Reviewerly/exHarmony huggingface dataset:
EM SCA White-Box Analysis-Based Reduced Leakage Cell Design and Presilicon Evaluation

This work presents a white-box modeling of the electromagnetic (EM) leakage from an integrated circuit (IC) to develop EM side-channel analysis (SCA)-aware design techniques. A new digital library cell layout design technique is proposed to minimize the EM leakage and is evaluated using a high-frequency structure simulator (HFSS)-based framework. Backed by our physics-based understanding of EM radiation, the proposed double-row power grid-based digital cell layout design shows <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;5\times $ </tex-math></inline-formula> reduction in the EM SCA leakage compared to the traditional digital logic gate layout design. Furthermore, exploiting the magneto-quasistatic (MQS) regime of operation of the EM leakage from the CMOS circuits, the HFSS-based framework is utilized to develop a pre-silicon (Si) EM SCA evaluation technique to assess the vulnerability of cryptographic implementations against such attacks during the design phase itself.
A Modeling Method for Face Image Deblurring

This paper aims to learn a face representation that can accurately reconstruct a clear face from a blurred face image. To this end, we propose an explicit model (ID-Face) to represent the human face as a concise expression, which is the sum of a series of analytic function units. And we also design a network, which we named it ID-Net, to train the model. ID-Net consists of two sub-networks with encoder-decoder structure: the encoder of one of the sub-networks learns a set of implicit variables, and the corresponding decoder decodes the implicit variables into the representation’s parameters, which are used to reconstruct the main structure and texture features of the face image. The other sub-network is a general autoencoder to restore the facial details and high frequency parts of the blurred image. Experiments on two kinds of blurred face images show that our method can effectively restore clear and lifelike human face.
Arbitrary-Sized JPEG Steganalysis Based on Fully Convolutional Network

Steganography detectors based on convolutional neural networks have achieved significant performance in recently years. Most existing networks, however, only focus on the detection performance of fixed-size images, and the sizes of training and testing images are usually 256 $$\times $$ 256 and 512 $$\times $$ 512. It can not meet the practical requirement of detecting arbitrary-sized steganographic images. Furthermore, there are few efficient methods for arbitrary-sized JPEG steganalysis. In this paper, we proposed a novel end-to-end steganalyzer based on fully convolutional network to address this issue. The characteristic of only containing convolutional layers allows the network to train and test arbitrary-sized images. In addition, the U-shaped network design can make element-wise classification, which provides state-of-the-art detection accuracy for both fixed and arbitrary size. The experimental results on standard image sources BOSSBase 1.01 and ALASKA #2 show that the proposed network can achieves superior performance.
Human Forest vs. Random Forest in Time-Sensitive COVID-19 Clinical Trial Prediction

How do we generate accurate predictions about the timely advancement of clinical trials? We report the methods and results of the first forecasting tournament comparing machine modeling and human crowdsourcing prediction methods in the clinical trial domain. Our focus is on predicting the probability of trial phase transition for COVID-19 vaccines and treatments by specific dates in late 2020 and early 2021. The machine approach is represented by a time-specific random survival forest (RSF) model. The crowdsourcing approach focuses on the Human Forest process and software, which enables forecasters to define custom reference classes, query a historical database and review resulting base rates. These base rates, and adjusted probabilistic estimates, are then aggregated across forecasters. We compared across 28 forecasting questions on COVID-19 vaccine and treatment clinical trials. The human crowdsourcing approaches, including Human Forest, significantly outperformed the random survival forest (RSF) model, yielding 32%-48% reductions in Brier scores. The Human Forest advantage was largely traceable to better calibration. The differences in accuracy among crowdsourcing methods were smaller. Including Human Forest-derived base rate estimates at the aggregation stage improved overall performance among elite forecasters, and was especially beneficial when few forecasters provided estimates. A follow-up tournament will explore hybrid prediction approaches, including the use of reference class selections by Human Forest forecasters to inform machine model specification.
WikiFlash: Generating Flashcards from Wikipedia Articles

