Reviewerly/exHarmony huggingface dataset:
Enhancing Convolutional Neural Network Architectures with Long Short-Term Memory for Improved Image Classification

In recent years, the application of artificial intelligence and deep learning has expanded to address a variety of tasks, including image classification. Researchers have de-veloped more complex network architectures with more pa-rameters to improve the convergence performance of neural networks. However, this has also increased the complexity of problem-solving and hardware requirements. To address this issue, we propose integrating Long Short-Term Memory (LSTM) into known Convolutional Neural Network (CNN) architectures to enhance their performance without significantly increasing complexity. Our experiments with several well-known neural network architectures, including some network architectures such as ResNet, DenseNet, and MobileNet, evaluated on different datasets such as 525 bird species, CIFAR10, and GTSRB for classification, demonstrate that the integration of LSTM into known CNN architectures can improve their performance in image classification tasks.
FIPS Compliant Quantum Secure Communication Using Quantum Permutation Pad

Quantum computing has entered a fast development track since Shor's algorithm was proposed in 1994. Multi-cloud services of quantum computing farms are currently available. One of which, IBM quantum computing, presented a road map showing their Kookaburra system with over 4158 qubits that will be available in 2025. For the standardization of Post-Quantum Cryptography or PQC, the National Institute of Standards and Technology or NIST recently announced the first candidates for standardization with one algorithm for key encapsulation mechanism (KEM), Kyber, and three algorithms for digital signatures. NIST has also issued a new call for quantum-safe digital signature algorithms due June 1, 2023. This timeline shows that FIPS-certified quantum-safe TLS protocol would take a predictably long time. However, 'steal now, crack later’ tactic requires protecting data against future quantum threat actors today. NIST recommended the use of a hybrid mode of TLS 1.3 with its extensions to support PQC. The hybrid mode works for certain cases but FIPS certification for the hybridized cryptomodule might still be required. This paper proposes to take a nested mode to enable TLS 1.3 protocol with quantum-safe data, which can be made available today and is FIPS compliant. We discussed the performance impacts of the handshaking phase of the nested TLS 1.3 with PQC and the symmetric encryption phase. The major impact on performance using the nested mode is in the data symmetric encryption with AES. To overcome this performance reduction, we suggest using quantum encryption with a quantum permutation pad for data encryption with a minor performance reduction of less than 10%.
VMCanary: Effective Memory Protection for WebAssembly via Virtual Machine-assisted Approach

WebAssembly is an emerging secure programming language and portable instruction set architecture, and has been deployed in diverse security-critical scenarios due to its safety advantages. However, WebAssembly’s linear memory is still vulnerable to buffer overflows due to the lack of effective protection mechanism, defeating its security guarantees. In this paper, we present VMCanary, the first framework for effective WebAssembly memory protection, by leveraging a canary approach but with the aid from WebAssembly virtual machines (VMs). Our key idea is that, due to the fact that WebAssembly is a managed programming language to be executed by underlying WebAssembly VMs, the VMs must understand any protection mechanisms already enforced in programs. With this key idea, we first propose the concept of canary in code, which is like a traditional canary in data but whose semantics is understandable by underlying WebAssembly VMs. To realize this kind of canary, we introduced two novel WebAssembly instructions by defining their semantics. Furthermore, we designed an instrumentation for WebAssembly binaries to instrument these two instructions automatically, hence no sources and compiler toolchain modifications are required. We have implemented a software prototype for VMCanary, and have conducted extensive experiment to evaluate it on micro benchmarks and 59 real-world CWEs. Experimental results demonstrated that VMCanary is effective in protecting Wasm memory with negligible overhead (3% on average).
Rapid and Efficient Facial Landmark Identification by Light and High Resolution Network using Artificial Intelligence

The ability to detect facial landmarks is in high demand because of its usefulness in monitoring driver health in real time. Heat map-based approaches are well-known for their exact accuracy in landmark position prediction, tiny, and excellent-resolution networks may accomplish quick estimations. While tiny high-resolution networks (THRN) have been successful in connecting feature maps of varying resolutions, the fusion block’s costly computation remains an issue. Furthermore, THRN does not make use of the powerful output module seen in high-resolution networks. We propose an entirely novel design called THRN to address these issues. Using multi-resolution feature maps and an innovative fusion block, THRN Plus improves in two ways. First, it reduces the processing timing of its output module. On two different face landmark datasets, we ran extensive trials and showed that THRN greatly improved baseline techniques while keeping the latest in exactness at a cost to computation of roughly ten million FLOPS.
A Comparative Analysis of Ethereum Solidity and Sui Move Smart Contract Languages: Advantages and Trade-Offs

