Reviewerly/exHarmony huggingface dataset:
Learning discriminative and representative feature with cascade GAN for generalized zero-shot learning

Zero-Shot Learning (ZSL) aims to employ seen images and their related semantics to identify unseen images through knowledge transfer. Among past numerous methods, the generative methods are more prominent and achieve better results than other methods. However, we find the input for generating samples is too monotonous, there are only semantics of each class and artificially defined noise, which makes the generated visual features non-discriminative and the classifier cannot effectively distinguish them. In order to solve this problem, we propose a novel approach with cascade Generative Adversarial Network (GAN) to generate discriminative and representative features. In this method, we define a latent space where the features from different categories are orthogonal to each other and the generator for this latent space is learned with a Wasserstein GAN. In addition, in order to make up for the deficiency that the features in this latent space cannot accurately simulate the true distribution of species, we utilize another Wasserstein GAN or Cramér GAN cascaded with the previous one to generate more discriminative and representative visual features. In this way, we can not only expand the content used as input in the generation process, but also make the final generated visual features clear and separable under the influence of latent spatial orthogonality. Extensive experiments on five benchmark datasets, i.e., AWA1, AWA2, CUB, SUN and APY, demonstrate that our proposed method can outperform most of the state-of-the-art methods on both conventional and generalized zero-shot learning settings.
The Effects of Class Label Noise on Highly-Imbalanced Big Data

This study explores the effects of class label noise on a highly-imbalanced big data set by injecting varying levels of class noise into a Medicare Part B fraud detection data set. Noise parameters are used to vary the total level of class noise and the proportion of class noise between the majority and minority classes. This allows us to better understand how class noise affects imbalanced data sets and where data cleaning efforts should be focused. Four popular machine learning algorithms are evaluated using six rounds of five-fold cross-validation to determine which learners are most robust to class noise. Area under the precision-recall curve (AUPRC) results shows that negative class noise, i.e. when positive instances are incorrectly labeled as negative, has the greatest adverse effect on classification performance. Statistical results show that the XGBoost learner performs significantly better than Random Forest, Multilayer Perceptron, and Logistic Regression Learners. True positive rates and true negative rates show that there is a trade-off that occurs as the noise proportion switches between the majority and negative classes. Finally, we show that the effects of class label noise can be combatted by regularizing the XGBoost learner through shallower decision trees.
Blockchains in Enterprise global risk management

The Enterprise Global Risk Management (EGRM) framework views risk management as composed of four elements: hierarchical level of management, risk management policy, planned actions and orientation of the risk management process. It was proposed as the next step in the evolution of Enterprise Risk Management (ERM) and Enterprise Integrated Risk Management (EIRM) by including the risks of Industry 4.0. Blockchains are an essential and very fast-growing technology. The purpose of this paper is to demonstrate how the EGRM framework, through the idea of including Industry 4.0 risks, could be used for decision making of risk management in organizations/enterprises that deploying blockchains. To achieve it, the quasi-multicriteria algorithm SIGMA is used for evaluation of optimal elements of the EGRM framework by different criteria for risk tolerance. It is proposed a reverse interpretation of the worst-case solutions as recommendation for preliminary guideline for risk management recourse allocation between these elements of EGRM. The presented results support the assumption that EGRM framework is applicable as an additional interpretive tool for risk management decision-making in blockchain deployment. The framework could be considered as promising tool for ex-ante guidelines for allocating resources (financial or other) of enterprises/organizations among the elements of the EGRM.
Implementation of UAV Smooth Path Planning by Improved Parallel Genetic Algorithm on Controller Area Network

Unmanned aerial vehicle (UAV) path planning is an essential branch in UAVs research. This paper presents the hardware implementation of the UAV path planning problem using an improved parallel genetic algorithm (GA) in a multi-microcontroller environment. A controller area network (CAN) bus is a robust bus designed to allow microcontrollers to communicate with each other in applications without a host computer. The CAN bus is used to communicate between the microcontrollers and solve the path planning problem with the parallel algorithm. The data exchange on this network is by the multi-master model, so it is possible to implement an asynchronous and multi-master parallel algorithm using CAN bus. Also, we use the 32-bit ARM Cortex-M3 microcontroller (with CPU clock up to 100 MHz) for hardware implementation. The comparison of both single and parallel GA shows that a multi-microcontroller structure produces better results on the CAN bus, and the parallel version experiences significantly faster speeds than the sequential version.
K-EPIC: Entity-Perceived Context Representation in Korean Relation Extraction