Flashcards, or any sort of question-answer pairs, are a fundamental tool in education. However, the creation of question-answer pairs is a tedious job which often defers independent learners from properly studying a topic. We seek to provide a tool to automatically generate flashcards from Wikipedia articles to make independent education more attractive to a broader audience. We investigate different state-of-the-art natural language processing models and propose a pipeline to generate flashcards with different levels of detail from any given article. We evaluate the proposed pipeline based on its computing time and the number of generated and filtered questions, given the proposed filtering method. In a user study, we find that the generated flashcards are evaluated as helpful. Further, users evaluated the quality of human created flashcards that are available open source as comparable to or only slightly better than the automatically generated cards (Our application is available at: flashcard.ethz.ch).
Dual-Stream Guided-Learning via <i>a Priori</i> Optimization for Person Re-identification

The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.
HinPLMs: Pre-trained Language Models for Hindi

It has been shown that the use of pre-trained models (PTMs) can significantly improve the performance of natural language processing (NLP) tasks for language with rich resources, and also reduce the amount of labeled sample data required in supervised learning. However, there are still few research and shared task datasets available for Hindi, and PTMs for the Romanized Hindi script has been rarely released. In this work, we construct a Hindi pre-training corpus in Devanagari and Romanized scripts, and train Hindi pre-trained models with two versions: Hindi-Devanagari-Roberta and Hindi-Romanized-Roberta. We evaluate our model on 5 types of downstream NLP tasks with 8 datasets, and compare them with existing Hindi pre-training models and commonly used methods. Experimental results show that the model proposed in this work can achieve the best results on the all tasks, especially on Part-of-Speech Tagging and Named Entity Recognition tasks, which proves the validity and superiority of our Hindi pre-trained models. Specifically, the performance of Devanagari Hindi pre-trained model is better than the Romanized Hindi pre-trained model in the tasks of single-label Text Classification, Part-of-Speech Tagging, Named Entity Recognition, and Natural Language Inference. However, Romanized Hindi pre-trained model performs better in multi-label Text Classification and Machine Reading Comprehension, which may indicate that the pre-trained model of Romanized Hindi script has advantages in such tasks. We will publish our model to the community with the intention of promoting the future development of Hindi NLP.
Design and Implementation of a Blockchain-Enabled Secure Sensing Data Processing and Logging System

In this paper, we present the design and implementation of a blockchain-enabled secure sensing data processing and logging system. Although in our implementation we use the IOTA distributed ledger, the main mechanisms introduced in the system are independent from any particular blockchain platform for maximum flexibility. A blockchain platform is used as an immutable datastore to store critical data for secure sensing data processing and logging. This system corporates several innovative mechanisms. First, a sensor identification mechanism is put in place to ensure that only sensing data submitted by eligible sensors are accepted. Second, only aggregated sensing data are transmitted to the blockchain for safe-keeping. This strategy has two benefits: (1) it reduces the throughput requirement on the public blockchain; and (2) it saves on the transaction fees for asking the blockchain to store the data. Furthermore, a mechanism is introduced to allow two-level logging using a local datastore in conjunction with the blockchain to extend the immutability property offered by the blockchain to the locally stored raw sensing data. Third, a set of mechanisms are designed to facilitate the query of the blockchain for a particular subset of the aggregated data, and to ensure that given an aggregated data item, the corresponding raw data can be quickly located, which is essential for data retrieval needed when situations arise such as forensic analysis in cases of incidents and for auditing purposes. The system is implemented in the Python programming language and a preliminary testing on the functionality of the system has been conducted.
Yolov3-tiny Object Detection SoC Based on FPGA Platform

