Reviewerly/exHarmony huggingface dataset:
Deep self-organizing cube: A novel multi-dimensional classifier for multiple output learning

Multi-dimensional classification (MDC) task can be considered the most inclusive description of all classifications tasks, as it joins multiple class spaces and their multiple class members into a single compound classification problem. The challenges in MDC arise from the possible class dependencies across different class spaces, as well as the imbalance of labels in training datasets due to lack of all possible combinations. In this paper, we propose a straightforward, yet efficient, MDC deep learning classifier, named Deep Self-Organizing Cube (DSOC) that can model dependencies among classes in multiple class spaces, while consolidating its ability to classify rare combinations of labels. DSOC is formed of two n-dimensional components, namely the Hypercube Classifier and the multiple DSOC Neural Networks connected to the hypercube. The multiple neural networks component is responsible for feature selection and segregation of classes, while the Hypercube classifier is responsible for creating the semantics among multiple class spaces and accommodate the model for rare sample classification. DSOC is a multiple-output learning algorithm that successfully classify samples across all class spaces simultaneously. To challenge the proposed DSOC model, we conducted an assessment on seventeen benchmark datasets in the four types of classification tasks, binary, multi-class, multi-label and multi-dimensional. The obtained results were compared to four standard classifiers and eight competitive state-of-the-art approaches reported in literature. The DSOC has achieved superior performance over standard classifiers as well as the state-of-the-art approaches in all the four classification tasks. Moreover, in terms of Exact Match accuracy metrics, DSOC has outperformed all state-of-the-art approaches in 77.8% of the cases, which reflects the superior ability of DSOC to model dependencies and successfully classify rare samples across all dimensions simultaneously.
High-resolution image compression algorithms in remote sensing imaging

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000.
BPFL: Blockchain-Enabled Distributed Edge Cluster for Personalized Federated Learning

Due to the fact that the client's data distribution in federated learning (FL) is highly heterogeneous and leads to poor convergence, the concept of personalized federated learning (PFL) is on the rise. PFL aims to tackle the effect of non-IID data and statistical heterogeneity problems along with achieving rapid model convergence and personalized models. Moreover, in the context of clustering-based PFL, it allows a multi-model method with group-level client relationships to accomplish personalization. However, since the current method still relies on the centralized method, where the server orchestrates all processes, this paper introduces a blockchain-enabled distributed edge cluster for PFL (BPFL) that exploits the benefits of blockchain and edge computing to address these shortcomings. Blockchain can be employed to enhance client privacy and security by recording all transactions in immutable distributed ledger networks as well as improving efficient client selection and clustering. Furthermore, an edge computing system offers reliable storage and computation, where computational processing is locally performed in the edge infrastructure to be closer to clients. Thus, it also improves real-time services and low-latency communication of PFL.
Evaluation of the drivers to the implementation of blockchain technology in the construction supply chain management in Nigeria

Purpose Blockchain technology (BT) is a relatively new technological innovation in all industries, including the construction industry, that is used to improve supply chain management. Therefore, this study assesses the drivers for the implementation of BT in the construction supply chain management in Nigeria. Design/methodology/approach This study used a quantitative research approach, with a questionnaire survey administered to professionals in the Nigerian construction industry using the snowball sampling method, yielding 155 respondents. The collected data were analysed using descriptive and exploratory factor analysis (EFA) while Cronbach’s alpha was used to evaluate the reliability. Findings The analysis revealed that all the identified drivers ranked higher than the average mean item score, with level of awareness of the new technology and data management ranking topmost. The identified drivers were clustered into five categories using EFA: technological driver, social-economic driver, management driver, transparency and security driver and information driver. Research limitations/implications This research was carried out in the Southwestern region which is one of the six geo-political zones in Nigeria using a cross-sectional survey method. Practical implications The findings will be extremely useful to both professionals and practitioners in the Nigerian construction industry in gaining knowledge about the potential drivers to the implementation of BT in construction supply chain management. Originality/value The research categorized the drivers into technological, social-economic, management, transparency and security and information driver. It also identified that level of awareness of BT as the major driver in the implementation of BT in construction supply chain management.
Voxento 4.0: A More Flexible Visualisation and Control for Lifelogs

