Reviewerly/exHarmony huggingface dataset:
The Acceptance of Technology in Agriculture: case in Dalangan Village

Dalangan Village in Sukoharjo Regency has designated a national experimental area for agricultural machinery and information technology implementation in farming activities since 2018. Because of its use in the domain, individuals unfamiliar with using digital technology and a lack of information so many are still hesitant to utilize it. The purpose of this study was to determine the factors that influence public acceptance of the innovation of agricultural machinery in Dalangan Village using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). This research method is quantitative with an explanative research type. Data collection using a questionnaire with a survey method. The Partial Least Square (PLS) method with the Structural Equation Modeling (SEM) model in the smart-PLS three applications for data processing. The results of this study indicate that of the 130 respondents, there are 25 people (19&#x0025;) who never used the technology of agricultural mechanization tools and machines, 16 people (12&#x0025;) used it, 38 people (29&#x0025;) sometimes use it, 41 people (32&#x0025;) frequently use. Then people (8&#x0025;) very often use agricultural mechanization technology and machinery. Factors that significantly influence public acceptance of the use of agricultural machinery innovation are performance expectations, facilitating conditions, habits, and behavioral intention.
User experience measurement on augmented reality mobile application for learning to read using a phonics-based approach

Reading skills in early childhood provide the foundation for acquiring and mastering other lessons that are useful for their lives. The use of fun interactive learning media is one technique to boost interest in reading for early childhood. The advantage of the phonics-based method that emphasizes word learning through the listening of letter sounds makes it easier for children to improve their reading skills. Augmented reality (AR) development is beneficial because it helps students pay more attention and raise passion by including them in the learning process. This study aimed to create an AR-based multimedia application for learning to read by utilizing the phonics-based method, which is beneficial for the first step in advancing the reading skills of the Indonesian language. At the final stage of the study, a user experience measurement was conducted to investigate the level of acceptance of the developed application. The application that has been developed has been experimentally confirmed to have a high level of user acceptability.
Performance Analysis of Stego Images Detection Using Shallow Denoising Autoencoders

Early detection of sensitive information leakage in communication systems is topical task today. This requires usage of advanced statistical processing methods for detection negligible changes of cover files, such as digital images, caused by message hiding. One of promising approaches for solving the task is learning an appropriate representation of cover and formed stego images that is sensitive to data embedding. This approach is widely used in modern stegdetectors based on utilization of convolutional neural networks. Achieving of high detection accuracy by stegdetector requires usage deep convolutional networks, whose computation-intensive re-train procedure limits fast adaptation to unknown embedding methods. For overcoming this limitation, we propose to use special types of neural networks, namely autoencoders that provides fast adaptation to changes of inputted data by preserving high restoration accuracy. The work is devoted to performance analysis of usage shallow denoising autoencoders for detection of stego images formed by advanced embedding methods. It is revealed that considered networks allows improving detection accuracy up to 1.5%-2% for the most difficult case of small cover image payload (less than 10%).
Deterministic algorithms for the hidden subgroup problem

We consider deterministic algorithms for the well-known hidden subgroup problem ($\mathsf{HSP}$): for a finite group $G$ and a finite set $X$, given a function $f:G \to X$ and the promise that for any $g_1, g_2 \in G, f(g_1) = f(g_2)$ iff $g_1H=g_2H$ for a subgroup $H \le G$, the goal of the decision version is to determine whether $H$ is trivial or not, and the goal of the identification version is to identify $H$. An algorithm for the problem should query $f(g)$ for $g\in G$ at least as possible. Nayak asked whether there exist deterministic algorithms with $O(\sqrt{\frac{|G|}{|H|}})$ query complexity for $\mathsf{HSP}$. We answer this problem by proving the following results, which also extend the main results of Ref. [30], since here the algorithms do not rely on any prior knowledge of $H$. (i)When $G$ is a general finite Abelian group, there exist an algorithm with $O(\sqrt{\frac{|G|}{|H|}})$ queries to decide the triviality of $H$ and an algorithm to identify $H$ with $O(\sqrt{\frac{|G|}{|H|}\log |H|}+\log |H|)$ queries. (ii)In general there is no deterministic algorithm for the identification version of $\mathsf{HSP}$ with query complexity of $O(\sqrt{\frac{|G|}{|H|}})$, since there exists an instance of $\mathsf{HSP}$ that needs $\omega(\sqrt{\frac{|G|}{|H|}})$ queries to identify $H$. $f(x)$ is said to be $\omega(g(x))$ if for every positive constant $C$, there exists a positive constant $N$ such that for $x>N$, $f(x)\ge C\cdot g(x)$, which means $g$ is a strict lower bound for $f$. On the other hand, there exist instances of $\mathsf{HSP}$ with query complexity far smaller than $O(\sqrt{\frac{|G|}{|H|}})$, whose query complexity is $O(\log \frac{|G|}{|H|})$ and even $O(1)$.
Hurricane Damage Detection by Classic and Hybrid Classic-Quantum Neural Networks

