Reviewerly/exHarmony huggingface dataset:
Global–Local Discriminative Representation Learning Network for Viewpoint-Aware Vehicle Re-Identification in Intelligent Transportation

Vehicle re-identification (Re-ID) that aims at matching vehicles across multiple non-overlapping cameras is prevalently recognized as an important application of computer vision in intelligent transportation. One of the major challenges is to extract discriminative features that are resistant to viewpoint variations. To address this problem, this paper proposes a novel vehicle Re-ID model from the perspectives of effective feature fusion and adaptive part attention. Firstly, we put forward a channel attention-based feature fusion (CAFF) module that can learn the significance of features from different layers of the backbone network. In such a way, our model can leverage complementary features for vehicle Re-ID. Then, to address the viewpoint variation problem, we present an adaptive part attention (APA) module that evaluates the significance of local vehicle parts based on the visible areas and the extracted features. By doing so, our model can concentrate more on the vehicle parts with rich discriminative information while paying less attention to the parts with limited distinctive capability. Finally, the whole model is trained by simultaneous classification and metric learning. Experiments on two large-scale vehicle Re-ID datasets are carried out to evaluate the proposed model. The results show that our model achieves competing performance compared with other state-of-the-art approaches.
A Siamese Network-Based Method for Automatic Stitching of Artifact Fragments

For the problem of fragmentation of cultural relic fragments caused by natural or man-made factors, this paper proposes a method of automatic splicing of cultural relic fragments based on the siamese network. First, the method employs an improved region growing segmentation algorithm to segment the fractured and non-fractured surfaces of the point cloud of artifact fragments. Second, a rigid-body mechanics simulation method is used to fragment virtual artifacts and establish a database of fragments for deep learning algorithm training. Then, point cloud similarity comparison using a neural network DGCNN-Siamese net to achieve matching of fracture surfaces of broken pieces. Third, the fracture surface point cloud registration is aligned by using Harris-3D feature point extraction, neighborhood point feature histogram (PFH) feature description, and iterative closest point (ICP) method. The experimental result shows that the overall matching accuracy of the method is 96.99%, the method is able to reduce the registration deviation and achieve more complete recovery of the fragmented artifacts through comparative analysis.
Monitoring for Resource-Awareness

We show that resource-aware semantics, an execution model recently developed in the research community working on substructural type systems, can be naturally seen as a kind of monitored reduction.
Verifying Well-Typedness Preservation of Refactorings using Scope Graphs

The goal of automated refactoring is to reduce maintenance effort. To realize this, programmers need to be able to trust or manually check that refactorings actually preserve behavior. To allow programmers to focus on such checks, automated refactorings should preserve program well-typedness. However, historically automated refactorings in popular IDEs could break well-typedness. The reason is that modern languages have complex name binding semantics which makes it hard to guarantee well-typedness in general.
Temporal Layers: Reactive Activation Scope of First-Class Layer Instances

Context-Oriented Programming (COP) is an approach to improve modularity of context-dependent behaviors. Most COP languages modularize context-dependent behaviors using layers, which are rarely instantiated explicitly. Using the existing layer mechanism, it is difficult to express a context like "a situation where several objects are selected in a GUI window." To tackle this problem, we propose a new type of layers, which we call temporal layers. A temporal layer is different from existing layers in that it is used by creating its instance, and the extent of its activation is its lifetime. The scope of its activation can be defined by declaratively specifying the exclusion condition in the layer's enclosing partial classes. This scope can be dynamically and reactively changing. Using a temporal layer, we show a case study of contextual timetraveling, which demonstrates the effectiveness of our proposal.
Towards Virtual Machine Support for Contextual Role-Oriented Programming Languages

Adaptive software becomes more and more important as computing is increasingly context-dependent. Runtime adaptability can be achieved by dynamically selecting and applying context-specific code. Role-oriented programming has been proposed as a paradigm to enable runtime adaptive software by design. Roles change the objects’ behavior at runtime, thus adapting the software to a given context. Most approaches focus on optimizing language implementations neglecting the fact that the generated code is a verbose description of contextual roles in an object-oriented paradigm, which incurs an overhead. This paper takes a novel approach to reduce the semantic gap. We propose ObjectTeams/Truffle, to the best of our knowledge, the first virtual machine that optimizes the dispatch of contextual roles. We evaluate the implementation with a benchmark for role-oriented programming languages achieving a speedup of up to 2.49× over the reference implementation ObjectTeams/Java and 1.2× over an optimized version ObjectTeams/Java using Dispatch Plans.
Gradual Guarantee for FJ with lambda-Expressions

