Reviewerly/exHarmony huggingface dataset:
Towards Software Engineering Perspective for BDI Agent

BDI agent is a cognitive architecture to model human reasoning based on a philosophical stream. Based on the survey, the BDI agent is widely adopted in agent based social simulation to model a complex and realistic human behaviour. To support BDI agent development, Prometheus and tactic development framework (extended Prometheus are introduced. Although Prometheus and TDF are introduced to model BDI agent, the modelling and simulating BDI agent is not a straightforward process. As simulation is one of the pillars in IR4.0, it is undoubtedly that there will be a higher demand and usage of agent simulation in real world problem. Hence, there is a need to call for software engineering perspective for BDI agent. Inline with the works on Software Engineering for AI application development, this paper introduces an alternative method to model BDI agent through a software engineering perspective. The requirement model, design and implementation of BDI based on software engineering approach are covered in this paper. A walkthrough example of BDI based a bush fire evacuation simulation has revealed the potential usage of the method.
Spatial Variability of Metal Elements in Soils of a Waste Disposal Site in Khulna: A Geostatistical Study

The aim of this study was to identify the spatial variability of various metal elements in soils and to compare the variability of the outputs of different geostatistical methods using the concentration of heavy metals in the ground of the waste disposal site at Rajbandh, Khulna, Bangladesh, to find the best-fitted prediction model. In this study, sixty soil samples were collected from the waste disposal site at a depth of 0–30 cm from the existing ground surface. The concentration of metal components like Al, As, Ba, Ca, Cd, Cr, Co, Cu, Fe, Hg, K, Mn, Na, Ni, Pb, Sb, Sc, Sr, Ti, V, and Zn in soils were measured through standard technique. ArcGIS 10.5 was used to prepare the spatial distribution of these metals to determine their extents in the soil. Four geostatistical methods named inverse distance weighting (IDW), universal kriging (UK), ordinary kriging (OK), and empirical Bayesian kriging (EBK) were performed to generate the spatial distribution of these elements in the soil. The interpolation technique was used in this regard to reduce the possibility of error prediction of results. Prediction maps prepared using interpolation techniques showed that most of the contaminated spots were near the dumping point. There was seen a significant amount of low concentration of metals in some spots. Moreover, cross-validation was conducted to determine the best-fitted model. The results indicated that EBK was a better method with more accurate predictions based on MAPE, RMSPE, and SRMSPE values than that of other counterparts.
Robust data expansion for optimised modelling using adaptive neuro-fuzzy inference systems

This work focuses on the problem of constructing accurate prediction models using an adaptive neuro-fuzzy inference system (ANFIS) from data that are scarce and poorly scaled. Many real-world problems have limited data to work with and yet require models with high prediction accuracy. Due to the fuzzy nature of many data sets, it is appropriate to construct prediction models using fuzzy inference systems (FIS), rather than traditional artificial neural network (ANN) models. However, the scarcity of the data raises serious concerns when attempting to construct models of this type. In this paper, we address the problem of constructing accurate prediction models using ANFIS in circumstances where capturing large amounts of data is difficult. We propose a new approach, using robust data expansion methods to enhance the data carefully. This approach provides the learning algorithm with enough representative data to allow all of the parameters (premise and consequent) to be optimised. We compare and contrast the effectiveness in reducing prediction error for three specific expansion models using radial basis function (RBF) kernels. When expanding data, for which the underlying smoothness properties are not known, there is often a danger of over-smoothing. This is usually noticeable at turning points and can occur when fitting the data using polynomials or cubic splines for example. To avoid over-smoothing, we introduce a modified multiquadric approach that allows smooth, near-interpolation while maintaining the desirable shape-preserving properties of the data. The proposed method is explained through two case studies — the first to predict gasoline consumption for the oil/fuel industry in Iraq and the second for predicting natural gas consumption in Iran. This is the first study that uses adjustable, shape-preserving re-sampling methods for optimising ANFIS models in low data environments.
The Construction of Knowledge Graph for Personalized Online Teaching

