Reviewerly/exHarmony huggingface dataset:
Deep hierarchical encoding model for sentence semantic matching

Sentence semantic matching (SSM) always plays a critical role in natural language processing. Measuring the intrinsic semantic similarity among sentences is very challenging and has not been substantially addressed. The latest SSM research usually relies on a shallow text representation and interaction between sentence pairs, which might not be enough to capture the complex semantic features and lead to limited performance. To capture more semantic context features and interactions, we propose a hierarchical encoding model (HEM) for sentence representation, further enhanced by a hierarchical matching mechanism for sentence interaction. Given two sentences, HEM generates intermediate and final representations in encoding layer, which are further handled by a novel hierarchical matching mechanism to capture more multi-view interactions in matching layer. The comprehensive experiments demonstrate that our model is capable to capture more sentence semantic features and interactions, which significantly outperforms the existing state-of-the-art neural models on the public real-world dataset.
Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions

As several home appliances, such as air conditioners, heaters, and refrigerators, were connecting to the Internet, they became targets of cyberattacks, which cause serious problems such as compromising safety and even harming users. We have proposed a method to detect such attacks based on user behavior. This method models user behavior as sequences of user events including operation of home IoT (Internet of Things) devices and other monitored activities. Considering users behave depending on the condition of the home such as time and temperature, our method learns event sequences for each condition. To mitigate the impact of events of other users in the home included in the monitored sequence, our method generates multiple event sequences by removing some events and learning the frequently observed sequences. For evaluation, we constructed an experimental network of home IoT devices and recorded time data for four users entering/leaving a room and operating devices. We obtained detection ratios exceeding 90% for anomalous operations with less than 10% of misdetections when our method observed event sequences related to the operation. In this article, we also discuss the effectiveness of our method by comparing with a method learning users' behavior by Hidden Markov Models.
Attentive Sequential Models of Latent Intent for Next Item Recommendation

Users exhibit different intents across e-commerce services (e.g. discovering items, purchasing gifts, etc.) which drives them to interact with a wide variety of items in multiple ways (e.g. click, add-to-cart, add-to-favorites, purchase). To give better recommendations, it is important to capture user intent, in addition to considering their historic interactions. However these intents are by definition latent, as we observe only a user’s interactions, and not their underlying intent. To discover such latent intents, and use them effectively for recommendation, in this paper we propose an Attentive Sequential model of Latent Intent (ASLI in short). Our model first learns item similarities from users’ interaction histories via a self-attention layer, then uses a Temporal Convolutional Network layer to obtain a latent representation of the user’s intent from her actions on a particular category. We use this representation to guide an attentive model to predict the next item. Results from our experiments show that our model can capture the dynamics of user behavior and preferences, leading to state-of-the-art performance across datasets from two major e-commerce platforms, namely Etsy and Alibaba.
Weighted Feature Pyramid Networks for Object Detection

Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and semantic information in the object detection. Thus, FPN and its variants cannot detect some objects on challenging images. In this paper, based on FPN, we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection. The proposed method, is called a Weighted Feature Pyramid Network (WFPN), and shows significant improvement over the traditional feature pyramids in several applications. Using WFPN in Faster R-CNN system, the proposed method achieves better performance on the PASCAL detection benchmark.
Automatic segmentation model combining U-Net and level set method for medical images

We introduce a new method that combines a constrained term and level set method for the automated segmentation of medical image. There are two types of constrained terms, fully automatic and semi-automatic. It is fully automatic to use the U-Net’s segmentation result as a constrained term, and the manual segmentation result as a constrained term is semi-automatic. The level set method does not require a large training set and is theoretically very explanatory, but is usually sensitive to the initial contour. The U-Net can segment more complex medical images, but requires a large number of manually labeled images and usually needs to be normalized to produce a good generalization. Therefore, the combination of these methods combines the advantages of both methods, resulting in a method that requires a small training set and produces accurate segmentation results. We test our method on the melanoma and left ventricle images. Among them, when segmenting melanoma images, our semi-automatic segmentation and full-automatic segmentation results are better than the U-Net and RSF segmentation results alone. When segmenting the left ventricle images, our semi-automatic segmentation result is better than the RSF segmentation result.
Design Patterns for Gas Optimization in Ethereum

