Reviewerly/exHarmony huggingface dataset:
An audio encryption scheme based on Fast Walsh Hadamard Transform and mixed chaotic keystreams

This paper introduces an audio encryption algorithm based on permutation of audio samples using discrete modified Henon map followed by substitution operation with keystream generated from the modified Lorenz-Hyperchaotic system. In this work, the audio file is initially compressed by Fast Walsh Hadamard Transform (FWHT) for removing the residual intelligibility in the transform domain. The resulting file is then encrypted in two phases. In the first phase permutation operation is carried out using modified discrete Henon map to weaken the correlation between adjacent samples. In the second phase it utilizes modified-Lorenz hyperchaotic system for substitution operation to fill the silent periods within the speech conversation. Dynamic keystream generation mechanism is also introduced to enhance the correlation between plaintext and encrypted text. Various quality metrics analysis such as correlation, signal to noise ratio (SNR), differential attacks, spectral entropy, histogram analysis, keyspace and key sensitivity are carried out to evaluate the quality of the proposed algorithm. The simulation results and numerical analyses demonstrate that the proposed algorithm has excellent security performance and robust against various cryptographic attacks.
Pancreatic Cancer Detection in Whole Slide Images Using Noisy Label Annotations

We propose an approach to accurately predict regions of pancreatic cancer in whole-slide images (WSIs) by leveraging a relatively large, but noisy, dataset. We employ a noisy label classification (NLC) method (called the NLC model) that utilizes a small set of clean training samples and assigns the appropriate weights to training samples to deal with sample noise. The weights are assigned online so that the network loss approximates the loss for the clean samples. This method results in a 9.7% performance improvement over the baseline non-NLC method (the Baseline-Noisy model). We use both methods in an ensemble setup to generate labels for a large training dataset to train a classifier. This classifier outperforms a classifier trained with manually annotated data by 2.94%–3.74% in terms of AUC for testing patches in WSIs.
Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search

A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as to maximize the loss on the perturbed example. In this work, we hypothesize that adversarial perturbations move the image away from the image manifold in the sense that there exists no physical process that could have produced the adversarial image. This hypothesis suggests that a successful defense mechanism against adversarial images should aim to project the images back onto the image manifold. We study such defense mechanisms, which approximate the projection onto the unknown image manifold by a nearest-neighbor search against a web-scale image database containing tens of billions of images. Empirical evaluations of this defense strategy on ImageNet suggest that it very effective in attack settings in which the adversary does not have access to the image database. We also propose two novel attack methods to break nearest-neighbor defense settings and show conditions under which nearest-neighbor defense fails. We perform a series of ablation experiments, which suggest that there is a trade-off between robustness and accuracy between as we use features from deeper in the network, that a large index size (hundreds of millions) is crucial to get good performance, and that careful construction of database is crucial for robustness against nearest-neighbor attacks.
Physical Layer Identification Based on Spatial–Temporal Beam Features for Millimeter-Wave Wireless Networks

With millimeter wave (mmWave) wireless communication envisioned to be the key enabler of next generation high data rate wireless networks, security is of paramount importance. While conventional security measures in wireless networks operate at a higher layer of the protocol stack, physical layer security utilizes unique device dependent hardware features to identify and authenticate legitimate devices. In this work, we identify that the manufacturing tolerances in the antenna arrays used in mmWave devices contribute to a beam pattern that is unique to each device, and to that end we propose a novel device fingerprinting scheme based on the unique beam pattern of different codebooks used by the mmWave devices. Specifically, we propose a fingerprinting scheme with multiple access points (APs) to take advantage of the rich spatial-temporal information of the beam pattern. We perform comprehensive experiments with commercial off-the-shelf mmWave devices to validate the reliability performance of our proposed method under various scenarios. We also compare our beam pattern feature with a conventional physical layer feature namely power spectral density feature (PSD). To that end, we implement PSD feature based fingerprinting for mmWave devices. We show that the proposed multiple APs scheme is able to achieve over 99% identification accuracy for stationary LOS and NLOS scenarios and significantly outperform the PSD feature fingerprinting method. For mobility scenario, the overall identification accuracy is 99%. In addition, we perform security analysis of our proposed beam pattern fingerprinting system and PSD fingerprinting system by studying the feasibility of performing impersonation attacks. We design and implement an impersonation attack mechanism for mmWave wireless networks using state-of-the-art 60 GHz software defined radios. We discuss our findings and their implications on the security of the mmWave wireless networks.
Towards Standardizing and Improving Classification of Bug-Fix Commits

