Reviewerly/exHarmony huggingface dataset:
SECHO: A deep reinforcement learning-based scheme for secure handover in mobile edge computing

The experience of users in Mobile Edge Computing (MEC) highly depends on data offloading whose major bottle neck is the design of handover scheme. Existing research advocates that offloading security and quality of service (QoS) should be both considered for an optimal handover. However, recent studies concentrate on improving QoS while neglect security. Due to the complexity of MEC scenarios, generating an optimal handover scheme improving both QoS and security remains a challenge. To solve this problem, we propose SECHO, a secure handover scheme for blockchain-based single-user mobile edge computing system. Then, we design a security policy selection algorithm of offloading tasks to guarantees security performance and controls overhead. Finally, we customize the neural network for a deep reinforcement learning algorithm to realize optimal handover, where a security policy selection method is engaged to collaboratively enhance offloading QoS while decreasing underlying risks. Simulation results show that SECHO leads a 29.6%–302% advantage in overall performance than existing approaches. It proves that our proposal is effective to achieve secure handover while not degrading offloading QoS.
On the spread and evolution of dead methods in Java desktop applications: an exploratory study

Abstract Background. Dead code is a code smell. It can refer to code blocks, fields, methods, etc. that are unused and/or unreachable—e.g., if a method is unused and/or unreachable, it is a dead method. Past research has shown that the presence of dead code in source code harms its comprehensibility and maintainability. Nevertheless, there is still little empirical evidence on the spread of this code smell in the source code of commercial and open-source software applications. Aims. Our goal is to gather, through an exploratory study, empirical evidence on the spread and evolution of dead methods in open-source Java desktop applications. Method. We quantitatively analyzed the commit histories of 23 open-source Java desktop applications, whose software projects were hosted on GitHub . To investigate the spread and evolution of dead methods, we focused on dead methods detected at a commit level. The total number of analyzed commits in our study is 1,587. The perspective of our exploratory study is that of both practitioners and researchers. Results. We can summarize the most important take-away results as follows: (i) dead methods affect open-source Java desktop applications; (ii) dead methods generally survive for a long time before being “buried” or “revived;” (iii) dead methods that are then revived tend to survive less, as compared to dead methods that are then buried; (iv) dead methods are rarely revived; and (v) most dead methods are stillborn, rather than becoming dead later. Given the exploratory nature of our study, we believe that its results will help researchers to conduct more resource- and time-demanding research on dead methods and, in general, on dead code. Conclusions. We can conclude that developers should carefully handle dead code (and thus dead methods) since it is harmful, widespread, rarely revived, and survives for a long time in software applications.
A Recommendation Algorithm Combining Local and Global Interest Features

Due to the ability of knowledge graph to effectively solve the sparsity problem of collaborative filtering, knowledge graph (KG) has been widely studied and applied as auxiliary information in the field of recommendation systems. However, existing KG-based recommendation methods mainly focus on learning its representation from the neighborhood of target items, ignoring the influence of other items on the target item. The learning focuses on the local feature representation of the target item, which is not sufficient to effectively explore the user’s preference degree for the target item. To address the above issues, in this paper, an approach combining users’ local interest features with global interest features (KGG) is proposed to efficiently explore the user’s preference level for the target item, which learns the user’s local interest features and global interest features for target item through Knowledge Graph Convolutional Network and Generative Adversarial Network (GAN). Specifically, this paper first utilizes the Knowledge Graph Convolutional Network to mine related attributes on the knowledge graph to effectively capture item correlations and obtain the local feature representation of the target item, then uses the matrix factorization method to learn the user’s local interest features for target items. Secondly, it uses GAN to learn the user’s global interest features for target items from the implicit interaction matrix. Finally, a linear fusion layer is designed to effectively fuse the user’s local and global interests towards target items to obtain the final click prediction. Experimental results on three real datasets show that the proposed method not only effectively integrates the user’s local and global interests but also further alleviates the problem of data sparsity. Compared with the current baselines for knowledge graph-based systems, the KGG method achieves a maximum improvement of 8.1% and 7.6% in AUC and ACC, respectively.
The Tsukamoto Fuzzy Method for Predicting the Availability Status of Goods Inventory Based on Identification with RFID Technology