Relation Extraction (RE) aims to predict the correct relation between two entities from the given sentence. To obtain the proper relation in Relation Extraction (RE), it is significant to comprehend the precise meaning of the two entities as well as the context of the sentence. In contrast to the RE research in English, Korean-based RE studies focusing on the entities and preserving Korean linguistic properties rarely exist. Therefore, we propose K-EPIC (Entity-Perceived Context representation in Korean) to ensure enhanced capability for understanding the meaning of entities along with considering linguistic characteristics in Korean. We present the experimental results on the BERT-Ko-RE and KLUE-RE datasets with four different types of K-EPIC methods, utilizing entity position tokens. To compare the ability of understanding entities and context of Korean pre-trained language models, we analyze HanBERT, KLUE-BERT, KoBERT, KorBERT, KoELECTRA, and multilingual-BERT (mBERT). The experimental results demonstrate that the F1 score increases significantly with our K-EPIC and that the performance of the language models trained with the Korean corpus outperforms the baseline.
A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy

When faced with complex optimization problems with multiple objectives and multiple variables, many multiobjective particle swarm algorithms are prone to premature convergence. To enhance the convergence and diversity of the multiobjective particle swarm algorithm, a multiobjective particle swarm optimization algorithm based on the grid technique and multistrategy (GTMSMOPSO) is proposed. The algorithm randomly uses one of two different evaluation index strategies (convergence evaluation index and distribution evaluation index) combined with the grid technique to enhance the diversity and convergence of the population and improve the probability of particles flying to the real Pareto front. A combination of grid technology and a mixed evaluation index strategy is used to maintain the external archive to avoid removing particles with better convergence based only on particle density, which leads to population degradation and affects the particle exploitation ability. At the same time, a variation operation is proposed to avoid rapid degradation of the population, which enhances the particle search capability. The simulation results show that the proposed algorithm has better convergence and distribution than CMOPSO, NSGAII, MOEAD, MOPSOCD, and NMPSO.
Spatial temporal and channel aware network for video-based person re-identification

As a challenging computer vision task, video-based person Re-IDentification (Re-ID) has been intensively studied, and recent works have achieved a series of satisfactory results by capturing spatial temporal relationships. However, extensive observations have found that the same feature vector generated by a convolutional neural network contains considerable redundant information in the channel dimension. This issue is seldom investigated. A Spatial Temporal and Channel Aware Network (STCAN) for video-based ReID is studied in this paper. It jointly considers spatial temporal and channel information. Firstly, the Spatial Attention Enhanced (SAE) convolutional network is developed as the backbone network to learn spatial enhanced features from video frames. Secondly, a Channel Segmentation and Group Shuffle (CSGS) convolution module is designed to jointly address temporal and channel relations. Finally, a Two Branch Weighted Fusion (TBWF) mechanism is introduced to enhance the robustness of the Re-ID network by fusing the output of the SAE backbone network and CSGS. Comprehensive experiments are conducted on three large-scale datasets MARS, LSVID, and P-DESTRE. The experimental results imply that the STCAN can effectively improve the performance of video-based Re-ID and outperform several state-of-the-art methods.
Applied corpus linguistics for language acquisition, pedagogy, and beyond

Abstract This plenary speech provides an overview of applications of corpus research in several core areas of applied linguistics, including second language acquisition and language assessment. It does this by showcasing a number of recent studies carried out by or with involvement of the author. These studies all focus on phraseological aspects of language and demonstrate the importance of studying its patterned nature. The studies also illustrate how corpora and corpus-analytic techniques can allow us as applied linguists to contribute to solving problems in other disciplines (such as legal scholarship or music theory) and hope to thereby encourage more interdisciplinary research collaborations between corpus linguists and scholars from other fields.
DeepCM: Deep neural networks to improve accuracy prediction of database cost models

