Reviewerly/exHarmony huggingface dataset:
Blockchain-Enabled Safety-as-a-Service for Industrial IoT Applications

The integration of Industrial Internet of Things (IIoT)-based technologies with the existing industrial manufacturing processes help to improve on-site safety of workers, reduce downtime of machines, improve productivity rate, and minimize near-miss incidents and casualties. However, a prior intimation of the various safety-related information may minimize the probability of accidents on the factory floor and casualty rates. Typically, the safety-as-a-service (Safe-aaS) infrastructure provides customized safety-related decisions to the registered end-users. However, there exist certain problems associated with the privacy of the information provided by the end-users during registration, decision parameters requested by the end-users, and data sensed by the sensor nodes. In this work, we provide an architecture of Blockchain integration into the Safe-aaS infrastructure. Additionally, we discuss the implementation and management of the Blockchain-enabled Safe-aaS architecture. Extensive analytical results demonstrate that the profit of the safety service provider (SSP) improves with the adoption of blockchain technology into the Safe-aaS architecture. On the other hand, the overall throughput in our proposed architecture follows an increasing trend, with the increase in the number of decisions successfully delivered. Additionally, the throughput decreases with the increase in the number of registered end-users.
Adversarial attack on DL-based massive MIMO CSI feedback

With the increasing application of deep learning (DL) algorithms in wireless communications, the physical layer faces new challenges caused by adversarial attack. Such attack has significantly affected the neural network in computer vision. We choose DL-based channel state information (CSI) to show the effect of adversarial attack on DL-based communication system. We present a practical method to craft white-box adversarial attack on DL-based CSI feedback process. Our simulation results show the destructive effect adversarial attack causes on DL-based CSI feedback by analyzing the performance of normalized mean square error. We also launch a jamming attack for comparison and find that the jamming attack could be prevented with certain precautions. As DL algorithm becomes the trend in developing wireless communication, this work raises concerns regarding the security in the use of DL-based algorithms.
Primer on silicon neuromorphic photonic processors: architecture and compiler

Abstract Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.
View-Invariant Probabilistic Embedding for Human Pose

Depictions of similar human body configurations can vary with changing viewpoints. Using only 2D information, we would like to enable vision algorithms to recognize similarity in human body poses across multiple views. This ability is useful for analyzing body movements and human behaviors in images and videos. In this paper, we propose an approach for learning a compact view-invariant embedding space from 2D joint keypoints alone, without explicitly predicting 3D poses. Since 2D poses are projected from 3D space, they have an inherent ambiguity, which is difficult to represent through a deterministic mapping. Hence, we use probabilistic embeddings to model this input uncertainty. Experimental results show that our embedding model achieves higher accuracy when retrieving similar poses across different camera views, in comparison with 2D-to-3D pose lifting models. We also demonstrate the effectiveness of applying our embeddings to view-invariant action recognition and video alignment. Our code is available at https://github.com/google-research/google-research/tree/master/poem .
Experimental Passive-State Preparation for Continuous-Variable Quantum Communications

In the Gaussian-modulated coherent state quantum key distribution (QKD) protocol, the sender first generates Gaussian distributed random numbers and then encodes them on weak laser pulses actively by performing amplitude and phase modulations. Recently, an equivalent passive QKD scheme was proposed by exploring the intrinsic field fluctuations of a thermal source [B. Qi, P. G. Evans, and W. P. Grice, Phys. Rev. A 97, 012317 (2018)]. This passive QKD scheme is especially appealing for chip-scale implementation since no active modulations are required. In this paper, we conduct an experimental study of the passively encoded QKD scheme using an off-the-shelf amplified spontaneous emission source operated in continuous-wave mode. Our results show that the excess noise introduced by the passive state preparation scheme can be effectively suppressed by applying optical attenuation and secure key could be generated over metro-area distances.
Single-photon nonreciprocal excitation transfer with non-Markovian retarded effects

We study at the single-photon level the nonreciprocal excitation transfer between emitters coupled with a common waveguide. Non-Markovian retarded effects are taken into account due to the large separation distance between different emitter-waveguide coupling ports. It is shown that the excitation transfer between the emitters of a small-atom dimer can be obviously nonreciprocal by introducing between them a coherent coupling channel with nontrivial coupling phase. We prove that for dimer models the nonreciprocity cannot coexist with the decoherence-free giant-atom structure although the latter markedly lengthens the lifetime of the emitters. In view of this, we further propose a giant-atom trimer which supports both nonreciprocal transfer (directional circulation) of the excitation and greatly lengthened lifetime. Such a trimer model also exhibits incommensurate emitter-waveguide entanglement for different initial states in which case the excitation transfer is, however. reciprocal. We believe that the proposals in this paper are of potential applications in large-scale quantum networks and quantum information processing.
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source.In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms.By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts.Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage.Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases 1 .
Pay Attention to Raw Traces: A Deep Learning Architecture for End-to-End Profiling Attacks

