Reviewerly/exHarmony huggingface dataset:
A Novel Low-Power Compression Scheme for Systolic Array-Based Deep Learning Accelerators

The proliferation of deep learning algorithms has catalyzed their utilization to solve a multitude of real-world problems. Algorithms such as deep neural networks (DNNs) are compute- and power-intensive, thereby accentuating the development of hardware platforms like DNN inference accelerators. However, inference execution of large DNNs in resource-constrained environments induces energy bottlenecks in these accelerators. Since large DNNs consist of hundreds of millions of trained parameters, accessing them from the accelerator memory incurs substantial energy. To address this challenge, we propose HardCompress, which, to the best of our knowledge, is the first low-power solution that uses traditional compression strategies pertaining to commercial DNN accelerators in resource-constrained IoT edge devices. The three-step approach involves hardware-based post-quantization trimming of weights, followed by their dictionary-based compression and subsequent decompression by a low-power hardware engine during inference in the accelerator. We evaluate the proposed solution on lightweight networks trained on the MNIST dataset, the compact model trained on the CIFAR-10 dataset, and large DNNs trained on the ImageNet dataset. Performance of HardCompress at different quantization levels has been analyzed. Furthermore, to quantify the effectiveness of the proposed solution, an energy framework that contrasts the DRAM energies of the original and HardCompressed models has been developed. Finally, a fault injection framework which compares the fault resilience of the original model with its HardCompressed counterpart is also proposed. Our results exhibit that HardCompress, without any performance degradation in large DNNs, furnishes a maximum compression of 99.27%, equivalent to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$137\times $ </tex-math></inline-formula> reduction in memory footprint and 0.07 J for 8-bit quantization in the systolic array-based DNN accelerator. Furthermore, our proposed low-power decompression engine incurs an area overhead of only 0.02%; thus, enabling HardCompress’ utilization in resource-constrained environments.
Cross-Company Data Exchange with Asset Administration Shells and Distributed Ledger Technology

Abstract To overcome boundaries in the cross-company data exchange, a new solution to use Distributed Ledger Technology (DLT) for the transfer of data between asset supplier and the Asset Administration Shell (AAS) in a remote repository is proposed in this paper. A short introduction to the AAS and an analysis of DLT in industrial context is given and showing useful properties for industrial usage as well as challenges to solve. The status of the exchange of AASs and possibilities to use DLT in supply chain tracking are described. The new solution combines both concepts by sending data through the distributed ledger from the supplier to the AAS. This is also shown in a proof-of-concept implementation of the solution using IOTA as a DLT and BaSyx as a framework for the AAS. The evaluation and discussion shows challenges and advantages from using the presented approach.
An Innovative Higher Education Institution Training Toolbox to Effectively Address Industry 4.0 Skills Gap and Mismatches

The development of the fourth industrial revolution (Industry 4.0) in the 2010s has paved the way to a digital transformation, fuelled by increased connectivity and intelligence in the manufacturing industry. This technological driven transformation has led to fast paced developments in a number of engineering fields in the past 10 years. Within higher education institutions, educators are finding difficulties to keep abreast of the latest technologies and developments in the field as complexity increases and the need for cross-disciplinary knowledge is required. This means that educators may not be in an effective position to pass on knowledge to their students who are the workers of future generations. This situation implies that there is a need for both educators and learners to be provided a tool which supports them in addressing their Industry 4.0 skills gaps and mismatches. This research therefore aims to contribute an open and digital training toolbox to educate both current and previous generations of higher education institution educators and learners in Industry 4.0 technologies and concepts.
Using bilateral filtering and autoencoder to defend against adversarial attacks for object detection

