Reviewerly/exHarmony huggingface dataset:
EcoBank: A flexible database platform for sharing ecological data

Environmental crisis challenges the human race harder than ever before. Ecologists have produced a massive amount of data to cope with the crisis. Accordingly, many national scale ecological database systems have been developed worldwide to manage and analyse these datasets. However, in Korea, ecological datasets produced by different research institutes for different purposes have not been integrated or serviced due to poorly designed information infrastructure. To address this obstacle, we present EcoBank (www.nie-ecobank.kr), an open, web-based ecological database platform designed to play an important role in ecosystem analysis, not only in Korea, but also worldwide.The architecture of EcoBank comprises core technologies of WebGIS, Application Programming Interface (API), responsive web and open-source software (OSS). Comprehensive ecological datasets from three different sources, including the National Institute of Ecology (NIE) in Korea, related national and international platforms and repositories, enter the three conceptual modules in EcoBank: data management, analysis and service. Diverse potential stakeholders of EcoBank can be classified into three groups: researchers, policy-makers and public users. EcoBank aims to expand its horizons through mutual communication amongst these stakeholders. We opened and launched the EcoBank service in December 2019 and have now begun to broaden its network by linking it to other data platforms and repositories over the globe to find possible solutions to ecological issues in Korea.
USING REMOTE TECHNOLOGIES DURING CLASSES: LMS MOODLE, GOOGLE CLASSROOM, MICROSOFT TEAMS

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A Framework for Modeling Non-Functional Requirements for Business-Critical Systems

Proper definition and implementation of NFRs is critical.In case they are Over-specify, then the solution may be too costly to be viable; in case they are underspecified or underachieve them, the system will be inadequate for its intended use.An adaptive and incremental approach to exploring, defining, and implementing NFRs is vital for the successful delivering of NFRs.NFRs are not product backlog items.The constraints on development that limit degree of design freedom while building system.These constraints are articulated in the acceptance criteria for multiple product backlog items.For e.g., SAML-based Single Sign-on -SSO is a requirement for the product.SSO is a functional requirement, while SAML is the constraint.In that sense, any backlog item building sign-on functionality would reference the SAML constraint in its acceptance criteria.The sections that follow describe the phases of NFR journey from discovery to deployment for a large complex business critical systems including the NFR modelling framework.
A theory of Automated Market Makers in DeFi

Automated market makers (AMMs) are one of the most prominent decentralized finance (DeFi) applications. AMMs allow users to trade different types of crypto-tokens, without the need to find a counter-party. There are several implementations and models for AMMs, featuring a variety of sophisticated economic mechanisms. We present a theory of AMMs. The core of our theory is an abstract operational model of the interactions between users and AMMs, which can be concretised by instantiating the economic mechanisms. We exploit our theory to formally prove a set of fundamental properties of AMMs, characterizing both structural and economic aspects. We do this by abstracting from the actual economic mechanisms used in implementations, and identifying sufficient conditions which ensure the relevant properties. Notably, we devise a general solution to the arbitrage problem, the main game-theoretic foundation behind the economic mechanisms of AMMs.
Dephasing-assisted entanglement in a system of strongly coupled qubits

Creation of entangled states of quantum systems with low decoherence rates is a cornerstone in practical implementation of quantum computations. Processes of separate dephasing in each qubit in experimentally feasible systems is commonly accepted to destroy entanglement. In this work, we consider a system of two strongly coupled qubits that interact with dephasing reservoirs. We demonstrate that interaction with dephasing reservoirs can contribute to the formation of a long-lived mixed entangled state with nonzero concurrence. The weight of the subradiant state in this mixed state tends toward unity if the dephasing rate is much larger than the radiative rate and less than the coupling constant between qubits. The lifetime of this state is proportional to the exponent of the ratio of the coupling constant to environmental temperature and can be, by orders of magnitude, larger than the system’s characteristic dephasing and dissipation times. Therefore, high dephasing, along with strong coupling, contributes to the creation of an entangled state with a long lifetime. This result paves the way for creation of long-lived entangled states.
Eigenfaces-Based Steganography

