Reviewerly/exHarmony huggingface dataset:
Impairment Identification for PAM-4 Transceivers and Links Using Machine Learning

We demonstrate simultaneous TDECQ estimation and impairment identification using convolutional neural networks. Robust training with representative impairments yielded identification accuracy of 100% for TDECQ &gt;2.6 dB, when considering limited bandwidth, signal compression and SNR degradation.
DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning

Self-supervised learning algorithms, including BERT and SimCLR, have enabled significant strides in fields like natural language processing, computer vision, and speech processing. However, these algorithms are domain-specific, meaning that new self-supervised learning algorithms must be developed for each new setting, including myriad healthcare, scientific, and multimodal domains. To catalyze progress toward domain-agnostic methods, we introduce DABS: a Domain-Agnostic Benchmark for Self-supervised learning. To perform well on DABS, an algorithm is evaluated on seven diverse domains: natural images, multichannel sensor data, English text, speech recordings, multilingual text, chest x-rays, and images with text descriptions. Each domain contains an unlabeled dataset for pretraining; the model is then is scored based on its downstream performance on a set of labeled tasks in the domain. We also present e-Mix and ShED: two baseline domain-agnostic algorithms; their relatively modest performance demonstrates that significant progress is needed before self-supervised learning is an out-of-the-box solution for arbitrary domains. Code for benchmark datasets and baseline algorithms is available at https://github.com/alextamkin/dabs.
FCA-BNN: Flexible and Configurable Accelerator for Binarized Neural Networks on FPGA

A series of Binarized Neural Networks (BNNs) show the accepted accuracy in image classification tasks and achieve the excellent performance on field programmable gate array (FPGA). Nevertheless, we observe existing designs of BNNs are quite time-consuming in change of the target BNN and acceleration of a new BNN. Therefore, this paper presents FCA-BNN, a flexible and configurable accelerator, which employs the layer-level configurable technique to execute seamlessly each layer of target BNN. Initially, to save resource and improve energy efficiency, the hardware-oriented optimal formulas are introduced to design energy-efficient computing array for different sizes of padded-convolution and fully-connected layers. Moreover, to accelerate the target BNNs efficiently, we exploit the analytical model to explore the optimal design parameters for FCA-BNN. Finally, our proposed mapping flow changes the target network by entering order, and accelerates a new network by compiling and loading corresponding instructions, while without loading and generating bitstream. The evaluations on three major structures of BNNs show the differences between inference accuracy of FCA-BNN and that of GPU are just 0.07%, 0.31% and 0.4% for LFC, VGG-like and Cifar-10 AlexNet. Furthermore, our energy-efficiency results achieve the results of existing customized FPGA accelerators by 0.8× for LFC and 2.6× for VGG-like. For Cifar-10 AlexNet, FCA-BNN achieves 188.2× and 60.6× better than CPU and GPU in energy efficiency, respectively. To the best of our knowledge, FCA-BNN is the most efficient design for change of the target BNN and acceleration of a new BNN, while keeps the competitive performance.
Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification

Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted features or model skeleton dynamics from the trajectory of body joints, while they rarely explore valuable relation information contained in body structure or motion. To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID. Specifically, considering that structurally-connected body components are highly correlated in a skeleton, we first propose a multi-head structural relation layer to learn different relations of neighbor body-component nodes in graphs, which helps aggregate key correlative features for effective node representations. Second, inspired by the fact that body-component collaboration in walking usually carries recognizable patterns, we propose a cross-level collaborative relation layer to infer collaboration between different level components, so as to capture more discriminative skeleton graph features. Finally, to enhance graph dynamics encoding, we propose a novel self-supervised sparse sequential prediction task for model pre-training, which facilitates encoding high-level graph semantics for person Re-ID. MG-SCR outperforms state-of-the-art skeleton-based methods, and it achieves superior performance to many multi-modal methods that utilize extra RGB or depth features. Our codes are available at https://github.com/Kali-Hac/MG-SCR.
Cross-shaped Hanning filter used in Fourier transform profilometry for accurate 3-D shape retrieval

