMOS devices and shows how such devices enable novel computing paradigms that will solve the challenges of today's architectures for certain applications. The paper covers not only the potential of memristor devices in enabling novel memory technologies, logic design styles, and arithmetic operations, but also their potential in enabling in-memory computing and neuromorphic computing.
Internet Privacy Concerns Confirm the Case for Intervention

yberspace is invading private space. Controversies about spam, cookies, and the clickstream are merely the tip of an iceberg. Behind them loom real-time person location technologies includ-It's small wonder that lack of public confidence is a serious impediment to the take-up rate of consumer e-commerce. The concerns are not merely about security of value, but about something much more significant: trust in the information society. Conventional thinking has been the Internet renders laws less relevant. On the contrary, this article argues that the current debates about privacy and the Internet are the harbingers of a substantial shift. Because the U.S. has held off general privacy protections for so long, it will undergo much more significant adjustments than European countries. Privacy is often thought of as a moral right or a legal right. But it's often more useful to perceive privacy as the interest that individuals have in sustaining a personal space, free from interference by other people and organizations. Personal space has multiple dimensions, in particular , privacy of the person (concerned with the integrity of the individual's body), privacy of personal behavior, privacy of personal communications , and privacy of personal data. Information privacy refers to the claims of individuals that data about themselves should generally not be available to other individuals and organizations, and that, where data is possessed by another party, the individual must be able to exercise a substantial degree of control over that data and its use. (Definitional issues are examined in .) Information privacy has been under increasing threat as a result of the rapid replacement of expensive physical surveillance by what I referred to in Communications over a decade ago as " dataveillance: " the systematic use of personal data systems in the investigation or monitoring of people's actions or communications . Intensive data trails about each individual provide a basis for the exercise of power over I In nt te er rv ve en nt ti io on n Public confidence in matters of online privacy seemingly lessens as the Internet grows. Indeed, there is mounting evidence the necessary remedy may be a protective framework that includes (gulp) legislative provisions.
Influence of saturation effects in a transverse flux machine

In this paper the torque density of transverse flux machines is investigated against the flux density inside the active parts with analytical calculations and FEM simulations. Especially saturation effects, which reduce the flux linkage with the coil, and core losses, are considered for both, the flux concentrating setup and the surface mounted magnets topology. The calculations are verified by measurements on an existing prototype. Finally new design guidelines are developed for the construction of TFMs with regard to the magnetic circuit.
New Comparative Study Between DES, 3DES and AES within Nine Factors

--With the rapid development of various multimedia technologies, more and more multimedia data are generated and transmitted in the medical, also the internet allows for wide distribution of digital media data. It becomes much easier to edit, modify and duplicate digital information .Besides that, digital documents are also easy to copy and distribute, therefore it will be faced by many threats. It is a big security and privacy issue, it become necessary to find appropriate protection because of the significance, accuracy and sensitivity of the information. , which may include some sensitive information which should not be accessed by or can only be partially exposed to the general users. Therefore, security and privacy has become an important. Another problem with digital document and video is that undetectable modifications can be made with very simple and widely available equipment, which put the digital material for evidential purposes under question. Cryptography considers one of the techniques which used to protect the important information. In this paper a three algorithm of multimedia encryption schemes have been proposed in the literature and description. The New Comparative Study between DES, 3DES and AES within Nine Factors achieving an efficiency, flexibility and security, which is a challenge of researchers.
Timing to Perfection: The Biology of Central and Peripheral Circadian Clocks

The mammalian circadian system, which is comprised of multiple cellular clocks located in the organs and tissues, orchestrates their regulation in a hierarchical manner throughout the 24 hr of the day. At the top of the hierarchy are the suprachiasmatic nuclei, which synchronize subordinate organ and tissue clocks using electrical, endocrine, and metabolic signaling pathways that impact the molecular mechanisms of cellular clocks. The interplay between the central neural and peripheral tissue clocks is not fully understood and remains a major challenge in determining how neurological and metabolic homeostasis is achieved across the sleep-wake cycle. Disturbances in the communication between the plethora of body clocks can desynchronize the circadian system, which is believed to contribute to the development of diseases such as obesity and neuropsychiatric disorders. This review will highlight the relationship between clocks and metabolism, and describe how cues such as light, food, and reward mediate entrainment of the circadian system.
Reactive stream processing for data-centric publish/subscribe

