Reviewerly/exHarmony huggingface dataset:
Fuzzy Matching Template Attacks on Multivariate Cryptography: A Case Study

Multivariate cryptography is one of the most promising candidates for post-quantum cryptography. Applying machine learning techniques in this paper, we experimentally investigate the side-channel security of the multivariate cryptosystems, which seriously threatens the hardware implementations of cryptographic systems. Generally, registers are required to store values of monomials and polynomials during the encryption of multivariate cryptosystems. Based on maximum-likelihood and fuzzy matching techniques, we propose a template-based least-square technique to efficiently exploit the side-channel leakage of registers. Using QUAD for a case study, which is a typical multivariate cryptosystem with provable security, we perform our attack against both serial and parallel QUAD implementations on field programmable gate array (FPGA). Experimental results show that our attacks on both serial and parallel implementations require only about 30 and 150 power traces, respectively, to successfully reveal the secret key with a success rate close to 100%. Finally, efficient and low-cost strategies are proposed to resist side-channel attacks.
An Improved Dual-Channel Network to Eliminate Catastrophic Forgetting

Catastrophic forgetting is a chronic problem during the online training process of deep neural networks. That is, once a new data set is used to train an existing neural network, the network will lose the ability to recognize the original data set. In literature, online contrastive divergence (CD) with generative replay (GR) exploits the generative capacity of the neural network to facilitate online training. It greatly alleviates catastrophic forgetting but cannot totally eliminate it. To overcome this shortcoming and further solve the challenging issue, in this article, we propose a novel approach named asynchronous dual-channel online restricted Boltzmann machine, where online CD with dual-channel GR plays an important role in further eliminating catastrophic forgetting. The asynchronous gradient estimation, by which the Markov chain sampling and the network calculation are conducted asynchronously on separate computing nodes, is designed to speed up training. The experimental results show that the proposed method outperforms several algorithms in increasing training speed and minimizing catastrophic forgetting. Besides, online learning with dual-channel can be effectively extended to other online learning neural networks with GR and has achieved excellent results in our verification experiments.
TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning

This paper introduces our approach to the EmotioNet Challenge 2020. We pose the AU recognition problem as a multi-task learning problem, where the non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head motion (the last 6 AUs) are modeled separately. The co-occurrence of the expression features and the head pose features are explored. We observe that different AUs converge at various speed. By choosing the optimal checkpoint for each AU, the recognition results are improved. We are able to obtain a final score of 0.746 in validation set and 0.7306 in the test set of the challenge.
VTIM: Video Title Identification Using Open Metadata

Video-streaming applications are very popular these days. Existing studies of video streaming have attempted to identify video titles of users using machine learning techniques to identify specific patterns of video packets transmitted over the network. However, these studies have limitations when applied to actual environments where the network is congested or there are multiple users in the same network. This paper proposes Video Title Identification using open Metadata (VTIM), which identifies video titles by analyzing storyboards and Media Presentation Description (MPD) of MPEG-DASH in connection with video packets transmitted over the network. Attack was carried out using VTIM on 13,291 videos selected from actual video-streaming environment of YouTube. Our experiments show that VTIM is able to identify video titles with 100% accuracy at nearly thirty times faster than existing methods based on machine learning techniques. The paper also proposes and evaluates a countermeasure against VTIM.
Real-time Tracking with Stabilized Frame

Deep learning methods have dramatically increased tracking accuracy benefitting from exquisite features extractor. Among these methods, siamese-based tracker performs well. However, in case of camera shaking, the objects are easily to be lost because of no consideration of camera judder, and the position of each pixel changes drastically between frames. In particular, the tracking performance would degrade dramatically in case that the target is small and moving fast, such as UAV tracking. In this paper, the S-Siam framework is proposed to deal with this problem and improves the performance of real-time tracking. Through stabilizing each frame by estimating where the object is going to move, the camera is adjusted adaptively to keep the object in its original position. Experimental results on the VOT2018 dataset show that the proposed method obtained an EAO score 0.449, and achieved 10% robustness improvement compared with existing three trackers, i.e., SiamFC, SiamMask and SiamRPN++, which demonstrates the effectiveness of the proposed algorithm.
Controlling Over Enhancement of Images Using Histogram Equalization Technique

