Reviewerly/exHarmony huggingface dataset:
A Novel Adaptive Undersampling Framework for Class-Imbalance Fault Detection

Class-imbalance is a prevalent and challenging problem in the field of fault detection. The undersampling ensemble framework is an effective method to deal with imbalance problems. However, designing a suitable sampling strategy to generate effective and divergent subsets is a major difficulty of this type of method. Hence, we propose a novel adaptive undersampling framework. It models the entire training process as a Markov decision process (MDP), thus, enabling dynamic decision-making for subsequent sampling strategies based on the current training performance of the ensemble framework. The sampler is optimized by the soft actor-critic reinforcement learning method. Considering the imbalance dataset's nature and the need for state definition, the clustering method is applied to the original training dataset. The state (training performance) and the action (sampling strategy) are determined according to the clustering results. The unique state definition and sampling decision mechanism are designed to ensure the convergence speed of MDP and improve the divergence of the subsets. We validate the effectiveness of the proposed framework on the real-world wind turbine blade cracking datasets and the high-speed train braking system dataset. The experimental results show that the classification performance and robustness of the proposed framework are significantly better than the 16 benchmark methods.
DKMI: Diversification of Web Image Search Using Knowledge Centric Machine Intelligence

Web Image Recommendation is quite important in the present-day owing to the large scale of the multimedia content on the World Wide Web (WWW) specifically images. Recommendation of the images that are highly pertinent to the query with diversified yet relevant query results is a challenge. In this paper the DKMI framework for web image recommendation has been proposed which is mainly focused on ontology alignment and knowledge pool derivation using standard crowd-sourced knowledge stores like Wikipedia and DBpedia. Apart from this the DKMI model encompasses differential classification of the same dataset using the GRU and SVM, which are two distinct differential classifiers at two different levels. GRU being a Deep Learning classifier and the SVM being a Machine Learning classifier, enhances the heterogeneity and diversity in the results. Semantic similarity computation using Cosine Similarity, PMI and SOC-PMI at several phases ensures strong relevance computation in the model. The DKMI model yields overall Precision of 97.62% with an accuracy of 98.36% along with the lowest FDR score of 0.03 and is much better than the other models that are considered to be the baseline models.
Using Topic Modeling and Word Embedding for Topic Extraction in Twitter

Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text's topic or theme. Topic analysis uses natural language processing (NLP) to break down human language into blocks (speech, words, sentences, context) so that you can find patterns and unlock semantic structures within texts to extract insights and help make data-driven decisions. The two most common approaches for topic analysis with machine learning are NLP topic modeling and NLP topic classification. Topic modeling faces several challenges: some are general to the NLP task (as extracting the context of document), and some are specific and are related to the nature (or properties) of the documents. One particular type of documents that raises several challenges are short-text documents that we find in Social Networks. First, we should note that with the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. However, despite their ubiquity, extracting topics from shorts texts remains a difficult task for several reasons. First, unlike traditional normal texts, short texts typically only include a few words. Therefore, directly applying traditional models on short texts will suffer from the severe data sparsity problem (i.e., the sparse word co-occurrence patterns in individual document). Second, the limited contexts make it more difficult to identify the senses of ambiguous words in short texts. Third, in general, the performance of the used machine learning models relies on labeled data. Unfortunately, due to their volume, labeling short-text from social networks remains a tedious and hard task. In this paper, we propose a model for extracting topics in short-texts, and more specifically in Twitter. The key feature of our proposal is the use of word embedding technique for topic modeling, and k-Means clustering for the semi-automatic annotation of tweets. In addition, and unlike most existing approaches, we assign in our model a set of topics to a tweet each with a confidence degree.
Pseudorandom (Function-Like) Quantum State Generators: New Definitions and Applications

Pseudorandom quantum states (PRS) are efficiently constructible states that are computationally indistinguishable from being Haar-random, and have recently found cryptographic applications. We explore new definitions, new properties and applications of pseudorandom states, and present the following contributions:
Multidirectional Gradient Feature With Shape Index for Effective Texture Classification

