Reviewerly/exHarmony huggingface dataset:
Object Detection in Aerial Images with Attention-based Regression Loss

Object detection is a computer vision technique used to identify objects that are usually present in natural scenes. However, the methods used for this case are not easily transferable to detect objects in aerial images. Objects in aerial images are mostly arbitrary-oriented, small, and in complex backgrounds compared to upright and well-focused objects in natural scenes. To effectively detect objects in aerial images, we propose a new regression loss function based on the attention mechanism through attention weights. Using the relative position of the attention weights to the bounding box, the foreground is given more attention, which highlights the target object and effectively suppresses the noise and background. Preliminary experiments are conducted on an attention-based object detector using the DOTA dataset to test the capability of attention mechanism in extracting the contextual information of objects, especially in complex environments.
Stereo Video Depth Estimation Based on Disparity Map Propagation

Stereo matching algorithm is used to estimating the depth value correspond with two frames taken from left and right cameras. Due to the movement of the objects, the output of depth map will easily vibrate without confidence. Moreover, surrounding information which cover by other objects may cause significant impacts and problems for the users. Therefore, the stability of depth maps is a crucial point for precisely outcomes. This paper proposes a new notion that considers the motion of objects, the frames in different time, and the relationship between left and right frames to propagate a new depth map. The regions of each objects are estimated using quadtree and contrast context histogram. The experimental results show the proposed method surpass conventional stereo matching methods.
AI-based intelligent blockchain for the authentication of the metering system

Smart grids, as all other IoT applications, are subject to numerous security challenges because of their component's constrained nature, namely smart meters, which are the weakest link in the network. Although several researchers have tried to solve the authentication issues, they did not provide a complete solution that ensures, at the same time, integrity, availability, and confidentiality of the authentication data and allows the mobility of the smart meters. The new architecture proposed in this article aims to strengthen the authentication process by adding blockchain and artificial intelligence to the current one. The artificial intelligence algorithms implemented in the MDMS server allow the detection of authentication fraud and therefore deny access. Moreover, in addition to storing the authentication data in an immutable ledger, the decentralized nature of the blockchain allows smart meters to connect to any MDMS server belonging to the cluster. The authentication policy is implemented in the smart contract and executed by the blockchain nodes. The other architecture components, namely, the smart meters and the MDMS servers, interact with this smart contract through transactions. The article also introduces the concept of the smart meter reputation, which prevents the authentication of a malicious smart meter. The smart contract used for this purpose is implemented on the remix as Ethereum integrated development environment. The simulation tests have shown the architecture's robustness and resistance against different attacks.
Performance Comparison of Containerized HBase Clusters on Kubernetes

The demand for large-volume database storage has become an essential issue with the rising trend of big data. Since the NoSQL database performs better than SQL databases when handling extensive data, many developers choose the NoSQL database as their first choice. Among all the NoSQL databases, HBase has become a popular choice due to its flexibility and high efficiency in the big data processing field. HBase is a column-oriented NoSQL database. It uses HDFS storage and is suitable for integrating with Hadoop ecosystem applications. However, deploying an HBase cluster on bare metal or virtual machines could be pretty complicated and time-consuming. The container technology can make HBase installation more convenient. Nevertheless, containerized HBase can be deployed in different ways. Deploying the HBase cluster in a proper approach can achieve higher performance. In this research, we propose two approaches, namely the Container-dedicated approach and the Container-shared approach, to containerize HBase on Kubernetes. Two benchmark tools are used to compare their performance under different workloads. According to experiment results, the Container-dedicated approach is suitable for writeheavy and read/write balanced applications. The container-shared approach shows a better performance in read-heavy applications. The test result will give future developers a reference when designing a containerized HBase cluster.
Bert Based Chinese Sentiment Analysis for Automatic Censoring of Dynamic Electronic Scroll

This article uses BERT[1] algorithm to judge Chinese sentiment. The goal is to automatically censor short advertising words on advertising boards or message boards, and filter out some inappropriate speeches. The method is to collect various speeches on the internet, including online shopping reviews, meal reviews, store reviews, etc., as well as emotion dictionaries, and use these data to train the algorithm so that the algorithm can correctly identify the emotions of short sentences.
Clustering-and-Bagging-based Ensemble for Novelty Detection in Passive Sonar Systems

