Reviewerly/exHarmony huggingface dataset:
Background Knowledge in Schema Matching: Strategy vs. Data

The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically. In this paper, we exploit six general-purpose knowledge graphs as sources of background knowledge for the matching task. The background sources are evaluated by applying three different exploitation strategies. We find that explicit strategies still outperform latent ones and that the choice of the strategy has a greater impact on the final alignment than the actual background dataset on which the strategy is applied. While we could not identify a universally superior resource, BabelNet achieved consistently good results. Our best matcher configuration with BabelNet performs very competitively when compared to other matching systems even though no dataset-specific optimizations were made.
Trajectory Planning for Hypersonic Vehicles with Reinforcement Learning

This paper discusses the problem of avoiding threats during the cruising flight of hypersonic vehicles (HV). Considering the constraints on kinematics of HV and changing environments, this paper proposes two methods of trajectory planning that taking the overload or the rotational angular velocity of the ballistic deflection angle as actions of agent. Meanwhile, the agent’s policy is optimized with policy gradient method, and Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are used for comparison. Experimental results show that PPO and SAC have similar performance in penetration missions. Moreover, in the complicated flight environment, the method of taking overload and exploration distance as actions has a higher penetration success ratio.
Brain Tumor Segmentation based on Knowledge Distillation and Adversarial Training

3D MRI brain tumor segmentation is a reliable method for disease diagnosis and treatment plans in the future. Early on, the segmentation of brain tumors is mostly done manually. However, manual segmentation of 3D MRI brain tumor requires professional anatomical knowledge and may be inaccurate. In this paper, we propose a 3D MRI brain tumor segmentation architecture based on the encoder-decoder structure. Specially, we introduce knowledge distillation and adversarial training methods, which compresses and improves the accuracy and robustness of the model. Furthermore, we obtain soft targets by designing multiple teacher network training and then apply them to the student network. Finally, we evaluate our method on a challenging BraTS dataset. As a result, the performance of our proposed model is superior to state-of-the-art methods.
Data-Free Evaluation of User Contributions in Federated Learning

Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device’s private data and computing resources. A critical issues is to evaluate individual users’ contributions so that (1) users’ effort in model training can be compensated with proper incentives and (2) malicious and low-quality users can be detected and removed. The state-of-the-art solutions require a representative test dataset for the evaluation purpose, but such a dataset is often unavailable and hard to synthesize. In this paper, we propose a method called Pairwise Correlated Agreement (PCA) based on the idea of peer prediction to evaluate user contribution in FL without a test dataset. PCA achieves this using the statistical correlation of the model parameters uploaded by users. We then apply PCA to designing (1) a new federated learning algorithm called Fed-PCA, and (2) a new incentive mechanism that guarantees truthfulness. We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset. The results demonstrate that our Fed-PCA outperforms the canonical FedAvg algorithm and other baseline methods in accuracy, and at the same time, PCA effectively incentivizes users to behave truthfully.
Automated Planning and BDI Agents: A Case Study

There have been many attempts to integrate automated planning and rational agents. Most of the research focuses on adding support directly within agent programming languages, such as those based on the Belief-Desire-Intention model, rather than using off-the-shelf planners. This approach is often believed to improve the computation time, which is a common requirement in real world applications. This paper shows that even in complex scenarios, such as in the Multi-Agent Programming Contest with 50 agents and a 4 s deadline for the agents to send actions to the server, it is possible to efficiently integrate agent languages with off-the-shelf automated planners. Based on the experience with this case study, the paper discusses advantages and disadvantages of decoupling the agents from the planners.
Sentiment Analysis of News Headlines Using Simple Transformers

With the rate at which the data is being generated, it is vital to use it and get some insights from it. When reporting on events, news expresses the opinions of news entities like people, locations, and things. In this paper, we obtain the sentiment of the news headlines using a new technique called transformers, in particular simple transformers that have been a significant advancement in the natural language processing field. Sentiment Analysis has been performed using the four transformers model. These models are pre-trained on extensive data, and we have fine-tuned them by training them on our own news headlines dataset. For our sentiment analysis task, classification models (specific simple transformer model) are used to classify news headlines as negative, neutral, positive. The idea behind taking four different models that are Bidirectional Encoder Representations from Transformers (BERT) base-cased [1], Robustly optimized BERT approach (RoBERTa) base [1], Distilled BERT (DistilBERT) base-cased [1], XLNet base-cased [1], is the different dataset on which they are pre-trained, parameters used by them, and different method used by them, which improve their performance significantly in comparison to different machine learning classifiers and prior deep learning models. The model that performed the best is bert-base-cased with Matthews Correlation Coefficient (MCC) score of 90.1%, an F1 score of 93.6%, and an Accuracy score of 93.6%.
AutoGuard: A Dual Intelligence Proactive Anomaly Detection at Application-Layer in 5G Networks

