Reviewerly/exHarmony huggingface dataset:
Adversarial point cloud perturbations to attack deep object detection models

The reliability of existing Cyber-Physical Systems(CPSs) developed on the basis of deep neural networks is not guaranteed, because deep models have been proved vulnerable to adversarial attacks with imperceptible perturbations. Considering that deep 3D models have potential applications to a wide variety of CPSs with safety-critical requirements, such as autonomous driving systems, we explore the robustness of deep 3D object detection models under adversarial point cloud perturbations in this paper. A novel method to generate 3D adversarial examples is developed from point cloud perturbations, which are common in practice due to the inherent characteristics of the LiDAR data produced by the 3D sensors. The method has the ability to successfully attack deep 3D models in most cases, and thereby gives rise to a great real-world concern about object detection models due to their vulnerability to physical-world attacks in the form of point cloud perturbations. We conduct a thorough robustness evaluation of popular deep 3D object detectors in an adversarial setting on the KITTI dataset. Experimental results show that current deep 3D object detection models are susceptible to adversarial attacks, and their performances are decreased to a large extent in the presence of adversarial point clouds generated by the proposed method.
Real-time shallow water image retrieval and enhancement for low-cost unmanned underwater vehicle using Raspberry Pi

Unmanned Underwater Vehicles (UUV) operated underwater without any human interference. UUVs can be used for many purposed i.e. exploration or spying. They have several designs and each one can be used for a specific purpose. Main problem that limits the functionality of UUV is poor image quality. Reason behind this poor image quality is scattering and wavelength absorption. Purpose of this paper is to propose an electrical assembly design of low-cost UUV. In order to carry out this work use of Raspberry-Pi is proposed which is a small computer own it's own. It can be used to enhance image quality and detect objects along with color detection. For controlling the propellers in accordance to any hindrance, pulse width modulation(PWM) GPIO pins are used. GPIO pins are also used to attach sensors. For enhancement of images python 3 was used and different image enhancement methods were tested on basis of execution time per frame. In the end, best model is selected which is more suitable on basis of execution time and image enhancement. In order to transmit the images from UUV to base computer wi-fi module of Raspberry pi is used.
Qiskit n-Bitstring Quantum Half-adder and Half-substractor

Quantum Computing fast development is leading to the emergence of a wide variety of software development frameworks. In general, in these frameworks users can implement quantum algorithms and circuits, evaluating their behavior through simulations, and in some cases, executing them on Noisy Intermediate-Scale Quantum (NISQ) devices. IBM has been a pioneer in this field, providing public access to their devices through the IBM Q Experience Platform, using Python's open-source framework Qiskit. In this paper, we present the development of a n-bitstring half-adder and half-subtractor algorithm in Qiskit, analyzing the behavior on the IBM Q Experience simulator and real quantum processors.
REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

Abstract We present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mi>a</mml:mi> <mml:mi>x</mml:mi> <mml:mo>-</mml:mo> <mml:mi>F</mml:mi> </mml:mrow> </mml:math> test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mi>a</mml:mi> <mml:mi>x</mml:mi> <mml:mo>-</mml:mo> <mml:mi>F</mml:mi> </mml:mrow> </mml:math> statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.
UPLX: Blockchain Platform for Integrated Health Data Management

Health data management currently needs a technology refresh in order to provide accurate, reliable and verifiable data for doctors and researchers to decide on the best medications and for the public to have their own dependable health information history as they continue with their daily lives. We propose UPLX (Unified Patient Ledger) as a blockchain-based data platform to securely record, “anonymize” and store patient health data for medical, academic and pharmaceutical research. UPLX is a blockchain powered health data platform which is designed to be interoperable and can be integrated with any hospital information systems (HIS) through API (Application Programming Interface) technology. To this end, a Hyperledger Fabric implementation is described to demonstrate the feasibility of the proposal and its use in healthcare organization. Successful implementation will accelerate the acceptance of Blockchain technology in protecting recorded health data while increasing the efficiency of healthcare delivery.
Measuring Risk Mitigation Techniques in Agile Global Software Development

The main purpose of an Agile development method is to reduce risks, leading to a more successful and effective information system as it had overcome limitations faced in a traditional software development method. This reason lead many to adopt the Agile methodology, especially for global software development (GSD) projects. However, most are not aware of the risks introduced when using agile methods in GSD projects. The risks in GSD projects are dynamic due to factor multiplicity (e.g., multi-locations, multi-groups, multi-cultures, multi-technologies, and multi-standards). This study aims to distinguish the difference between two risk mitigation techniques as an overall technique in tackling the same issue, which is communication issues introduced by the geographical distance between two development teams.
A Method for Recognition of Dynamic Hand Gestures Based on Wrist Tendon Sounds

