Reviewerly/exHarmony huggingface dataset:
"Hey IDE, Display Hello World": Integrating a Voice Coding Approach in Hands-on Computer Programming Activities

Following recent advancements in automatic speech recognition (ASR) technologies, we replicated an experiment four decades ago that utilized voice as an input modality for computer programming. We also extended this experiment by investigating the pedagogical effectiveness of ‘programming by voice’ in terms of attitude, self-efficacy, code correctness, and coding speed. A total of 96 students from an institute of technology in the capital region of the Philippines were randomly selected to participate in a quasi-experimental study using a one-group pretest-posttest design. We subjected students to programming activities with different levels of difficulty to compare voice and keyboard. Our results show that although voice decreases negativity, it likewise decreases control, which means that both attitude and self-efficacy are positively and negatively affected, respectively. Using voice as an input modality also allows students to code faster when the activities are easy but not when they are moderate or difficult. Code correctness analysis shows that voice is only preferable for easy and moderate machine problems. With the deviation of our findings from an experiment four decades ago, we can now conclude that ASR technologies and voice as input modality provide substantial implications and new opportunities for teaching and learning computer programming.
Joint Client Selection and Receive Beamforming for Over-the-Air Federated Learning With Energy Harvesting

Federated learning (FL) is a well-regarded distributed machine learning technology that leverages local computing resources while protecting privacy. The over-the-air (OTA) computation has been adopted for FL to prevent excessive consumption of communication resources by employing the superposition nature of wireless waveform. Meanwhile, energy harvesting technology can relieve the energy constraint of clients and enable durable computation for FL. However, few of the existing works on OTA FL have considered jointly performing client selection and receive beamforming optimization with energy harvesting clients. The objective of this work is to address this issue to improve the learning performance of OTA FL. Specifically, we first derive the expression of the optimality gap regarding client selection and receive beamforming design. Then, to minimize the optimality gap, a mixed-integer nonlinear programming (MINLP) problem is formulated and decomposed into two sub-problems. Next, the semidefinite relaxation method and the channel-energy-data (CED)-based method are developed to optimize the receive beamforming sub-problem and client selection sub-problem iteratively. One alternative optimization method is proposed to deal with the decoupled sub-problems for obtaining the solutions to the original MINLP problem. Our simulation results demonstrate that the proposed solution is superior to the other comparison schemes in various parameter settings.
READSUM: Retrieval-Augmented Adaptive Transformer for Source Code Summarization

Code summarization is the process of automatically generating brief and informative summaries of source code to aid in software comprehension and maintenance.In this paper, we propose a novel model called READSUM, REtrieval-augmented ADaptive transformer for source code SUMmarization, that combines both abstractive and extractive approaches.Our proposed model generates code summaries in an abstractive manner, taking into account both the structural and sequential information of the input code, while also utilizing an extractive approach that leverages a retrieved summary of similar code to increase the frequency of important keywords.To effectively blend the original code and the retrieved similar code at the embedding layer stage, we obtain the augmented representation of the original code and the retrieved code through multi-head self-attention.In addition, we develop a self-attention network that adaptively learns the structural and sequential information for the representations in the encoder stage.Furthermore, we design a fusion network to capture the relation between the original code and the retrieved summary at the decoder stage.The fusion network effectively guides summary generation based on the retrieved summary.Finally, READSUM extracts important keywords using an extractive approach and generates high-quality summaries using an abstractive approach that considers both the structural and sequential information of the source code.We demonstrate the superiority of READSUM through various experiments and an ablation study.Additionally, we perform a human evaluation to assess the quality of the generated summary.
Process-Oriented heterogeneous graph learning in GNN-Based ICS anomalous pattern recognition

Over the past few years, massive penetrations targeting an Industrial Control System (ICS) network intend to compromise its core industrial processes. So far, numerous advanced methods have been proposed to detect anomalous patterns in the numeric data streams with respect to the heterogeneous field devices involved in the industrial processes. These methods, despite reporting decent results, usually conduct system-wise detection instead of fine-grained anomalous pattern recognition at the device level. Furthermore, lacking explicit consideration of the exclusive process-related features with respect to each differentiated device, the fitness of their application in specified industrial processes is undermined. To tackle these issues, a GNN-based Attributed Heterogeneous Graph Analyzer (the AHGA) is designed to perform device-wise anomalous pattern detection via in-depth process-oriented associativity learning. The AHGA's framework is constructed with four building blocks: a graph processor, a feature analyzer, a link inference decoder, and an anomaly detector. Its performance is assessed and compared against multiple link inference and anomaly detection baselines over 2 popular ICS datasets (SWaT and WADI). Comparative results demonstrate the AHGA's reliability in capturing sophisticated process-oriented relations among heterogeneous devices as well as its effectiveness in boosting the performance of anomalous pattern recognition at device-level granularity.
Cross-modal Contrastive Learning for Generalizable and Efficient Image-text Retrieval