In this paper, we introduce Voxento 4.0 – an interactive voice-based retrieval system for lifelogs which has been developed to participate in the sixth Lifelog Search Challenge LSC’23, at ACM ICMR’23. Voxento has participated three times in the LSC editions and achieved the rank of 4th in LSC21 and 5th in LSC22 respectively. In this version, Voxento 4.0, we have focused on improving the previous system’s interface, voice interaction and retrieval functionality. The current version has implemented some processing and cleaning of the dataset and employs the CLIP model to extract image features. In addition, the system’s interface was redesigned for better visualisation of the elements and the images for effective interaction. This improvement in the interface will help to support voice interaction in future work. The interface developments include logging voice interaction and images displayed, submitted, selected and starred to enhance user experience with the system. The voice interaction part has also been enhanced in the workflow of the voice lifecycle interaction and with additional voice commands.
Research on adaptive obstacle avoidance algorithm of robot based on DDPG-DWA

In this paper, we propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic-Window Approach) to study the obstacle avoidance problem of robots in complex continuous state space. First, the obstacle avoidance problem is converted into an optimal learning incentive problem, and the self-learning of the obstacle avoidance policy is realized based on DDPG; second, the DWA obstacle avoidance trajectory evaluation function is optimized using the DDPG reward incentive mechanism, and the Experience Replay mechanism; finally, the algorithm model is simulated. The experiments show that the model can significantly circumvent the deficiency of the DWA algorithm in limiting to the optimal local solution in a complex environment and solve the action output problem in the continuous velocity and turning angle value interval of the robot; through the trial and error interaction with the environment and the trajectory evaluation incentive feedback, the obstacle avoidance passing ability of the robot in a complex environment is improved.
Can Bitcoin mining increase renewable electricity capacity?

Proponents of Bitcoin argue that demand for electricity from Bitcoin miners can lead to an increase in renewable electricity capacity. We rigorously evaluate this claim by estimating a Bitcoin electricity demand curve and include this demand curve in a long-run model of the Texas electricity market. We find that while Bitcoin mining can indeed increase renewable capacity, it also increases carbon emissions. When Bitcoin miners provide grid management services in the form of demand response, their emissions impact is largely mitigated.
Multi-Head multimodal deep interest recommendation network

From machine learning recommendation to deep learning recommendation, reinforcement learning recommendation, and recommendation model compression, the network structure of the recommendation algorithm becomes more complex, more expressive, and more lightweight with higher real-time performance. However, most of these models are dedicated to recommendations by mining ratings and text information, and researchers have optimized the network structures of many recommendation models. However, mining image information and enriching recommendation model features are less studied, and there is still some room for optimization of deep recommendation model structures and real-time performance. To this end, this paper proposes a model called MMDIN by adding multi-head and multi-modal modules to the DIN model. The multi-modal module extracts image features, which enriches the feature set that the model can use and strengthens the cross-combination and fitting expression capabilities of the model. Meanwhile, the multi-head mechanism extracts features from different dimensions, which further enhances the model prediction performance. Experimental results show that the proposed MMDIN model improves the recommendation prediction performance, and it outperforms state-of-the-art methods in comprehensive indicators.
Spoiler-Alert: Detecting Spoiler Attacks Using a Cuckoo Filter

Spoiler attacks leak physical address information, which is exploited to accelerate reverse engineering of virtual-to-physical address mapping, thus greatly boosting Rowhammer and cache attacks. However, existing approaches that detect data-leakage attacks no longer suit the requirements of identifying Spoiler. This paper proposes Spoiler-alert,the first hardware-level mechanism to detect the address-leakage Spoiler attacks in real time. It leverages a cuckoo filter module embedded into Memory Order Buffer component to screen buffer addresses on-the-fly. We further optimise the filtering algorithm to reduce false positives. We assess the effectiveness and performance based on prototype implementations, which achieve a detection rate of 99.99% and negligible performance loss. Finally, we discuss potential reactions of our detection mechanism after a Spoiler attack was discovered.
Integrating topology beyond descriptions for zero-shot learning

