Reviewerly/exHarmony huggingface dataset:
Adversarial Learning of Disentangled and Generalizable Representations of Visual Attributes

Recently, a multitude of methods for image-to-image translation have demonstrated impressive results on problems, such as multidomain or multiattribute transfer. The vast majority of such works leverages the strengths of adversarial learning and deep convolutional autoencoders to achieve realistic results by well-capturing the target data distribution. Nevertheless, the most prominent representatives of this class of methods do not facilitate semantic structure in the latent space and usually rely on binary domain labels for test-time transfer. This leads to rigid models, unable to capture the variance of each domain label. In this light, we propose a novel adversarial learning method that: 1) facilitates the emergence of latent structure by semantically disentangling sources of variation and 2) encourages learning generalizable, continuous, and transferable latent codes that enable flexible attribute mixing. This is achieved by introducing a novel loss function that encourages representations to result in uniformly distributed class posteriors for disentangled attributes. In tandem with an algorithm for inducing generalizable properties, the resulting representations can be utilized for a variety of tasks such as intensity-preserving multiattribute image translation and synthesis, without requiring labeled test data. We demonstrate the merits of the proposed method by a set of qualitative and quantitative experiments on popular databases such as MultiPIE, RaFD, and BU-3DFE, where our method outperforms other state-of-the-art methods in tasks such as intensity-preserving multiattribute transfer and synthesis.
A Hybrid Reinforcement Learning-Based Trust Model for 5G Networks

Trust investigation in 5G, which is the next-generation wireless network, is still at its infancy. This research proposes a hybrid trust model for the selection of a legitimate (or trusted) forwarding (relay) entity and to countermeasure intelligent attacks against a route selection scheme. The hybrid trust model has a centralized entity (i.e., a centralized controller C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> ) that provides the security level of the operating environment to network entities, and distributed entities that identify malicious or legitimate entities in the network. When the security level of the operating environment is high (low), the distributed entities learn more (lesser) from their respective operating environment. While legitimate entities can use artificial intelligence, such as reinforcement learning (RL), to enhance their trust models, the malicious entities can also use artificial intelligence to increase the detrimental effects of their attacks and minimize their likelihood of being detected. Our proposed trust model is feasible with the introduction of artificial intelligence and the central controller (C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> ) in 5G to support the hybrid trust model. We have explained in detail our proposed model that reinforcement learning based hybridization of trust model can enhance the performance of the network interms of learning and tackling intelligent attacks, whereby achieving context awareness and detection of malicious entities.
New Jochemsz–May Cryptanalytic Bound for RSA System Utilizing Common Modulus N = p2q

This paper describes an attack on the Rivest, Shamir and Adleman (RSA) cryptosystem utilizing the modulus N=p2q where p and q are two large balanced primes. Let e1,e2&lt;Nγ be the integers such that d1,d2&lt;Nδ be their multiplicative inverses. Based on the two key equations e1d1−k1ϕ(N)=1 and e2d2−k2ϕ(N)=1 where ϕ(N)=p(p−1)(q−1), our attack works when the primes share a known amount of least significant bits (LSBs) and the private exponents share an amount of most significant bits (MSBs). We apply the extended strategy of Jochemsz–May to find the small roots of an integer polynomial and show that N can be factored if δ&lt;1110+94α−12β−12γ−130180γ+990α−180β+64. Our attack improves the bounds of some previously proposed attacks that makes the RSA variant vulnerable.
Quantum Assisted Image Registration

Quantum Computing has been invigorated by the introduction of the first commercially available computers. Quantum Annealers (QAs) like the D-Wave are now housing ~5000 quantum-bits, and quantum computing, a potentially disruptive technology, is closer to becoming a reality than ever. Here we investigate quantum-assisted approaches to image registration of MODIS images using QAs. We present two approaches: (1) a mapping of image matching to a quadratic-unconstrained-binary optimization problem and (2) a machine learning approach that employs generative models trained using quantum annealing statistics. Results are shown and discussed.
Development of a Smart Solar Tracking (SST) system as a green renewable energy solution