With the development of adversarial attacks, the performance of object detection based on deep learning is threatened. When adversarial examples are introduced into the detection task, the detector will suffer from poor detection performance, causing a large number of false detections. To handle this problem, we propose a defense method by combing bilateral filtering and the denoising autoencoder. Taking the you only look once (YOLO) v4 detection model as the research target, the proposed method proceeds as follows. First, it performs weighted average in the spatial domain and the pixel-range domain. The method retains important edge texture information when it reduces the perturbations in the image. Then, a three-layer denoising reduction autoencoder is designed, and a new optimization algorithm is proposed to minimize the distance between the input and output. Finally, experiments show that the method proposed has a better defense effect than the existing defense methods. When facing the projected gradient descent-based object detection bounding box disappearance adversarial attack, our defense method can improve the detection true-box rate indicator to 83.04% on the visual object classes challenge (VOC) dataset and 72.20% on the common objects in context (COCO) data. The number of bounding boxes correctly detected is 88.09% and 86.09% of the original one on the PASCAL VOC dataset and the Microsoft COCO dataset, respectively.
Assessment in Preparing In-Service Teachers to Teach in the Digital Age

Current global technological transformations in education compel teachers to have technology skills that enable them to effectively function in digital classrooms. Thus, it is important for technology teacher training programs to equip teachers with the requisite skills. Assessment plays a crucial role in assisting one to cultivate these skills during training. In this study, we investigated assessment processes in a technology course in the in-service teacher education program at the University of Botswana to find out how they were contributing to technology skills development among the teachers. The Technological Pedagogical Content Knowledge (TPACK) model was used as the theoretical framework for the study. The findings indicated that the assessment pieces used for the course addressed the requisite skills that teachers need to use technology effectively. Evaluation of students’ performance in the various assessment pieces identified gaps in skill attainment that needed to be addressed. These included designing evaluation activities in lesson plans, identifying specific objectives for the use of tools, pedagogy, and application of design principles in developing digital resources. The results imply that assessment practices in in-service teacher training programs should focus more on requiring teachers to demonstrate technology skills in authentic situations to cement skill acquisition and improve their confidence. There is also an urgent need to provide requisite resources in the schools to promote skills application.
Teacher Education in the Digital Age: Opportunities and Challenges

This chapter considers some possibilities that may result from improved digital skills and competencies of teachers in the digital age. The chapter, conceptual in nature, also considers challenges that teachers face in the digital age. Available literature has been used to inform this conceptual chapter which looks through the lens of Gibson (1979) affordance theory. Bates (2016) argues that teachers must use ICTs for teaching to remain relevant. Digital technologies have proved that they can allow teaching and learning to continue during pandemics and crisis situations. This calls for stakeholders to engage and interact meaningfully through ICTs. Teachers, as key stakeholders in the education space, need to master technology use to enhance and improve their pedagogy and andragogy. However, research shows that digital technologies also present some challenges when used. Change in teacher education is key and needs to be prioritised. Teachers need to be nurtured into accepting and embracing technology, though some may resist (Fullan 2006). Citizens of Southern Africa continue to be frustrated by the digital gap in their countries. Therefore, it remains a challenge for their governments to support and give teacher education in the digital age the urgency it needs.
Low-complexity trainable denoising filter structure for image recognition applications

Even in modern imaging equipment, different types of noise make their way into the images especially under bad lighting conditions and rough communication channels. While applying denoising filters, the preservation of the edge in images has always been an essential albeit a difficult task. This is particularly important for accuracy of further image processing tasks, such as recognition. We described a denoising approach to process noisy images specific for the task of classification with minimal impact on image resolution using low-complexity operations. Specifically, support vector machine-based machine learning classifiers have been proposed to dynamically select an appropriate filtering kernel while processing individual pixels in the input image. The selection is based on the image features in a local window. The provided experimental results on standard dataset prove that the proposed scheme performs as well as contemporary complex convolutional neural network (CNN)-based approaches for the task of denoising images before classification in the presence of a variety of mixed-type noise. Moreover, the proposed filtering approach can run at much higher speeds than their CNN-based counterparts while delivering similar or better performance. The proposed framework has been made publicly available as open-source code to facilitate further work by researchers and practitioners interested in this area.
Latent Rank Analysis

Latent rank analysis (LRA; Shojima, 2008, 2009, 2011) is a model similar to latent class analysis (LCA) that supposes several latent classes.
An Analysis of Bitcoin Zakat Calculation