In this paper we propose a novel transform domain steganography technique—hiding a message in components of linear combination of high order eigenfaces vectors. By high order we mean eigenvectors responsible for dimensions with low amount of overall image variance, which are usually related to high-frequency parameters of image (details). The study found that when the method was trained on large enough data sets, image quality was nearly unaffected by modification of some linear combination coefficients used as PCA-based features. The proposed method is only limited to facial images, but in the era of overwhelming influence of social media, hundreds of thousands of selfies uploaded every day to social networks do not arouse any suspicion as a potential steganography communication channel. From our best knowledge there is no description of any popular steganography method that utilizes eigenfaces image domain. Due to this fact we have performed extensive evaluation of our method using at least 200,000 facial images for training and robustness evaluation of proposed approach. The obtained results are very promising. What is more, our numerical comparison with other state-of-the-art algorithms proved that eigenfaces-based steganography is among most robust methods against compression attack. The proposed research can be reproduced because we use publicly accessible data set and our implementation can be downloaded.
SARCASM DETECTION IN PERSIAN

Sarcasm is a form of communication where the individual states the opposite of what is implied. Therefore, detecting a sarcastic tone is somewhat complicated due to its ambiguous nature. On the other hand, identification of sarcasm is vital to various natural language processing tasks such as sentiment analysis and text summarisation. However, research on sarcasm detection in Persian is very limited. This paper investigated the sarcasm detection technique on Persian tweets by combining deep learning-based and machine learning-based approaches. Four sets of features that cover different types of sarcasm were proposed, namely deep polarity, sentiment, part of speech, and punctuation features. These features were utilised to classify the tweets as sarcastic and nonsarcastic. In this study, the deep polarity feature was proposed by conducting a sentiment analysis using deep neural network architecture. In addition, to extract the sentiment feature, a Persian sentiment dictionary was developed, which consisted of four sentiment categories. The study also used a new Persian proverb dictionary in the preparation step to enhance the accuracy of the proposed model. The performance of the model is analysed using several
Reinforcement Learning for Model Selection and Hyperparameter Optimization

With the development of machine learning technology, the number of machine learning algorithms grows rapidly and the models become more and more complex. That causes two major problems in practice: the selection of machine learning models and the hyperparameter optimization. In order to tackle these issues, this paper proposes a new method based on deep reinforcement learning. Long short-term memory (LSTM) network is used to build an agent which automatically selects the machine learning model and optimizes hyperparameters for a given dataset. The agent aims to maximize the accuracy of the selected machine learning model on the validation dataset. At each iteration, it utilizes the accuracy of the selected model on the validation dataset as a reward signal to improve its decision for the next time. The reinforcement learning algorithm is used to guide the learning process for the agent. To verify the idea, the proposed method is compared with two widely optimization methods, tree-structured Parzen estimator and random search on UCI datasets. The results show that the proposed method outperforms other methods in terms of stability, time efficiency and accuracy.
Enhanced Autonomous Navigation of Robots by Deep Reinforcement Learning Algorithm with Multistep Method

In this paper, we propose a new method to improve the autonomous navigation of mobile robots.The new method combines a multistep update method with a double deep Q-network (MS-DDQN) to realize reinforcement learning (RL) to enhance the navigation ability of mobile robots.The proposed MS-DDQN gives two types of rewards for taking actions: terminal and non-terminal rewards.These rewards are subdivided into several different ones including rewards for arrival and collision (terminal rewards) and rewards for distance, orientation, and danger (non-terminal rewards).With the subdivided reward system, the new method trains mobile robots more effectively to increase their autonomous navigation ability.In the experimental process of this study, a laser range finder was used as the sensor for the mobile robot to perceive the distance information of the obstacle.Experiment results validated the new method's enhanced ability, showing higher success rates (97% on average) than those of other methods such as the double deep Q-network (DDQN), prioritized deep Q-network (DQN), and prioritized DDQN.The higher success rates originated from the sophisticated reward system as the total reward of the proposed method was 7-94% higher than those of the other methods in simulations in five different environments.The learning speed was also improved, reducing the learning time, as the new method completed the learning in fewer episodes.The results of the new model suggest that MS-DDQN enables mobile robots to have higher learning efficiency and generalization ability than conventional deep reinforcement (DRL)-based methods and allows them to navigate autonomously even in unknown complex environments.
Assessing the Performance and Participation among Young Male and Female Entrepreneurs in Agribusiness: A Case Study of the Rice and Maize Subsectors in Cameroon

