Reviewerly/exHarmony huggingface dataset:
Exact Memory- and Communication-aware Scheduling of DNNs on Pipelined Edge TPUs

Deep neural networks (DNNs) represent the state-of-the-art in many applications but have substantial computational and memory requirements, which greatly limit their training and deployment in real-world systems. In particular, the deployment challenges further increase on edge systems with much more restricted resource-constrained (e.g., computation and memory bounded), which recently attracted significant interest in many application scenarios. Such devices like Edge TPUs usually provide limited on-chip storage and memory bandwidth, where the heuristic-based ahead-of-time compilation techniques are highly limited in optimizing the inference performance due to the lacks of performance guarantees. This work proposes a novel exact pipeline scheduling framework that enables model parameter caching, data dependency, and device-to-device communication-aware multi-objective optimizations. The framework is powered by novel versatile SDC+ILP formulations supporting both propositional logic and non-equality constraints. The experimental results demonstrate that the proposed scheduling frameworks consistently outperform commercial Edge TPU Compiler with up to more than 4 x speedups on eleven ImageNet models in physical pipelined Edge TPU setups. In addition, we have demonstrated consistent real-world energy efficiency improvements measured with high precision power meter. Finally, the proposed framework has also demonstrated the capability in multi-model co-deployment on pipeline Edge TPU system, which is not supported by Edge TPU Compiler.
GreenChain: A Solana Blockchain-based Management Platform for Academic/Commercial Areas

Technologies based on distributed chains (blockchain) are compared, by some authors, as being as innovative as the transition from analog to digital, with revolutionary consequences in the medium and long term regarding the strategies for generating, transmitting, and storing data/information and the relationships between them. This paper presents a proposal for a management platform solution based on Solana blockchain and Agile software methodologies, for academic areas (for example, for the dissemination of doctoral theses) and also, for the commercial area (various types of economic contracts, etc.).
Modelling and Estimation of The Optimum Tilt Angle of Photovoltaic Systems, Case Study: Libya

The most important parameter for designing solar photovoltaic (PV) systems is the tilt angle of PV panels with horizontal surface. It determines the amount of radiation incident on PV panels surfaces, thus it is required for an economic evaluation of solar PV systems. For this reason, this study aims to estimate the optimum tilt angle of solar PV panels to exploit the maximum daily solar radiation on the inclined surface. The analysis was achieved based on measured data recorded in four cities of Libya. The optimum tilt angle was determined by changing the angle of surface from 0° up to 90° with the 1° resolution and searching for the maximum value of the corresponding daily total solar radiation. In addition, Six empirical models were established to estimate the optimum tilt angle of PV panels. Their empirical coefficients were determined using non-linear regression technique. The models accuracies were evaluated using statistical criteria such as mean bias error, MBE, root mean square error, RMSE, and correlation coefficient, R. A combined statistical indicator, CSI, has been developed to assess and rank the performance of models. The presented results showed that the optimum tilt angle in the four studied sites varied from 0° to 59° throughout the year, it increased during the winter and decreased during the summer. The results also showed that both third-order polynomial and Fourier model presented the best efficiency in estimating optimum tilt angle with R = 0.9943.
Attention multiple instance learning with Transformer aggregation for breast cancer whole slide image classification

Recently, attention-based multiple instance learning (MIL) methods have received more concentration in histopathology whole slide image (WSI) applications. However, existing attention-based MIL methods rarely consider the cross-channel information interaction of pathology images when identifying discriminant patches. Additionally, they also have limitations on capturing the correlation between different discriminant instances for the bag-level classification. To address these challenges, we present a novel attention-based MIL model (AMIL-Trans) for breast cancer WSI classification. AMIL-Trans first embeds the efficient channel attention to realize the cross-channel interaction of pathology images, thus computing more robust features for instance selection without introducing too much computation cost. Then, it leverages vision Transformer encoder to directly aggregate selected instance features for better bag-level prediction, which effectively considers the correlation between different discriminant instances. Experiment results illustrate that AMIL-Trans respectively achieves its optimal AUC of 94.27% and 84.22% on the Camelyon-16 dataset and MSK external validation dataset, demonstrating the competitive performance compared with state-of-the-art MIL methods on breast cancer WSI classification task. The code will be available at https://github.con CunqiaoHou/AMIL-Trans.
Mixed-Net: A Mixed Architecture for Medical Image Segmentation