This article offers an extensive comparison between Ethereum Solidity and Sui Move, two prominent smart contract languages. As blockchain technology gains momentum, choosing the right smart contract language becomes paramount for building resilient decentralized applications. The study delves into the unique features, strengths, and limitations of both languages, assisting developers in making well-informed decisions. Ethereum Solidity, widely adopted due to its versatility, has been integrated into numerous blockchain projects. Conversely, Move, introduced by the Libra blockchain, prioritizes security and safety through a distinct approach, advantages that the Sui network has incorporated into the Sui Move language. The analysis covers various key elements of each language while at the same time, examines their support for overall security mechanisms, formal verification, and ease of use. By providing an evaluation of Ethereum Solidity and Sui Move, the article aims to empower developers with valuable insights during the process of creating robust and secure decentralized applications.
Intelligent Web Service System for Detecting Cyberbullying on Twitter Based on Support Vector Machine and Random Forest Algorithms

Cyberbullying is a prevalent form of digital crime within the social media sphere. A survey conducted in 2021 among 6,000 individuals aged 10 to 18 showed that 50% of the surveyed children had encountered instances of cyberbullying. Approximately 47% of participants recounted instances where they faced threats, derogatory comments, and offensive messages on social media platforms. These acts of intimidation and disparagement can significantly harm mental well-being. The integration of machine learning methodologies, such as Support Vector Machine (SVM) and Random Forest, was used as a viable approach to predict whether a given social media post, particularly tweets, falls under cyberbullying. This research starts with data collection, followed by tokenization, modeling, evaluation and deployment of web service. The research attained 84.24% and 84.98% accuracy using Random Forest and Support Vector Machines, respectively. Both algorithms produced models that were subsequently deployed as a web service API through various multiplatform applications. The findings of this study are available for reference at uns.id/winarSVMRF.
Leveraging Empowerment to Model Tool Use in Reinforcement Learning

Intrinsic motivation plays a key role in learning how to use tools, a fundamental aspect of human cultural evolution and child development that remains largely unexplored within the context of Reinforcement Learning (RL). This paper introduces “object empowerment” as a novel concept within this realm, showing its role as information-theoretic intrinsic motivation that underpins tool discovery and usage. Using empowerment, we propose a new general framework to model the utilization of tools within RL. We explore how maximizing empowerment can expedite the RL of tasks involving tools, highlighting its capacity to solve the challenge posed by sparse reward signals. By employing object empowerment as an intrinsically motivated regulariser, we guide the RL agent in simple grid-worlds towards states beneficial for learning how to master tools for efficient task completion. We will show how object empowerment can be used to measure and compare the effectiveness of different tools in handling an object. Our findings indicate efficient strategies to learn tool use and insights into the integration and modeling of tool control in the context of RL.
An Anomaly Detection Method for Data Center Devices

Data center demand is growing significantly due to the widespread use of remote working by businesses and cloud-based services. As a demand grows, the number of infrastructure devices, such as servers and storage, increase. An interruption or the failure of a device is costly for the data centers. To prevent thousands of electric devices from unpredictable failures, the data center must implement a speedy and accurate monitoring method. A visual monitoring method is considered reliable for detecting failures in the electric devices, but the method is very slow to monitor numerous devices due to fully manual work and a pattern of failures is not explicitly defined. Therefore, automation of the visual monitoring method is required for the data centers. This paper discusses an anomaly detection method from the video recording of the devices and development of a monitoring system. In an experimental environment of Niigata University, the proposed method performed 100% (anomaly: 4/4, regular: 225/225) accuracy for detecting anomalies from the video frames.
A Multilevel Learning Model for Predicting CPU Utilization in Cloud Data Centers

In the contemporary era of cloud computing, efficient and precise prediction of CPU utilization ensures optimal performance and energy efficiency in data centers. Traditional predictive models often need to be improved as these data centers grow in complexity and scale, necessitating more nuanced and integrative solutions. This paper introduces an advanced multi-layered learning framework meticulously designed to meet the demands of modern cloud data centers. Our innovative approach synergistically combines anomaly detection, data clustering, and ensemble-based regression prediction. The Isolation Forest algorithm is used during the preliminary stage to identify and address anomalies within the data. Subsequent phases harness the K-Means clustering algorithm, refine data categorization based on recurrent CPU usage patterns, and employ multilevel ensemble-based prediction models for accurate forecasting rooted in historical and real-time data trends. Through comprehensive evaluations, our model demonstrates significant improvements in prediction accuracy and robustness against the dynamism inherent in cloud environments. Our research paves the way for a more resilient, proactive, and efficient approach to CPU utilization prediction, laying the foundational stone for future innovations in cloud computing resource management.
Path Planning Techniques for Multi-robot Systems: A Systematic Review

