Reviewerly/exHarmony huggingface dataset:
Optical Flow in the Dark

Optical flow estimation in low-light conditions is a challenging task for existing methods and current optical flow datasets lack low-light samples. Even if the dark images are enhanced before estimation, which could achieve great visual perception, it still leads to suboptimal optical flow results because information like motion consistency may be broken during the enhancement. We propose to apply a novel training policy to learn optical flow directly from new synthetic and real low-light images. Specifically, first, we design a method to collect a new optical flow dataset in multiple exposures with shared optical flow pseudo labels. Then we apply a two-step process to create a synthetic low-light optical flow dataset, based on an existing bright one, by simulating low-light raw features from the multi-exposure raw images we collected. To extend the data diversity, we also include published low-light raw videos without optical flow labels. In our training pipeline, with the three datasets, we create two teacher-student pairs to progressively obtain optical flow labels for all data. Finally, we apply a mix-up training policy with our diversified datasets to produce low-light-robust optical flow models for release. The experiments show that our method can relatively maintain the optical flow accuracy as the image exposure descends and the generalization ability of our method is tested with different cameras in multiple practical scenes.
Exploring Affine Abstractions for Qubit Mapping

One of the key compilation steps in Quantum Computing (QC) is to determine an initial logical to physical mapping of the qubits used in a quantum circuit. The impact of the starting qubit layout can vastly affect later scheduling and placement decisions of QASM operations, yielding higher values on critical performance metrics (gate count and circuit depth) as a result of a quantum compilers introducing communication operations to meet the underlying physical neighboring and connectivity constraints of the quantum device.We present our ongoing work on qubit mapping with affine abstractions, which aims to compute effective initial qubit layouts. The key assumption is that the quantum program is represented in an affine representation, a property which permits us to apply aggressive program analyses to determine the potential of sharing qubit accesses among groups of quantum operations prescribed by linear and affine relations. We present preliminary results demonstrating the effectiveness of our novel approach on 69 quantum circuits, while using the QISKIT compiler on IBM Tokyo and Google Sycamore, obtaining between 16% and 21% average improvement on circuit depth and 24%–47% improvement on the number of added gates.
Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.
Robustness of Probabilistic U-Net for Automated Segmentation of White Matter Hyperintensities in Different Datasets of Brain MRI

White Matter Hyperintensities (WMHs) are neu-roradiological features often seen in T2-FLAIR brain MRI as white regions (i.e., hyperintensities) and characteristic of small vessel disease (SVD). Detailed measurements of WMHs (e.g., their volumes, locations, distributions) are vital for clinical research, but segmenting WMHs is challenging due to WMHs' ill-posed boundaries. In this study, we investigate the robustness of Probabilistic U-Net and other deterministic deep learning models (i.e., U-Net and its variations) for automatic segmentation of WMHs. In particular, we are interested in the robustness of U-Net based deep learning models, especially the Probabilistic U-Net, for segmenting WMHs in brain MRI from different datasets. Thus, we performed two different experiments, which are k- fold cross validation experiment (i.e., training and testing using the same dataset) and cross dataset experiment (i.e., testing in different dataset). Based on our experiments, Probabilistic U-Net outperformed other tested models in k-fold cross validation experiment. On the other hand, we found that Probabilistic U-Net captured different types of uncertainty when tested in different dataset.
Data Hiding in Vector Images

The suggested in the last few years methods of data hiding in the vector images are considered in the article. The individual elements of a vector graphic are described through special mathematical objects such as points, lines, curves of first and second order, Bezier curves, nodes, tangent lines, control points, etc. Such objects are being used for data concealing. The main aim lies in ensuring affine transformations resistance, and vector graphics allow its implementation. There are a few methods of data hiding, that are considered and programmable implemented in the article. The performed experimental research proves, that used techniques indeed allow to achieve high resistance indicators to affine transformations. Moreover, the suggested implementation is cross-platform because created with JavaScript program language. That means that scripts can be run on different types of platforms and browsers. Thus, developed implementation can be used in the future as a framework both for web-client applications and backend of more complex and professional tasks.
Anime Character Colorization using Few-shot Learning

In this paper, we propose an automatic Anime-style colorization method using only a small number of colorized reference images manually colorized by artists. To accomplish this, we introduce a few-shot patch-based learning method considering the characteristics of Anime line-drawing. To streamline the learning process, we derive optimal settings with acceptable colorization accuracy and training time for a production pipeline. We demonstrate that the proposed method helps to reduce manual labor for artists.
Efficient and Scalable Hardware Implementation of Montgomery Modular Multiplication

