Reviewerly/exHarmony huggingface dataset:
Pseudo Label Rectification via Co-Teaching and Decoupling for Multisource Domain Adaptation in Semantic Segmentation

Multi-source Unsupervised Domain Adaptation (MUDA) is an approach aiming to transfer the knowledge obtained from multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel self-training method for MUDA, which includes pseudo label-oriented coteaching and pseudo label decoupling that are attempted for the pseudo label rectification-based MUDA for semantic segmentation. Existing ensemble-based self-training methods which are well-known approaches for MUDA use pseudo labels made from the ensemble of the predictions of multiple models to transfer the knowledge of source domains to the target domain. In these methods, information from multiple models can be contaminated, or errors from incorrect pseudo labels can be propagated. On the other hand, the proposed pseudo label-oriented coteaching trains multiple models by using pseudo labels from the peer model without any integration of pseudo labels. Simultaneously, the pseudo label decoupling method is proposed for rectification of pseudo labels, which updates the models with two pseudo labels only if they disagree. It also alleviates the problem of class imbalance in semantic segmentation, in which dominant classes lead the update for training. The effects of the proposed pseudo label-oriented coteaching and pseudo label decoupling on the performance of semantic segmentation were verified by extensive experiments. The proposed method achieved the best semantic segmentation accuracy compared with the benchmark methods. In addition, we confirmed that the prediction accuracy of small objects was greatly improved by the proposed pseudo label rectification.
A Robust Visual Servoing Controller for Anthropomorphic Manipulators With Field-of-View Constraints and Swivel-Angle Motion: Overcoming System Uncertainty and Improving Control Performance

Human–robot collaboration has attracted significant attention in the industry due to the flexibility of humans and the accuracy of robots. Humanoid control of anthropomorphic robotic arms combined with visual servoing will enhance the intelligence of industrial robots. However, the robotic manipulator will introduce psychological discomfort to nearby humans, and the loss of visual features will induce visual servoing task failure. Aiming at these problems, this article proposes a humanoid control method based on visual servoing by utilizing the swivel angle derived from the human arm to realize the human-like behavior of anthropomorphic robot manipulators. To advance the visual servoing control performance, a function constraint is designed with the barrier Lyapunov function (BLF) to ensure that image features stay within the field of view (FoV). The sliding mode control (SMC) is combined with image-based visual servoing (IBVS) to dispose of the uncertainties of a seven-degree-of-freedom (7-DoF) redundant robot manipulator. The proposed algorithm is substantiated through comparison experiments based on the Sawyer robot and constructed visual servoing physical platform.
Uni3DA: Universal 3D Domain Adaptation for Object Recognition

Traditional 3D point cloud classification tasks focus on training a classifier in the closed-set scenario, where training and test data have the same label set and the same data distribution. In this work, we focus on a more challenging and realistic scenario in 3D point cloud classification task: universal domain adaptation (UniDA), where 1) data distributions for training and test data are different; and 2) for given label sets of training data and test data, they may have the shared classes and keep the private classes respectively, introducing an extra label set discrepancy. To solve UniDA problem, researchers have designed many methods based on 2D image datasets. However, due to the difficulty in capturing discriminative local geometric structures brought by the unordered and irregular 3D point cloud data, we cannot directly deploy the existing methods based on 2D image datasets to the 3D scenarios. To address UniDA in 3D scenarios, we develop a 3D universal domain adaptation framework, which consists of three modules: Self-Constructed Geometric (SCG) module, Local-to-Global Hypersphere Reasoning (LGHR) module and Self-Supervised Boundary Adaptation (SBA) module. SCG and LGHR generate the discriminative representation, which is used to acquire domain-invariant knowledge for training and test data. SBA is designed to automatically recognize whether a given label is from the shared label set or private label set, and adapts training and test data from the shared label set. To our best knowledge, this work is the first exploration of UniDA for 3D scenarios. Extensive experiments on public 3D point cloud datasets verify that the proposed method outperforms the existing UniDA methods.
Creation of open educational resources during educational practice by means of cloud technologies