This article describes a deep hybrid convolutional neural network (HNN) that uses one quantum device for binary image classification. The quantum device is a Ry quantum circuit with one qubit. This HNN configuration was evaluated on a practical problem - detection of damaged buildings on pictures taken from a satellite in addition to our previous attempts on the simple MNIST, MNIST Fashion and more sophisticated CIFAR10 and CIFAR100 datasets. The performance of HNN was compared with the reference classical model and two pretrained models: VGG-15 and RESNET-50. The measured metrics (accuracy and loss) during these experiments showed some drawbacks compared with the reference model but still support our assumption about the feasibility of HNN application for multiclass classification problems.
Research on Strategy Selection of Dynamic Defense Based on Game Theory

Aiming at the optimal selection of transition cycle in the design of dynamic defense strategy, this paper studies the dynamic change process of the interaction between attack and defense strategies by using game theory. Starting from the game process of attack and defense, this paper constructs the attack and defense game model, simulates the attack process of the attacker, obtains the best attack decision of the attacker, and then calculates the best defense income of the defender. This method can select the optimal defense strategy under the limited dynamic defense technology deployment, to maximize the network security defense revenue. Simulation results verify the effectiveness of the method and quantitatively analyze the improvement level of the dynamic defense technology optimal strategy compared with the traditional static defense technology.
Targeted Code Inspection based on Human Errors

As a direct cause of software defects, human error is the key to understanding and identifying defects. We propose a new code inspection method: targeted code inspection based on human error mechanisms of software engineers. Based on the common erroneous mechanisms of human cognition, the method targets error-prone codes with high efficiency and minimum effort. The proposed method is supported by preliminary evidence in a pilot study.
Patternika: A Pattern-Mining-Based Tool For Automatic Library Migration

Modern software projects typically include a number of third-party libraries. Library migrations (e.g., upgrade/ downgrade/replace some libraries with others) happen frequently due to license violations, known vulnerabilities, feature enhancements and so on, which is laborious and error-prone since that such tasks usually require developers to manually identify mappings between the libraries and then manually change the code. To address this problem, we propose and implement a pattern-mining based tool named Patternika which can help automate such activities. The key idea of the approach is to mine migration patterns from historical migration instances via static code differencing technique upon Abstract Syntax Tree (AST) structures and anti-unification technique upon code differencing graphs, and then apply these patterns to help automatically migrate libraries for future projects (i.e., find and patch source code fragments that contain API references to libraries to be migrated). With experimental evaluations on open data, the pattern mining algorithm provided by Patternika is demonstrated to be fully effective for commercial use. Patternika is now already integrated into an IDE plugin and can be freely downloaded and used by ninety thousands of internal developers intra an IT giant company.
Differential Cryptanalysis of TweGIFT-128 Based on Neural Network

It is a new trend of cryptographic analysis to realize automatic analysis on cryptographic algorithms by means of deep learning in recent years. TweGIFT-128 algorithm is an instantiation tweak block cipher algorithm for encryption authentication scheme ESTATE, and it was selected as a candidate cipher algorithm in the second round NIST challenge. Aiming at the security of TweGIFT-128 algorithm, the neural network model was used to realize automatic differential analysis in this paper, and the problem of searching differential distinguishers was transformed into a classification problem. Then based on DNN, the neural network differential distinguishers on TweGIFT-128 algorithm were constructed, and the 4&#x2013;8 round neural network differential distinguishers were trained by using fixed difference. Experimental results show that the obtained 4&#x2013;7 round TweGIFT-128 differential distinguishers can distinguish ciphertext from random data well, and the method constructing distinguisher based on neural network model is simpler and more general than the traditional methods.
Bit-FP: A Traffic Fingerprinting Approach for Bitcoin Hidden Service Detection

Bitcoin is a virtual encrypted digital currency based on a peer-to-peer network. In recent years, for higher anonymity, more and more Bitcoin users try to use Tor hidden services for identity and location hiding. However, previous studies have shown that Tor are vulnerable to traffic fingerprinting attack, which can identify different websites by identifying traffic patterns using statistical features of traffic. Our work shows that traffic fingerprinting attack is also effective for the Bitcoin hidden nodes detection. In this paper, we proposed a novel lightweight Bitcoin hidden service traffic fingerprinting, using a random decision forest classifier with features from TLS packet size and direction. We test our attack on a novel dataset, including a foreground set of Bitcoin hidden node traffic and a background set of different hidden service websites and various Tor applications traffic. We can detect Bitcoin hidden node from different Tor clients and website hidden services with a precision of 0.989 and a recall of 0.987, which is higher than the previous model.
Network Security Situation Prediction Model Based on Security Situation Factors

