Reviewerly/exHarmony huggingface dataset:
Multi-branch selection fusion fine-grained classification algorithm based on coordinate attention localization

Object localization has been the focus of research in Fine-Grained Visual Categorization (FGVC). With the aim of improving the accuracy and precision of object localization in multi-branch networks, as well as the robustness and universality of object localization methods, our study mainly focus on how to combine coordinate attention and feature activation map for target localization. The model in this paper is a three-branch model including raw branch, object branch and part branch. The images are fed directly into the raw branch. Coordinate Attention Object Localization Module (CAOLM) is used to localize and crop objects in the image to generate the input for the object branch. Attention Partial Proposal Module (APPM) is used to propose part regions at different scales. The three classes of input images undergo end-to-end weakly supervised learning through different branches of the network. The model expands the receptive field to capture multi-scale features by Selective Branch Atrous Spatial Pooling Pyramid (SB-ASPP). It can fuse the feature maps obtained from the raw branch and the object branch with Selective Branch Block (SBBlock), and the complete features of the raw branch are used to supplement the missing information of the object branch. Extensive experimental results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets show that our method has the best classification performance on FGVC-Aircraft and also has competitive performance on other datasets. Few parameters and fast inference speed are also the advantages of our model.
Dissipative stabilization of entangled qubit pairs in quantum arrays with a single localized dissipative channel

We study the dissipative stabilization of entangled states in arrays of quantum systems. Specifically, we are interested in the states of qubits (spin-1/2) which may or may not interact with one or more cavities (bosonic modes). In all cases only one element, either a cavity or a qubit, is lossy and irreversibly coupled to a reservoir. When the lossy element is a cavity, we consider a squeezed reservoir and only interactions which conserve the number of cavity excitations. Instead, when the lossy element is a qubit, pure decay and a properly selected structure of XY-interactions are taken into account. We show that in all cases, in the steady state, many pairs of distant, non-directly interacting qubits, which cover the whole array, can get entangled in a stationary way, by means of the interplay of dissipation and local interactions.
Hiding information in BMP based on ECC and LSB algorithm

Information hiding technology can embed specific information in the digital host. Because the host information changes little, it will not arouse the attention of the monitor. In order to improve the information hiding effect and anti-violence extraction ability, logistic chaotic mapping and ECC encryption are introduced, and random hiding of information after encryption is proposed. Before the data is hidden, the information is divided and combined, and the control information is also embedded in the BMP image to realize the improved LSB algorithm.
Determining the trustworthiness of DNNs in classification tasks using generalized feature-based confidence metric

Determining the confidence of Deep Neural Networks in predictions is crucial for building reliable and robust systems. However, it has received minor attention among other areas related to Deep Learning. The confidence of DNNs in predictions is highly correlated with their ability in feature extraction. Consequently, a more robust feature extractor in DNNs leads to a more confident and trustworthy model. In this study, a method is designed in order to determine the trustworthiness of DNNs based on the quality of their feature extraction components. The concept of feature quality is defined based on the models’ confidence in predictions. In a situation where two DNNs have approximately the same accuracy, the superior model has more confidence in its predictions. Hence, it is less influenced by overfitting, making it more robust and reliable in unseen and noisy environments. Determining such a model is not always possible with the well-known accuracy metric. Accordingly, a novel metric named Generalized Feature-Based Confidence Metric is proposed, which is capable of profoundly evaluating the models’ confidence in predictions. It analyzes layer-by-layer feature vectors generated by DNNs and evaluates their quality. Altogether, these utilities boost assessing and comparing different models with varying widths and depths, improving them, and picking the best one. The practicality of the proposed method and metric is investigated through four significantly diverse case studies and empirically proved. Three of them are reputable benchmarking datasets, namely, CIFAR-10, CIFAR-100, and Fashion-MNIST. Moreover, a new high-quality dataset for the Hand Rubbing problem (made by the authors) is used to analyze the proposed method’s performance in a real-world application. Overall, the proposed metric is able to distinguish between different models from about 1% to 8% in terms of confidence in predictions where the models possess almost the same accuracy (0.5% difference or lower).
A Case Study of Software Project Replacement: A Time Series Analysis