Neural network-based approaches have taken the lead in medical image segmentation with the encoder-decoder architecture. However, these approaches are still limited to one neural structure, which is short in leveraging the strengths of the three dominant structures (Convolutional Neural Network, Transformer, and Multilayer Perceptron) simultaneously. Furthermore, simple skip connections cannot effectively bridge the semantic gap between the encoder and decoder at the same level. To alleviate the above problems, this paper proposes Mixed-Net,haode which cleverly formulates a strategy to synergize three neural network structures for medical image segmentation. Specifically, our method innovatively designs two components, namely a Semantic Gap Bridging Module (SGBM) and a Global Information Compensation Decoder (GICD). Convolution-based SGBM can validly expand the receptive field and combine shallow and high-level representations by replacing original skip connections. Equally importantly, we present a GICD containing convolution and transformer, which can adequately incorporate local refinement features and global representations in the information decoding space. We evaluate Mixed-Net on 3 different medical image segmentation datasets. Surprisingly, our method sets the new state-of-the-art performance and demonstrates stronger generalization capability.
Drones network security enhancement using smart based block-chain technology

Drones, also known as unmanned aerial vehicles (UAVs), can sometimes put just on trying to fly Internet of Things (IoT) environment to perform a variety of tasks, including monitoring systems, crisis management, photogrammetry, remote monitoring opponents at borders, and even more. The development of high-speed data transmission protocols (i.e., 5G and 6G networks) is constantly evolving the paradigm of the Internet of Drones for diverse fields. Installed drones can sense and gather data while safely transmitting it to a ground server station (GSS). After turning the acquired data into safe encryption, the server station sends it to a peer-to-peer cloud server network (P2PCS). Finally, from that encryption blocks are created and added to the blockchain which is implemented by P2PCS network. In this paper, the proposed blockchain-enabled framework based on protected communication between Internets of Drones environments.
Bring Your Own Device Information Security Policy Compliance Framework: A Systematic Literature Review and Bibliometric Analysis (2017–2022)

The latest trend of using personal devices for work productivity is Bring Your Own Device (BYOD). This rapid growth trend has excellent benefits for employees and organizations, but the security risks remain increasing, such as malware, spyware, and malicious apps on employees' devices; organizations should manage an up-to-date information security policy. This paper aims to identify the risks caused by employees' non-compliance and identify factors and behaviours toward BYOD compliance. The factors of information security policy compliance have been found to follow the organization's policies while working with employees' devices and processing data. Influencing information security culture in an organizational setting avoids risks. A systematic review was conducted to identify which journals are most influential to BYOD policy compliance based on Scopus and Web of Science databases. The PRISMA approach is used the Scopus and WoS databases to gather the most related journals by analyzing 67 journals out of 207 from the past six years. It additionally analyzes a fully bibliometric analysis from 2017 to 2022. A further analysis was done using bibliometric with VOSviewer software, version 1.6.17. This paper reviews the selected journals from the extracted literature. It tracks research routes of BYOD policy compliance in terms of risks of non-compliance, impact factors of compliance, and influential theories based on BYOD issues. The BYOD issues are divided into three main categories: technology, management, and human issues. This paper contributes new insights and guidelines for BYOD policy compliance and security issues by listing the risks and impact factors.
Path Planning of Mobile Robot Based on an Improved Genetic Algorithm

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations; Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm; Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles; Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning.
Explainable Abuse Detection as Intent Classification and Slot Filling

Abstract To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent policy, moderators are provided with detailed guidelines. In contrast, most state-of-the-art models learn what abuse is from labeled examples and as a result base their predictions on spurious cues, such as the presence of group identifiers, which can be unreliable. In this work we introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We propose a machine-friendly representation of the policy that moderators wish to enforce, by breaking it down into a collection of intents and slots. We collect and annotate a dataset of 3,535 English posts with such slots, and show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.1
LCS : Alleviating Total Cold Start Latency in Serverless Applications with LRU Warm Container Approach

