Reviewerly/exHarmony huggingface dataset:
A Two-Way VoLTE Covert Channel With Feedback Adaptive to Mobile Network Environment

In the existing network covert channel research, the transmission of secret messages is one-way, lacking confirmation feedback on whether the secret message is successfully accepted. However, voice over LTE (VoLTE) has real-time interactive features, and the data packets between the sender and the receiver are transmitted in both directions, which facilitates the construction of a two-way covert channel with feedback. Therefore, we propose a two-way VoLTE covert channel adaptive to mobile network environment, which includes a sender-to-receiver covert timing channel that modulates covert message through actively dropping packets during the silence periods and a reverse covert storage channel that hides the acceptance of the covert message as feedback information into the feedback control information field of the real-time transport control protocol (RTCP) packet. The sender evaluates the current attack severity according to the feedback and adjusts the real-time parameters of the covert timing channel to weigh the robustness and other performance, thereby constructing a two-way covert channel adaptive to complex mobile network environments. Experimental results show that this solution can maintain voice quality which is an important performance indicator for VoLTE, while keeping the covert channel undetectable and robust.
Data-Anonymous Encoding for Text-to-SQL Generation

On text-to-SQL generation, the input utterance usually contains lots of tokens that are related to column names or cells in the table, called table-related tokens.These table-related tokens are troublesome for the downstream neural semantic parser because it brings complex semantics and hinders the sharing across the training examples.However, existing approaches either ignore handling these tokens before the semantic parser or simply use deterministic approaches based on string-match or word embedding similarity.In this work, we propose a more efficient approach to handle table-related tokens before the semantic parser.First, we formulate it as a sequential tagging problem and propose a two-stage anonymization model to learn the semantic relationship between tables and input utterances.Then, we leverage the implicit supervision from SQL queries by policy gradient to guide the training.Experiments demonstrate that our approach consistently improves performances of different neural semantic parsers and significantly outperforms deterministic approaches.
Coda: An End-to-End Neural Program Decompiler

Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial advantages. On the other hand, binary decompilation can be leveraged for code vulnerability analysis and malware detection. However, efficient binary decompilation is challenging. Conventional decompilers have the following major limitations: (i) they are only applicable to specific source-target language pair, hence incurs undesired development cost for new language tasks; (ii) their output high-level code cannot effectively preserve the correct functionality of the input binary; (iii) their output program does not capture the semantics of the input and the reversed program is hard to interpret. To address the above problems, we propose Coda1, the first end-to-end neural-based framework for code decompilation. Coda decomposes the decompilation task into of two key phases: First, Coda employs an instruction type-aware encoder and a tree decoder for generating an abstract syntax tree (AST) with attention feeding during the code sketch generation stage. Second, Coda then updates the code sketch using an iterative error correction machine guided by an ensembled neural error predictor. By finding a good approximate candidate and then fixing it towards perfect, Coda achieves superior with performance compared to baseline approaches. We assess Coda’s performance with extensive experiments on various benchmarks. Evaluation results show that Coda achieves an average of 82% program recovery accuracy on unseen binary samples, where the state-of-the-art decompilers yield 0% accuracy. Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70% program accuracy. Our work reveals the vulnerability of binary executables and imposes a new threat to the protection of Intellectual Property (IP) for software development.
Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019

The 1st ACL workshop on Gender Bias in Natural Language Processing included a shared task on gendered ambiguous pronoun (GAP) resolution.This task was based on the coreference challenge defined in Webster et al. (2018), designed to benchmark the ability of systems to resolve pronouns in real-world contexts in a gender-fair way.263 teams competed via a Kaggle competition, with the winning system achieving logloss of 0.13667 and near gender parity.We review the approaches of eleven systems with accepted description papers, noting their effective use of BERT (Devlin et al., 2019), both via fine-tuning and for feature extraction, as well as ensembling.
VerbNet Representations: Subevent Semantics for Transfer Verbs

This paper announces the release of a new version of the English lexical resource VerbNet with substantially revised semantic representations designed to facilitate computer planning and reasoning based on human language.We use the transfer of possession and transfer of information event representations to illustrate both the general framework of the representations and the types of nuances the new representations can capture.These representations use a Generative Lexicon-inspired subevent structure to track attributes of event participants across time, highlighting oppositions and temporal and causal relations among the subevents.
A Model for Availability and Security Risk Evaluation for Systems With VMM Rejuvenation Enabled by VM Migration Scheduling

