Reviewerly/exHarmony huggingface dataset:
Structure-Constrained Motion Sequence Generation

Video generation is a challenging task due to the extremely high-dimensional distribution of the solution space. Good constraints in the solution domain would thus reduce the difficulty of approximating optimal solutions. In this paper, instead of directly generating high-dimensional video data, we propose using object landmarks as explicit structure constraints to address this issue. Specifically, we propose a two-stage framework for an action-conditioned video generation task. In our framework, the first stage aims to generate landmark sequences according to predefined motion types, and a recurrent model (RNN/LSTM) is adopted for this purpose. The landmark sequence can be regarded as a low-dimensional structure embedding of high-dimensional video data, and generating landmark sequences is much easier than generating videos. The second stage is inspired by a conditional generative adversarial network (CGAN), and we take the generated landmark sequence as a structure condition to learn a landmark-to-image translation network. Such a one-to-one translation framework avoids the difficulty of generating videos and instead transfers the video generation task to image generation, which is resolvable due to the maturity of current GAN-based models. The experimental results demonstrate that our model not only achieves promising results on rigid/nonrigid motion generation tasks but also can be extended to multiobject motion situations.
Optimal construction of compressed indexes for highly repetitive texts

We propose algorithms that, given the input string of length n over integer alphabet of size σ, construct the Burrows-Wheeler transform (BWT), the permuted longest-common-prefix (PLCP) array, and the LZ77 parsing in O(n/ logσn + r polylog n) time and working space, where r is the number of runs in the BWT of the input. These are the essential components of many compressed indexes such as compressed suffix tree, FM-index, and grammar and LZ77-based indexes, but also find numerous applications in sequence analysis and data compression. The value of r is a common measure of repetitiveness that is significantly smaller than n if the string is highly repetitive. Since just accessing every symbol of the string requires Ω(n/ logσn) time, the presented algorithms are time and space optimal for inputs satisfying the assumption n/r ∈ Ω(polylog n) on the repetitiveness. For such inputs our result improves upon the currently fastest general algorithms of Belazzougui (STOC 2014) and Munro et al. (SODA 2017) which run in O(n) time and use O(n/ logσn) working space. We also show how to use our techniques to obtain optimal solutions on highly repetitive data for other fundamental string processing problems such as: Lyndon factorization, construction of run-length compressed suffix arrays, and some classical textbook problems such as computing the longest substring occurring at least some fixed number of times.
Bayesian Weight Decay on Bounded Approximation for Deep Convolutional Neural Networks

This paper determines the weight decay parameter value of a deep convolutional neural network (CNN) that yields a good generalization. To obtain such a CNN in practice, numerical trials with different weight decay values are needed. However, the larger the CNN architecture is, the higher is the computational cost of the trials. To address this problem, this paper formulates an analytical solution for the decay parameter through a proposed objective function in conjunction with Bayesian probability distributions. For computational efficiency, a novel method to approximate this solution is suggested. This method uses a small amount of information in the Hessian matrix. Theoretically, the approximate solution is guaranteed by a provable bound and is obtained by a proposed algorithm, where its time complexity is linear in terms of both the depth and width of the CNN. The bound provides a consistent result for the proposed learning scheme. By reducing the computational cost of determining the decay value, the approximation allows for the fast investigation of a deep CNN (DCNN) which yields a small generalization error. Experimental results show that our assumption verified with different DCNNs is suitable for real-world image data sets. In addition, the proposed method significantly reduces the time cost of learning with setting the weight decay parameter while achieving good classification performances.
Investigating identity fraud management practices in e-tail sector: a systematic review