Nowadays, the use of digital currency technology is recognised by a variety of industries, particularly those that use Bitcoin transactions.Bitcoin was created as a digital currency to facilitate online payment transactions between one party or individual and another without the use of a third party.However, Bitcoin is also used for money laundering, investment scheme fraud, and involvement in cybercrime, funding activities, and other illegal activities.Bitcoin is also unsuitable as a value storage tool because Bitcoin wallet virtual securities are frequently targeted by cyber criminals, and they can lose liquidity and become vulnerable to hyperinflation.This scenario compels experts and scientists from all over the world to pay close attention to hakam law and the risks of Bitcoin use.According to the Fatwa issued by the state of Perlis explains that any ownership of digital assets is subject to zakat if it is sufficient haul (storage period) and nisab (mandatory rate).This is because digital currency is property and currency that is mutaqawwim which is legal property according to sharia.The issue is whether zakat institutions are willing to accept zakat via Bitcoin transactions.How to make zakat payments with Bitcoin.This study's objective is to dismantle the method for calculating zakat on Bitcoin currency.This study collects data qualitatively through interviews with representatives of zakat institutions and Bitcoin practitioners.While data analysis is based on document analysis and data interpretation of information obtained through interviews.The findings of this study are calculation methods that can be applied to zakat Bitcoin currency.Thus, this study is able to provide new contributions to the academic development and management of zakat in the initial preparation and organise strategies for technological change and innovation that is growing rapidly in changing the way zakat collection today.
DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata Processor

Dynamic graph traversals (DGTs) currently are widely used in many important application domains, especially in this big-data era that urgently demands high-performance graph processing and analysis. Unlike static graph traversals, DGTs in real-world application scenarios require not only fast traversal acceleration itself but also, more importantly, a runtime strategy that can effectively accommodate the ever-evolving nature of the graph structure updates followed by a diverse range of graph traversal algorithms . Because of these special features, state-of-the-art designs on conventional compute-centric architectures (e.g., CPU and GPU) struggle to provide sufficient acceleration for DGT processing due to the dominating irregular memory access patterns in graph traversal algorithms and inefficient platform-specific update mechanisms. In this article, we explore the algorithmic features and runtime requirements of real-world DGTs and identify their unique opportunities of acceleration on the recent Micron Automata Processor (AP), an in-situ memory-centric pattern-matching architecture. These features include the natural mapping between traversal algorithms’ path exploration pattern to classic non-deterministic finite automata processing, AP’s architectural and compilation support for DGTs’ evolving traversal operations, and its inherent hardware fitness. However, despite these benefits, enabling highly efficient DGT execution on AP is non-trivial and faces several major challenges. To tackle them, we propose DynamAP , the first AP framework design that enables fast processing for general DGTs. DynamAP is oblivious to periodical traversal algorithm changes and can address the significant overhead caused by frequent graph updates and AP recompilation through our novel hybrid macro designs and associated efficient updating strategies. We evaluate DynamAP against the current DGT designs on a CPU, GPU, and AP with a range of widely adopted DGT algorithms and real-world graphs. For a single update request , our DynamAP achieves an average speedup of 21.3x (up to 39.2x ) over the state-of-the-art implementation on host-AP architecture; an average speedup of 9.2x (up to 14.7x ) and 1.7x (up to 2.8x ) over two highly optimized DGT design frameworks on a 64-GB Intel(R) Xeon CPU and a 32-GB NVIDIA Tesla V100 GPU. DynamAP also maintains high performance and resource utilization for high graph update ratios, and can significantly benefit natural graphs that present a high average vertex degree.
Multi-Tire CNN Model for Motor Imagery Based UAV Control

