Reviewerly/exHarmony huggingface dataset:
A Novel IoT-Based Explainable Deep Learning Framework for Intrusion Detection Systems

The growth of the Internet of Things (IoT) is accompanied by serious cybersecurity risks, especially with the emergence of IoT botnets. In this context, intrusion detection systems (IDSs) proved their efficiency in detecting various attacks that may target IoT networks, especially when leveraging machine/deep learning (ML/DL) techniques. In fact, ML/DL-based solutions make “machine-centric” decisions about intrusion detection in the IoT network, which are then executed by humans (i.e., executive cyber-security staff). However, ML/DL-based solutions do not provide any explanation of why such decisions were made, and thus their results cannot be properly understood/exploited by humans. To address this issue, explainable artificial intelligence (XAI) is a promising paradigm that helps to explain the decisions of ML/DL-based IDSs to make them understandable to cyber-security experts. In this article, we design a novel XAI-powered framework to enable not only detecting intrusions/attacks in IoT networks, but also interpret critical decisions made by ML/DL-based IDSs. Therefore, we first build an ML/DL-based IDS using a deep neural network (DNN) to detect and predict IoT attacks in real time. Then we develop multiple XAI models (i.e., RuleFit and SHapley Additive exPlanations, SHAP) on top of our DNN architecture to enable more trust, transparency, and explanation of the decisions made by our ML/DL-based IDS to cyber security experts. The in-depth experiment results with well-known IoT attacks show the efficiency and explainability of our proposed framework.
Coherence as a Resource for Shor’s Algorithm

Shor's factoring algorithm provides a superpolynomial speedup over all known classical factoring algorithms. Here, we address the question of which quantum properties fuel this advantage. We investigate a sequential variant of Shor's algorithm with a fixed overall structure and identify the role of coherence for this algorithm quantitatively. We analyze this protocol in the framework of dynamical resource theories, which capture the resource character of operations that can create and detect coherence. This allows us to derive a lower and an upper bound on the success probability of the protocol, which depend on rigorously defined measures of coherence as a dynamical resource. We compare these bounds with the classical limit of the protocol and conclude that within the fixed structure that we consider, coherence is the quantum resource that determines its performance by bounding the success probability from below and above. Therefore, we shine new light on the fundamental role of coherence in quantum computation.
Design and Evaluation Challenges of Conversational Agents in Health Care and Well-being: Selective Review Study

Health care and well-being are 2 main interconnected application areas of conversational agents (CAs). There is a significant increase in research, development, and commercial implementations in this area. In parallel to the increasing interest, new challenges in designing and evaluating CAs have emerged.This study aims to identify key design, development, and evaluation challenges of CAs in health care and well-being research. The focus is on the very recent projects with their emerging challenges.A review study was conducted with 17 invited studies, most of which were presented at the ACM (Association for Computing Machinery) CHI 2020 conference workshop on CAs for health and well-being. Eligibility criteria required the studies to involve a CA applied to a health or well-being project (ongoing or recently finished). The participating studies were asked to report on their projects' design and evaluation challenges. We used thematic analysis to review the studies.The findings include a range of topics from primary care to caring for older adults to health coaching. We identified 4 major themes: (1) Domain Information and Integration, (2) User-System Interaction and Partnership, (3) Evaluation, and (4) Conversational Competence.CAs proved their worth during the pandemic as health screening tools, and are expected to stay to further support various health care domains, especially personal health care. Growth in investment in CAs also shows the value as a personal assistant. Our study shows that while some challenges are shared with other CA application areas, safety and privacy remain the major challenges in the health care and well-being domains. An increased level of collaboration across different institutions and entities may be a promising direction to address some of the major challenges that otherwise would be too complex to be addressed by the projects with their limited scope and budget.
Predictive uncertainty estimation for out-of-distribution detection in digital pathology