The existing network security situation prediction (NSSP) methods are static prediction, and do not consider the impact of dynamic changes of security situation factors on the future NSS, which caused the prediction inaccurate. This paper proposes a NSSP model based on the prediction of security situation factors. This model comprehensively considers the change of factors of the security situation and ignores the factors of the situation that do not change in the short term, such as topology and service information. It mainly analyzes the dynamic changes of attack information, vulnerability information and defensive measures, and forecasts the situation from both the host and network levels. The method of prediction in this paper is verified by experiments, which can improve the prediction accuracy and is more in line with the attack scenario.
An efficient dual ensemble software defect prediction method with neural network

With the rapid development of technology, software projects are becoming increasingly complex, but the problem of defects is still not well solved, and the application of defective software will bring some security problems, therefore, it is necessary to identify the defective modules to ensure the quality of software. Software defect prediction (SDP) can achieve this goal and it is now an essential part of software testing. However, there is a problem of class imbalance in the defective datasets, which can easily cause the prediction models inaccuracy. Ensemble learning has been proven to be one of the best ways to address the problem of class imbalance. In this paper, we propose an efficient dual ensemble software defect prediction method with neural network (DE-SDP) to solve the class imbalance problem, thereby improving the performance of prediction model. Firstly, we combine cross-validation and seven different classifiers to build base ensemble classifiers. Then, we use stacking method and neural network model to re-ensemble the base ensemble classifiers. Finally, we evaluate the performance of proposed DE-SDP on eight public datasets, and the results demonstrate the effectiveness of the DE-SDP method.
Adversarial Residual Variational Graph Autoencoder with Batch Normalization

The variational graph autoencoder (VGAE), a framework for unsupervised learning on graph-structured data, has captured more attention recently in graph embedding area. However, it has been faced up with the challenge of KL vanishing, which will converge to local optimum and make the graph embedding unavailable for downstream tasks like link prediction. This paper proposes a novel variational graph autoencoder framework to achieve more effective graph embedding. Firstly, we introduce batch normalization to make sure the KL distribution consistent with the whole dataset by keeping its expectation positive, thus avoiding posteriori collapse. In addition, we invite residual connection and adversarial network to simultaneously embed the topology information and content information into graph representation stably, enhancing the expressive ability of latent vector. Finally, link prediction experiments on three citation datasets demonstrate that the AUC scores of our algorithm are higher than 92&#x0025; and the average accuracies are higher than 93&#x0025;, which is competitive comparing with the state-of-the-art variational graph autoencoders.
Modeling Connection Strength in Graph Neural Networks for Social Influence Prediction

Social media applications such as Twitter and Weibo have attracted tremendous interests in understanding and predicting online social influence. Among other factors, relationship closeness between users is one of the most important ones for social influence prediction. Recent studies have verified the effectiveness of employing graph neural networks(GNN) for social influence prediction. However, most of them are failed to or less effective to make use of relationship closeness explicitly and precisely (with a few exceptions in which relationship closeness is measured as connection strength). Instead, they tend to use simple binary information such as whether users are friends/followers or not. In this paper, we develop a new model, CSGNN, to exploit connection strength feature in GAT and GCN. We estimate connection strength by a combination of static interaction frequency and time duration of follow actions. We also propose a new attention mechanism to incorporate connection strength based on GAT, and a new method of convolution and aggregation based on GCN. Extensive experiments are conducted on multiple real-world social network datasets, including Digg and Weibo. The results demonstrate that CSGNN outperforms several state-of-the-art baselines, and the ablation study confirms that connection strength contributes to the performance improvement significantly.
BDCP: An Improved Nested Named Entity Recognition Model Based on LSTM

In construction of knowledge graphs (KGs), named entity recognition (NER) is a sub-task to identify the boundaries of entities with special meaning and predict their categories in texts. The instance that an entity contains one or more entities is called nested entity. The current work on NER usually ignores the nested NER. We propose an improved model named Boundary Detection and Category Prediction (BDCP) for Nested NER. Our model uses LSTM to extract the contextual features of the sequence, and uses boundary detection unit to mark the boundaries of entities in the text. By introducing boundary detection unit, our model extracts the boundaries of entities and restrict the number of candidate entities. We also design a layer-by-layer decoding module by boundary detection unit for Nested NER. Experiments on Nested NER datasets named GENIA [1] demonstrate the effectiveness of our model on nested NER.
PRFP: Privacy-respecting and Accurate Device Fingerprint Identification

