Reviewerly/exHarmony huggingface dataset:
A fast approach for bitcoin blockchain cryptocurrency mining system

The growth of the blockchain-based cryptocurrencies has attracted a lot of attention from a variety of fields, especially in academic research. One of them is Bitcoin, the most popular and highest valued cryptocurrency on the market. The SHA256 is the main processing part in Bitcoin mining, to date the difficulty of which is extremely high and still increases relentlessly. Hence, it is essential to improve the speed of the SHA256 cores in the Bitcoin mining system. In this paper, we propose a two-level pipeline hardware architecture for the SHA256 processing. The first-level pipeline helps the system reduce the number of operating cycles. Besides, the maximum frequency of the system is boosted by the second-level pipeline. The proposed hardware is implemented on FPGA Xilinx Virtex 7-VC707 (28 nm technology). The mining hash rate using the proposed pipeline SHA256 cores reaches 514.92 MH/s that improves 2.4 times compared to the FPGA based conventional technique. The throughput of SHA core of current study is 296.108 Gbps that is 240 times higher compared to the standard technique. The proposed architecture is also implemented in an ASIC design using ROHM 180 nm CMOS technology, which resulted in a throughput of 69.28 Gbps that is 18 times higher than that of conventional work implemented in Intel 14 nm process.
A Virtual Reality Simulation of Drug Users’ Everyday Life: The Effect of Supported Sensorimotor Contingencies on Empathy

Perspective taking techniques have been used to transport people into imaginary situations and the lives of others. Virtual Reality provides an immersive way to virtually experience the lives of stigmatized by society members. Through the support of sensorimotor contingencies, people can use natural movements to view and interact with the virtual world around them. In this study, we compared a perspective-taking immersive Virtual Reality system which supports a number of sensorimotor contingencies (SC group) with a perspective-taking desktop system of the same application but without support of any sensorimotor contingencies (NSC group), to investigate the effect of the supported sensorimotor contingencies in promoting empathy and positive attitudes toward drug users. Results demonstrate a strong correlation between closeness to the drug user and empathy in the SC group. In both groups there were a within group significant change in their reported attitudes before and after their exposure. Finally, participants in the SC condition reported significantly higher levels of Place Illusion (PI), body ownership, agency and plausibility of people. Further research is needed to investigate how sensorimotor contingencies can be exploited to the fullest to be used as an effective method to induce empathy and change attitudes toward stigmatized by society people.
Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study

In this paper, we study the challenging problem of automatic generation of citation texts in scholarly papers.Given the context of a citing paper A and a cited paper B, the task aims to generate a short text to describe B in the given context of A. One big challenge for addressing this task is the lack of training data.Usually, explicit citation texts are easy to extract, but it is not easy to extract implicit citation texts from scholarly papers.We thus first train an implicit citation text extraction model based on BERT and leverage the model to construct a large training dataset for the citation text generation task.Then we propose and train a multi-source pointer-generator network with cross attention mechanism for citation text generation.Empirical evaluation results on a manually labeled test dataset verify the efficacy of our model.This pilot study confirms the feasibility of automatically generating citation texts in scholarly papers and the technique has the great potential to help researchers prepare their scientific papers.
Non-Parametric Calibration for Classification

Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification frameworks, in particular deep neural networks, achieve high accuracy, they tend to incorrectly estimate uncertainty. In this paper, we propose a method that adjusts the confidence estimates of a general classifier such that they approach the probability of classifying correctly. In contrast to existing approaches, our calibration method employs a non-parametric representation using a latent Gaussian process, and is specifically designed for multi-class classification. It can be applied to any classifier that outputs confidence estimates and is not limited to neural networks. We also provide a theoretical analysis regarding the over- and underconfidence of a classifier and its relationship to calibration, as well as an empirical outlook for calibrated active learning. In experiments we show the universally strong performance of our methodacross different classifiers and benchmark data sets, in particular for state-of-the art neural network architectures.
Handwriting Based Gender Classification Using COLD and Hinge Features