The knowledge graph (KG) is widely used in various fields recently, while there are few achievements of knowledge graph related to educational applications. In this paper, some subject knowledge graphs are constructed to adapt to the educational development in big data environment, and the relationships between knowledge nodes are analyzed in order to provide students with a personalized online teaching for knowledge learning. Specially, a knowledge graph on python subject is constructed based on the neo4j graph database for students in programing courses. With three applications achieved on the knowledge graphs, the experimental results show that the knowledge points to students is described more clearly, and the logical relationships between the knowledge points are inferred according to the queries from students. It is verified that the knowledge graph on course learning performs significant effectiveness and efficiency in personalized online teaching.
Automated Identification and Deconstruction of Penalty Clauses in Regulation

Regulations are often complex and therefore difficult to understand. Violations are known to occur oftentimes because of this lack of understanding. Regulations contain penalty clauses that impose some kind of punitive measures on the defaulting party in case of non-compliance. In this paper, we present a model for extracting and deconstructing penalty clauses. The model allows for capturing the essence of penalty clauses in terms of their critical attributes such as Trigger, Consequent Action, Penalized Entity, Subsequent Offence and Exception. We augment the extracted penalty attributes with relevant Definitions and render the deconstructed and augmented penalty clauses for a better understandability. We discuss the results of automating the identification and deconstruction of penalty clauses using a combination of rule-based techniques and Recurrent Neural Network based sequence-to-sequence model designed around the Penalty Model. The evaluation carried out on 39 publicly available regulation documents from sixteen countries indicate that our method can identify penalty clauses with a F1-score of 95% and extract 85% of the attribute instances correctly. This will help the requirement analysts to identify critical software requirements implied in penalty clauses, prioritize them, and thereby contribute to building compliant software systems.
A Concise Review of Deep Learning Deployment in 3D Computer Vision Systems

Deep Learning (DL) has advanced the concept of digital image processing, but this does not rule out the relevance of the conventional computer vision techniques as they have continuously been rebranded even before the emergence of DL. DL techniques have over the years been the method of choice in several fields due to their superior performance compared to the state-of-the-art machine learning methods. Among the numerous DL methods, computer vision has to be one of the most prominent methods, and given this, this article overviewed the concept of DL in computer vision, focusing more on the recent advancements in tasks that involve 3D visual data. Some of the DL schemes that are relevant in solving computer vision problems were also briefly reviewed. Furthermore, the history, structure, merits, and drawbacks of these DL schemes were provided. The application of these DL schemes in various computer vision tasks, like object detection, was also described in this article.
Secret Keys in Genus-2 SIDH

We present a polynomial-time adaptive attack on the genus-2 variant of SIDH (G2SIDH) and describe an improvement to its secret selection procedure. G2SIDH is a generalisation of the Supersingular Isogeny Diffie–Hellman key exchange into the genus-2 setting and achieves the same security as SIDH while using fields a third of the size. We analyze the keyspace of G2SIDH and achieve an improvement to the secret selection by using symplectic bases for the torsion subgroups. This allows for the near uniform sampling of secrets without needing to solve multiple linear congruences as suggested by Flynn–Ti. More generally, using symplectic bases enables us to classify and enumerate isogeny kernel subgroups and thus simplify the secret sampling step for general genus-2 SIDH-style constructions. The proposed adaptive attack on G2SIDH is able to recover the secret when furnished with an oracle that returns a single bit of information. We ensure that the maliciously generated information provided by the attacker cannot be detected by implementing simple countermeasures, forcing the use of the Fujisaki–Okamoto transform for CCA2-security. We demonstrate this attack and show that it is able to recover the secret isogeny in all cases of G2SIDH using a symplectic basis before extending the strategy to arbitrary bases.
Sparse Feature Representation Learning for Deep Face Gender Transfer

