Reviewerly/exHarmony huggingface dataset:
Digital signature with cryptographic reverse firewalls

The Snowden's Prism incident warned people great threats from internal adversaries. Cryptographic Reverse Firewalls (CRFs), one mean to resolve the problem, are increasingly popular on account of its simplicity and convenience. The current related CRFs solutions are applied to encryption or key agreement. However, CRFs have extremely high requirements for re-randomization. Due to the irreversible hash function in the digital signature, it is difficult for signature to apply CRFs. Undoubtedly, as an important field in cryptography, digital signature cannot resist similar internal attacks, hence it is useful to find a way to build CRFs for signature. Therefore, we propose two signature schemes with CRFs. One is based on traditional signature and CRF is built for the signer, the other is based on identity-based signature (IBS) and CRFs are built for the signer and the public key generator (PKG). We also prove that these two schemes can resist exfiltration. One is against existence forgery under passive attacks. The other is against existence forgery under adaptive chosen message attack. Moreover, we use pypbc library to implement our schemes. Compared with signature schemes without CRFs, our schemes can not only use almost similar computational cost but also have advantages in resisting exfiltration.
A Selfish Attack on Chainweb Blockchain

It is well known that the Proof-of-Work (PoW) based blockchain scheme, first introduced in Bitcoin system, is not scalable, where the PoW implementation limits the transaction processing rate. In recent years, parallel chain techniques have been proposed to overcome this issue. Chainweb is one of the parallel chains to be studied in this paper with a focus on security related issues. It is worth noting that existing blockchain security studies mainly focus on traditional single-chain based protocols. There are not many security related studies on parallel blockchains. This paper for the first time reveals that selfish mining attack is possible on Chainweb blockchain, to the best of our knowledge. Specifically, we propose a selfish mining attack that exclusively mines blocks on a subset of parallel chains with the same block height and achieves gain through a proper withholding strategy. We develop a mathematical model to quantitatively evaluate the performance and demonstrate the effectiveness of the proposed attack. Our results show that the attacker can gain extra mining reward when his computational power is at least 38% of the total power in Chainweb network. Under the 50% computational power restriction, the attacker's extra gain increases monotonically with its computational power.
Robust General Twin Support Vector Machine with Pinball Loss Function

Twin support vector machines (TWSVM) with hinge loss suffer from noise sensitivity and instability. To overcome these issues, pinball loss based general twin support vector machines (Pin-GTSVM) was recently proposed. However, TWSVM and Pin-GTSVM implement the empirical risk minimization principle. Also, the matrices in their dual formulations are positive semi-definite. To overcome these issues, we propose pinball loss based robust general twin support vector machines (Pin-RGTSVM). Pin-RGTSVM implements the structural risk minimization principle which embodies the marrow of statistical learning and pinball loss function makes it more robust for noisy datasets. Also, the matrices appear in the dual formulation of the proposed Pin-RGTSVM are positive definite. The incorporation of the structural risk minimization principle via introduction of the regularisation term leads to the improved generalization performance of the proposed Pin-RGTSVM. Numerical experiments and statistical evaluation on the real world benchmark datasets show the efficacy of the proposed Pin-RGTSVM.
Automatic portrait image pixelization

Pixelization is one of the most distinctive image stylization methods, and it is often used in game production, icon design, and advertising printing. One of the most popular examples is portrait pixel art, which retains both the features and imagination of a character. However, the difficulty of pixelization in production and time consumption discourages many untrained people from working in pixel art. In this paper, we propose an automatic pixelization algorithm for portrait images based on Simple Linear Iterative Clustering (SLIC) and Fuzzy Iterative Self-Organizing Data Analysis (FISODATA) algorithms. Several improvements have been made for portrait images using superpixels reordering, cascade object detection, gaussian bilateral filtering, and feature edge enhancement. Our algorithm is particularly well suited for pixelating natural portrait images which have smooth color transition. The proposed algorithm not only satisfies the requirements of clear edges and limited colors of pixel images, but also automatically determines the width and height of the output image and the size of the palette through the image, avoiding intensive human involvement on the resulting image. We compared the results obtained by our proposed algorithm with those of manual pixel art and existing methods. Experimental results show that our algorithm can produce a unique style between realism and abstraction.
Two-Stage Biometric Identification Systems Without Privacy Leakage