The role played by youth in agriculture cannot be overemphasized, while agribusinesses are important generators of employment and income worldwide. Improving the sustainability of food value chains can benefit millions of rural poor people living in developing countries, especially young women. Despite policies and programs aimed at encouraging agricultural entrepreneurs, they are still facing challenges such as high-cost agrochemicals, limited access to credit, price volatility, etc. which seriously affect their capacity to remain in the sector. This study was undertaken to assess the performance and participation of young male and female entrepreneurs. This study made use of cross-section data from a survey conducted on 1019 young agricultural entrepreneurs in Cameroon. The data were analyzed using gross margin, student t-test, and logistic regression. Our study contributes to the literature by showing that young women agribusiness entrepreneurs are as competitive as their male counterparts and that the opportunities for young women in agriculture are good. Incentives such as single-digit interest rates and no collateral security should be directed to young women to receive more credit for purchasing agrochemicals and improved varieties of seed. Furthermore, a price control policy should be set up throughout the year to enable young women earn as their young men counterparts to enable them remain in production and marketing activities.
Adaptive Bagging Methods for Classification of Data Streams with Concept Drift

Data streams represent a continuous stream of data, in many forms, coming from different sources.Streams are often dynamic and its underlying structure usually changes over time.When solving predictive tasks on the streaming data, traditional models, trained on historical data, may become invalid, when such change occurs.Therefore, adaptive models, equipped with mechanisms to reflect the changes in the data, are suitable to solve these tasks.Adaptive ensemble models represent a popular group of such methods used in classification tasks on data streams.In this paper, we designed and implemented the modifications of the adaptive bagging methods, which utilizes internal class-weighting schemes for the model adaptation.Implemented models were evaluated on two simulated real-world data streams and compared with base classifiers and other adaptive methods.In addition to the performance evaluation, we also analyzed other models' characteristics, such as the duration of model update and memory requirements.
Pedestrian Detection in Thermal Images Using Deep Saliency Map and Instance Segmentation

Pedestrian detection is an established instance of computer vision task.Pedestrian detection from the color images has achieved robust performance but in the night time or in bad light conditions it has low detection accuracy.Thermal images are used for detecting people at night time, foggy weather or in bad lighting situations when color images have a lower vision.But in the daytime where the surroundings are warm or warmer than pedestrians then the thermal image has lower accuracy.Hence thermal and color image pair can be a solution but it is expensive to capture color-thermal pair and misaligned imagery can cause low detection accuracy.We proposed a network that achieved better accuracy by extending the prior works which introduced the use of the saliency map in pedestrian detection tasks from the thermal images into instance-level segmentation.We worked on a subdivision of KAIST Multispectral Pedestrian Detection Dataset [8] which has pixel-level annotations.We have trained Mask-RCNN for pedestrian detection task and report the added effect of saliency maps generated using PiCA-Net.We have achieved an accuracy of 88.14% over day and 91.84% over night images. .So, our model has reduced the miss rate by 24.1% and 23% over the existing state-of-the-art method in day and night images.
Probing Product Description Generation via Posterior Distillation

In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation.
An Ontology based Approach for Context-Aware Security in the Internet of Things (IoT)

Due to increased number of IoT devices, the marketplace is showing significant growth of sensor deployments around the world.The context involved in any IoT environment needs proper storage, processing and interpretation to get deeper insights from it.Previous research has not focussed much on context-ware security in IoT environment and has primarily relied on context-ware computing methods.In this research paper we implement logical decisions among IoT nodes in healthcare system using ontological approach.With the help of ontological method collected data is transferred between various healthcare devices to the knowledge base thereby achieving security of context like patient data by providing deeper insights, so as to generate intelligent suggested solutions.Incorporation of context-aware rules based on common experience for specific healthcare scenario is done to get implicit insight among IoT nodes.This work designs security ontology using Security Toolbox: Attacks & Countermeasures (STAC) framework that is implemented in Proté gé 5.Moreover, Pellet (Incremental) reasoned is used to evaluate the ontology.Emergency ontology that can prove helpful at emergency times has also been designed.Different parameters addressed in this work are authentication, access-control, authorization and privacy using context-awareness methodology that can enable naive users make informed security decision.
A Swarm Intelligence-based Approach for Dynamic Data Replication in a Cloud Environment

