Reviewerly/exHarmony huggingface dataset:
Datapath Extension of NPUs to Support Nonconvolutional Layers Efficiently

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes:</i> This article extends the datapath of a neural processing unit to support nonconvolutional layers enabling end-to-end execution of lightweight convolutional neural network models on the accelerator. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">—Tulika Mitra, National University of Singapore</i>
NNReArch: A Tensor Program Scheduling Framework Against Neural Network Architecture Reverse Engineering

Architecture reverse engineering has become an emerging attack against deep neural network (DNN) implementations. Several prior works have utilized side-channel leakage to recover the model architecture while the an DNN is executing on a hardware acceleration platform. In this work, we target an open-source deep-learning accelerator, Versatile Tensor Accelerator (VTA), and utilize electromagnetic (EM) side-channel leakage to comprehensively learn the association between DNN architecture configurations and EM emanations. We also consider the holistic system–including the low-level tensor program code of the VTA accelerator on a Xilinx FPGA, and explore the effect of such low-level configurations on the EM leakage. Our study demonstrates that both the optimization and configuration of tensor programs will affect the EM side-channel leakage.Gaining knowledge of the association between low-level tensor program and the EM emanations, we propose NNReArch, a lightweight tensor program scheduling framework against side-channel-based DNN model architecture reverse engineering. Specifically, NNReArch targets reshaping the EM traces of different DNN operators, through scheduling the tensor program execution of the DNN model so as to confuse the adversary. NNReArch is a comprehensive protection framework supporting two modes, a balanced mode that strikes a balance between the DNN model confidentiality and execution performance, and a secure mode where the most secure setting is chosen. We implement and evaluate the proposed framework on the open-source VTA with state-of-the-art DNN architectures. The experimental results demonstrate that NNReArch can efficiently enhance the model architecture security with a small performance overhead. In addition, the proposed obfuscation technique makes reverse engineering of the DNN architecture significantly harder.
Geochemical characterization of the Central Mineral Belt U ± Cu ± Mo ± V mineralization, Labrador, Canada: Application of unsupervised machine-learning for evaluation of IOCG and affiliated mineral potential

The Central Mineral Belt (CMB) in Labrador, Canada, hosts multiple U (±base ± precious metal) showings, prospects and deposits in metamorphosed and variably hydrothermally altered Neoarchean to Mesoproterozoic, igneous and sedimentary rocks. Previous work has recognized U mineralization locally associated with Fe-Ca and alkali metasomatism typical of metasomatic iron oxide and alkali-calcic alteration systems (IOAA) that host iron oxide-copper-gold (IOCG) and affiliated critical metal deposits. However, the type, extent and temporal or genetic relationships between the diverse Fe, Ca and alkali metasomatism and the regionally distributed U mineralization remains poorly understood. Combined unsupervised machine-learning and classification of alteration from a large geochemical dataset distinguish the main alteration phases in the CMB, identify compositional changes related to U mineralization, and infer lithological/mineralogical information from samples with censored (i.e., missing), limited and/or inaccurate metadata. Weak to intense Na and Na + Ca-Fe (Mg) metasomatism in the southwest (Two-Time and Moran Lake areas) and eastern (Michelin area) portions of the CMB pre-dates U mineralization and Fe-oxide breccia development, similar to albitite-hosted U and IOCG deposits globally. Rare earth elements and spider diagrams highlight both preservation and disruption of normally immobile elements. Principal component and cluster analysis indicate significant variations in Fe-Mg ± Na contents in the rocks from combinations of Na, Ca, Fe, and Mg-rich alteration, while protolith REE signatures can be locally preserved even after pervasive albitization-hematization. Cluster analysis identifies mineralized felsic and mafic rocks in the Michelin deposit and Moran Lake area, facilitating inference of relevant lithological/mineralogical information from samples lacking or with limited meta-data. The methods outlined provide rapid and relatively inexpensive means to optimize identification of mineral systems within large geochemical datasets, verify drill core or field observations, highlight potentially overlooked alteration, and refine economic mineral potential assessments. Based on our results and previous work, we suggest the mineral potential of the southwestern and eastern CMB needs to be re-assessed with modern exploration models for IOAA ore systems and their iron oxide-poor variants.
Multi-fidelity and learning-regularization for single image super resolution

