Reviewerly/exHarmony huggingface dataset:
Anomaly detection algorithm of IoT devices based on semi-supervised heterogeneous ensemble learning

This paper proposes an abnormal behavior detection method based on semi-supervised heterogeneous ensemble learning to detect the abnormal behavior of IoT devices and solve the problems of data heterogeneity, weak generalization ability, and high label cost-efficiently and accurately. Firstly, the equipment is divided into three types: periodic type, trend type and random type according to the time series decomposition method and correlation coefficient. Secondly, two kinds of direct detection methods based on probability statistics and similarity measurement are adopted for the random equipment and indirect detection methods based on prediction or reconstruction before diagnosis are used for periodic and trend equipment. Finally, the optimal sub-model of each class is selected, and the heterogeneous integration model based on accuracy weighting is designed to make the anomaly detection more robust and accurate. The experimental results show that the accuracy of the LSTMs-AE + 3sigma sub-model is the highest, up to 81.09%, compared with many detection combination algorithms. The accuracy rate of the heterogeneous integration model obtained by selecting the optimal sub-models from different types of algorithms is as high as 89.25%, which is 8.16% higher than the individual sub-model. The false positive rate and false negative rate also decreased, only 5.05% and 2.08%.
Do You Speak Cyber? Talking Security With Developers of Health Systems and Devices

Teams developing software for health-related applications often have little professional security support. To work effectively with them, we should accept the complexity of their decision making, create stories as a basis for discussion, and use different jargon from cyberprofessionals.
Mobile Edge Computing 5G with Distributed Blockchain Multi-Domain Collaborative in IoT Environment

The use of mobile edge computing is becoming more and more widespread by the day. It offers the advantages of various things including reach and other things that are not just convenient for the people but also profitable for the companies that act as providers to the people. This is one of the new generations of technology and has various other industries involved in it than just telecommunications and Internet providers. That means that it creates more jobs in the industries and benefits the local economies as well. This also helps the peer to peer connection from forming and makes easy networks for block chains in multi-domaincollaborative using the IoT environment.A survey has also been conducted to test the notion and the research questions. A group of 51 people were chosen from various companies and private individuals that perform MEC processes and use or are familiar with the process of block chain. Their opinions of the experts have been carefully considered and the analysis of it has been presented in the fourth chapter of the study.
Bronco: A Universal Authoring Language for Controllable Text Generation

We present Bronco: an in-development authoring language for Turing-complete procedural text generation. Our language emerged from a close examination of existing tools. This analysis led to our desire of supporting users in specifying yielding grammars, a formalism we invented that is more expressive than what several popular and available solutions offer. With this formalism as our basis, we detail the qualities of Bronco that expose its power in author-focused ways.
Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique

Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.
TS-DST: A Two-Stage Framework for Schema-Guided Dialogue State Tracking with Selected Dialogue History

The task-oriented dialogue systems aim to assist the users in completing specific tasks through natural language dialogue. Recently, word-level dialogue state tracking (DST) has become a core component of task-oriented dialogue systems. In this paper, we study the word-level DST task at the 8th dialogue system technology challenge (DSTC8), namely schema-guided dialogue state tracking, which focuses on cross-domain dialogue state tracking and zero-shot generalization to new services. Many approaches have been proposed to exploit the schema description for dialogue modeling, especially on unseen services. Despite their success, existing methods still suffer from two weaknesses: (1) the current methods do not fully exploit the dialogue history, which makes it difficult to solve the slot carryover problem from the multi-domain dialogues; (2) the current method treats the task as four independent sub tasks without considering the relevance of the subtasks. To address these issues, we propose a novel two-stage framework for schema-guided dialogue state tracking with selected dialogue history (TS-DST). Specifically, to solve the first issue, we propose a novel utterance selection module to select the most related previous utterances from the dialogue history by considering the specific schema element. To solve the second issue, we propose a two-stage framework to solve the four subtasks. Experiments conducted on the SGD dataset show that our method achieves new state-of-the-art performance. We also conduct ablation studies to demonstrate the effectiveness of the utterance selection module and the two-stage strategy.
(n, n) XOR-based Visual Multi-secrets Sharing Scheme with Meaningful Shares