Modular multiplication (MM) is an important arithmetic operation in public key cryptography (PKC). In this paper, we present the FPGA implementation of the MM using Montgomery MM (MMM) algorithm. The execution performances of this operation depend on the radix-[Formula: see text] and the operands length. In fact, when increasing the radix-[Formula: see text], the MMM algorithm requires multiplications of digit by operand. On the other hand, when a long modulus is used, the hardware implementation of the MMM needs a large area. Our objective in this work is to realize a scalable architecture able to support any operands length. In order to achieve a best trade-off between computation throughput and hardware resources, our implementation approach is based on the execution of the basic arithmetic operations in serial way. In addition, efficient parallel and pipelined strategies are realized at low-level abstraction for the optimization of the execution time. The implementations results on Virtex-7 circuit show that a 1024-bit MMM runs in 2.09[Formula: see text][Formula: see text]s and consumes 581 slices.
Modeling Human Skeleton Joint Dynamics for Fall Detection

The increasing pace of population aging calls for better care and support systems. Falling is a frequent and critical problem for elderly people causing serious long-term health issues. Fall detection from video streams is not an attractive option for real-life applications due to privacy issues. Existing methods try to resolve this issue by using very low-resolution cameras or video encryption. However, privacy cannot be ensured completely with such approaches. Key points on the body, such as skeleton joints, can convey significant information about motion dynamics and successive posture changes which are crucial for fall detection. Skeleton joints have been explored for feature extraction but with image recognition models that ignore joint dependency across frames which is important for the classification of actions. Moreover, existing models are over-parameterized or evaluated on small datasets with very few activity classes. We propose an efficient graph convolution network model that exploits spatio-temporal joint dependencies and dynamics of human skeleton joints for accurate fall detection. Our method leverages dynamic representation with robust concurrent spatiotemporal characteristics of skeleton joints. We performed extensive experiments on three large-scale datasets. With a significantly smaller model size than most existing methods, our proposed method achieves state-of-the-art results on the large scale NTU datasets.
Investigation of New Non-Binary Sequences Created on the Basis of Gold and de Bruijn Sequences

De Bruijn sequences are widely used in various fields of science and technology. These sequences have unique properties, a huge size of the ensemble compared to other pseudo-random sequences, and a nonlinear formation algorithm. But, despite this, there is a need to study new pseudorandom sequences. In this article, algorithms for the formation of new non-binary pseudo-random sequences based on binary Gold sequences and non-binary de Bruijn sequences are considered. New sequences have better correlation properties than the sequences on the basis of which they are created. The new sequences have a nonlinear algorithm for forming and a flexible ensemble size that is not smaller than the size of the ensemble of Gold sequences. The paper presents an overview of de Bruijn sequences and the results of experiments to study the correlation properties of new sequences.
A Catalog of Performance Measures for Self-Adaptive Systems

[Context] Self-adaptive systems (SAS) can evaluate their own behavior at runtime and change it when necessary to avoid failures. Many of those systems require a high processing power to carry out their dynamic and complex operations successfully. In this realm, performance arises as an important quality characteristic that has been widely used in the literature to drive the necessary adaptation strategies in SAS to achieve or maintain the QoS requirements. However, few studies evaluate the SAS performance efficiently due to the lack of knowledge on all possible performance measures. Still, there is a variety of measures scattered throughout the body of work in this area. [Objective] Our objective is to create a catalog of performance measures for assessing self-adaptive systems. [Method] To do that, we identified and analyzed the performance measures used in 32 primary studies found through a systematic literature mapping. [Results] As a result, 18 measures have been inserted in the catalog, having most of them addressed the sub-characteristics of time behavior and resource utilization. To validate the catalog, we implemented a subset of measures in a healthcare SAS.
Artificial Intelligence Approach For BAZNAS Website Using K-Nearest Neighbor (KNN)

Amil Zakat National Agency (BAZNAS) is a national institution for the distribution of zakat. As one of the main foundations in Islam, zakat is, obviously, very important to be fulfilled. However, it is very often that the data of the recipient became unclear that it caused problems in terms of a fair distribution of zakat. This research tried to offer a solution by doing a classification of the recipient of zakat on the BAZNAS websites into two categories: indigent and poor, using K-Nearest Neighbor method. This research concluded that the accuracy of KNN method by using classification report, confusion matrix, and ROC-AUC respectively resulted in accuracy of 97%, 96.7%, and 97.7%
Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks

Chinese ancient stone inscriptions contain Chinese traditional calligraphy culture and art information. However, due to the long history of the ancient stone inscriptions, natural erosion, and poor early protection measures, there are a lot of noise in the existing ancient stone inscriptions, which has adverse effects on reading these stone inscriptions and their aesthetic appreciation. At present, digital technologies have played important roles in the protection of cultural relics. For ancient stone inscriptions, we should obtain more perfect digital results without multiple types of noise, while there are few deep learning methods designed for processing stone inscription images. Therefore, we propose a basic framework for image denoising and inpainting of stone inscriptions based on deep learning methods. Firstly, we collect as many images of stone inscriptions as possible and preprocess these images to establish an inscriptions image dataset for image denoising and inpainting. In addition, an improved GAN with a denoiser is used for generating more virtual stone inscription images to expand the dataset. On the basis of these collected and generated images, we designed a stone inscription image denoising model based on multiscale feature fusion and introduced Charbonnier loss function to improve this image denoising model. To further improve the denoising results, an image inpainting model with the coherent semantic attention mechanism is introduced to recover some effective information removed by the former denoising model as much as possible. The experimental results show that our image denoising model achieves better results on PSNR, SSIM, and CEI. The final results have obvious visual improvement compared with the original stone inscription images.
Efficient Methods for SoC Trust Validation Using Information Flow Verification

Information flow properties are essential to identify security vulnerabilities in System-on-Chip (SoC) designs. Verifying information flow properties, such as integrity and confidentiality, is challenging as these properties cannot be handled using traditional assertion-based verification techniques. This paper proposes two novel approaches, a universal method and a property-driven method, to verify and monitor information flow properties. Both methods can be used for formal verification, dynamic verification during simulation, post-fabrication validation, and run-time monitoring. The universal method expedites implementing the information flow model and has less complexity than the most recently published technique. The property-driven method reduces the overhead of the security model, which helps speed up the verification process and create an efficient run-time hardware monitor. More than 20 information flow properties from 5 different designs were verified and several bugs were identified. We show that the method is scalable for large systems by applying it to an SoC design based on an OpenRISC-1200 processor.
A HEVC Steganography Algorithm Based on DCT/DST Coefficients with BLB Distortion Model

Currently, video steganography has become more important for information security. Most of the previous work focused on preventing the intra-frame distortion drift without considering the intra-block distortion caused by steganography and only embedded data in small-sized blocks. With higher resolution, large-sized blocks are more in HEVC videos. It is a large waste of embedding capacity if they are not used to hide data. In this paper, a HEVC steganography algorithm based on DCT (Discrete Cosine Transform)/DST (Discrete Sine Transform) coefficients with BLB (Block Based) distortion model is proposed. The proposed BLB distortion model considers both inter-block distortion and intra-block distortion. The algorithm uses the intra-frame error propagation-free method to hide data in small-sized blocks to eliminate inter-block distortion. Especially, the STC (Syndrome Trellis Codes) framework is used to hide data in large-sized blocks to reduce intra-block distortion. Experiments confirm that the proposed algorithm improves the embedding capacity largely with the same level of visual quality and compression bitrate as that of the previous work, and can resist the latest HEVC video steganalysis method.
When Robot (Vs. Human) Employees Say “Sorry” Following Service Failure

This paper aims to understand travelers' responses to apologies of robot (vs. human) employees following service failures and how travelers' age influences their responses. Using a scenario-based between-subject experimental design, Study 1 finds that human employees' apologies (vs. no apologies) enhance travelers' revisit intention while robot employees' apologies do not have such an effect. Study 2 reveals that human employees' apologies increase satisfaction among younger travelers, whereas robot employees' apologies increase satisfaction among older travelers. Managers in the hospitality and tourism industry may train their employees (vs. design robot employees' apology messages) effectively to serve travelers in different ages.
Affective reactions and test-driven development: Results from three experiments and a survey