Fourier transform profilometry (FTP) is widely used for real-time three-dimensional (3-D) surface shape measurement with a single frame of projection fringe pattern. The band-pass filter is very important to the shape retrieval accuracy of this technique. On the basis of the Hanning band-pass filter, a cross-shaped Hanning filter is developed in the paper to extract the first-order spectrum of deformed fringe pattern, and the effect of fringe angle on the proposed filter is analysed. The phase retrieval results using the proposed filter and Hanning filter are compared in the simulation and measurement experiment. The results show that the cross-shaped Hanning filter coordinating with the fringe pattern of about 45 degrees holds the higher accuracy of 3-D shape retrieval.
Dawn of artificial intelligence -Enable digital Pathology in Pakistan-A paradigm shift

Pathology particularly histopathology is considered to be a busy and challenging field. It is considered as gold standard for the diagnosis and management of patient particularly in cases of tumor. It has been more than twenty years since the introduction of whole slide imaging (WSI) in the developed part of the world. Various whole slide image (WSI) devices and use of artificial intelligence (AI) based softwares have transformed the field of Pathology1. Digital pathology is a novel technology and currently being implemented in most of the developed part of the world.2 Once the patient’s data becomes digital, it is easily stored, reproducible on a single click and quality remains same. This data can be used to make disease models, disease trends and predict the outcome of a particular disease through data mining which will open new horizons of precise medicine. The use of WSI with computational pathology and data storage devices have revolutionized the working in histopathology. The world witnessed an exponential rise in its adoption particularly after Covid-19 pandemic1. However, in the developing world either it is not being implemented or its use is still sub-optimal. By realizing the potential of digital and computational pathology along with the use of artificial intelligence software, we can bring a drastic change in the field of personalized medicine in the developing part of the world 3. Numerous validation studies have been published indicating that WSI is a reliable tool for routine diagnosis in surgical pathology 4 Continuous...
VisInReport: Complementing Visual Discourse Analytics Through Personalized Insight Reports

We present VisInReport, a visual analytics tool that supports the manual analysis of discourse transcripts and generates reports based on user interaction. As an integral part of scholarly work in the social sciences and humanities, discourse analysis involves an aggregation of characteristics identified in the text, which, in turn, involves a prior identification of regions of particular interest. Manual data evaluation requires extensive effort, which can be a barrier to effective analysis. Our system addresses this challenge by augmenting the users’ analysis with a set of automatically generated visualization layers. These layers enable the detection and exploration of relevant parts of the discussion supporting several tasks, such as topic modeling or question categorization. The system summarizes the extracted events visually and verbally, generating a content-rich insight into the data and the analysis process. During each analysis session, VisInReport builds a shareable report containing a curated selection of interactions and annotations generated by the analyst. We evaluate our approach on real-world datasets through a qualitative study with domain experts from political science, computer science, and linguistics. The results highlight the benefit of integrating the analysis and reporting processes through a visual analytics system, which supports the communication of results among collaborating researchers.
Uncertainty Analysis for Failure Prediction in Optical Transport Network Using Bayesian Neural Network

A Bayesian neural network-based uncertainty analysis technique is proposed for failure prediction in optical transport network, which can not only achieve F1-score up to 99.5%, but also give an uncertainty quantification for the prediction results.
What small businesses in developing country think of cybersecurity risks in the digital age: Indonesian case

Purpose This study aims to identify and analyse the issues faced by internet-based small businesses in developing countries regarding cybersecurity and document how these businesses address the risks. Design/methodology/approach This study used the qualitative method. Respondents were internet-based small businesses selected by using theoretical sampling. Data were collected by using interviews and observations. The validity of the analysis was ensured by using triangulation and member checking. Findings This study reveals that small businesses managed to identify the loss of physical and monetary assets as possible damage. However, only a few businesses identified loss of intangible assets as possible cyber risks. Most small businesses had used basic cybersecurity measures to protect data access and some primary business activities. Unfortunately, they rarely take initiatives in preventing and early detecting cyber risks. Research limitations/implications Findings of this study cannot be generalised as it aims to obtain new insights and document unexplored findings. Thus, if this study’s findings are going to be generalised, it is necessary to conduct an additional study. Secondly, this study did not assess how far small business had fulfilled the relevant information security framework as assessment required additional research, and this study only aimed to map the current situation in small businesses. Practical implications This study emphasised the importance of identifying valuable assets or resources when implementing cybersecurity measures. Focusing on security measures to protect identified assets from cyber risk will make the efforts more efficient and effective than using standardised cybersecurity measures. Third-party developers can also use this study to understand small businesses’ current cybersecurity implementation and their characters to design online platforms that suit these needs. Governments can also design educational activities that address small businesses’ lack of knowledge. Originality/value Most studies which focus on small businesses and information technology (IT) usually only discuss how they use IT. This study also brings new contributions by focusing on developing countries and specifically addresses internet-based technology cyber risk faced by e-commerce businesses. The qualitative method is used as most studies in e-commerce adoption were positivistic in nature, and inductive-based studies were rarely found on the topic.
When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks?