In recent years, there has been increasing interest in cloud computing research, especially replication strategies and their applications.When the number of replicas is increased and placed in different places, maintaining the system's data availability, performance and reliability will increase the cost.In this paper, two multi-objectives swarm intelligence algorithms are used to optimize the data replication selection and placement in a cloud environment.These algorithms are namely, multi-objective particle swarm optimization (MOPSO) and multi-objective ant colony optimization (MOACO).The first algorithm, (MOPSO), is used to find the best selected data replica according to the most popular data replication strategy.The improved time-based decay function (ITBDF), is used to enhance the proposed model.The second algorithm, (MOACO), is used to find the best data replica placement according to the minimum distance, the number of data transmissions and the availability of data replication.A simulation of the suggested strategy has been performed using CloudSim.the Cloud is formed to simulate different kinds of datacenters (DCs) with different structures.Moreover, 21 DCs are used.Each DC consists of a host that contains a set of virtual machines (VMs) that provides blocks of available data replications.Three different data placements for high datacenters were created.A total of one thousand cloudlets are randomly confirmed for the data replication order.All replication files are placed in high datacenters and randomly distributed in the suggested system.The performance of proposed strategy was evaluated relative to many well-known strategies such as, Enhance Fast Spread (EFS), Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S), Genetic Algorithm (GA), Genetic adaptive Selection Algorithm (GASA), Replica Selection and Placement (RSP), Dynamic Replica Selection Ant Colony Optimization (DRSACO), Adaptive Replica Dynamic Strategy (ARDS), Popular File Replication First (PFRF).The experimental results show that MOPSO, achieves better data replication than compared algorithms.Additionally, MOACO, achieves higher data availability, lower cost, and less bandwidth consumption than compared algorithms.
An Efficient Recommendation Algorithm Based on Heterogeneous Information Network

Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. However, the existing algorithms cannot extract and combine the structural features in heterogeneous information networks. Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to automatically learn node information to extract heterogeneous information and avoid errors caused by the manual search for metapaths. Furthermore, by fully considering the scoring relationship between nodes, a calculation strategy combining heterogeneous information and a scoring information fusion strategy is proposed to solve the scoring between nodes, which makes the prediction scoring more accurate. Finally, by updating the nodes, the training scale is reduced, and the calculation efficiency is improved. The study conducted a large number of experiments on three real data sets with millions of edges. The results of the experiments show that compared with PMF, SemRec, and other algorithms, the proposed algorithm improves the recommendation accuracy MAE by approximately 3% and the RMSE by approximately 8% and reduces the time consumption significantly.
Neural Network-Based Tree Translation for Knowledge Base Construction

Knowledge bases (KB), such as Probase and ConceptNet, play an important role in many natural language processing tasks.Compared with resource-poor languages such as Chinese, the scale and quality of English knowledge bases are obviously superior.To expand Chinese KBs by using English KB resources, translating English KBs into Chinese is an effective way.In this direction, two major challenges are how to model more structure semantics to improve translation quality and how to avoid labor-intensive feature engineering.We address these challenges by presenting a neural network approach, which learns tree representation by different structure features.We also build a new dataset for English-Chinese KB translation from Probase and ConceptNet, and compare our proposed approach with several baselines on it.Experimental results show that the proposed method improves the translation accuracy compared with baseline methods.Meanwhile, we translate Probase and ConceptNet into Zh-Probase and Zh-ConceptNet by our proposed model, and release them to the public, in hope of speeding up the research in Chinese natural language processing tasks.
Mermin polynomials for non-locality and entanglement detection in Grover’s algorithm and Quantum Fourier Transform

The entanglement of a quantum system can be valuated using Mermin polynomials. This gives us a means to study entanglement evolution during the execution of quantum algorithms. We first consider Grover's quantum search algorithm, noticing that states during the algorithm are maximally entangled in the direction of a single constant state, which allows us to search for a single optimal Mermin operator and use it to evaluate entanglement through the whole execution of Grover's algorithm. Then the Quantum Fourier Transform is also studied with Mermin polynomials. A different optimal Mermin operator is searched at each execution step, since in this case there is no single direction of evolution. The results for the Quantum Fourier Transform are compared to results from a previous study of entanglement with Cayley hyperdeterminant. All our computations can be replayed thanks to a structured and documented open-source code that we provide.