We present FJ&λ⋆, a new core calculus that extends Featherweight Java (FJ) with interfaces, λ-expressions, intersection types and a form of dynamic type. Intersection types can be used anywhere, in particular to specify target types of λ-expressions. The dynamic type is exploited to specify parts of the class tables and programs we want to exclude temporarily from static typing. Our main result is the gradual guarantee, which says that if a program is well typed in a class table, then replacing type annotations (from the program and from the class table) with the dynamic type always produces a program that is still well typed in the obtained class table. Furthermore, if a typed program evaluates to a value in a class table, then replacing type annotations with dynamic types always produces a program that evaluates to the same value in the obtained class table.
Segmentation of gastric cancer from microscopic biopsy images using deep learning approach

In Computer vision for microscopy image analysis applications, object segmentation, classification, and structure localization are essential processes for many biological researchers across the globe. The proposed method is significant as it provides automated cancer diagnosis. The manual interpretation of microscopic biopsy for cancer diagnosis is subjective, time-consuming, and dependent on pathologists’ interpretations, degree of skill. An automated cancer diagnosis is required to address these constraints. In this work, A deep learning-based architecture, namely enhanced EMD convolution neural networks (EECNNs) for object segmentation is proposed for microscopy images. The multiple IMFs give detailed frequency components of an image for feature extraction. This improves the performance significantly as compared to the previous works of feature extraction. The presented network is utilized for the segmentation of nuclei and cells in microscopy. The network uses multi-resolution deconvolution filters to train at several levels of an input image, it links the intervening layers for improved localization and context and provides the outcome. The additional convolution layers, which skip the max-pooling function, allow the network to train for varying input intensities and object dimensions while making it adaptable to noisy data. Here, we have compared the experimental results of the proposed method on widely accessible data sets which demonstrate that the proposed method provides superior results in terms of Accuracy of 0.9445, Specificity of 0.9256, Sensitivity of 0.9245, MCC of 0.9654, and BCR of 0.9250.
VCMix-Net: A hybrid network for medical image segmentation

With the continuous development of convolutional neural networks, their applications in medical image segmentation have become increasingly widespread. By using techniques such as channel attention and spatial attention, better segmentation results have been achieved. However, most existing networks lack the ability to select informative features when segmenting medical images. The combination of channel and spatial attention assumes that the interaction between channel and spatial information is independent, which can lead to biases and affect the model's performance. In certain cases, important information is assigned less weight, resulting in lower accuracy of small sample segmentation in large backgrounds due to the interdependence of this interaction. To address this issue, this paper proposes the Vip and Convolution Mixed (VCMix) module. It utilizes parallel operations of convolution and a multi-layer perceptron with class attention. By employing tensor shifting and linear projection, the module simultaneously captures local information and local-global information. It not only reduces the interdependence between channel and spatial information but also leverages the advantages of convolution in capturing local information to correct biases in local-global information, thereby obtaining more accurate feature information. The VCMix module can be integrated into the U-Net architecture. The model is evaluated on three datasets: LiTs, Lung, and ISIC-2016. Experimental results demonstrate the excellent performance of the VCMix module on all three datasets, highlighting its effectiveness in medical image segmentation. The parallel operations of the VCMix module provide insights for parallel operations of convolution with other methods, which contribute to the accurate delineation of lesion areas in medical image segmentation. Furthermore, it lays a foundation for the integration of artificial intelligence techniques from different domains and the development of AI in the medical field.
Operationalizing Real-Time Monitoring Data in Simulation Models Using the Public Domain HEC-DSSVue Software Platform

This paper describes a public domain semiautomated data screening and quality assurance processing tool useful for both discrete and continuous data. This application serves both a field research project assessing salinity and methylmercury drainage load control options and the requirements of a public domain watershed-based water quality forecasting model used to provide decision support for short-term salinity management in the San Joaquin River Basin of California. The US Army Corps of Engineers HEC-DSSVue data management and visualization platform was enhanced in the former application with Python scripts that performed the basic data quality assurance functions. Analogous tools were developed for use on the HOSTGATOR cloud server using the same Python scripts and algorithms to migrate quality-controlled data directly to a server for use by the Watershed Analysis Risk Management Framework (WARMF) water quality forecasting model. The Python scripts for both applications are easily adaptable by other potential users who do not currently have the resources to implement an enterprise-level hydrological data management and real-time quality assurance software system.
A Formal Treatment of Expressiveness and Relevanceof Digital Evidence

