Reviewerly/exHarmony huggingface dataset:
Recommending Bids on Dou-DiZhu Poker Games: A Deep Learning Approach

Computer aided game playing strategy provides a unique view towards artificial intelligence studies. In addition to symmetric games, establishing intelligent models for non-symmetric games are more challenging. A vivid example is the Dou-DiZhu game, a famous traditional Chinese poker game, which requires one player playing cards against the alliance of the other two players. This paper has made a first attempt to establish a machine-aided playing strategy to help player to call bids with a higher gain. In particular, a Convolutional Neural Network (CNN) based recommendation model is proposed to combine the feature of player's hand sequence and strength. To validate the feasibility of the proposed method, a real-world dataset is utilized to examine the performance of the recommendation results outputted the proposed model. The results of comparative experiments show that the proposed model achieves a higher precision, recall, F1 and accuracy performance than traditional machine learning models and single-feature based deep learning models.
A Multi-Stage DCNN Method for Liver Tumor Segmentation

Medical image segmentation is one of the most important tasks in machine learning. In order to help with the doctors, we focus on computer-aided automatic computer tomography (CT) segmentation of liver tumors. Instead of using a single model during the segmentation process, we argue that the filter made by the model of object detection before segmentation could improve the accuracy of segmentation. From the motivation above, we proposed a multi-stage deep convolutional neural network (DCNN) model, which can be used for segmentation of the tumors in the livers. To further improve the accuracy of the object detection model, we proposed an iterative training method which boosts the performance of our model and yield more accurate localization of the tumors. Finally, the evaluation of proposed method on our private dataset from hospital and public dataset from MICCAI-LiTS2017 suggested that our model is superior to the current state-of-the-art models.
Degradation Classification on Ancient Document Image Based on Deep Neural Networks

In this paper, we study degradation classification on ancient document images using three pre-trained models of benchmarking CNN architecture, i.e., Resnet101, Mobilenet V2, and Shufflenet. We use Document Image Binarization Contest (DIBCO), Persian Heritage Image Binarization Dataset (PHIBD), and private Jawi datasets for experimental purposes. We grouped the degradation into four categories, namely: bleedthrough/showthrough/ink-bleed, faint-text and low contrast, smear-spot-stain, and uniform degradation. In the training progress, we set optimizer to ADAM, the initial learn rate to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , and three epoch values: 5, 25, and 50 training epoch. To test the model, we conduct two testing stages: (1) unblind testing, (2) blind testing. The result shows that Shufflenet with 25 training epoch achieved 100% and 85% accuracy of unblind and blind testing, respectively, and obtained the fastest computational process. We concluded that Shufflenet could be chosen in classifying degradations based on its accuracy and computational time.
Distributional discrepancy: A metric for unconditional text generation

The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However, when different metrics are used for comparing the methods of unconditional text generation, contradictory conclusions are drawn. The difficulty is that both the diversity and quality of the sample should be considered simultaneously when the models are evaluated. To solve this problem, a novel metric of distributional discrepancy (DD) is designed to evaluate generators based on the discrepancy between the generated and real training sentences. However, it cannot compute the DD directly because the distribution of real sentences is unavailable. Thus, we propose a method for estimating the DD by training a neural-network-based text classifier. For comparison, three existing metrics, bi-lingual evaluation understudy (BLEU) versus self-BLEU, language model score versus reverse language model score, and Fréchet embedding distance, along with the proposed DD, are used to evaluate two popular generative models of long short-term memory and generative pretrained transformer 2 on both synthetic and real data. Experimental results show that DD is significantly better than the three existing metrics for ranking these generative models.
Childhood Medulloblastoma Classification Using EfficientNets

In this work, a deep learning methodology for accurate classification of histological images of the patients suffering from childhood medulloblastoma is proposed. Pre trained EfficientNets trained on the ImageNet dataset are considered in this work. Features are extracted from the average pooling layer of EfficienNets and are given to an error correcting output code classifier. Ensemble prediction from the selected pre-trained EfficientNets is employed. For the multi class classification, the proposed approach is able to predict with a mean classification accuracy of 98.78% for 10x level images and 95.67% for the 100x level images for an 80% train and 20% test split. The peak classification accuracy is 100% for both binary and multiclass case at cell level and architectural level. For the binary classification with same split, 100% mean classification accuracy is achieved even without ensemble prediction. The results are compared with an existing work on a similar dataset and the significant improvement is demonstrated with the experimental simulations.
The Ontological Classification of Conversational Agents

