Reviewerly/exHarmony huggingface dataset:
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection

Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear how existing solutions would perform under out-of-distribution scenarios, e.g., due to shifts in machine load or environmental noise. Grounded in the application of machine health monitoring, we propose a framework that adapts to new conditions with few-shot samples. Building upon prior work, we adopt a classification-based approach for anomaly detection and show its equivalence to mixture density estimation of the normal samples. We incorporate an episodic training procedure to match the few-shot setting during inference. We define multiple auxiliary classification tasks based on meta-information and leverage gradient-based meta-learning to improve generalization to different shifts. We evaluate our proposed method on a recently-released dataset of audio measurements from different machine types. It improved upon two baselines by around 10% and is on par with best-performing model reported on the dataset.
Establishment of geochemical thresholds for vanadium throughout Korea and at potential development sites using geochemical map data

Geochemical maps can be used for a variety of purposes, one of which is to establish regional or local geochemical thresholds for the analyzed elements. In the case of vanadium, as industrial demand and use increase, it is necessary to expand the development of vanadium in Korea. However, the environmental management standards are insufficient. Therefore, in this study, using geochemical data, we derived geochemical threshold values for the entire country and areas with potential for the development of vanadium deposits. The regional (country-wide) threshold value was derived using logarithmic transformation of raw data (N = 23,548) of the first- and second-order stream sediments collected across the country in the late 1990s and the early 2000s. The median + 2 median absolute deviation (MAD) and Tukey inner fence (TIF) values were 116 mg/kg and 200 mg/kg, respectively. Of these, the TIF standard, which showed 0.6% of data exceeding the threshold, was judged to be appropriate for distinguishing clear enrichment or contamination of vanadium. In the case of the Geumsan and Pocheon, areas with potential for vanadium development, the TIF and median + 2 MAD values of 259 mg/kg and 218 mg/kg, respectively, can be used as the criteria for evaluating the impact of environmental pollution before and after deposit development. Likewise, by deriving threshold values of the target elements using geochemical map data, it is possible to provide basic environmental information for geochemical evaluation and follow-up management in advance during large-scale site development.
Towards a recipe for language decomposition: quality assessment of language product lines

Abstract Programming languages are complex systems that are usually implemented as monolithic interpreters and compilers. In recent years, researchers and practitioners gained interest in product line engineering to improve the reusability of language assets and the management of variability-rich systems, introducing the notions of language workbenches and language product lines (LPLs). Nonetheless, language development remains a complex activity and design or implementation flaws can easily waste the efforts of decomposing a language specification into language features. Poorly designed language decompositions result in high inter-dependent components, reducing the variability space of the LPL system and its maintainability. One should detect and fix the design flaws posthaste to prevent these risks while minimizing the development overhead. Therefore, various aspects of the quality of a language decomposition should be quantitatively measurable through adequate metrics. The evaluation, analysis and feedback of these measures should be a primary part of the engineering process of a LPL. In this paper, we present an exploratory study trying to capture these aspects by introducing a design methodology for LPLs; we define the properties of a good language decomposition and adapt a set of metrics from the literature to the framework of language workbenches. Moreover, we leverage the LPL engineering environment to perform an empirical evaluation of 26 -based LPLs based on this design methodology. Our contributions form the foundations of a design methodology for -based LPLs. This methodology is comprised of four different elements: i) an engineering process that defines the order in which decisions are made, ii) an integrated development environment for LPL designers and iii) some best practices in the design of well-structured language decomposition when using , supported by iv) a variety of LPL metrics that can be used to detect errors in design decisions.
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification

Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms. However, such clustering-based scheme becomes computationally prohibitive for large-scale datasets, making it infeasible to be applied in real-world application. How to efficiently leverage endless unlabeled data with limited computing resources for better U-ReID is under-explored. In this paper, we make the first attempt to the large-scale U-ReID and propose a "small data for big task" paradigm dubbed Meta Clustering Learning (MCL). MCL only pseudo-labels a subset of the entire unlabeled data via clustering to save computing for the first-phase training. After that, the learned cluster centroids, termed as meta-prototypes in our MCL, are regarded as a proxy annotator to softly annotate the rest unlabeled data for further polishing the model. To alleviate the potential noisy labeling issue in the polishment phase, we enforce two well-designed loss constraints to promise intra-identity consistency and inter-identity strong correlation. For multiple widely-used U-ReID benchmarks, our method significantly saves computational cost while achieving a comparable or even better performance compared to prior works.
YOLOv3 based Real Time Social Distance Violation Detection in Public Places