Hand gesture recognition are widely applied in game industry and rehabilitation engineering. Some recognition methods based on physiological data are attracting attention. In this paper, a novel method for dynamic hand gesture recognition based on sounds of wrist tendon is proposed. Bone conduction sensors which automatically filter the ambient noise is utilized to collect the wrist tendon sounds of three types of dynamic hand gestures. Continuous Wavelet Transform(CWT) is utilized extract the features of sounds and Support Vector Machine(SVM) is utilized to train the classifier model. Five healthy subjects participated the experiment and the average test accuracy of three classes achieves 92.33%. The proposed method provides an effective perspective for hand gesture recognition.
Normalized Differential Power Analysis - for Ghost Peaks Mitigation

The attack efficacy of Differential Power Analysis (DPA), a popular side channel evaluation technique for key extraction, is compromised by the false highest Difference Of Means (DOMs) value ('ghost peaks') in the DOMs matrix produced in a conventional DPA. The ghost peak is generated by the wrong key guess and always occurs in the conventional DPA when the number of side channel traces is not enough. In this paper, an improved version of the conventional DPA termed as Normalized DPA (NDPA) is proposed to circumvent the ghost peak. With the analysis on the generation of ghost peaks in the conventional DPA, we observed that by normalizing the DOMs matrix, the ghost peaks can be greatly suppressed. We model the proposed NDPA mathematically and show that it performs better than the conventional DPA. We further provide the experimental validations on a set of 200k power simulation traces on AES S- Box and 500 EM traces from ASCAD dataset. Based on the attack results of these datasets, our proposed NDPA requires (up to 68%) lesser number of traces to reveal a correct key when compared to the conventional DPA.
An E-commerce Agreement Based on the Points System of the Blockchain and the Secure Multi-party Platform

Blockchain technology, as the supporting technology in digital currency, has brought considerable changes to e-commerce sector with its decentralised, tamper-proof and anonymous working methods. Based on the characteristics and the tamper-proof traceability of the block chain, this study constructs the application architecture of the e-commerce platform integration system, which not only realises the collaborative computing on the multi-party secure data platform, but also maintains the confidentiality of the calculation verification and input data. Additionally, the actual consumption process of consumers is introduced for case analysis to verify the feasibility of the method, which guides the collaborative application of blockchain technology and multi-party security computing technology in the field of e-commerce.
Reinforcement Learning for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMC) is an important yet challenging problem in the NLP community, which refers to the problem of assigning to each document its most relevant subset of class labels from an extremely large label collection. For example, the input text could be a story document on chinastory.cn and the labels could be story categories that implies the potential meaning. However, naively applying normal neural network models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue. In this paper, we presents the first attempt at applying reinforcement learning to XMC. Experimental results on public and our own engineering datasets demonstrate that our approach achieves expecting performance compared with the evaluation of the state-of-the-art methods.
ON ONE APPROACH TO SOLVING THE TRAVELING SALESMAN PROBLEM FOR AUTONOMOUS UNMANNED AERIAL VEHICLE PLANNING OF TARGET FLIGHT PATHS

Objective. Develop a principle of constructing tools and procedures for optimal routing of targets by an autonomous uncrewed aerial vehicle, which have an acceptable complexity for implementation on an onboard computer. Methods . The rules for optimizing the terrain map with the located targets, creating a penalty matrix that considers the disturbing factors of the unstable air environment in the planning process, and building a reference tree for finding the minimum route for the accepted cost of flying around the targets. Results . A principle is proposed to develop effective procedures for planning minimum cost-effective routes to fly around targets in an unstable air environment. The basis for behavior planning is the optimization of the formal description of a given terrain map, which leads to a significant reduction in the number of alternatives compared in the process of searching for a minimum route to fly around targets. This, in turn, makes it possible to implement planning procedures on the onboard computer system of an autonomous uncrewed aerial vehicle. A statement is proved to justify the existence of a minimum route for the accepted cost of overflying targets in the formal description of the terrain map obtained as a result of converting its original representation based on the developed optimization rules. Conclusion . The considered approach effectively solves various types of issues reduced to the traveling salesman problem, including planning the minimum route of flying around targets by an autonomous uncrewed aerial vehicle in an unstable air environment under uncertainty conditions.
Diversity Regularized StarGAN for Multi-style Fonts Generation of Chinese Characters