Trained submarine operators identify threats through passive sonar systems by analysing the acoustic waves captured by arrays of hydrophones. Automatic Classification Systems may be quite beneficial to address this task; however, typical operational settings require instruments to identify the occurrences of unknown classes of ships for promptly alerting this specialised crew. This article proposes a new approach for developing an accurate novelty detector of unknown classes of ships. This proposal comprises an architecture composed by a synergistic combination of cluster-specialised and bagging generated novelty detectors, in opposition to a previous solution exploiting a hierarchical class-specialised architecture. The proposed approach is experimentally evaluated using radiated noise from 8 classes of ships, acquired in an acoustic range from the Brazilian Navy. The proposed detector outperforms previous works in 4.4% of AUC (on average), assuming an evaluation scenario composed by five known and three supposedly unknown ship classes. This gain is relevant to this performance-critical application and is related to a more strict definition of class boundaries attained by the proposed strategical clustering and bagging combination.
Tangible Objects in Virtual Reality for Visuo-Haptic Feedback

Including tangible objects in Virtual Reality (VR) can improve the interaction and immersion in virtual experiences. Users’ hands are freed to interact directly with physical objects and thus receive realistic haptic feedback that complements the primarily visual information offered by the Head-Mounted Display (HMD). The challenge in this area relies on tracking and mapping physical objects into the Virtual Environment (VE). Most methods require attaching sensors to the objects being tracked, usually resulting in object-oriented solutions, making the system inflexible to track different props. In this paper, we propose a marker-based approach that uses computer vision to make the system flexible to different objects, requiring few additional hardware resources. Using the developed prototype, we conducted a user study to analyse hand representation’s impact on the performance of tangible object manipulation tasks. We studied three conditions for hand representation: no hands, a realistic model of the human hands, and an abstract model (two spheres). Our results showed that users were able to interact with the proposed tangible system easily. Of the three hand conditions, participants performed better without any hand representation and took longer to complete the task under the abstract model. These results suggest that passive haptic feedback may be sufficient to give users a good spatial perception of the tangibles, discarding the need for a visual representation of the hands in this context.
Classification of Czech Sign Language Alphabet Diacritics via LSTM

The paper presents the classification of image sequences of the single-handed Czech sign language alphabet diacritics. Since diacritics are expressed by the motion of the hand, the classification is performed by the Long Short-Term Memory recurrent neural network. Annotation of the dataset is done by the MediaPipe framework, and the neural network is constructed with the TensorFlow computational library. The paper describes the proposed method's flow, data acquisition, preprocessing, and training. Obtained results consist of the validation dataset's classification success rate and testing on whole signed words and sentences. The overall success rate was around 88%
Exploring Player Adaptivity through Level Design: A Platformer Case Study

Creating a game that engages everyone is not trivial and, ultimately, impossible. However, generating content adapted to players could improve their experience, thus maximizing the potential player base. With adaptivity, games can be designed to act differently according to several actions without developing different versions. Although it can help the designers, creating a generic approach that adapts content for every game and genre is not trivial. To tackle this problem, this work proposes the exploration of player adaptivity in level design, following three stages: (i) selection of a genre and a game; (ii) study to correlate players’ profiles, preferences, and actions with single elements; (iii) study, based on the previous, to evaluate the impact of generating and adapting game levels. Using Infinite Mario Bros, the first results show that correlating players’ profiles with game elements is not possible. However, preferences for specific levels’ configurations were spotted and used for the second study’s adaptation, which did not impact the players’ experience. In the future, other analyses of the study’s data should be performed, and the methodology should be applied to other games, ensuring similar results are obtained. Lastly, another study should explore correlations between players’ profiles and groups of game elements.
A New evolving Fuzzy System with Mechanisms to Deal With Uncertainties in Times Series Forecasting

In the information era, many of the collected data take the stream form, which is usually non-stationary and produced at high speed. Furthermore, the real-world data sets inevitably contain unnecessary and irrelevant information called noise, disturbing the model’s accuracy. This paper proposes recursive filtering to smooth the input data. This approach is implemented in the so-called evolving Participatory Learning with Kernel Recursive Least Square and Distance Correlation (ePL-KRLS-DISCO). The such implementation makes the model more robust when dealing with noise data, reducing the model’s error. The introduced model is tested using the Mackey-Glass time series and the nonlinear system identification with the injection of noise. The performance is evaluated in terms of error metrics and the number of final rules. Furthermore, the results are compared with the results of classical models and evolving Fuzzy Systems. Among the six simulations, the proposed model outperformed the compared models in five concerning the errors.
Human profiling based on computer vision : A survey with application