Background: The inventive idea of developing a brain-controlled UAV gave rise to a series of ongoing investigations which were pioneered by researchers at the University of Minnesota. These early efforts laid the groundwork for other research teams to develop more sophisticated prototypes of brain-controlled UAVs. However, in general, BCI signals complex because of their nonstationary and high-dimensionality properties. Therefore, high consideration needs to be taken in both feature extraction and classification process. Method: This study tried to apply the technique of hybridization of pretrained CNN with a classical neural network classifier and STFT spectrum in a multi-tire CNN model. This technique is identified in this study as a multi-tire CNN (MTCNN) model and used in this study for decoding two class MI signal to control the up/down movement of the UAV. Result: Practically, four experiments were conducted in the experimental part of this study. The first experiment conducted to evaluate the MTCNN model over a large dataset. The result indicated that the model achieved 0.991 of classification accuracy. The second and third experiment then conducted to evaluate the model on two different datasets for the same subjects to test their ability to overcome the problem of inter- subjects and intra-subjects associated with different brain complexities. The MTCNN model achieved 0.997 of classification accuracy on both datasets. Additionally, the fourth experiment conducted to validate the model on another dataset, and they achieved 100%, and 0.996 of classification accuracy. Interestingly, the MTCNN model is outperformed the accuracy of the literature on two BCI competition datasets. Conclusion: It can be deduced that, the MTCNN model appraises the capability in decoding the MI signal for the left and right hand, which then can be transferred to the field of brain-controlled UAV as a control command for up/down movement controls. Additionally, MTCNN model can contribute to the BCI-MI community by facilitating the deployment of the proposed model in a MI based UAV control system.
DeepMiCa: Automatic Segmentation and Classification of Breast Microcalcifications from Mammograms

Background and objective: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalized treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases.Methods: DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs. Results: The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95% and 0.89% respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results.Conclusion: To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualize and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.
A Study of Pre-editing Methods at the Lexical Level in the Process of Machine Translation

As the demand for translation increases, the role of machine translation in improving the efficiency of translation is increasingly prominent, but due to its inherent limitations, the translation quality of machine translation is not guaranteed. Pre-editing is one of the ways to enhance the quality of machine translated texts. With Google online translation as a tool, the effect of pre-editing on the quality of machine translation is studied by adopting the Bilingual Evaluation Understudy (BLEU) approach. The pre-editing methods of additions, omissions, replacing and terminology preprocessing based on the lexical level are proposed. The results show these methods can play a role in improving the quality of English-to-Chinese machine translations. In most cases, pre-editing at the lexical level is sufficient to generate high-quality machine translation output, but the effectiveness of pre-editing can be further improved when perspective-shifting is involved or when the target language involves contextually consistent verb or person usage.
Gpu Accelerated Scalable Parallel Coordinates Plots

Parallel coordinates are a powerful technique to visually analyze multi-parameter data, i.e., sets of data points with potentially many associated parameter values per data point. When these sets are large, line rendering becomes a severe performance bottle- neck, and since many lines fall into the same pixel the numerical precision of the color bu ff er is quickly reached. We propose a scalable GPU realization of parallel coordi- nates building upon 2D pairwise attribute bins, to significantly reduce the number of lines to be rendered. Our approach comprises a GPU compute pipeline that combines shader-based scattering with atomic increment operations to e ffi ciently count how often a line is drawn. These counts are then used to draw all pairwise sub-plots in the parallel coordinates plot, by analytically calculating the opacity for each count and rendering a line with end points determined by the 2D coordinates of the bin. In this way, frame- bu ff er precision issues that are paramount in classical approaches can be overcome. We demonstrate the e ffi ciency of the proposed realization for visualizing a weather forecast ensemble comprising 2.7 billion data points, each carrying 7 prognostic floating-point variables like temperature, precipitation and pressure, plus spatial and simulation input variables. We compare our pipeline to a rasterization-based approach regarding perfor- mance, and demonstrate interactive brushing at 4 seconds per frame at full HD viewport resolution.
Analysis of Grover’S Quantum Search Algorithm on a Classical Computer

In this paper, we analyzed the Grover’s search algorithm using a classical computer by calculating the amplitudes and the probabilities of finding a single marked state for n=5, 10, 15, 20, 25 and 27 qubit states. The calculations shows that the marked state can be found in [[EQUATION]] iterations, where [[EQUATION]] is the number of items. We also discuss the possibility of improving the Grover’s search algorithm to find a single item in N search elements. Again the amplitudes and hence the probabilities of finding the marked state were calculated for n=5, 10, 15, 20, 25, 30, 35, 40, 45 and 50 qubit states. The calculations showed that the marked state could be found with sufficiently high probability in some cases in [[EQUATION]] iterations.