Most companies and organizations rely nowadays on virtualized environments to host and run their applications.Some of these applications have stringent availability and security requirements.An important challenge for high availability in virtualized systems is software aging, which can lead the system to hangs or other types of failures.Software rejuvenation is applied to cope with software aging problems, whereas previous research suggests the use of Virtual Machine (VM) migration to reduce the downtime related to Virtual Machine Monitor (VMM) software rejuvenation.However, there is still a gap regarding the security implications of applying VM migration scheduling as support for VMM software rejuvenation.In this paper, we propose a security evaluation approach based on an availability model for virtualized systems with VM migration for VMM rejuvenation.The goal is to find the proper rejuvenation scheduling to reach the desired levels (or at least to avoid the undesired levels) of security risk and availability.We present three case studies comprising major security threats, namely Man-in-the-middle and Denial of Service attacks.Results provide insightful information regarding the tradeoff between availability and security risk when applying VM migration scheduling for rejuvenation purposes.
Extreme spin squeezing from deep reinforcement learning

Spin squeezing (SS) is a recognized resource for realizing measurement precision beyond the standard quantum limit $\propto 1/\sqrt{N}$. The rudimentary one-axis twisting (OAT) interaction can facilitate SS and has been realized in diverse experiments, but it cannot achieve extreme SS for precision at Heisenberg limit $\propto 1/{N}$. Aided by deep reinforcement learning (DRL), we discover size-independent universal rules for realizing nearly extreme SS with OAT interaction using merely a handful of rotation pulses. More specifically, only 6 pairs of pulses are required for up to $10^4$ particles, while the time taken to reach extreme SS remains on the same order of the optimal OAT squeezing time, which makes our scheme viable for experiments that reported OAT squeezing. This study highlights the potential of DRL for controlled quantum dynamics.
Fully Automatic Pediatric Echocardiography Segmentation Using Deep Convolutional Networks Based on BiSeNet

Accurate segmentation of pediatric echocardiography is an essential preprocessing step for a wide range of analysis tasks. Currently, it highly relies on sonographer's manual segmentation, which is time-consuming and redundant, and therefore might lead to mistakes. In this paper, we present a deep learning method based on Bilateral Segmentation Network (BiSeNet) to fully automatic segment pediatric echocardiography images in 4 chamber view. BiSeNet consists of two paths, a spatial path for capturing low-level spatial features, and a context path for exploiting high-level context semantic features. In addition, a feature fusion module is used to fuse features learned by both the two paths. Experiments based on our selfcollected dataset shows that our method achieves 0.932, and 0.908 in term of Dice index in the left ventricle and left atrium segmentation task, which outperforms different state-of-the-art U-Net architectures.
Hybrid QKD Protocol Outperforming Both DV- and CV-QKD Protocols

To overcome the limitations of both discrete variable (DV) and continuous variable (CV) QKD protocols, in this paper, a hybrid QKD protocol is proposed. In the proposed hybrid QKD protocol, Alice simultaneously performs discrete modulation (DM)-based encoding for CV-QKD subsystem and time-phase encoding for DV-QKD on a transmitter side and transmits such hybrid encoded pulse with optimized average number of photons per pulse. On receiver side, Bob employs a 1:2 optical space switch to select either DV-QKD receiver or CV-QKD receiver with the optimized probability of selection. Other compatible CV-QKD and DV-QKD protocols can also be used in hybrid QKD. Bob further performs the classical postprocessing applied to both subsystems so that resulting joint secure key is derived from both subsystems. The proposed hybrid QKD protocol significantly outperforms previously introduced both Gaussian modulation (GM)- and DM-based CV-QKD protocols as well as DV-QKD protocols in terms of both secret-key rate and achievable transmission distance.
Chosen-plaintext attack on the double random polarization encryption

Images can be optically encrypted by random encoding in the phase, the polarization, or even the coherence of a light field. It is important for these optical encryption methods to undergo rigorous cryptanalysis. However, only phase-encoding-based encryption has been rigorously analyzed to date. In this manuscript, we demonstrate that the double random polarization encryption (DRPolE) is vulnerable to chosen-plaintext attack (CPA). We show that the keys can be retrieved if one can choose the polarization states of two plaintext images and collect the corresponding cyphertext images. Our study reveals a serious concern regarding the DRPolE that should be addressed in the design of polarization-based optical encryption methods.
Mapping potential impact areas of Blockchain use in the public sector

