Reviewerly/exHarmony huggingface dataset:
Indonesian Sign Language Fingerspelling Recognition using Vision-based Machine Learning

Sign language is a method of communication using hand gestures that are usually used by Deaf people. In Indonesia, there are 2 types of sign language, namely SIBI and BISINDO. However, in everyday life, BISINDO is more often used. Communication gaps often occur between Deaf people and hearing people. So that we need media that can bridge their communication. one of the technologies that can be used is SLR (Sign Language Recognition). SLR itself has various kinds of approaches, one of which is a vision-based SLR. Vision-based SLR has an advantage, such as not requiring a special device attached to the hand, but simply making gestures with bare hands in front of the camera. In this study, we created a machine learning model with a vision-based SLR approach. The model we created was using the CNN (Convolutional Neural Network) architecture. The CNN model was trained and tested on the BISINDO alphabet (A-Z) dataset that we created on our own. This model achieves an accuracy of 99.28% on validation accuracy, 98.57% on testing accuracy, and 98.07% on real-time testing accuracy.
The IoT Supply Chain Attack Trends-Vulnerabilities and Preventive Measures

The lifestyle of people in this century will be categorized as Pre-COVID and Post-COVID lifestyle in the future. The pandemic has influenced every nation and every sector. The Internet of Things has helped the humankind to progress even during the pandemic time. Internet of Things has grown drastically with the industry switching their offices to homes. This has also increased the threat landscape and the attacks are also on rise. Several cyber-attacks are possible with Internet of Things devices. However, Supply chain attacks remain the largest threat as reactive measures to mitigate them are extremely difficult. In addition to that, supply chain attacks are difficult to detect in a network. In most of the cases, they remain unnoticed and they are used to launch espionage and ransom ware attacks. The impact of supply chain attacks is also extremely large compared to other attacks. The number of Internet of Things devices are going to grow by leaps and bounds, and it is necessary to take proactive steps in the network to mitigate such attacks. In this article, the elements of Internet of Things supply chain, motivation behind these attacks, vulnerabilities in the supply chain, and recent attacks are discussed in detail. In order to mitigate the supply chain attacks in Internet of Things environment, preventive measures that can be adopted at various levels are also suggested. These measures can be applied as best practices in any smart network environment against supply chain attacks.
Deep Autonomous Agents Comparison for Self-driving Cars

Autonomous driving is one of the most challenging problems of the last decades. The development in recent years is mainly due to the continuous expansion of Artificial Intelligence. Nowadays, most self-driving systems use Deep Learning techniques. In recent years, however, thanks to the successful learning demonstrations of Atari games and AlphaGo by Google DeepMind, new frameworks based on Deep Reinforcement Learning are being developed. The objective is to combine the advantages of image processing and feature extraction of convolutional networks, and the learning process through the interaction of one or multiple agents with their environment. This work aims to deepen and explore these new methodologies applied to autonomous driving cars. In particular, we developed a framework for controlling a car in a simulated environment. The agent learns to drive within a neighborhood with constant speed, variable light conditions, and avoiding collisions with external objects. The proposed techniques are based on Double Deep Q-learning and Dueling Double Deep Q-learning. We implemented two variants of the algorithms: one trained from random weights and one exploiting the concepts of Transfer Learning. After a simulation study, the Dueling Double Deep Q-learning with Transfer Learning has showed promising performance.
A Low Cost and Portable Mini Motor Car System with a BNN Accelerator on FPGA

To realize autonomous driving, a deep neural network (DNN) is one of the key technologies. However, since DNN needs a lot of computation, it is challenging for an edge device to support DNN with limited computation resources. A binarized neural network (BNN) has been proposed to reduce latency and parameter size and is suited for hardware implementation. Since current DNN technology is a growing and better algorithm change with time, implementing DNN on an FPGA is preferable to an ASIC. In this paper, we propose a low cost and portable mini motor car system with a BNN accelerator on an FPGA. We compare the road tracking demonstration with a similar motor car using Raspberry Pi and show the effectiveness of FPGA in a DNN implementation. The proposed system is implemented on Nexys A7, one of the most popular FPGA development boards using an Artix-7 FPGA.
ANALYTICAL REVIEW OF AUGMENTED AND MIXED REALITY SYSTEMS IN THE CONTEXT OF INDUSTRY 4.0

