Reviewerly/exHarmony huggingface dataset:
A Robust Security Scheme Based on Novel Encoding with LSB Steganography

Many Cryptanalysis trials performed on the Elliptic Curve Cryptography (ECC). Some of these trials succeeded to crack the privacy of data using ECC. A proposed method by Singh et al. has been executed successfully and the cipher image is decrypted revealing the original image. It took advantage that the order of elliptic curve is not large enough to protect from attacks A robust encoding method for elliptic curve cryptosystem is proposed in this paper to provide high security level, more efficient and complexity if compared with the traditional encoding method. The proposed method encodes data and key selection over prime field. Compared with hacked ECC algorithm, the proposed encoding scheme proved its robustness based on some statistical analyzes. Correlation Coefficient (CC), Entropy, Peak Signal to Noise Ratio (PSNR), Number of changing pixel rate (NPCR), and Unified Average Changing Intensity (UACI) are used to quantify ciphertext strength and securing performance on gray and colored image to measure how much robustness is the proposed encoding technique. Together with the encryption scheme, a Least Significant Bit (LSB) steganography technique is subjoined after encryption process to hide the ciphertext. Overall results of the previews metrics were satisfied and its recommended to use the proposed encoding method with the ECC.
Optimal Path Planning for Unmanned Vehicles Using Improved Ant Colony Optimization Algorithm

To prevent the locally optimal problem and slow convergence problem of unmanned vehicles (UVs) path planning, an improved ant colony algorithm is proposed by using a dynamic pheromone volatility coefficient. The best path is searched by selecting the appropriate pheromone volatility coefficient in ant colony algorithm, which has better searching ability, and converges to the optimal value quickly. The experimental results are illustrated to compare with other improved ant colony optimization algorithms to verify the effectiveness and efficiency of our proposed path planning method for UVs.
Research on Location of Chinese Handwritten Signature Based on EfficientDet

Nowadays, most of the time we need to obtain the location information of the signature in the documents for signature verification or other tasks. Manual processing can be completed for a small amount of documents, if the amount of documents is large, it will consume huge manpower and material resources to do it .When using deep learning methods to intelligently locate signatures, but there are several difficulties: First, for documents such as Chinese contracts, files, etc. the signature is covered by seal coverage or other interference in most cases, which brings a lot of interference to the location of the signature.Second , it is hard to get real-scene contracts, bills which can be used as training sets.Third, the signature itself has fewer features, this brings certain difficulties to the intelligent location. This paper proposes an improved feature extraction network based on EfficientNet to adapt to detection targets with fewer features and more interference. Training datasets mainly comes from algorithm synthesis.Finally we tested on 1000 scanned documents containing 2048 handwritten signatures,the test results on real-scenes data set containing Chinese handwritten signatures can achieve more than 90% of AP, Precision, and Recall.
Design and Realization of Distance Intelligent Learning System Based on Bayesian Network Training

Modern long-distance teaching utilizes the advantages of Internet information technology, making it not only comparable to traditional teaching in certain typical indicators, but also able to integrate with relevant educational technology, and develop new teaching models that cannot be supported by traditional teaching environments. However, judging from the current situation, most of the distance teaching systems have shortcomings in design. They only digitize the existing teaching models and existing teaching materials and move them to the Internet, which makes the system lacks intelligence and adaptability. It is difficult to provide personalized learning guidance for students' learning differences, and the quality of distance teaching cannot be well guaranteed. This paper proposes an intelligent teaching model based on Bayesian network and multi-agent technology. The teaching model can be different from person to person to formulate and implement guidance strategies, actively collect the most interesting information for learners through the intelligent agent proxy server, and automatically generate various questions and exercises. At the same time, the model uses agent communication protocol to process information through reasonable planning and adjustment of learning content and progress, and allows users to browse. The system can predict user needs, actively formulate learning plans, adjust learning progress, and diagnose learning progress. Problems and errors make them more in line with the learner's knowledge base and cognitive characteristics. Therefore, the teaching model has a certain pertinence and forward-looking.
Td-Net: Topology Destruction Network For Generating Adversarial Point Cloud

