Reviewerly/exHarmony huggingface dataset:
A General Framework for Geometrically Uniform Codes and Signal Sets Matched to Groups

Geometrically uniform codes are fundamental in communication systems, mainly for modulation. Typically, geometrically uniform codes are dependent on a given alphabet. The current work establishes the necessary and sufficient conditions for obtaining a matched labeling between a group G and a signal set S. It introduces the concept of the G-isometric signal set, allowing for the establishment of equivalences between different types of signal sets. In particular, we obtain isometries between groups and geometrically uniform codes with a minimal generator. We also draw attention to the influence of the environment metric space, the group metric, and the matched mapping on the labeling of a signal set. The results are valid for all environment metric spaces. The alphabet emerges naturally from the relationship between the signal set S and the label group derived from its symmetry group, Γ(S).
As Code Testing: Characterizing Test Quality in Open Source Ansible Development

Infrastructure as code (IaC) scripts, such as Ansible scripts, are used to provision computing infrastructure at scale. Existence of bugs in IaC test scripts, such as, configuration and security bugs, can be consequential for the provisioned computing infrastructure. A characterization study of bugs in IaC test scripts is the first step to understand the quality concerns that arise during testing of IaC scripts, and also provide recommendations for practitioners on quality assurance. We conduct an empirical study with 4,831 Ansible test scripts mined from 104 open source software (OSS) repositories where we quantify bug frequency, and categorize bugs in test scripts. We further categorize testing patterns, i.e., recurring coding patterns in test scripts, which also correlate with appearance of bugs. From our empirical study, we observe 1.8% of 4,831 Ansible test scripts to include a bug, and 45.2% of the 104 repositories to contain at least one test script that includes bugs. We identify 7 categories of bugs, which includes security bugs and performance bugs that are related with metadata extraction. We also identify 3 testing patterns that correlate with appearance of bugs: 'assertion roulette’, 'local only testing’, and 'remote mystery guest‘. Based on our findings, we advocate for detection and mitigation of the 3 testing patterns as these patterns can have negative implications for troubleshooting failures, reproducible deployments of software, and provisioning of computing infrastructure.
Baggage Threat Detection Under Extreme Class Imbalance

Automatic detection of prohibited items is a critical but difficult task during aviation security. Manual detection of such items is a time-consuming process that is also limited by the examination capacity of the security inspector. To overcome these constraints, several researchers have proposed deep learning solutions to identify contraband data contained within baggage X-ray imagery. However, when trained on the imbalanced data that is frequently encountered in real-world aviation screening, the performance of these models suffers significantly. Towards this end, this paper proposes the coupling of various imbalanced learning strategies that can be used to augment traditional threat detection models and enable them to effectively learn the extremely imbalanced distribution of normal and threat object categories. The proposed approach is validated on three public datasets, namely SIXray, OPIXray, and COMPASS-XP, where it achieved the performance improvement of 9.52%, 11.32%, and 10.98%, respectively, on all three datasets in terms of mean intersection-over-union as compared to the state-of-the-art threat detection frameworks.
FPGA-Based Pedestrian Detection for Collision Prediction System

Pedestrian detection (PD) systems capable of locating pedestrians over large distances and locating them faster are needed in Pedestrian Collision Prediction (PCP) systems to increase the decision-making distance. This paper proposes a performance-optimized FPGA implementation of a HOG-SVM-based PD system with support for image pyramids and detection windows of different sizes to locate near and far pedestrians. This work proposes a hardware architecture that can process one pixel per clock cycle by exploring data and temporal parallelism using techniques such as pipeline and spatial division of data between parallel processing units. The proposed architecture for the PD module was validated in FPGA and integrated with the stereo semi-global matching (SGM) module, also prototyped in FPGA. Processing two windows of different dimensions permitted a reduction in miss rate of at least 6% compared to a uniquely sized window detector. The performances achieved by the PD system and the PCP system in HD resolution were 100 and 66.2 frames per second (FPS), respectively. The performance improvement achieved by the PCP system with the addition of our PD module permitted an increase in decision-making distance of 3.3 m compared to a PCP system that processes at 30 FPS.
Part-Based Obstacle Detection Using a Multiple Output Neural Network