Numerous path planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper provides a comprehensive review of multi-robot path planning approaches, focusing on classical, AI-based, and hybrid methods. Bio-inspired techniques are commonly used, and AI approaches are gaining popularity. The challenges faced by multi-robot systems include the number of robots, energy efficiency, fault tolerance, robustness, and dynamic targets. Therefore, developers are continuously researching for optimal solutions that can gather the benefits of multi-robot systems while overcoming the possible challenges. This paper provides a comprehensive assessment of various path planning techniques, highlights key problems in this field, and identifies research gaps for a better multi-robot path planning experience.
microConceptBERT: Concept-Relation Based Document Information Extraction Framework

Extracting information from documents is a crucial task in natural language processing research. Existing information extraction methodologies often focus on specific domains, such as medicine, education or finance, and are limited by language constraints. However, more comprehensive approaches that transcend document types, languages, contexts, and structures would significantly advance the field proposed in recent research. This study addresses this challenge by introducing microConceptBERT: a concept-relations-based framework for document information extraction, which offers flexibility for various document processing tasks while accounting for hierarchical, semantic, and heuristic features. The proposed framework has been applied to a question-answering task on benchmark datasets: SQUAD 2.0 and DOCVQA. Notably, the F1 evaluation metric attains an outperforming 87.01 performance rate on the SQUAD 2.0 dataset compared to baseline models: BERT-base and BERT-large models.
Two-Stream Architecture Using RGB-based ConvNet and Pose-based LSTM for Video Action Recognition

Traditional methods for video recognition require hand-crafted features, which often involves offline pre-processing for real-world videos. In this study, we propose a conceptually simple framework that directly takes raw videos as an input source for activity recognition. Our framework consists of two streams, namely a spatial stream and a temporal stream. The spatial stream is trained on RepVGG-B0 ConvNet using cropped RGB features, while the temporal stream uses an attention-based Bi-directional Long Short-Term Memory (Bi-LSTM) network to learn posture vectors from human pose data obtained through Faster R-CNN pre-trained model. Our proposed method is evaluated on a standard video action recognition benchmark, MSR Daily Activity3D, and proves to be competitive with state-of-the-art action recognition methods. We achieve state-of-the-art performance on MSR Daily Activity3D with a precision and recall rate of 99.01% and 98.91%, respectively. Our results demonstrate the effectiveness of our approach in recognizing video actions.
Lexical Acquisition with Cross-Situational Learning Based on Mutual Segmentation of Sentences and Multi-Attribute Sensory Information

Human infants acquire lexicons by inferring word meanings through cross-situational learning (CSL) and word segmentation. This study models robot lexical acquisition by segmenting sentences using sensory information as clues and inferring word meanings through the co-occurrence of words and sensory information using CSL. This process is called the mutual segmentation of sentences and multi-attribute sensory information. There are two challenges in modeling lexical acquisition based on mutual segmentation: (i) Segmenting sentences and learning word meaning simultaneously for mutual segmentation; (ii) Consideration of words that are not associated with sensory information. The proposed method extends the probabilistic generative model of CSL to infer whether words are related to sensory information. In addition, the proposed method mathematically integrates an unsupervised word segmentation method. In the experiments, an agent acquired lexicons using multi-attribute sensory information collected in simulated environments and linguistic information. The results demonstrate that mutual segmentation leads to a 2.1% improvement in F1-score for word segmentation performance, and reduces the influence of word segmentation errors on the estimation of word attributes and meanings. Furthermore, the results indicate that considering ungrounded words improves the performance of sensory information clustering and word segmentation.
Project Risk Mitigation in Autonomous Vehicles Systems: The Tesla way

This research study examines Tesla's methods for reducing project risks in the area of autonomous car systems, providing useful information for the technology and automotive sectors. The study analyzes publicly accessible data on Tesla's project management, risk assessment, and results, as well as major risk mitigation techniques from pertinent documents using Natural Language Processing (NLP) and online scraping. Through proactive planning, iterative development, and thorough real-world testing, the analysis reveals Tesla's methodology. It helps to identify and resolve any dangers that the organization prefers real-world testing to simulations. Furthermore, Tesla's over-the-air upgrades enable quick reactions to developing issues and provide adaptive risk management. In summary, this study emphasizes the value of proactive risk mitigation techniques, placing particular emphasis on real-world testing and iterative development, especially in large-scale initiatives like autonomous vehicle systems. Tesla's strategy highlights the importance of ongoing innovation and adaptation in successful risk management, providing insightful lessons for many industries.
Exploring Cyber Investigators: An In-Depth Examination of the Field of Digital Forensics