Machine learning model deployment in clinical practice demands real-time risk assessment to identify situations in which the model is uncertain. Once deployed, models should be accurate for classes seen during training while providing informative estimates of uncertainty to flag abnormalities and unseen classes for further analysis. Although recent developments in uncertainty estimation have resulted in an increasing number of methods, a rigorous empirical evaluation of their performance on large-scale digital pathology datasets is lacking. This work provides a benchmark for evaluating prevalent methods on multiple datasets by comparing the uncertainty estimates on both in-distribution and realistic near and far out-of-distribution (OOD) data on a whole-slide level. To this end, we aggregate uncertainty values from patch-based classifiers to whole-slide level uncertainty scores. We show that results found in classical computer vision benchmarks do not always translate to the medical imaging setting. Specifically, we demonstrate that deep ensembles perform best at detecting far-OOD data but can be outperformed on a more challenging near-OOD detection task by multi-head ensembles trained for optimal ensemble diversity. Furthermore, we demonstrate the harmful impact OOD data can have on the performance of deployed machine learning models. Overall, we show that uncertainty estimates can be used to discriminate in-distribution from OOD data with high AUC scores. Still, model deployment might require careful tuning based on prior knowledge of prospective OOD data.
Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.
Salient-to-Broad Transition for Video Person Re-identification

Due to the limited utilization of temporal relations in video re-id, the frame-level attention regions of mainstream methods are partial and highly similar. To address this problem, we propose a Salient-to-Broad Module (SBM) to enlarge the attention regions gradually. Specifically, in SBM, while the previous frames have focused on the most salient regions, the later frames tend to focus on broader regions. In this way, the additional information in broad regions can supplement salient regions, incurring more powerful video-level representations. To further improve SBM, an Integration-and-Distribution Module (IDM) is introduced to enhance frame-level representations. IDM first integrates features from the entire feature space and then distributes the integrated features to each spatial location. SBM and IDM are mutually beneficial since they enhance the representations from video-level and frame-level, respectively. Extensive experiments on four prevalent benchmarks demonstrate the effectiveness and superiority of our method. The source code is available at https://github.com/baist/SINet.
Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression

Modeling latent variables with priors and hyperpriors is an essential problem in variational image compression. Formally, trade-off between rate and distortion is handled well if priors and hyperpriors precisely describe latent variables. Current practices only adopt univariate priors and process each variable individually. However, we find inter-correlations and intra-correlations exist when observing latent variables in a vectorized perspective. These findings reveal visual redundancies to improve rate-distortion performance and parallel processing ability to speed up compression. This encourages us to propose a novel vectorized prior. Specifically, a multivariate Gaussian mixture is proposed with means and covariances to be estimated. Then, a novel probabilistic vector quantization is utilized to effectively approximate means, and remaining covariances are further induced to a unified mixture and solved by cascaded estimation without context models involved. Furthermore, code books involved in quantization are extended to multi-codebooks for complexity reduction, which formulates an efficient compression procedure. Extensive experiments on benchmark datasets against state-of-the-art indicate our model has better rate-distortion performance and an impressive 3.18x compression speed up, giving us the ability to perform real-time, high-quality variational image compression in practice. Our source code is publicly available at https://github.com/xiaosu-zhu/McQuic.
A perspective on physical reservoir computing with nanomagnetic devices

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.
Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women’s health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.
A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition

Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.
A New Approach to Conformant Planning Using CNF∗

In this paper, we develop a heuristic, progression based conformant planner, called CNF, which represents belief states by a special type of CNF formulae, called CNF CNF-state. We define a transition function φCNF for computing the successor belief state resulting from the execution of an action in a belief state and prove that it is sound and complete with respect to the complete semantics defined in the literature for conformant planning. We evaluate the performance of CNF against other state-of-the-art conformant planners and identify the classes of problems where CNF is comparable with other state-of-the-art planners or scales up better than other planners. We also develop a technique called oneof relaxation which helps boost the performance of CNF. We characterize the domains where this technique can be applied and validate this idea by proposing a new set of benchmarks that is really difficult for other planners yet easy for CNF.
Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features

Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-ofthe-art algorithms on leveraging multiple features for multimedia analysis.
Fundamental building block for all-optical scalable quantum networks

The fundamental limits for achieving scalability in all-optical quantum communication settings are addressed. A specific scheme is proposed to reach such a limit in an efficient and fast way, in this respect outperforming the standard version of quantum repeaters and also advanced photonic and matter-based protocols.
Iterative Search for Weakly Supervised Semantic Parsing

