Reviewerly/exHarmony huggingface dataset:
Multi-view Spectral Clustering with High-order Similarity Learning

Clustering objects with diverse attributes obtained from multiple views is full of challenges in fusing the multi-view information. Many of the present multi-view clustering (MVC) methods concentrate on direct similarity learning among data points and fail to excavate the hidden high-order similarity among different views. Therefore, it is difficult to obtain a dependable clustering assignments. To address this problem, we propose the high-order similarity (HOS) learning model for multi-view spectral clustering (MCHSL). The proposed MCHSL learns the first-order similarity (FOS), second-order similarity (SOS), and the HOS collaboratively to excavate the local structure relations, proximity structure relations of paired data points and the interactive-view relations among different views instead of the common similarity learning. Then spectral clustering is performed to obtain the final clustering assignments. Extensive experiments performed on some public datasets indicate that the proposed MCHSL has better clustering performance than benchmark methods in most cases and is able to reveal a dependable underlying similarity structure hidden in multiple views.
Analysis of aspects of science literacy in hematology textbooks at Medan State University

This study aims to analyze the textbook of Hematology because most of the lecturers use textbooks as a learning guide in the classroom. The analysis carried out leads to an analysis of textbooks on scientific literacy, namely to determine the scope of scientific literacy in textbooks. This study is a descriptive study that aims to explain the scope of scientific literacy contained in the textbooks used on the Medan State University campus. The population of this research is all materials of Hematology. The sample of this study is one of the materials that are difficult to understand, namely Hematopoiesis. Samples were taken by purposive sampling technique. The textbooks analyzed were 2 books. The data was taken using an observation sheet containing scientific literacy indicators, which were then translated into percentages for each book and indicator. The results of the research analysis show that the aspects of scientific literacy that appear the most in each book are science as a body of knowledge as much as 25%, science as a way to investigate as much as 27.5%, science as a way of thinking as much as 32.5% and interaction. science and technology with the community as much as 0%. The results of the study concluded that the two textbooks used emphasized the presentation of facts, concepts, principles, laws, theories and models and emphasized that students could remember information through questions.
MoPo sane: Mobility Portal for Health Care Centers

The project “MoPo gesund—Mobilitätsportal für das Gesundheitswesen” (MoPo sane—mobility portal for the health care centers) is an example of a smart mobility system that provides a solution for flexible and networked mobility in order to improve the accessibility to health centers, especially for rural areas. Essential facilities of the health services (clinics, medical specialists, etc.) are located at the middle-order and/or higher-order regional centers with high traffic. In the era of the advanced Telemedicine (Projekt-Telepflege, 2019), outpatient and inpatient stay in health care facilities will continue to be indispensable in the frame of health care and services of general interest. A web-based and a mobile portal will be used to collect the mobility needs of patients, visitors, and employees with no or restricted individual mobility options (older people, young people without driving licenses, socially disadvantaged people) and connect them to sustainable mobility alternatives. The portal will offer a clearly defined regional backdrop with “mobility endpoints” that can be clearly located from a geographical location. The consideration of such system boundaries has significant advantages. Providing the necessary mobility services would be difficult if the sources and destinations of the paths covered by rural residents are weakly concentrated or widely dispersed. This project will concentrate the mobility endpoints onto various healthcare facilities.
Mixed-Precision Quantization of U-Net for Medical Image Segmentation

Network quantization can facilitate the practical deployment of U-Net in various medical applications, especially medical image segmentation. However, existing quantization methods for U-Net do not sufficiently exploit the skip connection between its encoder and decoder structures. In this paper, we propose a novel mixed-precision quantization scheme for U-Net that leverages split convolution and skip-supervised quantization aware training to address this problem. Specifically, split convolution decouples the feature maps obtained by skip connection and up-sampling to enable fine-grained bitwidth allocation for mixed-precision quantization. Furthermore, we introduce supervision on skip connection for quantization aware training to compensate gradients in back propagation and enhance the skip features in finetuning. To our best knowledge, this is the first attempt to design mixed-precision quantization specifically for medical image segmentation with U-Net. Experimental results on four medical image datasets demonstrate that the proposed scheme outperforms existing U-Net quantization strategy and recent general-purpose mixed-precision quantizaton methods in medical image segementation.
A Comparative Study on Object Detection Using Retinanet