Purpose Identity fraud is a growing issue for online retail organisations. The literature on this issue is scattered, and none of the studies presents a holistic view of identity fraud management practices in the online retail context. Therefore, the purpose of this paper is to investigate the identity fraud management practices and present a comprehensive set of practices for e-tail sector. Design/methodology/approach A systematic literature review approach was adopted, and the articles were selected through pre-set inclusion criteria. The authors synthesised existing literature to investigate identity fraud management in e-tail sector. Findings The research finds that literature on practices for identity fraud management is scattered. The findings also reveal that firms assume identity fraud issues as a technological challenge, which is one of the major reasons for a gap in effective management of identity frauds. This research suggests e-tailers to deal this issue as a management challenge and counter strategies should be developed in technological, human and organisational aspects. Research limitations/implications This study is limited to the published sources of data. Studies, based on empirical data, will be helpful to support the argument of this study; additionally, future studies are recommended to include a wide number of databases. Practical implications This research will help e-tail organisations to understand the whole of identity fraud management and help them develop and implement a comprehensive set of practices at each stage, for effective management identity frauds. Originality/value This research makes unique contributions by synthesising existing literature at each stage of fraud management and encompasses social, organisational and technological aspects. It will also help academicians understanding a holistic view of available research and opens new lines for future research.
Unfamiliar Face Matching With Driving Licence and Passport Photographs

Matching two different images of an unfamiliar face is difficult, although we rely on this process every day when proving our identity. Although previous work with laboratory photosets has shown that performance is error-prone, few studies have focussed on how accurately people carry out this matching task using photographs taken from official forms of identification. In Experiment 1, participants matched high-resolution, colour face photos with current UK driving licence photos of the same group of people in a sorting task. Averaging 19 mistaken pairings out of 30, our results showed that this task was both difficult and error-prone. In Experiment 2, high-resolution photographs were paired with either driving licence or passport photographs in a typical pairwise matching paradigm. We found no difference in performance levels for the two types of ID image, with both producing unacceptable levels of accuracy (around 75%-79% correct). The current work benefits from increased ecological validity and provides a clear demonstration that these forms of official identification are ineffective and alternatives should be considered.
WASSERSTEIN BARYCENTER MODEL ENSEMBLING

A method, system and apparatus of ensembling, including inputting a set of models that predict different sets of attributes, determining a source set of attributes and a target set of attributes using a barycenter with an optimal transport metric, and determining a consensus among the set of models whose predictions are defined on the source set of attributes.
A variable selection package driving Netica with Python

Bayesian Networks (BNs) are useful methods of probabilistically modelling environmental systems. BN performance is sensitive to the number of variables included in the model framework. The selection of the optimum set of variables to include in a BN (“variable selection”) is therefore a key part of the BN modelling process. While variable selection is an issue dealt with in the wider BN and machine learning literature, it remains largely absent from environmental BN applications to date, due in large part to a lack of software designed to work with available BN packages. CVNetica_VS is an open-source Python module that extends the functionality of Netica, a commonly used commercial BN software package, to perform variable selection. CVNetica_VS uses wrapper-based variable selection and cross-validation to search for the optimum variable set to use in a BN. The software will aid in objectifying and automating the development of BNs in environmental applications.
Give Ear to My Face: Modelling Multimodal Attention to Social Interactions

We address the deployment of perceptual attention to social interactions as displayed in conversational clips, when relying on multimodal information (audio and video). A probabilistic modelling framework is proposed that goes beyond the classic saliency paradigm while integrating multiple information cues. Attentional allocation is determined not just by stimulus-driven selection but, importantly, by social value as modulating the selection history of relevant multimodal items. Thus, the construction of attentional priority is the result of a sampling procedure conditioned on the potential value dynamics of socially relevant objects emerging moment to moment within the scene. Preliminary experiments on a publicly available dataset are presented.
Association of information technology and internal controls of Iranian state agencies

Purpose This study aims to investigate the relationship between information technology and internal controls of state agencies in Iran. Design/Methodology/Approach The research population includes all auditors and managers working in public sector. Data collection instrument is a questionnaire designed by the researcher and administrated during March 5, 2016. The collected data are analyzed through descriptive and inferential statistics (binomial test). Findings The findings of the research show that there is a significant relationship between information technology and internal controls (administrative, financial and accounting controls, risk assessment, information and communication, control activities and monitoring). Moreover, the alteration of data collection methods (from traditional to modern) and the written instructions (in information technology) have a positive effect on the internal control and its subscales. Originality/value With regard to the emphasis on the development of computer application and the use of new processing facilities and the exchange of information and its specific controlling consequences, this is an innovative research.
Micro‐ and macro‐optimizations of S<scp>aa</scp>T search