Application-layer protocols are widely adopted for signaling in telecommunication networks such as the 5G networks. However, they can be subject to application-layer attacks that are hardly detected by existing traditional network-based security tools that often do not support telecommunication-specific applications. To address this issue, we propose in this work AutoGuard, a proactive anomaly detection solution that employs application-layer Performance Measurement (PM) counters to train two different Deep Learning (DL) techniques, namely, Long Short Term Memory (LSTM) networks and AutoEncoders (AEs). We leverage recent advancements in Machine Learning (ML) that show the advantages brought by combining multiple ML models to build a dual-intelligence approach allowing the proactive detection of application layer anomalies. Our proposed dual-intelligence solution promotes signaling workload forecasting and anomaly prediction as a proactive security control in 5G networks. As a proof of concept, we implement our approach for the proactive detection of Diameter-related signaling attacks on the Home Subscriber Server (HSS) core network function. To evaluate our solution, we conduct a set of experiments using data collected from a real 5G testbed. Our results show the effectiveness of our dual intelligence approach on proactively detecting signaling anomalies with a precision reaching 0.86.
Realistic Smile Expression Recognition Approach Using Ensemble Classifier with Enhanced Bagging

A robust smile recognition system could be widely used for many real-world applications. Classification of a facial smile in an unconstrained setting is difficult due to the invertible and wide variety in face images. In this paper, an adaptive model for smile expression classification is suggested that integrates a fast features extraction algorithm and cascade classifiers. Our model takes advantage of the intrinsic association between face detection, smile, and other face features to alleviate the over-fitting issue on the limited training set and increase classification results. The features are extracted taking into account to exclude any unnecessary coefficients in the feature vector; thereby enhancing the discriminatory capacity of the extracted features and reducing the computational process. Still, the main causes of error in learning are due to noise, bias, and variance. Ensemble helps to minimize these factors. Combinations of multiple classifiers decrease variance, especially in the case of unstable classifiers, and may produce a more reliable classification than a single classifier. However, a shortcoming of bagging as the best ensemble classifier is its random selection, where the classification performance relies on the chance to pick an appropriate subset of training items. The suggested model employs a modified form of bagging while creating training sets to deal with this challenge (error-based bootstrapping). The experimental results for smile classification on the JAFFE, CK+, and CK+48 benchmark datasets show the feasibility of our proposed model.
Exploring the Eolang-Java Integration and Interoperability

In recent times, the subject of interoperability has become very popular. In large-scale software applications development, it is a common practice to combine multiple languages in solving peculiar problems and developing robust solutions. The ability to combine multiple languages allows an easy migration of an existing project from one language to another or use existing libraries in another language. This makes interoperability a force to be reckoned with when developing new programming languages. The Eolang programming language is a new research and development initiative aimed at achieving true Object-Oriented Programming by having all components of the program as objects. As such, the construct and syntax of Eolang is vastly different from that of Java. This makes integration and interoperability between these two languages a challenging issue related to method/object naming conventions, keywords and operators, etc. In this paper we explore the potential of Eolang interoperability with Java by looking at the interoperability mechanisms of some other languages with Java, describe ways to overcome these challenges with Eolang and develop the solution. Specifically, we focus on the possibility to call Java code from Eolang while the semantics of both languages remain preserved. Our solution allows Java code to be called in Eolang through wrappers that turn Java classes and methods into Eolang Objects.
Naagarik: A Machine Learning Framework for Intelligent Analysis of Civic Issues

This paper introduces Naagarik, a framework which analyses hyperlocal, real-time citizen-generated tweets filtered by location, and determines whether they are civic issues or not. It further classifies the civic issues into different categories like Waste/Garbage, Water, Potholes and more. In addition to this, we compute the sentiment of the reports to capture their severity. We use Machine Learning and Natural Language Processing techniques like Logistic Regression, Support Vector Machines and the VADER sentiment analyzer to perform the three-step process of identification, classification and sentiment analysis respectively. These models are chosen on the basis of better performance metrics exhibited, when compared to other text classification algorithms. Thus, our framework bridges the gap between a city's local government and its citizens by highlighting their grievances based on relevance, category and severity.
Kidney Stone Detection Using Digital Image Processing Techniques