Practical training is an integral part of the professional training of future teachers. During the practical training, trainees not only implement their own theoretical training in practice, but also increase the level of information competence. The rapid use of e-learning resources for distance learning during epidemiological constraints caused by biological threats poses new challenges to education in the availability of e-learning resources. The development of electronic educational resources is a difficult task, one of the ways to solve it is to involve future teachers in this process during the initial practice. The experiment of creating open educational resources by means of cloud technologies during the training practice showed the probability of solving this problem. Restrictions in direct contact between participants in the experiment, both due to epidemiological restrictions and through practical training in various educational institutions, are solved through the use of cloud technologies. The latter provides an opportunity to easily disseminate developed open educational resources and disseminate best practices in creating educational content.
An improved imperialist competition algorithm with adaptive differential mutation assimilation strategy for function optimization

In imperialist competition algorithm (ICA), the assimilation is an important step, which is responsible for exploring potential better position or solution for an optimization problem. In this paper, an improved ICA equipped with an adaptive differential mutation (ADM) assimilation strategy was proposed inspired by differential evolution (DE). The proposed algorithm is called ICA-ADM. The ADM assimilation strategy generates new positions for colonies in an empire using the "DE/best/1" mutation operator and crossover operator with adaptively varied mutation and crossover rate. Since the "DE/best/1" mutation and crossover can produce high quality solutions, ICA-ADM possesses good exploration capability. Meanwhile, since the mutation and crossover rates are adaptively varied, ICA-ADM also preserves a good population diversity. Thus, the performance of ICA-ADM is greatly increased. Optimization results on 16 well-known benchmark functions and comparisons verify that the proposed ICA-ADM exceeds the initial ICA and its several variants.
Quantum statistical mechanics of encryption: Reaching the speed limit of classical block ciphers

We cast encryption via classical block ciphers in terms of operator spreading in a dual space of Pauli strings, a formulation which allows us to characterize classical ciphers by using tools well known in the analysis of quantum many-body systems. We connect plaintext and ciphertext attacks to out-of-time order correlators (OTOCs) and quantify the quality of ciphers using measures of delocalization in string space such as participation ratios and corresponding entropies obtained from the wave function amplitudes in string space. In particular, we show that in Feistel ciphers the entropy saturates its bound to exponential precision for ciphers with 4 or more rounds, consistent with the classic Luby–Rackoff result that it takes these many rounds to generate strong pseudorandom permutations. The saturation of the string-space information entropy and the vanishing of the OTOCs to exponential precision are taken as necessary conditions for the security of the cipher — we conjecture these criteria are also sufficient, as physically they signal irreversibility and chaos. We argue that the conditions on both OTOCs and string entropies can be satisfied by n-bit block ciphers implemented via random reversible circuits with O(nlogn) gates. This paper focuses on a tree-structured cipher composed of layers of n/3 3-bit gates, for which a “key” specifies uniquely the sequence of gates that comprise the circuit. We show that in order to reach this “speed limit” one must employ a three-stage circuit consisting of a nonlinear stage implemented by layers of nonlinear gates that proliferate the number of strings, flanked by two linear stages, each deploying layers of a special set of linear “inflationary” gates that accelerate the growth of small individual strings. The close formal correspondence to quantum scramblers established in this work leads us to suggest that this three-stage construction is also required in order to scramble quantum states to similar precision and with circuits of similar size. A shallow, O(logn)-depth cipher of the type described here can be used in constructing a polynomial-overhead scheme for computation on encrypted data proposed in another publication as an alternative to Homomorphic Encryption.
Reachability Analysis of a General Class of Neural Ordinary Differential Equations

Continuous deep learning models, referred to as Neural Ordinary Differential Equations (Neural ODEs), have received considerable attention over the last several years. Despite their burgeoning impact, there is a lack of formal analysis techniques for these systems. In this paper, we consider a general class of neural ODEs with varying architectures and layers, and introduce a novel reachability framework that allows for the formal analysis of their behavior. The methods developed for the reachability analysis of neural ODEs are implemented in a new tool called NNVODE. Specifically, our work extends an existing neural network verification tool to support neural ODEs. We demonstrate the capabilities and efficacy of our methods through the analysis of a set of benchmarks that include neural ODEs used for classification, and in control and dynamical systems, including an evaluation of the efficacy and capabilities of our approach with respect to existing software tools within the continuous-time systems reachability literature, when it is possible to do so.
Development of a Novel Object Detection System Based on Synthetic Data Generated from Unreal Game Engine

