Reviewerly/exHarmony huggingface dataset:
Application of Structured-light 3D Reconstruction in Virtual Assembly

3D modeling is a time-consuming and laborious work in virtual assembly. How to reconstruct parts with various shapes quickly and accurately is an important topic in the field of reverse engineering. In this paper, a popgun and its special-shaped parts are reconstructed by using a self-developed surface structured light scanning system and a commercial line structured light scanning system, with an accuracy less than 0.1mm. And the virtual assembly of the popgun is completed according to the high-precision 3D solid models.
Classifying Emotions in Roman Urdu Posts using Machine Learning

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made; 1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user’s mental states using textual posts on social media plat-forms; Instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification.
Updated Trial of Meteorological Station Using PV Reference Cell Array

We report on an updated field trial of a new meteorological station concept for resource assessment at the site of a prospective utility-scale photovoltaic (PV) power plant to be developed by 7X Energy. Cost savings are achieved by substituting Atonometrics PV reference cells for thermopile pyranometers in a unique multi-reference-cell array configuration that allows accurate determination of global horizontal irradiance (GHI) and by using advanced remote diagnostics to minimize the need for on-site visits for sensor cleaning. Reference data are also obtained from a second station at the site employing thermopile pyranometers and maintained through weekly site visits for pyranometer cleaning. Data from both stations are compared to industry-leading SolarAnywhere® satellite-derived irradiance measurements from Clean Power Research and are used for tuning of the satellite model for long-term irradiance prediction and site resource assessment. Predicted long-term average annual irradiance using either the reference cell array or pyranometer ground station for satellite model tuning differed by only 0.6%.
Reward Design for Multi-Agent Reinforcement Learning with a Penalty Based on the Payment Mechanism

In this paper, we propose a novel method of reward design for multi-agent reinforcement learning (MARL). One of the main uses of MARL is building cooperative policies between self-interested agents. We take inspiration from the concept of mechanism design from game theory to modify how agents are rewarded in MARL algorithms. We defined the payment that reflects the negative contribution to other agents’ valuation in the same manner as the Vickrey-Clarke-Groves (VCG) mechanism. We give the individual learning agent a reward signal that consists of two elements. One is a reward evaluated solely on the basis of individual behavior that will follow a greedy and selfish policy, and the other is a negative reward as a penalty evaluated on the basis of the payment that will reflect the negative contribution to social welfare. We call this scheme reward design for MARL based on the payment mechanism (RDPM). We experimented with RDPM in two different scenarios. We show that RDPM can increase the social utility among agents while the other reward designs achieve far less, even for basic and simplistic problems. We finally analyze and discuss how RDPM affects the building of a cooperative policy.
Student Behavioral Analysis Using Computer Vision

Monitoring students through the camera in the classroom has now become a conventional approach and is losing its effectiveness. A student’s academic achievements depend on various factors. One of the main factors among these is student behavior and attention levels. For a long time, researchers and educators have heavily relied on teacher observation and student self-reporting to gauge student learning behaviors in class. The objective of this research work is to develop an automated model to enable informative-based functionalities to supervisors that helps the faculties to gather and summarize student behaviors in the classroom based on their facial expressions and other activities as a kind of information gathering to facilitate in making decisions. This paper uses some popular machine learning classifiers like Naive Bayes, neural network and KNN algorithm to train the model and used for pattern classification purposes. It was observed that an error rate of 0.2189 was minimum with diagonal seating arrangement while row-wise seating gave a high RMSE value of 0.3042. The highest classification accuracy rate of 92.5% was generated with the decision tree classifier while a relatively low accuracy of 84.6% was the outcome with Naive Bayes. The developed model can be effectively used to monitor the entire teaching session in the classroom or virtual classes, which helps faculties decide whether and when students are paying attention or not. This will result in an improvised learning session and will be more effective.
The nanoCoP 2.0 Connection Provers for Classical, Intuitionistic and Modal Logics

This paper introduces the full versions of the non-clausal connection provers nanoCoP for first-order classical logic, nanoCoP-i for first-order intuitionistic logic and nanoCoP-M for several first-order multimodal logics. The enhancements added to the core provers include several techniques to improve performance and usability, such as a strategy scheduling and the output of a detailed non-clausal connection proof for all covered logics. Experimental evaluations for all provers show the effectiveness of the integrated optimizations.
The Role of Finance in Inclusive Human Development in Africa Revisited