This paper focuses on the attributed nature of the voice-based agents Alexa and Google Assistant in conversational contexts. Using Piaget’s equilibration theory, enhanced by Hubbard’s concept of personhood the paper considers how people categorize voice-based agents along a thing–person spectrum and whether this categorization reflects assimilation or accommodation of these technologies. The results of two studies (a hypothetical conversation with the agent via an online-survey, N = 1288, and a real conversation with the agent, N = 105) are indicating a modified classification towards personified things, which is reinforced by younger age and a higher quality of interaction. Implications, limitations, and further research regarding a more detailed classification of conversational agents are discussed.
A TCM Constitution Analysis Algorithm Based on Association Rules in Hadoop Framework

In order to effectively utilize the parallel processing capability of the Hadoop framework of the cloud platform. The traditional association rule algorithm FP-growth algorithm in big data mining is improved. The association rules algorithm for a TCM constitution analysis based on in Hadoop framework(ARA-TCM) in machine learning is applied to the classification of TCM constitutions, and the corresponding classification model was established to observe the accuracy of category prediction and provide preliminary screening and decision support for TCM constitutions analysis. Meanwhile, the association rules algorithm for a TCM constitution analysis based on in Hadoop framework (ARA- TCM) is compared with the Apriori algorithm, and the results show that the association rules algorithm for a TCM constitution analysis based on in Hadoop framework (ARA-TCM) has better classification effect than the Apriori algorithm. This indicates that it is feasible to apply the association rules algorithm for a TCM constitution analysis based on in Hadoop framework (ARA- TCM) to the classification of TCM constitution.
Classification of rainfall data using support vector machine

Abstract Classification problems can be based on cross-section or time-series data. In general, some of the characteristics found in time series data are data that are very susceptible to containing noise and outliers. In this paper, time series data-based classification was carried out using the support vector machine (SVM) method. The SVM method is the most popular binary classification technique in machine learning. The advantage of this method is that it can find a global optimum solution and always achieve the same solution for every running. Another advantage is that it can solve the over-fitting problem by minimizing the upper limit of generalization errors. SVM classification model performance can be seen from classification accuracy and sensitivity and specificity tests. The results showed that based on the level of classification accuracy used the SVM method resulted in an accuracy rate of the prediction results with training data of 96,3% and the accuracy of the prediction results with testing data of 90,08%. Therefore, the classification of rainfall data using the SVM method has a very good performance.
Noise reduction in high-resolution speckle displacement measurements through ensemble averaging

To measure surface displacement on micro samples, a non-invasive method with both a low displacement measurement uncertainty below 100 nm and high spatial resolution of around 20 µm is required. In digital image speckle correlation, both requirements can be fulfilled individually but not simultaneously. To lower the displacement measurement uncertainty without deteriorating the spatial resolution, an ensemble averaging technique over multiple uncorrelated speckle patterns is presented. To generate and reproduce different speckle patterns, two concepts for the respective modulation of laser light illumination are investigated: a low-cost concept with a rotating glass diffuser, as well as a faster concept using a digital micromirror device combined with a stationary diffuser with a maximum pattern rate of 17.9 kHz. Both setups lead to a measurement uncertainty reduction by one order of magnitude over a wide range of spatial resolutions. As a result, displacements in the micrometer range are measured with a measurement uncertainty of 40 nm and spatial resolution of 20 µm.
Video super‐resolution with non‐local alignment network

Video super-resolution (VSR) aims at recovering high-resolution frames from their low-resolution counterparts. Over the past few years, deep neural networks have dominated the video super-resolution task because of its strong non-linear representational ability. To exploit temporal correlations, most deep neural networks have to face two challenges: (1) how to align consecutive frames containing motions, occlusions and blurring, and establish accurate temporal correspondences, (2) how to effectively fuse aligned frames and balance their contributions. In this work, a novel video super-resolution network, named NLVSR, is proposed to solve above problems in an efficient and effective manner. For alignment, a temporal-spatial non-local operation is employed to align each frame to the reference frame. Compared with existing alignment approaches, the proposed temporal-spatial non-local operation is able to integrate the global information of each frame by a weighted sum, leading to a better performance in alignment. For fusion, an attention-based progressive fusion framework was designed to integrate aligned frames gradually. To penalize the points with low-quality in aligned features, an attention mechanism was employed for a robust reconstruction. Experimental results demonstrate the superiority of the proposed network in terms of quantitative and qualitative evaluation, and surpasses other state-of-the-art methods by 0.33 dB at least.
WITHDRAWN: Sports activity recognition using classification techniques