Cross-modal image-text retrieval is a fundamental task in bridging vision and language. It faces two main challenges that are typically not well addressed in previous works. 1) Generalizability: Existing methods often assume a strong semantic correlation between each text-image pair, which are thus difficult to generalize to real-world scenarios where the weak correlation dominates. 2) Efficiency: Many latest works adopt the single-tower architecture with heavy detectors, which are inefficient during the inference stage because the costly computation needs to be repeated for each text-image pair. In this work, to overcome these two challenges, we propose a two-tower cross-modal contrastive learning (CMCL) framework. Specifically, we first devise a two-tower architecture, which enables a unified feature space for the text and image modalities to be directly compared with each other, alleviating the heavy computation during inference. We further introduce a simple yet effective module named multi-grid split (MGS) to learn fine-grained image features without using detectors. Last but not the least, we deploy a cross-modal contrastive loss on the global image/text features to learn their weak correlation and thus achieve high generalizability. To validate that our CMCL can be readily generalized to real-world scenarios, we construct a large multi-source image-text dataset called weak semantic correlation dataset (WSCD). Extensive experiments show that our CMCL outperforms the state-of-the-arts while being much more efficient.
Efficient image encryption algorithm based on dynamic high-performance S-box and hyperchaotic system

Abstract With the development of information technology, security, low latency, and instant messaging have become a major demand. To solve this problem, this paper designs a secure and efficient image cipher algorithm. Firstly, a new four-dimensional hyperchaotic system with strong chaotic performance is proposed. Secondly, a dynamic high-performance S-box generation algorithm is proposed based on GF(2 8 ) by improving the method of generating S-boxes in AES, selecting suitable irreducible polynomial as well as affine matrix, precomputing the corresponding affine multiplication matrix as well as multiplication inverse matrix. The S-box algebraic expressions generated by this algorithm have 255 terms and are more resistant to algebraic attacks than AES. Finally, based on the four-dimensional hyperchaotic system and dynamic high-performance S-box generation algorithm, the new image cipher algorithm that can encrypt and decrypt images of arbitrary size is proposed. The image cipher algorithm consists of two rounds of interleaved permutation, two rounds of dynamic S-box substitution and cyclic shift permutation with fixed point. Cyclic shift permutation with fixed point and dynamic S-box substitution ensure that the plaintext transformation can be transferred to the entire image. Through testing, analysis and comparison, the algorithm has proven to be a secure and efficient image cipher algorithm.
The Use of Cloud Computing and its Security Risks in a Philippine Education System: A Literature Review

Cloud computing is a technology that impacted the performance of modern markets and businesses. Because of this, cloud computing has become more significant to humans with its virtualization and distributed computing. However, according to the gathered literature for this paper, cloud computing imposes security concerns. This paper examines the definition of cloud computing by analyzing its historical perspective, architecture, and framework. We attempt to understand cloud security threats by classifying their type and affected cloud layers. From that, we correlate our assessments to the virtualization of Philippine education.
Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation

The complexity of urban physical environments at road intersections is a primary factor characterizing the difficulty of wayfinding, which is a fundamental spatial activity of human beings in cities. A complex intersection may increase the difficulty of understanding the environment, which may result in incorrect turning decisions and even bring road safety issues. Existing methods measure the complexity of road intersections by solely considering their visual or structural features. More importantly, they only output a single complexity value for each intersection, failing to differentiate the decision-making complexity based on the specific entry/exit branches of a passing branches. This study proposes a computational model to quantify the fine-grained decision-making complexity of road intersections for the navigation data models and navigation systems based on specific passing branches, using the visual, structural, and semantic features from human perspectives. For each pair of two branches (i.e., one entry and one exit) passing through the road intersection, the model will output a specific decision-making complexity score. Furthermore, this study develops a route planning algorithm for generating the minimum complexity route to serve relevant navigation applications. This study contributes to human-centered route planning and communication, as well as enabling potential innovative applications in traffic safety studies and sustainable urban and environmental development.
Deep Learning Inferencing with High-performance Hardware Accelerators