This study investigates direct and indirect linkages between financial development and inclusive human development in data panels for African countries. It employs a battery of estimation techniques, notably: Two-Stage Least Squares, Fixed Effects, Generalized Method of Moments and Tobit regressions. The dependent variable is the inequality adjusted human development index. All dimensions of the Financial Development and Structure Database (FDSD) of the World Bank are considered. The main finding is that financial dynamics of depth, activity and size improve inclusive human development, whereas the inability of banks to transform mobilized deposits into credit for financial access negatively affects inclusive human development. Policies should be tailored to improve mechanisms by which credit facilities can be provided to both households and business operators. Surplus liquidity issues resulting from the inability of banks to transform mobilized deposits into credit can be resolved by enhancing the introduction of information sharing offices (like public credit registries and private credit bureaus) that would reduce information asymmetry between lenders and borrowers. This study complements the extant literature by assessing the nexus between financial development and inclusive human development in Africa.
Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

Aiming at the difficulty in real-time measuring and the long offline measurement cycle for the content of cement clinker free lime (fCaO), it is very important to build an online prediction model for fCaO content. In this work, on the basis of Cholesky factorization, the online sequential multiple kernel extreme learning machine algorithm (COS-MKELM) is proposed. The LDLT form Cholesky factorization of the matrix is introduced to avoid the large operation amount of inverse matrix calculation. In addition, the stored initial information is utilized to realize online model identification. Then, three regression datasets are used to test the performance of the COS-MKELM algorithm. Finally, an online prediction model for fCaO content is built based on COS-MKELM. Experimental results demonstrate that the fCaO content model improves the performance in terms of learning efficiency, regression accuracy, and generalization ability. In addition, the online prediction model can be corrected in real-time when the production conditions of cement clinker change.
Performance Comparison of Different Machine Learning Algorithms on Hindi News Classification

Text classification is a process of categorizing text into predefined classes. It plays an important role in extracting useful information which can be further used for sentiment analysis, spam detection, content tagging, etc. In today's world a large number of Hindi text documents are generated by government sites, various magazines, and newspapers which are required to be classified. Since limited work has been done on Hindi language, so, we tried to classify Hindi news text collected from newspapers like Jagran, Navbharat, Patrika, etc. using different machine learning algorithms. Since machine learning algorithms are not able to work directly on the extracted text, so we applied pre-processing and feature engineering techniques such as count vectorizer, TF-IDF and word2vec. Such pre-processing is challenging due to the presence of multisense words, conjunctions, punctuations and special characters. Our model accepts Hindi news headlines of different predefined categories such as Entertainment news, Sports News, Tech news and Lifestyle news. The corpus size containing unique words after pre-processing was 54,44,997 words. Out of different combinations, multinomial Naïve Bayes with count vectorizer outperformed with an accuracy of 85.47%.
Research of 6‐DOF pose estimation in stacked scenes

Summary Recently, robot vision recognition has been widely used in modern logistics system. In the face of complex and changeable environment and objects, industrial robots need to identify and operate the target objects accurately. Therefore, this article proposes a method based on Mask R‐CNN and RGB‐D to estimate the 6‐DOF of the target object. We find the 6‐DOF of the object is identified and located accurately.
Adaptive Niche Radius Fireworks Algorithm for Multi-modal Function Optimization

In recent years, many multi-modal optimization algorithms have been proposed to find not only the one optimal solution but all global optimums of the problem. In solving multi-modal optimization problems, conventional fireworks algorithms only consider the global optima of the problem, but ignore the local optima. In this paper, inspired by the niching theory, a novel adaptive radius fireworks algorithm (ANRFWA) which introduces the concept of dominating space of local optima is proposed to solve multi-modal optimization problems. Local optima is absolutely dominant in its dominating space, we directly eliminate other fireworks in its dominating space in case of unnecessary iteration. We have tested a large number of multimodal optimization problems, and the ANRFWA performs well in solving most of them. Compared with the previous fireworks algorithm, the ANRFWA can find more global optima, and it is not inferior to some well-known evolutionary algorithms.
Bridging the gap