Recently, local gradient microstructure of image textures has become an important field of texture classification, but it is generally to investigate the multiscale local microstructures of image gradient, and rarely consider the multidirectional and multiscale local microstructure of image gradient. The proposed algorithm first extracts the two-order gradient feature of the image from different orthogonal directions and further constructs the multiple shape index of the image, and then calculates the histogram feature vectors of the shape index on different orthogonal directions and scales, and finally connects all histogram feature vectors on different orthogonal directions and scales to obtain the final matching feature vector of the image. To further enhance the discriminant ability of feature vector generated by multidirectional shape index schemes, the weight of each block of images is also considered. Experiments on two texture databases and one palmprint database have fully confirmed the effective of proposed algorithm.
Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition

We address the well-known wearable activity recognition problem of having to work with sensors that are non-optimal in terms of information they provide but have to be used due to wearability/usability concerns (e.g. the need to work with wrist-worn IMUs because they are embedded in most smart watches). To mitigate this problem we propose a method that facilitates the use of information from sensors that are only present during the training process and are unavailable during the later use of the system. The method transfers information from the source sensors to the latent representation of the target sensor data through contrastive loss that is combined with the classification loss during joint training (fig. 1). We evaluate the method on the well-known PAMAP2 and Opportunity benchmarks for different combinations of source and target sensors showing average (over all activities) F1 score improvements of between 5% and 13% with the improvement on individual activities, particularly well suited to benefit from the additional information going up to between 20% and 40%.
In rust we trust

Rust is a type-safe system programming language with a compiler checking memory and concurrency safety. For a smooth transition from existing C projects, a source-to-source transpiler can autotransform C programs into Rust using program transformation. However, existing C-to-Rust transformation tools (e.g. the open-source C2Rust transpiler1 project) have the drawback of preserving the unsafe semantics of C, while rewriting them in Rust's syntax. The work by Emre et el. [2] acknowledged these drawbacks, and used rustc compiler feedback to refactor one certain type of raw pointers to Rust references to improve overall safety and idiomaticness of C2Rust output. Focusing on improving API-safeness (i.e. lowering unsafe keyword usage in function signatures), we apply source-to-source transformation technique to auto-refactor C2Rust output using code structure pattern matching and transformation, which does not rely on rustc compiler feedback. And by relaxing the semantics-preserving constraints of transformations, we present CRustS2 a fully-automated source-to-source transformation approach that increases the ratio of the transformed code passing the safety checks of the rustc compiler. Our method uses 220 new TXL [1] source-to-source transformation rules, of which 198 are strictly semantics-preserving and 22 are semantics-approximating, thus reducing the scope of unsafe expressions and exposing more opportunities for safe Rust refactoring. Our method has been evaluated on both open-source and commercial C projects, and demonstrates significantly higher safe code ratios after the transformations, with function-level safe code ratios comparable to the average level of idiomatic Rust projects.
A Novel System Model for Managing Cyber Threat Intelligence

Cyber threat intelligence (CTI) systems, which collect CTI statistics since publically accessible resources, have been the subject of much study as a means of mitigating the ever-evolving cyber dangers. Due to the ever-increasing sophistication and persistence of cyber attackers, as well as the lightning-fast pace at which cyber assaults develop, quick decision making is now crucial to the sustained security of most companies. As a result, several businesses have started using threat intelligence management systems to better coordinate their defences against cyber threats and those of other companies. Getting a handle on how these platforms should be built, deployed, and utilised requires first knowing how successful they have been in the past. However, a lack of consensus on what aspects affect the performance of threat intelligence management systems exists between academia and industry. We used a review entrenched methodology to gather data from 152 security experts in order to empirically evaluate the concerns. Then, we determined a few variables that are critical to the effectiveness of management platform inside an organisation.
Development of E-Student Worksheets in the form of Picture Stories Using Live Worksheets in Primary Schools

The article aimed to determine the feasibility and effectiveness of the products developed, that was E-Student Worksheets in the form of Picture Stories Using Live Worksheets at Elementary Schools. The type of research used Research and Development (R&amp;D). The research procedure used Rowntree development with three stages, namely planning, development and assessment. The product developed has been declared feasible to use according to the validation results from material experts, media experts, and design experts. In field trials, it was found that the use of e-student worksheets in the form of illustrated stories using live worksheets, made students happier to learn, interested in learning, and made it easier for students to understand the learning material. The E-Student Worksheets in the Form of Picture Stories using the developed live worksheet could be said to be feasible and effective to use. This research was useful as a reference for the use of technology for educators in providing variations of worksheets to students. This live worksheet was also useful in making it easier for educators to check the work of students and students are also given the convenience of collecting their worksheets without being limited by distance. This research was useful as a reference for the use of technology for educators in providing variations of worksheets to students. Keywords: E-Student Worksheets, Picture Stories, Development Worksheets
Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning

Conventional NAS-based pruning algorithms aim to find the sub-network with the best validation performance. However, validation performance does not successfully represent test performance, i.e., potential performance. Also, although fine-tuning the pruned network to restore the performance drop is an inevitable process, few studies have handled this issue. This paper provides a novel Ensemble Knowledge Guidance (EKG) to solve both problems at once. First, we experimentally prove that the fluctuation of loss landscape can be an effective metric to evaluate the potential performance. In order to search a sub-network with the smoothest loss landscape at a low cost, we employ EKG as a search reward. EKG utilized for the following search iteration is composed of the ensemble knowledge of interim sub-networks, i.e., the by-products of the sub-network evaluation. Next, we reuse EKG to provide a gentle and informative guidance to the pruned network while fine-tuning the pruned network. Since EKG is implemented as a memory bank in both phases, it requires a negligible cost. For example, in the case of ResNet-50, just 315 GPU hours are required to remove around 45.04% of FLOPS without any performance degradation, which can operate even on a low-spec workstation. the source code is available at here .
Bridging Multi-Scale Context-Aware Representation for Object Detection

Feature Pyramid Network (FPN) exploits multi-scale fusion representation to deal with scale variances in object detection. However, it ignores the context information gap across different levels. In this paper, we develop a plug-and-play detector, the multi-scale context-aware feature pyramid network to unleash the power of feature pyramid representation. Based on the dilated feature map at the highest level of the backbone, we propose the cross-scale context aggregation block to make full use of context information in the feature pyramid. Moreover, we extract discriminative features among different levels by the adaptive context aggregation block for robust object detection. Comprehensive experiments on MS-COCO demonstrate the effectiveness and efficiency of the proposed network, where about 1.0~3.0 AP improvements are achieved compared with existing FPN-based methods. In addition, we also conduct extensive experiments on pixel-level prediction tasks, i.e., instance segmentation, semantic segmentation, and panoptic segmentation, which further verify the effectiveness of the proposed method.
Sigma workbook

Cloud data warehouses (CDWs) bring large-scale data and compute power closer to users in enterprises. However, existing tools for analyzing data in CDWs are either limited in ad-hoc transformations or difficult to use for business users. Here we introduce Sigma Workbook, a new interactive system that enables business users to easily perform visual analysis of data in CDWs at scale. For this, Sigma Workbook provides an accessible spreadsheet-like interface for analysis through direct manipulation. Sigma Workbook dynamically constructs matching SQL queries from user interactions, building on the versatility and expressivity of SQL. Constructed queries are directly executed on CDWs, leveraging the superior characteristics of the new generation CDWs, including scalability. We demonstrate Sigma Workbook through 3 real-life use cases---cohort analysis, sessionization, and data augmentation---and underline Workbook's ease of use, scalability, and expressivity.
A Non-conventional Sum-and-Max based Neural Network layer for Low Power Classification

The increasing need for small and low-power Deep Neural Networks (DNNs) for edge computing applications involves the investigation of new architectures that allow good performance on low-resources/mobile devices. To this aim, many different structures have been proposed in the literature, mainly targeting the reduction in the costs introduced by the Multiply and Accumulate (MAC) primitive. In this work, a DNN layer based on the novel Sum and Max (SAM) paradigm is proposed. It does not require either the use of multiplications or the insertion of complex non-linear operations. Furthermore, it is especially prone to aggressive pruning, thus needing a very low number of parameters to work. The layer is tested on a simple classification task and its cost is compared with a classic DNN layer with equivalent accuracy based on the MAC primitive, in order to assess the reduction of resources that the use of this new structure could introduce.
Novel Hybrid Low-Rank Tensor Approximation for Hyperspectral Image Mixed Denoising Based on Global-Guided-Nonlocal Prior Mechanism