Background: Open source software repositories like GitHub are mined to gain useful empirical software engineering insights and answer critical research questions. However, the present state of the art mining approaches suffers from high error rate in the labeling of data that is used for such analysis. This is particularly true when labels are automatically generated from the commit message, and seriously undermines the results of these studies. Aim: Our goal is to label commit comments with high accuracy automatically. In this work, we focus on classifying a commit as a “Bug-Fix commit” or not. Method: Traditionally, researchers have utilized keyword-based approaches to identify bug fix commits that leads to a significant increase in the error rate. We present an alternative methodology leveraging a deep neural network model called Bidirectional Encoder Representations from Transformers (BERT) that can understand the context of the commit message. We provide the rules for semantic interpretation of commit comments. We construct a hand-labeled dataset from real GitHub commits according to these rules and fine-tune BERT for classification. Results: Our initial evaluation shows that our approach significantly reduces the error rate, with up to 10% relative improvement in classification over keyword-based approaches. Future Direction: We plan on extending our dataset to cover more corner cases and reduce programming language specific biases. We also plan on refining the semantic rules. In this work, we have only considered a simple binary classification problem (Bug-Fix or not), which we plan to extend to other classes and extend the approach to consider multiclass problems. Conclusion: The rules, data, and the model proposed in this paper have the potential to be used by people analyzing open source repositories to improve the labeling of data used in their analysis.
Unakite: Scaffolding Developers' Decision-Making Using the Web

Developers spend a significant portion of their time searching for solutions and methods online. While numerous tools have been developed to support this exploratory process, in many cases the answers to developers' questions involve trade-offs among multiple valid options and not just a single solution. Through interviews, we discovered that developers express a desire for help with decision-making and understanding trade-offs. Through an analysis of Stack Overflow posts, we observed that many answers describe such trade-offs. These findings suggest that tools designed to help a developer capture information and make decisions about trade-offs can provide crucial benefits for both the developers and others who want to understand their design rationale. In this work, we probe this hypothesis with a prototype system named Unakite that collects, organizes, and keeps track of information about trade-offs and builds a comparison table, which can be saved as a design rationale for later use. Our evaluation results show that Unakite reduces the cost of capturing tradeoff-related information by 45%, and that the resulting comparison table speeds up a subsequent developer's ability to understand the trade-offs by about a factor of three.
Chronos+: An Accurate Blockchain-based Time-stamping Scheme for Cloud Storage

We propose Chronos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , an accurate blockchain-based time-stamping scheme for outsourced data, where both the storage and time-stamping services are provided by cloud service providers. Specifically, Chronos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> integrates a file into a transaction on a blockchain once the file is created, which guarantees the file's latest creation time to be the time when the block containing the transaction is appended to the blockchain. A sufficient number of consecutive blocks that are latest confirmed on the blockchain is embedded into the file at the creation time. These blocks serve as a time-dependent random seed to prove the earliest creation time, due to blockchains' chain quality property. Chronos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> makes the file's timestamp corresponding to a time interval formed by the earliest and latest creation times which are derived from the heights of the corresponding blocks. Due to blockchains' chain growth property, such a height-derived timestamp can ensure that the time intervals' range is within a few minutes so as to guarantee the accuracy. We also point out potential threats towards outsourced time-sensitive files and present security analyses to prove that Chronos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> is secure against these threats. Comprehensive performance evaluations demonstrate the efficiency and practicality of Chronos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> .
Systematic Review of the Use of Telepathology During Intraoperative Consultation

Abstract Objective To compare studies that used telepathology systems vs conventional microscopy for intraoperative consultation (frozen-section) diagnosis. Methods A total of 56 telepathology studies with 13,996 cases in aggregate were identified through database searches. Results The concordance of telepathology with the reference standard was generally excellent, with a weighted mean of 96.9%. In comparison, we identified seven studies using conventional intraoperative consultation that showed a weighted mean concordance of 98.3%. Evaluation of the risk of bias showed that most of these studies were low risk. Conclusions Despite limitations such as variation in reporting and publication bias, this systematic review provides strong support for the safety of using telepathology for intraoperative consultations.
Mentions of Security Vulnerabilities on Reddit, Twitter and GitHub