Digital forensics, the field concerned with investigating digital devices and data for legal purposes, is a continually evolving discipline of utmost importance in the current digital era. This literature review provides a comprehensive analysis of digital forensics, tracing its historical development, exploring its methodologies and tools, examining the legal and ethical considerations involved, and predicting future trends. It emphasizes the ongoing growth and relevance of the field, serving as a crucial element in both cybersecurity and law enforcement. Digital forensics not only uncovers enigmatic digital phenomena but also safeguards privacy, ensures the integrity of data, upholds the principles of justice, and delves into various types of digital evidence. In addition, it tackles the numerous difficulties and evolving patterns that influence the environment of this ever-changing discipline, clearing the path for inventive resolutions and methodologies.
AE-TGN: Attribute-Empowered Temporal Graph Networks for Dynamic Graph Link Prediction

Temporal/dynamic graph link prediction task requires the AI systems to predict the possible future edges based on the observation history of graph events. Existing works for the temporal graph learning mainly use the recurrent neural networks or self-attention mechanism to model the interaction information of graph, and ignore the node attribute features. In particular, they usually adopt a static attribute encoder where the attribute representation can not adjust according the graph structure evolution. To address this issue, we propose Attribute-Empowered Temporal Graph Networks (AE-TGN), which leverages a dynamic attribute leaning module to fine-tune the attribute embedding. In detail, we use the recurrent networks to encode the memory of nodes and the temporal self-attention mechanism to capture the spatial graph information. Next, a trainable multi-layer perceptron is employed to adjust the node attribute representation. Finally, the node attribute embeddings are concatenated with the memory vectors as the temporal embeddings of nodes, and fed into the link prediction decoder. Experimental results on two real-world datasets demonstrates the superiority of our proposal by beating competitive baselines. Specially, AE-TGN presents obvious improvements in the inductive setting, which validates that the attribute encoder can produce accurate node representation for the downstream task.
Acquiring Viewpoint Transformation to Reuse Self Body Image for Other on Self-Organized Internal Spatial Coordinate System

Viewpoint transformation is a prerequisite for understanding others by taking the perspective of the other individual. We propose a neural network model that can develop the ability of viewpoint transformation only through visuo-proprioceptive experiences. Our proposed model learns to reconstruct visual sensations corresponding to given propri-oceptive sensations after observing the environment, and its developmental capability was evaluated by learning on a simu-lated robot. The model first learned through a body babbling in a self alone situation, resulting in the development of an internal spatial coordinate system and self body image. Then, the model learned in a face- to- face situation with another individual, resulting in the development of viewpoint transformation ability to reuse the developed self visual body image to explain the visual appearance of the other: the internal body image was projected onto the other in visual sensations to reconstruct the other's visual appearance. It was also observed that the model internally generated imitative poses to reuse the self body image for the other. Although the developed viewpoint transformation is not to take the visual perspective of the other individuals, the ability is fundamental to imagine the visual perspective outside the self-egocentric perspective.
A Design of Smart Glasses-Based Gesture Recognition and Translation System for Sign Languages

Sign languages are not understood by most people with no hearing disabilities. Therefore, translation systems for sign languages to written or spoken languages are useful. However, existing software and hardware that can translate sign languages are very difficult to carry and use. Smart glasses are becoming increasingly widespread, and with the development of software and hardware, they have become capable of recognizing hand movements. Therefore, this study attempts to propose a real-time translation system based on smart glasses with an Android OS, to facilitate better communication with deaf people. When the user puts on the smart glasses, the sign language gestures can be translated in to written language, in real-time, in the smart glasses. This will make it smoother and easier for the people who are not versed in sign languages to communicate with the deaf people. The results will improve the functions of smart glasses and contribute to the development of the communication systems for the deaf people.
GLARE: Accelerating Sparse DNN Inference Kernels with Global Memory Access Reduction

Sparse deep neural networks (DNNs) leverage sparse representations to achieve faster inference and lower memory footprint. However, deploying sparse DNNs comes with challenges, such as irregular memory access patterns, workload imbalance, etc. To address these challenges, IEEE HPEC has organized the Sparse DNN Graph Challenge (SDGC), seeking new methods from the high-performance computing community. For many years, SDGC has yielded innovative works on accelerating sparse DNN inference. However, none of them have identified redundant global memory access that contributes to significant runtime overhead. To overcome this challenge, we propose GLARE, a framework that can assist existing sparse inference kernels in effectively reducing redundant global memory access. We have applied GLARE to previous SDGC champions and a recent sparse inference engine SNICIT. Evaluated on SDGC benchmarks, we demonstrate the promising performance of GLARE and its generalizability in accelerating existing sparse inference kernels, for instance, up to 31.56 x speed-up over one of the previous SDGC champions.