Hyperspectral image mixed denoising is a challenging task in the fields of remote sensing, environmental monitoring, mineral exploration, etc. A crucial difficulty is to acquire clean restoration from hyperspectral image (HSI) that encounters Gaussian noise, impulse noise, strip noise and deadlines. In the previous works, combining global information and nonlocal information is a popular way to learn the comprehensive characteristics of the clean HSI. However, the advantages of 2D spatial structure similarity and spectral low-rankness may not be fully exploited at the same time in global prior learning. The iterative update between global restoration and nonlocal restoration may cause high time consumption and certain loss of information. To address these issues, we propose a three-stage mixed denoising model based on novel hybrid low-rank tensor approximation and global-guided-nonlocal prior mechanism (HLTA-GN). Firstly, to learn a good global prior, hybrid low-rank tensor approximation incorporated with a useful nonconvex tensor rank estimation is presented to balance 2D spatial similarity and spectral low-rankness. Secondly, to learn a high-quality nonlocal prior, global-guided-nonlocal prior mechanism is proposed to help nonlocal restoration suppress the residual noise. At the same time, a regularized sequential low-rank tensor approximation is proposed to enhance the robustness to noisy patch groups. Thirdly, a weighted fusion on global prior and nonlocal prior helps to further balance global denoising and patch processing. An efficient learning algorithm is provided to solve HLTA-GN. Abundant experiments are conducted on various HSIs with several scenarios. The experimental results demonstrate the superiority of HLTA-GN.
GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval

Cognitive science has shown that humans perceive videos in terms of events separated by the state changes of dominant subjects. State changes trigger new events and are one of the most useful among the large amount of redundant information perceived. However, previous research focuses on the overall understanding of segments without evaluating the fine-grained status changes inside. In this paper, we introduce a new dataset called Kinetic-GEB+. The dataset consists of over 170k boundaries associated with captions describing status changes in the generic events in 12K videos. Upon this new dataset, we propose three tasks supporting the development of a more fine-grained, robust, and human-like understanding of videos through status changes. We evaluate many representative baselines in our dataset, where we also design a new TPD (Temporal-based Pairwise Difference) Modeling method for visual difference and achieve significant performance improvements. Besides, the results show there are still formidable challenges for current methods in the utilization of different granularities, representation of visual difference, and the accurate localization of status changes. Further analysis shows that our dataset can drive developing more powerful methods to understand status changes and thus improve video level comprehension. The dataset is available at https://github.com/Yuxuan-W/GEB-Plus .
Temporal Knowledge Graph Embedding for Link Prediction

Link prediction aims to infer the behavior of the network evolution process by predicting missed or future relationships based on currently observed connections. It has become an attractive area of research since it allows us to understand how networks will evolve. Early studies cast the link prediction task as an entity identifying problem on graphs and adopt vertex representation strategies to perform predictive analysis. Although these methods are effective to some extent, they overlook the special properties of network evolution. In this paper, we propose a new method named TKGE, short for Temporal Knowledge Graph Embedding, to learn the evolutional representations of temporal knowledge graph for link prediction task. Specifically, we employ the self-attention mechanism to incorporate the static structural information and dynamic temporal information by aggregating the context from related entities. By introducing the position embedding characterizing the dynamic information of temporal knowledge graph, TKGE can generate the evolutional embedding of entities and relations for downstream applications, such as link prediction, recommender system, and so on. We conduct experiments on several real datasets. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our TKGE method.
Deep Learning for Object Detection: A Survey

The ability to quickly and accurately detect and identify the many things in a picture is crucial for interacting with one’s surroundings. As a result, to ensure that no object instance is overlooked, all regions of a picture must be searched for objects of various sizes. However, classifying the content of an image region takes a significant amount of time and effort and an agent’s computational and time resources are limited. Because of the way our brains work, we can make rapid decisions about which parts of a picture require more attention and which may be overlooked. The detection of objects is a fascinating area in computer vision. The fast growth of deep learning has piqued the interest of a number of scholars who have developed new methods in the field. Area suggestions, split grid cells, multi-scale feature maps and novel loss functions are some of the advancements being presented. Because of this, object detection’s performance has lately improved significantly. However, most modern detectors, whether one-stage or two-stage, have difficulty picking up on tiny things. This makes it possible for us to effectively deal with the massive quantity of visual information we are exposed to on a daily basis. Researchers in computer vision face the same challenges, therefore studying human behaviour can help to enhance current algorithms. Deep learning models from both methodologies are compared in this study to see which is the most recent and cutting-edge. We analyze the benefits and drawbacks of models in great detail.