Why do people think Tom Hanks and Juliette Lewis look alike? Can we modify the gender appearance of a face image without changing its identity information? Is there any specific feature responsible for the perception of femininity/masculinity in a given face image? Those questions are appealing from both computer vision and visual perception perspectives. To shed light upon them, we propose to develop a GAN based approach toward face gender transfer and study the relevance of learned feature representations to face gender perception. Our key contributions include: 1) an architecture design with specially tailored loss functions in the feature space for face gender transfer; 2) the introduction of a novel probabilistic gender mask to facilitate achieving both the objectives of gender transfer and identity preservation; and 3) identification of sparse features (≈ 20 out of 256) uniquely responsible for face gender perception. Extensive experimental results are reported to demonstrate not only the superiority of the proposed face gender transfer technique (in terms of visual quality of reconstructed images) but also the effectiveness of gender feature representation learning (in terms of the high correlation between the learned sparse features and the perceived gender information). Our findings seem to corroborate a hypothesis about the independence between face recognizability and gender classifiability in the literature of psychology. We expect this work will stimulate more computational studies of different face perception attributes including race, age, attractiveness, and trustworthiness.
IC neuron: An efficient unit to construct neural networks

As a popular machine learning method, neural networks can be used to solve many complex tasks. Their strong generalization ability comes from the representation ability of the basic neuron models. The most popular neuron model is the McCulloch-Pitts (MP) neuron, which uses a simple transformation to process the input signal. A common trend is to use the MP neuron to design various neural networks. However, the optimization of the neuron structure is rarely considered. Inspired by the elastic collision model in physics, we propose a new neuron model that can represent more complex distributions. We term it the Inter-layer Collision (IC) neuron which divides the input space into multiple subspaces to represent different linear transformations. Through this operation, the IC neuron enhances the non-linear representation ability and emphasizes useful input features for a given task. We build the IC networks by integrating the IC neurons into the fully-connected, the convolutional, and the recurrent structures. The IC networks outperform the traditional neural networks in a wide range of tasks. Besides, we combine the IC neuron with deep learning models and show the superiority of the IC neuron. Our research proves that the IC neuron can be an effective basic unit to build network structures and make the network performance better.
Generation of entangled photons with a second-order nonlinear photonic crystal and a beam splitter

We discuss the generation of entangled photons using a nonlinear photonic crystal and beam splitter. In our method, the photonic crystal is assumed to be composed of a material with a large second-order nonlinear optical susceptibility $\chi^{(2)}$. Our proposal relies on two facts: (1) A nonlinear photonic crystal changes coherent incident light into squeezed light. (2) A beam splitter transforms the squeezed light into entangled light beams flying in two different directions. We estimate the yield efficiency of pairs of entangled photons per pulse of the very weak coherent light for our method at $0.0783$ for a specific concrete example. Our method is more effective because the conversion efficiency (entangled biphotons per incident pump photons) in the spontaneous parametric downconversion is of the order of $4\times 10^{-6}$. The only drawback is that it requires very fine tuning of the frequency of signal photons fed into the photonic crystal; for example, an adjustment is given by $\Delta\nu=3.11\times 10^{8}$ Hz for the signal light of $\nu=3.23\times 10^{14}$ Hz. We investigate the application of entangled photons produced by our method to the BB84 quantum key distribution protocol, and we explore how to detect an eavesdropper. We suggest that our method is promising for decoy-state quantum key distribution using weak coherent light.
Study on geological characteristics and critical metal deportment of the Huangzhuyuan silver polymetallic deposit in Luzong basin, Anhui Province