Abstract Contrast Enhancement is a powerful technique to procure high quality images with outstanding contrast enrichment and an appreciable improvement in visual quality. A multitude of contrast enhancement schemes based on Histogram Equalization accomplished to improve image perceptibility are available in literature. Although these perform quite well, but image over enhancement is a stumbling block, which cannot be overlooked. This paper is an effort to resolve the above problem by incorporating a novel recursive approach, taking Histogram Equalization (HE) as the base methodology for improving the subjective quality of image. The proposed mechanism searches those portions of image where contrast enhancement is actually required, thus avoiding over enhancement. Furthermore, contrast adjustment is performed on the overall image so as to suppress the excessively enhanced regions. A simulator is designed in MATLAB 2016a to fulfil the defined purpose. The comparison results of proposed technique with already reported ones show that ours is better than others in terms of the performance metrics, Contrast Improvement Index(CII) and Colour Enhancement Factor (CEF).
Tracking, tracing, trust: contemplating mitigating the impact of <scp>COVID</scp> ‐19 through technological interventions

To the Editor: The use of Bluetooth-enabled apps like Australia's COVIDSafe to contact trace people exposed to coronavirus disease 2019 (COVID-19) raises challenging moral and public health questions. Leins and colleagues1 rightly note that such tracing may endanger human rights. Yet the ethical decisions for governments and citizens are complex. The absence of vaccines and effective treatments, and the significant asymptomatic transmission of SARS-CoV-2, compels reliance on traditional tactics of social distancing, quarantine and contact tracing.2, 3 Although the added value of digital contact tracing over manual tracing remains uncertain, even marginal improvements may interrupt disease transmission, save lives and improve public health resourcing. This could especially benefit vulnerable and disadvantaged people who suffer disproportionate harms,4 without treating digital contact tracing as a “silver bullet”. Whether, and which, digital contact tracing options are warranted depends on tough cost–benefit judgements. COVIDSafe's centralised storage of data on Amazon's servers facilitates access by governments with extraordinary power to interfere in citizens’ lives. Alternatively, decentralised data storage on smartphones has privacy advantages — but providing individual app users with the discretion to act on notifications of potential exposure to COVID-19 may compromise disease control efforts. A hard choice exists between allowing personal data to be accessible by democratically elected governments versus powerful technology giants like Apple and Google which support decentralised data storage.5 Even greater invasions of privacy have been proposed, however, with location tracking options such as Norway's Smittestopp app (https://helsenorge.no/coronavirus/smittestopp) promoted as necessary to understand community interactions and the effects of social distancing policies for current (and future) outbreaks. While Leins and colleagues highlight significant ethical drawbacks, a full ethical analysis of digital contact tracing must also weigh its potential benefits. Certainly, citizens should agitate for strong protections to prevent abuse of power and misuse of personal information. However, even when governments offer ethically suboptimal contact tracing options, it may be permissible and even a moral requirement, all things considered, for citizens to support options to help protect the community. For its part, the Australian government should recognise that deploying digital tracing without sufficient transparency and community and expert input leaves citizens with harder moral decisions. No relevant disclosures.
Minimum-Energy Trajectory Planning for Industrial Robotic Applications: Analytical Model and Experimental Results

In this paper an analytical model and experimental results for minimum-energy trajectories applied to robotic axes are presented. The dynamic and electro-mechanical models of a linear axis are implemented and applied for the planning of optimal point-to-point trajectories, using trapezoidal and cycloidal motion profiles. A challenging (but realistic, in industrial environments) scenario is chosen, namely a linear axis of a Cartesian manipulator built in the 1990’s. Experimental tests are carried out measuring the energy consumption directly from the drive unit. Despite the limitations of the mechanical and power measurement systems, the results show a trend in accordance with the numerical predictions, demonstrating the feasibility of the approach in enhancing the energetic performance of the robotic system, even in worst-case conditions.
The Expression of Power in ICT's Knowledge Enterprise

ICT globalization continues to spread hardware, software, and accompanying technologies, so too does knowledges and trainings on those ICTs. This knowledge migration process has been linked by scholars to a 'colonial impulse' inherent in computing as a knowledge enterprise, which incorporates into broader colonizing forces. Through simultaneous explorations of dual case studies with a tribal ISP in California and an educational organization that works with indigenous First Nations communities in British Columbia, we depict how power circulates in this process, both empowering and disempowering communities. We then offer a brief argument for the need to foreground methods and approaches to disentangling these contradicting forces.
Designing &lt;Strike&gt;with&lt;/Strike&gt; as People