Object detection is a popular direction in computer vision and digital image processing and convolutional neural network have been widely used in this field. In the forward reasoning stage, many practical applications based on embedded platforms have stringent requirements for low latency and low power consumption. FPGA are undoubtedly the optimal solution to deal with such problems. Yolo [1] is one of the high-quality frameworks for object detection. Among them, Yolov3-tiny is a lightweight network that balances accuracy and network complexity. It is also the most popular object detection network in the industry. This article introduces the complete process of mapping the network structure to the FPGA based on the Yolov3tiny algorithm and optimizes the accelerator architecture for Zedboard to make it under limited resources to achieve the best performance. The experimental results show that the detection speed of 325.036ms/img and the performance of 24.32GOPS is obtained on Zedboard and the mAP of COCO is 32.6%. Compared with Xeon E5-2673 v4 CPU, the energy efficiency is 241times and the performance is 8 times; compared with single-core ARM-A9 CPU, its energy efficiency is 180.75 times and its performance is 402.12 times.
Updatable privacy-preserving $$\textit{K}$$-nearest neighbor query in location-based s-ervice

The K -nearest neighbor ( K -NN) query is an important query in location-based service (LBS), which can query the nearest k points to a given point, and provide some convenient services such as interest recommendations. Hence the privacy protection issue of K -NN query has been a popular research area, protecting the information of queries and the queried results, especially in the information era. However, most of existing schemes fail to consider the privacy protection of location points already stored on servers. Or some schemes support no update of location points. In this paper, we present an updatable and privacy-preserving K -NN query scheme to address the above two issues. Concretely, our scheme utilizes the K D-tree ( K -Dimensional tree) to store the location points of data owners in location service provider and encrypts the points with a distributed double-trapdoor public-key cryptosystem. Then, based on the Ciphertext Comparison Protocol and Ciphertext Euclidean Distance Calculation Protocol, our scheme can protect the privacy of location and query contents. Experimental analyses show our proposal supports some new location points for a fixed location service provider. Moreover, the queried results show a high accuracy of more than 95%.
Octopus-DF: Unified DataFrame-based cross-platform data analytic system

Nowadays, DataFrame serves as a core to model and implement numerous machine learning and data analytic algorithms. Traditional data analytic programming languages, such as Python, provide the DataFrame programming model natively. In the big data era, it is a natural demand to introduce the DataFrame model into distributed computing systems for convenient big data analysis. Therefore, various DataFrame libraries have been implemented on Spark and Dask. However, these distributed computing systems contain some parallelism semantics which are not very straightforward for data analysts. Also, a DataFrame-based algorithm may have quite different performance for various datasets over different platforms. And, it is difficult for data analysts to choose the optimal platforms that achieve the best performance for their programs. To address these problems, we build a unified DataFrame-based data analytic system Octopus-DF. Octopus-DF integrates Pandas, Dask, and Spark as the backend computing platforms and exposes the most widely used Pandas-style APIs to users. Then, as DataFrame computation performance plays a critical role in the computing efficiency of DataFrame-based data analytic algorithms, we designed a set of DataFrame computation optimizations which are divided into two parts: (1) multiple indexing and DAG optimizations, and (2) cross-platform scheduling strategy. Experimental results show that Octopus-DF outperformed the existing single platforms with 11.72× speedup on average. Compared with the existing platform combination strategies, Octopus-DF can achieve the optimal one. Moreover, the proposed optimizations can effectively speedup the execution workflow.
Atmospheric Convection Model Based Digital Confidentiality Scheme

Nonlinear dynamics is a fascinating area that is intensely affecting a wide range of different disciplines of science and technology globally. The combination of different innovative topics of information security and high-speed computing has added new visions into the behavior of complex nonlinear dynamical systems which uncovered amazing results even in the least difficult nonlinear models. The generation of complex actions from a very simple dynamical method has a strong relation with information security. The protection of digital content is one of the inescapable concerns of the digitally advanced world. Today, information plays an important role in everyday life and affects the surroundings rapidly. These digital contents consist of text, images, audio, and videos, respectively. Due to the vast usage of digital images in the number of social and web applications, its security is one of the biggest issues. In this work, we have offered an innovative image encryption technique based on present criteria of confusion and diffusion. The designed scheme comprises two major nonlinear dynamical systems. We have employed discrete fractional chaotic iterative maps to add confusion capability in our suggested algorithm and continuous chaotic Lorenz system. We have verified our offered scheme by using statistical analysis. The investigations under the statistical tests suggested that our proposed technique is quite reasonable for the security of digital data.
FSeCBIR: A Faster Secure Content-Based Image Retrieval for Cloud