With the rapid development of the mobile device, various online transactions have attracted a large number of black markets. Malicious users forge mobile equipments to register accounts, receive coupons, and get the best deal, which cause serious losses to merchants, enterprises, and other consumers. In order to combat these threats, device fingerprinting technology has been studied to derive unique identification and defeat malicious registrations and coupons from the same device. However, existing device fingerprinting methods require collecting sensitive information from users&#x0027; devices and hence leak privacy. Although there are studies protecting privacy by adding noises or perturbations to collected device information, they significantly reduce the usability of collected sensor data and hence degrade the accuracy of device fingerprinting. In this paper, we design a framework named PRFP, which achieves high accuracy of fingerprinting devices while effectively protecting users&#x0027; privacy. It enables deep learning models to extract informative intermediate feature representations related to device fingerprinting from raw device, while automatically removing user-related private information as much as possible. Our experimental results with a large device fingerprinting dataset show that PRFP can effectively protect users&#x0027; privacy while only introducing less than 4&#x0025; accuracy loss compared to fingerprinting devices with raw data.
Transfer Learning for Highlighting Diagnosis in Pathological Anatomy Based on Immunohistochemistry

In the medical field, the diagnostic phase is the most important, as the entire treatment process will be based on this step. Oncological diseases such as breast cancer require a precise anatomopathological study accompanied most of the time by an immunohistochemical study whose goal is to know the sensitivity of tumor tissues to hormone therapy and targeted therapy. This study relies on antibodies and their interpretation requires significant time and as it can suffer from poor reproducibility which negatively influences the treatment stage. In this work, the objective is to classify histopathological images stained with E-cadherin antibody to help pathologists in their work in order to facilitate oncologists in the choice of the most appropriate therapeutic protocol. The realization of this task is based on the choice of transfer learning as techniques and data augmentation due to the minimal number of images gathered. The results obtained are very satisfying both on accuracy where we reached a rate of 97.27% with a reduced number of parameters and very close to our basic model.
The Necessity of Low-code Engineering for Industrial Software Development: A Case Study and Reflections

Developing industrial software systems is challenging due to the complexity of industrial operations, as well as the distinct knowledge structures and cultures between industrial domains and professional software development. Industrial domain experts are often with very limited programming backgrounds, while most professional developers lack the understandings of industrial operations. In this paper, we reported a case study conducted in a large metal processing plant. The results revealed that: (1) the collaboration between industrial domain experts and professional software engineers is inefficient and ineffective; (2) industrial domain experts would rather build industrial applications by themselves; however, existing techniques are primarily developed for professional developers rather than them; (3) industrial domain experts expect to be assisted by visual language-based low-code platforms in building such applications. We reflected on the findings and thus argued the necessity and potentials of low-code engineering for industrial software development.
Audio Steganography with Speech Recognition System

Deep neural networks (DNNs) are vulnerable to adversarial examples that are intentionally crafted by adding small perturbations to the original input. Most works focus on generating such adversarial examples to reveal the security concerns of DNNs, while few of them explore the positive usage of the adversarial examples. In this paper, we generate adversarial audio that could fool DNNs for speech recognition, but can be utilized for audio steganography. Specifically, the generated adversarial audio contains secret information which is only recognized by DeepSpeech (an authorized speech recognition system), while unrecognizable to humans or other unauthorized speech recognition systems. Experimental results show that with an average SNR of &#x2212;28.75, the adversarial audio can achieve high success ratio of 88&#x0025;.
The Controlled Sharing Mechanism for Private Data Based on Blockchain

In order to solve the problems that private information is easy to be leaked and privacy violations are difficult to be traced in the process of data cross-domain transferring, we propose a blockchain-based controlled sharing mechanism for private data, which has the merits such as decentralization, tamper-proof, and traceability. By combining smart contract technology and cryptographic algorithms, we realize the user authentication and private data rights determination. On the basis of that, we extract privacy protection metadata by using technologies such as hash function and digital signature and then store it on the blockchain, which realizes the depositing and tracing of data transferring records. At the same time, we encrypt the original data and store it in a distributed file system to alleviate the problem of limited storage space in blockchain while ensuring the security of data storage.
Methods for deep learning model failure detection and model adaption: A survey

In real-world applications, deep learning models may fail to predict due to service switch, system upgrade, or other environmental changes. One main reason is that the model lacks generalization ability when data distribution changes. To detect model failures in advance, a direct and effective method is to monitor the data distribution in real time. This paper provides a taxonomy of data distribution shift detection methods, which is an important issue in model failure perception, and also gives a framework on model adaption and generalization under distribution shift scenario.