Reviewerly/exHarmony huggingface dataset:
Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss

State-of-the-art supervised local descriptor learning methods heavily rely on accurately labelled patches for training. However, since the process of labelling patches is laborious and inefficient, supervised training is limited by the availability and scale of training datasets. In comparison, unsupervised learning does not require burdensome data labelling; thus it is not restricted to a specific domain. Furthermore, extracting patches from training images in-volves minimal effort. Nevertheless, most of the existing unsupervised learning based methods are inherently inferior to the handcrafted local descriptors, such as the Scale-Invariant Feature Transform (SIFT). In this paper, we aim to leverage unlabelled data to learn descriptors for image patches by a deep convolutional neural network. We introduce a Relative Distance Ranking Loss (RDRL) that measures the deviation of a generated ranking order of patch similarities against a reference one. Specifically, our approach yields a patch similarity ranking based on the learned embedding of a neural network, and the ranking mechanism minimizes the proposed RDRL by mimicking a reference similarity ranking based on a competent handcrafted feature (i.e., SIFT). To our advantage, after the training process, our network is not only able to measure the patch similarity but also able to outperform SIFT by a large margin on several commonly used benchmark datasets as demonstrated in our extensive experiments.
The bare-bones differential evolutionary for stochastic joint replenishment with random number of imperfect items

In e-business running, not only uncertainties in customers’ demands cause great risks from the marketing side, but also imperfect qualities of multi-item induce great losses at the supplying side. Hence, in this paper a joint replenishment problem (IJRP) simultaneously considering the stochastic demands and random number of imperfect items for the first time is proposed and a meta-heuristic, namely, bare-bones differential evolutionary (BBDE) algorithm is redesigned based on the solution structure containing the continuous variable and discrete variable to solve the IJRP problem. Through intensive numerical experiments, it has been testified that BBDE is not only superior to genetic evolution algorithm (GA) and particle swarm optimization (PSO) algorithm at the best-found TC, the lowest mean value, and the smallest standard error for three small tested JRPs (GJRP, SJRP and IJRP), but also a competitive algorithm to differential evolution (DE) and bare bones PSO (BBPSO) for three small scale JRPs (GJRP, SJRP and IJRP) at the aspects of finding the best-found TC, the lowest mean value, the smallest standard error and the least of computation time. BBDE also shows great efficiency in solving some large scale IJRPs comparing to DE and BBPSO, the limitation of BBDE for large scale IJRP is also reported and analyzed.
Adaptive Steganalysis Based on Statistical Model of Quantized DCT Coefficients for JPEG Images

In current steganalysis, relying on a large scale of samples, widely-adopted supervised schemes require the training stage while few studies focus on the design of a training-free unsupervised adaptive detector with high efficiency. To fill the gap, we investigate an adaptive statistical model-based detector designed for detecting JPEG steganography. First, in virtue of hypothesis testing theory, together with the distribution of quantized DCT coefficients, we establish the general framework of the statistical model-based detector. Second, based on the framework, we mainly analyze the performance of the detector relying on the selection of the statistical model, parameters estimation, and less significant payload prediction. Third, to improve the reliability of detection, based on the strategy of assigning weights for DCT channels, the novel adaptive statistical model-based detectors are proposed to aim at detecting JPEG steganography, involving the channel-selected or non-channel-selected algorithm. Extensive experiments highlight the effectiveness of the proposed methodology. Moreover, when detecting JPEG images adopted by two steganographic schemes with the small payload, the experimental results show the Area Under Curve (AUC) of our proposed optimal adaptive detector can achieve as high as 0.9567 and 0.9895 respectively, which are both better than that of non-adaptive detector.
On matrix-product structure of repeated-root constacyclic codes over finite fields

For any prime number p, positive integers m,k,n, where n satisfies gcd(p,n)=1, and for any non-zero element λ0 of the finite field Fpm of cardinality pm, we prove that any λ0pk-constacyclic code of length pkn over the finite field Fpm is monomially equivalent to a matrix-product code of a nested sequence of pk λ0-constacyclic codes with length n over Fpm. As an application, we completely determine the Hamming distances of all negacyclic codes of length 7⋅2l over F7 for any integer l≥3.
Smart Contract Defense through Bytecode Rewriting