Object recognition has been useful in a variety of situations. In this, we have to select the selected regions from the image and have to classify them using a convolutional neural network. By using CNN we have to predict every selected region. RetinaNet seems to be the best effective algorithm in Deep learning for objects detection. The second algorithm based on the regression YOLOv3 method comes under this category. We predict the classes and the objects will be highlighted with the anchor boxes when multiple objects will display in a single frame using a RetinaNet. Yolo V3 the algorithm is fast as compared to other classification algorithms like Yolo. In real-time our algorithm process 45 frames per second. By capturing the streaming video for a specific time the system will give accurate results when compared with the non moving images. Detecting the objects from the images by considering the preprocessing techniques, Feature Extraction, RetinaNet and Kalman filter for tracking the object positions.
Blockchain Innovation and Information at HHH University: Issues of Methodology

Blockchain has become an epidemic and significant decision that organizations may make in the next few years, enabling institutions to integrate business functions, operations, and processes in a decen- tralized distributed ledger technology. This technology will transform the business world and economy in solving the limitations created by centralization and system inefficiency. Accordingly, with the high demand and complexity of growing economies such as the HHH University countries, the need for a typical solution technology is a game changer. This will lead GCC to a solid economic base. Blockchain technology can be applicable in many different fields such as Banking, education, Health, finance, government and trade. This article will address the literature review and methodology of Blockchain technology and innovation at the GCC, particularly in Saudi Arabia. Also, more research can be conducted in the future as the system may be integrated in these countries.
MOT-H: A Multi-Target Tracking Dataset Based on Horizontal View

The computer vision field is quickly developing, including multiple object tracking, as the big data age approaches. The majority of the effort is focused on tracking methods while less attention is paid to the most important aspect, data. After an analysis of existing datasets, we find that they commonly ignore the breakpoint problem in tracking and have low image quality. Thus we present the dataset named Multiple Object Tracking on Horizontal view (MOT-H). MOT-H is meticulously annotated on crowded scenes from the horizontal view, with the primary goal of proving anti-jamming performance against complicated occlusion or even complete occlusion. The breakpoint issue is emphasized, which means the target object temporarily leaves the scene and returns after a while. The proposed MOT-H dataset has 10 sequences, 20,311 frames, and 337,440 annotation boxes in total, with all pictures having the resolution of 3840 × 2160 and being filmed at 30 frames per second (fps). We establish a fair benchmark for the future object tracking method development. The whole dataset can be found at: https://drive.google.com/drive/folders/1SCUJAdbqXQStyV-F2M9UyGfsuCaxR73a?usp=sharing.
NeuroFabric: Hardware and ML Model Co-Design for A Priori Sparse Neural Network Training

Sparse Deep Neural Networks (DNN) offer a large improvement in model storage requirements, execution latency and execution throughput. DNN pruning is contingent on knowing model weights, so networks can be pruned only after training. A priori sparse neural networks have been proposed as a way to extend sparsity benefits to the training process as well. Selecting a topology a priori is also beneficial for hardware accelerator specialization, lowering power, chip area, and latency.We present NeuroFabric, a hardware-ML model co-design approach that jointly optimizes a sparse neural network topology and a hardware accelerator configuration. NeuroFabric replaces dense DNN layers with cascades of sparse layers with a specific topology. We present an efficient and data-agnostic method for sparse network topology optimization, and show that parallel butterfly networks with skip connections achieve the best accuracy independent of sparsity or depth. We also present a multi-objective optimization framework that finds a Pareto frontier of hardware-ML model configurations over six objectives: accuracy, parameter count, throughput, latency, power, and hardware area.
RELIGIOUS FEATURES OF RUSSIAN PAINTING WORKS OF THE 1920S

In contemporary theory the problem of the relationship between religion and art is posed in a number of research papers. In the scientific literature devoted to the study of topical problems of philosophy, theory and history of art, the authors devoted their works to the problem of mastering the essential unity of the religious and artistic-visual language, comprehending their common meaning and purpose for individual and social human life. This article explores the specifics of enthusiastic religiosity, which is considered on the example of three works of Russian painting of the 1920s – "People of the Future" by K. F. Yuon, "Fantasy" by K. S. Petrov-Vodkin and "Death of the Commissar" by K. S. Petrov-Vodkin . The role of a work of art as an intermediary between the human world and the divine world is affirmed, and within the framework of enthusiasm, it is a person who performs an action towards the Absolute. It also defines a special place for art in the atheistic period, when it would seem inappropriate to draw parallels between artistic creativity and the religious component. This connection of the two aspects is capable of satisfying the completely objective needs of a person - aesthetic. However, the absence of a religious interpretation of the fine arts of Russia at the beginning of the century, during the period of the eradication of religious consciousness, turns the interest of the study to the definition of the “religiosity” of a work of Russian painting of the 1920s. Determining the indicated features is the purpose of the study. The religiosity of the works is manifested in the desire for a perfect future. Determining the peculiarity of the enthusiastic religiosity of the works of Russian painting of the 1920s, it should be concluded that the specifics were influenced by the search for a new artistic language through the transformation of iconographic and symbolic techniques, as well as the artistic traditions of Western European masters, caused by the formation of a new worldview in the 1920s.
Understanding and Quantifying Adversarial Examples Existence in Linear Classification

