Reviewerly/exHarmony huggingface dataset:
Prediction Intervals for Learned Cardinality Estimation: An Experimental Evaluation

Cardinality estimation is a fundamental and challenging problem in query optimization. Recently, a number of learned models have been proposed for this task. Often, these models significantly outperform traditional approaches in terms of accuracy. One of the stumbling blocks that prevents their increased adoption is that the learned models do not quantify the uncertainty of their estimates. It is desirable to associate each cardinality estimate of the model with a prediction interval that will contain the true cardinality with an user-specified probability. The size of the prediction interval encodes the uncertainty allowing the query optimizer to make an informed decision. For example, knowing that the cardinality of a query <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> lies between 1–3% of the relation size with high probability is more informative than a single point estimate of 2%. While there has been some prior work on deriving bounds for traditional methods (such as sampling or histograms), they are not directly applicable for the learned models for cardinality estimation. In this paper, we conduct a systematic investigation of potential approaches for obtaining prediction intervals. We enumerate the list of desirable properties such as the ability to wrap around a learned model without significant internal modification and providing bounds with theoretical guarantees in a distribution agnostic manner among others. Based on an extensive literature survey, we identify four practical and high quality approaches for uncertainty quantification that satisfies these criteria. They span a wide spectrum in terms of theoretical guarantees, width of prediction interval and time taken for computing the prediction intervals. We conduct extensive experimental analysis of the efficacy of these approaches over three diverse and representative cardinality estimation algorithms. Our experiments covers diverse workloads involving both point and range queries and highlights the inherent trade-offs. Our results show that it is possible to obtain accurate prediction intervals in an efficient manner thereby opening up new avenues for future research.
Research and Implementation of Birds and Floating Objects Target Detection Technology in Transmission Lines

The transmission line environment is diverse and complex. Floating objects and birds account for a high proportion of transmission line failures. These two kinds of intrusions will cause line tripping, external force damage and other hazards. It is necessary to patrol and monitor them to ensure the safety of transmission lines. In this paper, the target detection technology of floating objects and birds is studied. According to the different types of intrusions and environmental background, combined with Gaussian background modeling and deep learning algorithm based on YOLO, the target detection algorithm based on deep learning is studied to realize the accurate detection and positioning of transmission line intrusions in complex scenes.
Special Subsets of Addresses for Blockchains Using the secp256k1 Curve

In 2020, Sala, Sogiorno and Taufer were able to find the private keys of some Bitcoin addresses, thus being able to spend the cryptocurrency linked to them. This was unexpected since the recovery of non-trivial private keys for blockchain addresses is deemed to be an infeasible problem. In this paper, we widen this analysis by mounting a similar attack on other small subsets of the set of private keys. We then apply it to other blockchains as well, examining Ethereum, Dogecoin, Litecoin, Dash, Zcash and Bitcoin Cash. In addition to the results, we also explain the techniques we have used to perform this exhaustive search for all the addresses that have ever appeared in these blockchains, and we give an estimate of the time needed to perform all the computations. Finally, we also examine the possibility of mounting a similar attack on other elliptic curves used in blockchains, i.e., Curve25519 and NIST P-256.
Multi-View Learning for Parallelism Discovery of Sequential Programs

Identifying suitable parallelizable regions in sequential programs is a crucial task for performance optimizations. Traditional methods like static and dynamic analysis have flaws like insufficient accuracy or high overhead runtime. Recent studies are more interested in applying machine learning techniques to this topic. The crux of parallelism discovery with machine learning is to generate meaningful code representations. One promising route is to exploit the dependence graph through Graph Neural Networks (GNNS). In this paper, a novel multi-view framework is proposed to automatically detect potential parallelism opportunities. Sequential programs are first repre-sented by program execution graphs encompassing both semantic and structural information. Then two independent views are defined: namely, a structural pattern view and a node feature view. In the structural view, local graph structural patterns are captured via random anonymous walks and then fed into a Graph Convolutional Network (GCN). The node features, both dynamic and static, are fed into another GCN in the node feature view. In addition, a multi-view model is designed to unify the node features and the structural features for parallelism detection. Our approach achieves comparable state-of-the-art performance on parallel region classification with an accuracy up to 92.6 % when evaluated with popular parallel eomputing benchmarks.
Decision Tree Algorithm Considering Distances Between Classes