Augmented reality systems are gaining popularity for solving problems in the field of production optimization and employee training. The article discusses augmented reality systems in the context of Industry 4.0. The analysis, generalization and synthesis of scientific publications on this topic was carried out. This article presents up-to-date information about the scientific and technological state of affairs in the field of the use of augmented reality systems in industrial enterprises, which can be useful to both scientific and engineering workers.
Quantum Error Correction in Continuous-Variable System

On the physical level, digital computers are not error free. However, by adding the redundancy and using the error-correction technique, the errors on the computational level can be mitigated to the point that the probability of error occurring is negligible for most tasks. In quantum computation, a similar strategy can be employed. Here, a main difference between classical computer and quantum computer is that the quantum error correction needs to mitigate the errors, while maintaining the quantum coherence. In this chapter, we will first discuss the conceptual ideas of how to implement quantum error correction and achieve fault tolerance in the continuous-variable system. In the case of the continuous-variable system, we need to first encode the information using a logical qubit which, in a sense, digitize the broad Hilbert space. We will give some examples of the logical qubits and then discuss a particular type of encoding called Gottesman-Kitaev-Preskill (GKP) qubit, which is one of the currently most promising encoding.
PrunedCaps: A Case For Primary Capsules Discrimination

Capsule Networks (CapsNets) are a generation of image classifiers with proven advantages over Convolutional Neural Networks (CNNs). Better robustness to affine transformation and overlapping image detection are some of the benefits associated with CapsNets. However, CapsNets cannot be classified as a resource-efficient deep learning architecture due to the high number of Primary Capsules (PCs). In addition, CapsNets’ training and testing are slow and resource hungry. This paper investigates the possibility of Primary Capsules pruning in CapsNets on MNIST handwritten digits, Fashion-MNIST, CIFAR-10, and SVHN datasets. We show that a pruned version of CapsNet performs up to 9.90x faster than the conventional architecture by removing 95% percent of Capsules without a loss of accuracy. Also, our pruned architecture saves on more than 95.36% of floating-point operations in the dynamic routing stage of the architecture. Moreover, we provide insight into why some datasets benefit significantly from pruning while others fall behind.
Assessing Feature Representations for Instance-Based Cross-Domain Anomaly Detection in Cloud Services Univariate Time Series Data

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets.
Comparing Facial Expressions for Face Swapping Evaluation with Supervised Contrastive Representation Learning

Measuring and comparing facial expression have several practical applications. One such application is to measure the facial expression embedding, and to compare distances between those expressions embeddings in order to determine the identity- and face swapping algorithms' capabilities in preserving the facial expression information. One useful aspect is to present how well the expressions are preserved while anonymizing facial data during privacy aware data collection. We show that a weighted supervised contrastive learning is a strong approach for learning facial expression representation embeddings and dealing with the class imbalance bias. By feeding a classifier-head with the learned embeddings we reach competitive state-of-the-art results. Furthermore, we demonstrate the use case of measuring the distance between the expressions of a target face, a source face and the anonymized target face in the facial anonymization context.
A Large Visual Question Answering Dataset for Cultural Heritage

Visual Question Answering (VQA) is gaining momentum for its ability of bridging Computer Vision and Natural Language Processing. VQA approaches mainly rely on Machine Learning algorithms that need to be trained on large annotated datasets. Once trained, a machine learning model is barely portable on a different domain. This calls for agile methodologies for building large annotated datasets from existing resources. The cultural heritage domain represents both a natural application of this task and an extensive source of data for training and validating VQA models. To this end, by using data and models from ArCo, the knowledge graph of the Italian cultural heritage, we generated a large dataset for VQA in Italian and English. We describe the results and the lessons learned by our semi-automatic process for the dataset generation and discuss the employed tools for data extraction and transformation.
A Neural Network Based Approach for Text-Level Sentiment Analysis Using Sentiment Lexicons

There have been many discussions on forums, e-commerce sites, sites for reviewing products, social media which helps in exchanging opinions, thoughts through free expression of users. Internet as well as web 2.0 is overflowing with the data generated by users which provides a good source for various sentiments, reviews, and evaluations. Opinion mining more popularly known as sentiment analysis classifies the text document based on a positive or negative sentiment that it holds. This is an open research domain and this particular research paper puts forth a model called Artificial Neural Network Based Model i.e., ANNBM. The model is trained and tested through Information Gain as well as three other popular lexicons to extract the sentiments. It’s a new approach that best utilizes the ANNBM model and the subjectivity knowledge which is available in sentiment lexicons. Experiments were conducted on the mobile phone review as well as car review to derive that this approach was successful in finding best output for sentiment-based classification of text and simultaneously reduces dimensionality.
Fast Online Character Recognition Using a Novel Local-Global Feature Extraction Method