Serverless computing offers "Function-as-a-Service"(FaaS), which promotes an application in the form of independent granular components called functions. FaaS goes well as a widespread standard that facilitates the development of applications in cloud-based environments. Clients can solely focus on developing applications in a serverless ecosystem, passing the overburden of resource governance to the service providers. However, FaaS platforms have to bear the degradation in performance originating from the cold starts of executables i.e. serverless functions. The cold start reflects the delay in provisioning a runtime container that processes the functions. Each serverless platform is handling the problem of cold start with its own solution. In recent times, approaches to deal with cold starts have received the attention of many researchers. This paper comes up with an extensive solution to handle the cold start problem. We propose a scheduling approach to reduce the cold start occurrences by keeping the containers alive for a longer period of time using the Least Recently Used warm Container Selection (LCS ) approach on Affinity-based scheduling. Further, we carried out an evaluation and compared the obtained results with the MRU container selection approach. The proposed LCS approach outperforms by approximately 48% compared to the MRU approach.
Electro-optical synaptic characteristics of ferroelectric liquid crystals for artificial intelligence

We propose electro-optical synaptic devices using surface-stabilized ferroelectric liquid crystals. Typical synaptic characteristics were observed for varying pulse time intervals, numbers of pulses, and signal voltages. Plasticity only occurred when pulses were applied at intervals shorter than the response time of the ferroelectric liquid crystal. Moreover, the plasticity increased with a higher pulse voltage and number of pulses. This demonstrates the importance of repeated learning. The synaptic weights required to make connections through learning in an artificial neural network can be determined by tuning the pulse signal. We discuss the high-speed computational potential of optical neuromorphic devices using liquid crystals.
GeoChain: A Locality-Based Sharding Protocol for Permissioned Blockchains

Blockchain is a distributed ledger that uses cryptography and consensus protocols to record a growing list of transactions in a tamper-resistant manner. Scalability is one of the main problems that limit its usage. This paper introduces a full sharding protocol, Geochain, for permissioned blockchains. We first clarify the limitations of state-of-the-art sharding protocols. Then, we propose a locality-based sharding protocol that achieves high scalability. We optimize inter-shard performance by clustering participants using their geographical properties, locality. In addition, the locality is also employed to decide the transaction placement which results in a low ratio of cross-shard transactions for applications, such as everyday banking, retail payments, and electric vehicle charging. We also propose a client-driven efficient mechanism to handle cross-shard transactions and present an analysis. This enables clients to manage their assets across different shards directly. A prototype is implemented on top of Hyperledger Fabric v2.3 and evaluated on Amazon EC2. The experiments show that our protocol doubles the peak throughput, even with a high ratio of cross-shard transactions, while minimizing the transaction latency.
cHPCe: Data Locality and Memory Bandwidth Contention-aware Containerized HPC

The High-Performance Computing (HPC) community started using Operating System virtualization, aka Containerization, due to its near-native performance compared to the BareMetal environment. Despite several existing container solutions for HPC, users are still unconvinced about the suitability of container orchestration solutions for their extreme-scale applications due to the lack of thorough performance assessment on recent advances. A key concern in current generation containerized HPC is the inherent performance degradation due to resource interference by co-hosted applications. This paper proposes an analytical model for data locality and memory bandwidth contention-aware container placement strategy for our developed containerized High-Performance Computing environment (cHPCe). Performance is evaluated and compared against LXD, Docker Swarm, Kubernetes, and Singularity using HPC Challenge and NAS parallel benchmarks. To the best of our knowledge, no study has been reported yet for such a comparative analysis with insight into data locality and memory bandwidth contention-aware container placement. The experimental result shows that data locality and memory bandwidth contention-awareness reduce the overall execution time of the benchmark in cHPCe by 51.41% in the best case compared to the Docker Swarm.
Probabilistic Logic Markov Decision Processes for Modeling Driving Behaviors in Self-driving Cars

Rule-based strategies and probability models are among the most successful techniques for selecting driving behaviors of self-driving cars. However, there is still the need to explore the combination of the flexibility and conceptual clarity of deterministic rules with probabilistic models to describe the uncertainty in the spacial relationships among the entities on the road. Therefore, in this paper we propose an action policy obtained from a probabilistic logical description of a Markov decision process (MDP) as a behavior selection scheme for a self-driving car to avoid collisions with other vehicles. We consider three behaviours: keep distance, overtaking, and steady motion. The state variables of the MDP signal the presence or absence of other vehicles in the surroundings of the ego car. Simple probabilistic logic rules characterize the probability distribution of the immediate future state of the autonomous car given the current state and action. The utility model of the MDP rewards the autonomous car when no car is ahead and it penalizes two types of crashes accordingly to their severity. We simulated our proposal in 16 possible scenarios. The results show the appropriateness of both, the overall system and the decision-making strategy to choose actions that prevents potential accidents of the self-driving car.
The Cryptoasset Auditing and Accounting Landscape