The Internet of Things (IoT) paradigm has given rise to a new class of applications wherein complex data analytics must be performed in real-time on large volumes of fast-moving and heterogeneous sensor-generated data. Such data streams are often unbounded and must be processed in a distributed and parallel manner to ensure timely processing and delivery to interested subscribers. Dataflow architectures based on event-based design have served well in such applications because events support asynchrony, loose coupling, and helps build resilient, responsive and scalable applications. However, a unified programming model for event processing and distribution that can naturally compose the processing stages in a dataflow while exploiting the inherent parallelism available in the environment and computation is still lacking. To that end, we investigate the benefits of blending Reactive Programming with data distribution frameworks for building distributed, reactive, and high-performance stream-processing applications. Specifically, we present insights from our study integrating and evaluating Microsoft .NET Reactive Extensions (Rx) with OMG Data Distribution Service (DDS), which is a standards-based publish/subscribe middleware suitable for demanding industrial IoT applications. Several key insights from both qualitative and quantitative evaluation of our approach are presented.
A new pedestrian detection method based on combined HOG and LSS features

Pedestrian detection is a critical issue in computer vision, with several feature descriptors can be adopted. Since the ability of various kinds of feature descriptor is different in pedestrian detection and there is no basis in feature selection, we analyze the commonly used features in theory and compare them in experiments. It is desired to find a new feature with the strongest description ability from their pair-wise combinations. In experiments, INRIA database and Daimler database are adopted as the training and testing set. By theoretic analysis, we find the HOG–LSS combined feature have more comprehensive description ability. At first, Adaboost is regarded as classifier and the experimental results show that the description ability of the new combination features is improved on the basis of the single feature and HOG–LSS combined feature has the strongest description ability. For further verifying this conclusion, SVM classifier is used in the experiment. The detection performance is evaluated by miss rate, the false positives per window, and the false positives per image. The results of these indicators further prove that description ability of HOG–LSS feature is better than other combination of these
Blind Deconvolution Using Alternating Maximum a Posteriori Estimation with Heavy-Tailed Priors

Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straightforward maximum a posteriory estimation combined with very sparse priors and an efficient numerical method can produce results, which compete with much more complicated state-of-the-art methods.
Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking

Traditional multiple object tracking methods divide the task into two parts: affinity learning and data association. The separation of the task requires to define a hand-crafted training goal in affinity learning stage and a hand-crafted cost function of data association stage, which prevents the tracking goals from learning directly from the feature. In this paper, we present a new multiple object tracking (MOT) framework with data-driven association method, named as Tracklet Association Tracker (TAT). The framework aims at gluing feature learning and data association into a unity by a bi-level optimization formulation so that the association results can be directly learned from features. To boost the performance, we also adopt the popular hierarchical association and perform the necessary alignment and selection of raw detection responses. Our model trains over 20× faster than a similar approach, and achieves the stateof-the-art performance on both MOT2016 and MOT2017 benchmarks.
Teaching the science of learning

The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.
Continuous noninvasive glucose monitoring technology based on "occlusion spectroscopy".

A truly noninvasive glucose-sensing device could revolutionalize diabetes treatment by leading to improved compliance with recommended glucose levels, thus reducing the long-term complications and cost of diabetes. Herein, we present the technology and evaluate the efficacy of a truly noninvasive device for continuous blood glucose monitoring, the NBM (OrSense Ltd.).



In vitro analysis was used to validate the technology and algorithms. A clinical study was performed to quantify the in vivo performance of the NBM device. A total of 23 patients with type 1 (n = 12) and type 2 (n = 11) diabetes were enrolled in the clinical study and participated in 111 sessions. Accuracy was assessed by comparing NBM data with paired self-monitoring of blood glucose meter readings.



In vitro experiments showed a strong correlation between calculated and actual glucose concentrations. The clinical trial produced a total of 1690 paired glucose values (NBM vs reference). In the paired data set, the reference glucose range was 40-496 mg/dl. No systematic bias was found at any of the glucose levels examined (70, 100, 150, and 200 mg/dl). The mean relative absolute difference was 17.2%, and a Clarke error grid analysis showed that 95.5% of the measurements fall within the clinically acceptable A&B regions (zone A, 69.7%; and zone B, 25.7%).