In recent years, content-based image retrieval in encrypted domain has emerged as an important challenge in cloud environment. We present FSeCBIR, a prototype that implements Faster and Secure Content-Based Image Retrieval ensuring secure search and retrieval in large encrypted image databases. This prototype is implemented using MATLAB on a server/high-end PC and it incorporates a copy deterrence module to identify data leaks through untrustworthy query users. This simulates a system that can be used in real-time cloud applications involving sensitive information which demand high level of security, retrieval precision, and less time/space complexity.
A survey of the extraction and applications of causal relations

Abstract Causationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai et al . (2019). Transformer-xl: Attentive language models beyond a fixed-length context . arXiv preprint arXiv:1901.02860 ) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.
Personality-aware recommendations: an empirical study in education

Recommender systems have been developed to deliver item recommendations to the users tailored to user preferences. The impact of the human personality has been realised in user decision makings. There are several personality-aware recommendation models which incorporate the personality traits into the recommendations. They have been demonstrated to be effective in improving the quality of the recommendations in several domains, including movies, music and social networks. However, the impact on the area of education is still under investigation. In this paper, we discuss and summarise state-of-the-art personality-based collaborative filtering techniques for recommendations and perform an empirical study on an educational data. Particularly, we collect the personality traits in two ways - user survey and a natural language processing system. We examine the effectiveness of the recommendation models by using these subjective and inferred personality traits, respectively. Our experimental results reveal that students with different personality traits may make different choices, and the inferred personality traits are more reliable and effective to be used in the process of recommendations.
Few-shot Egocentric Multimodal Activity Recognition

Activity recognition based on egocentric multimodal data collected by wearable devices has become increasingly popular recently. However, conventional activity recognition methods face the dilemma of the lack of large-scale labeled egocentric multimodal datasets due to the high cost of data collection. In this paper, we propose a new task of few-shot egocentric multimodal activity recognition, which has at least two significant challenges. On the one hand, it is difficult to extract effective features from the multimodal data sequences of video and sensor signals due to the scarcity of the samples. On the other hand, how to robustly recognize novel activity classes with very few labeled samples becomes another more critical challenge due to the complexity of the multimodal data. To resolve the challenges, we propose a two-stream graph network, which consists of a heterogeneous graph-based multimodal association module and a knowledge-aware activity classifier module. The former uses a heterogeneous graph network to comprehensively capture the dynamic and complementary information contained in the multimodal data stream. The latter learns robust activity classifiers through knowledge propagation among the classifier parameters of different classes. In addition, we adopt episodic training strategy to improve the generalization ability of the proposed few-shot activity recognition model. Experiments on two public datasets show that the proposed model achieves better performances than other baseline models.
Query-Adaptive Feature Fusion Base on Convolutional Neural Networks for Remote Sensing Image Retrieval

Content-based Remote sensing image retrieval (CBRSIR) becomes important research with the volume of remote sensing images rapidly expanding. Many image features have been proposed for CBRSIR, hence it has become a big challenge to effectively fuse these features for alleviating the huge variation in retrieval performance among different image queries when a single image feature is used. We proposed a query-adaptive feature fusion method based on a convolutional neural networks (CNN) regression model. We use the CNN regression model to estimate the DCG value for each feature and assign different features with different weights for each query according to these DCG values. Meanwhile, we use the image-to-query-class distance to further improve retrieval performance. Experiments on UCMD show that the proposed method can improve the CBRSIR performance.