An Ethereum bytecode rewriting and validation architecture is proposed and evaluated for securing smart contracts in decentralized cryptocurrency systems without access to contract source code. This addresses a wave of smart contract vulnerabilities that have been exploited by cybercriminals in recent years to steal millions of dollars from victims. Such attacks have motivated various best practices proposals for helping developers write safer contracts; but as the number of programming languages used to develop smart contracts increases, implementing these best practices can be cumbersome and hard to enforce across the development tool chain. Automated hardening at the bytecode level bypasses this source-level heterogeneity to enforce safety and code integrity properties of contracts independently of the sources whence they were derived. In addition, a binary code verification tool implemented atop the Coq interactive theorem prover establishes input-output equivalence between the original code and the modified code. Evaluation demonstrates that the system can enforce policies that protect against integer overflow and underflow vulnerabilities in real Ethereum contract bytecode, and overhead is measured in terms of instruction counts.
The Golden Shield Project of China: A Decade Later—An in-Depth Study of the Great Firewall

The following topics are dealt with: learning (artificial intelligence); cryptography; distributed databases; feature extraction; Internet; cryptocurrencies; cryptographic protocols; neural nets; authorisation; Internet of Things.
Facebook Analysis of Community Sentiment on 2019 Indonesian Presidential Candidates from Facebook Opinion Data

On April 17, 2019, Indonesia will hold a presidential general election. Analysis of public sentiment towards presidential candidates and vice-presidential candidates of Indonesia in 2019 through social media especially Facebook, is needed to find out the popularity and public sentiments towards the candidates. Data was obtained through comments on Facebook posts of three major news media in Indonesia, namely Detik (@detikcom), TribunNews (@tribunnews), and Liputan6 (@liputan6online). The Opinion is classified using the data mining approach using the Naive Bayes Classifier algorithm. This research found that 40.52% of the comments were talking about presidential and vice-presidential candidate Joko Widodo and Maruf Amin (Jokowi-Maruf) meanwhile presidential candidate Prabowo Subianto and his running mate Sandiaga Uno (Prabowo-Sandi) dominate the comments by 59.48%. On the contrary, for the sentiment polarity results, the Jokowi-Maruf dominate by 56.76% positive sentiment and 43.24% negative sentiment, while the Prabowo-Sandi received 24.21% positive sentiment and 75.79% negative sentiment.
Constrained Image Splicing Detection and Localization With Attention-Aware Encoder-Decoder and Atrous Convolution

Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task and plays an important role in verifying the generating process of a forged image. CISDL conducts dense matching between two investigated images and detects whether one image has forged regions pasted from the other. In this work, we introduce a novel attention-aware encoder-decoder deep matching network named as AttentionDM for CISDL. An encoder-decoder with atrous convolution is newly designed for hierarchical features dense matching and fine-grained masks generation. A novel attention-aware correlation computation module is built on normalization operations and informative features recalibration with channel attention blocks. Last but not least, VGG and ResNets are respectively formulated as feature extractors for comprehensive comparisons in CISDL. Extensive experiments demonstrate the superior performance of AttentionDM over the state-of-the-art methods.
Asymmetric Interface: User Interface of Asymmetric Virtual Reality for New Presence and Experience

This study proposes an asymmetric interface that can provide head-mounted display (HMD) and non-HMD users with improved presence and an experience consistent with the user’s environment in an asymmetric virtual reality. For the proposed asymmetric interface, a controller-based hand interface is designed for portability, easy and convenient use, and high immersion. Subsequently, a three-step decision-making structure that supports accurate and efficient decision-making is defined based on the asymmetric experience structure of each user (HMD and non-HMD). Based on this process, an optimal interface that distinguishes between HMD (direct interaction) and non-HMD users (multi-viewpoint interaction) is implemented. With the objective of surveying and analyzing each user’s experience along with the presence provided by the proposed interface, an asymmetric virtual reality application is developed directly, and an experiment is conducted with the participants. Finally, it is statistically analyzed and verified that the use of the proposed asymmetric interface can provide optimal presence and user-optimized experience to both HMD and non-HMD users.
Heterogeneous Domain Adaptation via Covariance Structured Feature Translators

Domain adaptation (DA) and transfer learning with statistical property description is very important in image analysis and data classification. This article studies the domain adaptive feature representation problem for the heterogeneous data, of which both the feature dimensions and the sample distributions across domains are so different that their features cannot be matched directly. To transfer the discriminant information efficiently from the source domain to the target domain, and then enhance the classification performance for the target data, we first introduce two projection matrices specified for different domains to transform the heterogeneous features into a shared space. We then propose a joint kernel regression model to learn the regression variable, which is called feature translator in this article. The novelty focuses on the exploration of optimal experimental design (OED) to deal with the heterogeneous and nonlinear DA by seeking the covariance structured feature translators (CSFTs). An approximate and efficient method is proposed to compute the optimal data projections. Comprehensive experiments are conducted to validate the effectiveness and efficacy of the proposed model. The results show the state-of-the-art performance of our method in heterogeneous DA.
Architecture Model of Information Technology Infrastructure based on Service Quality at Government Institution