Abstract Renewable energy is a type of energy generated from renewable resources that are naturally generated such as sunlight, waves, rain, wind and geothermal heat. Renewable energy provides energy in different categories such as electricity generation, air and water heating/cooling and transportation. Solar PV cells are the most common method for transforming the sun light to electrical power. But using a standstill PV cells is not very efficient due to sun movement as the PV cells must be perpendicularly aligned to the sun to get its maximum efficiency. Tracking the sun by a tracking system will increase the harvested energy and achieve the maximum power generated from the solar cells. In this work, we develop a solar tracking system that enables photovoltaic cells to continuously track the sun and study the effect of light incident angle on the photovoltaic cell’s efficiency. In this study, the use of PV cells with tracking capabilities is 21% more efficient as compared to conventional standstill PV cells.
Towards Real-time Object Recognition and Pose Estimation in Point Clouds

Object recognition and 6DoF pose estimation are quite challenging tasks in computer vision applications. Despite efficiency in such tasks, standard methods deliver far from real-time processing rates. This paper presents a novel pipeline to estimate a fine 6DoF pose of objects, applied to realistic scenarios in real-time. We split our proposal into three main parts. Firstly, a Color feature classification leverages the use of pre-trained CNN color features trained on the ImageNet for object detection. A Feature-based registration module conducts a coarse pose estimation, and finally, a Fine-adjustment step performs an ICP-based dense registration. Our proposal achieves, in the best case, an accuracy performance of almost 83\% on the RGB-D Scenes dataset. Regarding processing time, the object detection task is done at a frame processing rate up to 90 FPS, and the pose estimation at almost 14 FPS in a full execution strategy. We discuss that due to the proposal's modularity, we could let the full execution occurs only when necessary and perform a scheduled execution that unlocks real-time processing, even for multitask situations.
Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters

This study aims to decompose plot structures of stories in narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia). Since a story is interwoven with main plots and subplots (i.e., primary and ancillary story lines), decomposing a story into multiple story lines enables us to analyze how events in the story are allocated and logically connected. For the decomposition, the existing studies employed character networks (i.e., social networks of characters that appeared in a story) and assumed that characters’ social relationships are consistent in a story line. However, these studies overlooked that social relationships significantly change around major events. To solve this problem, we attempt to use the changes for distinguishing story lines rather than suffer from the changes. We concentrate on the changes in characters’ social relationships being the result of changes in their personalities. Moreover, these changes gradually proceed within a story line. Therefore, we first propose features for measuring changes in personalities of characters: (i) Degrees of characters in character networks, (ii) lengths of dialogues spoken by characters, and (iii) ratios of out-degrees for in-degrees of characters in character networks. We supposed these features reflect importance, inner/outer conflicts, and activeness of characters, respectively. Since characters’ personalities gradually change in a story line, we can suppose that the features also show gradual story developments in a story line. Therefore, we conduct regression for each feature to discover dominant tendencies of the features. By filtering scenes that do not follow the tendencies, we extract a story line that exhibits the most dominant personality changes. We can decompose stories into multiple story lines by iterating the regression and filtering. Besides, personalities of characters change more significantly in major story lines. Based on this assumption, we also propose methods for discriminating main plots. Finally, we evaluated the accuracy of the proposed methods by applying them to the movies, which is one of the most popular narrative multimedia.
Iterated learning for emergent systematicity in VQA

Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learning layouts and modules jointly, compositionality does not arise automatically and an explicit pressure is necessary for the emergence of layouts exhibiting the right structure. We propose to address this problem using iterated learning, a cognitive science theory of the emergence of compositional languages in nature that has primarily been applied to simple referential games in machine learning. Considering the layouts of module networks as samples from an emergent language, we use iterated learning to encourage the development of structure within this language. We show that the resulting layouts support systematic generalization in neural agents solving the more complex task of visual question-answering. Our regularized iterated learning method can outperform baselines without iterated learning on SHAPES-SyGeT (SHAPES Systematic Generalization Test), a new split of the SHAPES dataset we introduce to evaluate systematic generalization, and on CLOSURE, an extension of CLEVR also designed to test systematic generalization. We demonstrate superior performance in recovering ground-truth compositional program structure with limited supervision on both SHAPES-SyGeT and CLEVR.
Scalable balanced training of conditional generative adversarial neural networks on image data

We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score, Fréchet inception distance, and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1000 processes and 2000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.
Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening

Different samples can have different effects on learning support vector machine (SVM) classifiers. To rebalance an imbalanced dataset, it is reasonable to reduce non-informative samples and add informative samples for learning classifiers. Safe sample screening can identify a part of non-informative samples and retain informative samples. This study developed a resampling algorithm for Rebalancing imbalanced data using Safe Sample Screening (Re-SSS), which is composed of selecting Informative Samples (Re-SSS-IS) and rebalancing via a Weighted SMOTE (Re-SSS-WSMOTE). The Re-SSS-IS selects informative samples from the majority class, and determines a suitable regularization parameter for SVM, while the Re-SSS-WSMOTE generates informative minority samples. Both Re-SSS-IS and Re-SSS-WSMOTE are based on safe sampling screening. The experimental results show that Re-SSS can effectively improve the classification performance of imbalanced classification problems.
Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm

As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and unpredictable. Thus, the application of a multi-regime method would be problematic, even when separate sets of model parameters are prepared in advance. In the present study we devised a robust approach that facilitates multi-regime analysis. This approach employs an online parametric algorithm to determine the change-points for ambient conditions. An autoencoder was used to reduce the dimensions of input images, and reduced feature vectors were used to implement the online change-point algorithm. Seven separate periods were tagged with typical times in a given day. Multi-regime analysis was then performed so that the traffic density could be separately measured for each period. To train and test models for vehicle counting, 1,100 video images were randomly chosen for each period and labeled with traffic counts. The measurement accuracy of multi-regime analysis was much higher than that of an integrated model trained on all data.
A General Approach to Compute the Relevance of Middle-Level Input Features

This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) models in terms of middle-level features. One can isolate two different ways to provide explanations in the context of XAI: low and middle-level explanations. Middle-level explanations have been introduced for alleviating some deficiencies of low-level explanations such as, in the context of image classification, the fact that human users are left with a significant interpretive burden: starting from low-level explanations, one has to identify properties of the overall input that are perceptually salient for the human visual system. However, a general approach to correctly evaluate the elements of middle-level explanations with respect ML model responses has never been proposed in the literature.
A Neural Network Model for Text Detection in Chinese Drug Package Insert

The text information in the medical photocopies is of great significance to the construction of medical digital platform. Text region detection, the very first step of extracting medical photocopies information, is functional to detect text area or locate text instance on the sample. Researchers have done a lot works on text area detection in natural scenes, yet few of them in turn pay attention to the medical photocopies scenario which is urgent to be settled. Here, a text line area detection dataset based on Chinese medical photocopies (CMPTD) are created and a fine-grained text line region detection model based on multi-scale feature extraction and fusion are proposed in this paper. The detection model consists of three parts. The first part is feature extraction module. Cspdarknet53 in You Only Look Once version 4 (YOLOv4) is used as the backbone network of our model, and the spatial pyramid pool strategy is used to extract multi-scale features to enhance the robustness of the model. The second part is feature fusion module. By referring to the PANet structure, the three effective feature layers in feature extraction module are fused repeatedly. The last part is prediction module. The network outputs a series of fine-grained text proposals by referring to the CTPN structure, which are connected into text lines by text line construction algorithm. We experimentally demonstrate the effectiveness of the detection model with the precision of 92.46% and the recall of 91.74% in the text detection task of the dataset CMPTD.
Disposable Virtual Machines and Challenges to Digital Forensics Investigation

Digital forensics field faces new challenges with emerging technologies.Virtualization is one of the significant challenges in the field of digital forensics.Virtual Machines (VM) have many advantages either it be an optimum utilization of hardware resources or cost saving for organizations.Traditional forensics' tools are not competent enough to analyze the virtual machines as they only support for physical machines, to overcome this challenge Virtual Machine Introspection technologies were developed to perform forensic investigation of virtual machines.Until now, we were dealing with persistent virtual machines; these are created once and used many times.We have extreme version of virtual machine and that is disposable virtual machine.However, the disposable virtual machine once created and are used one time, it vanish from the system without leaving behind any significant traces or artifacts for digital investigator.The purpose of this paper is to discuss various disposable virtualization technologies available and challenges posed by them on the digital forensics investigation process and provided some future directions to overcome these challenges.
Resource Optimization-Based Software Risk Reduction Model for Large-Scale Application Development

Software risks are a common phenomenon in the software development lifecycle, and risks emerge into larger problems if they are not dealt with on time. Software risk management is a strategy that focuses on the identification, management, and mitigation of the risk factors in the software development lifecycle. The management itself depends on the nature, size, and skill of the project under consideration. This paper proposes a model that deals with identifying and dealing with the risk factors by introducing different observatory and participatory project factors. It is assumed that most of the risk factors can be dealt with by doing effective business processing that in response deals with the orientation of risks and elimination or reduction of those risk factors that emerge over time. The model proposes different combinations of resource allocation that can help us conclude a software project with an extended amount of acceptability. This paper presents a Risk Reduction Model, which effectively handles the application development risks. The model can synchronize its working with medium to large-scale software projects. The reduction in software failures positively affects the software development environment, and the software failures shall reduce consequently.
Network Security for Home IoT Devices Must Involve the User: A Position Paper

Many home IoT devices suffer from poor security design and confusing interfaces, lowering the bar for successful cyberattacks. A popular approach to identify compromised IoT devices is network-based detection, in which network traffic is analyzed to fingerprint and identify such devices. However, while several network-based techniques for identifying misbehaving devices have been proposed, the role of the user in remediating IoT security incidents has been conspicuously overlooked.In this paper, we argue that successful IoT security must involve the user, even if the user is not a technical expert, and that the form in which security findings are communicated is as important as the technique used to generate such warnings. Finally, we present the design of a research testbed designed to foster further research in IoT security warnings.
Fidelity-based supervised and unsupervised learning for binary classification of quantum states

Here, we develop two quantum-computational schemes for supervised and unsupervised classification tasks in a quantum world by employing the quantum information-geometric tools of quantum fidelity and quantum search algorithm. Presuming that pure states of a set of given quantum systems (or objects) belong to one of two known classes, the objective here is to decide to which of these classes each system belongs—without knowing its state. The supervised binary classification algorithm is based on having a training sample of quantum systems whose class memberships are already known. The unsupervised binary classification algorithm, however, uses a quantum oracle which knows the class membership of the states of the computational basis. Both algorithms require the ability to evaluate the fidelity between states of the quantum systems with unknown states, for which here we also develop a general scheme.
TOPIK BALANCE SCORECARD DALAM LITERATUR AKUNTANSI DI INDONESIA: STUDI BIBLIOGRAFI

This study aims to enrich the literature on performance assessment using the Balance Scorecard with the Charting The Field method. This study uses article data from 7 journals totaling 24 article with the key word "Balance Scorecard" published in Sinta 2 and Sinta 3 with the research period 2009-2019. This study classifies each sample article into 3 (three) categories, namely the journal group, year of publication, and research method. The results showed that the journal that published the most articles with the topic Balance Scorecard was the Udayana University Accounting E-Journal with 15 articles, the research topic most often used is Consequences with a percentage of 79% of the articles, 2013 was the year with the most number of articles with the theme of Balance Scorecard with 5 articles, and the method most used in research on the Balance Scorecard is the Analytical method uses an quantitative approach with a percentage of 67%.
Research on Small Object Detection in UAV Reconnaissance Images Based on Haar-Like Features and MobileNet-SSD Algorithm

In order to enhance the recognition rate of small object in UAV reconnaissance images, this paper proposes a detection algorithm based on MobileNet-SSD, soft-NMS and haar-like features cascade classifier. First, input the batch reconnaissance images into the improved SSD detection network based on MobileNet v1 to obtain the initial detection frame; then use the soft-NMS algorithm to remove redundant duplicate frames to improve the detection accuracy; finally, all the detected images obtained after non-maximum suppression are input into the Haar-cascade classifier in batches, and are jointly verified with the detection results of the SSD model to further boost the recognition accuracy.