Reviewerly/exHarmony huggingface dataset:
Representing Deep Neural Networks Latent Space Geometries with Graphs

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

We identify a fundamental implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradient updates decrease the expressivity of the current value network. We characterize this loss of expressivity via a rank collapse of the learned value network features and show that it corresponds to a drop in performance. We demonstrate this phenomenon on popular domains including Atari and Gym benchmarks and in both offline and online RL settings. We formally analyze this phenomenon and show that it results from a pathological interaction between bootstrapping and gradient-based optimization. Finally, we show that mitigating implicit under- parameterization by controlling rank collapse improves performance.
Hybrid Imperialist Competitive Evolutionary Algorithm for Solving Biobjective Portfolio Problem

Portfolio optimization is an effective way to diversify investment risk and optimize asset management. Many multiobjective optimization mathematical models and metaheuristic intelligent algorithms have been proposed to solve portfolio problem under an ideal condition. This paper presents a biobjective portfolio optimization model under the assumption of no short selling. In order to obtain sufficient number of portfolio optimal solutions uniformly distributed on the portfolio efficient Pareto front, a hybrid imperialist competitive evolutionary algorithm which combines a multi-colony levy crossover operator and a simple-colony moving operator with random perturbation is also given. The performance of the given algorithm is verified by four criterion portfolio test problems, and the simulation results and comparison analyses illustrate that the proposed algorithm could obtain faster convergence toward the portfolio true Pareto front compared with the other two state of the art multiobjective optimization methods. The results can provide optimal portfolio plans and investment strategies for investors to allocate and manage assets effectively.
Directive Explanations for Actionable Explainability in Machine Learning Applications

In this article, we show that explanations of decisions made by machine learning systems can be improved by not only explaining why a decision was made but also explaining how an individual could obtain their desired outcome. We formally define the concept of directive explanations (those that offer specific actions an individual could take to achieve their desired outcome), introduce two forms of directive explanations (directive-specific and directive-generic), and describe how these can be generated computationally. We investigate people’s preference for and perception toward directive explanations through two online studies, one quantitative and the other qualitative, each covering two domains (the credit scoring domain and the employee satisfaction domain). We find a significant preference for both forms of directive explanations compared to non-directive counterfactual explanations. However, we also find that preferences are affected by many aspects, including individual preferences and social factors. We conclude that deciding what type of explanation to provide requires information about the recipients and other contextual information. This reinforces the need for a human-centered and context-specific approach to explainable AI.
Counting and locating high-density objects using convolutional neural network

This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery.To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map.The proposed method was evaluated in two counting datasets: tree and car.For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R 2 ) of 0.986.For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods.In the these datasets, our approach achieved an MAE of 4. 45 and 3.16, an RMSE of 6.18 and 4.39, and an R 2 of 0.975 and 0.999, respectively.The proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects.
GOAL-DTU: Development of Distributed Intelligence for the Multi-Agent Programming Contest

We provide a brief description of the GOAL-DTU system for the Multi-Agent Programming Contest, including the overall strategy and how the system is designed to apply this strategy. Our agents are implemented using the GOAL programming language. We evaluate the performance of our agents for the contest, and finally also discuss how to improve the system based on analysis of its strengths and weaknesses.
Deep feature learning for gender classification with covered/camouflaged faces

The great attention to gender classification is increasing recently as genders carry rich information related to male and female social activities. Extracting discriminating visual representations for gender classification is challenging especially with covered or camouflaged faces. In this work, the authors propose a network that uses a combination of inceptions with variational feature learning (VFL) loss function. The proposed network recognises the gender of normal or covered/camouflaged faces through the middle face part. This network trained on the middle part of the faces that contain both eyes with a small margin from the top-left corner to the bottom-right corner of the area of the eyes. Experimental results showed that the proposed network achieved state-of-art performance on five public data sets: FEI, SCIEN, AR FACES, LFW, and ADIENCE. They also evaluated the authors’ network on another new collected data set for covered and camouflaged faces and obtained encouraging outcomes.
Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization

Temporally localizing actions in videos is one of the key components for video understanding. Learning from weakly-labeled data is seen as a potential solution towards avoiding expensive frame-level annotations. Different from other works, which only depend on the visual-modality, we propose to learn richer audio-visual representations for weakly-supervised action localization. First, we propose a multi-stage cross-attention mechanism to collaboratively fuse audio and visual features, which preserves the intra-modal characteristics. Second, to model both foreground and background frames, we construct an open-max classifier, which treats the background class as an open-set. Third, for precise action localization, we design consistency losses to enforce temporal continuity for the action-class prediction, and also help with foreground-prediction reliability. Extensive experiments on two publicly available video-datasets (AVE and ActivityNet1.2) show that the proposed method effectively fuses audio and visual modalities, and achieves the state-of-the-art results for weakly-supervised action localization.
Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing

The Mixup scheme suggests mixing a pair of samples to create an augmented training sample and has gained considerable attention recently for improving the generalizability of neural networks. A straightforward and widely used extension of Mixup is to combine with regional dropout-like methods: removing random patches from a sample and replacing it with the features from another sample. Albeit their simplicity and effectiveness, these methods are prone to create harmful samples due to their randomness. To address this issue, 'maximum saliency' strategies were recently proposed: they select only the most informative features to prevent such a phenomenon. However, they now suffer from lack of sample diversification as they always deterministically select regions with maximum saliency, injecting bias into the augmented data. In this paper, we present, a novel, yet simple Mixup-variant that captures the best of both worlds. Our idea is two-fold. By stochastically sampling the features and ‘grafting’ them onto another sample, our method effectively generates diverse yet meaningful samples. Its second ingredient is to produce the label of the grafted sample by mixing the labels in a saliency-calibrated fashion, which rectifies supervision misguidance introduced by the random sampling procedure. Our experiments under CIFAR, Tiny-ImageNet, and ImageNet datasets show that our scheme outperforms the current state-of-the-art augmentation strategies not only in terms of classification accuracy, but is also superior in coping under stress conditions such as data corruption and object occlusion.
Cloud accounting information systems: Threats and advantages

When the Corona-virus pandemic hit the world, executing and performing the jobs of the remote world became an imperative, this pandemic became as motivator to create this study which aimed to reveal the advantages and threats that encounter the implementing of Cloud Accounting Information Systems CAIS from the view point of external auditors. For that purpose, a questionnaire was developed and distributed then from 198 valid questionnaires which were analyzed by a T- test. It has been found that the main advantages are a reduction of labor and overtime costs because of the ability to access the system from anywhere, while penetration, interruption, and confidentiality are the main threats, also it was found that there is a strong relationship between implementing cloud accounting information systems (CAIS) and the limiting manipulation of financial information. The study therefore recommended upgrading the security procedures of cloud accounting systems and holding courses for auditors to enhance their abilities in auditing CAIS.
Provably robust classification of adversarial examples with detection

Adversarial attacks against deep networks can be defended against either by building robust classifiers or, by creating classifiers that can \emph{detect} the presence of adversarial perturbations. Although it may intuitively seem easier to simply detect attacks rather than build a robust classifier, this has not bourne out in practice even empirically, as most detection methods have subsequently been broken by adaptive attacks, thus necessitating \emph{verifiable} performance for detection mechanisms. In this paper, we propose a new method for jointly training a provably robust classifier and detector. Specifically, we show that by introducing an additional abstain/detection into a classifier, we can modify existing certified defense mechanisms to allow the classifier to either robustly classify \emph{or} detect adversarial attacks. We extend the common interval bound propagation (IBP) method for certified robustness under l∞ perturbations to account for our new robust objective, and show that the method outperforms traditional IBP used in isolation, especially for large perturbation sizes. Specifically, tests on MNIST and CIFAR-10 datasets exhibit promising results, for example with provable robust error less than 63.63% and 67.92%, for 55.6% and 66.37% natural error, for ϵ=8/255 and 16/255 on the CIFAR-10 dataset, respectively.
Educational Question Mining At Scale: Prediction, Analysis and Personalization

Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real students' answers to a large collection of questions. Based on p-VAE, we propose two novel metrics that quantify question quality and difficulty, respectively, and a personalized strategy to adaptively select questions for students. We apply our proposed framework to a real-world dataset with tens of thousands of questions and tens of millions of answers from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain experts' evaluation.
Adversarial Binary Mutual Learning for Semi-Supervised Deep Hashing

