Reviewerly/exHarmony huggingface dataset:
A Framework to Counter Statistical Ineffective Fault Analysis of Block Ciphers Using Domain Transformation and Error Correction

Right from its introduction, fault attacks (FA) have been established to be one of the most practical threats to both public key and symmetric key based cryptosystems. Statistical Ineffective Fault Analysis (SIFA) is a recently proposed class of fault attacks introduced at CHES 2018. The fascinating feature of this attack is that it exploits the correct ciphertexts obtained during a fault injection campaign, instead of the faulty ciphertexts. SIFA has been shown to bypass almost all of the existing fault attack countermeasures even when they are combined with masking schemes for side-channel resistance. The goal of this work is to propose a countermeasure framework for SIFA. It has been observed that a randomized domain transformation of the intermediate computation combined with bit-level error correction can prevent SIFA attacks. The domain transformation (Transform) can be realized by standard masking schemes. In fact, we prove that if biased faults are injected at the state register of a block cipher at a certain target round, then masking is sufficient for SIFA protection, until all the shares for a specific bit are corrupted. However, masking alone cannot prevent SIFA if the faults are injected at certain specific locations inside the S-Boxes. To address this issue, we incorporate a bit-level error-correction mechanism (Encode). An instantiation of this Transform-and-Encode (TaE) framework, called AntiSIFA, has been proposed and realized for the block cipher PRESENT as a proof-of-concept. Practical evaluation of the countermeasure implementation in both hardware and software ensures our theoretical claims regarding SIFA security, as well as protection against Side-Channel-Attacks (SCA).
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos

Deep neural networks (DNNs) have been widely applied in various applications, including autonomous driving and surveillance systems. However, DNNs are found to be vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead a learner to make incorrect predictions. While several defense and detection approaches are proposed for static image classification, many security-critical tasks use videos as their input and require efficient processing. In this paper, we propose an efficient and effective method advIT to detect adversarial frames within videos against different types of attacks based on temporal consistency property of videos. In particular, we apply optical flow estimation to the target and previous frames to generate pseudo frames and evaluate the consistency of the learner output between these pseudo frames and target. High inconsistency indicates that the target frame is adversarial. We conduct extensive experiments on various learning tasks including video semantic segmentation, human pose estimation, object detection, and action recognition, and demonstrate that we can achieve above 95% adversarial frame detection rate. To consider adaptive attackers, we show that even if an adversary has access to the detector and performs a strong adaptive attack based on the state of the art expectation of transformation method, the detection rate stays almost the same. We also tested the transferability among different optical flow estimators and show that it is hard for attackers to attack one and transfer the perturbation to others. In addition, as efficiency is important in video analysis, we show that advIT can achieve real-time detection in about 0.03--0.4 seconds.
Efficient dynamic multi-keyword fuzzy search over encrypted cloud data

Multi-keyword search of encrypted cloud data has attracted extensive attention worldwide in the recent years due to the increasing concern for data security and privacy in Cloud Computing. Fault-tolerance is important for multi-keyword fuzzy search which can provide accurate results even with the presence of minor spelling and typographical errors in the search keywords. But, existing fuzzy search schemes lack efficiency due to their high computational overhead and do not support file dynamic updates. This paper proposes an efficient dynamic multi-keyword fuzzy search scheme for encrypted cloud data to support dynamic file updates. Locality sensitive hashing (LSH) and Bloom filters are employed to generate index vectors and query vectors. Based on the generated vectors, a balanced binary tree is constructed as the index for the entire file set, and a Top-k search algorithm is developed to search k files that are most relevant to a given query with the help of the index tree. Extensive experiments conducted on real-world datasets demonstrate that our scheme is more efficient than existing similar schemes.
HoneyBadgerMPC and AsynchroMix