Zero-shot learning (ZSL) aims to discriminate object categories through the identification of their attributes and has received much attention for its capability to predict unseen categories without collecting training data. Recently, excellent works have been devoted to optimizing the model inference by mining the topology among categories/attributes, which proves that the topology learning is beneficial and important for ZSL. However, existing works focus almost exclusively on the construction of semantic topological knowledge with textual descriptions, which, though effective, still suffer from two deficiencies: first, the semantic gap between modalities makes it difficult for the category attributes to accurately describe the corresponding visual characters, resulting in the topology constructed in the semantic modality being distorted in the visual modality; second, it is difficult for one to enumerate all the attributes hidden in images, resulting in an incomplete topology mined only from the defined attributes. Therefore, we propose a Cross-Modality Topology Propagation Matcher (CTPM) to construct a more complete topology system by collaborative mining of topological knowledge in both the visual and semantic modalities. We stand at the dataset level to construct sample-based visual topological knowledge based on the global image features to preserve the integrity of visual information. Meanwhile, we exploit the matching relationship between visual and semantic modalities to make topological knowledge propagate effectively across modalities, and fully enjoy the benefits of multi-modality topological knowledge in category/attribute reasoning. We validate the effectiveness of our CTPM through extensive experiments and achieve state-of-the-art performance on four ZSL datasets.
Multi-sensors Fused IoT-based Home Surveillance via Bag of Visual and Motion Features

Internet of things (IoT) represent the small devices connected together wirelessly collecting data to make lifestyle convenient. Inertial measurement units (IMU) and cameras connected together to collect data from multiple indoor activities can also support home surveillance systems. The traditional closed-circuit television is out-fashioned due to the huge volume of storage requirements and not connected together to notify users immediately of apprehensive activities. Therefore, this paper proposes an IoT-based surveillance system for indoor environments that will upkeep the security methods inside the home. For this purpose, the fused multisensors-based data is acquired from two state-of-the-art datasets, namely, opportunity++ and CMU-MMAC. This acquired data from IoT devices is further pre-processed through multiple filtering techniques according to the type of data. Then, a skeleton model has been designed for the filtered video frame sequences. Furthermore, a bag of visual and motion features has been extracted using three different techniques followed by their discrimination. Finally, the IoT-based surveillance system detects indoor activities and provides feedback to the user.
One-Shot Face Recognition

Facial recognition is one of the most fascinating and interesting research areas. It has attracted the attention of many scientists and researchers for its amazing applications in identity authentication, policing, healthcare, marketing, and security. There are different face recognition algorithms available that give very good results but at the cost of huge data. Humans can recognize a person just by seeing a person once but this is not the case for computers they need enormous amounts of data just to recognize a person. In the case of a small dataset, only one algorithm stands out which is one-shot learning. In the case of ‘‘One-shot’’ learning, the model learns from a single input image. The thought is to train a CNN model with an enormous dataset of individuals with different faces, expressions, and lighting conditions specified model once given a single image of an individual will be recognized properly. For this, we tend to use the ‘‘Siamese neural network’’ to be told the similarity between faces.
A Safety-Guaranteed Framework for Neural-Network-Based Planners in Connected Vehicles under Communication Disturbance

Neural-network-based (NN-based) planners have been increasingly used to enhance the performance of planning for autonomous vehicles. However, it is often difficult for NN-based planners to balance efficiency and safety in complicated scenarios, especially under real-world communication disturbance. To tackle this challenge, we present a safety-guaranteed framework for NN-based planners in connected vehicle environments with communication disturbance. Given any NN-based planner with no safety-guarantee, the framework generates a robust compound planner embedding the NN-based planner to ensure overall system safety. Moreover, with the aid of an information filter for imperfect communication and an aggressive approach for the estimation of the unsafe set, the compound planner could achieve similar or better efficiency than the given NN-based planner. A comprehensive case study of unprotected left turn and extensive simulations demonstrate the effectiveness of our framework.
SparseMEM: Energy-efficient Design for In-memory Sparse-based Graph Processing

