nils-herrmann/clean_scirepeval huggingface dataset:
Offline arabic handwritten text recognition: A Survey

Research in offline Arabic handwriting recognition has increased considerably in the past few years. This is evident from the numerous research results published recently in major journals and conferences in the area of handwriting recognition. Features and classifications techniques utilized in recent research work have diversified noticeably compared to the past. Moreover, more efforts have been diverted, in last few years, to construct different databases for Arabic handwriting recognition. This article provides a comprehensive survey of recent developments in Arabic handwriting recognition. The article starts with a summary of the characteristics of Arabic text, followed by a general model for an Arabic text recognition system. Then the used databases for Arabic text recognition are discussed. Research works on preprocessing phase, like text representation, baseline detection, line, word, character, and subcharacter segmentation algorithms, are presented. Different feature extraction techniques used in Arabic handwriting recognition are identified and discussed. Different classification approaches, like HMM, ANN, SVM, k-NN, syntactical methods, etc., are discussed in the context of Arabic handwriting recognition. Works on Arabic lexicon construction and spell checking are presented in the postprocessing phase. Several summary tables of published research work are provided for used Arabic text databases and reported results on Arabic character, word, numerals, and text recognition. These tables summarize the features, classifiers, data, and reported recognition accuracy for each technique. Finally, we discuss some future research directions in Arabic handwriting recognition.
The Role of Service-provider's Attributes in Sharing Economy: a Data-driven Study from the Perspective of Trust

Trust has been found as the key determinant of consumer decisions in sharing economy platforms, and service-provider’s attributesare crucial fortrust building but rarelystudied. In this paper, we investigate the effect of host’s attributes on consumer’s purchasing behaviors in xiaozhu.com, a leading short rental platform in China. Specifically, we argue that host’s attributescan influencetrustworthiness from the perspective of benevolence, ability, integrity andhave significant effects on orders. Furthermore, it isfound that consumers prefer to choose women as hosts when book single roomsand genderis an influencing factor in the decision process of ordering. These findings imply that service-provider’s attributes can contribute to trust building and decisionmaking in sharing economy. It’s expected to make contributions to trust studytheoretically and provide practical trust development guidesfor sharing economy platforms.
The electrically noncontacting ECG measurement on the toilet seat using the capacitively-coupled insulated electrodes

The studies of ECG measurement on the toilet seat have been performed specifically for the ubiquitous health care. Instead of the mainly used dry electrodes having several problems such as the electrical safety or the environmental stability, we used the capacitively-coupled insulated electrodes, which were composed of the Cu plate and the PTFE film for the measurement. The biosignal sensed with the insulated electrodes was measured through the ultra-high input impedance system including OPA111 having the common mode impedance of 10/sup 14/ /spl Omega/ ft. As the result of measuring the signal, with the electrical ground on the neck or the hand, the R-peaks were detected very positively. However, without the electrical ground on the body, we could detect the heartbeat signal, a land of the motion artifacts by the variation of the blood vessel volume. It seems that this heartbeat signal can be also used as the important parameter like the R-peaks for the HRV (heart rate variability) analysis. In addition, it is thought that, without the directly electrical ground, the R-peak detection will be possible by the improvement of the SNR with the active common canceling system.
Generating Adversarial Examples for Speech Recognition

With the proliferation of natural language interfaces on mobile devices and in home personal assistants such as Siri and Alexa, many services and data are becoming available through transcription from a speech recognition system. One major risk factor in this trend is that a malicious adversary may attack this system without the primary user noticing. One way to accomplish this is to use adversarial examples that are perceived one way by a human, but transcribed differently by the Automatic Speech Recognition (ASR) system. For example, a recording that sounds like ”hello” to the human ear, but is transcribed as “goodbye” by the ASR system. Recent work has shown that adversarial examples can be created for convolutional neural networks to fool vision recognition systems. We show that similar methods can be applied to neural ASR systems. We show successful results for two methods of generating adversarial examples where we fool a high quality ASR system but the difference in the audio is imperceptible to the human ear. We also present a method for converting the adversarial MFCC features back into audio.
Communication-Efficient Distributed Online Learning with Kernels

We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterparts, communicating the support vector expansions becomes inefficient for large numbers of support vectors. The proposed extension allows for a larger class of online learning algorithms—including those alleviating the problem above through model compression. In addition, we characterize the quality of the proposed protocol by introducing a novel criterion that requires the communication to be bounded by the loss suffered.
Vision and Locomotion Shape the Interactions between Neuron Types in Mouse Visual Cortex