This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies. Training an object detection system can be challenging and time-consuming as machine learning requires substantial volumes of training data with associated metadata. Synthetic data can solve this by providing unlimited desired training data with automatic generation. However, the main challenge is creating a balanced dataset that closes the reality gap and generalizes well when deployed in the real world. A state-of-the-art game engine, Unreal Engine 4, was used to approach the challenge of generating a photorealistic dataset for deep learning model training. In addition, a comprehensive domain randomized environment was implemented to create a robust dataset that generalizes the training data well. The randomized environment was reinforced by adding high-dynamic-range image scenes. Finally, a modern neural network was used to train the object detection system, providing a robust framework for an adaptive and self-learning model. The final models were deployed in simulation and in the real world to evaluate the training. The results of this study show that it is possible to train a real-world object detection system on synthetic data. However, the models showcase a lot of potential for improvements regarding the stability and confidence of the inference results. In addition, the paper provides valuable insight into how the number of assets and training data influence the resulting model.
Joint clothes image detection and search via anchor free framework

Clothes image search is an important learning task in fashion analysis to find the most relevant clothes in a database given a user-provided query. To address this problem, most existing methods employ a two-step approach, i.e., first detect the target clothes, and then crop it to feed the model for similarity learning. But the two-step approach is time-consuming and resource-intensive. On the other hand, one-step methods provide efficient solutions to integrate clothes detection and search in a unified framework. However, since one-step methods usually explore anchor-based detectors, they inevitably inherit limitations, such as high computational complexity caused by dense anchors, and high sensitivity to hyperparameters. To address the aforementioned issues, we propose an anchor-free framework for joint clothes detection and search. Specifically, we first choose an anchor-free detector as backbone. We then add a mask prediction branch and a Re-ID embedding branch to the framework. The mask prediction branch aims to predict the masks of clothes, while Re-ID embedding branch aims to extract the rich embedding features of clothes, in which we aggregate the feature of clothes via a mask pooling module by referencing the estimated target clothes masks. In this way, the extracted target clothes features can grasp more information in the area of the clothes mask; finally, we further introduce a match loss to fine-tune the embedding feature in Re-ID branch for improving the retrieval performance. Simulation results based on real datasets demonstrate the effectiveness of the proposed work.
Blockchain-Based Autonomous Authentication and Integrity for Internet of Battlefield Things in C3I System

In the Internet of Battlefield Things (IoBT), users and sensor-equipped entities send multiple messages to the Command Control Center (CCC) over the network.The authentication and integrity of these messages are crucial because if an adversary or malicious node transmits, alters, or replays these messages, the consequences will be disasters.Current centralized authentication systems are not suitable for the distributed environment because such schemes are prone to a single point of failure, privacy, and scalability issues.Moreover, the high communication overhead caused by centralization increases energy consumption.In this work, we propose a technique called Blockchain-based Autonomous Authentication and Integrity for the Internet of Battlefield Things (BIoBT) for the C3I system.The proposed technique does not require an explicit authentication channel for the authentication of entities because it is performed on the blockchain side when receiving the data.In addition, it provides data integrity and non-repudiation.BIoBT prototype is created, deployed, and tested on the Ethereum test network.The results prove that BIoBT is efficient, cost-effective, and satisfies the security requirements of a distributed environment for IoBT.BIoBT also outperforms contemporary mechanisms in terms of the number of messages required to establish a secure channel, thereby reducing communication overhead and resource consumption.
An ensemble active learning for a fluidized bed granulation in the pharmaceutical industry

Active learning (AL), allowing to query the labels by an oracle, can dwindle the generalization error from a few labeled samples, thus well-motivating in the scenario where there are fewer labeled samples and abundant unlabeled samples. However, the query samples cannot be exploited efficiently in the existing methods based on AL for regression tasks, and AL is typically applied for classification problems. In this paper, an active learning framework (CALF) is proposed to improve the efficiency with fewer query samples, aiming at the moisture content prediction in fluidized bed granulation. The proposed method, based on conditional variational auto-encoder (CVAE) and selective ensemble algorithm, can efficiently incorporate the information in query samples into the labeled sample space. The game of CVAE and selective ensemble algorithm improves the performance of the framework effectively, and its effectiveness is verified by the results obtained from the large batch of fluidized bed granulating experiments.
PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits

Promoting healthy discourse on community-based online platforms like Reddit can be challenging, especially when conversations show ominous signs of toxicity. Therefore, in this study, we find the turning points (i.e., toxicity triggers) making conversations toxic. Before finding toxicity triggers, we built and evaluated various machine learning models to detect toxicity from Reddit comments. Subsequently, we used our best-performing model, a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model that achieved an area under the receiver operating characteristic curve (AUC) score of 0.983 to detect toxicity. Next, we constructed conversation threads and used the toxicity prediction results to build a training set for detecting toxicity triggers. This procedure entailed using our large-scale dataset to refine toxicity triggers' definition and build a trigger detection dataset using 991,806 conversation threads from the top 100 communities on Reddit. Then, we extracted a set of sentiment shift, topical shift, and context-based features from the trigger detection dataset, using them to build a dual embedding biLSTM neural network that achieved an AUC score of 0.789. Our trigger detection dataset analysis showed that specific triggering keywords are common across all communities, like 'racist' and 'women'. In contrast, other triggering keywords are specific to certain communities, like 'overwatch' in r/Games. Implications are that toxicity trigger detection algorithms can leverage generic approaches but must also tailor detections to specific communities.
An Efficient Deep Learning Model with Interrelated Tagging Prototype with Segmentation for Telugu Optical Character Recognition

More than 66 million people in India speak Telugu, a language that dates back thousands of years and is widely spoken in South India. There has not been much progress reported on the advancement of Telugu text Optical Character Recognition (OCR) systems. Telugu characters can be composed of many symbols joined together. OCR is the process of turning a document image into a text-editable one that may be used in other applications. It saves a great deal of time and effort by not having to start from scratch each time. There are hundreds of thousands of different combinations of modifiers and consonants when writing compound letters. Symbols joined to one another form a compound character. Since there are so many output classes in Telugu, there’s a lot of interclass variation. Additionally, there are not any Telugu OCR systems that take use of recent breakthroughs in deep learning, which prompted us to create our own. When used in conjunction with a word processor, an OCR system has a significant impact on real-world applications. In a Telugu OCR system, we offer two ways to improve symbol or glyph segmentation. When it comes to Telugu OCR, the ability to recognise that Telugu text is crucial. In a picture, connected components are collections of identical pixels that are connected to one another by either 4- or 8-pixel connectivity. These connected components are known as glyphs in Telugu. In the proposed research, an efficient deep learning model with Interrelated Tagging Prototype with Segmentation for Telugu Text Recognition (ITP-STTR) is introduced. The proposed model is compared with the existing model and the results exhibit that the proposed model’s performance in text recognition is high.
Training Strategy of Music Expression in Piano Teaching and Performance by Intelligent Multimedia Technology

Teaching using a multimedia technology in the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mn>21</mn> </mrow> <mrow> <mi>s</mi> <mi>t</mi> </mrow> </msup> </math> century affords the possibility of developing novel instructional strategies and paves the way for the all-around extension of musical educational functions. The importance of multimedia teaching technology in piano instruction has started to emerge in our country and society due to the ongoing development of this kind of technology in music educational institutions here. The conventional method of teaching piano has several drawbacks that may be mitigated by using one of the several alternative methods of instruction for the instrument, especially in light of the ongoing advancements in science and technology. A pianist’s methods of expression are the tools they use to convey their thoughts and emotions about a piece of music to the audience. Teachers may demonstrate their musical skills to students and they must immediately focus on a musical expression which is vital for performers. In this paper, the Multimedia-based Piano Teaching Model (MPTM) has been proposed to improve the piano teaching quality. Traditional piano instruction is improved and developed using multimedia technology in this article. The Internet education model is used for teacher assessment, and the systematic way representing piano teaching combines different music educational materials. It begins building a sufficiently broad music network infrastructure resource sharing framework and benefits society’s amateur music literacy. The use of machine learning in students’ concrete piano instruction has the potential to thoroughly promote contemporary piano instruction and enhance the overall quality of instruction. To begin, an explanation of the intelligent piano’s features and capabilities is provided. The neural network is used to suggest a technique for detecting a piano note on a set. The network can assess the input piano music signal’s time frequency by translating the original time-domain waveform into the time-varying frequency distribution. Intelligent piano instruction analysis can effectively achieve the overall optimization of piano performance. The test results show that MPTM has a significant role in boosting the desire to learn to play the instrument. The experimental results show that the proposed MPTM achieves a learning skills ratio of 97.6%, a learning activity ratio of 98.5%, a student performance ratio of 93.8%, a teaching evaluation ratio of 90.3%, and a learning behavior ratio of 94.2% when compared to other methods.