State-of-art deep neural networks (DNN) are vulnerable to attacks by adversarial examples: a carefully designed small perturbation to the input, that is imperceptible to human, can mislead DNN. To understand the root cause of adversarial examples, we quantify the probability of adversarial example existence for linear classifiers. Previous mathematical definition of adversarial examples only involves the overall perturbation amount, and we propose a more practical relevant definition of strong adversarial examples that separately limits the perturbation along the signal direction also. We show that linear classifiers can be made robust to strong adversarial examples attack in cases where no adversarial robust linear classifiers exist under the previous definition. The results suggest that designing general strong-adversarial-robust learning systems is feasible but only through incorporating human knowledge of the underlying classification problem.
ACTSS: Input Detection Defense against Backdoor Attacks via Activation Subset Scanning

Deep neural networks are vulnerable to backdoor attacks where adversaries inject the trigger into partial training data to manipulate the trained model misclassification. In addition, the poisoned model behaves normally on clean inputs, and the malicious behavior only occurs when the secret trigger is present, making backdoor attacks hard to be detected. Most existing input detection methods leverage the link between triggers and outputs to reveal the poisoned inputs, which suffer from the trigger-size or the “all-to-all” attack scenario. We show that the internal activations produced by benign and poisoned inputs are significantly different in the poisoned model. In this paper, we propose a novel and run-time input detection algorithm, Activation Subset Scanning (ACTSS), which extracts the activations of incoming inputs and leverages an anomaly detection algorithm to identify malicious inputs. We search and score for the abnormal activation subset according to the statistical difference of activations between benign and poisoned data using nonparametric statistics technology. Extensive experiments are conducted on three public datasets: CIFAR10, GTSRB, and ImageNet, with three representative models. The results verify our approach's effectiveness and state-of-the-art performance, which achieve over 98% false rejection rate for different types of triggers.
An optimum approach to vector machine with K-nearest neighbor

Kernel-based Virtual Machine (KVM) is an open-source computer technology built into Linux. The hybrid classification system is built by combining KNN and Support Vector Machine is abbreviated as K-VM. Due to its flexibility and reliability in handling different forms of classification activities, the KNN has been stated as one of the most frequently used classification approaches. The KNN faces a significant challenge in determining the acceptable value for parameter K to ensure good classification efficacy. This is because the value of parameter K has a significant effect on the KNN classifier’s accuracy. The KNN is a method of learning that is based on laziness that holds the entire training examples before classification time, in addition to deciding the optimum value of parameter K. As a result, as the value of parameter K increases, the KNN’s computational method becomes more intensive. To construct a new text message classifier, this paper combines the K-nearest neighbor (KNN)classification approach with the support vector machine (SVM) training algorithm. This paper proposes the K-VM hybrid classification system to reduce the impact of parameters on classification accuracy. The Euclidean distance function is used to measure the average distance between the testing data point and each range in SVs in various categories. Experiments on a variety of benchmark datasets show that the K-VM approach outperforms the conventional KNN classification model in classification accuracy.
Research on the Characteristics of Knowledge Diffusion of Seminal H-Index Papers

The diffusion of h-index papers in the field of scientific knowledge was investigated by retrieving 3679 papers that cited the h-index from the Web of Science's SCI/SSCI core database.With using Histcite, Pajek, and VOSviewer software, this paper measures the knowledge diffusion over the last 16 years using indicators in three dimensions: the time and space diffusion, the diffusion indicators, and the main path of the top 30 cited papers on h-index. The knowledge diffusion index, keyword co-occurrence, and main path analysis methods are used. The results show that h-index papers have been continuously cited and spread to 96 countries and regions, including the United States, China, Spain and Germany. The University of Antwerp and the Max Planck Society are the primary research institutes, and most of the authors are from these institutions. The analysis of co-occurrence keywords shows that h-index diffusion was initially concentrated in bibliometrics, and has recently spread to other fields such as artificial intelligence. Through the path analysis of highly cited papers, this work can help researchers understand the current development trend of the h index better and provide guidance for new index innovation.
Improving the FQF Distributional Reinforcement Learning Algorithm in MinAtar Environment