Ill-posedness results in regularization-based methods being widely used in single image super-resolution (SISR). However, producing super-resolved images with finer details and fewer artifacts is still a great challenge. With the help of the plug-and-play framework, we introduce a novel fidelity term and learned prior knowledge to produce a powerful SISR model. In the proposed fidelity term, called multi-fidelity, the similarity between the observed data and simulated data is measured in terms of both intensity and gradient; these measurements can reflect the tendency of feature response results to degrade into multiple image layers. Based on the half quadratic splitting (HQS) method, the proposed SISR model is split into two sub-problems, which include the multi-fidelity and regularization terms, respectively. In this paper, we design a deep network, named as R&BED, to learn prior image knowledge. Compared with the manually designed regularization term, learned knowledge can preserve easily ignored features. Experimental results indicate that the subjective and objective metrics corresponding to the proposed method are better than those obtained using the comparison methods.
An Empirical Study on Vulnerability Detection for Source Code Software based on Deep Learning

In recent years, the complexity of software vulnera-bilities has continued to increase. Manual vulnerability detection methods alone no longer meet the demand. With the rapid development of the deep learning, many neural network models have been widely applied to source code vulnerability detection. The variant of recurrent neural network (RNN), bidirectional Long Short-Term Memory (BiLSTM), has been a popular choice in vulnerability detection. However, is BiLSTM the most suitable choice? To answer this question, we conducted a series of experiments to investigate the effectiveness of different neural network models for source code vulnerability detection. The results shows that the variants of RNN, gated recurrent unit (GRU) and bidirectional GRU, are more capable of detecting source code fragments with mixed vulnerability types. And the concatenated convolutional neural network is more capable of detecting source code fragments of single vulnerability types.
Human Action Captioning based on a GRU+LSTM+Attention Model

To quickly understand human actions in the videos, this paper proposes to solve the human action captioning problem which aims to automatically generate text descriptions based on human action videos. A sequence-to-sequence method based on GRU+LSTM+Attention (GLA) model is proposed to solve this problem. Specifically, GRU is applied as the encoder to capture the temporal information of actions. The LSTM is applied as the decoder to generate the fluent fine-grained descriptions for human actions. To focus on the most relevant part of actions and capture the correlation between actions and descriptions, an attention mechanism is applied in the proposed method. Experiments on the WorkoutUOW-18 dataset demonstrate the effectiveness of the proposed method.
A modified ACO with K-Opt for restricted covering salesman problems in different environments

In this study, the ant colony optimization (ACO) algorithm is modified with the K-Opt operation to solve the covering salesman problem (CSP) under one restriction in crisp and imprecise (fuzzy, rough) environments. A CSP involves two phases—the division of cities into groups with the selection of the visiting cities and searching of the Hamiltonian circuit through the visiting cities. But, none of the studies in the literature is made following the direct approach. Also, none of the studies in the literature gives attention to reduce the total travel distance of the unvisited cities from the visited city of a group. Moreover, there is no algorithm in the literature which provides the solution of a CSP with the specified coverage range r. Also, none has introduced any algorithm to solve CSPs in imprecise environments. Though algorithms are available to solve the Traveling Salesman Problems in the imprecise environments, the approach cannot deal with the problems involving fuzzy data with nonlinear membership functions or the problems involving rough data where the rough estimation can not be done using Lebesgue measure. The well-established algorithm for any routing problem is the ACO, but not much attention has been paid to solve the CSP using ACOs. To overcome these limitations on the studies of the ACO on the CSPs, here, an algorithm is proposed for the division of groups of the set of cities depending upon the maximum number of cities in a group and the total number of groups. Then, ACO is used to find the shortest/minimum-cost path of the problem by selecting only one visiting the city from each group without violating the restriction of the specified coverage range r of the location of the unvisited cities. K-Opt operation is applied periodically at the end of ACO operation to improve the quality of the best found solution so far by the ACO algorithm and to arrest any premature convergence. For the restricted problems, paths are searched in such a manner that the total distance/travel cost of different unvisited cities of a group from the visited city of the group should not exceed a predefined upper limit. To solve the problem in an imprecise environment, some approach is followed so that the tour is searched without transferring the imprecise optimisation problem into an equivalent crisp optimisation problem. Also, the simulation approaches in fuzzy and rough environments are proposed to deal with the CSPs with any type of estimation of the imprecise data set. Algorithm is tested with the standard benchmark crisp problems available in the literature. To test the algorithm in the imprecise environments, the imprecise instances are derived randomly from the standard crisp instances using a specified rule. Test results imply that the proposed algorithm is efficient enough in solving the CSPs in the crisp as well as in the imprecise environments.
Leveraging Content-Style Item Representation for Visual Recommendation