Gender Classification from handwriting is still considered to be challenging due to homogeneous vision comparing male and female handwritten documents. This paper presents a new method based on Cloud of Line Distribution (COLD) and Hinge feature for distinguishing the gender from handwriting. The SVM classifier combination decides the assigned class based on the maximum of the two decisions values resulting from COLD and Hinge feature. The proposed approach is evaluated on the standard QUWI dataset and following the framework protocol described in the ICFHR 2016 competition. Obtained results are promising regarding the classification rates announced in the literature.
A Big Data-Enabled Hierarchical Framework for Traffic Classification

According to the critical requirements of the Internet, a wide range of privacy-preserving technologies are available, e.g. proxy sites, virtual private networks, and anonymity tools. Such mechanisms are challenged by traffic-classification endeavors which are crucial for network-management tasks and have recently become a milestone in their privacy-degree assessment, both from attacker and designer standpoints. Further, the new Internet era is characterized by the capillary distribution of smart devices leveraging high-capacity communication infrastructures: this results in huge amount of heterogeneous network traffic, i.e. big data. Hence, herein we present BDeH, a novel hierarchical framework for traffic classification of anonymity tools. BDeH is enabled by big data-paradigm and capitalizes the machine learning workhorse for operating with encrypted traffic. In detail, our proposal allows for seamless integration of data parallelism provided by big-data technologies with model parallelism enabled by hierarchical approaches. Results prove that the so-achieved double parallelism carries no negative impact on traffic-classification effectiveness at any granularity level and achieves non negligible performance enhancements with respect to non-hierarchical architectures (+4.5% F-measure). Also, it significantly gains over either pure data or pure model parallelism (resp. centralized) approaches by reducing both training completion time-up to 78% (resp. 90%)-and cloud-deployment cost-up to 31% (resp. 10%).
Elicitation of Nonfunctional Requirements in Agile Development Using Cloud Computing Environment

Nonfunctional requirements get less attention because functional requirements are considered more important in the domain of agile software methodologies. This is due to the lack of mature requirement elicitation methodologies and the nature of the software agile software development process. The less attention caused few solutions in the domain which lead to software project failure. Cloud computing helps to practice twelve (12) agile principles including nonfunctional requirement elicitation. This study proposed a semi-automated methodology which will help analyst and developers in eliciting nonfunctional requirements in agile development and cloud computing environment. The methodology used an NLP based automatic NFR extraction approach to fast the NFR elicitation process. The methodology is evaluated by applying on eProcurement dataset. The results are improved by 8.77% and 1.76% in terms of “Successful” NFR. It is decreased by 7.02% and 1.75% in term of “Partial success”, and 1.76% to 0.0% in term of “Failure” as compared to existing studies.
Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation

An increasing number of studies are leveraging unsupervised cross-modality synthesis to mitigate the limited label problem in training medical image segmentation models. They typically transfer ground truth annotations from a label-rich imaging modality to a label-lacking imaging modality, under an assumption that different modalities share the same anatomical structure information. However, since these methods commonly use voxel/pixel-wise cycle-consistency to regularize the mappings between modalities, high-level semantic information is not necessarily preserved. In this paper, we propose a novel anatomy-regularized representation learning approach for segmentation-oriented cross-modality image synthesis. It learns a common feature encoding across different modalities to form a shared latent space, where 1) the input and its synthesis present consistent anatomical structure information, and 2) the transformation between two images in one domain is preserved by their syntheses in another domain. We applied our method to the tasks of cross-modality skull segmentation and cardiac substructure segmentation. Experimental results demonstrate the superiority of our method in comparison with state-of-the-art cross-modality medical image segmentation methods.
High-speed dual-view band-limited illumination profilometry using temporally interlaced acquisition