Due to the rise of the Internet and the development of digital technology, lifestyles have become rich and diverse. People are also used to transmitting important information over the Internet. With the popularity of the Internet, the methods of cybercrime are constantly innovating, becoming common and complex, and information security is under increasing threat. Naor and Shamir put up the fundamental design for visual cryptography (VC) in 1994. When the secret image is restored without relying on any complicated equipment, it is only necessary to stack those shares, and the secret image can be observed through human vision. Since then, scholars have started to research visual cryptography (VC). One of the recent advancements in visual cryptography, XOR-based VC addresses the issue of OR-based VC's poor image quality of the restored image through recent hardware cost reductions. After all n shares are collected, the restoration operation is performed by the XOR operation, so that the restored image has higher visual quality. In this study, we propose an XOR-based VCS that encrypts multiple secret images at the same time and makes each share meaningful separately so that its protection is improved. Encrypting by XOR operation makes the restored image with high visual quality. According to theoretical analysis and experimental findings, the approach is safe and efficient. The proposed VCS has better capabilities than earlier approaches.
Localised active contour method via local similarity measure for image segmentation

The accuracy of active contour methods is not always exact since there are many uncertainty factors, e.g., abundant noise, lack of clear boundaries, intensity inhomogeneity. To tackle these issues, a localised region-based segmentation framework is presented in this paper. In our method, a new adaptive local similarity measure is built in local regions as the spatial constraint to guarantee noise suppression and outlier resistance. Second, we construct an objective equation by integrating the local similarity measure into an active contour algorithm based on the local region. Furthermore, we design the local mean difference energy as a control constraint to enhance the efficiency and smoothness of the profile curve. Experimental data demonstrate that our algorithm, when compared with other classical region-based models, can achieve higher accuracy and has stronger robustness for images with higher noise levels.
Students’ Grading System by Fuzzy Approach

Assessment of student’s performance is a crucial challenge in schools, colleges, universities. In this work we propose a performance evaluation system for students utilizing fuzzy logic approach considering the perseverance factor. The different variables used for evaluation system are the marks scored by the students in Test-1, Test-2, Mid Term and End Term. Crisp parameters’ values are transformed to compatible parameters which is fuzzy, and performance is evaluated in the fuzzy domain in a more efficient procedure than the traditional system. The proposed system is simulated in MATLAB using a fuzzy inference technique.
Chinese Font Generation from Stroke Semantic and Attention Mechanism

In order to help designers develop computer Chinese fonts efficiently, a Chinese character font generation algorithm is proposed. The algorithm can generate full font character images with a relatively consistent style based on small font datasets. For the sake of solving problems in stroke adhesion and structural errors when using adversarial generative network (GAN), the prior information of stroke semantics is introduced to improve the deep neural network for alleviating wrong stroke generation. Moreover, a skip connection module equipped with attention mechanism is integrated in the network model to reduce incomplete font structure and training period. In this module, features in encoder can be projected to that in de- coder, which is helpful in eliminating the information loss of the decoder so as to avoid generating structural errors. Comparative experiments between the proposed algorithm and other methods including pix2pix, zi2zi and DCFont are conducted on public Chinese font datasets. The results show the proposed algorithm is better that the others from the perspective of SSIM and average in loss function. The industry application and scope of our algorithm are given in finally.
Convergence of Blockchain and Next Generation Information Technology

Blockchain technology ensures secure data transmission and use through features such as anti-tampering and traceability. However, blockchain can only ensure of data on the blockchain. To ensure end-to-end security and trustworthiness of data, blockchain needs to cooperate with other technologies. In this way, we can see that blockchain + IOT, blockchain + AI are complementary in technology capabilities and jointly maintain end-to-end secure data transmission.
A Real-time Vibration Haptization System Using High-Speed Active Vision

This study develops a real-world haptization system based on high-speed active vision that can virtually touch a remote human-untouchable object vibrating at the audio frequency level such as product machines operating at high speed and heated systems in factories. A high-speed active vision system can simultaneously observe small vibrations at hundreds of Hertz by executing digital image correlation analysis with sub-pixel accuracy in real time at 500 fps. A remote user enables wide-angle observation by operating its pan-tilt mirror direction with a trackball interface, and virtually feels the vibration by exciting his finger with a high-speed one-axis vibrator. Several experimental results show the effectiveness of our vibration haptization system that enables remote touch on human-untouchable vibrating objects such as a boiling electric kettle, a heated boiler, and a fast-rotating pump motor.
Experience-based Intelligence Augmentation with Decisional DNA: Upcoming Direction