When customers' choices may depend on the visual appearance of products (e.g., fashion), visually-aware recommender systems (VRSs) have been shown to provide more accurate preference predictions than pure collaborative models. To refine recommendations, recent VRSs have tried to recognize the influence of each item's visual characteristic on users' preferences, for example, through attention mechanisms. Such visual characteristics may come in the form of content-level item metadata (e.g., image tags) and reviews, which are not always and easily accessible, or image regions-of-interest (e.g., the collar of a shirt), which miss items' style. To address these limitations, we propose a pipeline for visual recommendation, built upon the adoption of those features that can be easily extracted from item images and represent the item content on a stylistic level (i.e., color, shape, and category of a fashion product). Then, we inject such features into a VRS that exploits attention mechanisms to uncover users' personalized importance for each content-style item feature and a neural architecture to model non-linear patterns within user-item interactions. We show that our solution can reach a competitive accuracy and beyond-accuracy trade-off compared with other baselines on two fashion datasets. Code and datasets are available at: https://github.com/sisinflab/Content-Style-VRSs .
Energy-Efficient and High-Throughput CNN Inference on Embedded CPUs-GPUs MPSoCs

Nowadays, many application scenarios, such as mobile phones, drones, mobile robots, require Convolutional Neural Networks (CNNs) inference on embedded CPUs-GPUs MPSoCs. CNN model inference is usually computation intensive while the embedded CPUs-GPUs MPSoCs are usually energy consumption constrained. Therefore, how to achieve computationally-intensive CNN inference in an energy-efficient and high-throughput way is an important issue. However, existing Deep Learning (DL) frameworks only pay attention to achieving high-throughput inference when deploying CNN models on CPU or GPU processors without specifically considering the energy consumption. In this paper, we propose a novel methodology which features design-time optimization techniques in order to achieve energy efficiency and high throughput when deploying CNN models on embedded CPUs-GPUs MPSoCs. Our methodology finds Pareto-optimal mappings of a CNN model onto a CPUs-GPUs MPSoC with voltage and frequency scaling (VFS) configurations with the help of a two-objective Genetic Algorithm (GA) which optimizes the system throughput and energy consumption simultaneously. Moreover, we propose two analytical models, that are used as fitness functions in the two-objective GA to evaluate very fast the system throughput and energy consumption of CNNs mapped onto embedded CPUs-GPUs MPSoCs. Also, we confirm the high accuracy of these two analytical models by experimental evidence. Finally, our experimental results show that our novel methodology is able to achieve both energy efficiency and high throughput when deploying CNN models on embedded CPUs-GPUs MPSoCs, in comparison with TensorRT which is the best-known CNN deployment optimizer designed for NVIDIA embedded MPSoCs.
ExKGR: Explainable Multi-hop Reasoning for Evolving Knowledge Graph

Knowledge graph reasoning is a popular approach to predict new facts in knowledge graphs (KGs) suffering from inherent incompleteness. Compared with the popular embedding-based approach, multi-hop reasoning approach is more interpretable. Multi-hop reasoning can be modeled as reinforcement learning (RL) in which the RL agent navigates in the KG. Despite high interpretability, the knowledge in real world evolves by the minute, previous approaches are based on static KG. To address the above challenges, we propose an explainable multi-hop reasoning approach (ExKGR) for practical scenario, aiming to reason the emerging entity in evolving KGs and provide evidentiary reasoning paths. Specifically, ExKGR can represent emerging entities by inductive learning of neighbors and the query. Furthermore, we restrict the RL action space of supernodes. Also, we use a dynamic reward instead of a binary reward in prior approaches. The experimental results on four benchmark datasets demonstrate that our approach significantly outperforms prior approaches.
Classification of Fire Related Tweets on Twitter Using Bidirectional Encoder Representations from Transformers (BERT)

Bidirectional Encoder Representation from Transformers (BERT) is a transfer learning model approach in natural language processing (NLP). BERT has different types of pre-trained models that can pre-train a language representation to create a model that can be finetuned on specific tasks using a dataset like text classification to produce state of the art predictions. Recent studies providing the use of BERT in natural language processing have highlighted that there are no publicly available Filipino tweet datasets regarding fire reports on social media that lead to a lack of classification models. This paper aims to design and implement a system to classify Filipino tweets using different pre-trained BERT models. Upon creating a model exclusive for organizing Filipino tweets using 2,081 tweets as a dataset that contains fire-related tweets, the researchers were able to compare the accuracy of the different finetuned pre-trained BERT models. The data shows a significant difference in the accuracy of each pre-trained BERT model. The highest of which is the BERT Base Uncased WWM model with a test accuracy of 87.50% and a train loss of 0.06 during training of the dataset. The least accurate among the pre-trained BERT models is the BERT Base Cased WWM model, with a test accuracy of 76.34% and a train loss of 0.2. The result shows that BERT Base Uncased WWM model can be a reliable model in classifying fire-related tweets. The accuracy obtained by the models may vary depending on how large the dataset is.
Multi-modal Sentiment and Emotion Joint Analysis with a Deep Attentive Multi-task Learning Model

