Reviewerly/exHarmony huggingface dataset:
Algebraic Cryptanalysis of Variants of Frit

Frit is a cryptographic 384-bit permutation recently proposed by Simon et al. and follows a novel design approach for built-in countermeasures against fault attacks. We analyze the cryptanalytic security of Frit in different use cases and propose attacks on the full-round primitive. We show that the inverse $$\textsc {Frit}^{-1}$$ of Frit is significantly weaker than Frit from an algebraic perspective, despite the better diffusion of the inverse of the mixing functions $$\sigma $$: Its round function has an effective algebraic degree of only about 1.325. We show how to craft structured input spaces to linearize up to 4 (or, conditionally, 5) rounds and thus further reduce the degree. As a result, we propose very low-dimensional start-in-the-middle zero-sum partitioning distinguishers for unkeyed Frit, as well as integral distinguishers for reduced-round Frit and full-round $$\textsc {Frit}^{-1}$$. We also consider keyed Frit variants using Even-Mansour or arbitrary round keys. By using optimized interpolation attacks and symbolically evaluating up to 5 rounds of $$\textsc {Frit}^{-1}$$, we obtain key-recovery attacks with a complexity of either $$2^{59}$$ chosen plaintexts and $$2^{67}$$ time, or $$2^{18}$$ chosen ciphertexts and time (about 5 seconds in practice).
Mining behavioural patterns from spatial data

Behaviour of objects are determined from collections of responses, reactions, movements, properties, which are noted as attributes along with time and location in some spatial dataset. Behavioural Pattern finds a set of objects having similar behaviour. We have explored all types of possible measures to define similarity among objects having some similar features. Objects from same entity will have same set of features, whereas objects from different entity may have some similar features. A similarity measure has been proposed for mining behavioural patterns in different entity set. An approach for computing behavioural patterns is also proposed in this paper. Recent applications are focusing to understand the interaction between objects from various entity set, needs to measure behavioural similarity among objects within a region of interest. This newly defined measure and mining approach will defiantly help many applications to analyse data and extract important knowledge.
Fab-IoT-Lab: Technological Expertise, Guidance and Prototyping Skills in a Single Place

Designing connected objects or introducing them in industry processes is complex because it requires many skills. These skills can be found in different places. Research labs are studying advanced topics and can provide focused expertise. Some projects offer a shared infrastructure to elaborate and test new products and algorithms. Some organizations provide guidance and technological advice. The Fab-IoT-Lab project brings all these skills and competencies in a single place so that entrepreneurs and industries can benefit from the strengths of these existing structures without sacrificing the high-level, multidisciplinary point of view.
Migrating gradual types

Abstract Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically typed to more statically typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared. In this paper, we address this problem by developing migrational typing , which efficiently types all possible ways of replacing dynamic types with fully static types for a gradually typed program. The typing result supports automatically migrating a program to be as static as possible or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2–4 times longer than plain gradual typing.
On declarative rewriting for sound and complete union, intersection and negation types

Implementing the type system of a programming language is a critical task that is often done in an ad-hoc fashion. Whilst this makes it hard to ensure the system is sound, it also makes it difficult to extend as the language evolves. We are interested in describing type systems using rewrite rules from which an implementation can be automatically generated. Whilst not all type systems are easily expressed in this manner, those involving unions, intersections and negations are well-suited for this. For example, subtyping in such systems is naturally expressed as a maximal reduction over the intersection of types involved. In this paper, we consider a relatively complex type system involving unions, intersections and negations developed previously in the context of type checking. This system was not developed with rewriting in mind, though clear parallels are immediately apparent from the original presentation. For example, the system presented required types be first converted into a variation on Disjunctive Normal Form. However, some aspects of the original system are more challenging to express with rewrite rules. We identify that the original system can, for the most part, be reworked to enable a natural expression using rewrite rules. We present an implementation of our rewrite rules in the Whiley Rewrite Language (WyRL), and report performance results compared with a hand-coded solution. We also present an implementation of our system in the Rascal rewriting system, and find different trade offs.
Semantic convex matrix factorisation for cross‐media retrieval