Product line engineering emerged from a fruitful interaction of applied research in academia, industry research, and software engineering practice. SPLC was created as the primary venue to exchange ideas on this emerging topic and integrate the communities. Yet, today, SPLC is mostly regarded as an academic conference with little industry participation. Since a strong integration of academia and industry is often seen positive, here, we try to better understand motivations for practitioners to visit academic conferences like SPLC and the impact this has on such conferences. This analysis is based on nine systematic interviews with practitioners and researchers, who have been members of the SPLC community and other leading software engineering communities for a long time. Our preliminary results clarify the relevance and interest of practitioners and researchers to exchange knowledge and learn when attending scientific software engineering conferences such as SPLC. Yet, the results also highlight the differences between the goals of industry and academic conference participants, which often lead to inefficiencies and even barriers for constructive interaction at scientific conferences such as SPLC. We use this as a basis for pointing out further discussion points, both from the perspective of the interviewees as well as the authors.
Quantum differential cryptanalysis

The work is devoted to the study quantum versions of the differential cryptanalysis based on using a combination of the quantum minimum/maximum search algorithm and the quantum counting algorithm. We have estimated the complexity and the required resources for applying the quantum differential and quantum linear cryptanalysis to searching round keys of block ciphers. It is shown that the implementation of the quantum linear method requires less logical qubits than for the implementation of the quantum differential method. The acceleration of calculations due to “quantum parallelism” in the quantum differential cryptanalysis, based on a combination of Grover’s quantum algorithms and quantum counting algorithm, is apparently absent, because the using of quantum counting as “subprogram” in the Grover algorithm eliminates quantum acceleration, as far as $$ O (\sqrt{K}) \cdot O (\sqrt{K}) \approx O (K) $$ .
Infrared scene prediction of night unmanned vehicles based on multi-scale feature maps

Recent advances achieve good performance in scene prediction, however, most approaches generate blurry and short-term predictions due to pixel-level prediction. Meanwhile, there is hardly any research on infrared scene prediction of night unmanned vehicles. In this paper, we propose a Multi-Scale Feature Map Network (MSFMNet) for infrared scene prediction of night unmanned vehicles to achieve real-time, realistic future predictions and long-term predictions. First, Contrast-Limited Adaptive Histogram Equalization and gamma transformation are used to preprocess infrared images, which improves the ability of network to extract more useful feature information. Second, an Atrous Spatial Pyramid network is designed to extract spatial features of small objects and contours, which benefits the modeling of detailed structure during decoding. Finally, a Multi-Scale Feature Maps Loss is proposed to supervise and guide the network, which achieves multi-frame infrared scene predictions. Extensive experiments on DHUdataset demonstrate the effectiveness of the proposed model with the state-of-the-art approaches, and at the same time, the model meets the real-time requirements and can predict future images after 1.2 s.
Regularization Effect of Random Node Fault/Noise on Gradient Descent Learning Algorithm

For decades, adding fault/noise during training by gradient descent has been a technique for getting a neural network (NN) tolerant to persistent fault/noise or getting an NN with better generalization. In recent years, this technique has been readvocated in deep learning to avoid overfitting. Yet, the objective function of such fault/noise injection learning has been misinterpreted as the desired measure (i.e., the expected mean squared error (mse) of the training samples) of the NN with the same fault/noise. The aims of this article are: 1) to clarify the above misconception and 2) investigate the actual regularization effect of adding node fault/noise when training by gradient descent. Based on the previous works on adding fault/noise during training, we speculate the reason why the misconception appears. In the sequel, it is shown that the learning objective of adding random node fault during gradient descent learning (GDL) for a multilayer perceptron (MLP) is identical to the desired measure of the MLP with the same fault. If additive (resp. multiplicative) node noise is added during GDL for an MLP, the learning objective is not identical to the desired measure of the MLP with such noise. For radial basis function (RBF) networks, it is shown that the learning objective is identical to the corresponding desired measure for all three fault/noise conditions. Empirical evidence is presented to support the theoretical results and, hence, clarify the misconception that the objective function of a fault/noise injection learning might not be interpreted as the desired measure of the NN with the same fault/noise. Afterward, the regularization effect of adding node fault/noise during training is revealed for the case of RBF networks. Notably, it is shown that the regularization effect of adding additive or multiplicative node noise (MNN) during training an RBF is reducing network complexity. Applying dropout regularization in RBF networks, its effect is the same as adding MNN during training.
Using Zero-Shot Transfer to Initialize azWikiNER, a Gold Standard Named Entity Corpus for the Azerbaijani Language