With the renaissance of deep learning, the side-channel community also notices the potential of this technology, which is highly related to the profiling attacks in the side-channel context. Many papers have recently investigated the abilities of deep learning in profiling traces. Some of them also aim at the countermeasures (e.g., masking) simultaneously. Nevertheless, so far, all of these papers work with an (implicit) assumption that the number of time samples in raw traces can be reduced before the profiling, i.e., the position of points of interest (PoIs) can be manually located. This is arguably the most challenging part of a practical black-box analysis targeting an implementation protected by masking. Therefore, we argue that to fully utilize the potential of deep learning and get rid of any manual intervention, the end-to-end profiling directly mapping raw traces to target intermediate values is demanded.In this paper, we propose a neural network architecture that consists of encoders, attention mechanisms and a classifier, to conduct the end-to-end profiling. The networks built by our architecture could directly classify the traces that contain a large number of time samples (i.e., raw traces without manual feature extraction) while whose underlying implementation is protected by masking. We validate our networks on several public datasets, i.e., DPA contest v4 and ASCAD, where over 100,000 time samples are directly used in profiling. To our best knowledge, we are the first that successfully carry out end-to-end profiling attacks. The results on the datasets indicate that our networks could get rid of the tricky manual feature extraction. Moreover, our networks perform even systematically better (w.r.t. the number of traces in attacks) than those trained on the reduced traces. These validations imply our approach is not only a first but also a concrete step towards end-to-end profiling attacks in the side-channel context.
VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild

We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VoxelTrack</i> for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. In contrast to previous efforts which require to establish cross-view correspondence based on noisy 2D pose estimates, it directly estimates and tracks 3D poses from a 3D voxel-based representation constructed from multi-view images. We first discretize the 3D space by regular voxels and compute a feature vector for each voxel by averaging the body joint heatmaps that are inversely projected from all views. We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint. Similarly, a Re-ID feature is computed for each voxel which is used to track the estimated 3D poses over time. The main advantage of the approach is that it avoids making any hard decisions based on individual images. The approach can robustly estimate and track 3D poses even when people are severely occluded in some cameras. It outperforms the state-of-the-art methods by a large margin on four public datasets including Shelf, Campus, Human3.6 M and CMU Panoptic.
Kraken

The growing popularity of microservices has led to the proliferation of online cloud service-based applications, which are typically modelled as Directed Acyclic Graphs (DAGs) comprising of tens to hundreds of microservices. The vast majority of these applications are user-facing, and hence, have stringent SLO requirements. Serverless functions, having short resource provisioning times and instant scalability, are suitable candidates for developing such latency-critical applications. However, existing serverless providers are unaware of the workflow characteristics of application DAGs, leading to container over-provisioning in many cases. This is further exacerbated in the case of dynamic DAGs, where the function chain for an application is not known a priori. Motivated by these observations, we propose Kraken, a workflow-aware resource management framework that minimizes the number of containers provisioned for an application DAG while ensuring SLO-compliance. We design and implement Kraken on OpenFaaS and evaluate it on a multi-node Kubernetes-managed cluster. Our extensive experimental evaluation using DeathStarbench workload suite and real-world traces demonstrates that Kraken spawns up to 76% fewer containers, thereby improving container utilization and saving cluster-wide energy by up to 4x and 48%, respectively, when compared to state-of-the art schedulers employed in serverless platforms.
Boundary Smoothing for Named Entity Recognition

Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration.Inspired by label smoothing and driven by the ambiguity of boundary annotation in NER engineering, we propose boundary smoothing as a regularization technique for span-based neural NER models.It re-assigns entity probabilities from annotated spans to the surrounding ones.Built on a simple but strong baseline, our model achieves results better than or competitive with previous stateof-the-art systems on eight well-known NER benchmarks. 1 Further empirical analysis suggests that boundary smoothing effectively mitigates over-confidence, improves model calibration, and brings flatter neural minima and more smoothed loss landscapes.
Adversarial attacks and adversarial robustness in computational pathology

Abstract Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks.
Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

Spam represents a genuine irritation for email users, since it often disturbs them during their work or free time. Machine learning approaches are commonly utilized as the engine of spam detection solutions, as they are efficient and usually exhibit a high degree of classification accuracy. Nevertheless, it sometimes happens that good messages are labeled as spam and, more often, some spam emails enter into the inbox as good ones. This manuscript proposes a novel email spam detection approach by combining machine learning models with an enhanced sine cosine swarm intelligence algorithm to counter the deficiencies of the existing techniques. The introduced novel sine cosine was adopted for training logistic regression and for tuning XGBoost models as part of the hybrid machine learning-metaheuristics framework. The developed framework has been validated on two public high-dimensional spam benchmark datasets (CSDMC2010 and TurkishEmail), and the extensive experiments conducted have shown that the model successfully deals with high-degree data. The comparative analysis with other cutting-edge spam detection models, also based on metaheuristics, has shown that the proposed hybrid method obtains superior performance in terms of accuracy, precision, recall, f1 score, and other relevant classification metrics. Additionally, the empirically established superiority of the proposed method is validated using rigid statistical tests.
Online Scale Adaptive Visual Tracking Based on Multilayer Convolutional Features