When utilising matrix factorisation to extract latent features for cross-media retrieval, semantic information may be lost in the process of factorisation. In addition, many presented approaches directly mapped different modalities into an isomorphic semantic space to conduct the similarity measurement of different modalities, which also resulted in the loss of crucial information. To address these problems, a semantic convex matrix factorisation subspace learning approach is proposed for cross-media retrieval between image and text. The proposed method can extract an intermediate-level feature representation for the high dimensional image modality in order to weaken the loss of information, in the meantime, learn a semantic feature representation with semantic information for the lower dimension text modality to strengthen the discriminated capability. After that, the intermediate-level feature representation of image is mapped into a latent semantic space by a projection matrix. Then the similarity of different modalities can be estimated in terms of uniform dimensional latent feature representations. Experimental results on three benchmark datasets demonstrate the superiority of the proposed approach over several state-of-the-art approaches.
The influence of textese on adolescents’ perceptions of text message writers

Text messages are characterised by a casual language style, ‘textese’ (e.g., c u on thurs). This study investigated adolescents’ perceptions of the use of different levels of textese in digital messages which varied in their intended recipient (friend, teacher). Grade 8 students in Australia (N = 90, aged 13–14 years) each read six text messages purportedly written by a fellow student, and rated the extent to which they agreed (on a 7-point Likert scale) that the message writer was intelligent, paid attention to detail, used an appropriate writing style, and was friendly. Overall, participants rated message senders most favourably on all measures when they used no textese, and least favourably when they used high levels of textese. This pattern was even stronger when messages were addressed to teachers rather than same-aged friends. The findings suggest that adolescents are sensitive to both writing style and recipient when considering digital messages.
Under what conditions can an application service firm with in-house computing benefit from cloudbursting?

Cloudbursting, a hybrid cloud computing model, helps firms supplement their internal computing capacity from a private cloud by using external computing resources from a public cloud to meet increased demand. This paper examines whether cloudbursting benefits an application service firm that uses only its in-house capacity. Cloudbursting provides computing scalability and cost-effectiveness, but poses potential risks from data leakage when bursting into a public cloud. A private cloud reduces such risks, however, is constrained by resource limitations. We develop quantitative models under both non-competitive and competitive systems, and then determine the best choice between cloudbursting and a private cloud. Overall, we show that a profit-maximizing firm will benefit from migrating to cloudbursting if risk is considerably low and will maintain a private cloud if risk is considerably high. Interestingly, one exception occurs in a competition system whereby two competing firms access external resources as needed from the same public cloud. In this case, one firm will counterintuitively remain in a private cloud even though risk is considerably low while its competitor will migrate to cloudbursting. The numerical study conducts a sensitivity analysis to link a firm's profit performance with its best cloud choice.
Low‐complexity unidirectional systolic Dickson basis multiplier for lightweight cryptosystems

Finite field multiplier is a very important operation for realising elliptic curve cryptography. Dickson basis is a recently developed basis for representing finite elements in GF(2m). This study will propose a unidirectional systolic multiplier for such Dickson basis. The unidirectional systolic structure offers low space and time complexities and can be easily modified to have error detection capability which can resist side-channel attacks.
Magnification-invariant surface profiling technique for structured illumination imaging and microscopy

The structured illumination (SI) surface profiling method that is fast and has a depth-independent image magnification ratio is presented. Utilizing an electrically tunable lens (ETL) makes it possible to scan the object plane rapidly and motionlessly. The change in magnification ratio that generally occurs with the variation in the focal length of an objective lens is minimized by using a 4f relay system and placing the ETL at the confocal plane. The high reflection at the uncoated membrane surfaces of the ETL is bypassed by using polarization optics. It is also proposed that the fast response of the ETL can be fully exploited by switching the scanning sequence from that of a conventional SI imaging system; performing the depth scan before the pattern shift. It is shown that the scanning speed was increased up to 25 times. The principle and the related problems of SI profiling based on an ETL are fully analyzed, and the concept is confirmed experimentally. In the experiment, scanning depth variation of 35 mm could be achieved while keeping the magnification ratio variation below 0.03. The ability of 3D profiling is demonstrated by SI imaging a cone-shape 3D object and a face-shape plaster figure. In the fields requiring fast scanning, such as an intraoral scanner or biomedical imaging, the proposing SI imaging method can be fully utilized.
GAN-Based Pose-Aware Regulation for Video-Based Person Re-Identification