Digital investigations are largely concerned with reconstructing past events based on traces in digital systems. Given their importance, many concepts have been established to describe their quality—most of them concerned with procedural aspects, i.e., authenticity and integrity, for example. Besides that, there exist principal concepts that have been overlooked in the past: Two of those criteria are relevance and expressiveness of digital evidence. Unlike others, those are directly concerned with reaching the investigative goal. Therefore, we approach these two overlooked concepts of digital evidence by giving formal definitions. To illustrate the usefulness, we present two applications: First, we demonstrate that the notions of expressiveness and completeness can be used to guide investigations by presenting the Facet-oriented Criminalistic Cycle as a thinking model, which extends the well-established criminalistic cycle. Second, we put the concepts into practice by calculating the expressiveness of facets from a state machine representation of a digital system utilizing temporal logic and a model checker. Furthermore, we sketch out the implications of this improved way of defining relevance and expressiveness. Accordingly, this article aims to improve the understanding of these critical aspects of the overall investigative process.
Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy

The increasing demand for automation and material transportation has shown an incline toward optimal path navigation. The present work implements an intelligent Memory-based gravitational search algorithm (MGSA) with an evolutionary learning strategy to achieve a globally optimal collision-free path. The Evolutionary learning strategy helps improve the diversity among the Gravitational masses/agents, hence improving the overall exploration capability of the model. While the other approaches focus more on an evolutionary strategy based on mutation and cross-overs, the present technique implements the evolutionary strategy based on the position of the fit agents to improve the position of the unfit agents in the population. It ensures a fast-converging path planning result with an improved trajectory. Further, adding a memory-based approach helps the model remember the location of the best agent within the population. The controller is tested with multiple Humanoids on even and uneven terrains and showed a minimal improvement of more than 4% in path length with a minimum 5% deviation in the simulation and experimental results. The proposed approach showed a further improvement of more than 6% compared to the different intelligent path-planning approaches in a similar environment.
Developing a model semantic‐based image retrieval by combining KD‐Tree structure with ontology

Abstract The paper proposes an alternative approach to improve the performance of image retrieval. In this work, a framework for image retrieval based on machine learning and semantic retrieval is proposed. In the preprocessing phase, the image is segmented objects by using Graph‐cut, and the feature vectors of objects presented in the image and their visual relationships are extracted using R‐CNN. The feature vectors, visual relationships, and their symbolic labels are stored in KD‐Tree data structures which can be used to predict the label of objects and visual relationships later. To facilitate semantic query, the images use the RDF data model and create an ontology for the symbolic labels annotated. For each query image, after extracting their feature vectors, the KD‐Tree is used to classify the objects and predict their relationship. After that, a SPARQL query is built to extract a set of similar images. The SPARQL query consists of triple statements describing the objects and their relationship which were previously predicted. The evaluation of the framework with the MS‐COCO dataset and Flickr showed that the precision achieved scores of 0.9218 and 0.9370, respectively.
A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models

Face verification and recognition are important tasks that have made great progress in recent years. However, recognizing low-resolution faces from small images is still a difficult problem. In this paper, we advocate using diffusion models (DMs) to enhance face resolution and improve their quality for various downstream applications. Most existing DMs for super-resolution use U-Net as their backbone network, which only exploits multi-scale features along the spatial dimension. These approaches result in a slow convergence of corresponding DMs and the inability to capture complex details and fine textures. To address this issue, we propose a novel conditional generative model based on DMs called BPSR3, which replaces the U-Net in super-resolution via repeated refinement (SR3) with a multi-scale deep back-projection network structure. BPSR3 can extract richer features not only in depth but also in breadth. This helps to effectively refine the image quality at different scales. The experimental results on facial datasets show that BPSR3 significantly improved both convergence speed and reconstruction performance. BPSR3 has about 1/4 of the parameters of SR3 but achieves a 50.1% improvement in PSNR, a 19.8% improvement in SSIM, and a 15.4% reduction in FID. Our contribution lies in achieving less time and space consumption and better reconstruction results. In addition, we propose an idea of enhancing the performance of DMs by replacing the U-Net with a better network.
Fine-Grained Forward Secure Firmware Update in Smart Home