黄竹园银多金属矿床位于长江中下游成矿带庐枞矿集区东南缘,是成矿带断凹区火山岩盆地内首次发现的浅成低温热液型银多金属矿床,亟待开展矿床地质特征、金属元素特别是关键金属的赋存状态和矿床成因研究。黄竹园矿床矿体主要赋存于下白垩统砖桥组、双庙组火山岩及断裂破碎带中,呈层状、似层状产出;矿石类型主要有脉状、浸染状、细网脉状。本次工作基于矿床地质特征研究,通过对矿床中采集样品进行手标本及室内镜下观察、短波红外分析测试(SWIR)、扫描电子显微镜(SEM-EDS)、电子探针分析(EPMA)和自动矿物综合分析(TIMA)等方法,阐明了矿床中的主要蚀变矿化特征和成矿期次,重点开展银和关键金属赋存状态及矿床成因类型研究。研究结果表明,黄竹园矿床总体表现出靠近矿体部位发育绢云母-伊利石化,向外围矿化较弱部位变为蒙脱石-高岭石化;根据不同的矿物共生组合及其相互关系,将矿床的成矿过程从早到晚分为无矿化石英脉阶段、石英-银矿物-硫化物脉阶段和石英-碳酸盐脉阶段,其中石英-银矿物-硫化物脉阶段为该矿床最重要的银、铜成矿阶段。主要的银矿物为自然银、硫铜银矿和硫汞铜银矿。矿床中的主要关键金属矿物为辉砷钴矿和铁硫砷钴矿,呈不规则粒状镶嵌在辉铜矿和黄铁矿边缘。通过黄竹园矿床与国内外其他浅成低温热液矿床地质特征对比,本次工作认为黄竹园矿床是高硫型浅成低温热液矿化叠加于斑岩型矿化之上的复合成矿的典型实例。通过与矿区北部钱铺酸性蚀变岩帽(<1km)之间对比研究,提出黄竹园-钱铺酸性蚀变岩帽地区可能存在一大型斑岩-浅成低温热液成矿系统,为庐枞盆地下一步深部找矿提供了方向。;Huangzhuyuan silver polymetallic deposit is located in the southeastern margin of Luzong volcanic basin in the Middle-Lower Yangtze River Valley Metallogenic Belt (MLYB). It is the first discovered epithermal silver polymetallic deposit in the volcanic basin in the fault depression area of the MLYB. It is urgent to carry out a systematic study on the geological characteristics, the deportment of metals, especially critical metals, and the genesis of the deposit. Ore bodies are mainly hosted in the alteration zones and fractures of volcanic rocks of Zhuanqiao Formation and Shuangmiao Formation of Lower Cretaceous, as stratiform and lenses. Mineralization occurs as veins, disseminations and thin stockworks. Alteration and mineralization characteristics, mineral assemblages, especially the occurrence of Ag and critical metals and genesis of the deposit were discussed by hand specimens and microscopic observation, short wave infrared analyses (SWIR), scanning electron microscopy (SEM-EDS), electron probe analysis (EPMA) and TESCAN Integrated Mineral Analyzer (TIMA). The results show that Huangzhuyuan is characterized by muscovite-illite alteration of the ore body and montmorillonite-kaolinite alteration in the periphery of the deposit. The mineralization process of the deposit can be divided into three stage such as sulfide-barren quartz vein stage, quartz-silver-sulfides vein stage and quartz-calcite vein stage, of which quartz-silver-sulfides vein stage is the most important silver and copper mineralization stage. The main silver minerals are native silver, stromeyerite and danielsite. The main critical element minerals in the deposit are cobaltite and glaueodot, and are embedded in the margin of chalcopyrite and pyrite in irregular granular form. By comparing the geological characteristics of Huangzhuyuan deposit with other epithermal deposits globally, the Huangzhuyuan deposit is a porphyry-deposit overprinted by high sulfidation epithermal mineralization. By establishing relations with the surrounding Qianpu lithocap (less than 1km distance), it is indicated that there may be a large porphyry system in this area, and this indication provides a direction for further exploration in the deeper part of Luzong basin.
Deep Learning for Hate Speech Detection in social media

In recent years, many people on the internet write and post abusive language on online social media platforms such as Twitter, Facebook, etc. Detection of hate speech is very difficult to solve manually, especially in social media. Thus, we need to be automatic detection of hate speech in social media. We have used a benchmark dataset of approximately 25 thousand annotated tweets and proposed a model based on deep learning methods. We also compare the performance of our deep learning methods to the traditional machine learning classifier in terms of F1 Score and Accuracy. The results obtained through the proposed method is very promising.
Multi-modal and Multi-perspective Machine Translation by Collecting Diverse Alignments