This study indicates the potential use of OrSense's NBM device as a noninvasive sensor for continuous blood glucose evaluation. The device was safe and well tolerated.
Learning Filter Banks Using Deep Learning For Acoustic Signals

Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urbansound8K dataset [1], the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.
Printed $\lambda/8$ -PIFA for Penta-Band WWAN Operation in the Mobile Phone

A small-size printed planar inverted-F antenna (PIFA) operated at its one-eighth wavelength (lambda/8) mode as the fundamental resonant mode for achieving WWAN (wireless wide area network) operation in the mobile phone is presented. The proposed PIFA has a simple structure of comprising two radiating strips of length about lambda/8 at 900 MHz and is fed using a coupling feed. Compared to the traditional PIFA using a direct feed, the coupling feed greatly decreases the very large input impedance seen at the lambda/8 mode for the traditional PIFA and results in successful excitation of the lambda/8 mode for the proposed PIFA. Two lambda/8 modes are generated by the two radiating strips and occur at close frequencies at about 900 MHz to form a wide lower band to cover GSM850/900 operation. The two radiating strips also generate two higher-order modes or lambda/4 modes at about 1900 MHz to form a wide upper band for GSM1800/1900/UMTS operation. Penta-band WWAN operation is hence achieved, yet the proposed PIFA only occupies a small printed area of 15times31 mm2 or 465 mm2 on the system circuit board of the mobile phone, which is about the smallest for the internal uniplanar printed antenna capable of penta-band operation that have been reported. Details of the proposed PIFA are presented. The specific absorption rate (SAR) and hearing aid compatibility (HAC) results for the proposed PIFA are also analyzed.
Optimal Tuning of PID Controller in Automatic Voltage Regulator System using Improved Harmony Search Algorithm

This paper presents a new tuning approach based on an Improved Harmony Search algorithm (IHSA) to obtain the proportional-integral-derivative (PID) controller parameters in Automatic Voltage Regulator (AVR) system. In the tuning processes the IHSA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore it can improve the dynamic of the AVR system. The results analysis reveals that the new approach is able to find better control system performances compared with that of the particle swarm optimization (PSO).
Document Image Binarization Using Recurrent Neural Networks

In the context of document image analysis, image binarization is an important preprocessing step for other document analysis algorithms, but also relevant on its own by improving the readability of images of historical documents. While historical document image binarization is challenging due to common image degradations, such as bleedthrough, faded ink or stains, achieving good binarization performance in a timely manner is a worthwhile goal to facilitate efficient information extraction from historical documents. In this paper, we propose a recurrent neural network based algorithm using Grid Long Short-Term Memory cells for image binarization, as well as a pseudo F-Measure based weighted loss function. We evaluate the binarization and execution performance of our algorithm for different choices of footprint size, scale factor and loss function. Our experiments show a significant trade-off between binarization time and quality for different footprint sizes. However, we see no statistically significant difference when using different scale factors and only limited differences for different loss functions. Lastly, we compare the binarization performance of our approach with the best performing algorithm in the 2016 handwritten document image binarization contest and show that both algorithms perform equally well.
Consumers' online information adoption behavior: Motives and antecedents of electronic word of mouth communications

Electronic word of mouth influences the consumer bu ying behavior through the online exchange of customer opinions and experiences about commodit ies using social networking sites. Reviews or comments posted on the Internet by consumers and experts are based on their personal experiences and the evaluation and expert analysis of a product. The current study explains an overview of key elements of eWOM communication ante cedents, addresses eWOM motives to analyze food products purchasing factors associated with consumer engagement. We found certain factors of eWOM that should be considered i n ecision making according to the model for information adoption. It revealed that consumer s’ need for social interaction, economic incentives, and self-worth reinforcement are the pr ima y drivers of eWOM involvement. However, argument quality was found to be more appe ling then eWOM source credibility for food product purchasing. Practitioners should make their strategies to follow the credibility standards, trustworthy vehicles, perceived values, informative and digital channels for the enhancement of eWOM communications. eWOM communicat ion is an emerging area, further M AN US CR IP T AC CE PT ED ACCEPTED MANUSCRIPT research can be focused on the meta-analysis of rel ative factors, such as responses, moderators, and credibility to eWOM communication.
A System to Verify Network Behavior of Known Cryptographic Clients

Numerous exploits of client-server protocols and applications involve modifying clients to behave in ways that untampered clients would not, such as crafting malicious packets. In this paper, we develop a system for verifying in near real-time that a cryptographic client’s message sequence is consistent with its known implementation. Moreover, we accomplish this without knowing all of the client-side inputs driving its behavior. Our toolchain for verifying a client’s messages explores multiple candidate execution paths in the client concurrently, an innovation useful for aspects of certain cryptographic protocols such as message padding (which will be permitted in TLS 1.3). In addition, our toolchain includes a novel approach to symbolically executing the client software in multiple passes that defers expensive functions until their inputs can be inferred and concretized. We demonstrate client verification on OpenSSL and BoringSSL to show that, e.g., Heartbleed exploits can be detected without Heartbleed-specific filtering and within seconds of the first malicious packet. On legitimate traffic our verification keeps pace with Gmail-shaped workloads, with a median lag of 0.85s.