As computer architectures continue to integrate application-specific hardware, it is critical to understand the relative performance of devices for maximum app acceleration. The goal of benchmarking suites, such as MLPerf for analyzing machine learning (ML) hardware performance, is to standardize a fair comparison of different hardware architectures. However, there are many apps that are not well represented by these standards that require different workloads, such as ML models and datasets, to achieve similar goals. Additionally, many apps, like real-time video processing, are focused on latency of computations rather than strictly on throughput. This research analyzes multiple compute architectures that feature ML-specific hardware on a case study of handwritten Chinese character recognition. Specifically, AlexNet and a custom version of GoogLeNet are benchmarked in terms of their streaming latency and maximum throughput for optical character recognition. Considering that these models are composed of fundamental neural network operations yet architecturally different from each other, these models can stress devices in different yet insightful ways that generalizations of the performance of other models can be drawn from. Many devices featuring ML-specific hardware and optimizations are analyzed including Intel and AMD CPUs, Xilinx and Intel FPGAs, NVIDIA GPUs, and Google TPUs. Overall, ML-oriented hardware added to the Intel Xeon CPUs helps to boost throughput by 3.7× and to reduce latency by up to 34.7×, which makes the latency of Intel Xeon CPUs competitive on more parallel models. The TPU devices were limited in terms of throughput due to large data transfer times and not competitive in terms of latency. The FPGA frameworks showcase the lowest latency on the Xilinx Alveo U200 FPGA achieving 0.48 ms on AlexNet using Mipsology Zebra and 0.39 ms on GoogLeNet using Vitis-AI. Through their custom acceleration datapaths coupled with high-performance SRAM, the FPGAs are able to keep critical model data closer to processing elements for lower latency. The massively parallel and high-memory GPU devices with Tensor Core accelerators achieve the best throughput. The NVIDIA Tesla A100 GPU showcases the highest throughput at 42,513 and 52,484 images/second for AlexNet and GoogLeNet, respectively. 1
Automatic classification of ultrasound thyroids images using vision transformers and generative adversarial networks

Ultrasound has become the main imaging modality for the diagnosis of thyroid nodules. However, opinions issued and decisions taken by doctors are always subjective and depend on the experience of the doctor and his entourage. It is often essential to use a tool to guide doctors during decision-making. In this paper, we develop a new model, based on a deep learning approach, more specifically convolutional neural networks and vision transformers. Our goal was the classification of thyroid ultrasound images which were categorized into two classes (class: Benign and class: Malignant). At the beginning of this work, we implemented a Deep Convolutional Generative Adversarial Network (DCGAN) technique to fix the data shortage and the problem of the imbalanced data set. Then, we used two different methods for the splitting of the data, the first is the classic data split and the second is the cross-validation method. For the modeling techniques, we used two types of models such as: Convolutional Neural Network: (VGG16, EffecientNetB0 and ResNet50) and Vision Transformers: (ViT_B16 and Hybrid ViT). All the models were trained with Softmax and Support Vector Machine (SVM) classifiers. After evaluation of both the CNN and ViT models, we concluded that the SVM classification produces better performance than the Softmax classification for all of the models with the Hybrid ViT being on top in the matter of the metrics evaluation and the curves representation, the model achieved 97.63% Accuracy, resulting in a slight improvement over the existing studies. Our study achieved promising results, hopefully, this will help doctors better diagnose thyroid patients.
Model vs system level testing of autonomous driving systems: a replication and extension study

Abstract Offline model-level testing of autonomous driving software is much cheaper, faster, and diversified than in-field, online system-level testing. Hence, researchers have compared empirically model-level vs system-level testing using driving simulators. They reported the general usefulness of simulators at reproducing the same conditions experienced in-field, but also some inadequacy of model-level testing at exposing failures that are observable only in online mode. In this work, we replicate the reference study on model vs system-level testing of autonomous vehicles while acknowledging several assumptions that we had reconsidered. These assumptions are related to several threats to validity affecting the original study that motivated additional analysis and the development of techniques to mitigate them. Moreover, we also extend the replicated study by evaluating the original findings when considering a physical, radio-controlled autonomous vehicle. Our results show that simulator-based testing of autonomous driving systems yields predictions that are close to the ones of real-world datasets when using neural-based translation to mitigate the reality gap induced by the simulation platform. On the other hand, model-level testing failures are in line with those experienced at the system level, both in simulated and physical environments, when considering the pre-failure site, similar-looking images, and accurate labels.
Efficient, Traceable and Privacy-Aware Data Access Control in Distributed Cloud-Based IoD Systems