Decision tree algorithm (DT) is a commonly used data mining method for classification and regression. DT repeatedly divides a dataset into pure subsets based on impurity measurements such as entropy and Gini. Then relatively &#x201C;pure&#x201D; partitions consisting of observations with the (almost) same class are obtained. Gini index is one of the representative indices for measuring the impurity of data. However, the Gini index does not take into account distances between classes. If the distances between classes are considered when measuring impurity, the decision tree algorithm can distinguish clearly observations with different classes. To the end, a new decision tree algorithm based on Rao-Stirling index is proposed considering distances between classes. Rao-Stirling index considers distances between classes in such a way that weights more to pairs of references in more distant classes when measuring data impurity. Experimental results indicate that the proposed method is superior in terms of accuracy, implying that considering the distances between classes can help improve accuracy in DT.
Privacy-Preserving Image Captioning with Deep Learning and Double Random Phase Encoding

Cloud storage has become eminent, with an increasing amount of data being produced daily; this has led to substantial concerns related to privacy and unauthorized access. To secure privacy, users can protect their private data by uploading encrypted data to the cloud. Data encryption allows computations to be performed on encrypted data without the data being decrypted in the cloud, which requires enormous computation resources and prevents unauthorized access to private data. Data analysis such as classification, and image query and retrieval can preserve data privacy if the analysis is performed using encrypted data. This paper proposes an image-captioning method that generates captions over encrypted images using an encoder–decoder framework with attention and a double random phase encoding (DRPE) encryption scheme. The images are encrypted with DRPE to protect them and then fed to an encoder that adopts the ResNet architectures to generate a fixed-length vector of representations or features. The decoder is designed with long short-term memory to process the features and embeddings to generate descriptive captions for the images. We evaluate the predicted captions with BLEU, METEOR, ROUGE, and CIDEr metrics. The experimental results demonstrate the feasibility of our privacy-preserving image captioning on the popular benchmark Flickr8k dataset.
Generative Adversarial Networks Enhanced Pre-training for Insufficient Electronic Health Records Modeling

In recent years, automatic computational systems based on deep learning are widely used in medical fields, such as automatic diagnosing and disease prediction. Most of these systems are designed for data sufficient scenarios. However, due to the disease rarity or privacy, the medical data are always insufficient. When applying these data-hungry deep learning models with insufficient data, it is likely to lead to issues of over-fitting and cause serious performance problems. Many data augmentation methods have been proposed to solve the data insufficiency problem, such as using GAN (Generative Adversarial Networks) to generate training data. However, the augmented data usually contains lots of noise. Directly using them to train sensitive medical models is very difficult to achieve satisfactory results.
Query-Guided Prototype Learning with Decoder Alignment and Dynamic Fusion in Few-Shot Segmentation

Few-shot segmentation aims to segment objects belonging to a specific class under the guidance of a few annotated examples. Most existing approaches follow the prototype learning paradigm and generate category prototypes by squeezing masked feature maps extracted from images in the support set. These support prototypes may lead to inaccurate predictions when directly compared with features extracted from the query set due to the considerable distribution discrepancy between support and query features. We propose a query-guided prototype learning architecture to address this problem from two aspects: (i) We propose a cross-alignment loss for training the segmentation decoder. This loss function will help the decoder improve its robustness against the distribution discrepancy between support and query features. (ii) We build a dynamic fusion module to strengthen the original support prototype with another prototype extracted from query features. Experiments show that our method achieves promising results compared to previous prototype learning methods on PASCAL-5 i and COCO-20 i datasets.
Hybrid Feature Set based Mitotic Detection in Breast Histopathology Images

Breast cancer is a very serious threat to women and requires faster diagnosis methods to decrease the mortality rates. Automatic detection of mitosis is very difficult when compared to other pattern detection since mitotic cells are irregularly shaped objects, therefore there is no simple way of extracting features of a mitotic cell. This paper proposes a novel method with which a dataset comprising of shape and texture features are created from the image patches extracted from whole-slide histopathology images (MITOS-ATYPIA-14). The extracted image patches are converted to blue ratio images and hysteresis thresholding is applied for better edge detection. The ROI is extracted and a set of 10 shape features and 34 texture features are then extracted from each segmented image to form a hybrid feature dataset. Correlation-based feature selection is used to obtain the dominant features which can classify the data points as mitotic and non-mitotic. The dataset is then used to train seven models and classification is performed on unannotated image patches and the best model is selected based on the accuracy.
URBM: User-Rank-Based Management of Flows in Data Center Networks through SDN