Machine learning is currently a prominent issue due to the direct training of machines with minimal human input. In the present period, the scenario of manual machine feeding has changed, it now learns automatically. Machine learning approaches are utilized for a variety of tasks, such as feature extraction, pattern recognition, object recognition, and classification. In computer vision, machine learning is used to extract important information from visual data. Computer vision contributes to a variety of disciplines, including surveillance, robotics, suspect detection, optical character recognition, and numerous other technologies. Computer vision helps in extracting metadata from humans, such as emotions, sentiments, personality, and so on. This paper presents a thorough yet current survey of human profiling based on computer vision. However, we present a taxonomy of data profiling and computer vision as well as how to profile humans from image data. As an experience, we will discuss our program about human faces recognising emotions from a person’s image using deep learning. In any event, the outcomes we have achieved are critical and important.
Visualizing Streaming of Ordinal Big Data

Horizontal transitions are used in the Streaming of Big Data when there is the need to change the aggregation level of the data being presented. For example, data in a heat map may be aggregated into a line chart. Although these transitions have already been studied for quantitative streamed big data, ordinal data remains unchecked. In this study, we conducted an empirical study to explore horizontal transitions for ordinal data using Graceful Degradation, a concept that allows an overview of the received data at different periods via different levels of aggregation. We chose four visual idioms (Histogram, Ordinal Scatter Plot, Heat Map, and Line Chart), created several transitions between them, and tested how effectively could people perceive data in each idiom before, during, and after each corresponding transition. Participants had to watch numerous videos showcasing the idioms and transitions, and then they had to answer a questionnaire for us to measure how effective was their perception. All the four idioms tested were effective, and we were able to define numerous design guidelines for the creation of horizontal transitions in Streaming of Ordinal Big Data.
Frame-Level Matching Scheme Using Posteriorgram Probability Distance of Spoken Data to Improve Search Accuracy of Spoken Term Detection

Active research has been conducted of Spoken Term Detection (STD), which uses text queries (search words) to seek specific scenes in large amounts of data, including audio data. In subword-to-subword and state-to-state matching using results of automatic speech recognizer (ASR) for speech data, if a query includes an out-of-vocabulary (OOV) word that does not exist in the lexicon of ASR, retrieval accuracy deterioration occurs. Frame-level matching performs more detailed matching at the frame level using a maximum likelihood series that arranges the most probable states for each frame of the posterior probability vector, which is the output of a Deep Neural Network (DNN) used as an acoustic model for ASR, whereas subword-to-subword or state-to-state uses preconstructed acoustic distances between subwords or states. For query by example (QbE), which uses spoken queries, high retrieval accuracy is obtained using the posterior probability vector of speech data. As described herein, we propose a new frame-level matching scheme for a text query using the posterior probabilities of speech data instead of the preconstructed acoustic distance between subwords of states. Thereby, the retrieval accuracy is improved. Machine learning models of two types with different numbers of states are introduced to improve the retrieval accuracy further. Because each model shows different retrieval results, integration of these models is expected to be mutually complementary, thereby improving the retrieval accuracy. Experiments were conducted using public test collections of the SpokenDoc task from NTCIR-10 and NTCIR-12 workshops as benchmarks. Results indicate that the proposed method outperforms the best accuracy obtained at the workshop by 26.95 points for NTCIR-10 and 4.44 points for NTCIR-12. These results demonstrate the effectiveness of the proposed method.
Multi-Task Adversarial Training Algorithm for Multi-Speaker Neural Text-to-Speech

We propose a novel training algorithm for a multi-speaker neural text-to-speech (TTS) model based on multi-task adversarial training. A conventional generative adversarial network (GAN)-based training algorithm significantly improves the quality of synthetic speech by reducing the statistical difference between natural and synthetic speech. However, the algorithm does not guarantee the generalization performance of the trained TTS model in synthesizing voices of unseen speakers who are not included in the training data. Our algorithm alternatively trains two deep neural networks: multi-task discriminator and multi-speaker neural TTS model (i.e., generator of GANs). The discriminator is trained not only to distinguish between natural and synthetic speech but also to verify the speaker of input speech is existent or non-existent (i.e., newly generated by interpolating seen speakers' embedding vectors). Meanwhile, the generator is trained to minimize the weighted sum of the speech reconstruction loss and adversarial loss for fooling the discriminator, which achieves high-quality multi-speaker TTS even if the target speaker is unseen. Experimental evaluation shows that our algorithm improves the quality of synthetic speech better than a conventional GANSpeech algorithm.
Joint Speech Activity and Overlap Detection with Multi-Exit Architecture