The research on the claimed effects of Test-Driven Development (TDD) on software quality and developers’ productivity has shown inconclusive results. Some researchers have ascribed such results to the negative affective reactions that TDD would provoke when developers apply it. In this paper, we studied whether and in which phases TDD influences the affective states of developers, who are new to this development approach. To that end, we conducted a baseline experiment and two replications, and analyzed the data from these experiments both individually and jointly. Also, we performed methodological triangulation by means of an explanatory survey, whose respondents were experienced with TDD. The results of the baseline experiment suggested that developers like TDD significantly less, compared to a non-TDD approach. Also, developers who apply TDD like implementing production code significantly less than those who apply a non-TDD approach, while testing production code makes TDD developers significantly less happy. These results were not confirmed in the replicated experiments. We found that the moderator that better explains these differences across experiments is experience (in months) with unit testing, practiced in a test-last manner. The higher the experience with unit testing, the more negative the affective reactions caused by TDD. The results from the survey seem to confirm the role of this moderator. • Experience with unit testing causes more negative affective reactions to TDD. • When learning TDD, it is better to have little experience with unit testing. • Negative affective reactions to TDD can explain contrasting results of TDD research.
Indian Sign Language Translation using Deep Learning

Indian Sign Language is the language used by specially abled population in the Indian subcontinent to communicate with each other. Unfortunately the general population is not aware of the semantics of Indian Sign Language. In this work we present three deep architectures to translate a given video sequence containing the Indian Sign Language sentence to English Language sentence. We have tried to solve this problem using three approaches. First using an LSTM based Sequence to Sequence model(Seq2Seq), second using an LSTM based Seq2Seq model utilising attention, third using an Indian Sign Language Transformer. These models were evaluated on BLEU scores and the transformer model gave a perfect BLEU score of 1.0 on test data.
Preordonance correlation filter for feature selection in the high dimensional classification problem

Feature selection is a crucial pre-processing phase in the analysis of high-dimensional datasets. Due to the presence of insignificant and redundant features in the dataset, the classification performance of learning algorithms is generally not satisfactory. The main purpose of feature selection is to surmount the high dimensionality problem by saving relevant features and removing irrelevant and redundant ones from the original features. This research proposes a novel filter feature selection algorithm, termed Preordonance Correlation Filter (PCF), utilized to pinpoint the most discriminating features from the high-dimensional dataset. The performance of the proposed PCF approach is investigated on three artificial datasets of high dimensions. Experimental results demonstrate that the PCF algorithm is able to successfully identify the right significant features and discard the irrelevant ones from all of the three simulated datasets.
Using Dynamic Difficulty Adjustment to Improve the Experience and Train FPS Gamers

The concept of Dynamic Difficulty Adjustment (DDA) refers to software techniques that make a game adjust itself in real time to make the game better fit the player's abilities and therefore to improve his/her gaming experience. But DDA can also be used to improve and render more effective the training of e-sports players by creating even more challenging environments that always push the gamer to his/her limits. This article presents a first step towards that objective: in this study, the First-Person Shooter (FPS) game Half-Life was modified to allow several game mechanics (and, accordingly, the game's difficulty) to be adjusted in real-time creating what was expected to be an improved and more immersive experience to the player. The DDA algorithm makes these adjustments according to the previous actions of the player and towards a predefined performance goal. Preliminary results with a group of e-sports players showed positive results in terms of their perceived experience.
Omnidirectional Mobile Robot Path Finding Using Deep Deterministic Policy Gradient for Real Robot Control

Recently, workers are in short supply in the distribution industry. Therefore, the objective of this study is to develop an autonomous mobile robot that can search for a path to the goal while avoiding static and dynamic obstacles to support workers in a warehouse. In this study, we apply five learning types in a hybrid static and dynamic environment in a simulation environment as a preparation for learning in an actual machine and verify the effectiveness of these learning methods.
Implementation of Machine Learning Using Google's Teachable Machine Based on Android

Many people think that machine learning is difficult to build and do not know how to implement it. This is one of the problems in writing this report. In this study, the system development method used is the waterfall which starts from the requirements analysis stage, system design, system implementation, system testing and system maintenance. The design of this system uses flowcharts, Unified Modeling Languages (UML) and interface design. In this study, it is known that Android devices can be implemented with a machine learning model. Each model in this study has been tested for models and each model has an accuracy, precision and sensitivity ranging from 97-100&#x0025;. Google&#x0027;s Teachable Machine can create a machine learning model with a test accuracy rate of up to 100&#x0025;. The level of accuracy can be reduced according to the level of lighting at the detection site.