Reviewerly/exHarmony huggingface dataset:
Speechmoe2: Mixture-of-Experts Model with Improved Routing

Mixture-of-experts based acoustic models with dynamic routing mechanisms have proved promising results for speech recognition. The design principle of router architecture is important for the large model capacity and high computational efficiency. Our previous work SpeechMoE only uses local grapheme embedding to help routers to make route decisions. To further improve speech recognition performance against varying domains and accents, we propose a new router architecture which integrates additional global domain and accent embedding into router input to promote adaptability. Experimental results show that the proposed SpeechMoE2 can achieve lower character error rate (CER) with comparable parameters than SpeechMoE on both multi-domain and multi-accent task. Primarily, the proposed method provides up to 1.6% ∼ 4.8% relative CER improvement for the multi-domain task and 1.9% ∼ 17.7% relative CER improvement for the multi-accent task respectively. Besides, increasing the number of experts also achieves consistent performance improvement and keeps the computational cost constant.
Explainable Artificial Intelligence in Healthcare: Opportunities, Gaps and Challenges and a Novel Way to Look at the Problem Space

Explainable Artificial Intelligence (XAI) is rapidly becoming an emerging and fast-growing research field; however, its adoption in healthcare is still at the early stage despite the potential that XAI can bring to the application of AI in this industry. Many challenges remain to be solved, including setting standards for explanations, the degree of interaction between different stakeholders and the models, the implementation of quality and performance metrics, the agreement on standards for safety and accountability, its integration into clinical workflows, and IT infrastructure. This paper has two objectives. The first one is to present summarized outcomes of a literature survey and highlight the state-of-the-art for explainability including gaps, challenges, and opportunities for XAI in healthcare industry. For easier comprehension and onboarding to this research field we suggest a synthesized taxonomy for categorizing explainability methods. The second objective is to ask the question if applying a novel way of looking at explainability problem space, through a specific problem/domain lens, and automating that approach in an AutoML similar fashion, would help mitigate the challenges mentioned above. In the literature there is a tendency to look at the explainability of AI from model-first lens, which puts concrete problems and domains aside. For example, the explainability of a patient's survival model is treated the same as explaining a hospital cost procedure calculation. With a well-identified problem/domain that XAI should be applied to, the scope is clear and well-defined, enabling us to (semi-) automatically find suitable models, optimize their parameters and their explanations, metrics, stakeholders, safety/accountability level, and suggest means of their integration into clinical workflow .
Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach

Malicious cyber activities impose substantial costs on the U.S. economy and global markets. Cyber-criminals often use information-sharing social media platforms such as paste sites (e.g., Pastebin) to share vast amounts of plain text content related to Personally Identifiable Information (PII), credit card numbers, exploit code, malware, and other sensitive content. Paste sites can provide targeted Cyber Threat Intelligence (CTI) about potential threats and prior breaches. In this research, we propose a novel Bidirectional Encoder Representation from Transformers (BERT) with Latent Dirichlet Allocation (LDA) model to categorize pastes automatically. Our proposed BERT-LDA model leverages a neural network transformer architecture to capture sequential dependencies when representing each sentence in a paste. BERT-LDA replaces the Bag-of-Words (BoW) approach in the conventional LDA with a Bag-of-Labels (BoL) that encompasses class labels at the sequence level. We compared the performance of the proposed BERT-LDA against the conventional LDA and BERT-LDA variants (e.g., GPT2-LDA) on 4,254,453 pastes from three paste sites. Experiment results indicate that the proposed BERT-LDA outperformed the standard LDA and each BERT-LDA variant in terms of perplexity on each paste site. Results of our BERT-LDA case study suggest that significant content relating to hacker community activities, malicious code, network and website vulnerabilities, and PII are shared on paste sites. The insights provided by this study could be used by organizations to proactively mitigate potential damage on their infrastructure.
Multi-Objective Trajectory Planning for Slung-Load Quadrotor System

In this article, multi-objective trajectory planning has been carried out for a quadrotor carrying a slung load.The goal is to obtain non-dominated solutions for path length, mission duration, and dissipated energy cost functions.These costs are optimized by imposing constraints on the slung-load quadrotor system's endpoints, borders, obstacles, and dynamical equations.The dynamic model of a slung-load quadrotor system is used in the Euler-Lagrange formulation.Although the differential flatness feature is mostly used in this system's trajectory planning, a fully dynamic model has been used in our study.A new multi-objective Genetic Algorithm has been developed to solve path planning, aiming to optimize trajectory length, mission time, and energy consumed during the mission.The solution process has a three-phase algorithm: Phase-1 is about randomly generating waypoints, Phase-2 is about constructing the initial non-dominated pool, and the final phase, Phase-3, is obtaining the solution.In addition to conventional genetic operators, simple genetic operators are proposed to improve the trajectories locally.Pareto Fronts have been obtained corresponding to exciting scenarios.The method has been tested, and results have been presented at the end.A comparison of the solutions obtained with MOGA operators and MOPSO over hypervolume values is also presented.
AN-Coded Redundant Residue Number System for Reliable Neural Networks