Hashing is a popular search algorithm for its compact binary representation and efficient Hamming distance calculation. Benefited from the advance of deep learning, deep hashing methods have achieved promising performance. However, those methods usually learn with expensive labeled data but fail to utilize unlabeled data. Furthermore, the traditional pairwise loss used by those methods cannot explicitly force similar/dissimilar pairs to small/large distances. Both weaknesses limit existing methods’ performance. To solve the first problem, we propose a novel semi-supervised deep hashing model named adversarial binary mutual learning (ABML). Specifically, our ABML consists of a generative model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G_{H}$ </tex-math></inline-formula> and a discriminative model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{H}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{H}$ </tex-math></inline-formula> learns labeled data in a supervised way and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G_{H}$ </tex-math></inline-formula> learns unlabeled data by synthesizing real images. We adopt an adversarial learning (AL) strategy to transfer the knowledge of unlabeled data to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{H}$ </tex-math></inline-formula> by making <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G_{H}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{H}$ </tex-math></inline-formula> mutually learn from each other. To solve the second problem, we propose a novel Weibull cross-entropy loss (WCE) by using the Weibull distribution, which can distinguish tiny differences of distances and explicitly force similar/dissimilar distances as small/large as possible. Thus, the learned features are more discriminative. Finally, by incorporating ABML with WCE loss, our model can acquire more semantic and discriminative features. Extensive experiments on four common data sets (CIFAR-10, large database of handwritten digits (MNIST), ImageNet-10, and NUS-WIDE) and a large-scale data set ImageNet demonstrate that our approach successfully overcomes the two difficulties above and significantly outperforms state-of-the-art hashing methods.
Trustful: A Decentralized Public Key Infrastructure and Identity Management System

Modern Internet TCP uses Secure Sockets Layers (SSL)/Transport Layer Security (TLS) for secure communication, which relies on Public Key Infrastructure (PKIs) to authenticate public keys. Conventional PKI is done by Certification Authorities (CAs), issuing and storing Digital Certificates, which are public keys of users with the users identity. This leads to centralization of authority with the CAs and the storage of CAs being vulnerable and imposes a security concern. There have been instances in the past where CAs have issued rogue certificates or the CAs have been hacked to issue malicious certificates. Motivated from these facts, in this paper, we propose a method (named as Trustful), which aims to build a decentralized PKI using blockchain. Blockchains provide immutable storage in a decentralized manner and allows us to write smart contracts. Ethereum blockchain can be used to build a web of trust model where users can publish attributes, validate attributes about other users by signing them and creating a trust store of users that they trust. Trustful works on the Web-of-Trust (WoT) model and allows for any entity on the network to verify attributes about any other entity through a trusted network. This provides an alternative to the conventional CA-based identity verification model. The proposed model has been implemented and tested for efficacy and known major security attacks.
Virtual laboratory application based on virtual reality simulation as training tool of turning machine using goal-directed design method

Abstract This study aims to develop a virtual laboratory application based on virtual reality simulation as a training tool for turning machines. The application is built as a solution for manufacturing engineering education problems at the Production System Laboratory (PSL) in the Industrial Engineering Department, Diponegoro University. Problem based on work accident data shows human error from the student when using turning machines. The data shows that lack of student understanding due to insufficient of preliminary training at the beginning of manufacturing engineering education especially physical skills training. Virtual Reality Simulation is chosen because conventional machining training has some restrictions in execution. Furthermore, virtual reality simulation can reduce cost, improve safety, and add flexibility along with advances in computer technology. For this Research the Goal-Directed Design (GDD) method was chosen to develop VR simulation. This method is used as a procedure to accomplish the good design of application which including research, modelling, requirements, framework, refinement, and support. The application is built using Unity 3D software and run on Oculus Quest hardware. At the evaluation phase for assessing the needs, researchers use functional and non-functional requirements. Functional requirements consist of interaction, visualization, and information. Non-functional requirements consist of usability, performance, and reliability.
Multiple convolutional features in Siamese networks for object tracking

Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks.The source code and trained models are available at https://github.com/zhenxili96/MFST .
A new pseudorandom bits generator based on a 2D-chaotic system and diffusion property

A remarkable correlation between chaotic systems and cryptography has been established with sensitivity to initial states, unpredictability, and complex behaviors. In one development, stages of a chaotic stream cipher are applied to a discrete chaotic dynamic system for the generation of pseudorandom bits. Some of these generators are based on 1D chaotic map and others on 2D ones. In the current study, a pseudorandom bit generator (PRBG) based on a new 2D chaotic logistic map is proposed that runs side-by-side and commences from random independent initial states. The structure of the proposed model consists of the three components of a mouse input device, the proposed 2D chaotic system, and an initial permutation (IP) table. Statistical tests of the generated sequence of bits are investigated by applying five evaluations as well as the ACF and NIST. The results of five standard tests of randomness have been illustrated and overcome a value of 0.160 in frequency test. While the run test presents the pass value t0=4.769 and t1=2.929. Likewise, poker test and serial test the outcomes was passed with 3.520 for poker test, and 4.720 for serial test. Finally, autocorrelation test passed in all shift numbers from 1 to 10.