We study the training of finite-width two-layer smoothed ReLU networks for binary classification using the logistic loss. We show that gradient descent drives the training loss to zero if the initial loss is small enough. When the data satisfies certain cluster and separation conditions and the network is wide enough, we show that one step of gradient descent reduces the loss sufficiently that the first result applies.
Educational Software based on Augmented Reality: A Systematic Literature Review

Augmented Reality (AR) is a technology that has been seen to have potential in the educational sector for some years due to the tremendous positive impact that it could generate when used in teaching and learning processes. However, to achieve this, it is essential to consider educational and technological aspects to develop appropriate applications that provide the student and the teacher with innovative teaching methods. For this reason, this study presents a Systematic Review of Literature (SRL) under the protocol proposed by Kitchenham &amp; Stuart (2007) to collect relevant information that provides new research opportunities. This review focuses on augmented reality's technological aspect since the authors previously conducted an initial study considering the educational aspect. As part of this SRL, four sources of information were consulted: IEEE, ACM, Springer, and Elsevier, from which forty-two primary studies were analyzed that describe educational initiatives that incorporate Augmented Reality technology as part of their instructional design in the last decade. Finally, for this study's purposes, information is reported about the devices and mechanisms of interaction, tools and programming languages, recognition models, and the scarcity of methodologies used to develop educational applications with Augmented Reality is identified. In this way, an opportunity arises for research and innovation in the field of Software Engineering Methods and Models.
Access control scheme on blockchain and decentralized attributed-based algorithm with identity

The identity system on Blockchain is defective. There is a challenge of how to verify users identities and ensure the endorsements realities on Blockchain. The decentralized public key infrastructure running on Blockchain can solve the above problems to some extent, but we can provide an identity-model closer to the real society with the combination of the attribute-based algorithm and Blockchain. This paper proposes an access control scheme based on Blockchain and decentralized the attribute-based algorithm with the identity. It uses mutual authorization between users and organizations to get endorsements of identitys attributes for trust-cost links, and uses the attribute-based algorithm to control and share data on the chain for fine-grained access control and privacy protection. By designing a multi-user collaborative attribute-based algorithm, it offers endorsement capabilities for organizations in the identity-model. Through experimental simulation and comparative analysis, the solution meets the requirements of the current universal Blockchain in terms of security and performance, and provides a universal identity-model.
A Shallow Neural Network for Real-Time Embedded Machine Learning for Tensorial Tactile Data Processing

This paper presents a novel hardware architecture of the Tensorial Support Vector Machine (TSVM) based on Shallow Neural Networks (NN) for the Single Value Decomposition (SVD) computation. The proposed NN achieves a comparable Mean Squared Error and Cosine Similarity to the widely used one-sided Jacobi algorithm. When implemented on an FPGA, the NN offers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$324\times $ </tex-math></inline-formula> faster computations than the one-sided Jacobi with reductions up to 58% and 67% in terms of hardware resources and power consumption respectively. When validated on a touch modality classification problem, the NN-based TSVM implementation has achieved a real-time operation while consuming about 88% less energy per classification than the Jacobi-based TSVM with an accuracy loss of at most 3%. Such results offer the ability to deploy intelligence on resource-limited platform for energy-constrained applications.
Attention Mechanism-Driven Potential Fault Cause Identification in Optical Networks

A potential fault cause identification scheme is proposed by using attention mechanism with actual datasets, which finds out the potential fault causes for two types of optical equipment by calculating the attention weight.
Development of an Early Alert System for an Additional Wave of COVID-19 Cases using a Recurrent Neural Network with Long Short-Term Memory

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Damages Liability for Harm Caused by Artificial Intelligence – EU Law in Flux