Detecting the objects surrounding a moving vehicle is essential for autonomous driving and for any kind of advanced driving assistance system; such a system can also be used for analyzing the surrounding traffic as the vehicle moves. The most popular techniques for object detection are based on image processing; in recent years, they have become increasingly focused on artificial intelligence. Systems using monocular vision are increasingly popular for driving assistance, as they do not require complex calibration and setup. The lack of three-dimensional data is compensated for by the efficient and accurate classification of the input image pixels. The detected objects are usually identified as cuboids in the 3D space, or as rectangles in the image space. Recently, instance segmentation techniques have been developed that are able to identify the freeform set of pixels that form an individual object, using complex convolutional neural networks (CNNs). This paper presents an alternative to these instance segmentation networks, combining much simpler semantic segmentation networks with light, geometrical post-processing techniques, to achieve instance segmentation results. The semantic segmentation network produces four semantic labels that identify the quarters of the individual objects: top left, top right, bottom left, and bottom right. These pixels are grouped into connected regions, based on their proximity and their position with respect to the whole object. Each quarter is used to generate a complete object hypothesis, which is then scored according to object pixel fitness. The individual homogeneous regions extracted from the labeled pixels are then assigned to the best-fitted rectangles, leading to complete and freeform identification of the pixels of individual objects. The accuracy is similar to instance segmentation-based methods but with reduced complexity in terms of trainable parameters, which leads to a reduced demand for computational resources.
Content based Image Retrieval: A Literature Review

Content based image retrieval (CBIR) is a prime research issue in computer vision, which retrieves the query image from the database of images using the visual features of the image. Even though CBIR was proposed to overcome the drawbacks of Text based image retrieval (TBIR), many challenging issues need to be addressed in CBIR. The research focuses on the different methods of CBIR. Literature from the basic feature descriptor for feature extraction to the recent transformer based image retrieval is reviewed and summarized in a table, which helps the researchers to quickly grasp the working of CBIR.
Chinese News Text Classification Method Based On Attention Mechanism

Combining the convolution neural network (CNN) model and bidirectional long short-term memory (BiLSTM) model, an ATT-CN-BILSTM Chinese news classification model is proposed based on the attention mechanism. The model uses the attention mechanism to improve the feature extraction process of CNN and BiLSTM. After cancelling the CNN pooling layer, it pays attention to the critical local features obtained by CNN convolution according to the timing features output by BiLSTM, giving full play to the respective advantages of CNN and BiLSTM models. The experimental results on Thucnews dataset show that the accuracy of the model for Chinese news text classification is 97.87%, and the recall rate and F1 score are better than the comparison model.
Supply Chain Management Finance Application in Bank Official Website using Blockchain

Until 2015 people used to make transactions using the bank’s official website, or else with the help of the mobile application. Even though using these kinds of technologies to make transactions people would use only paper currency. Only after 2015 maximum number of people start earning bitcoin. Blockchain is one of the important concepts to be concentrated on in recent days because the entire country market is focusing on Blockchain and crypto-currencies too. And one of the common disadvantages of the blockchain means security issues. Both in average marketing and also in finance management security should not be weaker, security, trust once it is lost then it is the hardest situation to get it back again. In this research, Financial Maximally Filtered Graph (FMFG) algorithm is proposed to track the financial supply chain with blockchain. Later the results are compared with the existing Neural Network Algorithm and found that the proposed algorithm has obtained 98.85% of accuracy in tracking and exact delivery of the financial data. This accuracy result is 3.31% higher than the existing model.
Improved DeepSORT Algorithm Based on Multi-Feature Fusion

At present, the detection-based pedestrian multi-target tracking algorithm is widely used in artificial intelligence, unmanned driving cars, virtual reality and other fields, and has achieved good tracking results. The traditional DeepSORT algorithm mainly tracks multiple pedestrian targets continuously, and can keep the ID unchanged. The applicability and tracking accuracy of the algorithm need to be further improved during tracking. In order to improve the tracking accuracy of the DeepSORT method, we propose a novel algorithm by revising the IOU distance metric in the matching process and integrating Feature Pyramid Network (FPN) and multi-layer pedestrian appearance features. The improved algorithm is verified on the public MOT-16 dataset, and the tracking accuracy of the algorithm is improved by 4.1%.
Affixal rivalry and its purely semantic resolution among English derived adjectives

This paper aims to fill in a long missing piece in the paradigmatic word-formation research: a set of rival affixes whose members are differentiated in meaning. We argue that such a set can be found in English derivational adjectivalization, in the affixal rivalry between the adjectivalizing suffixes -ed and -y. Using the traditional method of doublet comparison (Aronoff 1976, 2020), we reveal that adjectives of the form Xed and those of the form Xy ( X standing for the source word) differ in the scale type. Xed adjectives are closed-scale adjectives, but Xy adjectives are totally open-scale adjectives. The scale-type difference explains why Xed adjectives combine with certain degree modifiers, whereas Xy adjectives do not. Furthermore, we show that the rival affixes are doubly differentiated in the deverbal domain in terms of the said output scale type and the input base selection. In this domain, the major sources of the closed-scale -ed adjectives and the open-scale -y adjectives are result and manner verbs, respectively.
Optical Noise in a Free-Space Quantum Communications Link from Natural and Nuclear Disturbed Environments