Activity on social media is seen as a relevant sensor for different aspects of the society. In a heavily digitized society, security vulnerabilities pose a significant threat that is publicly discussed on social media. This study presents a comparison of user-generated content related to security vulnerabilities on three digital platforms: two social media conversation channels (Reddit and Twitter) and a collaborative software development platform (GitHub). Our data analysis shows that while more security vulnerabilities are discussed on Twitter, relevant conversations go viral earlier on Reddit. We show that the two social media platforms can be used to accurately predict activity on GitHub.
Attention-based Densely Connected LSTM for Video Captioning

Recurrent Neural Networks (RNNs), especially the Long Short-Term Memory (LSTM), have been widely used for video captioning, since they can cope with the temporal dependencies within both video frames and the corresponding descriptions. However, as the sequence gets longer, it becomes much harder to handle the temporal dependencies within the sequence. And in traditional LSTM, previously generated hidden states except the last one do not work directly to predict the current word. This may lead to the predicted word highly related to the last generated hidden state other than the overall context. To better capture long range dependencies and directly leverage early generated hidden states, in this work, we propose a novel model named Attention-based Densely Connected Long Short-Term Memory (DenseLSTM). In DenseLSTM, to ensure maximum information flow, all previous cells are connected to the current cell, which makes the updating of the current state directly related to all its previous states. Furthermore, an attention mechanism is designed to model the impacts of different hidden states. Because each cell is directly connected with all its successive cells, each cell has direct access to the gradients from later ones. In this way, the long-range dependencies are more effectively captured. We perform experiments on two publicly used video captioning datasets: the Microsoft Video Description Corpus (MSVD) and the MSR-VTT, and experimental results illustrate the effectiveness of DenseLSTM.
MultiLock

In this paper we evaluate how discriminative are behavior-based signals obtained from the smartphone sensors. The main aim is to evaluate these signals for person recognition. The recognition based on these signals increases the security of devices, but also implies privacy concerns. We consider seven different data channels and their combinations. Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are all collected during human-mobile interaction to authenticate the users. We evaluate two approaches: one-time authentication and active authentication. In one-time authentication, we employ the information of all channels available during one session. For active authentication we take advantage of mobile user behavior across multiple sessions by updating a confidence value of the authentication score. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises of smartphone sensor signals acquired during natural human-mobile interaction. Our results show that different traits can be complementary and multimodal systems clearly increase the performance with accuracies ranging from 82.2% to 97.1% depending on the authentication scenario. These results confirm the discriminative power of these signals.
A Service-Oriented Permissioned Blockchain for the Internet of Things

Recently, the emergence of blockchain has stirred great interests in the field of Internet of Things (IoT). However, numerous non-trivial problems in the current blockchain system prevent it from being used as a generic platform for large-scale services and applications in IoT. One notable drawback is the scalability problem. Lots of projects and researches have been done to solve this problem. Nevertheless, they do not consider different users' conditions, only using a single consensus protocol as the best fit one, as well as the IoT system is heavily constrained by computing and networking resources. In this article, we study a permissioned blockchain-based IoT architecture. In order to improve the scalability of the blockchain system and meet the needs of different users, we propose a service-oriented permissioned blockchain, where different consensus protocols are launched according to users' quality of service (QoS) requirements. Specially, we quantify a few popular consensus protocols. Additionally, we select block producers, which need a great number of computation resources, as well as dynamically allocate network bandwidth to the blockchain system. We formulate consensus protocols selection, block producers selection, and network bandwidth allocation as a joint optimization problem. We then use a dueling deep reinforcement learning approach to solve the problem. Simulation results demonstrate the effectiveness of our proposed scheme.
Design and Implementation of a Fast and Scalable NTT-Based Polynomial Multiplier Architecture

In this paper, we present an optimized FPGA implementation of a novel, fast and highly parallelized NTT-based polynomial multiplier architecture, which proves to be effective as an accelerator for lattice-based homomorphic cryptographic schemes. As I/O operations are as time-consuming as NTT operations during homomorphic computations in a host processor/accelerator setting, instead of achieving the fastest NTT implementation possible on the target FPGA, we focus on a balanced time performance between the NTT and I/O operations. Even with this goal, we achieved the fastest NTT implementation in literature, to the best of our knowledge. For proof of concept, we utilize our architecture in a framework for Fan-Vercauteren (FV) homomorphic encryption scheme, utilizing a hardware/software co-design approach, in which polynomial multiplication operations are offloaded to the accelerator via PCIe bus while the rest of operations in the FV scheme are executed in software running on an off-the-shelf desktop computer. Specifically, our framework is optimized to accelerate Simple Encrypted Arithmetic Library (SEAL), developed by the Cryptography Research Group at Microsoft Research, for the FV encryption scheme, where large degree polynomial multiplications are utilized extensively. The hardware part of the proposed framework targets Xilinx Virtex-7 FPGA device and the proposed framework achieves almost 11x latency speedup for the offloaded operations compared to their pure software implementations. We achieved a throughput of almost 800K polynomial multiplications per second, for polynomials of degree 1024 with 32-bit coefficients.
The Significance of Blockchain Technology in Digital Transformation of Logistics and Transportation