The Kidney stones are a hard collection of salt and minerals, often calcium and uric acid that form in the kidneys. The majority of persons with kidney stones do not recognize them at first, and their organs gradually deteriorate. For surgical procedures, it is critical to determine the exact and precise location of a kidney stone. Speckle noise is present in most ultrasound images, which cannot be removed by humans. The paper consists of problems of kidney stones in the human body and detection mechanisms by using Image processing techniques. The Techniques like preprocessing, segmentation and Morphological Analysis. The Results of techniques are evaluated based on the output parameters and analyzed to conclude the methods working efficiently.
A Novel Zero Watermarking Based on DT-CWT and Quaternion for HDR Image

This paper presents a high dynamic range (HDR) image zero watermarking method based on dual tree complex wavelet transform (DT-CWT) and quaternion. In order to be against tone mapping (TM), DT-CWT is used to transform the three RGB color channels of the HDR image for obtaining the low-pass sub-bands, respectively, since DT-CWT can extract the contour of the HDR image and the contour change of the HDR image is small after TM. The HDR image provides a wide dynamic range, and thus, three-color channel correlations are higher than inner-relationships and the quaternion is used to consider three color channels as a whole to be transformed. Quaternion fast Fourier transform (QFFT) and quaternion singular value decomposition (QSVD) are utilized to decompose the HDR image for obtaining robust features, which is fused with a binary watermark to generate a zero watermark for copyright protection. Furthermore, the binary watermark is scrambled for the security by using the Arnold transform. Experimental results denote that the proposed zero-watermarking method is robust to TM and other image processing attacks, and can protect the HDR image efficiently.
Quantum Identity-Based Encryption from the Learning with Errors Problem

To prevent eavesdropping and tampering, network security protocols take advantage of asymmetric ciphers to establish session-specific shared keys with which further communication is encrypted using symmetric ciphers. Commonly used asymmetric algorithms include public key encryption, key exchange, and identity-based encryption (IBE). However, network security protocols based on classic identity-based encryption schemes do not have perfect forward secrecy. To solve this problem, we construct the first quantum IBE (QIBE) scheme based on the learning with errors (LWE) problem, which is also the first cryptographic scheme that applies the LWE problem to quantum encryption. We prove that our scheme is fully secure under the random oracle model and highlight the following advantages: (1) Network security protocols with our QIBE scheme provide perfect forward secrecy. The ciphertext is transmitted in the form of a quantum state unknown to the adversary and cannot be copied and stored. Thus, in network security protocols based on QIBE construction, the adversary does not have any previous quantum ciphertext to decrypt for obtaining the previous session key, even if the private identity key is threatened. (2) Classic key generation centre (KGC) systems can still be used in the QIBE scheme to generate and distribute private identity keys, reducing the cost when implementing this scheme. The classic KGC systems can be used because the master public and secret keys of our scheme are both in the form of classic bits. Finally, we present quantum circuits to implement this QIBE scheme and analyse its required quantum resources for given numbers of qubits, Hadamard gates, phase gates, T gates, and CNOT (controlled-NOT) gates. One of our main findings is that the quantum resources required by our scheme increase linearly with the number of plaintext bits to be encrypted.
A Few-Shot Learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-Identification

Many object re-identification (Re-ID) methods that depend on large-scale training datasets have been proposed in recent years.However, the performance of these methods degrades dramatically when insufficient training data are available.To address this challenging problem, we propose a few-shot object re-identification (FSOR) method that enhances the generalization and discrimination abilities of object Re-ID models trained on small datasets.This method applies two novel techniques: reparameterization for feature vectors and dual-distance metric learning.The reparameterization mechanism transforms the primary feature vector of each input image into a Gaussian distribution to enhance the robustness of the FSOR method when performing object Re-ID tasks.The dual-distance metric learning technique, called H&C learning, considers both the hard mining distance and the center-point distance between each query sample and each support set of different object identities.H&C learning extracts the characteristics of the entire training dataset more precisely than other approaches and thus improves the discriminative abilities of object Re-ID models.Extensive experiments on both person and vehicle Re-ID datasets, such as Market-1501, DukeMTMC-ReID, CUHK03, and VeRi-776, show that the FSOR method has improved performance and outperforms state-ofthe-art methods when the amount of labeled training data is small.
Epilepsy Radiology Reports Classification Using Deep Learning Networks