Cortical computation arises from the interaction of multiple neuronal types, including pyramidal (Pyr) cells and interneurons expressing Sst, Vip, or Pvalb. To study the circuit underlying such interactions, we imaged these four types of cells in mouse primary visual cortex (V1). Our recordings in darkness were consistent with a "disinhibitory" model in which locomotion activates Vip cells, thus inhibiting Sst cells and disinhibiting Pyr cells. However, the disinhibitory model failed when visual stimuli were present: locomotion increased Sst cell responses to large stimuli and Vip cell responses to small stimuli. A recurrent network model successfully predicted each cell type's activity from the measured activity of other types. Capturing the effects of locomotion, however, required allowing it to increase feedforward synaptic weights and modulate recurrent weights. This network model summarizes interneuron interactions and suggests that locomotion may alter cortical computation by changing effective synaptic connectivity.
Word Sense Disambiguation using a Bidirectional LSTM

In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.
Matchmaking for online games and other latency-sensitive P2P systems

The latency between machines on the Internet can dramatically affect users' experience for many distributed applications. Particularly, in multiplayer online games, players seek to cluster themselves so that those in the same session have low latency to each other. A system that predicts latencies between machine pairs allows such matchmaking to consider many more machine pairs than can be probed in a scalable fashion while users are waiting. Using a far-reaching trace of latencies between players on over 3.5 million game consoles, we designed Htrae, a latency prediction system for game matchmaking scenarios. One novel feature of Htrae is its synthesis of geolocation with a network coordinate system. It uses geolocation to select reasonable initial network coordinates for new machines joining the system, allowing it to converge more quickly than standard network coordinate systems and produce substantially lower prediction error than state-of-the-art latency prediction systems. For instance, it produces 90th percentile errors less than half those of iPlane and Pyxida. Our design is general enough to make it a good fit for other latency-sensitive peer-to-peer applications besides game matchmaking.
Microwave imaging via space-time beamforming for early detection of breast cancer

A backscatter-based microwave imaging technique that compensates for frequency-dependent propagation effects is proposed for detecting early-stage breast cancer. An array of antennas is located near the surface of the breast and an ultrawideband pulse is transmitted sequentially from each antenna. The received backscattered signals are passed through a space-time beamformer that is designed to image backscattered signal energy as a function of location. As a consequence of the significant dielectric-properties contrast between normal and malignant tissue, locations corresponding to malignant tumors are associated with large energy levels in the image. The effectiveness of these algorithms is demonstrated using simulated backscattered signals obtained from an anatomically realistic MRI-derived computational electromagnetic breast model. Very small (2 mm) malignant tumors embedded within the complex fibroglandular structure of the breast are easily detected above the background clutter.
The Impact of Knowledge Coordination on Virtual Team Performance Over Time

As the role of virtual teams in organizations becomes increasingly important, it is crucial that companies identify and leverage team members’ knowledge. Yet, little is known of how virtual team members come to recognize one another’s knowledge, trust one another’s expertise, and coordinate their knowledge effectively. In this study, we develop a model of how three behavioral dimensions associated with transactive memory systems (TMS) in virtual teams—expertise location, Ritu Agarwal was the accepting senior editor for this paper. Alberto Espinosa and Susan Gasson served as reviewers. The associate editor and a third reviewer chose to remain anonymous. Authors are listed alphabetically. Each contributed equally to the paper. task–knowledge coordination, and cognition-based trust—and their impacts on team performance change over time. Drawing on the data from a study that involves 38 virtual teams of MBA students performing a complex web-based business simulation game over an 8-week period, we found that in the early stage of the project, the frequency and volume of task-oriented communications among team members played an important role in forming expertise location and cognition-based trust. Once TMS were established, however, task-oriented communication became less important. Instead, toward the end of the project, task–knowledge coordination emerges as a key construct that influences team performance, mediating the impact of all other constructs. Our study demonstrates that TMS can be formed even in virtual team environments where interactions take place solely through electronic media, although they take a relatively long time to develop. Furthermore, our findings show that, once developed, TMS become essential to performing tasks effectively in virtual teams.
Diversity driven Attention Model for Query-based Abstractive Summarization

Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encodeattend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.ive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encodeattend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.
Fetal intracranial translucency and cisterna magna at 11 to 14 weeks : reference ranges and correlation with chromosomal abnormalities.

To measure the intracranial translucency (IT) and the cisterna magna (CM), to produce reference ranges and to examine the interobserver and intraobserver variability of those measurements. To examine the possible association of IT with chromosomal abnormalities.



Prospective study on pregnancies assessed at 11 to 14 weeks. IT was measured retrospectively in 17 cases with aneuploidy.



To produce reference ranges, 465 fetuses were used. IT and CM correlated linearly with crown-rump-length (CRL) and were independent of maternal demographic characteristics and biochemical indices. IT had a weak positive correlation with nuchal translucency. For IT the intraclass correlation coefficient was 0.88 for intraobserver variability and 0.83 for interobserver variability. For CM the intraclass correlation coefficient was 0.95 for intraobserver variability and 0.84 for interobserver variability. The IT multiple of the median was significantly increased in the chromosomally abnormal fetuses (1.02 for the normal and 1.28 for the chromosomally abnormal fetuses, Mann Whitney p < 0.001). IT multiple of the median was a significant predictor of chromosomal abnormality (Receiver Operator Characteristic curve analysis: Area under the curve = 0.86, CI=0.76-0.96, p<0.001).