Abstract A major challenge for many database management tasks including admission control, query scheduling, progress monitoring and self‐driving data storage systems is to enhance queries performances which are based on computational models known as database cost models. One of the most challenging aspects of developing accurate database cost models is identifying their parameters and capturing their relationships, consequently we can derive the query execution cost on the basis of a specific database hosted on a given platform. Furthermore, the highly dynamic workload (i.e., a set of queries) and the query execution variation lead to performance degradation risk, therefore cost models need to be improved by considering newer software configuration and future workload characteristics. In this article, we propose a framework called DeepCM that is based on a min–max optimization for building robust database cost model against uncertainty parameters. Furthermore, our framework is based on Robust Deep Neural Networks to build database cost models that guarantee a high accuracy regardless of variations from software configuration and workload characteristics. Several experiments have been done to evaluate the robustness of produced cost models and findings show that DeepCM provides a high cost model prediction accuracy and stable performance.
GeoReservoir: An ontology for deep-marine depositional system geometry description

An ontology is a logical theory that accounts for a domain vocabulary's intended meaning, allowing us to develop a computational artifact that explicitly and formally represents the community's conceptualization related to a vocabulary. This paper presents the GeoReservoir ontology — the result of the ontological analysis of the terminology adopted by geologists in sedimentological studies about deep-marine depositional systems, which is one of the most relevant types of oil and gas reservoirs around the world. Despite the variety of studies describing the patterns of productive reservoirs, this domain demanded new approaches in conceptual modeling methodologies because the available terminology presents issues such as ambiguity and contamination of different interpretations in the terms' definitions. Previous work has dealt with these issues in geology, resulting in the GeoCore, an ontology that explicitly defines the most generic entities in geology. However, the domain in focus still demanded a specialized ontology containing the particular terms found in reports about deep-marine deposits. GeoReservoir is an extension of GeoCore, which, in its turn, extends the Basic Formal Ontology (BFO), a foundational ontology for scientific domains. GeoReservoir takes advantage of both BFO and GeoCore's foundations, allowing us to focus our effort on defining the domain-specific entities. A team of professional reservoir geologists supported the knowledge acquisition process. We developed the ontology in iterative steps from an initial prototype to a complete artifact containing the taxonomy of entities and the relations between the entities, being increasingly refined by the experts. We further present a case study demonstrating how one could describe a depositional system in GeoReservoir terms. Moreover, we validated the ontology against defined competency questions (CQs). This work's final result is offering a sound, consistent and unambiguous terminology to support the integration of data and knowledge about deep-marine depositional system geometry and lithology.
What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.
Privacy-Protection Scheme of a Credit-Investigation System Based on Blockchain

In response to the rapid growth of credit-investigation data, data redundancy among credit-investigation agencies, privacy leakages of credit-investigation data subjects, and data security risks have been reported. This study proposes a privacy-protection scheme for a credit-investigation system based on blockchain technology, which realizes the secure sharing of credit-investigation data among multiple entities such as credit-investigation users, credit-investigation agencies, and cloud service providers. This scheme is based on blockchain technology to solve the problem of islanding of credit-investigation data and is based on zero-knowledge-proof technology, which works by submitting a proof to the smart contract to achieve anonymous identity authentication, ensuring that the identity privacy of credit-investigation users is not disclosed; this scheme is also based on searchable-symmetric-encryption technology to realize the retrieval of the ciphertext of the credit-investigation data. A security analysis showed that this scheme guarantees the confidentiality, the availability, the tamper-proofability, and the ciphertext searchability of credit-investigation data, as well as the fairness and anonymity of identity authentication in the credit-investigation data query. An efficiency analysis showed that, compared with similar identity-authentication schemes, the proof key of this scheme is smaller, and the verification time is shorter. Compared with similar ciphertext-retrieval schemes, the time for this scheme to generate indexes and trapdoors and return search results is significantly shorter.
Sending-or-not-sending twin-field quantum key distribution with multiphoton states

The twin-field quantum key distribution (TF-QKD) protocol has led to extensive investigation for its capacity of overcoming the rate-distance limit. One variant for TF-QKD named sending-or-not-sending (SNS) TF-QKD has also been proposed. The SNS-TF-QKD encodes the classical keys 0 and 1 in sending or not sending the phase-randomized coherent states, which helps it to tolerate large misalignment errors due to optical phase fluctuations. Nevertheless, the original SNS-TF-QKD scheme utilizes only single-photon interference in generating secret keys, which seems quite different from other TF-QKD protocols. In this paper, we prove that multiphoton interference in the SNS TF-QKD protocol can also contribute to key generation. Accordingly, an improved SNS-TF-QKD scheme with higher key rate is proposed, where events of not only single-photon but also two-photon emissions are considered as effective events.
A multiplatform energy-aware OWL reasoner benchmarking framework