Video-based person re-identification deals with the inherent difficulty of matching sequences with different length, unregulated, and incomplete target pose/viewpoint structure. Common approaches operate either by reducing the problem to the still images case, facing a significant information loss, or by exploiting inter-sequence temporal dependencies as in Siamese Recurrent Neural Networks or in gait analysis. However, in all cases, the inter-sequences pose/viewpoint misalignment is considered, and the existing spatial approaches are mostly limited to the still images context. To this end, we propose a novel approach that can exploit more effectively the rich video information, by accounting for the role that the changing pose/viewpoint factor plays in the sequences matching process. In particular, our approach consists of two components. The first one attempts to complement the original pose-incomplete information carried by the sequences with synthetic GAN-generated images, and fuse their features vectors into a more discriminative viewpoint-insensitive embedding, namely Weighted Fusion (WF). Another one performs an explicit pose-based alignment of sequence pairs to promote coherent feature matching, namely Weighted-Pose Regulation (WPR). Extensive experiments on two large video-based benchmark datasets show that our approach outperforms considerably existing methods.
Non-Local Texture Optimization With Wasserstein Regularization Under Convolutional Neural Network

Example-based texture synthesis aims to generate a new texture from an exemplar texture and has long been drawing attention in the fields of computer graphics, computer vision, and image processing. Nevertheless, synthesizing structured textures remains a challenging task. Most previous methods rely on additional guidance channels, which encode the structured features of textures. However, estimating the guidance channel is very difficult, and often fails when a texture has unpronounced features. In this paper, we propose a novel texture synthesis method, based on non-local operators, which captures the long-range structure of a texture without the additional guidance channel. The synthesized texture is generated by minimizing non-local texture energy through an expectation-maximization like optimization algorithm. A statistical constraint based on the Wasserstein distance is also proposed to ensure that the synthesized texture preserves the global statistics of the exemplar texture. Extensive experiments show that the proposed method can stably handle textures with different scale structures.
Automatic Pupillary Light Reflex Detection in Eyewear Computing

There are many benefits to facilitating “always-on” pupillary light reflex (PLR)-aware pupil size measurement in eyewear, including improving the reliability of pupil-based cognitive and affective load monitoring and enabling PLR-based diagnosis of cognitive and eye-related diseases which have neurological symptoms manifested in the form of aberrant PLR responses. However, the detection of PLR responses for application in eyewear devices for everyday usage, beyond PLR measurement in confined clinical sessions, has not been investigated. To this end, a means of characterizing PLRs in less controlled environmental settings is investigated and subsequently a method of PLR detection is developed and evaluated. A low-cost head-mounted Web camera was used to record near-field eye video sequences which were processed with the self-tuning threshold algorithm for pupil diameter estimation and blink detection. PLR was induced by luminance change of a monitor and brightness change of the displayed image on a monitor. A transient model-based PLR detection algorithm which utilizes the general correlation of PLR amplitude and velocity was developed and evaluated on the data sets in terms of false alarm and false rejection rates. The findings from this research suggest that the PLR can be detected reliably using low-cost wearable pupil-measurement systems without using a separate sensor for detecting the luminance conditions. The correlation between pupil diameter amplitude and maximum velocity of PLR was shown to be sufficiently consistent for PLR detection.
Visual-Quality Guided Global Backlight Dimming for Video Display on Mobile Devices

This proposes a visual-quality guided global backlight dimming (VQG-GBD) algorithm to reduce the power consumption of liquid-crystal display on mobile devices. We build a backlight scaling ratio (BSR) prediction model via visual-quality assessment that not only considers the display contents but also the backlight intensity while measuring video quality. Also, we add visual uncertainty as an indicator to dim the backlight without being noticed by observers. The VQG-GBD includes a training stage and an online stage. For the training stage, first, we collect videos with distinct attributes of brightness and uncertainty. Then, the subjective rating obtains the relationship among the visual quality, BSR, brightness, and visual uncertainty. Finally, we use the trust-region method to build the BSR prediction model. In the online stage, the model is applied to mobile devices for real-time video display and a BSR optimization strategy is proposed to eliminate the flicker effect between frames, followed by three techniques to accelerate the process: 1) motion vector extraction; 2) pixel subsampling to reduce the computation while analyzing frame content; and 3) GPU rendering to speed up the pixel compensation. The experimental results show that VQG-GBD achieves 21% of the power demand reduction on average for displaying videos on mobile devices while preserving good visual quality. The VQG-GBD delivers more power reduction than the state-of-the-art algorithm image integrity-based gray-level error control and multi-histogram-based gray-level error control by 10% and 8%, respectively.
Statistics of orbital entanglement for the full scattering state in a chaotic quantum dot