Intelligence amplification systems and technologies have gained significant interest from academia and industry during the past few decades. One of the main reasons behind this trend is the fact that most experts agree that truly intelligent artificial system is yet to be developed. The question increasing often asked is this: Is full replication of human intelligence desirable key aim in intelligence related technology and research? In this context, the concept of Augmented Intelligence, also known as Cognitive Augmentation or Intelligence Amplification (IA) comes into play. One of the main reasons behind this interest in this concept is the potential of such technologies to revolutionize human life as they intend to work under complex decision-making environments, adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior. The combination of these properties aims to enhance human capabilities and create more intelligent and efficient human-centered environments. In this paper we discuss how Decisional DNA (DDNA), a multi-domain knowledge structure that has the Set of Experience Knowledge Structure (SOEKS) at its core can be utilized as an experience-based intelligence augmentation in a number of different domains.
Using Counterfactuals to Proactively Solve Service Level Agreement Violations in 5G Networks

A main challenge of using 5G network slices is to meet all the quality of service requirements of the slices (which are agreed with the customer in a service level agreement (SLA)), throughout the network slices' lifecycle. To avoid the penalty for violation, a proactive solution is presented, including predicting the SLA violation, explaining the violation cause, and then providing an adaptation to traffic. This work uses counterfactual (CF) explanations to 1) explain the main factors affecting the identified model's SLA violation prediction and 2) present modifications in the input values, which are required to configure the network traffic to avoid such a violation. We evaluate the CF explanation at two different levels where the generated CF instance is fed to the predictive model, and then actuation data are generated to evaluate the result in the real network. Our solution minimizes the violation up to 98%. This information can be utilized to reconfigure the system, either by humans or by the system automatically, to make it fully autonomous on the one hand and comply with the 'right to explanation' introduced by the European Union's General Data Protection Regulation on the other hand.
The Randomizer Community does Procedural Content Generation Research

Academic Procedural Content Generation research has until recently overlooked a significant real-world application of generative methods to existing games: game randomizers. These programs remix existing games by changing things like item locations, enemy stats, or even room connections to create a fresh experience based on a beloved game, and are especially popular among speedrunning and streaming communities. They generate where high-production-quality full-scale games, explicitly geared towards replay value. Randomizers fulfill many of the stated motivations of the academic PCG research community, and important new research directions can be developed by investigating this space.
Distributed Random Number Generation Method on Smart Contracts

We propose N-choice game (NCG), a decentralized pseudo-random number generation method that can be executed on smart contracts. Of the M participants, one is a dealer, and the rest are players, each with a different role. Each participant randomly chooses one value between 0 and N − 1 and receives a score determined by the NCG rule. The amount of reward each participant receives is determined by the score. The values chosen by the participants are combined and hashed into a pseudo-random number. The NCG framework is designed to achieve the following three goals: (1) Incentivize participants to provide random choices, (2) Evaluate the level of randomness in the decentralized environment, and (3) Establish high performance. We implement the NCG framework in Solidity and evaluate its performance. Our extensive experiments revealed that unless more than 90% of NCG players collide, the generated random numbers have high randomness that can pass the NIST randomness test. The experiments also demonstrated that the throughput of random number generation in NCG was 129 times faster than in the existing framework, Random Bit Generator [2].
Skeleton-Based Sign Language Recognition with Graph Convolutional Networks on Small Data

Sign language is an important means of communication for people with speech or hearing impairments. On the other hand, it is difficult for normal people to understand sign language. Therefore, We need technology to support communication between people with speech or hearing impairments and normal people, and sign language recognition (SLR) is important to facilitate communication. In this work, we propose an approach to recognize sign language from dynamic skeletons using graph convolutional networks (GCNs). In this method, the convolution is performed by capturing the complex dynamic skeleton of sign language as graph structures. In addition, we suggest a skeleton data augmentation method, which uses MediaPipe and 3D motion data to create a new skeleton dataset for SLR from small data. We use 20 signs from Kogakuin University Japanese Sign Language Multi-Dimensional Database (KoSign) and achieve an average accuracy of 44.6% on top1 and 90.2% on top5 for two subjects.
Specification-Guided Reinforcement Learning