Performing analysis on large graph datasets in an energy-efficient manner has posed a significant challenge; not only due to excessive data movements and poor locality, but also due to the non-optimal use of high sparsity of such datasets. The latter leads to a waste of resources as the computation is also performed on zero's operands which do not contribute to the final result. This paper designs a novel graph processing accelerator, SparseMEM, targeting sparse datasets by leveraging the computing-in-memory (CIM) concept; CIM is a promising solution to alleviate the overhead of data movement and the inherent poor locality of graph processing. The proposed solution stores the graph information in a compressed hierarchical format inside the memory and adjusts the workflow based on this new mapping. This vastly improves resource utilization, leading to higher energy and permanence efficiency. The experimental results demonstrate that SparseMEM outperforms a GPU-based platform and two state-of-the-art in-memory accelerators on speedup and energy efficiency by one and three orders of magnitude, respectively.
Accurate yet Efficient Stochastic Computing Neural Acceleration with High Precision Residual Fusion

Stochastic computing (SC) emerges as a fault-tolerant and area-efficient computing paradigm for neural acceleration. However, existing SC accelerators suffer from an intrinsic trade-off between inference accuracy and efficiency: accurate SC re-quires high precision computation but suffers from an exponential increase of bitstream length and inference latency. In this paper, we discover the high precision residual as a key remedy and propose to combine a low precision datapath with a high precision residual to improve inference accuracy with minimum efficiency overhead. We also propose to fuse batch normalization with the activation function to further improve the inference efficiency. The effectiveness of our proposed method is verified on a recently proposed SC accelerator. With extensive results, we show that our proposed SC-friendly network achieves 9.43% accuracy im-provements compared to the baseline low precision networks with only 1.3% area-delay product (ADP) increase. We further show <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{3.01\times}$</tex> ADP reduction compared to the baseline SC accelerator with almost iso-accuracy.
Maximizing the Potential of Custom RISC-V Vector Extensions for Speeding up SHA-3 Hash Functions

SHA-3 is considered to be one of the most secure standardized hash functions. It relies on the Keccak-f[1 600] permutation, which operates on an internal state of 1 600 bits, mostly represented as a 5 x 5 x 64-bit matrix. While existing implementations process the state sequentially in chunks of typically 32 or 64 bits, the Keccak-f[1 600] permutation can benefit a lot from speedup through parallelization. This paper is the first to explore the full potential of parallelization of Keccak-f[1 600] in RISC-V based processors through custom vector extensions on 32-bit and 64-bit architectures. We analyze the Keccak <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{f}[1 \ 600]$</tex> permutation, composed of five different step mappings, and propose ten custom vector instructions to speed up the computation. We realize these extensions in a SIMD processor described in System Verilog. We compare the performance of our designs to existing architectures based on vectorized application-specific instruction set processors (ASIP). We show that our designs outperform all related work in throughput due to our carefully selected custom vector instructions.
Constructor Development: Predicting Object Communication Errors

A fundamental and computationally challenging task in the development of dynamic software is organizing the optimization of resource consumption to complete the work within a given time limit. Certain robust approaches are essential to meet the users’ requirements for preventing or repairing software defects, particularly when object communications are expected to inherit in a variety of environments. In this study, a technique for anticipating run-time issues in object creation is demonstrated when a large number of objects must communicate with each other, in multiple and multi-level object inheritance. An object fault refers to an error that occurs as a result of inheritance or a breach of object restrictions. In the discipline of software testing, the troublesome classes have to be separated using a defect prediction model. The inheritance fault observed in a specific class is used to divide classes. This model can be used to forecast software reliability, according to the findings.
Efficient-Net ASPP Deep Network for Malignant Ultrasound Breast Cancer Segmentation

Worldwide, breast cancer is one of the main causes of mortality among women. Only through early recognition of symptoms is it possible to limit the incidence of premature deaths. Utilizing standard deep-learning semantic segmentation, tumors are automatically segmented. It requires minimal human contact. The standard methods are less efficient and precise in accuracy. In this paper, we propose an Efficient-Net based Atrous Spatial Pyramid Pooling (Efficient-Net ASPP) model for efficient and improved segmentation. The proposed model is comprised of an EfficientNetV2B3 backbone with dilated convolutional, skip connection, max pooling, and an ASSP block. A publicly available Malignant Breast Cancer Ultrasound (MBCU) subset image dataset was investigated in our study. Several baseline segmentation methods were compared with our proposed method. The quantitative benchmark analysis shows the proposed model for segmenting MBCU images achieved 70% intersection over union (IOU) and a 62% score on the Dice Similarity Coefficient (DSC) metrics. The proposed network improves MBCU image segmentation by 14% on DSC and 18% on IOU when compared to baseline models.