We study the statistical properties of orbital entanglement for the full scattering state in a chaotic quantum dot attached to two nonideal two-channel leads via random matrix theory. Using the entanglement entropy as the entanglement quantifier, we obtain its average and probability density function varying the opacity of each lead in the presence and absence of time-reversal symmetry by two methods: (a) exact integration over random matrix ensembles when one contact is ideal and (b) random matrix simulations for arbitrary opacity values. All results show that these two methods are in agreement with each other. The Person correlation coefficient between the entanglement entropy and the entanglement production factor was also analyzed. We verify that the latter predicts a good level of entanglement of the full scattering state.
Word and Character Information Aware Neural Model for Emotional Analysis

Background: Social media texts are often highly unstructured in accordance with the presence of hashtags, emojis and URLs occurring in abundance. Thus, a sentiment or emotion analysis on these kinds of texts becomes very difficult. The difficulty increases even more when such texts are in local languages like Arabic. Methods: This work utilizes novel deep learning architectures in the form of character-level Convolutional Neural Network (CNN) module and the word-level Recurrent Neural Network (RNN) module to produce a hybrid architecture that makes use of the character level analysis and the word level analysis to obtain state-of-the-art results on a totally new Arabic Emotions dataset. Results: The proposed method works the best among the traditional bag-of-words and Term Frequency and Inverse Document Frequency methods for emotion analysis. It also outperforms the state-of-the-art deep learning methods which are known to perform very well in an English corpus. Conclusion: The proposed deep end-to-end architecture utilizes the character level information from a text through the Character CNN Module and the word level information from a text through the Word-Level RNN Module.
On feature selection in network flow based traffic sign tracking models

We use the network flow based tracking model to process videos recorded by taxicabs to collect information, such as the locations and types, of traffic signs. One of the key questions in this type of tracking models is the feature selection problem, which determines the features and their associated weights to construct the costs on the edges of the underlying network. However, in existing studies, the features and their weights are set mostly by intuition and there are no studies that systematically investigate this problem. We investigate the feature selection problem by considering a wide range of features related to traffic sign candidates, including its position, size, frame index, color histogram, speed up robust feature (SURF) descriptors, and template matching similarities. We propose to use the multinomial logit model to identify features that have significant influence on the edge costs and to estimate their corresponding weights. We test the performance of our selected features using real videos recorded by taxicabs in the city of Beijing. We find that the position and frame index of the traffic sign candidates are the top two features that influence the tracking results. We also find that image similarity measures are helpful features, but their contribution is not as large as the previous two features.
UX Design Requirements: User Stories and Requirements

In this chapter, UX design wants and needs discovered in usage research are codified as user stories and UX requirements. A user story is a short narrative describing a feature or capability needed by a user in a specific work role and a rationale for why it is needed, which is used as an agile UX design “requirement.” Although rarely needed in today’s projects, more formal UX design requirements can also be extracted from the usage research data, organized by subsystem, feature, etc. and documented in formal requirements statements. User stories and requirements are validated with users and stakeholders.
A spatial-frequency-temporal domain based saliency model for low contrast video sequences

The last few decades have witnessed rapid development of visual saliency detection, as it can detect object-of-interest from clutter environments to substantially facilitate a wide range of applications. However, traditional visual saliency detection models primarily rely on image features, which may face great challenges in low contrast video stream captured from low lighting scenarios. This paper proposes a dynamic multimodal fusion based visual saliency detection model towards low contrast videos, which combines saliency information from spatial, frequency, and temporal domains. In spatial domain, superpixel covariance is utilized to compute the region dissimilarity under low lighting scenarios; in frequency domain, the amplitude spectrum tuned method is used to suppress the background noise; in temporal domain, the incremental learning is employed to efficiently update background model from high dimensional video streams. Extensive experiments have been conducted to validate the effectiveness of the proposed model.