Reviewerly/exHarmony huggingface dataset:
Unsupervised steganographer identification via clustering and outlier detection

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users, called actors, are guilty of using steganography, which is typically defined as the steganographer identification problem (SIP). We might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning-based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when nonadaptive steganographic embedding was applied. In this chapter, we present foundational concepts and review advanced methodologies in SIP. It is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.
Monadic Decomposition in Integer Linear Arithmetic

Monadic decomposability is a notion of variable independence, which asks whether a given formula in a first-order theory is expressible as a Boolean combination of monadic predicates in the theory. Recently, Veanes et al. showed the usefulness of monadic decomposability in the context of SMT (i.e. the input formula is quantifier-free), and found various interesting applications including string analysis. However, checking monadic decomposability is undecidable in general. Decidability for certain theories is known (e.g. Presburger Arithmetic, Tarski’s Real-Closed Field), but there are very few results regarding their computational complexity. In this paper, we study monadic decomposability of integer linear arithmetic in the setting of SMT. We show that this decision problem is coNP-complete and, when monadically decomposable, a formula admits a decomposition of exponential size in the worst case. We provide a new application of our results to string constraint solving with length constraints. We then extend our results to variadic decomposability, where predicates could admit multiple free variables (in contrast to monadic decomposability). Finally, we give an application to quantifier elimination in integer linear arithmetic where the variables in a block of quantifiers, if independent, could be eliminated with an exponential (instead of the standard doubly exponential) blow-up.
Upgrading the Newsroom

We propose an automated image selection system to assist photo editors in selecting suitable images for news articles. The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs. It is equipped with char-level word embeddings to help both modeling morphologically rich languages, e.g., German, and transferring knowledge across nearby languages. The text encoder adopts a hierarchical self-attention mechanism to attend more to both key words within a piece of text and informative components of a news article. We extensively experiment our system on a large-scale text-image database containing multimodal multilingual news articles collected from Swiss local news media websites. The system is compared with multiple baselines with ablation studies and is shown to beat existing text-image retrieval methods in a weakly supervised learning setting. Besides, we also offer insights on the advantage of using multiple textual sources and multilingual data.
A tale of two contact-tracing apps – comparing Australia’s CovidSafe and New Zealand’s NZ Covid Tracer

Purpose The Australian and New Zealand governments have released smartphone-based apps to complement contact tracing in the event that they face a resurgence of COVID-19 infections. The apps form part of both countries’ policies to support a return to social and economic engagement following extended lockdowns. This paper aims to investigate the extent to which the two approaches are fit for purpose and compare their functional characteristics. Design/methodology/approach Using process mapping and analysis, this paper evaluates the potential of the two apps to improve the performance of existing contact tracing systems across a range of efficiency and effectiveness criteria with an emphasis on the framework proposed by Verrall (2020). Findings The Bluetooth-based Australian app appears likely to assist that country’s contact tracing system to perform more efficiently and effectively in the event of a resurgence of the virus and should increase confidence in re-engagement. The New Zealand QR code-based app, however, is not well-aligned with these objectives. Its interaction with a range of other regulations and obligations, combined with the inconvenience it imposes on its users, is likely to militate against its use. Bluetooth-based apps based on interactions between individuals likely better support these population-based objectives than QR code-based location-specific apps. Originality/value This paper provides an original and extensive analysis of the functionality and effectiveness of Australia and New Zealand’s official contact-tracing apps.
Learning from Interpretable Analysis: Attention-Based Knowledge Tracing

Knowledge tracing is a well-established problem and non-trivial task in personalized education. In recent years, many existing works have been proposed to handle the knowledge tracing task, particularly recurrent neural networks based methods, e.g., Deep Knowledge Tracing (DKT). However, DKT has the problem of vibration in prediction outputs. In this paper, to better understand the problem of DKT, we utilize a mathematical computation model named Finite State Automaton(FSA), which can change from one state to another in response to the external input, to interpret the hidden state transition of the DKT when receiving inputs. And we discover the root cause of the two problems is that the DKT can not handle the long sequence input with the help of FSA. Accordingly, we propose an effective attention-based model, which can solve the above problem by directly capturing the relationships among each item of the input regardless of the length of the input sequence. The experimental results show that our proposed model can significantly outperform state-of-the-art approaches on several well-known corpora.
Positional Games and QBF: The Corrective Encoding

Positional games are a mathematical class of two-player games comprising Tic-tac-toe and its generalizations. We propose a novel encoding of these games into Quantified Boolean Formulas (QBFs) such that a game instance admits a winning strategy for first player if and only if the corresponding formula is true. Our approach improves over previous QBF encodings of games in multiple ways. First, it is generic and lets us encode other positional games, such as Hex. Second, structural properties of positional games together with a careful treatment of illegal moves let us generate more compact instances that can be solved faster by state-of-the-art QBF solvers. We establish the latter fact through extensive experiments. Finally, the compactness of our new encoding makes it feasible to translate realistic game problems. We identify a few such problems of historical significance and put them forward to the QBF community as milestones of increasing difficulty.
Investigating Differential Error Types Between Human and Simulated Learners

Simulated learners represent computational theories of human learning that can be used to evaluate educational technologies, provide practice opportunities for teachers, and advance our theoretical understanding of human learning. A key challenge in working with simulated learners is evaluating the accuracy of the simulation compared to the behavior of real human students. One way this evaluation is done is by comparing the error-rate learning curves from a population of human learners and a corresponding set of simulated learners. In this paper, we argue that this approach misses an opportunity to more accurately capture nuances in learning by treating all errors as the same. We present a simulated learner system, the Apprentice Learner (AL) Architecture, and use this more nuanced evaluation to demonstrate ways in which it does and does not explain and accurately predict student learning in terms of the reduction of different kinds of errors over time as it learns, as human students do, from an Intelligent Tutoring System (ITS).
Didactic problems of digital transformation of vocational secondary education against the background of fighting the pandemic

The digital transformation of education is one of the federal priorities of the Russian Federation, the implementation of which in the vocational education system is designed to provide effective training for the digital economy. The study of the digitalization process of secondary vocational education and the identification of the organizational and pedagogical conditions for its effectiveness is carried out on the basis of an interregional network of experimental sites - professional educational organizations that have been implementing online training programs for several years. Describes the problems and limitations associated with the introduction of online into the secondary vocational education system, as well as possible solutions. It is shown that distance learning technology can be used in the implementation of vocational education programs only in force majeure circumstances (forced self-isolation in the fight against a pandemic) with a significant loss in the quality of education and its practical orientation. The optimal form of introducing online courses in the educational process of secondary vocational education is the technology of blended learning, combining online work with contact forms of training. The key condition for ensuring the effectiveness of the digital transformation of the educational process is the development by teachers of a complex of general competencies, including: self-organization, remote motivation management, work with granular educational content.
Instagram User Experience in Learning Graphic Design

&lt;span lang="EN-US"&gt;Mobile phone technology offers new opportunities to integrate face-to-face learning also other models of learning. Instagram is mostly only used for business and public figure exposure. But we see a tendency to use Instagram that students use in learning graphic design as a way to access content, publish work and learning outcomes using mobile phone. The purpose of this study is to find out the comparison of the average value of In-diagram assisted creative learning and measure the user experience (UX) of Instagram in learning graphic design. This study uses a quantitative approach with a pseudo experiment of non equivalent control group de-sign to find a comparison of the average value of student learning outcomes in the Statistical Test with MANOVA and to find out the average value of UX with UEQ Instagram benchmarks for learning graphic design. The results of the study there are significant differences (real) the average value between the experimental class group and the control class group. UX test results are on four scales that are categorized as ex-excellent, namely the scale of Attractiveness, efficiency, dependability, stimulation. One perspicuity scale is above average and the novelty scale is good, the mean range is 1.33-2.00 according to the UEQ benchmark interval. The implication of this research is that Instagram through mobile phone technology is effectively used for the creative learning of graphic design lessons and needs to be developed as a medium for the development of teaching materials on all subject matter.&lt;/span&gt;
An improved TFIDF algorithm based on dual parallel adaptive computing model

The double parallel cloud computing framework based on graphics processing unit (GPU) and MapReduce is proposed. The method aims at the low efficiency for the large data sets on the stand-alone by text categorisation algorithm, constructs the adaptive computation process of double parallel computing and combines the advantage of improved term frequency-inverse document frequency (TFIDF) algorithm, and improves TFIDF text categorisation algorithm with double parallel adaptive computing. In different operating environments, the efficiency of improved TFIDF algorithm will be compared with different computing nodes. The result shows that the improved TFIDF based on dual parallel adaptation has an increase of 6.48% on Macro_F1 compared to the TFIDF based on CPU, and the operating efficiency has increased by nearly seven times. With the number of nodes increasing, the algorithm execution efficiency with double parallel adaptive computing is getting more and more effective.
A Study of Gradient Variance in Deep Learning

The impact of gradient noise on training deep models is widely acknowledged but not well understood. In this context, we study the distribution of gradients during training. We introduce a method, Gradient Clustering, to minimize the variance of average mini-batch gradient with stratified sampling. We prove that the variance of average mini-batch gradient is minimized if the elements are sampled from a weighted clustering in the gradient space. We measure the gradient variance on common deep learning benchmarks and observe that, contrary to common assumptions, gradient variance increases during training, and smaller learning rates coincide with higher variance. In addition, we introduce normalized gradient variance as a statistic that better correlates with the speed of convergence compared to gradient variance.
Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction

The development of predictive models for clinical application requires the availability of electronic health record (EHR) data, which is complicated by patient privacy concerns. We showcase the "Model to Data" (MTD) approach as a new mechanism to make private clinical data available for the development of predictive models. Under this framework, we eliminate researchers' direct interaction with patient data by delivering containerized models to the EHR data.We operationalize the MTD framework using the Synapse collaboration platform and an on-premises secure computing environment at the University of Washington hosting EHR data. Containerized mortality prediction models developed by a model developer, were delivered to the University of Washington via Synapse, where the models were trained and evaluated. Model performance metrics were returned to the model developer.The model developer was able to develop 3 mortality prediction models under the MTD framework using simple demographic features (area under the receiver-operating characteristic curve [AUROC], 0.693), demographics and 5 common chronic diseases (AUROC, 0.861), and the 1000 most common features from the EHR's condition/procedure/drug domains (AUROC, 0.921).We demonstrate the feasibility of the MTD framework to facilitate the development of predictive models on private EHR data, enabled by common data models and containerization software. We identify challenges that both the model developer and the health system information technology group encountered and propose future efforts to improve implementation.The MTD framework lowers the barrier of access to EHR data and can accelerate the development and evaluation of clinical prediction models.
A Novel Biogeography-Based Optimization Algorithm with Momentum Migration and Taxonomic Mutation

Biogeography-based optimization (BBO) algorithm is not good at dealing with regions where function values change dramatically or barely. A novel biogeography-based optimization algorithm is proposed in this paper based on Momentum migration and taxonomic mutation. The momentum item is added to the original migration operation of BBO. It makes the algorithm more advantageous in dealing with regions where function values change dramatically or barely. At the same time, taxonomic mutation strategy divides the solutions into three categories: promising class, middle class and inferior class. Promising solutions do not take part in this mutation operation. Solutions of middle class use balanced differential mutation, and inferior solutions adopt exploration-biased random mutation. This strategy further increases the diversity of population. The simulation experiments are carried out with different types of CEC2014 benchmark functions. The proposed algorithm is compared with other algorithms and shows stronger global search ability, faster convergence speed and higher convergence accuracy.
Object Detector Combination for Increasing Accuracy and Detecting More Overlapping Objects

Object detection is considered as the cornerstone of many modern applications such as Drone vision and Self-driven cars. Object detectors, mainly those which are based on Convolutional Neural Net-works (CNNs) have received great attention from many researchers because they were able to yield remarkable results. However, most of them fail when it comes to detecting overlapping and small objects in images. There are two families of detectors: the first family detects more objects but with imprecise bounding boxes, while those of the second family do the opposite. In this paper, we propose a solution to this problem by combining the two families, in a way similar to classifier combination. Our solution has been validated through the combination of two famous detectors, Faster R-CNN which detects more objects and YOLO which produces accurate bounding boxes. However, it is more general and it can be applied to other detectors. The evaluation of our method has been applied to the PASCAL VOC dataset and it gave promising results.
A Modified Binary Pigeon-Inspired Algorithm for Solving the Multi-dimensional Knapsack Problem