Blockchain technology is an emerging technology that allows new forms of decentralized architectures, designed to generate trust among users, without the intervention of mediators or knowledge between the parties. Since 2015, thanks to the introduction of Smart Contracts by Ethereum, it is possible to run programs on the blockchain, greatly extending the potential of this technology. The programming of Smart Contract, through the Solidity language is different from the traditional one. First of all, any action that requires to modify the blockchain costs gas, which corresponds to a fraction of the currency used by that given blockchain, and therefore to real money. Gas optimization is a unique challenge in this context and has obvious implications. This document aims to provide a set of design patterns and tips to help gas saving in developing Smart Contracts on Ethereum. The provided patterns are presented divided into five main categories, based on their features.
Deep Learning in Breast Cancer Detection and Classification

Breast cancer is considered one of the primary causes of mortality among women aged 20–59 worldwide. Early detection and treatment can allow patients to have proper treatment and consequently reduce rate of morbidity of breast cancer. Research indicates that most experienced physicians can diagnose cancer with 79% accuracy while 91% correct diagnosis is achieved using machine learning techniques. In this paper, we present the most recent breast cancer detection and classification models that are machine learning based models by analyzing them in the form of comparative study. Also, in this paper, the datasets that are public for use and popular as well are listed in the recent work to facilitate any new experiments and comparisons. The comparative analysis shows that the recent highest accuracy models based on simple detection and the classification architectures are You Only Look Once (YOLO) and RetinaNet.
Efficient Low-Cost Ship Detection for SAR Imagery Based on Simplified U-Net

Due to the rapid development of chip technology and deep learning revolution, many ship detection frameworks for synthetic aperture radar (SAR) imagery based on convolutional neural networks (CNNs) have been proposed and achieved great success.However, there are problems hampering their development: 1) For the SAR ship detection task, it is uneconomic to apply heavy backbone network to extract features because it results in heavy computing load and prolongs the inference time cost; 2) The anchor-based methods usually have massive hyper-parameters, which typically need to be tuned carefully and easily lead to weak detection performance.To alleviate the problems, an efficient low-cost ship detection network for SAR imagery is proposed in this paper.Firstly, a simplified U-Net as the backbone to extract features is proposed.It only contains ∼ 0.47 million learnable weights, which is 2.37%, 0.76%, 0.34%, 1.01%, 0.55% and 1.07% of DarkNet-19, DarkNet-53, VGG-16, ResNet-50, ResNet-101 and ResNext-101, respectively.Secondly, an anchor-free SAR ship detection framework consisting of a bounding boxes regression sub-net and a score map regression sub-net based on simplified U-Net is proposed.To evaluate the effectiveness of our method, extensive experiments have been conducted and a more comprehensive set of evaluation metrics have been applied.Results demonstrate that the proposed network achieves 68.1% average precision and 67.6% average recall on the SAR ship detection dataset (SSDD), respectively.Compared with the state-of-the-art works, our proposed network achieves very competitive detection performance and extreme lightweight (∼ 0.93 million learnable weights in total).
Efficient, Coercion-free and Universally Verifiable Blockchain-based Voting

Most electronic voting systems today satisfy the basic requirements of privacy, unreusability, eligibility and fairness in a natural and rather straightforward way. However, receipt-freeness, incoercibility and universal verifiability are much harder to implement and in many cases they require a large amount of computation and communication overhead. In this work, we propose a blockchain-based voting system which achieves all the properties expected from secure elections without requiring too much from the voter. Coercion resistance and receipt-freeness are ensured by means of a randomizer token – a tamper-resistance source of randomness which acts as a black box in constructing the ballot for the user. Universal verifiability is ensured by the append-only structure of the blockchain, thus minimizing the trust placed in election authorities. Additionally, tallying the votes has linear overhead, hence our system is scalable and practical for large scale elections.
Measurement of Freeform Optical Surfaces: Trade‐Off between Accuracy and Dynamic Range

Abstract Freeform optical surfaces are advantageous to optical designers, as they provide additional degrees of freedom for optimization. The loss of rotational symmetry, however, makes measurement of freeforms more challenging. Herein, advances in interferometric areal measurement of freeform optical surfaces, including null tests, non‐null tests, near‐null tests, and adaptive null tests, are reviewed. Some new developments, in the Hartmann test, deflectometry, and phase retrieval, as representatives of non‐interferometric areal measurement, are then presented. Overall, the focus is on single‐point‐probe‐based profilometry categorized into coordinate measurements and slope‐ or curvature‐based measurements. Innovative measurement technology is trying to bridge the gap between high accuracy and high dynamic range as freeform optical surfaces are finding more applications in visible and shorter‐wavelength optics.
7.4 GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation

Generative adversarial networks (GAN) have a wide range of applications, from image style transfer to synthetic voice generation [1]. GAN applications on mobile devices, such as face-to-Emoji conversion and super-resolution imaging, enable more engaging user interaction. As shown in Fig. 7.4.1, a GAN consists of 2 competing deep neural networks (DNN): a generator and a discriminator. The discriminator is trained, while the generator is fixed, to distinguish whether the generated image is real or fake. On the other hand, the generator is trained to generate fake images to fool the discriminator. The minimax rivalry between the 2 sub-DNNs enables the model to generate high-quality images, difficult even for humans to distinguish.
High-fidelity manipulation of a qubit enabled by a manufactured nucleus

The recently demonstrated trapping and laser cooling of $^{133}$Ba$^+$ has opened the door to the use of this nearly ideal atom for quantum information processing. However, before high fidelity qubit operations can be performed, a number of unknown state energies are needed. Here, we report measurements of the $^2$P$_{3/2}$ and $^2$D$_{5/2}$ hyperfine splittings, as well as the $^2$P$_{3/2}$$\leftrightarrow$ $^2$S$_{1/2}$and $^2$P$_{3/2}$ $\leftrightarrow$ $^2$D$_{5/2}$ transition frequencies. Using these transitions, we demonstrate high fidelity $^{133}$Ba$^+$ hyperfine qubit manipulation with electron shelving detection to benchmark qubit state preparation and measurement (SPAM). Using single-shot, threshold discrimination, we measure an average SPAM fidelity of $\mathcal{F} = 0.99971(6)$, a factor of $\approx$ 2 improvement over the best reported performance of any qubit.
Analysis of Histopathological Images for Prediction of Breast Cancer Using Traditional Classifiers with Pre-Trained CNN

Breast cancer is one of the most commonly found and dangerous cancer among women which leads to a major research topic in medical science. Most of the times, it is identified by using a biopsy method where tissue is removed and studied under a microscope. If a histopathologist is not well trained then this may lead to the wrong diagnosis. In order to facilitate better diagnosis, automatic analysis of histopathology images can help pathologists to identify malignant tumors and cancer subtypes. Recently, Convolutional Neural Networks (CNNs) have become preferred deep learning approaches for breast cancer classification and detection. In this research, two machine learning approaches i.e. Support Vector Machine (SVM) and Logistic Regression (LR) are used for comparative analysis. This paper mainly focuses on leveraging pre-trained (CNN) activation features on traditional classifiers to perform automatic classification of breast cancer images. For this purpose, a two-phase model has been proposed for automatic classification on the basis of magnification subsequently classify the samples for benign and malignant. This model is trained separately with respect to various image magnifications (40x, 100x, 200x and 400x). In this study, the dataset is partitioned into the following fashion: 80% for the training phase and 20% for testing phase The performance is analyzed by using Accuracy, Precision, Recall and F1-score in order to find out the best-suited model that can be used for automation system. The experimental results demonstrate that ResNet50 network has achieved maximum accuracy for LR in comparison to SVM in magnification factor. In addition, results show that the performance of CNN +LR is slightly better than CNN +SVM for classification of benign and malignant classes. The proposed model helps in extracting more accurate image features and significantly improves the overall accuracy of classification.
Virtual reality for teaching and learning in crime scene investigation

Simulated crime scene investigation is an essential component of forensic science education, but its implementation is costly and poses challenges to accessibility; offering personal investigations in higher education scenarios is often impossible. Virtual reality (VR) is an emerging technology which offers exciting prospects for teaching and learning, especially for imparting practical skills. We document here a multidisciplinary experimental study in which a bespoke VR crime scene app was designed and implemented, after which it was tested by both undergraduate student and staff/postgraduate student cohorts. Through both qualitative and quantitative analyses, we demonstrate that VR applications support learning of practical crime scene processing skills. VR-based practical sessions have the potential to add value to forensic science courses, through offering cost-effective practical experience, the ability to work in isolation and in a variety of different scenarios. Both user groups reported high levels of satisfaction with using the app and reports of adverse effects (motion sickness) were minimal. With reference to user feedback, we proceed to evaluate the scalability and development challenges associated with large-scale implementation of VR as an adjunct to forensic science education.
A Novel Method for Generation of Strong Substitution-Boxes Based on Coset Graphs and Symmetric Groups