Public service activities in several government institutions are currently using information technology services so that information technology services must also have criteria in accordance with the government's vision and mission. One fundamental problem is providing quality and reliable information services that provide infrastructure support that has the capability and capacity. The provision of this infrastructure can be approached with accurate and complete planning and design. The purpose of this research is to produce infrastructure architecture planning and design that forms infrastructure services. As a service, infrastructure architecture has the duty to provide quality and reliable services for end users. Applications and data which are business processes of government institutions require infrastructure parameters that are presented from the service side perception. One way to present infrastructure services for information services in business processes is by planning right before the implementation and deployment phase. At present, the discussion of infrastructure architecture that is widely used is based on the core functions of infrastructure architecture which are limited to the main development and use. These core functions usually focus on availability and modularity (scalability). Availability is usually discussed in the criticality of services that require infrastructure backup. Whereas modularity is related to an increase in the number of users or services. In this study a collaborative role was added, namely an open contribution to the service delivery cycle from the infrastructure section. This open collaborative method is a strategy to "develop synergies" between infrastructure service providers and service users (end-users).
Structured Pruning for Efficient Convolutional Neural Networks via Incremental Regularization

Modern Convolutional Neural Networks (CNNs) are usually restricted by their massive computation and high storage. Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance degradation. Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fragility of the expressiveness of CNNs, and thus calls for a more gentle regularization scheme so that the networks can adapt during pruning. To achieve this, we propose a novel regularization-based pruning method, named IncReg, to incrementally assign different regularization factors to different weights based on their relative importance. Empirical analysis on CIFAR-10 dataset verifies the merits of IncReg. Further extensive experiments with popular CNNs on CIFAR-10 and ImageNet datasets show that IncReg achieves comparable to even better results compared with state-of-the-arts. Moreover, to resolve the problem that column pruning cannot be directly applied to off-the-shelf deep learning libraries for acceleration, we generalize IncReg from column pruning to spatial pruning, which can equip existing structured pruning methods (such as channel pruning) for further acceleration with ignorable accuracy loss. Our source codes and trained models are available at: https://github.com/mingsun-tse/caffe_increghttps://github.com/mingsun-tse/caffe_increg.
Simultaneous Segmentation and Recognition: Towards More Accurate Ego Gesture Recognition

Ego hand gestures can be used as an interface in AR and VR environments. While the context of an image is important for tasks like scene understanding, object recognition, image caption generation and activity recognition, it plays a minimal role in ego hand gesture recognition. An ego hand gesture used for AR and VR environments conveys the same information regardless of the background. With this idea in mind, we present our work on ego hand gesture recognition that produces embeddings from RBG images with ego hands, which are simultaneously used for ego hand segmentation and ego gesture recognition. To this extent, we achieved better recognition accuracy (96.9%) compared to the state of the art (92.2%) on the biggest ego hand gesture dataset available publicly. We present a gesture recognition deep neural network which recognises ego hand gestures from videos (videos containing a single gesture) by generating and recognising embeddings of ego hands from image sequences of varying length. We introduce the concept of simultaneous segmentation and recognition applied to ego hand gestures, present the network architecture, the training procedure and the results compared to the state of the art on the EgoGesture dataset.
Deterministic dropout for deep neural networks using composite random forest

Dropout prevents overfitting in deep neural networks. Typical strategy of dropout involves random termination of connections irrespective of their importance. Termination blocks the propagation of class discriminative information across the network. As a result, dropout may lead to inferior performance. We propose a deterministic dropout where only unimportant connections are dropped ensuring propagation of class discriminative information. We identify the unimportant connections using a novel composite random forest, integrated into the network. We prove that better generalization is achieved by terminating these unimportant connections. The proposed algorithm is useful in preventing overfitting in noisy datasets. The proposal is equally good for datasets with smaller number of training examples. Experiments on several benchmark datasets show up to 8% improvement in classification accuracy.
Karna: A Gate-Sizing based Security Aware EDA Flow for Improved Power Side-Channel Attack Protection