Residue Number Systems (RNS) can simultaneously improve computing acceleration, area reduction and power saving. For high-reliability applications like automotive electronics, partition-ability empowers the redundant RNS fault tolerance. However the parallel multiple modular redundancy will take a huge number of converters. This is the first paper to incorporate AN codes to the RRNS applied in high-reliable neural networks. From experimental results, the k-modulus redundancy can be reduced from 2 paths to only one, and the residue-to-binary converters can be saved from (k+2)(k+1) to only k+1 for the external-coding structure, and even to only one for the internal-coding structure in the single residue arithmetic-weight error correcting. From BLER simulations, about 130 times of reliability can be achieved for a 16-bit 47N-coded MAC.
AI Customer Service System with Pre-trained Language and Response Ranking Models for University Admissions

The application of chatbots in the field of customer service systems is already an indispensable technology. In the admissions of colleges and universities, the customer service system is one of the most important models. The objective of this study is to evaluate the efficacy of an intelligent enrollment customer service robot system established using the Generative Pre-Training-2 (GPT-2) model. In this study, we combine the customer service field of chat robots with the enrollment field to construct a customer service system with enrollment as the field. This study uses the GPT-2 natural language processing model proposed by OpenAI to construct a customer service system in the university admissions field. BLEU is utilized to generate an evaluation model to evaluate the similarity between the text generated by the university admissions customer service system and the reference text to determine if the generated text by this model is good or bad. The research contributions of this paper are that we proposed an artificial intelligence (AI) customer service system with a pre-trained language model and a response-ranking model for university admissions and conducted a user evaluation of the proposed AI system.
Detection of Image Malware Steganography Using Deep Transfer Learning Model

Alodat, Iyas Alodat, MohammadThe malware would offer the attacker or an un-legitimate user access to the device without authorization as a legitimate user. In this paper, we will discuss how malware hides inside images which can transfer between computers in the background of any system. Also, we will discuss how deep transfer learning will detect malware that hides under images. Lastly, we will make a comparison between deep transfer's models to detect malware in images. We will also conclude which model is the best to use in the system to detect malware.
The Status Quo of Information Technology-based English Informatization Education in Colleges and Universities

Project of Realization of Education Informatization 2.0 issued by the Ministry of Education sends out a signal that English education reform in higher schools is an irresistible tendency facing all English teachers as a whole, thus posing higher demand for teaching ability based on informatization. This present paper adopts a combination of questionnaires with interviews as a solid basis for quantity and quality analysis. A high-ranked provincial application-oriented university in Anhui province is chosen as the target for research with 78 target teachers as the research samples. The present study focuses on perception and application of information technology among the teachers, as well as on constraints of application of big data and artificial intelligence into English teaching. Naturally, the paper comes to suggested counter-measures aiming at the above problems.
Critical Overview of Visual Tracking with Kernel Correlation Filter

With the development of new methodologies for faster training on datasets, there is a need to provide an in-depth explanation of the workings of such methods. This paper attempts to provide an understanding for one such correlation filter-based tracking technology, Kernelized Correlation Filter (KCF), which uses implicit properties of tracked images (circulant matrices) for training and tracking in real-time. It is unlike deep learning, which is data intensive. KCF uses implicit dynamic properties of the scene and movements of image patches to form an efficient representation based on the circulant structure for further processing, using properties such as diagonalizing in the Fourier domain. The computational efficiency of KCF, which makes it ideal for low-power heterogeneous computational processing technologies, lies in its ability to compute data in high-dimensional feature space without explicitly invoking the computation on this space. Despite its strong practical potential in visual tracking, there is a need for an in-depth critical understanding of the method and its performance, which this paper aims to provide. Here we present a survey of KCF and its method along with an experimental study that highlights its novel approach and some of the future challenges associated with this method through observations on standard performance metrics in an effort to make the algorithm easy to investigate. It further compares the method against the current public benchmarks such as SOTA on OTB-50, VOT-2015, and VOT-2019. We observe that KCF is a simple-to-understand tracking algorithm that does well on popular benchmarks and has potential for further improvement. The paper aims to provide researchers a base for understanding and comparing KCF with other tracking technologies to explore the possibility of an improved KCF tracker.
Optimized Online Path Planning Algorithms Considering Energy

Plug and produce demonstrators handles multiple processes in the industry, appropriate path planning is essential and at the same time there is an increasing emphasis on more sustainable processes. To ensure the sustainability and automate these processes optimized path planning is required. We present an implementation of a path planning algorithm, which creates a smooth collision free path and considers energy use. In the paper, we demonstrated the implementation of PRM (Probabilistic Road Map) path planning and Dijkstra based optimization algorithm in a simulation environment and thereafter test in a real plug and produce demonstrator. To validate the simulated results the real energy was measured through the signal analyzer online. The measured results outlined in this paper includes; computational time, move along path time, and energy use with different loads. From the experiments and results we conclude that the combination of the two algorithms, PRM with Dijkstra, can be used to generate a collision free optimized path. Here we have considered the distance as the cost function for Dijkstra optimization algorithm and measured the energy of the collision free optimized path. The practical implication of this research is as an enabler for any kind of application where there are large variations of orders e.g., kitting techniques in assembly operations for manufacturing industry.
Approach to automatic population of ontologies of scientific subject domain using lexico-syntactic patterns

Abstract This paper presents an approach to automatic population of ontologies of a scientific subject domain (SSD) using Lexico-Syntactic Patterns (LSPs) and a corpus of texts related to modeled domain. The main feature of this approach is that such patterns are automatically built based on Ontology Design Patterns of other types provided by the system for the automated development of SSD ontologies using heterogeneous Ontology Design Patterns. The implementation of the ontology population using constructed LSPs is described in detail. The results of the experiments on the SSD ontology population are presented. It is noted that there is a problem in establishing a subject of a relation when extracting facts. To address this problem, the authors are planning to employ the coreference resolution methods.
Catching Jellies in Immersive Virtual Reality: A Comparative Teleoperation Study of ROVs in Underwater Capture Tasks

Remotely Operated Vehicles (ROVs) are essential to human-operated underwater expeditions in the deep sea. However, piloting an ROV to safely interact with live ecosystems is an expensive and cognitively demanding task, requiring extensive maneuvering and situational awareness. Immersive Virtual Reality (VR) Head-Mounted Displays (HMDs) could address some of these challenges. This paper investigates how VR HMDs influence operator performance through a novel telepresence system for piloting ROVs in real-time. We present an empirical user study [N=12] that examines common midwater creature capture tasks, comparing Stereoscopic-VR, Monoscopic-VR, and Desktop teleoperation conditions. Our findings indicate that Stereoscopic-VR can outperform Monoscopic-VR and Desktop ROV capture tasks, effectively doubling the efficacy of operators. We also found significant differences in presence, task load, usability, intrinsic motivation, and cybersickness. Our research points to new opportunities towards VR with ROVs.
Attendance Monitoring System based on Artificial Intelligence Facial Recognition Technology

Attendance monitoring is a basic system that is required for the operation of a company. However, traditional paper-based clock-in devices and radio frequency identification methods cannot avoid consigning others to the punch card problem. This paper therefore presents an attendance monitoring system based on Artificial Intelligence (AI) facial recognition technology. The proposed system uses a deep learning Tensorflow framework to build a face recognition model that can detect images in real time and upload this information to a cloud server. Our face recognition system can be used to store ‘punch card’ records as part of an attendance monitoring system.
OHODIN – Online Anomaly Detection for Data Streams

We propose OHODIN an online extension for data streams of the knn-based ODIN anomaly detection approach and presents a detection-threshold heuristic that is based on extreme value theory. In contrast to sophisticated anomaly and novelty detection approaches the decision-making process of ODIN is interpretable by humans, making it interesting for certain applications. This article presents the algorithms itself and an experimental evaluation with competing state-of-the-art anomaly detection approaches.
An Adaptive Threshold for the Canny Algorithm With Deep Reinforcement Learning