We report dual-view band-limited illumination profilometry (BLIP) with temporally interlaced acquisition (TIA) for high-speed, three-dimensional (3D) imaging. Band-limited illumination based on a digital micromirror device enables sinusoidal fringe projection at up to 4.8 kHz. The fringe patterns are captured alternately by two high-speed cameras. A new algorithm, which robustly matches pixels in acquired images, recovers the object’s 3D shape. The resultant TIA–BLIP system enables 3D imaging over 1000 frames per second on a field of view (FOV) of up to 180 mm × 130 mm (corresponding to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mn>1180</mml:mn> <mml:mo>×</mml:mo> <mml:mn>860</mml:mn> <mml:mtext> </mml:mtext> <mml:mtext>pixels</mml:mtext> </mml:mrow> </mml:math> ) in captured images. We demonstrated TIA–BLIP’s performance by imaging various static and fast-moving 3D objects. TIA–BLIP was applied to imaging glass vibration induced by sound and glass breakage by a hammer. Compared to existing methods in multiview phase-shifting fringe projection profilometry, TIA–BLIP eliminates information redundancy in data acquisition, which improves the 3D imaging speed and the FOV. We envision TIA–BLIP to be broadly implemented in diverse scientific studies and industrial applications.
Effects of Multimedia Instructional Message on Motivation and Academic Performance of Elementary School Students in Croatia

The study examined motivation and learning outcomes of the use of multimedia instructional message during the introductory part of 18 lessons of school subject Nature and Society in 4th grade.Learning outcomes were measured by immediate knowledge tests and global exams.The multimedia model was developed containing activities used either through multimedia presentations, or in a traditional manner, such as using printed text and picture.Multimedia presentations, created according to the multimedia learning principles, represented multimedia instructional and motivational messages.They consisted of six groups of activities: association games, videos, interactive stories, interactive maps, visual stimuli, and crossword puzzles and quizzes.Field quasi-experiment was conducted with the experimental group exposed to instructional and motivational message on computer, and the control groups exposed to the message by traditional teaching aids.The participants were 170 pupils from four Croatian schools.The experimental group consisted of 97 and the control group of 73 students.Using multimedia instructional messages gave better learning outcomes, measured by immediate knowledge tests, while the effect was partially visible in the more global exams.The experimental group rated the activities more fun, interesting and instructional than the control group.The higher motivation was positively correlated to better learning outcomes.
Mapping topological characteristics of dynamical systems into neural networks: A reservoir computing approach

Significant advances have recently been made in modeling chaotic systems with the reservoir computing approach, especially for prediction. We find that although state prediction of the trained reservoir computer will gradually deviate from the actual trajectory of the original system, the associated geometric features remain invariant. Specifically, we show that the typical geometric metrics including the correlation dimension, the multiscale entropy, and the memory effect are nearly identical between the trained reservoir computer and its learned chaotic systems. We further demonstrate this fact on a broad range of chaotic systems ranging from discrete and continuous chaotic systems to hyperchaotic systems. Our findings suggest that the successfully reservoir computer may be topologically conjugate to an observed dynamical system.
Classification techniques’ performance evaluation for facial expression recognition

&lt;p&gt;&lt;span&gt;Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. &lt;/span&gt;&lt;/p&gt;
Towards Secure and Privacy-Preserving IoT Enabled Smart Home: Architecture and Experimental Study

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested.
A Precisely Xtreme-Multi Channel Hybrid Approach for Roman Urdu Sentiment Analysis

In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this article for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove.The integrity of generated neural word embeddings is evaluated using intrinsic and extrinsic evaluation approaches.Considering the lack of publicly available benchmark datasets, it provides a first-ever Roman Urdu public dataset which consists of 3241 sentiments annotated against positive, negative, and neutral classes.To provide benchmark baseline performance over the presented dataset for Roman Urdu sentiment analysis, we adapt diverse machine learning (Support Vector Machine, Logistic Regression, Naive Bayes), deep learning (convolutional neural network, recurrent neural network), and hybrid deep learning approaches.Performance impact of generated neural word embeddings based representation is compared with other most widely used bag of words based feature representation approaches using diverse machine and deep learning classifiers.In order to improve the performance of Roman Urdu sentiment analysis, it proposes a novel precisely extreme multi-channel hybrid methodology which makes use of convolutional and recurrent neural networks along with pre-trained neural word embeddings.The proposed hybrid approach outperforms adapted machine learning approaches by the significant figure of 9% and deep learning approaches by the figure of 4% in terms of F1-score.
Robust facial landmark detection by cross-order cross-semantic deep network