The problem of reinforcement learning (RL) is to generate an optimal policy w.r.t. a given task in an unknown environment. Traditionally, the task is encoded in the form of a reward function which becomes cumbersome for long-horizon goals. An appealing alternate is to use logical specifications, opening the direction of RL from logical specifications. This paper summarizes the trials and triumphs in developing highly performant algorithms and obtain theoretical guarantees in RL from logical specifications.
Comparative Valuation of Global Solar Radiation on Horizontal and Tilt Surfaces in Owerri, Nigeria

Comparative valuation of global solar radiation on horizontal and tilt surfaces with the use of Angstrom-Page and four isotropic models for Owerri has been carried out in this paper. Angstrom linear regression model is developed from thirty years (1992-2022) of sunshine hour and average monthly global solar radiation sourced from NIMET and NASA, respectively. The Angstrom regression coefficients 𝑎 and 𝑏determined for Owerri is 0.210 and 0.696, respectively. The developed model is used to estimate average monthly global solar radiation on horizontal surface and consequently, the amount of incident solar radiation received on tilt surface by the isotropic models. The MBE, RMSE and MPE values are estimated to be 0.0083, 0.0029 and -0.0041%, respectively, for the developed Angstrom-Page model. A strong coefficient of determination of 76.6% was obtained and Analysis of Variance (ANOVA) significant of 100% was observed, which shows the goodness of fitness of the model. Among the isentropic models studied, one of the models poorly estimated solar radiation on horizontal and tilt surfaces for the study location
Traditional Public-Key Cryptosystems and Elliptic Curve Cryptography

The need to establish safer communication channels in a world where technological development is progressing in leaps and bounds is indispensable. Thus, implementing cryptographic algorithms, which are more complex to compromise, improves the possibilities of securing our sensitive data. In this paper, the authors analyze the algorithmic foundations and perform a comparative analysis of the traditional public-key cryptographic algorithms (e.g., RSA, ElGamal, Schnorr, DSA) and elliptic curve cryptography with NIST recommended curves. In the study, they focus on six different security strengths: 80-, 96-, 112-, 128-, 192-, and 256-bit key sizes. Moreover, this study provides a benchmark among different curves (NIST, SEC2, and IEFT Brainpool) that can be used with various security levels. The authors study and compare the characteristics and performance of the traditional asymmetric algorithms and the elliptic curve algorithms for information security. The results obtained in this study will be graphically visualized through statistical graphs and tables with quantification response times.
Discrete-Time Differential Dynamic Programming on SO(3) With Pose Constraints

The motion of many robotics systems, such as the rotation of unmanned aerial vehicles, can be modeled by SO(3). However, the difficulties in the parameterization of the SO(3) makes it hard to implement the state-of-the-art collision avoidance algorithm. In this paper, we present a new method for control on SO(3) via the combination of the nonlinear state constraints and geometric control technique. We first define the nonlinear constraints for trajectory optimization on SO(3). Then we solve the trajectory optimization problem by constrained Differential Dynamic Programming (DDP) in a Riemannian geometry framework. The first and second-order expansion of the cost function and dynamics are derived in a coordinate-free manner. The safety condition represented by nonlinear constraints is incorporated into the DDP via an active set method, where we find the active set in the backward path to obtain the optimal control policy. We validated our methods on motion planning of rigid model on SO(3) manifold where pose constraints are imposed. Our methods outperform the baseline methods in terms of convergence speed and numerical robustness to disturbance. The numerical robustness is essential when the system is initialized far from the local optimum.
Selecting Related Knowledge via Efficient Channel Attention for Online Continual Learning

Continual learning aims to learn a sequence of tasks by leveraging the knowledge acquired in the past in an online-learning manner while being able to perform well on all previous tasks. This ability is crucial to the artificial intelligence (AI) system. Compared to the traditional learning pattern, continual learning is more suitable for most real-world and complex applicative scenarios. However, the current models usually learn a generic representation based on the class label on each task and an effective strategy is selected to avoid catastrophic forgetting. We postulate that selecting the related and useful parts only from the knowledge obtained to perform each task is more effective than utilizing the whole knowledge. Based on this fact, in this paper we propose a new framework, named Selecting Related Knowledge for Online Continual Learning (SRKOCL), which incorporates an additional efficient channel attention mechanism to pick the particularly related knowledge for every task. Our model also combines experience replay and knowledge distillation to circumvent catastrophic forgetting. Finally, extensive experiments are conducted on different benchmarks and the competitive experimental results demonstrate that our proposed SRKOCL is a promising approach against the state-of-the-art.