Multiparty computation as a service (MPSaaS) is a promising approach for building privacy-preserving communication systems. However, in this paper, we argue that existing MPC implementations are inadequate for this application as they do not address fairness, let alone robustness. Even a single malicious server can cause the protocol to abort while seeing the output for itself, which in the context of an anonymous communication service would create a vulnerability to censorship and de-anonymization attacks. To remedy this we propose a new MPC implementation, HoneyBadgerMPC, that combines a robust online phase with an optimistic offline phase that is efficient enough to run continuously alongside the online phase. We use HoneyBadgerMPC to develop an application case study, called AsynchroMix, that provides an anonymous broadcast functionality. AsynchroMix features a novel MPC program that trades off between computation and communication, allowing for low-latency message mixing in varying settings. In a cloud-based distributed benchmark with 100 nodes, we demonstrate mixing a batch of 512 messages in around 20 seconds and up to 4096 messages in around two minutes.
Emotion-aware Chat Machine

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
A cloud data deduplication scheme based on certificateless proxy re-encryption

Cloud data deduplication removes redundant data blocks or files and keeps only one copy in the cloud storage server. In order to improve on security, we need to encrypt data files and blocks such that all same files and blocks are detectable based on ciphertext for deduplication. So how to detect a ciphertext to find the same files is a challenging problem. In this paper, we propose a cloud data deduplication scheme based on certificateless proxy re-encryption. It contains certificateless proxy re-encryption (CL-PRE) and proof of ownership based on certificateless signature (PoW-CLS). Compared with the existing scheme, we use certificateless cryptography to solve the problem of key escrow and avoid the situation where a key generation center (KGC) impersonates a user to decrypt the ciphertext. Our CL-PRE realizes data deduplication across users and our PoW-CLS improves the efficiency of the proof of ownership (PoW).
ExcitNet Vocoder: A Neural Excitation Model for Parametric Speech Synthesis Systems

This paper proposes a WaveNet-based neural excitation model (ExcitNet) for statistical parametric speech synthesis systems. Conventional WaveNet-based neural vocoding systems significantly improve the perceptual quality of synthesized speech by statistically generating a time sequence of speech waveforms through an auto-regressive framework. However, they often suffer from noisy outputs because of the difficulties in capturing the complicated time-varying nature of speech signals. To improve modeling efficiency, the proposed ExcitNet vocoder employs an adaptive inverse filter to decouple spectral components from the speech signal. The residual component, i.e. excitation signal, is then trained and generated within the WaveNet framework. In this way, the quality of the synthesized speech signal can be further improved since the spectral component is well represented by a deep learning framework and, moreover, the residual component is efficiently generated by the WaveNet framework. Experimental results show that the proposed ExcitNet vocoder, trained both speaker-dependently and speaker-independently, outperforms traditional linear prediction vocoders and similarly configured conventional WaveNet vocoders.
Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management

Social or humanoid robots do hardly show up in "the wild", aiming at pervasive human benefits such as child health. This paper presents the socio-cognitive engineering (SCE) methodology for the required field research \& development of robots, focusing on the incremental development of a social robot and child-robot activities that support the daily diabetes management processes of children, aged between 7 and 14 years (i.e., supporting a healthy lifestyle). The SCE methodology helps to integrate into the human-agent/robot system: (a) theories, models and methods from different scientific disciplines, (b) technologies from different fields, (c) varying diabetes management practices, and (d) last but not least, the diverse individual and context-dependent needs of the patients and caregivers. The resulting system represents a new type of long-term human-robot partnerships with evolving collective intelligence. The current prototype is based on four human-robot partnership functions, a knowledge-base and interaction design for child's prolonged disease self-management. It has been developed and tested in three cycles, and proved to support the children on the three basic needs of the Self-Determination Theory: autonomy, competence and relatedness. To our knowledge, this is the first design \& test of a robot system "in the wild" for prolonged "blended" care of children with a chronic disease, showing positive results in a 3 month evaluation period.
SenseHAR