Abstract The generation of stylish Chinese fonts plays a central role in many applications such as the design of art fonts and Chinese calligraphy generation. Most of existing methods focus on the generation of a single-style Chinese font, while few works focus on the multi-style font generation. In this paper, we exploit the star generative adversarial networks (StarGAN), a very popular generative adversarial networks (GAN) model recently developed in the literature, to realize the generation of multi-style Chinese fonts via a single model. Furthermore, in order to tackle the generation issue of Chinese characters having similar strokes for StarGAN, i.e., generating the same mode for these different but similar Chinese characters, we introduce a diversity regularizer such that the generator can generate high-quality characters with better diversity. A series of experiments are conducted on a handwritten Chinese character dataset called CASIA-HWDB1.1 and three standard printing font datasets to show the effectiveness of the proposed method. The experiment results show that the proposed method can effectively tackle the generation issue of Chinese characters having similar strokes in terms of the quality and diversity of generated results, via comparing to the baseline StarGAN, and is scalable to the multi-font generation via comparing to existing methods for the single-style font generation.
Creating Batik Lasem through a comparative study of Batik Lasem and Champa in the 15<sup>th</sup> to 19<sup>th</sup> century

Abstract Batik Lasem of Indonesia is a part of the national cultural heritage. Playing an important role in the production of traditional Indonesian textiles, batik Lasem has a specific motif mixing elements of Chinese and Javanese culture, which produces meaningful, unique motifs and colors. It is believed that Admiral Cheng Ho, who first arrived at the Binangun port in 1413, had brought this culture with his people from Champa. The purpose of this research was to create a new batik motif by combining Batik Lasem and the Champa motif. The research method used was the literature studies by analyzing the acculturation of Champa ceramic ornaments in Batik Lasem and interviewing with some batik observers as well as batik entrepreneurs. Through Design Thinking Process, the author develops four motifs of the acculturation motif from the surrounding natural environment and the daily stories of the local people. In the future, the author hopes that this sustainable new motif will inspire young people to preserve this extinct Batik Lasem as our precious cultural heritage.
Paired Augmentation for Improved Image Classification using Neural Network Models

The lack of sufficient data points and class imbalances in datasets is a serious problem that mitigates against the success of deep learning classification models. These problems result in model overfitting, poor accuracy, and poor generalization. Traditional augmentation techniques, and advanced augmentation, such as adversarial approaches, have individually been found to be effective. In this work, we present a method to determine the most effective augmentation techniques to combine into the machine learning pipeline. We propose using only two augmentations in the pipeline, an advanced technique applied in an offline manner followed by a simple technique applied in an online manner. This approach is validated by application to two medical image problems using datasets characterized by class imbalances and small sizes. The former is a binary classification problem using a brain tumor dataset and the latter is a multi-label classification problem using a white blood cell dataset. Exhaustive experimentation with approximately 170 different combinations of augmentations methods is reported. Experimental results indicate a 15% and 11% improvement in validation accuracy over using no augmentation and a 2% improvement over using a single augmentation. We conclude that the pairing of augmentation methods results in improvements in the classification tasks.
Interactive Mixed Reality Technology for Boosting the Level of Museum Engagement

Holographic immersive technology such as ‘Mixed Reality’ is nowadays extending in the cultural heritage sector to open new prospects to engage visitors in museums. This paper investigates the level of engagement in the museum space by conducting observations and time consuming at the Egyptian Museum in Cairo. An interactive mixed reality system named ‘MuseumEye’ was developed and used Microsoft HoloLens as a mixed reality head mounted display to boost the level of engagement with the exhibited antiques. This system was experienced by 171 of the Egyptian museum visitors and another observation was conducted to record their behaviours and the time they consumed next to each antique. Results of this study showed the time consumed to engage with holographic visuals and the exhibited has been increased 4 times compared to the time the visitors consumed before without using technological gadgets. The implications of these immersive technologies can be an important vehicle for driving the tourism industries towards achieving successful engaging experiences.
LiveEar: An Efficient and Easy-to-use Liveness Detection System for Voice Assistants