Convolutional neural networks can efficiently exploit sophisticated hierarchical features which have different properties for visual tracking problem. In this paper, by using multilayer convolutional features jointly and constructing a scale pyramid, we propose an online scale adaptive tracking method. We construct two separate correlation filters for translation and scale estimations. The translation filters improve the accuracy of target localization by a weighted fusion of multiple convolutional layers. Meanwhile, the separate scale filters achieve the optimal and fast scale estimation by a scale pyramid. This design decreases the mutual errors of translation and scale estimations, and reduces computational complexity efficiently. Moreover, in order to solve the problem of tracking drifts due to the severe occlusion or serious appearance changes of the target, we present a new adaptive and selective update mechanism to update the translation filters effectively. Extensive experimental results show that our proposed method achieves the excellent overall performance compared with the state-of-the-art methods.
Postevent Reconnaissance Image Documentation Using Automated Classification

Reconnaissance teams are charged with collecting perishable data after a natural disaster. In the field, these engineers typically record their observations through images. Each team takes many views of both exterior and interior buildings and frequently collects associated metadata that reflect information represented in images, such as global positioning system (GPS) devices, structural drawings, timestamp, and measurements. Large quantities of images with a wide variety of contents are collected. The window of opportunity is short, and engineers need to provide accurate and rich descriptions of such images before the details are forgotten. In this paper, an automated approach is developed to organize and document such scientific information in an efficient and rapid manner. Deep convolutional neural network algorithms were successfully implemented to extract robust features of key visual contents in the images. A schema is designed based on the realistic needs of field teams examining buildings. A significant number of images collected from past earthquakes were used to train robust classifiers to automatically classify the images. The classifiers and associated schema were used to automatically generate individual reports for buildings.
The Role of Knowledge Management Infrastructure in Enhancing Job Satisfaction: A Developing Country Perspective

Aim/Purpose: This research aims to examine the role of Knowledge Management (KM) infrastructure (technological, structural, and cultural) in enhancing job satisfaction in the context of developing countries, as exemplified by Jordan. Background: Despite the presence of job satisfaction studies conducted in educational institutions across the world, knowledge management issues have not been taken into consideration as influencing factors. Methodology: A total of 168 responses to a questionnaire survey were collected from the academic staff at Zarqa University in Jordan. Multiple regression analysis was conducted to test the research hypotheses. Contribution: This study offers deeper understanding about the role that knowledge management infrastructure plays in enhancing job satisfaction from a developing country perspective. The proposed model is tested the first time in Jordan. Findings: Results of the current study revealed that there are significant positive impacts of technological and cultural KM infrastructures on job satisfaction, whereas structural KM infrastructure does not have a significant impact on job satisfaction. Also, the results revealed significant gender difference in perception of the impact of knowledge management infrastructure on job satisfaction. On the other hand, an ANOVA test found no significant difference in the impact of knowledge management infrastructure on job satisfaction among groups by age, experience, and academic rank. Recommendation for Researchers: Our findings can be used as a base of knowledge for further studies about knowledge management infrastructure and job satisfaction following different criteria and research procedures. Future Research: The current model can be applied and assessed further in other sectors, including public universities and other services sectors in developed and developing countries.
ComQA: Question Answering Over Knowledge Base via Semantic Matching

Question answering over knowledge base (KBQA) is a powerful tool to extract answers from graph-like knowledge bases. Here, we present ComQA-a three-phase KBQA framework by which end-users can ask complex questions and get answers in a natural way. In ComQA, a complex question is decomposed into several triple patterns. Then, ComQA retrieves candidate subgraphs matching the triple patterns from the knowledge base and evaluates the semantic similarity between the subgraphs and the triple patterns to find the answer. It is a long-standing problem to evaluate the semantic similarity between the question and the heterogeneous subgraph containing the answer. To handle this problem, first, a semantic-based extension method is proposed to identify entities and relations in the question while considering the underlying knowledge base. The precision of identifying entities and relations determines the correctness of successive steps. Second, by exploiting the syntactic pattern in the question, ComQA constructs the query graphs for natural language questions so that it can filter out topology-mismatch subgraphs and narrow down the search space in knowledge bases. Finally, by incorporating the information from the underlying knowledge base, we fine-tune general word vectors, making them more specific to ranking possible answers in KBQA task. Extensive experiments over a series of QALD challenges confirm that the performance of ComQA is comparable to those state-of-the-art approaches with respect to precision, recall, and F1-score.