Power side-channel attacks pose a serious threat to the security of embedded devices. Most available countermeasures have significant overheads resulting in the application not meeting its requirements of low-power, high-performance and small area. We propose an algorithm called Karna <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Karna, much like Achilles from Greek mythology, was born with a shield that protected him from attacks. Similarly, Our proposed scheme, Karna protects the design from power side-channel attacks in the manufacturing phase or in other words the chip is manufactured(born) with a shield. that can be incorporated in the Electronic Design Automation (EDA) flow, in order to significantly improve the side-channel security of the device, without impacting the other device characteristics. Karna does not add additional logic but rather achieves this by first identifying vulnerable gates in the design and then reconfiguring these gates to increase side-channel resistance. Unlike contemporary works, Karna does not require any specialized gate library but uses the gates available in the standard cell library. We integrate Karna into the Synopsys Design Compiler and demonstrate its efficacy at reducing side-channel leakage in implementations of AES, PRESENT and Simon block ciphers, synthesized for a 28nm technology node. An interesting observation is that Karna only uses the available space around the gates to perform this optimization and does not incur any additional area overheads. We showcase the side-channel resistance of these optimized designs using a Differential Power Analysis attack. Our proposed approach is able to reduce the power side-channel of the designs while incurring no penalty in delay, power and gate-count.
Reliable Decentralized Oracle with Mechanisms for Verification and Disputation

Smart contract using Blockchain technology provides a mechanism to automatically exchange "cash" and "service" according to programmed conditions without requiring reliable third-party intervention. This results in reduction of time and cost for complex contract execution. Some contract execution require external information outside Blockchain as a trigger to execute the code specifying process for a certain contract. However, because Blockchain technology itself does not provide a function to directly access such external information, these applications require a proxy system called "oracle". Oracle is in charge accessing external information, to verify it, and to write it on Blockchain. To avoid security incidents such as oracle writing malicious information on Blockchain, reliability of oracle must be required. This paper introduces a decentralized oracle equipping with verification and disputation mechanisms. To evaluate reliability of the proposed mechanisms, a simulation-based experiment was conducted. The experimental results showed that our solution could effectively suppress the interference of malicious participants and obtained reliable consensus results even if relatively many malicious participants joined in the consensus process on the proposed decentralized oracle.
Blockchain Technologies in BIM Workflow Environment

Blockchain technology has been devised from the first cryptocurrency known as Bitcoin and was soon noted to have a much more full range of applications beyond serving as the platform for the digital cryptocurrency. Conceptually, a blockchain (BC) is defined as a decentralized ledger that registers every transactional operation made in encrypted data format. The implementation of such concept bring about augmented security, enforce accountability, and could potentially accelerate a shift in the work environment dynamics from the current centralized structure to a decentralized, cooperative chain of transactions by encouraging trust and transparency. This paper presents an overview of BC technology and its applications in the AEC industry and its potential integration with the building information (BIM) process. Furthermore, the study explores how BC technologies can improve the BIM working environment by reinforcing network security, providing more reliable data storage and management of permissions, ensuring change tracing and data ownership. The paper discusses the fundamental principles of distributed ledgers, their current applications and potential future advances, and their classification based on inherent characteristics of consensus reaching and permission management. The study presents some examples the prospective application of BC technologies in enhancing the framework for automating the code compliance checking process such as smart contract technologies and Hyperledger Fabric, as well as discussing the pros, cons, possible directions, and future research directions.
Software Process Simulation Modeling: Systematic literature review

Changes and continuous progress in logistics and productive systems make the realization of improvements in decision making necessary. Simulation is a good support tool for this type of decisions because it allows reproducing processes virtually to study their behavior, to analyze the impact of possible changes or to compare different design alternatives without the high cost of scale experiments. Although process simulation is usually focused on industrial processes, over the last two decades, new proposals have emerged to bring simulation techniques into software engineering. This paper describes a Systematic Literature Review (SLR) which returned 8070 papers (published from 2013 to 2019) by a systematic search in 4 digital libraries. After conducting this SLR, 36 Software Process Simulation Modeling (SPSM) works were selected as primary studies and were documented following a specific characterization scheme. This scheme allows characterizing each proposal according to the paradigm used and its technology base as well as its future line of work. Our purpose is to identify trends and directions for future research on SPSM after identifying and studying which proposals in this topic have been defined and the relationships and dependencies between these proposals in the last five years. After finishing this review, it is possible to conclude that SPSM continues to be a topic that is very much addressed by the scientific community, but each contribution has been proposed with particular goals. This review also concludes that Agent-Based Simulation and System Dynamics paradigm is increasing and decreasing, respectively, its trend among SPSM proposals in the last five years. Regarding Discrete-Event Simulation paradigm, it seems that it is strengthening its position among research community in recent years to design new approaches.