Performance evaluation is increasingly relevant for Web Ontology Language (OWL) reasoners, due to the expanding availability of knowledge corpuses on the Web, the growing variety of applications, and the rise to prominence of mobile and pervasive computing. Motivated mainly by the difficulty of comparing reasoning engines in the Semantic Web of Things (SWoT), this paper introduces evOWLuator, a novel approach and a multiplatform framework devised to be both flexible and expandable. It features integration of traditional and mobile/embedded engines as well as ontology dataset management, reasoning test execution, and report generation. A case study consisting of an experimental setting for time, memory peak and energy footprint evaluation with eight reasoners and four different platforms allows showcasing usage and validating features and usability of the tool.
BSI: Blockchain to secure routing protocol in Internet of Things

Abstract Blockchain is a database that contains the history of all exchanges made between its users since its creation. Blockchain is a new tool that enables essential distributed applications without the need for centralized trust. It is a transparent and secure technology for storing and transmitting information that operates without a central control body. Moreover, the large‐scale presence of wireless sensors, especially in the IoT, produce a large volume of data. This data must be protected to guarantee its correctness. However, due to the difficulty of encrypting this data, there are many problems, including limited resources, whether in terms of computation, memory, or energy, as well as the complexity of encryption algorithms. Therefore, we wanted, through this article, to show the importance of adopting the blockchain in wireless sensors. This enables it to send data as quickly and securely as possible, as little of the literature has dealt with this topic. For this, one imaginable solution must be managed in WSN which is to put the necessary keys for networks or groups at the level of each sensor for the different scenarios implemented, mobile node and orphan nodes, so that the latter can authenticate and link to these multiple networks or clusters, using our blockchain security IoT (BSI) protocol. The latter makes it possible to design a decentralized, transparent, and scalable authentication system, which does not negatively impact the performance of sensors or that of networks. This system must provide complete freedom to change the state of the sensor from one network to another, while ensuring the safety of the latter. This means that if a sensor is authenticated in a legitimate network, it becomes reliable and accepted by all other networks. This allows you to have a global vision on all networks. We evaluate the performance of our protocol with simulations using MATLAB. The results confirm that the BSI protocol is robust and efficient, provides lower power consumption and fast computing time.
A Remote-Sensing-Based Alteration Zonation Model of the Duolong Porphyry Copper Ore District, Tibet

Remote sensing (RS) of alteration zones and anomalies can provide information that is useful for geological prospecting and exploration. RS is an effective method for porphyry copper mineral exploration and prospecting prediction. More specifically, the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data, which include 14 spectral channels from visible light to thermal infrared, are useful in such cases. This study uses visible-shortwave infrared and thermal infrared ASTER data together with surface material spectra from the Duolong porphyry copper ore district to construct an RS-based alteration zonation model of the deposit. In this study, an RS alteration zoning model is established based on ground-spectral alteration zoning results. The methods include PCA (Principal Component Analysis), Ratio, and Slope methods. The information obtained by each method is different. RS-based alteration zonation is developed based on the intersection of maps, resultant from the different methods for extracting information related to different minerals. The alteration zonation information extracted from ASTER RS data is consistent with geological observations. Using information from the RS-based model, we mapped the alteration minerals and zones of the Duolong ore district, thereby identifying prospecting target areas of the deposit.
Sign to Speech Convolutional Neural Network-Based Filipino Sign Language Hand Gesture Recognition System

Sign Language Recognition is a breakthrough for helping deaf-mute people and has been studied for many years. Unfortunately, every research has its own limitation and are still unable to be used commercially. In this study, we developed a real-time Filipino sign language hand gesture recognition system based on Convolutional Neural Network. A manually gathered dataset consists of 237 video clips with 20 different gestures. This dataset underwent data cleaning and augmentation using image pre-processing techniques. The Inflated 3D convolutional neural network was used to train the Filipino sign language recognition model. The experiments considered retraining the pretrained model with top layers and all layers. As a result, the model retrained with all layers using imbalanced dataset was shown to be more effective and achieving accuracy up to 95% over the model retrained with top layers to classify different signs or hand gestures. Using the Rapid Application Development model, the Filipino sign language recognition application was developed and assessed its usability by the target users. With different parameters used in the evaluation, the application found to be effective and efficient.