Reviewerly/exHarmony huggingface dataset:
Facial Image Privacy Protection Based on Principal Components of Adversarial Segmented Image Blocks

The features in facial images, which are utilized for a variety of technological applications, pose a significant privacy concern for users.This paper proposes a method for protecting privacy in facial images based on the principal components of adversarial segmented image blocks.Generative adversarial network parameters are compressed by segmenting the facial images into blocks and extracting the principal components of the segmented image.The generator and discriminator in the generative adversarial network then generate images similar to the original facial images; the facial images generated by the generator, as-driven by the target recognition network, markedly different from the original facial images.As the generator, discriminator, and target recognition network compete with each other, minor perturbation is added to the principal components of the facial images to protect the users' privacy and prevent distinct face-related features of the images from being easily extracted.Experimental results show that the proposed method outperforms other similar methods in terms of generated image quality, operation speed, and target recognition network accuracy.
Interpolating splines on graphs for data science applications

We introduce intrinsic interpolatory bases for data structured on graphs and derive properties of those bases. Polyharmonic Lagrange functions are shown to satisfy exponential decay away from their centers. The decay depends on the density of the zeros of the Lagrange function, showing that they scale with the density of the data. These results indicate that Lagrange-type bases are ideal building blocks for analyzing data on graphs, and we illustrate their use in kernel-based machine learning applications.
Plundervolt: How a Little Bit of Undervolting Can Create a Lot of Trouble

Historically, fault injection was the realm of adversaries with physical access. This changed when research revealed that remote attackers could use software to inject faults. Plundervolt is a new software-based attack on Intel's trusted execution technology (SGX). Plundervolt can break cryptography and inject memory-safety bugs into secure code.
TargetFinder

With the proliferation of IoT cameras, it is possible to use crowdsourced videos to help find interested targets (e.g., crime suspect, lost child, lost vehicle) on demand. Due to the ubiquity of IoT cameras such as dash mounted and phone cameras, the crowdsourced videos have much better spatial coverage compared to only using surveillance cameras, and, thus, can significantly improve the effectiveness of target search. However, this may raise privacy concerns when workers (owners of IoT cameras) are provided with photos of the target. Also, the videos captured by the workers may be misused to track bystanders. To address this problem, we design and implement TargetFinder, a privacy preserving system for target search through IoT cameras. By exploiting homomorphic encryption techniques, the server can search for the target on encrypted information. We also propose techniques to allow the requester (e.g., the police) to receive images that include the target, while all other captured images of the bystanders are not revealed. Moreover, the target’s face image is not revealed to the server and the participating workers. Due to the high computation overhead of the cryptographic primitives, we develop optimization techniques in order to run our privacy preserving protocol on mobile devices. We also formulate and solve a worker selection problem to maximize the probability of finding the target under some budget constraint. A real-world demo and extensive evaluations demonstrate the effectiveness of TargetFinder.
Myscéal: An Experimental Interactive Lifelog Retrieval System for LSC'20

The Lifelog Search Challenge (LSC), is an annual comparative bench-marking activity for comparing approaches to interactive retrieval from multi-modal lifelogs. Being an interactive search challenge, issues such as retrieval accuracy, search speed and usability of inter-faces are key challenges that must be addressed by every participant. In this paper, we introduce Myscéal, an interactive lifelog retrieval engine designed to support novice users to retrieve items of interest from a large multimodal lifelog. Additionally, we also introduce anew similarity measure called "aTFIDF", to match a user's free-text information need with the multimodal lifelog index.
Early Prediction of a Team Performance in the Initial Assessment Phases of a Software Project for Sustainable Software Engineering Education

Software engineering is a competitive field in education and practice. Software projects are key elements of software engineering courses. Software projects feature a fusion of process and product. The process reflects the methodology of performing the overall software engineering practice. The software product is the final product produced by applying the process. Like any other academic domain, an early evaluation of the software product being developed is vital to identify the at-risk teams for sustainable education in software engineering. Guidance and instructor attention can help overcome the confusion and difficulties of low performing teams. This study proposed a hybrid approach of information gain feature selection with a J48 decision tree to predict the earliest possible phase for final performance prediction. The proposed technique was compared with the state-of-the-art machine learning (ML) classifiers, naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), simple logistic regression (SLR), repeated incremental pruning to produce error reduction (RIPPER), and sequential minimal optimization (SMO). The goal of this process is to predict the teams expected to obtain a below-average grade in software product development. The proposed technique outperforms others in the prediction of low performing teams at an early assessment stage. The proposed J48-based technique outperforms others by making 89% correct predictions.
Phishing Attacks: Detecting and Preventing Infected E-mails Using Machine Learning Methods

The main aim of the current study has been to provide a novel tool for detecting phishing attacks and finding a solution to counteract such types of threats. In this article we describe the process of how to develop a Scrum-based implementation of algorithms for automatic learning, Feature Selection and Neural Networks. This tool has the ability to detect and mitigate a phishing attack registered inside the e-mail server. For the validation of the obtained results, we have used the source of information of blacklist of PhishTank, which is a collaborative clearing house for data and information about phishing on the Internet. The conducted proof of concept demonstrated that the implemented feature selection algorithm discards the irrelevant characteristics of electronic mail and, that the neural network algorithm adopts these characteristics, establishing an optimal level of learning without redundancies. It also reveals the functionality of the proposed solution.
A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis

Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant virus data research. In this work, we conduct a proposed approach that is non-medical/clinical, generate graphs from five features of the SARS outbreak data in five countries and regions, and offer insights from a visual analysis perspective. The results show that prevention measures such as quarantine are the most common control policies used, and areas with strict measures did have fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis perfective.
Performance Analysis of ACTE

HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer Encoding) [6], a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely, (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate on ACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.
DAM: Transformer-based relation detection for Question Answering over Knowledge Base

Relation Detection is a core component of Knowledge Base Question Answering (KBQA). In this paper, we propose a Transformer-based deep attentive semantic matching model (DAM), to identify the KB relations corresponding to the questions. The DAM is completely based on the attention mechanism and applies the fine-grained word-level attention to enhance the matching of questions and relations. On the basis of the DAM, we build a three-stage KBQA pipeline system. The experimental results on multiple benchmarks demonstrate that our DAM model outperforms previous methods on relation detection. In addition, our DAM-based KBQA system also achieves state-of-the-art results on multiple datasets.
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space (e.g. attribute space). Considering the seen and unseen classes as two domains, a big domain gap often exists which challenges ZSL. In this work, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap. Specifically, we first build a class hierarchy of multiple superclass layers and a single class layer, where the superclasses are automatically generated by data-driven clustering over the semantic representations of all seen and unseen class names. We then exploit the superclasses from the class hierarchy to tackle the domain gap challenge in two aspects: deep feature learning and projection function learning. First, to narrow the domain gap in the feature space, we define a recurrent neural network over superclasses and then plug it into a convolutional neural network for enforcing the superclass hierarchy. Second, to further learn a transferrable projection function for ZSL, a novel projection function learning method is proposed by exploiting the superclasses to align the two domains. Importantly, our transferrable feature and projection learning methods can be easily extended to a closely related task—few-shot learning (FSL). Extensive experiments show that the proposed model outperforms the state-of-the-art alternatives in both ZSL and FSL tasks.
Sketch-then-Edit Generative Adversarial Network

Generative Adversarial Network (GAN) has been widely used to generate impressively plausible data. However, it is a non-trivial task to train the original GAN model in practice due to the vanishing gradient problem. This is because the JS divergence could be a constant (i.e., log2) when original data distribution and generated data distribution hold a negligible overlapping area. Under such a scenario, the gradient of generator is 0. Most efforts have been devoted to designing a more proper difference measure while few attentions have been paid to the former aspect of the issue. In this paper, we propose a new method to design a noise distribution having a guaranteed non-negligible overlapping area with raw data distribution. The key idea is to transform the noise from the randomized space into the raw data space. We propose to obtain the transformation as the basis matrix in non-negative matrix factorization because the basis matrix has the underlying features of the raw data. The proposed idea is instantiated as Sketch-then-Edit GAN (SEGAN) where sketches are the noises after transformation and are adopted as the name since they contains basic features of the raw data. Moreover, a new generator for editing the sketches into realistic-like data is designed. We mathematically prove that SEGAN solves the gradient vanishing problem, and conduct extensive experiments on the MNIST, CIFAR10, SVHN and Celeba datasets to demonstrate the effectiveness of SEGAN.
The GAN That Warped: Semantic Attribute Editing With Unpaired Data

Deep neural networks have recently been used to edit images with great success, in particular for faces. However, they are often limited to only being able to work at a restricted range of resolutions. Many methods are so flexible that face edits can often result in an unwanted loss of identity. This work proposes to learn how to perform semantic image edits through the application of smooth warp fields. Previous approaches that attempted to use warping for semantic edits required paired data, i.e. example images of the same subject with different semantic attributes. In contrast, we employ recent advances in Generative Adversarial Networks that allow our model to be trained with unpaired data. We demonstrate face editing at very high resolutions (4k images) with a single forward pass of a deep network at a lower resolution. We also show that our edits are substantially better at preserving the subject's identity. The robustness of our approach is demonstrated by showing plausible image editing results on the Cub200 birds dataset. To our knowledge this has not been previously accomplished, due the challenging nature of the dataset.
Offline Signature Verification on Real-World Documents

Research on offline signature verification has explored a large variety of methods on multiple signature datasets, which are collected under controlled conditions. However, these datasets may not fully reflect the characteristics of the signatures in some practical use cases. Real-world signatures extracted from the formal documents may contain different types of occlusions, for example, stamps, company seals, ruling lines, and signature boxes. Moreover, they may have very high intra-class variations, where even genuine signatures resemble forgeries. In this paper, we address a real-world writer independent offline signature verification problem, in which, a bank's customers' transaction request documents that contain their occluded signatures are compared with their clean reference signatures. Our proposed method consists of two main components, a stamp cleaning method based on CycleGAN and signature representation based on CNNs. We extensively evaluate different verification setups, fine-tuning strategies, and signature representation approaches to have a thorough analysis of the problem. Moreover, we conduct a human evaluation to show the challenging nature of the problem. We run experiments both on our custom dataset, as well as on the publicly available Tobacco-800 dataset. The experimental results validate the difficulty of offline signature verification on real-world documents. However, by employing the stamp cleaning process, we improve the signature verification performance significantly.
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation

We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.
Attack to Explain Deep Representation

Deep visual models are susceptible to extremely low magnitude perturbations to input images. Though carefully crafted, the perturbation patterns generally appear noisy, yet they are able to perform controlled manipulation of model predictions. This observation is used to argue that deep representation is misaligned with human perception. This paper counter-argues and proposes the first attack on deep learning that aims at explaining the learned representation instead of fooling it. By extending the input domain of the manipulative signal and employing a model faithful channelling, we iteratively accumulate adversarial perturbations for a deep model. The accumulated signal gradually manifests itself as a collection of visually salient features of the target label (in model fooling), casting adversarial perturbations as primitive features of the target label. Our attack provides the first demonstration of systematically computing perturbations for adversarially non-robust classifiers that comprise salient visual features of objects. We leverage the model explaining character of our algorithm to perform image generation, inpainting and interactive image manipulation by attacking adversarially robust classifiers. The visually appealing results across these applications demonstrate the utility of our attack (and perturbations in general) beyond model fooling.
Working principle, Application areas and Challenges for Blockchain Technology

In the modern world where we reside in everything is shifting towards being digital, from online transaction to teaching”, thus this paper aims at something like this only that is blockchain and its application in multiple domains. The paper investigates blockchain and its current scenario, it first focuses on the emergence of blockchain and its working and also briefly explains the characteristics and challenges faced by technology. The blockchain serves as an immutable way that allows the transactions to happen in a decentralized manner and by allowing the digital information to be distributed, blockchain has built a backbone for a new type of internet. In this paper, application areas of blockchain such as banking, healthcare, etc. are also discussed.
Approximating text-to-pattern Hamming distances

We revisit a fundamental problem in string matching: given a pattern of length m and a text of length n, both over an alphabet of size σ, compute the Hamming distance (i.e., the number of mismatches) between the pattern and the text at every location. Several randomized (1+ε)-approximation algorithms have been proposed in the literature (e.g., by Karloff (Inf. Proc. Lett., 1993), Indyk (FOCS 1998), and Kopelowitz and Porat (SOSA 2018)), with running time of the form O(ε−O(1) nlognlogm), all using fast Fourier transform (FFT). We describe a simple randomized (1+ε)-approximation algorithm that is faster and does not need FFT. Combining our approach with additional ideas leads to numerous new results (all Monte-Carlo randomized) in different settings:
Time Flies: Animating a Still Image With Time-Lapse Video As Reference

Time-lapse videos usually perform eye-catching appearances but are often hard to create. In this paper, we propose a self-supervised end-to-end model to generate the time-lapse video from a single image and a reference video. Our key idea is to extract both the style and the features of temporal variation from the reference video, and transfer them onto the input image. To ensure both the temporal consistency and realness of our resultant videos, we introduce several novel designs in our architecture, including classwise NoiseAdaIN, flow loss, and the video discriminator. In comparison to the baselines of state-of-the-art style transfer approaches, our proposed method is not only efficient in computation but also able to create more realistic and temporally smooth time-lapse video of a still image, with its temporal variation consistent to the reference.
Helios

Helios is a distributed, highly-scalable system used at Microsoft for flexible ingestion, indexing, and aggregation of large streams of real-time data that is designed to plug into relational engines. The system collects close to a quadrillion events indexing approximately 16 trillion search keys per day from hundreds of thousands of machines across tens of data centers around the world. Helios use cases within Microsoft include debugging/diagnostics in both public and government clouds, workload characterization, cluster health monitoring, deriving business insights and performing impact analysis of incidents in other large-scale systems such as Azure Data Lake and Cosmos. Helios also serves as a reference blueprint for other large-scale systems within Microsoft. We present the simple data model behind Helios, which offers great flexibility and control over costs, and enables the system to asynchronously index massive streams of data. We also present our experiences in building and operating Helios over the last five years at Microsoft.
Composing Good Shots by Exploiting Mutual Relations

Finding views with a good composition from an input image is a common but challenging problem. There are usually at least dozens of candidates (regions) in an image, and how to evaluate these candidates is subjective. Most existing methods only use the feature corresponding to each candidate to evaluate the quality. However, the mutual relations between the candidates from an image play an essential role in composing a good shot due to the comparative nature of this problem. Motivated by this, we propose a graph-based module with a gated feature update to model the relations between different candidates. The candidate region features are propagated on a graph that models mutual relations between different regions for mining the useful information such that the relation features and region features are adaptively fused. We design a multi-task loss to train the model, especially, a regularization term is adopted to incorporate the prior knowledge about the relations into the graph. A data augmentation method is also developed by mixing nodes from different graphs to improve the model generalization ability. Experimental results show that the proposed model performs favorably against state-of-the-art methods, and comprehensive ablation studies demonstrate the contribution of each module and graph-based inference of the proposed method.