The emerging combination of Internet of Things (IoT) and aerospace integration aided by satellite and 6G communication techniques has stimulated the Internet of Unmanned Aerial Vehicles (UAVs), i.e., Internet of Drones (IoD).To accommodate and share the enormous real-time UAV data, cloud-based IoD is an inevitable choice to lower the heavy burden of mobile UAVs.Nevertheless, how to protect highly sensitive UAV data in such a honest-but-curious, open and distributed environment with resourcelimited UAVs is a significant challenge.Although our previous work (PATLDAC) in SPNCE'21 devises a cloud-based UAV data access control scheme with policy privacy protection, limited access time and user traceability, it incurs inflexible and centralized cloud data storage and access as well as untrustworthy metadata in untrusted cloud environment for data access and user tracing.To this end, we further propose a blockchain-based privacy-aware data access control (BPADAC) scheme for distributed and secure UAV data sharing in cloud-based IoD.Based on fine-grained, traceable and privacy-preserving UAV data access characteristic of our previous work, we extend it by leveraging blockchain and Distributed Hash Table (DHT) for distributed and trustful UAV data access and storage, together with reliable and limited access mechanism to guarantee cloud UAV data sharing service provision.We also design public and undeniable user tracing mechanism to prevent user key abuse with traitor denial.Finally, we present formal security analysis and prototype the system leveraging the smart contracts of Ethereum blockchain for performance evaluation to show the feasibility of BPADAC.
A multi-behavior recommendation method exploring the preference differences among various behaviors

User behavior data has been widely used in recent research of recommendation systems. Existing work usually utilize only single behavior instead of multi-behavior. However, there are various typed of user behaviors in practical scenarios. For example, there are view, purchase, add-to-cart and add-to-favorite behaviors in a real-world e-commerce platform. For recommendation systems, using more user information is better for exploiting intrinsic user characteristics, as the single behavior records are usually too sparse to conduct preference mining. Therefore, multi-behavior based recommendation methods are increasingly emphasized by researchers. In this paper, we propose a novel framework (FPD) for utilizing multi-behavior data: generating embedding and building training losses. We compute an additional supplementary score by capturing user's preference difference among behaviors, instead of merely using initial embedding to obtain a ordinary prediction score. For better optimization model parameters, we use various behaviors to build multiple training losses. We optimize the loss function of the non-sampling strategy and set personal positive weights for each user. Experimental results on two real-world datasets demonstrates that our model outperformed the state-of-the-art methods in terms of various evaluation measurements. Furthermore, extensive ablation experiments and visualization analysis are conducted to verify the effectiveness of the proposed idea and to further explain the principle of the proposed method proposed.
The van der Pol physical reservoir computer

Abstract The van der Pol oscillator has historical and practical significance to spiking neural networks. It was proposed as one of the first models for heart oscillations, and it has been used as the building block for spiking neural networks. Furthermore, the van der Pol oscillator is also readily implemented as an electronic circuit. For these reasons, we chose to implement the van der Pol oscillator as a physical reservoir computer (PRC) to highlight its computational ability, even when it is not in an array. The van der Pol PRC is explored using various logical tasks with numerical simulations, and a field-programmable analog array circuit for the van der Pol system is constructed to verify its use as a reservoir computer. As the van der Pol oscillator can be easily constructed with commercial-off-the-shelf circuit components, this PRC could be a viable option for computing on edge devices. We believe this is the first time that the van der Pol oscillator has been demonstrated as a PRC.
Robust visual tracking based on modified mayfly optimization algorithm

In this study, MOA is applied to visual target tracking for the first time, and a novel meta-heuristic tracking algorithm with efficiency and precision is obtained. Similar to the common problems of other classical swarm intelligence algorithms, standard MOA faces a high probability of falling into local extremals, early maturity and a low efficiency of a late convergence speed. Therefore, super-MOA, a modified MOA method, is proposed in this study. By designing the updating mechanism, the position and velocity parameters of ephemera are monitored and dynamically adjusted with the iteration degree, and the balance of the global and local optimization process in different iteration stages is improved. A mayfly progeny mutation strategy was proposed to alleviate the prematurity problem of the standard MOA algorithm. Meanwhile, we introduce a chaos algorithm to reconstruct the velocity parameter iteration mechanism, which alleviates the efficiency loss caused by the frequent repeated searching of historical locations by mayflies in standard MOA. In addition, we further understand and improve the parameters such as the dynamic gravitational coefficient and dance coefficient during the courtship flight of mayflies. We also design a frame size adaptive adjustment strategy, which effectively decreases the interference of invalid features. In terms of experiments, we compare this algorithm with other classical trackers from the qualitative, quantitative and statistical perspectives through OTB2015, VOT2018 and typical large-scale benchmarks. Sufficient tracking experiments in various tracking scenes show that our tracker performs great in terms of efficiency, robustness and accuracy.