The Canny algorithm is widely used for edge detection. It requires the adjustment of parameters to obtain a high-quality edge image. Several methods can select them automatically, but they cannot cover the diverse variations on an image. In the Canny algorithm, we need to set values of three parameters. One is related to smoothing window size, and the other two are the low and high threshold. In this paper, we assume that the smoothing window size is fixed to a predefined size. This paper proposes a method to provide adaptive thresholds for the Canny algorithm, which operates well on images acquired under various variations. We select optimal values of two thresholds adaptively using an algorithm based on the Deep Q-Network (DQN). We introduce a state model, a policy model, and a reward model to formulate the given problem in deep reinforcement learning. The proposed method has the advantage that it can adapt to a new environment using only images without labels, unlike the existing supervised way. We show the feasibility of the proposed algorithm by diverse experimental results.
Prevention of SQL Injection and Security Enhancement in Cyber Networks

Now-a-days, the majority of web applications are related to databases at the back-end so there are potential outcomes of SQL injection attacks (SQLIA) on it. Various preventive measures have been found by different researchers to conquer this attack, however which measure is more helpful and gives quick admittance to applications without compromising the security is likewise a significant concern. In this paper work, we explicate complete review on various types of SQL injection, attacks, vulnerabilities, and detection techniques. Here we developed a webpage and SQL Injection is prevented using String binding method.
The Application of an Improved C4.5 Decision Tree

Decision tree classification method is an effective instance-based learning and data-mining method. In this paper, several decision tree classification algorithms were analyzed, including ID3 and C4.5 algorithm, followed by some improved algorithms. Taylor series and Maclaurin's series are applied to simplify the information gain ratio formula in which way the time efficiency of calculation can be improved. The improved C4.5 algorithm can be applied in the student employment recommendation system. Through this improved C4.5 decision tree algorithm, graduates could have a good guide while seeking for a job and teachers could also help their students in decision-making and job recommendation.
A Blockchain-Based Fine-Grained Access Data Control Scheme With Attribute Change Function

A distributed storage system (DSS) has the ad-vantages of low price and high throughput, but it also raises serious data confidentiality and privacy issues. Fine-grained access control (AC) is important for further applications. Although attribute-based encryption (ABE) schemes can achieve fine-grained AC, almost all schemes distribute a private key to a visitor by a private key generator that may leak data. Besides, attribute revocation functions of the ABE scheme will lead to collusion attacks. Blockchain-based ABE schemes rarely consider ciphertext-based search and attribute revocation functions which lead to a large communication overhead. In this paper, we propose a secure ABE scheme in which a data owner is the only person who has the right to distribute keys. Then we combine blockchain with the proposed ABE scheme to realize fine-grained AC over a distributed storage system, which supports the attribute change function and resists collusive attacks. In addition, we build a series of smart contracts to ensure the fairness of trade between the data owner and visitor. The auxiliary information of data stored on the blockchain can realize ciphertext-based search. Finally, we prove the scheme resists collusion attacks under the computational Diffie-Hellman assumption (CDH) and conduct multiple experiments based on Ethernet test Network. The experimental results demonstrate our scheme has the running time of algorithms reached a millisecond-level and the ET H cost is acceptable.
Zoom4PF: A Tool for Refining Static and Dynamic Domain Descriptions in Problem Frames

Problem analysis has long been considered the key to requirements engineering, and the Problem Frames (PF) approach provides a structured method by deploying a common model for analyzing various types of problems. Problem decomposition is an important technique in structuring the software solution and also the key to reducing problem size and complexity. However, there has not been a suite of flexible and effective tools to describe details of problem domains in PF models. In this paper, we combine model-driven engineering and PF to provide a tool that can refine domain descriptions. In order to support modeling between domain stakeholders and software designers, we provide a technique and tool to allow the modeller to zoom in the details of a problem diagram, by adding UML State Machine Diagrams and SysML Block Definition Diagrams to domain descriptions.A demo video of this tool is available at https://youtu.be/BcQPlDYiOa8. More details of this tool and the appendix to this article are available at https://github.com/Wsfff-lf/ZOOM4PF/tree/main.
Optimal Adaptive Strategies for Sequential Quantum Hypothesis Testing

We consider sequential hypothesis testing between two quantum states using adaptive and non-adaptive strategies. In this setting, samples of an unknown state are requested sequentially and a decision to either continue or to accept one of the two hypotheses is made after each test. Under the constraint that the number of samples is bounded, either in expectation or with high probability, we exhibit adaptive strategies that minimize both types of misidentification errors. Namely, we show that these errors decrease exponentially (in the stopping time) with decay rates given by the measured relative entropies between the two states. Moreover, if we allow joint measurements on multiple samples, the rates are increased to the respective quantum relative entropies. We also fully characterize the achievable error exponents for non-adaptive strategies and provide numerical evidence showing that adaptive measurements are necessary to achieve our bounds.