Reviewerly/exHarmony huggingface dataset:
Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length.
GAP-Gen: Guided Automatic Python Code Generation

Automatic code generation from natural language descriptions can be highly beneficial during the process of software development. In this work, we propose GAP-Gen, a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. We first introduce Python syntactic constraints in the form of Syntax-Flow, which is a simplified version of Abstract Syntax Tree (AST) reducing the size and high complexity of Abstract Syntax Tree but maintaining crucial syntactic information of Python code. In addition to Syntax-Flow, we introduce Variable-Flow which abstracts variable and function names consistently through out the code. In our work, rather than pretraining, we focus on modifying the finetuning process which reduces computational requirements but retains high generation performance on automatic Python code generation task. GAP-Gen fine-tunes the transformer based language models T5 and CodeT5 using the Code-to-Docstring datasets CodeSearchNet, CodeSearchNet AdvTest and Code-Docstring Corpus from EdinburghNLP. Our experiments show that GAP-Gen achieves better results on automatic Python code generation task than previous works
Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking

Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, Michael Witbrock. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
COVID-VTS: Fact Extraction and Verification on Short Video Platforms

We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19- focused information from both the real world and machine generation. We propose, TwtrDetective, an effective model incorporating cross-media consistency checking to detect token-level malicious tampering in different modalities, and generate explanations. Due to the scarcity of training data, we also develop an efficient and scalable approach to automatically generate misleading video posts by event manipulation or adversarial matching. We investigate several state-of-the-art models and demonstrate the superiority of TwtrDetective.
Overview of the ImageCLEF 2023: Multimedia Retrieval in Medical, Social Media and Internet Applications

This paper presents an overview of the ImageCLEF 2023 lab, which was organized in the frame of the Conference and Labs of the Evaluation Forum – CLEF Labs 2023. ImageCLEF is an ongoing evaluation event that started in 2003 and that encourage the evaluation of the technologies for annotation, indexing and retrieval of multimodal data with the goal of providing information access to large collections of data in various usage scenarios and domains. In 2023, the 21st edition of ImageCLEF runs three main tasks: (i) a medical task which included the sequel of the caption analysis task and three new tasks, namely, GANs for medical images, Visual Question Answering for colonoscopy images, and medical dialogue summarization; (ii) a sequel of the fusion task addressing the design of late fusion schemes for boosting the performance, with two real-world applications: image search diversification (retrieval) and prediction of visual interestingness (regression); and (iii) a sequel of the social media aware task on potential real-life effects awareness of online image sharing. The benchmark campaign was a real success and received the participation of over 45 groups submitting more than 240 runs.
Insider Threat Prevention in the US Banking System

Insider threats have been a major problem for the US banking sector in recent years, costing billions of dollars in damages. To combat this, the implementation of effective cybersecurity measures is essential. This paper investigates the current state of insider threats to banks in the U.S., the associated costs, and the potential measures that can be taken to mitigate this risk. The development of a framework for the adoption of cybersecurity measures within the banking industry is the primary emphasis in order to stop fraud and lessen financial losses. Through a detailed examination of the literature, in-depth interviews with experts in the banking sector, and case studies of existing cybersecurity measures, this paper provides a comprehensive overview of the problem and potential remedies. Analysis of the research reveals that identity and access management, data encryption, and secure authentication are key components of any cybersecurity strategy. Furthermore, it is recommended that banks increase their technical capabilities and improve their employee awareness and training. The study concludes with a series of suggestions for enhancing banking industry cybersecurity and eventually reducing the danger of insider attacks. This paper explores the topic of insider threats in the US banking industry and presents cybersecurity measures to prevent fraud. Insider threats from people with access to sensitive data and systems present serious hazards to the banking industry, resulting in monetary losses, reputational harm, and compromised data integrity.
Progressive Mixup Augmented Teacher-Student Learning for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain, mostly through learning a domain invariant feature representation. Currently, the best performing UDA methods use category level domain alignment to capture fine-grained information, resulting in significantly improved performance over global alignment. While successful, category level UDA methods suffer from the unreliable pseudo-labels for target data. In this paper, we propose an UDA approach with teacher-student learning where the teacher network is used to provide more reliable target pseudo-labels for the student during training. Furthermore, we use a progressive mixup augmentation strategy which generates intermediate samples that become increasingly target-dominant as training progresses. Aligning the source and intermediate domains allows the model to gradually transfer fine-grained domain knowledge from the source to the target domain while minimizing the negative impact of noisy target pseudo-labels. This progressive mixup augmented teacher-student (PMATS) training strategy achieves state-of-the-art performance on two public UDA benchmark datasets: Office-31 and Office-Home.
Using packet trimming at the edge for in-network video quality adaption