Emotion is seen as the external expression of sentiment, while sentiment is the essential nature of emotion. They are tightly entangled with each other in that one helps the understanding of the other, leading to a new research topic, i.e., multi-modal sentiment and emotion joint analysis. There exists two key challenges in this field, i.e., multi-modal fusion and multi-task interaction. Most of the recent approaches treat them as two independent tasks, and fail to model the relationships between them. In this paper, we propose a novel multi-modal multi-task learning model, termed MMT, to generically address such issues. Specially, two attention mechanisms, i.e., cross-modal and cross-task attentions are designed. Cross-modal attention is proposed to model multi-modal feature fusion, while cross-task attention is to capture the interaction between sentiment analysis and emotion recognition. Finally, we empirically show that this method alleviates such problems on two benchmarking datasets, while getting better performance for the main task, i.e., sentiment analysis with the help of the secondary emotion recognition task.
CyberIoT: An Initial Conceptualization of a Web-based Cyber Range for IoT

Digital technologies are changing the way people live and work. Despite the many benefits of digital technologies, they expose users to sophisticated security threats, both in home networks and in enterprise networks. User awareness of security issues surrounding their networks, hardware devices, and software is one of the best practices for mitigating security risks. Cyber range is a promising platform for enabling participants to learn cybersecurity concepts and create awareness on cyber threats through simulated exercises and training. This paper presents CyberIoT, a novel conceptual design of a cyber range platform designed as Platform-as-a-Service that allows users to instantiate, run, and operate cybersecurity training exercises. The platform is designed for implementation using OpenStack. The functional and non-functional requirements are identified. The platform architecture is based on the three-tier Web application architecture, with a Web portal, data store, and various management modules. Further research should include development of training use cases, design validation, and full system implementation.
Thresholding of prominent biomarkers of breast cancer on overall survival using classification and regression tree

HER2, ER, PR, and ERBB2 play a vital role in treating breast cancer. These are significant predictive and prognosis biomarkers of breast cancer.We aim to obtain a unique biomarker-specific prediction on overall survival to know their survival and death risk.Survival analysis is performed on classified data using Classification and Regression Tree (CART) analysis. Hazard ratio and Confidence Interval are computed using MLE and the Bayesian approach with the CPH model for univariate and multivariable illustrations. Validation of CART is executed with the Brier score, and accuracy and sensitivity are obtained using the k-nn classifier.Utilizing CART analysis, the cut-off value of continuous-valued biomarkers HER2, ER, PR, and ERBB2 are obtained as 14.707, 8.128, 13.153, and 6.884, respectively. Brier score of CART is 0.16 towards validation of methodology. Survival analysis gives a demonstration of the survival estimates with significant statistical strategies.Patients with breast cancer are at low risk of death, whose HER2 value is below its cut-off value, and ER, PR, and ERBB2 values are greater than their cut-off values. This comparison is with the patient having the opposite side of these cut-off values for the same biomarkers.
XceptionUnetV1: A Lightweight DCNN for Biomedical Image Segmentation

This paper proposes a Deep Convolutional Neural Network (DCNN) architecture for biomedical image segmentation. The proposed work explores the role of separable convolution within the context of biomedical image segmentation. The proposed architecture, XceptionUnetV1, is an amalgam of the Xception and U-Net models. Additionally, the model uses dilated convolution to increase the field view of the filters. The proposed model achieves better results than various versions of U-Net. The proposed DCNN architecture requires much lesser parameters, approximately $$1/4^{th}$$ of the U-Net model. The proposed model has been tested on chest X-rays for lung segmentation.
Hospital-Agnostic Image Representation Learning in Digital Pathology

Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability in the histopathology images, thus making consistent diagnosis challenging. These differences may stem from variability in image acquisition through multi-vendor scanners, variable acquisition parameters, and differences in staining procedure; as well, patient demographics may bias the glass slide batches before image acquisition. These variabilities are assumed to cause a domain shift in the images of different hospitals. It is crucial to overcome this domain shift because an ideal machine-learning model must be able to work on the diverse sources of images, independent of the acquisition center. A domain generalization technique is leveraged in this study to improve the generalization capability of a Deep Neural Network (DNN), to an unseen histopathology image set (i.e., from an unseen hospital/trial site) in the presence of domain shift. According to experimental results, the conventional supervisedlearning regime generalizes poorly to data collected from different hospitals. However, the proposed hospital-agnostic learning can improve the generalization considering the lowdimensional latent space representation visualization, and classification accuracy results.