Although the vigorous development of smart homes brings great convenience to people’s lives, smart homes usually suffer from various security threats due to firmware vulnerabilities. Firmware update is a possible solution, but existing methods cannot address the security issues during the update process well. To enable secure firmware updates, a Forward Secure Firmware Update (FSFU) system was realized based on the proposed Puncturable-Ciphertext Policy-Attribute-Based Encryption (P-CP-ABE) scheme. In FSFU, when the service provider delivers the latest firmware, it specifies an access policy and some tags to encrypt the data and appends its signature to achieve both fine-grained access control and authentication. Authorized customers can obtain the latest firmware by decrypting the encrypted data through their private key. In particular, after a successful update, each authorized customer can realize forward security by updating his/her puncturable key, which is an important private key component. In addition, FSFU is further enhanced by outsourcing a part of the parameters and computational tasks. Finally, FSFU was proven to be secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Our proposed FSFU is efficient from both the theoretical analysis and the experimental results.
Legitimacy and space in the use of technologies for environmental and social governance: The cases of human trafficking and COVID-19 contact tracing

This article develops the concept of legitimacy to analyze the capacity of technologies such as phone apps to mobilize collective commitments to shared environmental and social outcomes by constituting new governance spaces. This concept of governance spaces highlights the variable configurations of technologies and their interactions with humans, and helps avoid the tendency to see technologies as passive relays that transmit power originating elsewhere, or, in contrast, to overstate the almost magical autonomous capacities of technology. The concept of legitimacy is valuable for evaluating the degree to which technologies are effective and deserving of support. The article draws on Mark Suchman’s distinction between pragmatic, moral and cognitive legitimacy, which correspond in turn to interests, ethical values, and facts. In contrast to more conventional state-centered conceptions of legitimacy, these aspects of legitimacy can be applied to governance spaces constituted by technologies. The article then examines and compares the cases of technologies for countering human trafficking and COVID-19 digital contact tracing apps. In both cases all three aspects of legitimacy are present, important, and interconnected. An examination of a recent report issued by the Organization for Security and Cooperation in Europe and the Tech Against Trafficking coalition on 305 anti-trafficking tools shows the role of ethics and facts in their legitimacy, but also the degree to which the tools are skewed towards interests other than those at risk of being trafficked. Acceptance and evaluations of digital contract tracing apps are similarly shaped by the interactions between interests, ethical values, and facts, including evidence about their effectiveness. The legitimacy of COVID-19 digital contact tracing apps involves a wider presence of a public interest in health while the risks associated with power inequalities are greater with anti-trafficking technologies, highlighting the importance of variability in the legitimacy of governance spaces constituted by technologies.
Neural machine translation for in‐text citation classification

Abstract The quality of scientific publications can be measured by quantitative indices such as the h‐index, Source Normalized Impact per Paper, or g‐index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full‐text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning‐based architecture for citation context classification. Unlike feature‐based state‐of‐the‐art models, our proposed focal‐loss and class‐weight‐aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state‐of‐the‐art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings.
Ur Dad Is My Gardener: Antiracist Conversations for Educational Leaders

Racism, social media, and free speech collide on a high school campus when a student creates an offensive poster that a classmate posts on social media. This case describes a school administrator’s conversations and actions with students, teachers, and the community addressing bigotry, race, and free speech on campus. The scenario is presented from the perspective of a school leader leading a campus through an incident of offensive speech aimed at a rival high school and posted on social media. This case focuses on educators’ responses to the issues presented through critical conversations and an intergroup dialogue protocol. The case elicits questions about hate speech, antiracist conversations, and accountability.
Market-Based Analysis: Apriori approach to analyze purchase patterns

In this paper, a data mining approach has been developed to analyze customer behavioral patterns and to get a further idea of the scale of the products purchased. Using the approach we found the best and least performing products, the average number of the products that the customer is interested in buying, And using the apriori algorithm we can generate the frequent items which can be bought together which will help us in suggesting products for the customer to buy. Each pattern is supported by the support and confidence generated using the apriori algorithm.