This chapter examines some emerging topics and trends as it connects cryptoassets to accounting, auditing, and financial reporting. It discusses the treatment of cryptoassets from a financial reporting perspective under the US and international accounting standards, frequently asked questions about auditing and attestation, and potential best practices for auditing various cryptoassets. It also discusses accounting for now and potential in the future. The chapter outlines how the rapidly changing cryptoasset landscape could lead to differentiated accounting treatment and audit best practices for practitioners seeking to attest to certain aspects of cryptoassets.
A Systolic Neural CPU Processor Combining Deep Learning and General-Purpose Computing With Enhanced Data Locality and End-to-End Performance

While neural network (NN) accelerators are being significantly developed in recent years, CPU is still essential for data management and pre-/post-processing of accelerators in a commonly used heterogeneous architecture, which usually contains an NN accelerator and a processor core with data transfer performed by direct memory access (DMA) engine. This work presents a special neural processor, referred to as a systolic neural CPU processor (SNCPU), which is a unified architecture combining deep learning and general-purpose computing for fifth-generation of reduced instruction set computer (RISC-V) to improve end-to-end performance for machine learning (ML) tasks compared with a common heterogeneous architecture with CPU and accelerator. With 64%–80% processing elements (PEs) logic reuse and 10% area overhead, SNCPU can be configured into ten RISC-V CPU cores. Special bi-directional dataflow and four different working modes are developed to enhance the utilization of deep NN (DNN) accelerator and eliminate the expensive data transfer between CPU and DNN accelerator in existing heterogeneous architecture. A 65-nm test chip was fabricated demonstrating a 39%–64% performance improvement on end-to-end image classification tasks for ImageNet, Cifar10, and MNIST datasets with over 95% PE utilization and up to 1.8TOPs/W power efficiency.
Methodology for development of an expert system to derive knowledge from existing nature-based solutions experiences

Learning from past experiences is essential for the adoption of Nature-Based Solutions (NBS). There is a growing number of knowledge repositories sharing the experience of NBS projects implemented worldwide. These repositories provide access to a large amount of information, however, acquiring knowledge from them remains a challenge. This paper outlines the technical details of the NBS Case-Based System (NBS-CBS), an expert system that facilitates knowledge acquisition from an NBS case repository. The NBS-CBS is a hybrid system integrating a black-box Artificial Neural Network (ANN) with a white-box Case-Based Reasoning model. The system involves:•a repository that stores the information of past NBS projects, and an input collection component, guiding the collection and encoding of the user's inputs;•a classifier that predicts solutions (i.e., generates a hypothesis), based on user input (target case), drawing on a pre-trained ANN model to guide the case retrieval, and a case retrieval engine that identifies cases similar to the target case;•a case adaption and retainment process in which the user assesses the provided recommendations and retains the solved problem as a new case in the repository.
Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT

Nowadays, Wireless Sensor Network (WSN) is a modern technology with a wide range of applications and greatly attractive benefits, for example, self-governing, low expenditure on execution and data communication, long-term function, and unsupervised access to the network.The Internet of Things (IoT) is an attractive, exciting paradigm.By applying communication technologies in sensors and supervising features, WSNs have initiated communication between the IoT devices.Though IoT offers access to the highest amount of information collected through WSNs, it leads to privacy management problems.Hence, this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique (LRECC) to establish a secure IoT structure for preventing, detecting, and mitigating threats.This approach uses the Elliptical Curve Cryptography (ECC) algorithm to generate and distribute security keys.ECC algorithm is a light weight key; thus, it minimizes the routing overhead.Furthermore, the Logistic Regression machine learning technique selects the transmitter based on intelligent results.The main application of this approach is smart cities.This approach provides continuing reliable routing paths with small overheads.In addition, route nodes cooperate with IoT, and it handles the resources proficiently and minimizes the 29.95% delay.
Co-Design within and between Communities in Cultural Heritage: Current and Open Questions

In this Special Issue, we explore emerging theories, methods, and case studies focusing on co-design activities within and between communities in cultural heritage contexts [...]