Abstract This paper describes the effects of running in-network quality adaption by trimming the packets of layered video streams at the edge. The video stream is transmitted using the BPP transport protocol, which is like UDP, but has been designed to be both amenable to trimming and to provide low-latency and high reliability. The traffic adaption uses the Packet Wash process of Big Packet Protocol (BPP) on the transmitted Scalable Video Coding (SVC) video streams as they pass through a network function which is BPP-aware and embedded at the edge. Our previous work has either demonstrated the use of Software Defined Networking (SDN) controllers to implement Packet Wash directly, or the use of a network function in the core of the network to do the same task. This paper presents our effort to deploy and evaluate such a process at the edge, highlighting the packet trimming algorithm and showing the packet trimming effects on the streams. We compare the performance of transmitting video using BPP and the Packet Wash trimming, against alternative transmission schemes, namely UDP and HTTP adaptive streaming (HAS), presenting a number of quality parameters. The results demonstrate that providing traffic engineering using in-network quality adaption using packet trimming, provides high quality at the receiver.
Enhanced mammogram classification with convolutional neural network: An improved algorithm for automated breast cancer detection

The current use of medical image processing can be employed to detect various diseases or tumors present in the human body. Breast cancer, which predominantly affects women and can result in death, can be identified at an early stage using mammography, a special screening method of computer tomography (CT) scan, which may potentially save the patient's life. Despite advancements in medical imaging technology, radiologists encounter difficulties in precisely discriminating between non-cancerous and cancerous brain tumors owing to their cellular composition. This study aims to solve the issue of automatic detection of breast cancer utilizing efficient image processing methods. The quality of mammography pictures is increased using Non-Local-Means (NLM) filtering, adaptive histogram equalization (AHE), and clustering with a morphological technique. The Gray Level Run Length Matrix (GLRLM), which considers the shape, surface, and location of the breast tumour cells, is utilized to extract important details from the mammography image. The K-nearest neighbor (K-NN) method is used to categorize the image as normal or abnormal based on the selected attributes. Using a kernel-based convolutional neural network, the tumour is then isolated from the aberrant image and categorized as benign or malignant (KBCNN). This approach has demonstrated improved precision compared to existing methods, achieving more than 95% accuracy.
Review of visual analytics methods for food safety risks

Abstract With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.
Comparison Between Explainable AI Algorithms for Alzheimer’s Disease Prediction Using EfficientNet Models

Alzheimer's disease (AD) is a common form of dementia that affects brain regions that control cognition, memory, and language. Globally, more than 55 million people are suffering from dementia. Given these troubling statistics, predicting AD is critical for future medications and treatment. Mild Cognitive Impairment (MCI) is a vital stage for patients because from this stage a majority of patients turn into AD patients. Early MCI (EMCI) and Late MCI (LMCI) are vital two stages of MCI. Successful prediction of Cognitive Normal (CN), AD, MCI, EMCI, and LMCI stages is a big challenge. Although there are models for predicting these stages but all models are not near to accurate. One major concern for these lacking is not having sufficient datasets to train the model. Data augmentation can be a solution to create an abundance of MRI data. One major issue regarding Machine Learning (ML) is the black box nature. Due to this limitation, user satisfaction as well as trust in the model's prediction is missing. Explainable Artificial Intelligence (XAI) is the torchbearer approach and through this, the reason behind every decision can be observed by the user. This paper proposes a comparison between four XAI models named Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, Score-weighted CAM (Score-CAM), and Faster Score-CAM. For performing the five classes (CN, AD, EMCI, MCI, and LMCI) prediction, EfficientNet models are used because these models have performed well on the augmented dataset. Among EfficientNet models (B0-B7), EfficientNetB7 has performed the best. The testing accuracy and loss are 96.34% and 0.12, respectively. After that, the last layer of the EfficientNetB7 model is passed to the XAI models. Comparing the four XAI models it is observed that Grad-CAM++ and Score-CAM are better performing than others.
Improving the Science blog – The Rogue Scholar service.