The prevalent COVID 19 pandemic is incessantly taking toll on the lives of people throughout the world. Moreover, the dearth of effectual remedies has caused an expeditious rise in the total COVID 19 cases. Though vaccines have been developed, the enormous task of vaccinating a large population is still challenging. Also, as new variants emanate, the resilience from infections conceivably decreases. Hence, it’s most unlikely that we’ll achieve herd immunity globally so soon. Thus, since the transmission of COVID causing coronavirus roots mainly to social proximity between people, it is necessary to stringently comply to the non pharmaceutical preventive measures of wearing masks and maintaining physical distancing. Howbeit, it has evidently been found that people are being lethargically ignorant to the social distancing norms with passing time. Hence, an autonomous mechanism intended at social distancing violation detection through monitoring of people is needed to be introduced at an authority level. In this paper, the implementation of YOLO Object detection transfer learning process has been used for accomplishing this aim of real time detection of social distancing violation. Our social distance prediction approach uses a pre-trained YOLOv3 object tracking algorithm for identifying people in an input video stream. A Distance estimation algorithm is further used, that works by computing euclidean distance between the centroids of each pair of detected people. This approach highlights the people violating the social distancing criteria as well as calculates the number of times social distancing gets violated as any two people get closer than a set threshold value of minimum permissible distance. A number of experiments on various pre-recorded video streams has been conducted in order to estimate the viability of this method. Through experimental outcomes, it has been found that this YOLO based object detection method with the proposed social distance prediction algorithm produces favourable results for tracking social distancing in public spaces.
Structured Term Pruning for Computational Efficient Neural Networks Inference

The state-of-the-art convolutional neural network accelerators are showing a growing interest in exploiting the bit-level sparsity and eliminating the ineffectual computations of zero bits. However, the excessive redundancy and the irregular distribution of nonzero bits limit the real speedup in the accelerators. To address this, we propose an algorithm-architecture codesign, named structured term pruning (STP), to boost the computation efficiency of neural networks inference. Specifically, we enhance the bit sparsity by guiding the weights toward the value with fewer power-of-two terms. Then, we structure the terms with layer-wise group budgets. Retraining is adopted to recover the accuracy drop. We also design the hardware of the group processing element and the fast signed-digital encoder for efficient implementation of STP networks. The system design of STP is realized with some easy alterations on an input stationary systolic array design. Extensive evaluation results demonstrate that STP can reduce significant inference computation costs, and achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.35\times $ </tex-math></inline-formula> computational energy saving for the ResNet18 network on the ImageNet dataset.
Batch covariance neural network for image recognition

Recent work has shown that convolutional neural networks (CNN) can achieve state of the art if the datasets are well built. However, the existing convolutional layer is affected by various datasets with the inevitable problems of local abnormal features, i.e., illumination intensity and feature interaction. This paper replaces the convolutional layer with a batch covariance layer (BCL) to locate the category-related region unaffected by the problems. The BCL is regarded as a 3D covariance operation, which calculates the correlation between the kernels and feature maps in kernel size of all channels. Forward propagation, backward propagation, gradient updating, and testing procedure of the BCL are described. The comparison between BCL and convolutional layer shows the ability of BCL to reduce the influence of illumination intensity and feature interaction for discriminating and generating tasks. Complexity analysis shows that BCL can improve the accuracy with a thimbleful time consumption increase. Besides, the batch covariance neural network (BCovNN) is extended from the CNN by replacing the convolutional layer with BCL. Ablation experiment verifies the improvement of BCovNN is provided by BCL separately. BCovNN is evaluated on several popular datasets (i.e., MNIST, STL-10, CIFAR-10, and ImageNet) for image recognition and PASCAL VOC (2007 and 2012) datasets for object localization. Experimental results reveal that BCovNN achieves significant improvements over the corresponding CNN.
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks

Backdoor (Trojan) attacks are emerging threats against deep neural networks (DNN). A DNN being attacked will predict to an attacker-desired target class whenever a test sample from any source class is embedded with a backdoor pattern, while correctly classifying clean (attack-free) test samples. Existing backdoor defenses have shown success in detecting whether a DNN is attacked and in reverse-engineering the backdoor pattern in a "post-training" scenario: the defender has access to the DNN to be inspected and a small, clean dataset collected independently, but has no access to the (possibly poisoned) training set of the DNN. However, these defenses neither catch culprits in the act of triggering the backdoor mapping, nor mitigate the backdoor attack at test-time. In this paper, we propose an "in-flight" unsupervised defense against backdoor attacks on image classification that 1) detects use of a backdoor trigger at test-time; and 2) infers the class of origin (source class) for a detected trigger example. The effectiveness of our defense is demonstrated experimentally for a wide variety of DNN architectures, datasets, and backdoor attack configurations.
Trustable service discovery for highly dynamic decentralized workflows

The quantity and capabilities of smart devices and sensors deployed as part of the Internet of Things (IoT) and accessible via remote microservices is set to rise dramatically as the provision of interactive data streaming increases. This introduces opportunities to rapidly construct new applications by interconnecting these microservices in different workflow configurations. The challenge is to discover the required microservices, including those from trusted partners and the wider community, whilst being able to operate robustly under diverse networking conditions. This paper outlines a workflow approach that provides decentralized discovery and orchestration of verifiably trustable services in support of multi-party operations. The approach is based on adoption of patterns from self-sovereign identity research, notably Verifiable Credentials, to share information amongst peers based on attestations of service descriptions and prior service usage in a privacy preserving and secure manner. This provides a dynamic, trust-based framework for ratifying and evaluating the qualities of different services. Collating these new service descriptions and integrating with existing decentralized workflow research based on vector symbolic architecture (VSA) provides an enhanced semantic search space for efficient and trusted service discovery that is necessary to support a diverse range of emerging edge-computing environments. An architecture for a dynamic decentralized service discovery system, is designed, and described through application to a scenario which uses trusted peers' reported experiences of an anomaly detection service to determine service selection.
Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends

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Continuous Software Engineering in the Wild

Software is becoming a critical component of most products and organizational functions. The ability to continuously improve software determines how well the organization can respond to market opportunities. Continuous software engineering promises numerous advantages over sprint-based or plan-driven development. However, implementing a continuous software engineering pipeline in an existing organization is challenging. In this invited position paper, we discuss the adoption challenges and argue for a more systematic methodology to drive the adoption of continuous engineering. Our discussion is based on ongoing work with several industrial partners as well as experience reported in both state-of-practice and state-of-the-art. We conclude that the adoption of continuous software engineering primarily requires analysis of the organization, its goals, and constraints. One size does not fit all purposes, meaning that many of the principles behind continuous engineering are relevant for most organizations, but the level of realization and the benefits may still vary. The main hindrances to continuous flow of software arise from sub-optimal organizational structures and the lack of alignment. Once those are removed, the organization can implement automation to further improve the software delivery.
Lattice Puncturable Attribute Based Proxy Re-encryption Scheme and Its Application in Information Centric Network

This work presents the first attempt to build a feasible puncturable attribute-based proxy encryption from the lattice. Our system provides flexible and fine-grained access control. In this scheme, the private keys are punctured, allowing the decryption abilities to be revoked for specific messages or receivers and securing sensitive content even if the users’ present keys are compromised. As opposed to the traditional encryption scheme, the proposed technique has the advantage of allowing users to update their keys without requiring key re-issuance from the key generator. Also, it does not necessitate regular communication between the user and the key generation authority, nor does it necessitate deleting elements to remove current keys to generate fresh keys. We developed a new approach for embedding attribute-based keys and punctured keys. This was done in such a way that the secret key size is nearly the same as that of the original attribute-based encryption without a punctured key. Also, we demonstrate that our scheme is secure against a selected plaintext attack in the selective security model under the Learning With Errors (LWE) assumptions. Finally, we implemented our scheme and integrated it with ICN. The implantation results show that our scheme is practical.
W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image