A data center network (DCN) is composed of many servers and switches, and how to manage flows makes a great impact on its performance. The paper applies the software-defined networking technology to flow management in a fat-tree DCN with hybrid switches, where electrical switches form the network backbone, while optical switches provide fast links. Considering that flows have different ranks, we propose a user-rank-based management (URBM) scheme to improve DCN throughput and offer differentiated QoS supports to flows based on their ranks. Low-rank flows have limits on using fast links, and their users should pay extra fees. If some links become busy, URBM finds substitute paths to reroute their flows. Alternatively, it restricts bandwidth usage of flows, where high-rank flows can use more bandwidth to satisfy QoS demands. With the price elasticity of demand model, we develop a pricing method to compute extra fees for low-rank users to use fast links. Simulation results show that URBM can improve flow throughput, provide better QoS supports for high-rank users, and raise the operator’s revenue.
VISION: VIdeo StabilisatION using automatic features selection for image velocimetry analysis in rivers

VISION is open-source software written in MATLAB for video stabilisation using automatic features detection. It can be applied for any use, but it has been developed mainly for image velocimetry applications in rivers. It includes a number of options that can be set depending on the user’s needs and intended application: 1) selection of different feature detection algorithms (seven to be selected with the flexibility to choose two simultaneously), 2) definition of the percentual value of the strongest features detected to be considered for stabilisation, 3) geometric transformation type, 4) definition of a region of interest on which the analysis can be performed, and 5) visualisation in real-time of stabilised frames. One case study was deemed to illustrate VISION stabilisation capabilities on an image velocimetry experiment. In particular, the stabilisation impact was quantified in terms of velocity errors with respect to field measurements obtaining a significant error reduction of velocities. VISION is an easy-to-use software that may support research operating in image processing, but it can also be adopted for educational purposes.
BARM: A Batch-Aware Resource Manager for Boosting Multiple Neural Networks Inference on GPUs With Memory Oversubscription

Modern intelligent devices usually execute multiple neural networks to improve service quality. However, system performance degrades significantly when the working set exceeds the physical memory capability, a phenomenon called memory oversubscription. To support the execution of multiple independent neural networks with limited physical memory, this article explores resource management in GPUs with unified virtual memory and demand paging. We first analyze the relationship between the simultaneous execution of multiple neural networks from streaming multiprocessors (SM) assignment and page fault overhead from memory thrashing. To boost performance by reducing the page fault penalty, we propose a batch-aware resource management approach, BARM, including (1) batch-aware SM resource allocation to increase the batch size and (2) thrashing-preventing memory allocation to eliminate run-time thrashing. The performance of the proposed method was evaluated using a series of workloads, and response latency is reduced significantly over the state-of-the-art page fault prefetcher and batch-aware TLP management. The proposed framework was also implemented on the real platform and evaluated by a case study, and impressive results were obtained.
Boosting lesion annotation via aggregating explicit relations in external medical knowledge graph

Predicting a comprehensive set of relevant labels on chest X-ray images faces great challenges towards bridging visual and textual modalities. Despite the success of Graph Convolutional Networks (GCN) on modeling label dependencies using co-occurrence matrix generated from dataset, they still suffer from inherent label imbalance in dataset and ignore the explicit relations among labels presented in external medical knowledge graph (KG). We argue that jointly exploiting both the label co-occurrence matrix in dataset and the label relations in external knowledge graph facilitates multi-label lesion annotation. To model relevant lesion labels more comprehensively, we propose a KG-augmented model via Aggregating Explicit Relations for multi-label lesion annotation, called AER-GCN. The KG-augmented model employs GCN to learn the explicit label relations in external medical KG, and aggregates the explicit relations into statistical graph built from label co-occurrence information. Specially, we present three approaches on modeling the explicit label correlations in external knowledge, and two approaches on incorporating the explicit relations into co-occurrence relations for lesion annotation. We exploit SNOMED CT as the source of external knowledge and evaluate the performance of AER-GCN on the ChestX-ray and IU X-ray datasets. Extensive experiments demonstrate that our model outperforms other state-of-the-art models.
Efficient Two-Stream Network for Online Video Action Segmentation

Temporal action segmentation is a task of predicting frame-level classes of untrimmed longtermed videos.It can be widely used in various applications including customer analysis, data collection, video indexing, and surveillance.Learning good representations from videos such as motion and spatial representation is a critical factor for model performance.Furthermore, the used model should make reliable decisions with low latency for practical use in a resource-constrained environment.However, many works in action segmentation consider just an accuracy using the pre-calculated representations from heavy 3-dimensional(3D) networks.In this paper, we propose a two-stream action segmentation pipeline that can learn motion and spatial information efficiently and operate online.While the temporal stream combines frame-grouping and TSM for capturing short-term dynamics and long-term temporal information at the same time, the spatial stream captures information on color and appearance complementary to representations from the temporal stream.In addition, the results of both the streams are combined by a cross-attention module to provide a desired classification result for the task.Since it can be operated without heavy 3D convolutional neural networks (CNNs), it takes much less memory and computation than conventional 3D-CNN-based methods.Our proposed network using a non-overlapping sliding window achieved segmentation performance on two action segmentation datasets comparable to many recent works that require full temporal resolution and pre-calculated features of 3D CNNs.
Detection of SQL Injection Attack Using Machine Learning Based On Natural Language Processing