The success of AES encryption standard created challenges for the cryptographers to construct strong substitution-boxes using different underlying approaches. It is because they are solely responsible to decide the robustness of cryptosystem against linear and differential cryptanalyses. With an aim to fulfill the mentioned requirement of robustness, a novel group theoretic and graphical method is proposed to construct S-box with optimal features. Firstly, a strong S-box is generated with the help of orbits of coset graphs and the action of proposed powerful permutation of symmetric group S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">256</sub> . In addition, a specific group is designed the action of whose pairs of permutations has the ability to generate as many as 462422016 strong S-boxes. Few of such proposed S-boxes are reported and assessed against standard performance parameters to validate the effectiveness of proposed findings. The features of proposed S-boxes are compared with most of the recent S-boxes to validate the superior performance. Moreover, they are also applied for image encryption to demonstrate their suitability for multimedia security applications.
Learning-based denoising for polarimetric images

Based on measuring the polarimetric parameters which contain specific physical information, polarimetric imaging has been widely applied to various fields. However, in practice, the noise during image acquisition could lead to the output of noisy polarimetric images. In this paper, we propose, for the first time to our knowledge, a learning-based method for polarimetric image denoising. This method is based on the residual dense network and can significantly suppress the noise in polarimetric images. The experimental results show that the proposed method has an evident performance on the noise suppression and outperforms other existing methods. Especially for the images of the degree of polarization and the angle of polarization, which are quite sensitive to the noise, the proposed learning-based method can well reconstruct the details flooded in strong noise.
Privacy Protection Based on Stream Cipher for Spatiotemporal Data in IoT

In the participatory sensing framework, privacy protection of the Internet of Things (IoT) is very important. In this article, cryptography-based methods are utilized to protect participants' privacy information in unsecured network channels for dynamic and real-time sensing tasks. The edge computing paradigm is introduced in the traditional participatory sensing framework to reduce network latency. Then, the Rivest Cipher 4 stream cipher and logistic mapping are combined to deal with the problems of participants' limited resources and untruthful third-party platforms. Finally, the product algebra and logistic mapping are combined to deal with the problems of large numbers of participants' access and poor randomness of keystream. Through extensive performance evaluation and comparison experiments on the real-world data, the effectiveness and adaptation of the proposed privacy protection based on stream cipher are verified. It could effectively solve the problem of poor network latency and improve the privacy protection level of IoT.
Quantum reservoir computing with a single nonlinear oscillator

Realizing the promise of quantum information processing remains a daunting task, given the omnipresence of noise and error. Adapting noise-resilient classical computing modalities to quantum mechanics may be a viable path towards near-term applications in the noisy intermediate-scale quantum era. Here, we propose continuous variable quantum reservoir computing in a single nonlinear oscillator. Through numerical simulation of our model we demonstrate quantum-classical performance improvement, and identify its likely source: the nonlinearity of quantum measurement. Beyond quantum reservoir computing, this result may impact the interpretation of results across quantum machine learning. We study how the performance of our quantum reservoir depends on Hilbert space dimension, how it is impacted by injected noise, and briefly comment on its experimental implementation. Our results show that quantum reservoir computing in a single nonlinear oscillator is an attractive modality for quantum computing on near-term hardware.
Intelligent detection of hate speech in Arabic social network: A machine learning approach

Nowadays, cyber hate speech is increasingly growing, which forms a serious problem worldwide by threatening the cohesion of civil societies. Hate speech relates to using expressions or phrases that are violent, offensive or insulting for a person or a minority of people. In particular, in the Arab region, the number of Arab social media users is growing rapidly, which is accompanied with high increasing rate of cyber hate speech. This drew our attention to aspire healthy online environments that are free of hatred and discrimination. Therefore, this article aims to detect cyber hate speech based on Arabic context over Twitter platform, by applying Natural Language Processing (NLP) techniques, and machine learning methods. The article considers a set of tweets related to racism, journalism, sports orientation, terrorism and Islam. Several types of features and emotions are extracted and arranged in 15 different combinations of data. The processed dataset is experimented using Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF), in which RF with the feature set of Term Frequency-Inverse Document Frequency (TF-IDF) and profile-related features achieves the best results. Furthermore, a feature importance analysis is conducted based on RF classifier in order to quantify the predictive ability of features in regard to the hate class.