Online handwriting recognition is one of the most active subjects of research in the field of pattern recognition. Feature extraction is one of the main steps in handwritten recognition which has a significant impact on accuracy and speed of recognition. Low time complexity of feature extraction method and short feature vector length reduce the computational cost and memory consumption. In this paper, a new feature extraction method is presented for fast online Persian/Arabic character recognition. In this method, in order to recognize the character pattern, the character signal sequence is first segmented into several equal parts in terms of number of points. Then the angle between horizon line and transient line of first and last points of each segment is considered as local feature. The global features are the angles between horizon line and transient line of first point of the character and the end point of segments. This method has been tested on two standard datasets including numbers and characters of Persian/Arabic languages. The experimental results show that the proposed method, in addition to high recognition accuracy, has less computational complexity and shorter vector length compared to other new methods of feature extraction and handwriting recognition.
Agricultural based blockchain and food chain network

Blockchain is a developing digital technology that allows distributors to interact financially across all components without such middlemen as banks. This article analyzes and summarizes current research and efforts on blockchain-based agriculture and food supply chain implications, problems and potential. The research indicates that blockchain is a viable choice for a transparent supply chain with a great lot of work on foodstuffs and food issues. This research showed that a number of companies and efforts are establishing a dependable food supply chain with the involvement of key stakeholders within the distribution chain using blockchain technology. However, there are still certain obstacles and problems which hinder farms and systems from becoming more attractive. The difficulties include technological, educational, policy and regulatory frameworks. In the near future, study will demonstrate, in order to provide a secure, trustworthy and transparent approach, how government and trade activities should guarantee food security and integrity, as well as address such problems.
Towards Selfie Drone: Spatial Localization and Navigation of drone Using Human Pose Estimation

Remotely piloted drones are popular for photography and selfies are the most popular type of pictures. These drones require piloting skills and expertise in controlling maneuvers for taking pictures. We create a selfie drone software solution that eliminates the requirement for manual drone maneuvering to the desired location. The RC transmitter is linked to an Android smartphone that runs the software programme. The user selects or provides a template for a selfie and the drone takes off automatically. The software locks the drone’s home position and uses human pose estimation to find the user’s pose. It computes the position vector of the drone camera for the desired selfie, flies the drone to the desired position vector, and captures the selfie. After that, the drone hovers in the same spot, either waiting for the next template or returning to its home position. After careful review of the existing options, we decided to use DJI Phantom 4 as a selfie drone that is controlled through an RC transmitter by default. We control the drone with DJI SDK and use support vector regression to generate the position vector for capturing selfie images similar to the template provided by the user. The algorithm’s performance on the mobile platform is assessed qualitatively: the regressor is tested on real-world settings, yielding an accuracy of 80%, and the images acquired by the drone resemble the desired template images.
Prediction Method of the Industrial Economic Operation Index Based on an Improved Genetic Algorithm

Aiming at the problems of low prediction accuracy and large error economic loss in the existing industrial economic operation index prediction methods, an industrial economic operation index prediction method based on an improved genetic algorithm is proposed. According to the industrial operation mode, the mathematical model of industrial economic operation is established. Under this model, the industrial economic operation indicators are selected from the aspects of stability and sustainability. Through the analysis of the relationship between industrial economic operation indicators, the filtering treatment of selection indicators is realized. Collect the historical operation data of the industrial economy, extract the characteristics of historical data by using an improved genetic algorithm, summarize the change law of historical data, and predict industrial economic operation indicators. Through the prediction test experiment, it is concluded that the average prediction error of the design prediction method is less than 0.05, and the error loss is less than 200000 yuan, which meets the design and application requirements.
FLAG: Flow Representation Generator based on Self-supervised Learning for Encrypted Traffic Classification

Due to its excellent ability in learning features from large scale raw data, deep learning (DL) has attracted much attention for encrypted traffic classification. However, most DL-based traffic classifiers usually rely on enormous labeled samples. Motivated by this, we investigate a self-supervised traffic classifier (FLAG) without sacrifice of identification accuracy, only depending on small labeled traffic samples and highly available unlabeled traffic samples. Specifically, focusing on local short-term characteristics of traffic, we design a preprocessing algorithm, termed as N-phrase Extration, to convert unlabeled raw traffic dataset into sequences of high-frequency phrases as input of Bidirectional Encoder. On account of their significance, potential timing characteristics from input sequences are mined by Bidirectional Encoder and embedded into robust representations with distributed vectors to enhance classifier’s performance significantly. Our comprehensive experiments indicate FLAG can achieve 98.65% in 100% of dataset and 98.07% in 10% of dataset in terms of true positive rate in UNB ISCX VPN-nonVPN dataset, which are better than p-FP, FS-Net and Deep Packet.
Inference of collective Gaussian hidden Markov models

We consider inference (filtering) problems for a class of continuous state collective hidden Markov models, where the data is recorded in aggregate (collective) form generated by a large population of individuals following the same dynamics. We propose an aggregate inference algorithm called collective Gaussian forward-backward algorithm, extending recently proposed Sinkhorn belief propagation algorithm to models characterized by Gaussian densities. Our algorithm enjoys convergence guarantee. In addition, it reduces to the standard Kalman filter when the observations are generated by a single individual. The efficacy of the proposed algorithm is demonstrated through multiple experiments.
AI-Based Language Chatbot 2.0 – The Design and Implementation of English Language Concept Learning Agent App

In this paper, we propose an English Language Concept Learning App by combining English concept learning and AI technologies such as automatic options generation and speech recognition analysis. The application is based on WeChat mini-program aimed at assisting and conducting English learning by providing various exercises such as multiple-choice and phonetic questions. To generate wrong options for multiple-choice questions, we used new words from WordNet, a large English lexical database with collocations detected by using statistical natural language processing and neural network models such as CBOW in Word2vec or BERT. User voice input is embedded in voice interaction for recognition and analysis. Based on the framework, this application is expected to be combined with other Artificial Intelligence (AI) technologies to analyze users’ performance and adjust subsequent teaching curriculum accordingly.
Velocity Estimator Augmented Image-Based Visual Servoing for Moving Targets with Time-Varying 3-D Motion

This paper presents a novel image-based visual servoing (IBVS) technique for moving targets. A target velocity estimation law is designed using dynamic surface-like filters and incorporated in the control law to compensate for the uncertainties due to the unknown target motion. Rigorous Lyapunov analysis is performed to show that the errors converge exponentially to an ultimate bound for time-varying target motion and asymptotically to zero if the target velocity is constant. The simulation results considering a realistic environment show the adaptability of the proposed scheme to track an arbitrary moving target in 3-dimension (3-D).
Feature-Based Attack on Visual Tracking

Machine learning can categorize various algorithms, for example, classification model, object detection, segmentation and visual tracking. The adversarial attack comes up to addresses the generalization weakness which learns to anticipate the deep neural network by causing model malfunction. Although the adversarial attack models can effectively suppress the performance of the classification model, there are narrow attacking models in visual tracking because the tracking model is different from the generalization model. In this paper, we propose a feature-based attack model that mildly generates perturbation in the template region with two loss functions: minimizing malicious noise and interfering with the heatmap feature which gradually forces the tracker into the erroneous position. We train our proposed attacking model based on SiamRPN++. Our training model requires only a template image which is sufficient for attacking the tracker and leads to the training resource decreasing. In the experiment section, we benchmark our model with OTB100, VOT2018, LaSOT and UAV123 datasets. Our model can successfully deceive the tracker. It is superior to the other existing adversarial attack models in visual tracking. Besides, our noise generator is the fastest model.
Towards Occlusion Robust Facial Landmark Detector

Facial landmark detection plays an essential role in many face analysis tasks, such as face recognition, facial action unit detection, etc. However, when occlusion occurs, the existing methods might not perform well. This paper studies a new framework for facial landmark detection referred to as the Occlusion Robust Facial Landmark Detector (OR-FLD), which attempts to solve the occlusion problem. Specifically, the proposed OR-FLD consists of three main modules: Feature Random Dropping Module (FRD), Feature Activation Diversity Module (FAD), and Landmark Refinement Attention Module (LRA). The FRD forces the model to focus on the geometric relationships between different facial components by randomly dropping some appearance information and then automatically recovering them. The FAD tries to enhance the model representation and make the extracted features discriminative and robust to occlusions. Besides, the recovered features contribute differently to the final results. Therefore, we propose a simple and effective attention mechanism to refine the landmark location and make the model more robust to occlusions. We extensively evaluate OR-FLD on four facial landmark detection benchmarks (300W, WFLW, COFW, and AFLW). The results show that our method outperforms the state-of-the-art by a significant margin, especially on the datasets with large occlusion.