Artificial intelligence (AI) is an integral part of our everyday lives, able to perform a multitude of tasks with little to no human intervention. Many legal issues related to this phenomenon have not been comprehensively resolved yet. In that context, the question arises whether the existing legal rules on damages liability are sufficient for resolving cases involving AI. The EU institutions have started evaluating if and to what extent new legislation regarding AI is needed, envisioning a European approach to avoid fragmentation of the Single Market. This article critically analyses the most relevant preparatory documents and proposals with regard to civil liability for AI issued by EU legislators. In addition, we discuss the adequacy of existing legal doctrines on private liability in terms of resolving cases where AI is involved. While existing national laws on damages liability can be applied to AI-related harm, the risk exists that case outcomes are unpredictable and divergent, or, in some instances, unjust. The envisioned level playing field throughout the Single Market justifies harmonisation of many aspects of damages liability for AI-related harm. In the process, particular AI characteristics should be carefully considered in terms of questions such as causation and burden of proof.
The Role of Practical Teaching Platform Based on Virtual Reality Technology in Solving the Problems of Traditional Practical Training Education

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Research on the Application of AI Technology in Computer-Assisted Instruction

Abstract The application of AI tech in the field of auxiliary teaching still has many shortcomings and needs to be improved. We need to combine the relevance between AI and auxiliary system to carry out organic application. Based on this, this paper first studies the utilization status of AI tech in Computer-Assisted Instruction (CAI), then analyses the specific utilization of AI tech in CAI, and finally gives the utilization process of AI tech in CAI.
Detection of nucleated red blood cells using the Mindray BC-6800Plus hematology analyzer: a clinical performance evaluation

Background: With the development of instrument technology, the functions and detection methods of automatic blood cell analyzers have become more complex.To ensure optimal clinical applicability, it is crucial to select an automatic blood cell analyzer with excellent clinical detection performance.This study evaluated the latest Mindray BC-6800Plus automatic blood cell analyzer and assessed its performance in the detection of nucleated red blood cells (NRBCs).Methods: A total of 490 clinical blood samples were used to assess the performance of the instrument, including parameters such as precision, linearity, conformity rate of manual microscopic examination, carryover, and limit of quantitation. Results:The instrument showed a small carryover (≤0.02) and excellent linearity (R 2 ≥0.9986).The reproducibility of the sample tests was satisfactory, and the coefficient of variation (CV) of the test results [0.98-1.72%and 0.62-6.97%for white blood cells (WBCs) and NRBCs, respectively] were significantly lower than that declared by the manufacturer (2.5% and 20% for WBCs and NRBCs, respectively).Thus, the BC-6800Plus satisfies the requirements of clinical testing.Two separate Mindray BC-6800Plus machines were tested and found to be in good agreement with each other and with manual microscopy methods.Furthermore, WBC and NRBC counts were highly consistent with results obtained using the XN-9100 blood analyzer. Conclusions:The Mindray BC-6800Plus is an excellent analyzer that can provide timely and accurate reports for clinical laboratory detection of NRBC.
Relationship between Fe-Cu-REEs mineralization and magnetic basement faults using multifractal modeling in Tarom region, NW Iran

In this paper, a Concentration-Distance to Magnetic Faults (C-DMF) fractal model was applied to distinguish Fe, Cu and REEs mineralized zones based on their distance to basement faults derived via airborne geophysical data and field survey maps in the Tarom region at the western Alborz-Azerbaijan belt, NW Iran.The application of the C-DMF model was utilized for mineralization classification in the Tarom 1:100,000 sheet which reveals that the main Fe, Cu and REEs mineralization have an inverse correlation with their distances to magnetic faults (fault lineaments).The Cu ores within acidic rocks and the Fe/ REEs mineralization within intermediated rocks are located in the Northern and Southern parts of the Tarom region, respectively.Furthermore, the basement fault and intrusive rocks have overlapped and they have important roles for these metallic deposits/occurrences. Based on the C-DMF log-log plots, Fe, REEs and Cu high-grade mineralization values are larger than 48.31%, and 3.8%, with respective distances of 451 and 732 m to the nearest magnetic faults.The results obtained from the fractal modeling indicate a positive relationship between Fe, REEs mineralization and the basement faults.Consequently, Cu mineralization has a weak relationship to faults in comparison with Fe and REEs.
Celebrity profiling through linguistic analysis of digital social networks

Abstract Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame.