Reviewerly/exHarmony huggingface dataset:
Distributed Steganography in PDF Files—Secrets Hidden in Modified Pages

This paper shows how to diffuse a message and hide it in multiple PDF files. Presented method uses dereferenced objects and secret splitting or sharing algorithms. It is applicable to various types of PDF files, including text documents, presentations, scanned images etc. Because hiding process is based on structure manipulation, the solution may be easily combined with content-dependent steganographic techniques. The hidden pages are not visible in typical application usage, which was tested with seven different programs.
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open challenge to build models that generalize well across multiple tasks and datasets. One path to achieve this is to learn meaningful and compact representations, in which different semantic aspects of data are structurally disentangled. The focus of disentanglement approaches has been on separating independent factors of variation despite the fact that real-world observations are often not structured into meaningful independent causal variables to begin with. In this work we bridge the gap to real-world scenarios by analyzing the behavior of most prominent methods and disentanglement scores on correlated data in a large scale empirical study (including 3900 models). We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations, while widely used disentanglement scores fall short of capturing these latent correlations. Finally, we demonstrate how to disentangle these latent correlations using weak supervision, even if we constrain this supervision to be causally plausible. Our results thus support the argument to learn independent mechanisms rather than independent factors of variations.
Statistical limits of supervised quantum learning

Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the bound on the accuracy is taken into account, quantum machine learning algorithms for supervised learning---for which statistical guarantees are available---cannot achieve polylogarithmic runtimes in the input dimension. We conclude that, when no further assumptions on the problem are made, quantum machine learning algorithms for supervised learning can have at most polynomial speedups over efficient classical algorithms, even in cases where quantum access to the data is naturally available.
The Design of Multi-Metric Load Balancer for Kubernetes

Load balancer is an important component in the distributed system. Load balancing can offload external access to different service instances, thereby increasing the overall system throughput. In kubernetes, the built-in load balancing component functions has few functions, only supports simple static load balancing strategies, and it has design flaws and cannot adapt to complex business needs. For solving those problems, this paper designs a load balancer for kubernetes. In addition to the static load balancing strategy, the load balancer can also dynamically distribute requests based on the running status of the server and applications, such as CPU, network status, and so on. As a result, a more complex dynamic load balancing strategy is implemented, which can more flexibly configure the load balancing strategy according to the actual business needs.
Growing Action Spaces

In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress. In this work, we use a curriculum of progressively growing action spaces to accelerate learning. We assume the environment is out of our control, but that the agent may set an internal curriculum by initially restricting its action space. Our approach uses off-policy reinforcement learning to estimate optimal value functions for multiple action spaces simultaneously and efficiently transfers data, value estimates, and state representations from restricted action spaces to the full task. We show the efficacy of our approach in proof-of-concept control tasks and on challenging large-scale StarCraft micromanagement tasks with large, multi-agent action spaces.
Automated Design Space Exploration for Optimized Deployment of DNN on Arm Cortex-A CPUs

The spread of deep learning on embedded devices has prompted the development of numerous methods to optimise the deployment of deep neural networks (DNN). Works have mainly focused on: i) efficient DNN architectures, ii) network optimisation techniques such as pruning and quantisation, iii) optimised algorithms to speed up the execution of the most computational intensive layers and, iv) dedicated hardware to accelerate the data flow and computation. However, there is a lack of research on cross-level optimisation as the space of approaches becomes too large to test and obtain a globally optimised solution. Thus, leading to suboptimal deployment in terms of latency, accuracy, and memory. In this work, we first detail and analyse the methods to improve the deployment of DNNs across the different levels of software optimisation. Building on this knowledge, we present an automated exploration framework to ease the deployment of DNNs. The framework relies on a Reinforcement Learning search that, combined with a deep learning inference framework, automatically explores the design space and learns an optimised solution that speeds up the performance and reduces the memory on embedded CPU platforms. Thus, we present a set of results for state-of-the-art DNNs on a range of Arm Cortex-A CPU platforms achieving up to 4x improvement in performance and over 2x reduction in memory with negligible loss in accuracy with respect to the BLAS floating-point implementation.
Combination of LBP Bin and Histogram Selections for Color Texture Classification

LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.
Virtual Reality Therapy for Social Phobia: A Scoping Review.

Virtual reality (VR) has become an interesting alternative for the treatment of social phobia. This scoping review explored the impact of VR technologies in the treatment of social phobia (25 papers were included). Treatment approaches to social phobia included virtual reality exposure therapy, cognitive behaviour therapy, exposure group therapy, in vivo exposure therapy, and waiting list. VR was identified as the most successful approach, not only in improving social phobia, but also resulting in real life changes such as improvement in occupational success. VR Head Mounted Displays were used in most of studies (76%), followed by VR-based Specific Systems (24%). Although VR techniques indicated positive impact on patients to reduce their anxiety, it is paramount to consider variables such as the way therapists control the environment as well as individuals' characteristics for achieving the best outcome.
Q-Tree Search: An Information-Theoretic Approach Toward Hierarchical Abstractions for Agents With Computational Limitations