While the financial sector was the first to investigate the potential of Blockchain, and in more general terms, Distributed Ledger Technology (DLT), with an initial focus on digital currencies including cryptocurrencies such as Bitcoin and Ether, the study of potential uses of this technology is gradually expanding to also include other areas of application including public-sector applications. Academic and non-academic publications on the subject have so far burgeoned mainly in and around the so-called fintech (financial technology) area, a fact that is underlined by a surprisingly high number of literature reviews already performed in this relatively novel area of research. For the public sector and the study domain of Digital Government, Blockchain and DLT have remained under-researched topics, and the number of respective publications is still relatively low. The aim of this meta-level literature study is to map the existing Blockchain and DLT-related body of knowledge and link its prevalent concepts to known needs and requirements studied in the context of the public sector and in Digital Government. Among the concepts, which intersect the existing literature on Blockchain strongly with the public sector-related research and practice, the study found cost reduction, innovation, regulation, taxation, security, privacy, transparency, among others, all of which deserve increased attention from scholars active in the domain of Digital Government research.
Deep Learning–based Number Detection and Recognition for Gas Meter Reading

The meter reading.system field has been researched from conventional methods centered on image processing technology to techniques based on learning methods such as machine learning or deep learning. The biggest problem for meter reading systems based on computer vision is difficulty in recognizing the various kinds of meters. In fact, there are more than five major manufacturers for the meters installed in Korea. There are different meter reading areas, ID regions, and number formats by version. Because of these problems, most of the meter reading is still done hands-on. In this paper, we present an automatic meter.reading system that can work simply and efficiently, compared to existing meter reading systems that need a skilled worker. Our meter reading system consists of three parts: i) detection of meter-reading and ID regions using You Only Look Once (YOLO), ii) digit segmentation for recognition, and iii) convolutional neural network (CNN)-based digit recognition. It is possible to robustly detect and recognize various meter types by using the method presented here. Therefore, it can provide an environment where gas meter checkers can work efficiently without inconvenient procedures.
Firefly Algorithm-Based Kapur’s Thresholding and Hough Transform to Extract Leukocyte Section from Hematological Images

Computerized disease examination techniques are widely adopted in the literature to evaluate a considerable number of medical images ranging from the RGB scale to gray scale. This work proposes a novel image extraction method by combining Kapur’s thresholding and Hough transform (HT) to extract the leukocyte segment from the RGB-scaled blood smear image (BSI). Automated mining of the leukocyte region is always preferred in medical clinics for fast disease examination and treatment for the planning process. This study aims to implement a hybrid procedure to extort the leukocyte segment. Kapur’s is considered to enhance the RGB-scaled test image, and the HT is used to detect and extract the circle section from the image. In this work, the hematological images of leukocyte images for segmentation and classification (LISC) database are adopted for the examination. The extracted leukocyte picture is then evaluated with ground truth, and the essential image performance parameters (IPP) are then computed. This work is then validated against the semiautomated approaches, such as Shannon’s entropy-based Chan-Vese and level-set segmentation techniques existing in the literature. The outcome of the proposed techniques confirms that proposed procedure gives better IPP values compared to the existing semiautomated techniques.
Multi-Authority Non-Monotonic KP-ABE With Cryptographic Reverse Firewall

The revelations of Snowden show that hardware and software of devices may corrupt users’ machine to compromise the security in various ways. To address this concern, Mironov and Stephen-Davidowitz introduce the Cryptographic Reverse Firewall (CRF) concept that is able to resist the ex-filtration of secret information for some compromised machine (Eurocrypt 2015). There are some applications of CRF deployed in many cryptosystems, but less studied and deployed in Attribute-Based Encryption (ABE) field, which attracts a wide range of attention and is employed in real-world scenarios (i.e., data sharing in cloud). In this work, we focus how to give a CRF security protection for a multi-authority ABE scheme and hence propose a multi-authority key-policy ABE scheme with CRF (acronym, MA-KP-ABE-CRF), which supports attribute distribution and non-monotonic access structure. To achieve this, beginning with revisiting a MA-KP-ABE with non-trivial combining non-monotonic formula, we then give the randomness of ciphertexts and secret keys with reverse firewall and give formal security analysis. Finally, we give a simulation on our MA-KP-ABE-CRF system based on Charm library whose the experimental results demonstrate practical efficiency.
Firefly Algorithm and Its Variants in Digital Image Processing: A Comprehensive Review