Abstract Satellite communications at radio frequencies can experience a ‘blackout’ period following the atmospheric detonation of a nuclear weapon. The wavelengths used for free-space quantum communications will not incur the same ‘blackout’ effects from a nuclear detonation, but the optical systems will suffer from a phenomenon called redout . Redout occurs in an optical detector when ambient light scatters into the optical receiver, causing elevated background photon counts in the detector such that background noise overwhelms the signal. In this work, the duration of the redout effect is quantified from a nuclear disturbed environment on a ground-to-space quantum optical link. In addition, we comment on various techniques for reducing ambient and nuclear disturbed background counts in a quantum free-space optical link. For low-altitude nuclear detonations (i.e., under 50 km), the maximum interference time will be less than 1 min. Implementing a telescope, timing gate, and wavelength filter to the detector can reduce the background counts in the detector significantly. Aerosol levels and ground albedo are major contributors to background noise in a ground-to-satellite quantum channel, and ground station location should factor in both variables.
Empowering linked data in cultural heritage institutions: A knowledge management perspective

This reported research explores the barriers and challenges in linked data implementation in cultural heritage institutions, i.e., libraries, archives, and museums. Various data were collected from different sources regarding the linked data use cases related to libraries, archives, and museums over the past decade and analyzed from multiple facets. The analysis revealed very few activities of effective knowledge management in the linked data implementation and suggested that the crucial role of knowledge management and innovation should deserve enough attention in linked data projects and services. The findings will add value to the literature on knowledge management in the context of linked data and the semantic web.
Higher criticism for discriminating word-frequency tables and authorship attribution

We adapt the Higher Criticism (HC) goodness-of-fit test to measure the closeness between word-frequency tables. We apply this measure to authorship attribution challenges, where the goal is to identify the author of a document using other documents whose authorship is known. The method is simple yet performs well without handcrafting and tuning; reporting accuracy at the state of the art level in various current challenges. As an inherent side effect, the HC calculation identifies a subset of discriminating words. In practice, the identified words have low variance across documents belonging to a corpus of homogeneous authorship. We conclude that in comparing the similarity of a new document and a corpus of a single author, HC is mostly affected by words characteristic of the author and is relatively unaffected by topic structure.
Sommelier: Curating DNN Models for the Masses

Deep learning model repositories are indispensable in machine learning ecosystems today to facilitate model reuse. However, existing model repositories provide a bare-bone interface for model retrieval. The onus is on the user to profile and select from potentially hundreds of choices, barely relieving an average user of the expertise required to design the model in the first place. In this paper, we present Sommelier, an indexing and query system above typical DNN model repositories to interface directly with inference serving or other use cases. Given a desirable accuracy target and resource budget for an inference task category, Sommelier automatically searches through the repository for the most suitable model, without requiring manual profiling from the user. Motivated by manual iterative model search processes and typical model design strategies that generate model variants or models with common segments, Sommelier organizes DNN models based on their semantic correlation, defined as the probability of models producing the same results. This is further combined with a resource index based on relative resource consumption. Sommelier is implemented as a standalone query engine that can interface with an existing repository such as TF-Hub. A case study of 163 models in TF-Hub highlights the extent of model correlation across different model series, suggesting the best candidate model can easily evade manual profiling. Extensive evaluation shows that Sommelier returns the ideal model for over 95% of the queries; When interfaced with an inference server, Sommelier can reduce the 90th percentile tail latency of inference tasks by a factor of 6 via automatic model switching, far more than typical scale-out system optimizations.
Poster: Toward Zero-Trust Path-Aware Access Control

In this poster, we introduce path-aware risk scores for access control (PARSAC), a novel context-sensitive technique to enrich access requests with risk scoring of the path taken by those requests between the authenticated user and the resources they access. These path-aware risk scores enable another layer of security for traditional access control systems that addresses the need for fine-grained monitoring and enforcement within a zero-trust architecture. We define rules for general functions that can be used to determine risk and instantiate a specific approach to calculate path risk scores. We evaluate our approach with realistic network graphs; PARSAC finds more paths with lower risk when compared with traditional routing algorithms that select the shortest path.
BlueSky: Combining Task Planning and Activity-Centric Access Control for Assistive Humanoid Robots