Despite a great progress has been made in 3D point cloud recognition, recent studies find that deep models are vulnerable to adversarial attacks generated through various point cloud transformations. However, existing spoofing attack methods neglect the influence of point cloud topology on the model recognition accuracy. In this paper, we propose a novel adversarial point cloud generation network, named Topology Destruction Network (TD-Net), which destroys topological structure of a point cloud by selectively dropping points leading to the formulation of holes on the surface. The network consists of an encoder and a decoder. The encoder first leverages a PointNet architecture to extract geometric information of the original point cloud, from which a topological adjacency matrix is encoded describing adjacency relationships between points. The decoder selects a specific point to drop associated with its neighboring points derived from the learned adjacency matrix, resulting in an adversarial point cloud with a destructed topology. Experiments on the ModelNet40 dataset demonstrate that the proposed method surpasses existing adversarial attacks in terms of reducing model recognition accuracy.
Human-Object Interaction Detection with Missing Objects

Existing studies on human-object interaction (HOI) assume that human and object instances can be detected. This paper proposes a more practical HOI detection method for when object instances are not necessarily easily detectable. To our knowledge, we introduce the first method for such challenging HOI detection that incorporates global scene information. The two most widely used public HOI benchmark datasets are shown to contain many cases of HOI with missing objects (HOI-MO). We label these to compose new test sets for the proposed method. The effectiveness and superiority of the proposed method are demonstrated through extensive experiments and comparisons.
Recurrent Neural Networks for Pilot-aided Wireless Communications

Recently, deep learning (DL) has been successfully applied in physical-layer communications and shown great success and competitive results to conventional systems. In this paper, we propose a novel recurrent neural network (RNN)-based communication system, based on the autoencoder concept. We develop a structure to mimic the working principle of a pilot-aided equalizer and integrate it as a learnable part of the system to support the task of channel estimation and equalization. The system shows competitive results under flat and frequency selective fading channels. The model can be trained to deal with any predefined number of channel taps (multipath components) of specific strengths. The system can also be generalized to deal with arbitrary strengths of the taps, which was infeasible in previous deep learning-based communication systems due to the absence of a guiding pilot. We assess the system performance for various alphabet and encoding sizes showing the BLER vs EBNO and the learned constellations.
Going “Live” Again: Reflections on Zoom, Copresence, &amp; Liveness in a (Post)Pandemic World

In April 2020—only weeks after the World Health Organization declared COVID-19 a global pandemic—the New York Times published an article titled “Why Zoom Is Terrible.” Quoting a gustatory simile from Sheryl Brahnam of Missouri State University, the article declared, “In-person communication resembles video conferencing about as much as a real blueberry muffin resembles a packaged blueberry muffin that contains not a single blueberry but artificial flavors, textures and preservatives.” 1 It has been a year marked by the absence of “in-person” connection, or in the language of our field, of spatial copresence. The COVID-19 pandemic has fundamentally disrupted our ability to share space. Spatial copresence, it turns out, is what the coronavirus requires to spread. The virus, in this sense, is a phenomenon of the live. While technologies like Zoom have maintained our capacity for temporal copresence, the now ubiquitous status of “Zoom fatigue” points to new ways to consider spatial copresence, and by extension “liveness.”
Learning and planning with logical automata

Abstract We introduce a method to learn policies from expert demonstrations that are interpretable and manipulable . We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic. We achieve manipulability by integrating this automaton into planning via Logical Value Iteration , so that changes to the automaton have predictable effects on the learned behavior. These qualities allow a human user to first understand what the model has learned, and then either correct the learned behavior or zero-shot generalize to new, similar tasks. Our inference method requires only low-level trajectories and a description of the environment in order to learn high-level rules. We achieve this by using a deep Bayesian nonparametric hierarchical model. We test our model on several domains of interest and also show results for a real-world implementation on a mobile robotic arm platform for lunchbox-packing and cabinet-opening tasks.
Adversarial Segmentation Loss For Sketch Colorization

We introduce a new method for generating color images from sketches or edge maps. Current methods either require some form of additional user-guidance or are limited to the “paired” translation approach. We argue that segmentation information could provide valuable guidance for sketch colorization. To this end, we propose to leverage semantic image segmentation, as provided by a general purpose panoptic segmentation network, to create an additional adversarial loss function. Our loss function can be integrated to any baseline GAN model. Our method is not limited to datasets that contain segmentation labels, and it can be trained for “unpaired” translation tasks. We show the effectiveness of our method on four different datasets spanning scene level indoor, outdoor, and children book illustration images using qualitative, quantitative and user study analysis. Our model improves its baseline up to 35 points on the FID metric. Our code and pretrained models can be found at https://github.com/giddyyupp/AdvSegLoss.
Room-Temperature Implementation of the Quantum Streaming Algorithm in a Single Solid-State Spin Qubit