The significance and requirements of digital image processing arise from two main areas of applications: the improvement of visual information for human interpretation and the encoding of scene data for the independent perception of machines. However, human is often involved in such processing for manually tuning up the parameters, which takes a long time, and it remains as an unresolved issue. Nature is a brilliant and enormous source of inspiration for resolving difficult and complicated problems in computer science, as it possesses incredibly diverse, vibrant, flexible, complicated, and intriguing phenomena. In practice, selecting the optimum parameters for any technique is an optimization problem. Nature-inspired algorithms are metaheuristics that imitate the works of nature to solve optimization issues, leading to a new era in computing. There are several dozens of classical metaheuristic optimization algorithms reported in the literature, such as genetic algorithm, ant colony optimization, and particle swarm optimization. Though due to the efficacy and success in solving various digital image analysis problems, the firefly algorithm which is also a metaheuristic algorithm, inspired by fireflies' flashing behaviour in nature, is used in various image analysis optimization studies. This work is dedicated to a comprehensive review of the firefly algorithm to solve optimization problems in various steps of digital image analysis, like image preprocessing, segmentation, compression, feature selection, and classification. Various applications of the firefly algorithm in image analysis are also discussed in this review. Key issues and future research directions will also be highlighted.
Blockchain: The New Technology and its Applications for Libraries

Reporter: Li Zhang, Mississippi State University LibrariesPresenters: Sandra Hirsh, San Jose State University and Bohyun Kim, University of Rhode IslandThe Special Libraries Association 2019 Annual...
Face Identification Based on K-Nearest Neighbor

Face identification has been widely applied this time, such as security on gadgets, smart home security, and others. Face dominates as a biometric which is most increase in the next few years. Face is used for biometric identification which is considered successful among several other types of biometrics and accurate results. Face recognition utilizes facial features for security purposes. The classification method in this paper is K-nearest Neighbor (KNN). The K-Nearest Neighbor algorithm uses neighborhood classification as the predictive value of a good instance value. K-NN includes an instance-based learning group. This paper developed face identification using Principal Component Analysis (PCA) or eigenface extraction methods. The stages of face identification research using the KNN method are pre-processing in the input image. Preprocessing used in this research are contrass stretching, grayscale, and segmentation used haar cascade. This research is registered 30 people, each person had 3 images used for training and 2 images used for testing. The results obtained from several trials of k values are as follows. Experiments with a value of k=1 get the best accuracy, namely 81%, k=2 get 53% accuracy, and k=3 get 45% accuracy.
Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks

We study the generalization properties of deep convolutional neural networks for image denoising in the presence of varying noise levels. We provide extensive empirical evidence that current state-of-the-art architectures systematically overfit to the noise levels in the training set, performing very poorly at new noise levels. We show that strong generalization can be achieved through a simple architectural modification: removing all additive constants. The resulting bias-free networks attain state-of-the-art performance over a broad range of noise levels, even when trained over a limited range. They are also locally linear, which enables direct analysis with linear-algebraic tools. We show that the denoising map can be visualized locally as a filter that adapts to both image structure and noise level. In addition, our analysis reveals that deep networks implicitly perform a projection onto an adaptively-selected low-dimensional subspace, with dimensionality inversely proportional to noise level, that captures features of natural images.
On Universal Equivariant Set Networks

Using deep neural networks that are either invariant or equivariant to permutations in order to learn functions on unordered sets has become prevalent. The most popular, basic models are DeepSets (Zaheer et al. 2017) and PointNet (Qi et al. 2017). While known to be universal for approximating invariant functions, DeepSets and PointNet are not known to be universal when approximating equivariant set functions. On the other hand, several recent equivariant set architectures have been proven equivariant universal (Sannai et al. 2019, Keriven and Peyre 2019), however these models either use layers that are not permutation equivariant (in the standard sense) and/or use higher order tensor variables which are less practical. There is, therefore, a gap in understanding the universality of popular equivariant set models versus theoretical ones. In this paper we close this gap by proving that: (i) PointNet is not equivariant universal; and (ii) adding a single linear transmission layer makes PointNet universal. We call this architecture PointNetST and argue it is the simplest permutation equivariant universal model known to date. Another consequence is that DeepSets is universal, and also PointNetSeg, a popular point cloud segmentation network (used e.g., in Qi et al. 2017) is universal. The key theoretical tool used to prove the above results is an explicit characterization of all permutation equivariant polynomial layers. Lastly, we provide numerical experiments validating the theoretical results and comparing different permutation equivariant models.