Reinforcement learning algorithms allow agents to learn from experience, without the need for prior knowledge. For this reason, they have been widely used and the use of low and medium complexity digital games as benchmark environments has become a common practice. In 2013, a new algorithm, called DQN (Deep Q Network), caused a great impact in the academic environment by obtaining human-level results in several Atari 2600 games, using artificial neural networks. Consequently, new lines of research emerged and new derived algorithms were proposed. Among these, the FQF (Fully Parameterized Quantile Function) stands out, an algorithm that has become the state of the art among the non-distributed algorithms in the Atari 2600 domain. However, the FQF has not yet achieved results obtained by a human expert in all evaluated games, thus demonstrating that better results than current ones can still be obtained. Therefore, this work sought to combine two improvements in the FQF that brought success in algorithms proposed before the FQF, with the objective of improving it. The improvements applied to the FQF are: the use of 3 steps in the temporal difference and the application of the Munchausen approach. The FQF changed with the improvements was evaluated in 5 MinAtar games and the results obtained brought gains of approximately 147% over the original FQF in the median of agent's returns.
Zero-Shot Unseen Speaker Anonymization via Voice Conversion

Speech-based interfaces provide convenient methods for controlling various smart devices. For these interfaces to work reliably, considerable speech data with various noise and speaker characteristics must be collected to train the associated speech-processing models. Gathering spoken commands from actual users of devices can improve those devices’ performance by familiarizing each device with the individual acoustic characteristic of its particular user’s speech. However, the direct acquisition of spoken commands could threaten the privacy of users, as the spoken data would contain sensitive speaker-specific information. Speaker anonymization algorithms can be applied to suppress such sensitive information, while preserving the linguistic content of a user’s speech. Previous speaker anonymization algorithms could handle only the voice of speakers who contributed to the training datasets. As speaker anonymization algorithms are typically applied to new speakers (who are absent from the training datasets), a method of handling such speakers (commonly referred to as “unseen speakers”) should be developed. In this paper, we propose a novel method that can effectively suppress the individual characteristics in an unseen speaker’s voice, while retaining the linguistic content of the speech. It adopts zero-shot voice conversion methods for the unseen speaker anonymization. Since the proposed method utilizes speaker identity vectors commonly used in many-to-many voice conversion algorithms and does not modify the conversion algorithm itself, it can be easily combined with many other voice conversion algorithms. The proposed method is evaluated using the VCC2018 and VCTK corpora. Speaker identification rate and speech recognition rate are used for quantitative analysis. The experimental results showed that the average speaker identification accuracy was decreased by 92.3% point absolutely and the average speech recognition accuracy was decreased by 17.7% point absolutely after the speaker anonymization by the proposed method.
Compactly Restrictable Metric Policy Optimization Problems

We study policy optimization problems for deterministic Markov decision processes (MDPs) with metric state and action spaces, which we refer to as metric policy optimization problems (MPOPs). Our goal is to establish theoretical results on the well-posedness of MPOPs that can characterize practically relevant continuous control systems. To do so, we define a special class of MPOPs called compactly restrictable MPOPs (CR-MPOPs), which are flexible enough to capture the complex behavior of robotic systems but specific enough to admit solutions using dynamic programming methods such as value iteration. We show how to arrive at CR-MPOPs using forward-invariance. We further show that our theoretical results on CR-MPOPs can be used to characterize feedback linearizable control affine systems.
Intelligent Transfer Optimization for Ironmaking Process With Nonanalytic Constraints

In order to guarantee the smooth operation of the blast furnace ironmaking process, it is essential to consider the constraints for the optimization of this process. Due to the complexity of the process, it is challenging to obtain the description of the constraint functions and quantify the feasibility of solutions, making traditional optimization methods helpless. To address this problem, a transfer optimization framework is proposed that consists of a two-stage generation mapping algorithm, and an improved grey wolf optimizer (GWO) algorithm. The method achieves the transformation from constrained optimization to unconstrained optimization by establishing a proper mapping with distribution- and boundary-sensitive two-stage generation algorithm. Meanwhile, the density-amended GWO algorithm with adaptive search steps depended on the solution density distribution is applied to locate the optimal solutions. The intelligent transfer optimization method is validated by both numerical tests and practical data, and the results demonstrate the effectiveness of the proposed algorithm.
Event Source Page Discovery via Policy-Based RL with Multi-task Neural Sequence Model

The problem of finding event announcement pages for any given website is called event source page discovery. In this paper, we show a policy-based deep reinforcement learning (RL) model for the event source page discovery agent. We use two stages to train our agent, pre-training and fine-tuning. In the pre-training phase, the model is trained with limited labeled data, where each episode has a fixed number of steps. In the fine-tuning phase, the agent is trained using unlabeled data and a reward system based on an event source page classifier. The agent learns whether to continue exploring or stop exploring through an adaptive threshold. The proposed agent achieves 74% precision with a 1.28 unit cost (the average number of clicks for each event source page) on the real word data set.