The expression ‘designing with people’ predefines paradigmatic roles for designers and people collaborating in a design process. This paper challenges this paradigm and asks what other forms of collaboration would look like and what expressions could we use to define them. It shares a personal account of a project originally aimed at designing tools for doing sensory therapies in a psychiatric unit, but where the people working and living there did not assume the role of participants and I had to collaborate with them in their own terms and ended up making an arts trolley. Building on autonomous design, I propose the expression ‘designing as people’ to make sense of this experience. In the context of this conference, ‘designing as people’ is a provocation and invitation to explore forms of collaborative design where designers move away from the role of facilitators to become participants of creative processes of communities and learn to design in the ways that those who are not designers do it.
Autonomous Malicious Video Content Categorization Using Convolutional Neural Networks

Autonomous Video Content Categorization, as the name indicates, describes a technique where the input content gets reviewed automatically. Here, categorization means that given a video as an input to the system, the system classifies if it contains nudity content, gambling or alcoholic content. With the increasing volumes of data, bad video content review is becoming an important component in every field. Considerable time and effort get consumed in performing the manual content review, also the process of the creative display is delayed to the user. Here Convolutional Neural Network i.e. a class of deep neural network is used which consists of an input and an output layer, as well as multiple hidden layers. Deep learning is one such field which can give good results in that respect. Feature extractors like Convolutional neural networks (CNN) have proved to be significant in the field of video classification.
Adversarial Examples on Deep-Learning-Based ADS-B Spoofing Detection

Deep neural networks have been applied to many tasks in air traffic management, ranging from anomaly detection to flight trajectory prediction. However, it has been shown that such algorithms are susceptible to adversarial examples. In this letter, we manage to show that current deep learning algorithms proposed for spoofing detection are vulnerable to maliciously crafted ADS-B data. To inject the false messages into the ADS-B system without being detected, we need to find adversarial perturbations to balance the need of overwhelming the channel noise and keeping the decoding error low. Simulation results demonstrate the viability of our approach to evade the DNN-based spoofing detector without increasing the decoding error.
Participant's View: Short-Term Evaluation of Realizing PD Ideals

Participatory Design (PD) has long been described as a way to democratize technology development by involving those affected by the outcomes. Besides a good fit of technology as the outcome, PD allows participants to ‘have a say’, and supports ‘mutual learning’ and ‘co-realization’. A diverse range of PD methods has been developed, but there is a shortage of empirical studies showing whether (and why) these methods help pursue PD ideals. We therefore report on a case study of a short-term evaluation approach that may exemplary close this gap, and that allowed us to gain more knowledge on how our method affected participants. Participants perceived moderate to high feelings of ‘having a say’, ‘mutual learning’, and ‘co-realization’. In future research, mutual learning may exemplarily be improved by introducing a changing peer-to-peer procedure to the method. The evaluation may further be advanced by taking expectations into account.
Human-centric Quality Management of Immersive Multimedia Applications

Augmented Reality (AR) and Virtual Reality (VR) multimodal systems are the latest trend within the field of multimedia. As they emulate the senses by means of omnidirectional visuals, 360° sound, motion tracking and touch simulation, they are able to create a strong feeling of presence and interaction with the virtual environment. These experiences can be applied for virtual training (Industry 4.0), tele-surgery (healthcare) or remote learning (education). However, given the strong time and task sensitiveness of these applications, it is of great importance to sustain the end-user quality, i.e. the Quality-of-Experience (QoE), at all times. Lack of synchronization and quality degradation need to be reduced to a minimum to avoid feelings of cybersickness or loss of immersiveness and concentration. This means that there is a need to shift the quality management from system-centered performance metrics towards a more human, QoE-centered approach. However, this requires for novel techniques in the three areas of the QoE-management loop (monitoring, modelling and control). This position paper identifies open areas of research to fully enable human-centric driven management of immersive multimedia. To this extent, four main dimensions are put forward: (1) Task and well-being driven subjective assessment; (2) Real-time QoE modelling; (3) Accurate viewport prediction; (4) Machine Learning (ML)-based quality optimization and content recreation. This paper discusses the state-of-the-art, and provides with possible solutions to tackle the open challenges.
The challenge of identifying historic ‘private browsing’ sessions on suspect devices

Those subject to supervision orders requiring the lawful regulation of their Internet activity provide an investigatory challenge to law enforcement practitioners. Where a defendant's Internet history must be retained and made accessible for review at defined intervals, investigators must apply principles of digital triage to quickly evaluate existing content in order to identify any potential breaching activity. Such processes are often undertaken ‘on-scene’ and under time constraints. Whilst in some cases detecting offending actions may be simple, particularly if illegal history records are retained, it may be the case that some techniques such as private browsing functions leave minimal trace of usage post-event. As a result, live monitoring and activity capture may provide the only viable solution with regards to the effective regulation of Internet usage for those under supervision. This work examines the use of AppLocker and a bespoke developed solution ‘Private-Spy’ for the purpose of private browsing session detection. Both solutions are evaluated and findings are offered.
Optical estimation of unitary Gaussian processes without phase reference using Fock states