Enterprise software requires constant updates to keep it usable. These updates originate in correcting errors and mainly in new organizational demands. Over time, these demands generate a significant workload that becomes increasingly complex than the first requirements. For this reason, the organization providing the software may choose to continue updating the old product or make it obsolete and replace it with a new one. Identifying the ideal moment to carry out this migration involves, in addition to the costs of keeping the product obsolete for a while, the effort to develop a new one. This work addresses a case study that comprises fifteen years with two migrations of the software project. Due to the availability of the collection of activities performed by the development and support team, performed sequentially over time, the applicability of time series was possible. Furthermore, the historical base of the activities performed made it possible to use the time series decomposition to obtain its trend, seasonality and noise. Time series decomposition indicated many random events in the first migration, while in the second, the team self-regulated, but there were tension points. This study identified a preliminary model whose purpose is to determine when to develop a new software version.
Securing trustworthy evidence for robust forensic cloud-blockchain environment for immigration management with improved ECC encryption

At present in this digitalized era, the passport is still a substantial thing. It is taken at every place together with other journey-related ID like Immigration stamps and VISA, which are also an element of a passport. In this research, a novel forensic method is developed for privacy-preserved management of migration in cloud-assisted blockchain networks. The decentralized model of blockchain made public ledger tampering a chief problem. The conservation of confidentiality is the main demand in legal forensics. The developed approach adapts appropriate techniques for conserving anonymity and confidentiality, which assured that no private data, is disclosed throughout the blockchain-based procedure derivation function. The technique integrated six stages, like"Initialization phase, Log File collection phase, Key generation and management, partial proof generation, certificate and file encryption, adding encryption proofs on public key infrastructure, proof verification by cloud forensic investigator". As a new thing, we choose the public keys optimally via Combined Archimedes and Hunger Games Search Algorithm with Cauchy's Mutation (CAHSACM). In addition, "improved Elliptical curve cryptography (IECC) based encryption" is done to authenticate the data. At last, the improvements of CAHSACM are proven using varied analyses.
ConCoNet: Class-agnostic counting with positive and negative exemplars

Class-agnostic counting is usually phrased as a matching problem between a user-defined exemplar patch and a query image. The count is derived based on the number of objects similar to the exemplar patch. However, defining a target class using only positive exemplar patches inevitably miscounts unintended objects that are visually alike to the exemplar. In this paper, we propose to include negative exemplars that define what not to count. This allows the model to calibrate its notion of what is similar based on both positive and negative exemplars. It effectively disentangles visually similar negatives, leading to a more discriminative definition of the target object. We designed our method such that it can be incorporated with other class-agnostic counting models. Moreover, application-wise, our model can be used into a semi-automatic labeling tool to simplify the job of the annotator
Supervised Classification of UML Class Diagrams Based on F-KNB

Often most software development doesn’t start from scratch but applies previously developed artifacts. These reusable artifacts are involved in various phases of the software life cycle, ranging from requirements to maintenance. Software design as the high level of software development process has an important impact on the following stages, so its reuse is gaining more and more attention. Unified modeling language (UML) class diagram as a modeling tool has become a de facto standard of software design, and thus its reuse also becomes a concern accordingly. So far, the related research on the reuse of UML class diagrams has focused on matching and retrieval. As a large number of class diagrams enter the repository for reuse, classification has become an essential task. The classification is divided into unsupervised classification (also known as clustering) and supervised classification. In our previous work, we discussed the clustering of UML class diagrams. In this paper, we focus on only the supervised classification of UML class diagrams and propose a supervised classification method. A novel ensemble classifier F-KNB combining both dependent and independent construction ideas is built. The similarity of class diagrams is described, in which the semantic, structural and hybrid matching is defined, respectively. The extracted feature elements are used in base classifiers F-KNN and F-NBs that are constructed based on improved K-nearest neighbors (KNNs) and Naive Bayes (NB), respectively. A series of experimental results show that the proposed ensemble classifier F-KNB shows a good classification quality and efficiency under the condition of variable size and distribution of training samples.
Understanding the Role of Stack Overflow in Supporting Software Development Tasks: A Research Perspective