Modern smartphones and smartwatches are equipped with inertial sensors (accelerometer, gyroscope, and magnetometer) that can be used for Human Activity Recognition (HAR) to infer tasks such as daily activities, transportation modes and, gestures. HAR requires collecting raw inertial sensor values and training a machine learning model on the collected data. The challenge in this approach is that the models are trained for specific devices and device configurations whereas, in reality, the set of devices carried by a person may vary over time. Ideally, one would like activity inferencing to be robust of this variation and provide accurate predictions by making opportunistic use of information from available devices. Moreover, the devices may be located at different parts of the body (e.g. pocket, left and right wrist), may have different sets of sensors (e.g. a smartwatch may not have gyroscope while a smartphone might), and may differ in sampling frequencies. In this paper, we provide a solution which makes use of the information from available devices while being robust to their variations. Instead of training an end-to-end model for every permutation of device combinations and configurations, we propose a scalable deep learning based solution in which each device learns its own sensor fusion model that maps the raw sensor values to a shared low dimensional latent space which we call the 'SenseHAR'-a virtual activity sensor. The virtual sensor has the same format and similar behavior regardless of the subset of devices, sensor's availability, sampling rate, or a device's location. This would help machine learning engineers to develop their application specific (e.g., from gesture recognition to activities of daily life) models in a hardware-agnostic manner based on this virtual activity sensor. Our evaluations show that an application model trained on SenseHAR achieves the state of the art accuracies of 95.32%, 74.22% and 93.13% on PAMAP2, Opportunity(gestures) and our collected datasets respectively.
Multi-Level Features Extraction for Discontinuous Target Tracking in Remote Sensing Image Monitoring

Many techniques have been developed for computer vision in the past years. Features extraction and matching are the basis of many high-level applications. In this paper, we propose a multi-level features extraction for discontinuous target tracking in remote sensing image monitoring. The features of the reference image are pre-extracted at different levels. The first-level features are used to roughly check the candidate targets and other levels are used for refined matching. With Gaussian weight function introduced, the support of matching features is accumulated to make a final decision. Adaptive neighborhood and principal component analysis are used to improve the description of the feature. Experimental results verify the efficiency and accuracy of the proposed method.
Ambient Information Visualisation and Visitors’ Technology Acceptance of Mixed Reality in Museums

The visualisation of historical information and storytelling in museums is a crucial process for transferring knowledge by directly and simplistically engaging the museum audience. Until recently, technological limitations meant museums were limited to 2D and 3D screen-based information displays. However, advancements in Mixed Reality (MR) devices permit the propagation of a virtual overlay that amalgamates both real-world and virtual environments into a single spectrum. These holographical devices project a 3D space around the user which can be augmented with virtual artefacts, thus potentially changing the traditional museum visitor experience. Few research studies focus on utilising this virtual space to generate objects that do not visually inhibit or distract the operator. Therefore, this article aims to introduce the Ambient Information Visualisation Concept (AIVC) as a new form of storytelling, which can enhance the communication and interactivity between museum visitors and exhibits by measuring and sustaining an optimum spatial environment around the user. Furthermore, this article investigates the perceptual influences of AIVC on the users’ level of engagement in the museum. This article utilises the Microsoft HoloLens, which is one of the most cutting-edge imagining technologies available to date, in order to deploy the AIVC in a historical storytelling scene “The Battle” in the Egyptian department at The Manchester Museum. This research further seeks to measure the user acceptance of the MR prototype by adopting the Technology Acceptance Model (TAM). The operational approaches investigated in this study include personal innovativeness (PI), enjoyment (ENJ), usefulness (USF), ease of use (EOU), and willingness of future use (WFU). The population sampling methodology utilised 47 participants from the museum's daily visitors. Results of this research indicate that the WFU construct is the primary outcome of this study, followed by the usefulness factor. Further findings conclude that the majority of users found this technology highly engaging and easy to use. The combination of the proposed system and AIVC in museum storytelling has extensive applications in museums, galleries, and cultural heritage places to enhance the visitor experience.
Deep Multi-Scale Context Aware Feature Aggregation for Curved Scene Text Detection

Scene text plays a significant role in image and video understanding, which has made great progress in recent years. Most existing models on text detection in the wild have the assumption that all the texts are surrounded by a rotated rectangle or quadrangle. While there also exist lots of curved texts in the wild, which would not be bounded by a regular bounding box. In this paper, we develop a novel architecture to localize the text regions, which can deal with curved-shape scene texts. Specifically, we first design a text-related feature enhancement module by incorporating the prior knowledge of the text shape to enhance the feature representations. After that, based on the enhanced features, we employ a region proposal network to generate the candidate boxes of scene texts. For each text candidate, a pyramid region-of-interest pooling attention module is utilized to extract the fixed-size features. Finally, we exploit the box-aware context-based text segmentation module and box refinement network to obtain the location of scene text. Experiments are conducted on four challenging benchmarks CTW1500, totalTEXT, ICDAR-2015 and MLT, and the experimental results have demonstrated the superiority of our model.
Reasoning About Human-Object Interactions Through Dual Attention Networks