In this paper, we develop a framework to obtain graph abstractions for decision-making by an agent where the abstractions emerge as a function of the agent's limited computational resources. We discuss the connection of the proposed approach with information-theoretic signal compression, and formulate a novel optimization problem to obtain tree-based abstractions as a function of the agent's computational resources. The structural properties of the new problem are discussed in detail, and two algorithmic approaches are proposed to obtain solutions to this optimization problem. We discuss the quality of, and prove relationships between, solutions obtained by the two proposed algorithms. The framework is demonstrated to generate a hierarchy of abstractions for a non-trivial environment.
Cheating Death in Damascus

Evidential Decision Theory (EDT) and Causal Decision Theory (CDT) are the leading contenders as theories of rational action, but both face counterexamples. We present some new counterexamples, including one in which the optimal action is causally dominated. We also present a novel decision theory, Functional Decision Theory (FDT), which simultaneously solves both sets of counterexamples. Instead of considering which physical action of theirs would give rise to the best outcomes, FDT agents consider which output of their decision function would give rise to the best outcome. This theory relies on a notion of subjunctive dependence, where multiple implementations of the same mathematical function are considered (even counterfactually) to have identical results for logical rather than causal reasons. Taking these subjunctive dependencies into account allows FDT agents to outperform CDT and EDT agents in, for example, the presence of accurate predictors.
Active Learning for Identification of Linear Dynamical Systems

We propose an algorithm to actively estimate the parameters of a linear dynamical system. Given complete control over the system's input, our algorithm adaptively chooses the inputs to accelerate estimation. We show a finite time bound quantifying the estimation rate our algorithm attains and prove matching upper and lower bounds which guarantee its asymptotic optimality, up to constants. In addition, we show that this optimal rate is unattainable when using Gaussian noise to excite the system, even with optimally tuned covariance, and analyze several examples where our algorithm provably improves over rates obtained by playing noise. Our analysis critically relies on a novel result quantifying the error in estimating the parameters of a dynamical system when arbitrary periodic inputs are being played. We conclude with numerical examples that illustrate the effectiveness of our algorithm in practice.
Autoencoding With a Classifier System

Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size comes at the price of additional time and computational cost. Conditional computation, sparsity, and model pruning techniques can reduce these costs while maintaining performance. Learning classifier systems (LCS) are a framework for adaptively subdividing input spaces into an ensemble of simpler local approximations that together cover the domain. LCS perform conditional computation through the use of a population of individual gating/guarding components, each associated with a local approximation. This article explores the use of an LCS to adaptively decompose the input domain into a collection of small autoencoders where local solutions of different complexity may emerge. In addition to benefits in convergence time and computational cost, it is shown possible to reduce code size as well as the resulting decoder computational cost when compared with the global model equivalent.
The Classical Capacity of Additive Quantum Queue-Channels

We consider a setting where a stream of qubits is processed sequentially. We derive fundamental limits on the rate at which classical information can be transmitted using qubits that decohere as they wait to be processed. Specifically, we model the sequential processing of qubits using a single server queue, and derive expressions for the classical capacity of such a quantum `queue-channel.' Focusing on two important noise models, namely the erasure channel and the depolarizing channel, we obtain explicit single-letter capacity formulas in terms of the stationary waiting time of qubits in the queue. Our capacity proof also implies that a `classical' coding/decoding strategy is optimal, i.e., an encoder which uses only orthogonal product states, and a decoder which measures in a fixed product basis, are sufficient to achieve the classical capacity of both queue-channels. Our proof technique for the converse theorem generalizes readily - in particular, whenever the underlying quantum noise channel is additive, we can obtain a single-letter upper bound on the classical capacity of the corresponding quantum queue-channel. More broadly, our work begins to quantitatively address the impact of decoherence on the performance limits of quantum information processing systems.
Robust Multi-Modal Sensor Fusion: An Adversarial Approach

In recent years, multi-modal fusion has attracted a lot of research interest, both in academia, and in industry. Multimodal fusion entails the combination of information from a set of different types of sensors. Exploiting complementary information from different sensors, we show that target detection and classification problems can greatly benefit from this fusion approach and result in a performance increase. To achieve this gain, the information fusion from various sensors is shown to require some principled strategy to ensure that additional information is constructively used, and has a positive impact on performance. We subsequently demonstrate the viability of the proposed fusion approach by weakening the strong dependence on the functionality of all sensors, hence introducing additional flexibility in our solution and lifting the severe limitation in unconstrained surveillance settings with potential environmental impact. Our proposed data driven approach to multimodal fusion, exploits selected optimal features from an estimated latent space of data across all modalities. This hidden space is learned via a generative network conditioned on individual sensor modalities. The hidden space, as an intrinsic structure, is then exploited in detecting damaged sensors, and in subsequently safeguarding the performance of the fused sensor system. Experimental results show that such an approach can achieve automatic system robustness against noisy/damaged sensors.
Objective function estimation for solving optimization problems in gate-model quantum computers

Abstract Quantum computers provide a valuable resource to solve computational problems. The maximization of the objective function of a computational problem is a crucial problem in gate-model quantum computers. The objective function estimation is a high-cost procedure that requires several rounds of quantum computations and measurements. Here, we define a method for objective function estimation of arbitrary computational problems in gate-model quantum computers. The proposed solution significantly reduces the costs of the objective function estimation and provides an optimized estimate of the state of the quantum computer for solving optimization problems.
Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition

An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of optimal geometric features to classify six basic facial expressions: happiness, sadness, surprise, fear, anger, and disgust. Inspired by the Facial Action Coding System (FACS) and the Moving Picture Experts Group 4th standard (MPEG-4), an initial set of 89 features was proposed. These features are normalized distances and angles in 2D and 3D computed from 22 facial landmarks. To select a minimum set of features with the maximum classification accuracy, two selection methods and four classifiers were tested. The first selection method, principal component analysis (PCA), obtained 39 features. The second selection method, a genetic algorithm (GA), obtained 47 features. The experiments ran on the Bosphorus and UIVBFED data sets with 86.62% and 93.92% median accuracy, respectively. Our main finding is that the reduced feature set obtained by the GA is the smallest in comparison with other methods of comparable accuracy. This has implications in reducing the time of recognition.
A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering

Legislation can be viewed as a body of prescriptive rules expressed in natural language. The application of legislation to facts of a case we refer to as statutory reasoning, where those facts are also expressed in natural language. Computational statutory reasoning is distinct from most existing work in machine reading, in that much of the information needed for deciding a case is declared exactly once (a law), while the information needed in much of machine reading tends to be learned through distributional language statistics. To investigate the performance of natural language understanding approaches on statutory reasoning, we introduce a dataset, together with a legal-domain text corpus. Straightforward application of machine reading models exhibits low out-of-the-box performance on our questions, whether or not they have been fine-tuned to the legal domain. We contrast this with a hand-constructed Prolog-based system, designed to fully solve the task. These experiments support a discussion of the challenges facing statutory reasoning moving forward, which we argue is an interesting real-world task that can motivate the development of models able to utilize prescriptive rules specified in natural language.
Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning

Recently, deep convolutional neural networks have been successfully applied in different fields of computer vision and pattern recognition. Offline handwritten signature is one of the most important biometrics applied in banking systems, administrative and financial applications, which is a challenging task and still hard. The aim of this study is to review of the presented signature verification/recognition methods based on the convolutional neural networks and also evaluate the performance of some prominent available deep convolutional neural networks in offline handwritten signature verification/recognition as feature extractor using transfer learning. This is done using four pretrained models as the most used general models in computer vision tasks including VGG16, VGG19, ResNet50, and InceptionV3 and also two pre-trained models especially presented for signature processing tasks including SigNet and SigNet- F. Experiments have been conducted using two benchmark signature datasets: GPDS Synthetic signature dataset and MCYT- 75 as Latin signature datasets, and two Persian datasets: UTSig and FUM-PHSD. Obtained experimental results, in comparison with literature, verify the effectiveness of the models: VGG16 and SigNet for signature verification and the superiority of VGG16 in signature recognition task.
Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

We describe the 7th edition of the international data mining competition held at Knowledge Pit in association with the FedCSIS conference series.The goal was to predict workloadrelated characteristics of monitored network devices.We analyze solutions uploaded by the most successful participants.We investigate prediction errors which had the greatest influence on their results.We also present our own baseline solution which turned out to be the most reliable in the final evaluation.
The Use of Audio-Visual Assisted Google Classroom for Mathematics Course

The use of technology in the form of mathematics learning media has not been maximally applied amidst the rapid advances in technology today, coupled with a change in learning patterns from face-to-face in class to online learning patterns due to calls to work from home. This of course will have an impact on the learning outcomes of students. Seeing these conditions, innovation is needed in mathematics learning, namely by utilizing the use of media in learning, one of which is the use of audio-visual media assisted by google classroom. The purpose of this research is to determine the effect of using audio-visual media assisted by google classroom on mathematics courses at STMIK STIKOM Indonesia. The research method used was a quasi-experimental with a non-equivalent control group design. The instrument used in this study was a mathematics learning outcome test. The data analysis technique in this research includes descriptive analysis. The results showed that there was an effect of the use of audio-visual media on mathematics learning outcomes with an average score of 76.00 mathematics learning outcomes which was declared significant so that learning using audio-visual media had an effect on mathematics learning outcomes.
Cognitive Structures of Space-Time

Causation refers to relation, while space-time constitutes an abstract schema for causal connections between events, objects, and processes. Moreover, the mental representations of such events and relations seem to afford compositionality. Considering these notions, we posit an interplay between the physical structural properties of space-time and the compositional and operational modalities upon object and relational representations available to the reasoner therein. This hypothetical relation is defined and discussed. For instance, a “topological-temporal” schema, motivated by causal considerations from narrative information, might license talk of temporal precedence, succedence, and concurrency between some events, but not necessarily the duration of events, which requires an augmented “metric-topological” temporal schema to speak of the temporal distance between points in time. Therefore, our proposed model emphasizes that hierarchies of structural properties of certain space-time schema are important to explore, together with attendant considerations on the levels of complexity of settling causal queries within them. This discussion motivates advanced contributions to the psychological, physical, and philosophical discourse on causal cognition.