Some 13 years ago, I speculated about the longevity of the type of science communication then (and still now) represented by Blogs. I noted one new project called ArchivePress that was looking into providing solutions equivalent to what scientific journals have done for some 350 years of science communication. The link to ArchivePress no longer works, but details of the project can still be found here.
A Novel Hybrid Bio-Inspiration Technique for Service Composition

There are many challenges facing the service composition process. These challenges include, how to integrate services to satisfy global user requirements, missing or changeable values of QoS, and how to reduce the large solution space of candidate services. In this paper, we proposed a framework to address these challenges. The proposed framework consists of three phases. The Normalizer phase gives a certain range for all QoS attributes and historical user orders. During the Clustering phase, the search space is reduced. Finally, the composition process is done, and a list of candidate composite services is generated through the Service composer phase. We present the hybrid bio-inspiration technique to implement the proposed framework and prove its applicability. In addition, we introduced the MR-FPSO algorithm to implement this phase by merging PSO and FOA optimization algorithms over the MapReduce framework to handle the large scale of data in the cloud environment. Our technique is compared to different techniques, including MR-GA, MR-IDPSO, and MRPSO. The simulation results proved that our technique outperforms the other techniques.
Capturing Types

Type systems usually characterize the shape of values but not their free variables. However, many desirable safety properties could be guaranteed if one knew the free variables captured by values. We describe CC &lt; :◻ , a calculus where such captured variables are succinctly represented in types, and show it can be used to safely implement effects and effect polymorphism via scoped capabilities. We discuss how the decision to track captured variables guides key aspects of the calculus, and show that CC &lt; :◻ admits simple and intuitive types for common data structures and their typical usage patterns. We demonstrate how these ideas can be used to guide the implementation of capture checking in a practical programming language.
Bright and dark Talbot pulse trains on a chip

Abstract Temporal Talbot effect, the intriguing phenomenon of the self-imaging of optical pulse trains, is extensively investigated using macroscopic components. However, the ability to manipulate pulse trains, either bright or dark, through the Talbot effect on integrated photonic chips to replace bulky instruments has rarely been reported. Here, we design and experimentally demonstrate a proof-of-principle integrated silicon nitride device capable of imprinting the Talbot phase relation onto in-phase optical combs and generating the two-fold self-images at the output. We show that the GHz-repetition-rate bright and dark pulse trains can be doubled without affecting their spectra as a key feature of the temporal Talbot effect. The designed chip can be electrically tuned to switch between pass-through and repetition-rate-multiplication outputs and is compatible with other related frequencies. The results of this work lay the foundations for the large-scale system-on-chip photonic integration of Talbot-based pulse multipliers, enabling the on-chip flexible up-scaling of pulse trains’ repetition rate without altering their amplitude spectra.
The Role of Knowledge Management in the Financial Performance of Small and Medium Companies

This research aims to determine the effect knowledge management processes (knowledge generation, knowledge distribution, and application knowledge) on financial performance in the Malaysian small and medium companies. The descriptive analytical secondary and primary data was utilised as a tool for gathering data from 130 companies as a sample. The results display that there is a positive association among knowledge management with financial performance. The knowledge management represented in generation, distribute, and apply knowledge, and express it about performance through the perspectives of the financial perspective companies. As the research reached a set of conclusions, including that there the effect of knowledge management processes on raising level of performance.
Optimizing PID Gains of a Vehicle using the state-of-the-art Metaheuristic Methods