Great challenges are encountered when solving problems based on continuously generated data streams due to the online data input fashion and limited memory space. Quantum properties are helpful in dealing with stream data. In this work, we design and experimentally implement a quantum streaming algorithm (QSA) with various lengths of input data in a single-defect spin system in silicon carbide at room temperature. We explore the schemes with trusted advice on the output results during the algorithm execution. Quite contrary to the classical case, outdated advice is still useful in the quantum case, as revealed by a proper performance function. Our work demonstrates the advantage of the QSA, which would be useful for investigating complex online problems. The room-temperature solid-state spin platform based on such a technological material opens the door for scalable quantum-information processing.
Donohue+: Developing performer-specific electronic improvisatory accompaniment for instrumental improvisation

Electronic systems designed to improvise with a live instrumental performer are a constant mediation of musical language and artificial decision-making. Often these systems are designed to elicit a reaction in a very broad way, relying on segmenting and playing back audio material according to a fixed or mobile set of rules or analysis. As a result, such systems can produce an outcome that sounds generic across different improvisers, or restrict meaningful electroacoustic improvisation to those performers with a matching capacity for designing improvisatory electroacoustic processing. This article documents the development of an improvisatory electroacoustic instrument for pianist Maria Donohue as a collaborative process for music-making. The Donohue+ program is a bespoke electroacoustic improvisatory system designed to augment the performance capabilities of Maria, enabling her to achieve new possibilities in live performance. Through the process of development, Maria’s performative style, within the broader context of free improvisation, was analysed and used to design an interactive electronic system. The end result of this process is a meaningful augmentation of the piano in accordance with Maria’s creative practice, differing significantly from other improvising electroacoustic instruments she has previously experimented with. Through the process of development, Donohue+ identifies a practice for instrument design that engages not only with a performer’s musical materials but also with a broader free improvisation aesthetic.
Accelerating Federated Learning With a Global Biased Optimiser

Federated Learning (FL) is a recent development in distributed machine learning that collaboratively trains models without training data leaving client devices, preserving data privacy.In real-world FL, the training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, harming model convergence speed and final performance.To address this challenge, we propose a novel, generalised approach for incorporating adaptive optimisation into FL with the Federated Global Biased Optimiser (FedGBO) algorithm.FedGBO accelerates FL by employing a set of global biased optimiser values during training, reducing 'client-drift' from non-IID data whilst benefiting from adaptive optimisation.We show that in FedGBO, updates to the global model can be reformulated as centralised training using biased gradients and optimiser updates, and apply this framework to prove FedGBO's convergence on nonconvex objectives when using the momentum-SGD (SGDm) optimiser.We also conduct extensive experiments using 4 FL benchmark datasets (CIFAR100, Sent140, FEMNIST, Shakespeare) and 3 popular optimisers (SGDm, RMSProp, Adam) to compare FedGBO against six state-of-the-art FL algorithms.The results demonstrate that FedGBO displays superior or competitive performance across the datasets whilst having low data-upload and computational costs, and provide practical insights into the trade-offs associated with different adaptive-FL algorithms and optimisers.
InterCriteria Analysis of Data Obtained from University Students Practicing Sports Activities

Intellectual qualities of the university students are important factors for the successful participation in different academic activities. In view of this it is important to evaluate the initial level of intellectual development by applying a group of specific tests. In this paper we propose the application of the approach of InterCriteria Analysis to data of intellectual status parameters, obtained from university students practicing sports activities in order to evaluate the appropriateness of the used tests.
Rendering-based free-view image synthesis using peripheral view and depth images

Unlike light-field image captured with camera based on micro lens array, light-field image captured with a camera matrix has a large disparity, so it is difficult to synthesize a new view of image. When the new view image is generated from the light-field image having the large disparity by using the conventional depth image based rendering, there is a problem that a hole occurs in the warped color image. To solve this problem, we propose rendering-based free-view image synthesis using adjacent view and depth images. In the proposed algorithm, the camera position of the floating-point level of target view image, the image of four adjacent view images and each depth image are used as inputs. Then, warp the input images to the target view by projection and the projected images are synthesized to generate the image of target view. Unlike conventional methods, even if the target view image camera position is a floating-point, target view image is created that has no holes. Therefore, the proposed algorithm enables image synthesis at free-view, free from the limitations of the conventional stereo image synthesis method. And this can be confirmed by the experimental results.
Study on Computer English Teaching Reform under the Background of the Construction of High-quality Vocational Colleges