Summary We introduce and test several micro‐ and macro‐optimizations to the Score‐at‐a‐Time approach to processing impact‐ordered postings lists in a search engine. Our micro‐optimizations are at the single‐assembly instruction level, but our macro‐optimizations are algorithmic. Overall, we see an improvement of 37% on our baseline (22% on state of the art). We experiment with parallel search and present evidence that we have hit the memory wall.
Decomposition of a symmetric multipartite observable

Production and verification of multipartite quantum state are an essential step in quantum information processing. In this work, we propose an efficient method to decompose symmetric multipartite observables, which are invariant under permutations between parties, with only $(N+1)(N+2)/2$ local measurement settings, where $N$ is the number of qubits. We apply the decomposition technique to evaluate the fidelity between an unknown prepared state and any target permutation invariant state. In addition, for some typical permutation invariant states, such as the Dicke state with a constant number of excitations, $m$, we derive a tight linear bound on the number of local measurement settings, $m(2m+3)N+1$. Meanwhile, for the Greenberger-Horne-Zeilinger state, the $W$ state, and the Dicke state, we prove a linear lower bound, $\mathrm{\ensuremath{\Theta}}(N)$. Hence, for these particular states, our decomposition technique is optimal.
Operation characteristics of multiple solar trackers under typical weather conditions in a large-scale photovoltaic base

The most significant characteristics of solar energy, randomness, intermittency, and volatility have limited the development of large-scale photovoltaic power plants. Since different types of solar trackers would generate different types of electrical output, it is necessary to comprehend the operation characteristics of multiple solar trackers. The purpose of this research is to figure out the characteristics of multiple solar trackers under typical weather conditions. Results indicate that: (1) There are obvious differences in the solar tracker’s generation under different typical weathers, the largest annual power generation gain of which can reach 28.4% compared to fixed-tilted panels. (2) The horizontal-axis tracker and its bifacial panel can effectively reduce fluctuations. (3) There are complementary possibilities for multiple solar trackers. The research on the characteristics of multiple solar trackers could provide basis for the operation of large-scale photovoltaic power plants and could also serve as a feasible reference to the optimal utilization of other renewable energy.
Neural Speed Reading with Structural-Jump-LSTM

Recurrent neural networks (RNNs) can model natural language by sequentially 'reading' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as 'neural speed reading', either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word. A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text.
The Solar Resource and Meteorological Parameters

Total solar resource reserve refers to solar radiation energy in the form of electromagnetic energy that reaches Earth and is directly or indirectly utilized by humans, whereas total solar energy reserve refers to all solar radiation energy in the form of electromagnetic energy that reaches Earth, whether utilized by humans or not. Solar resource technical exploitable capacity refers to that part of the total solar resource reserve that has been and is to be exploited under current technical conditions without considering economic and other conditions. Solar resource economic exploitable capacity refers to that part of the total solar resource reserve that has been and is to be exploited under local economic conditions at present and that is technically exploitable within a foreseeable period, the exploitation costs of which can compete with those of other energies.
Password-Authenticated Public-Key Encryption

We introduce password-authenticated public-key encryption (PAPKE), a new cryptographic primitive. PAPKE enables secure end-to-end encryption between two entities without relying on a trusted third party or other out-of-band mechanisms for authentication. Instead, resistance to man-in-the-middle attacks is ensured in a human-friendly way by authenticating the public key with a shared password, while preventing offline dictionary attacks given the authenticated public key and/or the ciphertexts produced using this key. Our contributions are three-fold. First, we provide property-based and universally composable (UC) definitions for PAPKE, with the resulting primitive combining CCA security of public-key encryption (PKE) with password authentication. Second, we show that PAPKE implies Password-Authenticated Key Exchange (PAKE), but the reverse implication does not hold, indicating that PAPKE is a strictly stronger primitive than PAKE. Indeed, PAPKE implies a two-flow PAKE which remains secure if either party re-uses its state in multiple sessions, e.g. due to communication errors, thus strengthening existing notions of PAKE security. Third, we show two highly practical UC PAPKE schemes: a generic construction built from CCA-secure and anonymous PKE and an ideal cipher, and a direct construction based on the Decisional Diffie-Hellman assumption in the random oracle model. Finally, applying our PAPKE-to-PAKE compiler to the above PAPKE schemes we exhibit the first 2-round UC PAKE’s with efficiency comparable to (unauthenticated) Diffie-Hellman Key Exchange.
USE OF NEARPOD AS INTERACTIVE LEARNING METHOD