Stack Overflow is a Q&amp;A website that is popular among developers and extensively used in software engineering (SE) research. A significant body of research has examined how Stack Overflow can assist with software development tasks, such as recommending APIs. However, while researchers have recognized the importance of Stack Overflow in SE research related to software development tasks, the specific ways in which it is utilized and the reasons for its widespread usage in research have not been thoroughly explored. To address these knowledge gaps, we conducted the first study to understand the role of Stack Overflow in assisting with SE research regarding software development tasks by systematically examining relevant and high-quality research works. Meanwhile, we carried out a qualitative survey to gain insight into why researchers choose to utilize Stack Overflow in SE research and to solicit suggestions for the better use of Stack Overflow in research. The study identifies trends in the research area, prominent researchers and organizations, and the types of tasks that utilize Stack Overflow in research, with coding and debugging being the most common. Moreover, it examines how Stack Overflow data is utilized in SE research regarding software development tasks, including searching, training models, and mining associations. Our qualitative survey of researchers indicates that the popularity of Stack Overflow stems from its comprehensive explanations of technical topics that are often not found in documentation or manuals. The findings provide a comprehensive understanding of the role of Stack Overflow in SE research regarding software development tasks, and offer actionable implications for both researchers and stakeholders of Stack Overflow to facilitate future research and improvements.
Semantic Clone Detection Based on Code Feature Fusion Learning

Code clones are duplicated code snippets that significantly threaten software maintenance and the public corpora of code representation learning. Traditionally, code context and its structure information abstract syntax tree (AST), control flow graph (CFG) are typical representations of source code, and context-based models and structure-based models contributed significantly to the development of code clone detection. In this paper, we present a hybrid embedding model for code clone detection (HEM-CCD), a fusion method of token sequential information and graph-based structure information. We insert tokens’ global context information encoded by a bi-directional recurrent neural network into the AST-based graph for comprehensive code semantic representation. Then, feeding the graph into a gated graph neural network we generate code semantic vectors for similarity evaluation. We have implemented our model on two public clone datasets (BigCloneBench and GoogleCodeJam), and the results indicate that HEM-CCD outperforms several state-of-the-art approaches.
A prototype selection technique based on relative density and density peaks clustering for k nearest neighbor classification

k-nearest neighbor classifier (KNN) is one of the most famous classification models due to its straightforward implementation and an error bounded by twice the Bayes error. However, it usually degrades because of noise and the high cost in computing the distance between different samples. In this context, hybrid prototype selection techniques have been postulated as a good solution and developed. Yet, they have the following issues: (a) adopted edition methods are susceptible to harmful samples around tested samples; (b) they retain too many internal samples, which contributes little to the classification of KNN classifier and (or) leading to the low reduction; (c) they rely on many parameters. The main contributions of our work are that (a) a novel competitive hybrid prototype selection technique based on relative density and density peaks clustering (PST-RD-DP) are proposed against the above issues at the same time; (b) a new edition method based on relative density and distance (EMRDD) in PST-RD-DP is first proposed to remove harmful samples and smooth the class boundary; (c) a new condensing method based on relative density and density peaks clustering (CMRDDPC) in PST-RD-DP is second proposed to retain representative borderline samples. Intensive experiments prove that PST-RD-DP outperforms 6 popular hybrid prototype selection techniques on extensive real data sets in weighing accuracy and reduction of the KNN classifier. Besides, the running time of PST-RD-DP is also acceptable.
Learning binary codes for fast image retrieval with sparse discriminant analysis and deep autoencoders

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods.
Modeling multi-attribute and implicit relationship factors with self-supervised learning for recommender systems

Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users’ specific preferences. A user-item bipartite graph could be used to understand the differences in motivation. However, existing research has not clearly proposed and modeled the factors that affect the differences, ignoring the similarities between user pairs and item pairs, preventing them from capturing fine-grained user preferences more effectively. In addition to the two points mentioned above, most GNN-based models for recommendation have the following two limitations: First, the model’s accuracy depends on the number of observed interactions in the dataset. Secondly, node representations are vulnerable to noisy interactions. This work has developed a novel recommendation model called “Multi-Attribute and Implicit Relationship Factors With Self-Supervised Learning for Collaborative filtering” (MIS-CF), which explicitly proposes and models multi-attribute and implicit relationship factors for collaborative filtering recommendation. Meanwhile, an auxiliary self-supervised learning task is designed to help the downstream task optimize the node representation. MIS-CF aggregates multi-attribute spaces through the user-item bipartite graph and establishes user-user and item-item graphs to model the similar relationship information of neighbor pairs through a memory model. The self-supervised learning task generates contrastive learning via self-discrimination, thus mining the rich auxiliary signals within the data, improving the accuracy and robustness of our model. Moreover, the sparse regularizer is utilized to alleviate the overfitting problem. Extensive experimental results on three public datasets not only show the significant performance and robustness gain of the proposed model but also prove the effectiveness and interpretability of fine-grained implicit factors modeling.
Design and development of dynamic Agri‐ontology for IoT interoperability

Summary In recent years, the usage and applications of Internet of Things (IoT) have increased exponentially. IoT connects multiple heterogeneous devices like sensors, micro controllers, actuators, smart devices like mobiles, watches, etc. IoT contributes the data produced in the context of data collection, including the domains like military, agriculture, healthcare, etc. The diversity of possible applications at the intersection of the IoT and the web semantics has prompted many research teams to work at the interface between these two disciplines. This makes it possible to collect data and control various objects in transparent way. The challenge lies in the use of this data. Ontologies address this challenge to meet specific data needs in the IoT field. This paper presents the implementation of a dynamic agriculture ontology‐building tool that parses the ontology files to extract full data and update it based on the user needs. The technology is used to create the angular library for parsing the OWL files. The proposed ontology framework would accept user‐defined ontologies and provide an interface for an online updating of the owl files to ensure the interoperability in the agriculture IoT.
Multilayer Convolutional Processing Network Based Cryptography Mechanism for Digital Images Infosecurity

Digital images can be easily shared or stored using different imaging devices, storage tools, and computer networks or wireless communication systems. However, these digital images, such as headshots or medical images, may contain private information. Hence, to protect the confidentiality, reliability, and availability of digital images on online processing applications, it is crucial to increase the infosecurity of these images. Therefore, an authorization encryption scheme should ensure a high security level of digital images. The present study aimed to establish a multilayer convolutional processing network (MCPN)-based cryptography mechanism for performing two-round image encryption and decryption processes. In the MCPN layer, two-dimensional (2D) spatial convolutional operations were used to extract the image features and perform scramble operations from grayscale to gray gradient values for the first-image encryption and second-image decryption processes, respectively. In the MCPN weighted network, a sine-power chaotic map (SPCM)-based key generator was used to dynamically produce the non-ordered pseudorandom numbers to set the network-weighted values as secret keys in a sufficiently large key space. It performs the second and first encryption processes using the diffusion method, modifying the image pixel values. Children’s headshots and medical images were used to evaluate the security level by comparing the plain and cipher images using the information entropy, number of pixel change rate, and unified averaged changed intensity indices. Moreover, the plain and decrypted images were compared to verify the decrypted image quality using the structural similarity index measurement and peak signal-to-noise ratio.
SURE: A Smart Failover Blockchain-Based Solution for the Recycling Reuse Process

Currently, human activity has a substantial impact on the environment, and we are responsible for determining what it will look like in a few or a dozen decades. Numerous IT solutions are being developed to reduce the negative influence on the environment. In particular, the main problem is the amount of plastic found in circulation and its recycling. Unfortunately, only few solutions exist that, on the one hand, support the reuse of the raw material and, on the other hand, give tangible benefits to users. In this work, we present a blockchain-based system for monitoring the recycling process of plastic bottles. The solution was described by technical, social, and performance characteristics. It should be emphasized that the adopted features of the new blockchain, such as a simplified code or complete decentralization, distinguish the solution from those currently created. Moreover, performance and fraud detection tests were performed. The results present that the solution for a PoW difficulty level of 3 still achieves decent times when generating a block with transactions (from the point of view of the recycling process). In addition, fraud detection tests have proven the ability to detect forged transactions. The outcomes from performed experiments show that the proposed concept can be used as an efficient and fraud-resilient solution in the case of the plastic recycling process.