There has been a significant increase in the number of cyberattacks. This is not only happening in Indonesia, but also in many countries. Thus, the issue of cyber attacks should receive attention and be interesting to study. Regarding the explored security vulnerabilities, the Open Web Application Security Project has published the Top-10 website vulnerabilities. SQL Injection is still become one of the website vulnerabiliteis which is often exploited by attacker. This research has implemented and tested five algorithms. They are Naïve Bayes, Logistic Regression, Gradient Boosting, K-Nearest Neighbor, and Support Vector Machine. In addition, this study also uses natural language processing to increase the level of detection accuracy, as a part of text processing. Therefore, the main dataset was converted to corpus to make it easier to be analyzed. This process was carried out on feature enginering stage. This study used two datasets of SQL Injection. The first dataset was used to train the classifier, and the second dataset was used to test the performance of classifier. Based on the tests that have been carried out, the Support Vector Machine get the highest level of accuracy detection. The accuracy of detection is 0.9977 with 0,00100 micro seconds per query time of process. In performance testing, Support Vector Machine classifier can detect 99,37% of second dataset. Not only Support Vector Machine, the study have also revealed the detection accuracy level of further tested algorithms: K-Nearest Neighbor (0,9970), Logistic Refression (0,9960), Gradient Boosting (0,99477), and Naïve Bayes (0,9754).
Energy and Spectrum Efficient Radio Frequency Fingerprint Intelligent Blind Identification

Radio frequency fingerprint identification (RFFI) technology identifies the emitter by extracting one or more unintentional features of the signal from the emitter. To solve the problem that the traditional deep learning network is not highly adaptable for the contour features extracted from the signal, this paper proposes a novel RFFI method based on a deformable convolutional network. This network makes the convolution operation more biased towards the useful information content in the feature map with higher energy, and ignores part of the background noise information. The proposed blind identification method requires less information and no training sequences and pilots, Thus, it achieves energy and spectrum efficient radio communications. Simulation verifies that the proposed method can achieve better recognition performance and is beneficial for green radios.
Progressive Localization Networks for Language-Based Moment Localization

This article targets the task of language-based video moment localization. The language-based setting of this task allows for an open set of target activities, resulting in a large variation of the temporal lengths of video moments. Most existing methods prefer to first sample sufficient candidate moments with various temporal lengths, then match them with the given query to determine the target moment. However, candidate moments generated with a fixed temporal granularity may be suboptimal to handle the large variation in moment lengths. To this end, we propose a novel multi-stage Progressive Localization Network (PLN) that progressively localizes the target moment in a coarse-to-fine manner. Specifically, each stage of PLN has a localization branch and focuses on candidate moments that are generated with a specific temporal granularity. The temporal granularities of candidate moments are different across the stages. Moreover, we devise a conditional feature manipulation module and an upsampling connection to bridge the multiple localization branches. In this fashion, the later stages are able to absorb the previously learned information, thus facilitating the more fine-grained localization. Extensive experiments on three public datasets demonstrate the effectiveness of our proposed PLN for language-based moment localization, especially for localizing short moments in long videos.
An IOT Driven Eyeball And Gesture-Controlled Smart Wheelchair System for Disabled Person

The area of smart homes has evolved with goal of restoring the capacity of physically disabled people, aged, and others with limited mobility to accomplish critical every day activities by giving sufficient help via current technology devices. One of most difficult aspects of smart wheelchair&#x0027;s design is assuring that it functions appropriately and effectively. Smart wheelchairs make it easier for challenged people to travel indoor areas and perform daily activities on their own. In this device, manual wheelchair operation is substituted by automatic wheelchair control, which is operated by eyeball movement, allowing patients to feel freer and with less or no trouble in their movements. To determine position of eye pupil, a continuous image is acquired with assistance of a camera, which will then be subjected to numerous image processing techniques. The Gesture Functions are incorporated as well, allowing the user to use gestures for basic specifications/commands, allowing this wheelchair to be used for a variety of applications.