Recently, convolutional neural networks (CNNs)-based facial landmark detection methods have achieved great success. However, most of existing CNN-based facial landmark detection methods have not attempted to activate multiple correlated facial parts and learn different semantic features from them that they can not accurately model the relationships among the local details and can not fully explore more discriminative and fine semantic features, thus they suffer from partial occlusions and large pose variations. To address these problems, we propose a cross-order cross-semantic deep network (CCDN) to boost the semantic features learning for robust facial landmark detection. Specifically, a cross-order two-squeeze multi-excitation (CTM) module is proposed to introduce the cross-order channel correlations for more discriminative representations learning and multiple attention-specific part activation. Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection. It is interesting to show that by integrating the CTM module and COCS regularizer, the proposed CCDN can effectively activate and learn more fine and complementary cross-order cross-semantic features to improve the accuracy of facial landmark detection under extremely challenging scenarios. Experimental results on challenging benchmark datasets demonstrate the superiority of our CCDN over state-of-the-art facial landmark detection methods.
Grounding Conversations with Improvised Dialogues

Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people.Modern dialogue systems are not explicitly trained to build common ground, and therefore overlook this important aspect of communication.Improvisational theater (improv) intrinsically contains a high proportion of dialogue focused on building common ground, and makes use of the yes-and principle, a strong grounding speech act, to establish coherence and an actionable objective reality.We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier.We fine-tune chit-chat dialogue systems with our corpus to encourage more grounded, relevant conversation and confirm these findings with human evaluations.
Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT

Using a language model (LM) pretrained on two languages with large monolingual data in order to initialize an unsupervised neural machine translation (UNMT) system yields stateof-the-art results.When limited data is available for one language, however, this method leads to poor translations.We present an effective approach that reuses an LM that is pretrained only on a high-resource language.The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model.To reuse the pretrained LM, we have to modify its predefined vocabulary, to account for the new language.We therefore propose a novel vocabulary extension method.Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq), yielding more than +8.3 BLEU points for all four translation directions.
Experimental quantification of four-photon indistinguishability

Photon indistinguishability plays a fundamental role in information processing, with applications such as linear-optical quantum computation and metrology. It is then necessary to develop appropriate tools to quantify the amount of this resource in a multiparticle scenario. Here we report a four-photon experiment in a linear-optical interferometer designed to simultaneously estimate the degree of indistinguishability between three pairs of photons. The interferometer design dispenses with the need of heralding for parametric down-conversion sources, resulting in an efficient and reliable optical scheme. We then use a recently proposed theoretical framework to quantify genuine four-photon indistinguishability, as well as to obtain bounds on three unmeasured two-photon overlaps. Our findings are in high agreement with the theory, and represent a new resource-effective technique for the characterization of multiphoton interference.
Prior-knowledge and attention based meta-learning for few-shot learning

Recently, meta-learning has been shown to be a promising way to solve few-shot learning. In this paper, inspired by the human cognition process, which utilizes both prior-knowledge and visual attention when learning new knowledge, we present a novel paradigm of meta-learning approach that capitalizes on three developments to introduce attention mechanism and prior-knowledge to meta-learning. In our approach, prior-knowledge is responsible for helping the meta-learner express the input data in a high-level representation space, and the attention mechanism enables the meta-learner to focus on key data features in the representation space. Compared with the existing meta-learning approaches that pay little attention to prior-knowledge and visual attention, our approach alleviates the meta-learner’s few-shot cognition burden. Furthermore, we discover a Task-Over-Fitting (TOF) problem,1 which indicates that the meta-learner has poor generalization across different K-shot learning tasks. To model the TOF problem, we propose a novel Cross-Entropy across Tasks (CET) metric.2 Extensive experiments demonstrate that our techniques improve the meta-learner to state-of-the-art performance on several few-shot learning benchmarks while also substantially alleviating the TOF problem.