Data collection of goods entering and leaving the warehouse is very important. However, in large companies with hundreds of items being traded, manual recording can take a long time. To solve this problem, an RFID reader is used to record the name of the goods in the warehouse. By using RFID, the recording time can be reduced. Combined with the application of the Tsukamoto fuzzy algorithm, the system can predict the amount of inventory in the coming month to create sufficient inventory or not excess or less and the company can obtain optimal profits due to the process of buying and selling goods. This study aims to create an inventory system that can record goods through scans and can predict inventory in the coming month. The method used in this research is RFID technology and the Fuzzy Tsukamoto method. The results of this study indicate that the application can scan incoming or outgoing goods tags using RFID technology. The system can also predict the amount of inventory in the coming month using the Tsukamoto fuzzy method.
A Shallow System Prototype for Violent Action Detection in Italian Public Schools

This paper presents a novel low-cost integrated system prototype, called School Violence Detection system (SVD), based on a 2D Convolutional Neural Network (CNN). It is used for classifying and identifying automatically violent actions in educational environments based on shallow cost hardware. Moreover, the paper fills the gap of real datasets in educational environments by proposing a new one, called Daily School Break dataset (DSB), containing original videos recorded in an Italian high school yard. The proposed CNN has been pre-trained with an ImageNet model and a transfer learning approach. To extend its capabilities, the DSB was enriched with online images representing students in school environments. Experimental results analyze the classification performances of the SVD and investigate how it performs through the proposed DSB dataset. The SVD, which achieves a recognition accuracy of 95%, is considered computably efficient and low-cost. It could be adapted to other scenarios such as school arenas, gyms, playgrounds, etc.
Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology

Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for the classification of histological images is a rapidly expanding field of research. The popularity of CNNs has led to a rapid growth in the number of works related to CNNs in histopathology. This paper aims to provide a clear overview for better navigation. In this paper, recent DL-based classification studies in histopathology using strongly annotated data have been reviewed. All the works have been categorized from two points of view. First, the studies have been categorized into three groups according to the training approach and model construction: 1. fine-tuning of pre-trained networks for one-stage classification, 2. training networks from scratch for one-stage classification, and 3. multi-stage classification. Second, the papers summarized in this study cover a wide range of applications (e.g., breast, lung, colon, brain, kidney). To help navigate through the studies, the classification of reviewed works into tissue classification, tissue grading, and biomarker identification was used.
Verifiable Keyword Search Encryption Scheme That Supports Revocation of Attributes

In recent years, searchable encryption technology and attribute encryption technology have been widely used in cloud storage environments, and attribute-based searchable encryption schemes can both achieve the retrieval of encrypted data and effectively solve the access control problem. Considering that existing attribute-based searchable encryption schemes for cloud storage only support keyword search and do not support attribute revocation, most of the schemes that support attribute revocation only consider the computational overhead of users and ignore the large amount of computational resources consumed by attribute authorization centers when updating keys. In addition, keyword search may lead to partial errors in the returned search results, leading to the wastage of computational and broadband resources. To solve these issues, this paper proposes an attribute-based searchable encryption scheme that supports attribute revocation and is verifiable. To realize fine-grained ciphertext search of encrypted data, support scenarios of dynamic changes of user attributes, and ensure that third-party servers perform the search process reliably and honestly while minimizing computation and storage costs, first, this paper implements attribute revocation with the attribute authorization center by creating a user revocation list and an attribute key revocation list. At the same time, the system updates the attribute key at the time of user search request, which effectively reduces the computational overhead. Second, a third-party auditor is introduced to ensure the correctness of the search results. Finally, the security of this paper is verified by theoretical analysis, and the efficiency and practicality of this paper are verified by comparing it to other schemes through simulation experiments.
Frequency Seismic Response for EEWS Testing on Uniaxial Shaking Table