Abstract The pigeon-inspired optimization algorithm is a category of a newly proposed swarm intelligence-based algorithm that belongs to the population-based solution technique. The MKP is a class of complex optimization problems that have many practical applications in the fields of engineering and sciences. Due to the practical applications of MKP, numerous algorithmic-based methods like local search and population-based search algorithms have been proposed to solve the MKP in the past few decades. This paper proposes a modified binary pigeon-inspired optimization algorithm named (Modified-BPIO) for the 0 - 1 multidimensional knapsack problem (MKP). The utilization of the binary pigeon-inspired optimization (BPIO) for solving the multidimensional knapsack problem came with huge success. However, it can be observed that the BPIO converges prematurely due to lost diversity during the search activities. Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space. The MKP benchmarks from the Operations Research (OR) library are utilized to test the performance of the proposed binary method. Experimentally, it is concluded that the proposed Modified-BPIO has a better performance when compared with the BPIO and existing state-of-the-arts that worked on the same MKP benchmarks.
Multi-Dialect Arabic BERT for Country-Level Dialect Identification

Arabic dialect identification is a complex problem for a number of inherent properties of the language itself. In this paper, we present the experiments conducted, and the models developed by our competing team, Mawdoo3 AI, along the way to achieving our winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task. The dialect identification subtask provides 21,000 country-level labeled tweets covering all 21 Arab countries. An unlabeled corpus of 10M tweets from the same domain is also presented by the competition organizers for optional use. Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask at hand. We publicly release the pre-trained language model component of our winning solution under the name of Multi-dialect-Arabic-BERT model, for any interested researcher out there.
A two-parameter family of measure-valued diffusions with Poisson–Dirichlet stationary distributions

We give a pathwise construction of a two-parameter family of purely-atomic-measure-valued diffusions in which ranked masses of atoms are stationary with the Poisson–Dirichlet(α,θ) distributions, for α∈(0,1) and θ≥0. These processes resolve a conjecture of Feng and Sun (Probab. Theory Related Fields 148 (2010) 501–525). We build on our previous work on (α,0)- and (α,α)-interval partition evolutions. The extension to general θ≥0 is achieved by the construction of a σ-finite excursion measure of a new measure-valued branching diffusion. Our measure-valued processes are Hunt processes on an incomplete subspace of the space of all probability measures and do not possess an extension to a Feller process. In a companion paper, we use generators to show that ranked masses evolve according to a two-parameter family of diffusions introduced by Petrov (Funktsional. Anal. i Prilozhen. 43 (2009) 45–66), extending work of Ethier and Kurtz (Adv. in Appl. Probab. 13 (1981) 429–452).
A Security Perspective on Unikernels

Cloud-based infrastructures have grown in popularity over the last decade leveraging virtualisation, server, storage, compute power and network components to develop flexible applications.The requirements for instantaneous deployment and reduced costs have led the shift from virtual machine deployment to containerisation, increasing the overall flexibility of applications and increasing performances.However, containers require a fully fleshed operating system to execute, increasing the attack surface of an application.Unikernels, on the other hand, provide a lightweight memory footprint, ease of application packaging and reduced start-up times.Moreover, Unikernels reduce the attack surface due to the self-contained environment only enabling low-level features.In this work, we provide an exhaustive description of the unikernel ecosystem; we demonstrate unikernel vulnerabilities and further discuss the security implications of Unikernel-enabled environments through different use-cases.
Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene

At present, deep learning drives the rapid development of face recognition. However, in the unconstrained scenario, the change of facial posture has a great impact on face recognition. Moreover, the current model still has some shortcomings in accuracy and robustness. The existing research has formulated two methods to solve the above problems. One method is to model and train each pose separately. Then, a fusion decision will be made. The other method is to make “frontal” faces on the image or feature level and transform them into “frontal” face recognition. Based on the second idea, we propose a profile to the frontal revise mapping (PTFRM) module. This module realizes the revision of arbitrary poses on the feature level and transforms the multi-pose features into an approximate frontal representation to enhance the recognition ability of the existing recognition models. Finally, we evaluate the PTFRM on unconstrained face validation benchmark datasets such as Labeled Faces in the Wild (LFW), Celebrities in Frontal Profile (CFP), and IARPA Janus Benchmark A(IJB-A). Results show that the chosen method for this study achieves good performance.