The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given text report our systems first cleans HTML/XML tags, tokenize, erase punctuation, normalize text, (ii) then it converts into MRI text reports numeric sequences by using index-based word encoding, (iii) then we applied the deep learning models that are uni-directional long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network and convolutional neural network (CNN) for the classifying comparison of the data, (iv) finally, we used 70% of used for training, 15% for validation, and 15% for test observations. Unlike previous methods, this study encompasses the following objectives: (a) to extract significant text features from radiologic reports of epilepsy disease; (b) to ensure successful classifying accuracy performance to enhance epilepsy data attributes. Therefore, our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models. The traditional method is numeric sequences by using index-based word encoding which has been made for the first time in the literature, is successful feature descriptor in the epilepsy data set. The BiLSTM network has shown a promising performance regarding the accuracy rates. We show that the larger sized medical text reports can be analyzed by our proposed method.
An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers

The Conventional Cloud Common Data Model (CDM) uses a centralized method of user identification and credentials. This needs to be solved in a decentralized way because there are limitations in interoperability such as closed identity management and identity leakage. In this paper, we propose a DID (Decentralized Identifier)-based cloud CDM that allows researchers to securely store medical research information by authenticating their identity and to access the CDM reliably. The proposed service model is used to provide the credential of the researcher in the process of creating and accessing CDM data in the designed secure cloud. This model is designed on a DID-based user-centric identification system to support the research of enrolled researchers in a cloud CDM environment involving multiple hospitals and laboratories. The prototype of the designed model is an extension of the encrypted CDM delivery method using DID and provides an identification system by limiting the use cases of CDM data by researchers registered in cloud CDM. Prototypes built for agent-based proof of concept (PoC) are leveraged to enhance security for researcher use of ophthalmic CDM data. For this, the CDM ID schema and ID definition are described by issuing IDs of CDM providers and CDM agents, limiting the IDs of researchers who are CDM users. The proposed method is to provide a framework for integrated and efficient data access control policy management. It provides strong security and ensures both the integrity and availability of CDM data.
A bias–variance evaluation framework for information retrieval systems

In information retrieval (IR), the improvement of the effectiveness often sacrifices the stability of an IR system. To evaluate the stability, many risk-sensitive metrics have been proposed. Since the theoretical limitations, the current works study the effectiveness and stability separately, and have not explored the effectiveness–stability tradeoff. In this paper, we propose a Bias–Variance Tradeoff Evaluation (BV-Test) framework, based on the bias–variance decomposition of the mean squared error, to measure the overall performance (considering both effectiveness and stability) and the tradeoff between effectiveness and stability of a system. In this framework, we define generalized bias–variance metrics, based on the Cranfield-style experiment set-up where the document collection is fixed (across topics) or the set-up where document collection is a sample (per-topic). Compared with risk-sensitive evaluation methods, our work not only measures the effectiveness–stability tradeoff of a system, but also effectively tracks the source of system instability. Experiments on TREC Ad-hoc track (1993–1999) and Web track (2010–2014) show a clear effectiveness–stability tradeoff across topics and per-topic, and topic grouping and max–min normalization can effectively reduce the bias–variance tradeoff. Experimental results on TREC Session track (2010–2012) also show that the query reformulation and increase of user data are beneficial to both effectiveness and stability simultaneously.
Empirical Analysis on Consensus Algorithms of Blockchain

Nowadays, the rapid growth in the count of cryptocurrencies has been observed. Blockchain is the core technology used in the development of cryptocurrencies. In addition, blockchain also supports various security applications as it has converted the centralized structure of the network into the decentralized structure. Various consensus algorithms exist for deciding how to reach an agreement to append block into the verified and distributed ledger. However, there is a lack of empirical analysis on different consensus algorithms based on various parameters. To support detailed analysis, this study first discusses the most widely used consensus algorithms based on proof-based consensus, voting-based consensus and directed acyclic graph. Further, the analysis on these algorithms has been performed using tolerated power of adversary, verification speed and throughput as performance parameters. This analysis would help the reader to select the best consensus algorithm for a specific application.