Earthquake early warning systems are used as important tools in earthquake risk management, providing timely information to residents and both public and private emergency managers. By doing this, the potential impact of large magnitude seismic events is significantly reduced. These systems use seismic sensors in order to acquire real-time data for the weaker but fast moving P wave (usually the first 3-5 s of the earthquake) and specific algorithms to predict the magnitude and the arrival time of the slower but more destructive surface waves. Most of these projection algorithms make use only of the vertical component of the acceleration and need extensive training in earthquake simulators in order to enhance their performance. Therefore, a low-inertial-mass uniaxial shaking table is proposed and analyzed in terms of frequency response in this paper, providing an effective cost/control ratio and high daily duty cycle. Furthermore, with the large variety of prediction algorithms, which use different frequency ranges, a new concept of selective frequency band error is also introduced and discussed in this paper as being a necessary tool for the final assessment of magnitude estimation algorithm error.
Decentralized UAV Swarm Scheduling with Constrained Task Exploration Balance

Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-Based Bundle Algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called “task consideration” is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster.
Bridging the Gap between Medical Tabular Data and NLP Predictive Models: A Fuzzy-Logic-Based Textualization Approach

The increasing use of electronic health records (EHRs) generates a vast amount of data, which can be leveraged for predictive modeling and improving patient outcomes. However, EHR data are typically mixtures of structured and unstructured data, which presents two major challenges. While several studies have focused on using machine learning models to predict patient outcomes, these models often require data to be in a structured format, which may lead to the loss of important information. On the other hand, unstructured data, such as narrative reports, can be noisy and challenging for natural language processing applications and interoperability. Therefore, there is a need to bridge the gap between structured EHR data and NLP-based predictive models. In this paper, we propose a fuzzy-logic-based pipeline that generates medical narratives from structured EHR data and evaluates its performance in predicting patient outcomes. The pipeline includes a feature selection operation and a reasoning and inference function that generates medical narratives. We then extensively evaluate the generated narratives using transformer-based NLP models for a patient-outcome-prediction task. We furthermore assess the interpretability of the generated text using Shapley values. Our approach has demonstrated comparable performance to the benchmark baseline models with an F1-score of 93.7%, while exhibiting slightly improved results in terms of recall. The model demonstrated proficiency in the preservation of information and interpretability inherited from nuanced and structured narratives. To the best of our knowledge, this is the first study to demonstrate the ability to transform tabular data into text to apply NLP for a prediction task.
An efficient optimizer for the 0/1 knapsack problem using group counseling

The field of optimization is concerned with determining the optimal solution to a problem. It refers to the mathematical loss or gain of a given objective function. Optimization must reduce the given problem's losses and disadvantages while maximizing its earnings and benefits. We all want optimal or, at the very least, suboptimal answers because we all want to live a better life. Group counseling optimizer (GCO) is an emerging evolutionary algorithm that simulates the human behavior of counseling within a group for solving problems. GCO has been successfully applied to single and multi-objective optimization problems. The 0/1 knapsack problem is also a combinatorial problem in which we can select an item entirely or drop it to fill a knapsack so that the total weight of selected items is less than or equal to the knapsack size and the value of all items is as significant as possible. Dynamic programming solves the 0/1 knapsack problem optimally, but the time complexity of dynamic programming is O(n3). In this article, we provide a feature analysis of GCO parameters and use it to solve the 0/1 knapsack problem (KP) using GCO. The results show that the GCO-based approach efficiently solves the 0/1 knapsack problem; therefore, it is a viable alternative to solving the 0/1 knapsack problem.
A Lossless-Recovery Secret Distribution Scheme Based on QR Codes

The visual cryptography scheme (VCS) distributes a secret to several images that can enhance the secure transmission of that secret. Quick response (QR) codes are widespread. VCS can be used to improve their secure transmission. Some schemes recover QR codes with many errors. This paper uses a distribution mechanism to achieve the error-free recovery of QR codes. An error-correction codeword (ECC) is used to divide the QR code into different areas. Every area is a key, and they are distributed to n shares. The loss of any share will make the reconstructed QR code impossible to decode normally. Stacking all shares can recover the secret QR code losslessly. Based on some experiments, the proposed scheme is relatively safe. The proposed scheme can restore a secret QR code without errors, and it is effective and feasible.
Butterfly Transforms for Efficient Representation of Spatially Variant Point Spread Functions in Bayesian Imaging