Abstract Voice assistants, such as Amazon Alexa, Apple Siri and Tmall Genie, using voice biometrics for the identity authentication, are becoming pervasive in our daily lives. However, voice assistants are vulnerable to reply attack due to the open nature of voice-input channels. An attacker can record the voice commands of victims and replay them to spoof voice assistants. Existing liveness detection approaches are mostly based on machine learning methods, which are expensive and complex. Recently, several approaches are proposed to leverage the human specific voice features or the distinctness of voice played by loudspeaker. However, they require the users and the voice assistant to be in a fixed position and at a very close distance, which is not user-friendly in practice. This paper proposes LiveEar, an efficient and easy-to-use liveness detection system for voice assistant. LiveEar utilizes the differences in phoneme positions between live-human voices and voices replayed through loudspeakers. Specifically, it calculates the time-difference-of-arrival (TDoA) in a sequence of phoneme sounds to the microphone on the voice assistant. Then, an SVM-based classification model is trained with the extracted TDoA features. This paper implements a prototype of LiveEar and evaluates its performance using real-world data. Results show that LiveEar achieves high detection accuracy in various flexible positions, with negligible runtime overhead.
APPLICATION OF CLOUD TECHNOLOGIES IN PEDAGOGICAL MANAGEMENT

The concept “cloud technology” in the context of pedagogical management has been revealed in the article. It has been found that cloud technologies are relevant in education, are intensively integrated into general secondary education, constitute the basis for the formation of information and educational space of an educational institution, are indispensable during the COVID-19 national quarantine. Considering the cloud environment as an environment of effective pedagogical management, the principles on the basis of which cloud technologies of educational institution management are implemented have been determined. It has been revealed that the use of cloud technologies enables the head of the general secondary education institution to fully realize the main functions and directions of pedagogical management. The main modern processes in the cloud environment have been outlined (digitalization, feedback, collaboration, delegation of powers, crowdsourcing, strategic planning, educational benchmarking, joint planning, management of professional development of teachers, PR-activities of educational institutions, control, monitoring, evaluation, testing online). The powerful capabilities of Google’s cloud services in terms of management of teachers and educational institutions in general have been analyzed. Diagnosis has been carried out to determine the state and level of awareness of the possibilities of implementing effective pedagogical management through cloud services. Principals of general secondary education institutions from two regions of Ukraine were involved. The study was conducted in three stages: before COVID-19 quarantine, during the full lockdown and during the adaptive quarantine. As a result of the diagnosis, we have identified three levels of formation of information and digital competence of the principal of an educational institution in the context of the implementation of cloud technologies in pedagogical management: harmonious, functional and unproductive. It has been found that in the conditions of quarantine restrictions, educational managers accumulated their capabilities to the maximum, mastering the necessary cloud services in a short period of time, which led to a sharp increase in their level of information and digital competence.
HeadCap: A Hierarchical Encoder-And-Decoder Image Captioning Model

Image captioning achieves its goal by two steps traditionally, it first extracts image features using deep networks, and then translates the features into correct text descriptions. Though recent compelling captioning results have surged continuously, the bridge between visual cues and texts is usually limited by weak single-layer CNN features and one-word one-attention strategy. In this paper, we propose a simple yet effective Hierarchical Encoder-And-Decoder image CAPtioning model (HeadCap) to capture visual cues across different levels whilst conducting one-word multi-attention scheme in a unified hierarchical architecture. In specific, we collect multi-layer features from feature pyramid network (FPN), which allows stringing visual features across different levels hierarchically. More importantly, a multi-attention LSTM module (mLSTM) is established on such hierarchical features, which performs multiple attentions over hierarchical features progressively when predicting a word. This provides LSTM more attention chance to reduce the risk of the mismatch between visual cues and texts, and further to bridge the gap between each other. Experiments on MSCOCO image captioning dataset show that HeadCap yields favorable performance against several well-behaved deep captioning models.
ChannelMix: A Mixed Sample Data Augmentation Strategy for Image Classification

Recent emerging convolutional neural networks (CNNs) have powerful feature extraction ability and can achieve the state-of-the-art classification performance. However, there exists a problem that CNNs have the memorization to training samples and sensitivity to adversarial examples, resulting in overfitting and generalization decline. To solve the problem, this paper proposes a novel mixed sample data augmentation (MSDA) strategy named ChannelMix to improve network performance. Specifically, ChannelMix uses the multi-channel information and labels of paired samples to regularize the training process through convex combination, which can guide the network to pay more attention to the less discriminative parts. Extensive experiments on the CIFAR-10, CIFAR-100 and WHU-RS19 datasets demonstrate that ChannelMix can significantly improve the generalization and the classification performance of CNNs and stabilize the training process simultaneously.