Pathological diagnosis is the gold standard for cancer diagnosis, but it is labor-intensive, in which tasks such as cell detection, classification, a nd c ounting a re particularly prominent. A common solution for automating these tasks is using nucleus segmentation technology. However, it is hard to train a robust nucleus segmentation model, due to several challenging problems,i.e., the nucleus adhesion, stacking, and excessive fusion with the background. Recently, some researchers proposed a series of automatic nucleus segmentation methods based on point annotation, which can significant i mprove t he m odel performance. Nevertheless, the point annotation needs to be marked by experienced pathologists. In order to take advantage of segmentation methods based on point annotation, further alleviate the manual workload, and make cancer diagnosis more efficient and accurate, it is necessary to develop an automatic nucleus detection algorithm, which can automatically and efficiently l ocate the position of the nucleus in the pathological image and extract valuable information for pathologists. In this paper, we propose a W-shaped network for automatic nucleus detection. Different from the traditional U-Net based method, mapping the original pathology image to the target mask directly, our proposed method split the detection task into two sub-tasks. The first sub-task maps the original pathology image to the binary mask, then the binary mask is mapped to the density mask in the second subtask. After the task is split, the task's difficulty i s significantly reduced, and the network's overall performance is improved. Our proposed network can automatic identify the center of each nucleus. Combined with the NuClick, a semi-supervised recognition model based on point annotation, we implement a fully automatic nucleus annotation framework.
Learning Personal Food Preferences via Food Logs Embedding

Diet management is key to managing chronic dis-eases such as diabetes. Automated food recommender systems may be able to assist by providing meal recommendations that conform to a user’s nutrition goals and food preferences. Current recommendation systems suffer from a lack of accuracy that is in part due to a lack of knowledge of food preferences. In this work, we propose a method for learning food preferences from food logs, a comprehensive but noisy source of information about users’ dietary habits. We also introduce accompanying metrics to evaluate personal learning food preferences. The method generates and compares word embeddings to identify the parent food category of each food entry and then calculates the most popular. Our proposed approach identifies 82% of a user’s ten most frequently eaten foods. Our method is publicly available on (https://github.com/aametwally/LearningFoodPreferences).
Experiments in cross-domain few-shot learning for image classification

Cross-domain few-shot learning has many practical applications. This paper attempts to shed light on suitable configurations of feature exactors and ‘shallow’ classifiers in this machine learning setting. We apply ResNet-based feature extractors pretrained on two versions of the ImageNet dataset to five target domains with different degrees of similarity to ImageNet, varying the feature extractor size, the network stage at which features are extracted, and the learning algorithm applied to the extracted features. We evaluate standard learning algorithms such as logistic regression and linear discriminant analysis, as well as variants thereof, and additionally consider the effect of normalising the feature vectors using various p-norms. We also apply multi-instance learning to improve training image utilisation. In our experiments, the cosine similarity classifier and ℓ2-regularised 1-vs-rest logistic regression generally exhibit the best classification performance. We also find that algorithms such as linear discriminant analysis yield consistently higher accuracy using ℓ2-normalised feature vectors. Features extracted from the penultimate stage of a ResNet-101 model, and multi-instance learning techniques, produce the highest accuracy for most target domains. Our results will inform practitioners who are considering the application of pretrained ImageNet feature extractors in cross-domain few-shot settings.
Error-rate-agnostic decoding of topological stabilizer codes

Efficient high-performance decoding of topological stabilizer codes has the potential to crucially improve the balance between logical failure rates and the number and individual error rates of the constituent qubits. High-threshold maximum-likelihood decoders require an explicit error model for Pauli errors to decode a specific syndrome, whereas lower-threshold heuristic approaches such as minimum weight matching are "error agnostic". Here we consider an intermediate approach, formulating a decoder that depends on the bias, i.e., the relative probability of phase-flip to bit-flip errors, but is agnostic to error rate. Our decoder is based on counting the number and effective weight of the most likely error chains in each equivalence class of a given syndrome. We use Metropolis-based Monte Carlo sampling to explore the space of error chains and find unique chains, that are efficiently identified using a hash table. Using the error-rate invariance the decoder can sample chains effectively at an error rate which is higher than the physical error rate and without the need for "thermalization" between chains in different equivalence classes. Applied to the surface code and the XZZX code, the decoder matches maximum-likelihood decoders for moderate code sizes or low error rates. We anticipate that, because of the compressed information content per syndrome, it can be taken full advantage of in combination with machine-learning methods to extrapolate Monte Carlo-generated data.