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Blended Learning Between Team Games Tournament (TGT) And Web-Based Module as an Effort To Improve The Achievement Of Student Competency In Automotive Departement

This research focuses on creating a web-based module collaboration with team games tournament as a blended learning model for automotive department students. The research method used a class action research purposed to know the impact of blended learning to use team games tournament and web-based module as an effort to improve competence achievement for students in the automotive department with 32 students as the research subject. The research focused on the teacher's activities, student's activities, and post-test results. The pre-action result showed that the completeness level was 67,74%. After the first act was done, the student's competence increased up to 93,5%. After the 2nd action has done, the student's completeness in learning was 87,09%. From these 2 actions implemented, the average result found was higher than the minimum standard that has been targeted by 75%. Implementing blended learning team games tournament and Web-Based Module suggested being implemented in other learning subjects because it is acknowledge as the creative, innovative, and interesting method to improve student's competence accomplishment.
Unsupervised Learning Of Visual Odometry Using Direct Motion Modeling

Data for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on unsupervised learning methods and achieved remarkable results. Many unsupervised approaches rely on single-view predicted depth and so ignore motion information. Some unsupervised methods incorporate motion information indirectly by designing the depth prediction network as an RNN. However, none of the existing methods make direct use of multiple frames when predicting depth, which are readily available in videos. In this work, we show that it is possible to achieve superior pose prediction results by modeling motion more directly. Our method uses a novel learning-based formulation for depth propagation and refinement which warps predicted depth maps forward from the current frame onto the next frame where it serves as a prior for predicting the next frame’s depth map. Code will be made available upon acceptance.
Dormitory allocation method oriented to heuristic particle swarm genetic algorithm

research-article Dormitory allocation method oriented to heuristic particle swarm genetic algorithm Share on Author: Cai Muzhen Harbin Engineering University, China Harbin Engineering University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 51Pages 1–10https://doi.org/10.1145/3469213.3470251Online:18 August 2021Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Approximate Petz Recovery from the Geometry of Density Operators

We derive a new bound on the effectiveness of the Petz map as a universal recovery channel in approximate quantum error correction using the second sandwiched Rényi relative entropy $$\widetilde{D}_{2}$$ . For large Hilbert spaces, our bound implies that the Petz map performs quantum error correction with order- $$\epsilon $$ accuracy whenever the data processing inequality for $$\widetilde{D}_{2}$$ is saturated up to terms of order $$\epsilon ^2$$ times the inverse Hilbert space dimension. Conceptually, our result is obtained by extending (Cree and Sorce in J Phys A Math Theor, 2022. http://iopscience.iop.org/article/10.1088/1751-8121/ac5648 ), in which we studied exact saturation of the data processing inequality using differential geometry, to the case of approximate saturation. Important roles are played by (i) the fact that the exponential of the second sandwiched Rényi relative entropy is quadratic in its first argument, and (ii) the observation that the second sandwiched Rényi relative entropy satisfies the data processing inequality even when its first argument is a non-positive Hermitian operator.
Empowering reading and writing skills of primary students with contextual teaching learning approach

Background: To some students, learning Indonesian is very boring especially for students who are not used to read and write.&#x0D; Purpose: This study aims to improve the Indonesian Language Learning Outcomes in grade II students at Sukajadi 02 Elementary School in the Indonesian language subject.&#x0D; Design and methods: This classroom action research refers to the Kemmis and Mc Taggart Model which is commonly referred to as the spiral model consisting of four stages. The planning stage, the action stage, the observation stage, the reflection stage.&#x0D; Results: The result showed that Through the Contextual Teaching and Learning Approach, learning Indonesian in reading and writing is easier to apply in everyday life that students encounter. It can improve Indonesian language learning outcomes in literacy.
Application of Learning Motivation Theory in Mathematics Teaching in Primary Schools

Mathematics is an important subject in the primary school curriculum, in order to achieve a good classroom effect and learning effect, the "teaching" of teachers and the "learning" of students have become particularly important. So how can we improve the quality of teaching and learning while making students fall in love with mathematics? This paper applies learning motivation theory to primary school mathematics teaching, and fully explores the influence and function of learning motivation theory on primary school mathematics teaching.