Multi-modal machine translation (MMT) is one of the most active research directions in the natural language processing. Recently, Seq2Seq translation model with images shows promising performance in enhancing translation quality. However, the existing technologies inadequately consider diverse multi-modal alignments when using different ways of adding images. We observe that this diversity can produce differentiated knowledge and further generates distinct translation outputs. In order to address the above problem, this paper proposes a Multi-perspective and Multi-modal Machine Translation Method by Collecting Diverse Alignments (M\(^3\)-CoDA) which introduces different granularities of image features to the attention mechanism, aiming at forming diverse implicit multi-modal alignments. Moreover, those implicit alignments will produce MMT results from different perspectives. This paper further designs a sequence ensemble to aggregate multiple translation results. The experimental results on the Multi30k dataset show that our proposed method significantly improves translation quality compared with several popular baselines in terms of BLEU.
Evaluating BERT-based scientific relation classifiers for scholarly knowledge graph construction on digital library collections

The rapid growth of research publications has placed great demands on digital libraries (DL) for advanced information management technologies. To cater to these demands, techniques relying on knowledge-graph structures are being advocated. In such graph-based pipelines, inferring semantic relations between related scientific concepts is a crucial step. Recently, BERT-based pre-trained models have been popularly explored for automatic relation classification. Despite significant progress, most of them were evaluated in different scenarios, which limits their comparability. Furthermore, existing methods are primarily evaluated on clean texts, which ignores the digitization context of early scholarly publications in terms of machine scanning and optical character recognition (OCR). In such cases, the texts may contain OCR noise, in turn creating uncertainty about existing classifiers' performances. To address these limitations, we started by creating OCR-noisy texts based on three clean corpora. Given these parallel corpora, we conducted a thorough empirical evaluation of eight Bert-based classification models by focusing on three factors: (1) Bert variants; (2) classification strategies; and, (3) OCR noise impacts. Experiments on clean data show that the domain-specific pre-trained Bert is the best variant to identify scientific relations. The strategy of predicting a single relation each time outperforms the one simultaneously identifying multiple relations in general. The optimal classifier's performance can decline by around 10% to 20% in F-score on the noisy corpora. Insights discussed in this study can help DL stakeholders select techniques for building optimal knowledge-graph-based systems.
A Combination of Statistical Extraction and Texture Features Based on KNN for Batik Classification

Batik has various motives in every region in Indonesia. Various types of batik motifs have characteristic patterns that differ from one another. There are many computer systems that can detect various types of batik motifs to classify the types of batik motifs. One of the systems that have been used to solve the problem is K Nearest Neighbor (KNN) combined with Gray Level Co-Occurrence Matrix (GLCM). The dataset used is an image of a batik motif, amounting to 300 images divided into 50 classes. These images are RGB (Red Green Blue) color with the JPG format. Confusion Matrix was carried out to do the classification accuracy testing. There are five stages of the research process: the image input process, pre-processing, feature extraction, classification, and testing. The highest accuracy results are 96&#x0025; at the value of k &#x003D; 1, while the lowest accuracy results are 86&#x0025; at the value of k &#x003D; 6. The accuracy results obtained from this study are much higher than the previous research.
Gaze Assisted Visual Grounding

There has been an increasing demand for visual grounding in various human-robot interaction applications. However, the accuracy is often limited by the size of the dataset that can be collected, which is often a challenge. Hence, this paper proposes using the natural implicit input modality of human gaze to assist and improve the visual grounding accuracy of human instructions to robotic agents. To demonstrate the capability, mechanical gear objects are used. To achieve that, we utilized a transformer-based text classifier and a small corpus to develop a baseline phrase grounding model. We evaluate this phrase grounding system with and without gaze input to demonstrate the improvement. Gaze information (obtained from Microsoft Hololens2) improves the performance accuracy from 26% to 65%, leading to more efficient human-robot collaboration and applicable to hands-free scenarios. This approach is data-efficient as it requires only a small training dataset to ground the natural language referring expressions.