Intracranial translucency and CM can be measured reliably at the 11 to 14 weeks examination and the measurements are highly reproducible. IT appears to be increased in fetuses with chromosomal abnormalities.
SoK : Consensus in the Age of Blockchains

The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work (PoW), (ii) proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.
A Soft Robotic Gripper With Gecko-Inspired Adhesive

Previous work has demonstrated the versatility of soft robotic grippers using simple control inputs. However, these grippers still face challenges in grasping large objects and in achieving high-strength grasps. This work investigates the combination of fluidic elastomer actuators and gecko-inspired adhesives to both enhance existing soft gripper properties and generate new capabilities. On rocky or dirty surfaces where adhesion is limited, the gripper retains the functionality of a pneumatically actuated elastomer gripper with no measured loss in performance. Design strategies for using the unique properties of the gecko-inspired adhesives are presented. By modeling fluidic elastomer actuators as a series of joints with associated joint torques, we designed an actuator that takes advantage of the unique properties of the gecko-inspired adhesive. Experiments showed higher strength grasps at lower pressures compared to nongecko actuators, in many cases enabling the gripper to actuate more quickly and use less energy. The gripper weighs 48.7 g, uses $7.25 of raw materials, and can support loads of over 50 N. A second gripper, using three fingers for a larger adhesive surface, demonstrated a grasping force of 111 N (25 lbf) when actuated at an internal pressure of 40 kPa.
A new NMOS four-quadrant analog multiplier

In this paper, an all NMOS voltage-mode four-quadrant analog multiplier, based on a basic NMOS differential amplifier that can produce the output signal in voltage form without using resistors, is presented. The proposed circuit has been simulated with SPICE and achieved -3 dB bandwidth of 120 MHz. The power consumption is about 3.6 mW from a /spl plusmn/2.5 V power supply voltage, and the total harmonic distortion is 0.85% with a 1 V input signal.
Investigating the Relationship between Internet Privacy Concerns and Online Purchase Behavior

Many organizations now emphasize the use of technology that can help them get closer to consumers and build ongoing relationships with them. The ability to compile consumer data profiles has been made even easier with Internet technology. However, it is often assumed that consumers like to believe they can trust a company with their personal details. Lack of trust may cause consumers to have privacy concerns. Addressing such privacy concerns may therefore be crucial to creating stable and ultimately profitable customer relationships. Three specific privacy concerns that have been frequently identified as being of importance to consumers include unauthorized secondary use of data, invasion of privacy, and errors. Results of a survey study indicate that both errors and invasion of privacy have a significant inverse relationship with online purchase behavior. Unauthorized use of secondary data appears to have little impact. Managerial implications include the careful selection of communication channels for maximum impact, the maintenance of discrete “permission-based” contact with consumers, and accurate recording and handling of data.
Batteries and battery management systems for electric vehicles

The battery is a fundamental component of electric vehicles, which represent a step forward towards sustainable mobility. Lithium chemistry is now acknowledged as the technology of choice for energy storage in electric vehicles. However, several research points are still open. They include the best choice of the cell materials and the development of electronic circuits and algorithms for a more effective battery utilization. This paper initially reviews the most interesting modeling approaches for predicting the battery performance and discusses the demanding requirements and standards that apply to ICs and systems for battery management. Then, a general and flexible architecture for battery management implementation and the main techniques for state-of-charge estimation and charge balancing are reported. Finally, we describe the design and implementation of an innovative BMS, which incorporates an almost fully-integrated active charge equalizer.
Few-Shot Object Recognition from Machine-Labeled Web Images

With the tremendous advances made by Convolutional Neural Networks (ConvNets) on object recognition, we can now easily obtain adequately reliable machine-labeled annotations easily from predictions by off-the-shelf ConvNets. In this work, we present an abstraction memory based framework for few-shot learning, building upon machine-labeled image annotations. Our method takes large-scale machine-annotated dataset (e.g., OpenImages) as an external memory bank. In the external memory bank, the information is stored in the memory slots in the form of key-value, in which image feature is regarded as the key and the label embedding serves as the value. When queried by the few-shot examples, our model selects visually similar data from the external memory bank and writes the useful information obtained from related external data into another memory bank, i.e., abstraction memory. Long Short-Term Memory (LSTM) controllers and attention mechanisms are utilized to guarantee the data written to the abstraction memory correlates with the query example. The abstraction memory concentrates information from the external memory bank to make the few-shot recognition effective. In the experiments, we first confirm that our model can learn to conduct few-shot object recognition on clean human-labeled data from the ImageNet dataset. Then, we demonstrate that with our model, machine-labeled image annotations are very effective and abundant resources for performing object recognition on novel categories. Experimental results show that our proposed model with machine-labeled annotations achieves great results, with only a 1% difference in accuracy between the machine-labeled annotations and the human-labeled annotations.