Abstract Since a general Gaussian process is phase-sensitive, a stable phase reference is required to take advantage of this feature. When the reference is missing, either due to the volatile nature of the measured sample or the measurement’s technical limitations, the resulting process appears as random in phase. Under this condition, we consider two single-mode Gaussian processes, displacement and squeezing. We show that these two can be efficiently estimated using photon number states and photon number resolving detectors. For separate estimation of displacement and squeezing, the practical estimation errors for hundreds of probes’ ensembles can saturate the Cramér–Rao bound even for arbitrary small values of the estimated parameters and under realistic losses. The estimation of displacement with Fock states always outperforms estimation using Gaussian states with equivalent energy and optimal measurement. For estimation of squeezing, Fock states outperform Gaussian methods, but only when their energy is large enough. Finally, we show that Fock states can also be used to estimate the displacement and the squeezing simultaneously.
Coarse-to-fine two-stage semantic video carving approach in digital forensics

Video (e.g. CCTV) plays a crucial role in digital forensics. Existing video carving methods either assume the existence of file system information or resort to the impractical exhaustive matching for all pairs of fragments. In this paper, a coarse-to-fine two-stage semantic video carving approach is proposed to improve the efficiency and precision of content-based video carving. The proposed approach introduces a perceptual grouping stage to quickly group video fragments first based on the structural similarity of the fragments, followed by a precise stitching stage which sorts the fragments within each group depending on the pixel-level content of the fragments to reconstruct each original video file. The proposed approach can reduce the computational complexity and achieve a high carving precision since the two complementary methods used in two stages focus on different-scale features of video content. Experimental results based on a YouTube-8M video clip dataset show that the overall carving precision of the proposed approach is very high (e.g. 97.2% even when the number of mixed video fragments is increased to 288, which are from 36 video files with a fragmentation degree of 8). The overall carving time is 326.22 s, about 10 times lower than that of our previous optical flow-based approach.
Reflexive account-giving through ‘practice notations’:

This paper takes contemporary infrastructuring discourses further to explore ways to notice, articulate, and learn from dynamic shifts that characterize contingent contexts of participatory designing. We explore the knowledge(s) that can emerge from generating understandings of movements in relation to changing conditions through the use of ‘practice notation’ in a long-term collaboration with an organization working to end the prison industrial complex in the US. Informed by dance notation, the project experimented through practices of inquiry that enabled understandings across multiple scales, from the local to the systemic, with a focus on how collectivity was imagined, built, challenged, and sustained. We consider how practice notations can complement dynamic accounts of infrastructuring by layering participants’ movement, ideas, and contexts to create new understandings of practice. In so doing, we commit to the ethics of providing richer and more grounded articulations of participatory practices-in-motion.
Deep Learning vs Hand-Crafted Features for Face Recognition under Uncontrolled Conditions

Facial recognition in degraded conditions has become a very active and widely used area in several fields such as video surveillance and access control. Indeed, the presence of facial degradation such as the variation in face expressions, head poses, illumination or presence of partial occlusion, presents a great challenge for facial recognition. To overcome this problem, several approaches and techniques have been proposed in the literature. In this paper, we make a comparison between usual hand-crafted features, pre-trained architecture models and learned features extracted from the final layers of these pre-trained models. In particular, exploiting the large learning capability of deep networks improves the facial recognition results compared to usual methods based on handcrafted features. The results based on the Eurecom Kinect Face DB dataset demonstrate the effectiveness of learned features for face recognition classification.
Review of Deep Learning Techniques for Improving the Performance of Machine Reading Comprehension Problem

The amazing research of Artificial Intelligence is natural language processing (NLP) and the mesmerizing field in NLP is machine reading comprehension (MRC). MRC alleviates the efforts of making machines behave like a human as it helps information accessing in natural language by developing Question answering systems. MRC is summarized as a task to read a piece of text, understand it, and answer the related question of the text. Reading text can be cloze style reading (fill in the blanks from the text) as well as open style reading (separate question) and understanding the piece of text as well as the query is accomplished by contextual representation and Attention mechanism. In the MRC literature, various methodologies have been used for extracting answers from the given text including primitive methods to the deep learning methods to have a step towards deploying machine intelligence. The introduction of deep learning and large datasets in the recent few years has encouraged the success of MRC. This paper gives a recent review of MRC models based on deep learning, datasets on which they have been evaluated, and also their word representations.