PID controllers are important control methods that are widely used in industrial processes. Proper tuning of PID gains is critical for achieving the state-of-the-art system performance. Therefore, optimizing PID gains is an important research topic in the field of control engineering. In this study, PID controller gains are automatically tuned using metaheuristic optimization methods. These methods use an iterative approach to calculate optimal values of PID controller gains based on different optimization techniques. The interaction between artificial intelligence and control systems requires a multidimensional approach across different disciplines. In the study, we perform Particle Swarm Optimization, Gray Wolf Optimization, Whale Optimization Algorithm, Firefly Algorithm, Harris Hawks Optimization, Artificial Hummingbird Algorithm and African Vulture Optimization Algorithm to determine PID gains. In the simulation, step input is applied to the dynamic equation of the unmanned free-swimming submersible vehicle. The fitness function is determined with respect to controller integral square error, settling time value, and maximum percent overshoot value. We also evaluate the optimization time of the selected algorithms based on the fitness function. Experimental results present that Artificial Hummingbird Algorithm, Gray Wolf Optimization and Particle Swarm Optimization achieve significant performance. This underlines that using metaheuristic methods in PID gain optimization increase overall system performance.
Privacy-Preserving DNN Training with Prefetched Meta-Keys on Heterogeneous Neural Network Accelerators

The embedded software may migrate the collected data to the server for DNN computation acceleration, which may compromise privacy. We propose a DNN computation framework that combines TEE and NNA to address the privacy leakage problem. We design an NNA-friendly encryption method that enables NNA to correctly compute the encrypted linear input. Facing the overhead of TEE-NNA interaction, we design a pipeline-based prefetch mechanism that can reduce the TEE interaction overhead. Experimentally, our approach proves to be compatible with a wide range of NPUs and TPUs, and improves the performance by 8-19 times over the TEE scheme.
Patch SVDD(support vector data description)-based channel attention embedding and improvement of classifier

Some anomaly detection methods are based on CNN to fuse spatial and channel-wise information together within local receptive fields. However, the correlation between feature channels has not been fully utilized. Channel attention has been shown to model the interdependence between convolution feature channels and improve network representation. It is possible to introduce channel attention into anomaly detection. We attempt to directly embed the SE(Squeeze and Excitation) module into the convolutional layer but reduced anomaly detection performance. Therefore, we propose a lightweight channel attention module C-SE(Current Squeeze and Excitation) suitable for anomaly detection. C-SE module not only improves the representation ability of depth convolutional neural network but also has a significant effect on texture anomaly detection. C-SE module body is constructed by average pooling and maximum pooling branches, which ensure that local salient features of the image are not lost. Then reduce the negative impact of feature calibration through a long connection. In addition, the improvement of classifier plays an important role. Experimental results have shown that the proposed method outperforms the Patch SVDD methods by 3% in image-level AUROC and 0.7% in pixel-level AUROC on the MVTec benchmark. The higher AUROC score and the faster rate of convergence prove the effectiveness of the method.
MWPRanker: An Expression Similarity Based Math Word Problem Retriever

Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences by interpreting the linguistic information in them. To test the mathematical reasoning capabilities of the learners, sometimes the problem is rephrased or the thematic setting of the original MWP is changed. Since manual identification of MWPs with similar problem models is cumbersome, we propose a tool in this work for MWP retrieval. We propose a hybrid approach to retrieve similar MWPs with the same problem model. In our work, the problem model refers to the sequence of operations to be performed to arrive at the solution. We demonstrate that our tool is useful for the mentioned tasks and better than semantic similarity-based approaches, which fail to capture the arithmetic and logical sequence of the MWPs. A demo of the tool can be found at https://www.youtube.com/watch?v=gSQWP3chFIs .