We study two-stage biometric identification systems that allow authentication without privacy leakage. In the enrollment phase, secret keys and two layers of the helper data for each user are generated. Additional to the helper data and secret keys, we also introduce private keys in the systems. In the identification phase, an unknown but previously enrolled user is observed, and the user's private key is also presented to the system. The system firstly compares the user with the first layer helper database, outputs a list, and obtains a set of user indices. Then the system compares the observed user with the users in the set. Therefore, the identification procedure avoids an exhaustive search and only has to do a comparison with some part of the users in the system, which leads to a systematic reduction of the search complexity. Fundamental trade-offs among the identification rate, the secret key rate, the private key rate, the helper data rate, and the list size rate are derived in the imposed two-stage biometric identification systems without privacy leakage. Moreover, the obtained results show that a private key can boost the identification rate and the secret key rate, as well as preserve privacy.
An Enhanced Decentralized Virtual Machine Migration Approach for Energy-Aware Cloud Data Centers

Cloud computing is an increasingly important technology to deliver pay-as-you-go online computing services. In this study, the cloud service provider permits the cloud user to pay according to the user’s needs. Various methods have been used to reduce energy utilization in the cloud. The rapid increase of cloud users has led to increased energy consumption and higher operating costs for cloud providers. A key issue in cloud data centers is their massive energy consumption to operate and maintain computing services. Virtual machine (VM) migration is a method to reduce energy consumption. This study proposes enhanced decentralized virtual machine migration (EDVMM), based on a linear prediction model, to decrease energy utilization in cloud data centers, reduce service-level agreement violations with a minimum number of migrated VMs, and enhance resource utilization. The enhanced decentralized approach is used to select the appropriate VMs, and prediction is used to determine the VMs for a host. This EDVM algorithm uses virtualization technology and migrates VMs from overloaded and under-loaded hosts to physical machines (PMs). The work was implemented and evaluated using CloudSim with 10 days of real workload trace provided by PlanetLab.
Deep Learning-based approach for detection and classification of Micro/Mini drones

In the recent years, the micro/mini drones' industry has witnessed an explosive growth, making these flying objects become highly accessible to Terrorist groups. This phenomenon has caused specific security concerns due to the fact that these suspicious flying gadgets can cause serious hazards. To protect the sensitive locations and restricted areas, we suggest, in this paper, a drone detection method that integrates deeplearning-based classification and localization tasks. Specially, we selected a family of fast and accurate one-stage object detector: YOLOv3. So, we use and improve YOLOv3 deep learning neural network, by upgrading its architecture and fine-tuning its parameters to better accommodate small object detection such as micro/mini drone. Furthermore, to train our algorithm to classify the detected drone, we have constructed a multi-class drone dataset consisting of drones' images that may fly in Tunisian airspace and among which some may be a possible threat.
Time Series Based Trend Analysis for Hate Speech in Twitter During COVID 19 Pandemic

In Sri Lanka social media platforms were used as information propagation medium stupendously during COVID 19 outbreak period.The context of the post creates a high impact on the Social media engagement trends.This research primarily aims at how hate speech was propagated in twitter platform during the COVID 19 outbreak period.This paper describes the results of the study performed on how contents in the Sinhala language, and Sinhala language words written using English text were disseminated.Moreover, and the paper discusses the trend analysis techniques used in performing the trends analysis to identify the hate speech propagation trends over the period.It was observed that there was a rapid interaction with social media platforms during the first half of the posts identified, and in the latter part, the interactions were gradually decreased.Finally, this abstract paper discusses a trend line of hate speech posts identified on twitter data.
Functional Segmentation for Preoperative Liver Resection Based on Hepatic Vascular Networks

Successful liver resection relies on accurate estimation of future liver remnant volume (FLRV).According to Couinaud's scheme, a liver is composed of eight functionally independent segments, each of which has its own vascular in-and out-flow tracks.Segmenting a liver by this scheme is vital to postoperative regeneration and hence prognosis outcome.Conventionally, estimation of liver segments was often done by hand on 3D computed tomography.The process is generally tedious, time consuming, and prone to observer variability.Alternatively, computerized methods had been proposed but impeded by anatomically irrelevant approximation and manually specified markers.To resolve the issues, this paper presents a novel method for functional liver segmentation.Its main contribution was performing analyses of differential geometry directly on a liver surface and interior venous system.Except for a few points being placed on major vessels, anatomical references required for defining all separating surfaces were automatically identified.To demonstrate its merits, virtual liver resection was implemented on the standard MICCAI SLIVER07 dataset, and the resultant segments were benchmarked against four most related works.Visual and numerical assessments reported herein indicated that our method could faithfully label all Couinaud's segments, especially the caudate, with lesser degree of user interaction.The preliminary findings suggested that it can be integrated into augmented surgical planning and intervention.
An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks

The problem of estimating event truths from conflicting agent opinions in a social network is investigated. An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations. A Bayesian network model is proposed to guide the learning process by modeling the relationship of the autoencoder's outputs with different variables. At the same time, it also models the social relationships between agents in the network. The proposed approach is unsupervised and is applicable when ground truth labels of events are unavailable. A variational inference method is used to jointly estimate the hidden variables in the Bayesian network and the parameters in the autoencoder. Experiments on three real datasets demonstrate that our proposed approach is competitive with, and in most cases better than, several state-of-the-art benchmark methods.
A Deep Learning Facial Emotion Classification system: a VGGNet-19 based approach

after studying the pretrained VGGNet 19 model, we figured out that this model contains a large number of parameters that tend likely towards overfitting, which blocks the face expression recognition performance. This indicates that there is always some room for improvement. In this manuscript, we propose a new approach based on the VGGNet-19 network, in which we use several convolution layers with small filters and a dropout strategy. In the adopted model, the addition of convolution layers is recommended in order to give more precision to image classification. The experiment results suggest that the proposed model give promising results.
Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture

This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network (the convolutional neural network and bidirectional long short-term memory network with a multihead attention mechanism) is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods.
Inverse Kinematics and Temporal Convolutional Networks for Sequential Pose Analysis in VR

Drawing from a recent call to advance generalizability and causal inference in psychological science using contextually representative research designs [1], we introduce a conceptual framework that integrates techniques in machine perception of poses with VR-driven inverse kinematic character animation, leveraging the Unity game engine to mediate between the human user and the machine learner. This Computational Virtual Reality (C-VR) system contains the following components: a) Human motion capture (VR), b) Human to avatar character animation (inverse kinematics), c) character animation recordings (virtual cameras), d) avatar pose detection (OpenPose), d) avatar pose classification (SVM), and e) sequential avatar moving pose analyses (TCN). By leveraging the precision in representation afforded in virtual environments and agents and the precision in perception afforded in computer vision and machine learning in a unified system, we may take steps towards understanding a wider range of human complexity.
Research on phishing webpage detection technology based on CNN-BiLSTM algorithm

Abstract The rapid development of the Internet has also brought opportunities for some illegal elements. Network attackers steal sensitive information from victims through phishing webpages to obtain economic benefits. Currently, the commonly used detection methods for phishing webpages, based on blacklist detection and webpage content feature detection, have the problems of being unable to detect newly emerging phishing webpages or requiring manual extraction of webpage features. Therefore, researchers have used Convolution Neural Network (CNN) to detect phishing webpages by automatically extracting URL features. However, its method has some limitations: (1) The memory is limited when the URL is transformed into the feature matrix, and the embedding vector of new words cannot be obtained or the effective information of sensitive words is lost; (2) the long-distance dependent feature of the URL cannot be obtained. In response to the above challenges, we proposes a phishing detection method based on CNN and Bi-directional Long Short-Term Memory (Bi-LSTM) based on existing work: based on sensitive word segmentation-- comprehensively using two existing URL segmentation methods before converting URL into eigenvector matrix; adding Bi-LSTM on the basis of convolutional neural network to obtain URL long-distance dependent features. Experimental results show that this method can achieve high accuracy, recall rate and F1 value.
Overview of RCD-2020, the FIRE-2020 track on Retrieval from Conversational Dialogues