The purpose of this article is to identify blockchain's role in achieving logistic objectives, with the aim of pointing out the significance of blockchain technology in the digital transformation of logistics and transportation. Implementation of the blockchain technology in combination with IoT elements in logistics and transportation contributes to business process optimization, supply chain traceability and transparency, and significant financial savings. There are limitations as blockchain is at a relatively early stage of development with most projects. Therefore, the approach of theory building from multiple case studies was used. The article provides a comprehensive overview of current blockchain initiatives and use-cases. This article is believed to be the first to address the role of blockchain technology in achieving logistics objectives. Results from this research indicate that blockchain technology contributes to the achievement of logistics objectives.
MONET: Multiview Semi-Supervised Keypoint Detection via Epipolar Divergence

This paper presents MONET-an end-to-end semi-supervised learning frameworkfor a keypoint detector using multiview image streams. In particular, we consider general subjects such as non-human species where attaining a large scale annotated dataset is challenging. While multiview geometry can be used to self-supervise the unlabeled data, integrating the geometry into learning a keypoint detector is challenging due to representation mismatch. We address this mismatch by formulating a new differentiable representation of the epipolar constraint called epipolar divergence-a generalized distance from the epipolar lines to the corresponding keypoint distribution. Epipolar divergence characterizes when two view keypoint distributions produce zero reprojection error. We design a twin network that minimizes the epipolar divergence through stereo rectification that can significantly alleviate computational complexity and sampling aliasing in training. We demonstrate that our framework can localize customized keypoints of diverse species, e.g., humans, dogs, and monkeys.
Beyond Human

Virtual reality setups are particularly suited to create a tight bond between players and their avatars up to a degree where we start perceiving the virtual representation as our own body. We hypothesize that such an illusion of virtual body ownership (IVBO) has a particularly high, yet overlooked potential for nonhumanoid avatars. To validate our claim, we use the example of three very different creatures---a scorpion, a rhino, and a bird---to explore possible avatar controls and game mechanics based on specific animal abilities. A quantitative evaluation underpins the high game enjoyment arising from embodying such nonhuman morphologies, including additional body parts and obtaining respective superhuman skills, which allows us to derive a set of novel design implications. Furthermore, the experiment reveals a correlation between IVBO and game enjoyment, which is a further indication that nonhumanoid creatures offer a meaningful design space for VR games worth further investigation.
Outstanding: A Multi-Perspective Travel Approach for Virtual Reality Games

In virtual reality games, players dive into fictional environments and can experience a compelling and immersive world. State-of-the-art VR systems allow for natural and intuitive navigation through physical walking. However, the tracking space is still limited, and viable alternatives \commor extensions are required to reach further virtual destinations. Our work focuses on the exploration of vast open worlds -- an area where existing local navigation approaches such as the arc-based teleport are not ideally suited and world-in-miniature techniques potentially reduce presence. We present a novel alternative for open environments: Our idea is to equip players with the ability to switch from first-person to a third-person bird's eye perspective on demand. From above, players can command their avatar and initiate travels over large distance. Our evaluation reveals a significant increase in spatial orientation while avoiding cybersickness and preserving presence, enjoyment, and competence. We summarize our findings in a set of comprehensive design guidelines to help developers integrate our technique.
Social Media Popularity Prediction

Social media popularity prediction (SMPD) aims to predict the popularity of the post shared on online social media platforms. This task is crucial for content providers and consumers in a wide range of real-world applications, including multimedia advertising, recommendation system and trend analysis. In this paper, we propose to fuse features from multiple sources by deep neural networks (DNNs) for popularity prediction. Specifically, high-level image and text features are extracted by the advanced pretrained DNN, and numerical features are captured from the metadata of the posts. All of the features are concatenated and fed into a regressor with multiple dense layers. Experiments have demonstrated the effectiveness of the proposed model on the ACM Multimedia Challenge SMPD2019 dataset. We also verify the importance of each feature via univariate test and ablation study, and provide the insights of feature combination for social media popularity prediction.