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 8908-8915ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.2219Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Secure Computation of Any Boolean Function Based on Any Deck of Cards

It is established that secure computation can be achieved by using a deck of physical cards. Almost all existing card-based protocols are based on a specific deck of cards. In this study, we design card-based protocols that are executable using any deck of cards (e.g., playing cards, UNO, and trading cards). Specifically, we construct a card-based protocol for any Boolean function based on any deck of cards. As corollaries of our result, a standard deck of playing cards (having 52 cards) enables secure computation of any 22-variable Boolean function, and UNO (having 112 cards) enables secure computation of any 53-variable Boolean function.
Developing Adobe Flash-based mathematics learning media for 7<sup>th</sup>-grade students of junior high school

The purpose of this study was to develop Adobe Flash-based mathematics learning media for 7th-grade students of junior high school. This research was conducted at SMP N 191 Jakarta. It is a public junior high school in the capital city of Indonesia. We used the ADDIE model to develop the media, which stands for analysis, design, development, implementation, and evaluation. In this study, the media was verified by two material experts, two media experts, one expert on development studies. The product testing conducted towards 20 students of SMP N 191 Jakarta. The results of the trial through formative evaluation of media experts suggested improving economic aspects. The development expert suggested to give a short button and to add animation. Therefore, they have been improved so that Adobe Flash learning media is "decent and good" to be used and able to motivate students. Adobe Flash learning media is also an effective learning media that can be used by teachers as a tool in the process of teaching and learning activities in the classroom. The material published by the researcher is a one-year teaching media containing material, simulation, and evaluation so that it is very helpful for the teacher.
Reconnecting the Body and the Mind

Mindfulness meditation has the potential to help practitioners cope with their stress. Currently, projects often use corrective feedback models to help people understand when they are out of a mindfulness state. My dissertation uses research by design to build a technology intervention for mindfulness meditation that adopts a strategy of gently guiding and supporting the user's in-the-moment experience of practising meditation through a natural soundscape that responds to the user's brainwave activity collected from the Muse EEG Headset.
Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.
Trinity of Pixel Enhancement: a Joint Solution for Demosaicking, Denoising and Super-Resolution.

Demosaicing, denoising and super-resolution (SR) are of practical importance in digital image processing and have been studied independently in the passed decades. Despite the recent improvement of learning-based image processing methods in image quality, there lacks enough analysis into their interactions and characteristics under a realistic setting of the mixture problem of demosaicing, denoising and SR. In existing solutions, these tasks are simply combined to obtain a high-resolution image from a low-resolution raw mosaic image, resulting in a performance drop of the final image quality. In this paper, we first rethink the mixture problem from a holistic perspective and then propose the Trinity Enhancement Network (TENet), a specially designed learning-based method for the mixture problem, which adopts a novel image processing pipeline order and a joint learning strategy. In order to obtain the correct color sampling for training, we also contribute a new dataset namely PixelShift200, which consists of high-quality full color sampled real-world images using the advanced pixel shift technique. Experiments demonstrate that our TENet is superior to existing solutions in both quantitative and qualitative perspective. Our experiments also show the necessity of the proposed PixelShift200 dataset.
2FA-PP

Two factor authentication (2FA) schemes provide strong user authentication guarantees and increase the security of a wide range of web services. However, 2FA schemes still largely remain vulnerable to phishing attacks in which attackers also phish users' second factor (e.g., their OTP tokens). We propose 2FA-PP, a phishing detection scheme that protects users' 2nd authentication factor from phishing attacks. 2FA-PP uses novel browser APIs that support direct communication between browsers and external devices (e.g., mobile phones) and enables the user's phone to check the domain to which the user is connected. The second factor is then only made available to the user if he is accessing the correct domain. 2FA-PP can be combined with different 2FA schemes, both interactive, based on OTP, QR codes and non-interactive, based on device pairing or proximity.