This paper describes an overview of the track - ’Retrieval from Conversational Dialogues’ (RCD) organized as a part of Forum of Information Retrieval and Evaluation (FIRE), 2020. The motivation of the track is to develop a dataset towards a controlled and reproducible laboratory based experimental setup for investigating the effectiveness if conversational assistance systems. Specifically, the manner of conversational assistance which this track addresses is contextualization of certain concepts within the content either written (e.g. a chat system) or uttered (e.g. in an audio or video communication) by a user about which the other users participating in the communication are not well versed. To study the problem under a laboratory-based reproducible setting, we took a collection of four movie scripts and manually annotated spans of text that may require contextualization. The two tasks involved in RCD track are: a) Task-1:, where participants were required to estimate the annotated span of text likely to be benefited by contextualization from a given sequence of dialogue based interactions from the script; and b) Task-2:, which involved retrieving a ranked list of documents corresponding to the concepts requiring contextualization. To evaluate the effectiveness of Task-1, we used i) a character n-gram based variant of the BLEU score, and ii) bag-of-words based Jaccard coefficient to measure the overlap between the manually annotated ground-truth and the automatically extracted text spans at two different granularity levels of character and word matches, respectively. To evaluate the effectiveness of the retrieved documents for Task-2, we employed two standard precision-oriented information retrieval (IR) metrics, namely precision at top-5 ranks ([email protected]) and mean reciprocal rank (MRR), along with a both precision and recall oriented metric, namely the mean average precision (MAP). We received a total of 5 submissions from a single participating team for both the tasks. A general trend from the submitted runs is that statistical-based unsupervised approaches of term extraction and summarization from movie scripts turned out to be more effective for both the tasks (i.e. query identification and retrieval) than supervised approaches, such as pre-trained transformer (BERT) based ones.
Single-Server Private Information Retrieval Schemes are Equivalent to Locally Recoverable Coding Schemes

The Private Information Retrieval (PIR) problem has recently attracted a significant interest in the information-theory community. In this problem, a client wants to download one or more messages belonging to a database while protecting the identity of the downloaded message(s). In this article, we focus on the scenarios in which (i) the entire database is stored on a single server and (ii) the client has prior side information, namely a subset of messages unknown to the server. Such prior side information is necessary to enable efficient private information retrieval in the single server scenario. In the last decade, there has also been a significant interest in Locally Recoverable Codes (LRCs), a class of storage codes in which each symbol can be recovered from a limited number of other symbols. More recently, there is an interest in cooperative locally recoverable codes, i.e., codes in which multiple symbols can be recovered from a small set of other code symbols. The central problem in this context is given a set of code parameters to design an LRC scheme that includes a locally recoverable code along with encoding, decoding, and repair functions. The paper establishes an equivalence between the single-server PIR schemes and LRC schemes. In particular, we present explicit algorithms that transform a given PIR scheme into an LRC scheme and vice versa. We show that (i) PIR schemes for retrieving a single message are equivalent to classical LRC schemes; and (ii) PIR schemes for retrieving multiple messages are equivalent to cooperative LRC schemes. These equivalence results allow us to recover upper bounds on the download rate for PIR schemes, and to obtain a novel rate upper bound on cooperative LRC schemes. Our results cover schemes based on both linear and non-linear codes.
Embedding metadata in images at time of capture using physical Quick Response (QR) codes

Maintaining metadata records for scientific imaging is challenging where the link between the metadata and the image is labour intensive to create and can easily be broken. We propose a method for using QR codes in images of samples to embed the metadata in an open and robust manner, so that it can be readily extracted on demand. By using a novel pipeline for generating QR codes, displaying them in images, reading the QR codes in the images and extracting the metadata for later action such as renaming the image file, a streamlined process for metadata management is introduced. This method was simulated using a range of image types and QR code parameters to identify the limits of various parameter combinations, providing practical insight into code design and usability. The pipeline was also tested with hundreds of images in both laboratory and field situations and proved to be extremely efficient and robust. This method offers potential for anyone taking images of samples who needs to guarantee the existence and correctness of metadata without relying on an external association mechanism.
Mitigating the Position Bias of Transformer Models in Passage Re-ranking

Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear anywhere in a given passage. However, we observe a bias in the position of the correct answer in the text in two popular Question Answering datasets used for passage re-ranking. The excessive favoring of earlier positions inside passages is an unwanted artefact. This leads to three common Transformer-based re-ranking models to ignore relevant parts in unseen passages. More concerningly, as the evaluation set is taken from the same biased distribution, the models overfitting to that bias overestimate their true effectiveness. In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results. We propose a debiasing method for retrieval datasets. Our results show that a model trained on a position-biased dataset exhibits a significant decrease in re-ranking effectiveness when evaluated on a debiased dataset. We demonstrate that by mitigating the position bias, Transformer-based re-ranking models are equally effective on a biased and debiased dataset, as well as more effective in a transfer-learning setting between two differently biased datasets.