Overlapped speech detection (OSD) is critical for speech applications in scenario of multi-party conversion. Despite numerous research efforts and progresses, comparing with speech activity detection (VAD), OSD remains an open challenge and its overall performance is far from satisfactory. The majority of prior research typically formulates the OSD problem as a standard classification problem, to identify speech with binary (OSD) or three-class label (joint VAD and OSD) at frame level. In contrast to the mainstream, this study investigates the joint VAD and OSD task from a new perspective. In particular, we propose to extend traditional classification network with multi-exit architecture. Such an architecture empowers our system with unique capability to identify class using either low-level features from early exits or high-level features from last exit. In addition, two training schemes, knowledge distillation and dense connection, are adopted to further boost our system performance. Experimental results on benchmark datasets (AMI and DIHARD-III) validated the effectiveness and generality of our proposed system. Our ablations further reveal the complementary contribution of proposed schemes. With F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of 0.792 on AMI and 0.625 on DIHARD-III, our proposed system outperforms several top performing models on these datasets, but also surpasses the current state-of-the-art by large margins across both datasets. Besides the performance benefit, our proposed system offers another appealing potential for quality-complexity trade-offs, which is highly preferred for efficient OSD deployment.
Camera-Based Log System for Human Physical Distance Tracking in Classroom

In the pandemic of COVID-19, the indoor physical distancing protocol has been one of the recommendations for people to avoid close contact with each other in order to prevent contagious clusters. This paper proposes an end-to-end camera-based human physical distancing recording system for an indoor environment, specifically, a classroom. The recording system aims to automatically trace the locations of persons and the directions of their movements in a classroom, also with respect to the on- and off-seat activities. No identity of persons is kept in the recording log system, but locations of individual persons at each timestamp are obtained; hence, the spatial and temporal distribution can be studied further. In this paper, we illustrate the overview workflow of the human and seat detection as well as the log system storing human physical distancing actions.
Correcting, Rescoring and Matching: An N-best List Selection Framework for Speech Recognition

In recent years, automatic speech recognition (ASR) has been widely used in various scenarios, and it is usually the first step in many applications. Therefore, more and more studies concentrate on enhancing the recognition results. Among them, N -best reranking and error correction models are two active research subjects. Various models have been proposed and demonstrated their success. However, as the N -best reranking models aim to select the best hypothesis from a set of candidates, their performance upper bound is limited by the given set of hypotheses. The error correction models detect and correct recognition errors so as to provide better results, but they usually perform the process on the highest-scored hypothesis only. Therefore, the information embedded in other candidates is ignored. Besides, we note that almost all of the N -best reranking and error correction models consider the acoustic information implicitly, indirectly, or even omitted. In order to mitigate these flaws, we propose an N -best list selection framework, which consists of a text correction module, a text rescoring module, and a text-speech matching module, for speech recognition. Based on the proposed framework, a set of corrected hypotheses can be deduced, and then the text rescoring module is introduced to accurately rescore them. In addition, the text-speech matching module is employed to calculate the alignment score between each hypothesis and its own speech. The proposed framework is evaluated on the AISHELL-l dataset, and the experimental results reveal that the proposed framework can deliver over 30 % character error reduction rates compared to the baseline systems.
Design and Analysis of Data Sharing Scheme Based on Blockchain and Trusted Computing

Confidentiality and security are the key points in data use. For data sharing, through the research and analysis of some platforms based on block chains and trusted computing, this paper puts forward a security data sharing platform scheme with clear roles to improve the platform role building process and encryption design. Design a data sharing platform with clear process and moderate number of objects. The Trusted Computing Platform (TEE) in this scenario provides a secure computing environment to ensure its neutrality and security. The communication process uses asymmetric encryption to ensure reliable transactions and block chain technology which cannot be tampered with and traceable to the source. It also increases the traceability and monitoring ability while keeping data confidential, to avoid information leakage and inefficiency.
Epoch-Wise Double Descent Triggered by Learning a Single Sample

Recently, it has been empirically shown that if neural networks are trained on a single training image to reconstruct the input image, fully-connected neural networks (FCNs) learn to output the training image regardless of the test image (memorization), whereas fully-convolutional networks learn to output the given test images (generalization). We further investigated FCNs and empirically found that optimizers play an important role in the memorization process by splitting the model into the bias of the output layer (the bias component) and the rest (the weight component). Specifically, neural networks memorize the training image by the weight component at the early stage of training irrespective of the optimizer. Conversely, as the epoch increases, some optimizers force the weight component to forget the training image and the bias component to memorize the training image. By assuming that FCNs converge to the constant function during this shift, and measuring the generalization error, we observed epoch-wise double descent, which explains why early-stopping contributes to better generalizability.