Named Entity Recognition (NER) is one of the primary fields of Natural Language Processing, focused on analyzing and determining the entities in a given text. In this paper, we present a gold standard named entity dataset for Azerbaijani created from the Azerbaijani portion of WikiAnn, a ‘silver standard’ NER dataset generated from Wikipedia. In a zero-shot cross-lingual transfer scenario, we used an M-BERT-based NER model trained on the English Ontonotes corpus to add new entity types to the corpus. The output of the model was then hand-corrected. We evaluate the accuracy of the original WikiAnn corpus, the zero-shot performance of two models trained on the Ontonotes corpus, and two transformer-based NER models trained on the training part of the final corpus: one based on M-BERT and another based XLM-RoBERTa. We release the corpus and the trained models to the public.
Metric Learning with Deep Features for Highly Imbalanced Face Dataset

Learning faces from large databases having a highly uneven class distribution is subject to bias toward the majority class that has the maximum number of instances. The problem becomes more complex when the number of classes is very large with samples from the minority classes often getting mis-classified. This phenomenon, popularly known as the class imbalance problem, is yet to be explored in its entirety for the deep learning techniques that form the state of the art in computer vision today. Metric learning is sometimes used to mitigate the effect of class imbalance due to its data space transformation properties that bring samples of a class closer. In this paper, we investigate three popular metric learning schemes, namely LMNN, NCA, and MLKR, for transforming the input space comprising of deep feature vectors extracted from the VGG-Face pre-trained network. To reduce the computational overload associated with metric learning, we learn the distance metric from the majority classes only and use the learnt metric to transform the entire input space. Experiments on the benchmark LFW facial dataset prove the efficiency of our approach from the higher accuracies obtained as compared to the existing techniques.
Anonymous Certificate-Based Inner Product Broadcast Encryption

Broadcast encryption scheme enables a sender distribute the confidential content to a certain set of intended recipients. It has been applied in cloud computing, TV broadcasts, and many other scenarios. Inner product broadcast encryption takes merits of both broadcast encryption and inner product encryption. However, it is crucial to reduce the computation cost and to take the recipient’s privacy into consideration in the inner product broadcast encryption scheme. In order to address these problems, we focus on constructing a secure and practical inner product broadcast encryption scheme in this paper. First, we build an anonymous certificate-based inner product broadcast encryption scheme. Especially, we give the concrete construction and security analysis. Second, compared with the existing inner product broadcast encryption schemes, the proposed scheme has an advantage of anonymity. Security proofs show that the proposed scheme achieves confidentiality and anonymity against adaptive chosen-ciphertext attacks. Finally, we implement the proposed anonymous inner product broadcast encryption scheme and evaluate its performance. Test results show that the proposed scheme supports faster decryption operations and has higher efficiency.
Discrimination of quantum states under locality constraints in the many-copy setting

We study the discrimination of a pair of orthogonal quantum states in the many-copy setting. This is not a problem when arbitrary quantum measurements are allowed, as then the states can be distinguished perfectly even with one copy. However, it becomes highly nontrivial when we consider states of a multipartite system and locality constraints are imposed. We hence focus on the restricted families of measurements such as local operation and classical communication (LOCC), separable operations (SEP), and the positive-partial-transpose operations (PPT) in this paper. We first study asymptotic discrimination of an arbitrary multipartite entangled pure state against its orthogonal complement using LOCC/SEP/PPT measurements. We prove that the incurred optimal average error probability always decays exponentially in the number of copies, by proving upper and lower bounds on the exponent. In the special case of discriminating a maximally entangled state against its orthogonal complement, we determine the explicit expression for the optimal average error probability, thus establishing the associated Chernoff exponent. Our technique is based on the idea of using PPT operations to approximate LOCC. Then, we show an infinite asymptotic separation between SEP and PPT operations by providing a pair of states constructed from an unextendible product basis (UPB): they can be distinguished perfectly by PPT measurements, while the optimal error probability using SEP measurements admits an exponential lower bound. On the technical side, we prove this result by providing a quantitative version of the well-known statement that the tensor product of UPBs is UPB.