Pradeep Dasigi, Matt Gardner, Shikhar Murty, Luke Zettlemoyer, Eduard Hovy. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Environmental pollution and geo-ecological risk assessment of the Qhorveh mining area in western Iran

In order to evaluate the effect of mining activity on the environment of the Qhorveh mining area in the west of Iran, the geological, ecological and environmental data, related to social development and regional economic status, were used. The geological data included seven sub-indices, such as vegetation coverage, land utilization type, and fault activity; ecological data, with two sub-indices, such as degree of ecological environment recovery; and finally, environmental data, with three sub-indices, such as soil and dust pollutions. These were selected based on the literature and expert opinion which were utilized for environmental pollution and geo-ecological (EPGE) risk assessment of the study site. Remote sensing (RS) image, field sampling, digital elevation map, and data retrieved from different government agencies were used to generate layers for the sub-indices in the geographic information system (GIS) environment. In addition, the analytical hierarchy process (AHP) method was used to determine the weight of sub-indices. Five levels consisting of best, good, middle, poor and worst were used to describe the EPGE risk assessment of the Qhorveh mining area. Results showed that worst and poor levels of EPGE risk are in the east and northeast of the study area where the gold and pumice mines are located while best and good levels of EPGE risk are in its center where the stone mines are located. According to the results of this research, the EPGE risk assessment of the Qhorveh mining area is affected by the environmental pollution index with its highest weight (0.3908). It can be concluded that the integration of the RS, GIS and AHP methods proposed in this study improved the evaluation quality of EPGE risk assessment.
A Deep Neural Network for Short-Segment Speaker Recognition

Todays interactive devices such as smart-phone assistants and smart speakers often deal with short-duration speech segments. As a result, speaker recognition systems integrated into such devices will be much better suited with models capable of performing the recognition task with short-duration utterances. In this paper, a new deep neural network, UtterIdNet, capable of performing speaker recognition with short speech segments is proposed. Our proposed model utilizes a novel architecture that makes it suitable for short-segment speaker recognition through an efficiently increased use of information in short speech segments. UtterIdNet has been trained and tested on the VoxCeleb datasets, the latest benchmarks in speaker recognition. Evaluations for different segment durations show consistent and stable performance for short segments, with significant improvement over the previous models for segments of 2 seconds, 1 second, and especially sub-second durations (250 ms and 500 ms).
Vecalign: Improved Sentence Alignment in Linear Time and Space

We introduce Vecalign, a novel bilingual sentence alignment method which is linear in time and space with respect to the number of sentences being aligned and which requires only bilingual sentence embeddings.On a standard German-French test set, Vecalign outperforms the previous state-of-the-art method (which has quadratic time complexity and requires a machine translation system) by 5 F 1 points.It substantially outperforms the popular Hunalign toolkit at recovering Bible verse alignments in medium-to low-resource language pairs, and it improves downstream MT quality by 1.7 and 1.6 BLEU in Sinhala→English and Nepali→English, respectively, compared to the Hunalign-based Paracrawl pipeline.
A new random diffusion algorithm based on the multi-scroll Chua's chaotic circuit system

In this paper, a new image encryption algorithm was proposed based on the Chua's multi-scroll chaotic circuit system. In comparison with other classical discrete systems, the new multi-scroll system has higher complexity, thus it was more suitable for the design of the image encryption algorithm. The correlation of the adjacent pixels was greatly eliminated by using the Arnold cat transformation. According to the scroll number of the multi-scroll chaotic system on x-y direction, parameter p and q of the Arnold transformation can be determined. Especially, the pixel value of the ciphered image of the new random diffusion algorithm depends on two random non-adjacent pixels and the interference value of the multi-scroll chaotic system, it significantly reduced the correlation between the adjacent pixels, and it greatly increases the ability to resist deciphering. The experimental results show that the encryption effect of the ciphered image were significantly higher than that of other encryption algorithms. It provided a data support and theoretical direction for the research of the related cryptography fields.