In the not too distant future, assistive humanoid robots will provide versatile assistance for coping with everyday life. In their interactions with humans, not only safety, but also security and privacy issues need to be considered. In this Blue Sky paper, we therefore argue that it is time to bring task planning and execution as a well-established field of robotics with access and usage control in the field of security and privacy closer together. In particular, the recently proposed activity-based view on access and usage control provides a promising approach to bridge the gap between these two perspectives. We argue that humanoid robots provide for specific challenges due to their task-universality and their use in both, private and public spaces. Furthermore, they are socially connected to various parties and require policy creation at runtime due to learning. We contribute first attempts on the architecture and enforcement layer as well as on joint modeling, and discuss challenges and a research roadmap also for the policy and objectives layer. We conclude that the underlying combination of decentralized systems' and smart environments' research aspects provides for a rich source of challenges that need to be addressed on the road to deployment.
A Survey on Security Threats in Cloud Computing Service Models

The advent of cloud computing has enabled many users and organizations to make better use of system and business resources. Cloud computing's primary benefits are its cheaper service costs and the absence of the requirement for users to invest in expensive computing hardware. Because of the accessibility of its resources and flexibility for user computing operations, individuals and organizations migrate their programs, data, and resources to cloud storage services. A transition from localized to remote computing has brought with it a plethora of security risks and challenges for both users and service providers. However, there is one significant disadvantage to cloud computing: the storage of your data is in the hands of a third party. Amongst the most daunting concerns in providing powerful storage and processing as on-demand Services is the security threat posed by resource sharing in Cloud computing. Increasing efficiency and better performance are driving governments and organizations around the world to use cloud computing, either from scratch or as part of existing infrastructure. Risk and challenges associated with cloud computing, as well as the measures that can be taken to help shield the cloud from security threats, are discussed in this paper.
Intelligent and novel multi-type cancer prediction model using optimized ensemble learning

Cancer is known to be highly severe disease and gets incurable even when the treatment has started at the time of diagnosis owing to the occurrence of cancer cells. Diverse machine learning approaches are implemented for predicting the cancer recurrence that needs to be evaluated for showing the appropriate approach for cancer prediction. This paper provides intelligent optimized ensemble learning for predicting multiple types of cancers. At first, the different types of cancer data are collected and performed the data cleansing. Then, the feature extraction is done using statistical features, 'Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)'. With these features, a new Adaptive Condition Searched-Harris hawks Whale Optimization (ACS-HWO) is used for selecting the optimal features and transformed into weighted features with meta-heuristic update. The prediction is carried out by Optimized Ensemble-based Multi-disease Detection (OEMD) with Support Vector Machine (SVM), Autoencoder, Adaboost, 'Deep Neural Network (DNN), and Recurrent Neural Network (RNN)' with high ranking strategy. The same ACS-HWO is used for improvising the weighted feature selection and optimized ensemble learning. The comparative analysis over existing models shows that the suggested method can be highly applicable for the healthcare system to ensure the consistent prediction with the multi-type of cancers.
A Dichotomy in Consistent Query Answering for Primary Keys and Unary Foreign Keys

Since 2005, significant progress has been made in the problem of Consistent Query Answering (CQA) with respect to primary keys. In this problem, the input is a database instance that may violate one or more primary key constraints. A repair is defined as a maximal subinstance that satisfies all primary keys. Given a Boolean query q, the question then is whether q holds true in every repair.
Virtual design and machining of core and cavity for fabrication of dining plate tableware with Kawung batik pattern

Batik Kawung is one of the most popular batik patterns in Indonesia. This pattern can be displayed as an attractive and artistic ornament of ceramic plate products. This ornament can increase the selling value and storytelling of the product. Computer-based virtual design and manufacturing (VDM) technology is used to produce Kawung batik patterns that can be affixed to ceramic surfaces. The wrapping method in PowerShape is used to paste this pattern. The stages of designing a ceramic plate from the beginning of the Kawung pattern to form the core & cavity of the Kawung patterned plate into a 3D image and a virtual cutting milling motion simulation on a CNC machine using PowerMill software. The final result shows that the virtual machining simulation produces plate molds that are precise, accurate, and can be directly manufactured on a CNC router machine. The simulation time of a pair of plate molds is 234 hours 21 minutes 53 seconds, while the actual machining time is about 343 hours.