Objects are entities we act upon, where the functionality of an object is determined by how we interact with it. In this work we propose a Dual Attention Network model which reasons about human-object interactions. The dual-attentional framework weights the important features for objects and actions respectively. As a result, the recognition of objects and actions mutually benefit each other. The proposed model shows competitive classification performance on the human-object interaction dataset Something-Something. Besides, it can perform weak spatiotemporal localization and affordance segmentation, despite being trained only with video-level labels. The model not only finds when an action is happening and which object is being manipulated, but also identifies which part of the object is being interacted with.
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement

We present a method to improve the visual realism of low-quality, synthetic images, e.g. OpenGL renderings. Training an unpaired synthetic-to-real translation network in image space is severely under-constrained and produces visible artifacts. Instead, we propose a semi-supervised approach that operates on the disentangled shading and albedo layers of the image. Our two-stage pipeline first learns to predict accurate shading in a supervised fashion using physically-based renderings as targets, and further increases the realism of the textures and shading with an improved CycleGAN network. Extensive evaluations on the SUNCG indoor scene dataset demonstrate that our approach yields more realistic images compared to other state-of-the-art approaches. Furthermore, networks trained on our generated ``real'' images predict more accurate depth and normals than domain adaptation approaches, suggesting that improving the visual realism of the images can be more effective than imposing task-specific losses.
Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning

The rapid growth of e-commerce and social networking sites has created various challenges for the extraction of user-generated content (UGC). In the era of big data, customer opinions from social media are utilized for investigating consumer preferences to support product redesigns. Opinion mining, including the various automatic text classification algorithms using sentiment analysis is a capable tool to deal with a large amount of comments on the social networking sites. In which, sentiment analysis is used to determine the contextual polarity within a comment by searching sentimental words. However, the inconsistency on choosing the sentiment words leads to the inaccurate interpretation of the opinion strength of sentiment words. An approach to summarize the UGC from social networking media using fuzzy and ER without the need to review all the comments is proposed in this paper. The inaccuracy on determination of the polarity of sentiment words and corresponding opinion strengths is rectified by fuzzy approximation and ER. The result is presented in ranking therefore the effort for result interpretation significantly reduced. The incorporation of sentiment analysis with ER to analyze the UGC for product designs is a new attempt in investigating consumer preferences. The proposed approach is shown to be handy, sufficient, and cost effective for the product design and re-design, particularly in the preliminary stage. This project can be further extended by employing alternative fuzzy approximate techniques in the fuzzy-ER approach to support the sentiment analysis to enhance the accuracy of sentiment values for determining the distribution assessments of ER.
Bayesian Adaptive Superpixel Segmentation

Superpixels provide a useful intermediate image representation. Existing superpixel methods, however, suffer from at least some of the following drawbacks: 1) topology is handled heuristically; 2) the number of superpixels is either predefined or estimated at a prohibitive cost; 3) lack of adaptiveness. As a remedy, we propose a novel probabilistic model, self-coined Bayesian Adaptive Superpixel Segmentation (BASS), together with an efficient inference. BASS is a Bayesian nonparametric mixture model that also respects topology and favors spatial coherence. The optimizationbased and topology-aware inference is parallelizable and implemented in GPU. Quantitatively, BASS achieves results that are either better than the state-of-the-art or close to it, depending on the performance index and/or dataset. Qualitatively, we argue it achieves the best results; we demonstrate this by not only subjective visual inspection but also objective quantitative performance evaluation of the downstream application of face detection. Our code is available at https://github.com/uzielroy/BASS.
HyperSched

Prior research in resource scheduling for machine learning training workloads has largely focused on minimizing job completion times. Commonly, these model training workloads collectively search over a large number of parameter values that control the learning process in a hyperparameter search. It is preferable to identify and maximally provision the best-performing hyperparameter configuration (trial) to achieve the highest accuracy result as soon as possible.