Bayesian imaging algorithms are becoming increasingly important in, e.g., astronomy, medicine and biology. Given that many of these algorithms compute iterative solutions to high-dimensional inverse problems, the efficiency and accuracy of the instrument response representation are of high importance for the imaging process. For efficiency reasons, point spread functions, which make up a large fraction of the response functions of telescopes and microscopes, are usually assumed to be spatially invariant in a given field of view and can thus be represented by a convolution. For many instruments, this assumption does not hold and degrades the accuracy of the instrument representation. Here, we discuss the application of butterfly transforms, which are linear neural network structures whose sizes scale sub-quadratically with the number of data points. Butterfly transforms are efficient by design, since they are inspired by the structure of the Cooley-Tukey fast Fourier transform. In this work, we combine them in several ways into butterfly networks, compare the different architectures with respect to their performance and identify a representation that is suitable for the efficient representation of a synthetic spatially variant point spread function up to a 1% error. Furthermore, we show its application in a short synthetic example.
Sensor Management Method of Giving Priority to Confirmed Identified Targets

The optimization objective function of sensor management for target identification is commonly established based on information theory indicators such as information gain, discrimination, discrimination gain, and quadratic entropy, which can control the sensors to reduce the overall uncertainty of all targets to be identified but ignores the speed of target being confirmed as identified. Therefore, inspired by the maximum posterior criterion of target identification and the target identification confirmation mechanism, we study a sensor management method that preferentially allocates resources to identifiable targets. Firstly, in the distributed target identification framework based on Bayesian theory, an improved identification probability prediction method that provides feedback the global identification results to local classifiers is proposed, which can improve the accuracy of identification probability prediction. Secondly, an effective sensor management function based on information entropy and expected confidence level is proposed to optimize the identification uncertainty itself rather than its variation, which can increase the priority of targets that satisfy the desired confidence level. In the end, the sensor management for target identification is modeled as a sensor allocation problem, and the optimization objective function based on the effective function is constructed, which can improve the target identification speed. The experimental results show that the correct identification rate of the proposed method is comparable to the methods based on information gain, discrimination, discrimination gain, and quadratic entropy in different scenarios, but the average time to confirm the identification is the shortest.
Comparison of tristimulus spectrocolorimeter measurements and human visual assessment of South Carolina field moist Munsell soil colors

Abstract Soil color has historically been estimated visually, but inaccuracies with this method have been documented, including disagreement in evaluators, color blindness, and differences in physical color books due to weathering. As new technologies become available to soil scientists, a reliable digital method to determine soil color in the field could offer a solution to these inaccuracies. This research aimed to assess if visual estimates of soil matrix color from 111 soil horizons differed from digital measurements of Munsell soil color across three South Carolina regions. Visual estimates of soil color were conducted using Munsell color books. The XRite Capsure gave digital measurements in Munsell notation specific for soils. The absolute difference between hues was similar among assessment methods. Visual estimates of color value and chroma were statistically greater than digital measurements. However, color values and chromas were less than one chip different, suggesting no practical difference between the two assessment methods. Utilizing a digital soil color assessment tool can assist with training and validate human estimates.
Toward cross‐domain object detection in artwork images using improved YoloV5 and XGBoosting

Abstract Object recognition in natural images has achieved great success, while recognizing objects in style‐images, such as artworks and watercolor images, has not yet achieved great progress. Here, this problem is addressed using cross‐domain object detection in style‐images, clipart, watercolor, and comic images. In particular, a cross‐domain object detection model is proposed using YoloV5 and eXtreme Gradient Boosting (XGBoosting). As detecting difficult instances in cross domain images is a challenging task, XGBoosting is incorporated in this workflow to enhance learning of the proposed model for application on hard‐to‐detect samples. Several ablation studies are carried out by training and evaluating this model on the StyleObject7K, ClipArt1K, Watercolor2K, and Comic2K datasets. It is empirically established that this proposed model works better than other methods for the above‐mentioned datasets.