Wearable sensors for activity recognition can give a response to the person about their daily life concerning sports physical activity. Moreover, it encourages a more active lifestyle. Mostly, supervised laboratory settings are used to study activity recognition. Therefore, this paper gives a comparative analysis of different types of classification techniques for classifying data obtained from body-worn magnetic and inertial sensors. The algorithms used for classification and comparison are Decision Tree (DT), Gradient Boosting Classification, Bagging Classification, Random Forest Classifier, Extra Trees Classifier, K-Nearest Neighbours (K-NN), Support Vector Machines (SVM), Neural Networks and its variants. The daily and sports activity are recorded using sensors worn on the right hand, left hand, torso, right leg, left leg. Each sensor consists of a triaxial gyroscope, triaxial accelerometer, triaxial magnetometer. Features extracted from raw sensor data using Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) is used for classification.
Newton-ADMM: A Distributed GPU-Accelerated Optimizer for Multiclass Classification Problems

First-order optimization techniques, such as stochastic gradient descent (SGD) and its variants, are widely used in machine learning applications due to their simplicity and low per-iteration costs. However, they often require larger numbers of iterations, with associated communication costs in distributed environments. In contrast, Newton-type methods, while having higher per-iteration computation costs, typically require a significantly smaller number of iterations, which directly translates to reduced communication costs. We present a novel distributed optimizer for classification problems, which integrates a GPU-accelerated Newton-type solver with the global consensus formulation of Alternating Direction of Method Multipliers (ADMM). By leveraging the communication efficiency of ADMM, a highly efficient GPUaccelerated inexact-Newton solver, and an effective spectral penalty parameter selection strategy, we show that our proposed method (i) yields better generalization performance on several classification problems; (ii) significantly outperforms state-of-the-art methods in distributed time to solution; and (iii) offers better scaling on large distributed platforms.
Biometric Attendance Management System Using Raspberry Pi

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Comprehensive Study of Artificial Intelligence Tools in Supply Chain

The global supply chain has become more complex in recent years, and the advent of artificial intelligence tools is set to improve the functioning of supply chain. This paper examines the effect of artificial intelligence tools on key parameters of supply chain such as cost, quality, pace, reliability, and sustainability. The Blockchain, the internet of things, the big data technologies, and the machine learning are the new potential enablers of sustainable manufacturing supply chain. This study reviews the current state-of-art research efforts and provides a systematic overview of the current and potential research directions to recognize the market trend in the adoption of these new technologies and some of the challenges as well.
PBFT-based ordering service for IoT domains

This work proposes and evaluates a Practical Byzantine Fault Tolerance (PBFT)-inspired ordering service for IoT data collection and block formation in a permissioned blockchain environment. We implement an algorithm for atomic insertion of request to ordering service in which each ordering node can initiate insertion and lead the consensus protocol, unlike traditional current implementations of ordering service which rely on a single point of entry. We have modeled record insertion service into the P2P ordering service with constant number of nodes, variable request rate, and known distribution of one-way propagation delays among the ordering peers. Performance results show the behavior of system descriptors and the limits of system capacity expressed in terms of total request rate.
Federated Learning with Heterogeneous Quantization

Quantization of local model updates before uploading to the parameter server is a primary solution to reduce the communication overhead in federated learning. However, prior literature always assumes homogeneous quantization for all clients, while in reality devices are heterogeneous and they support different levels of quantization precision. This heterogeneity of quantization poses a new challenge: fine-quantized model updates are more accurate than coarse-quantized ones, and how to optimally aggregate them at the server is an unsolved problem. In this paper, we propose FEDHQ: Federated Learning with Heterogeneous Quantization. In particular, FEDHQ allocates different weights to clients by minimizing the convergence rate upper bound, which is a function of quantization errors of all clients. We derive the convergence rate of FEDHQ under strongly convex loss functions. To further accelerate the convergence, the instantaneous quantization error is computed and piggybacked when each client uploads the local model update, and the server dynamically calculates the weight accordingly for the current round. Numerical experiments demonstrate the performance advantages of FEDHQ+ over conventional FEDAVG with standard equal weights and a heuristic scheme which assigns weights linearly proportional to the clients’ quantization precision.
Cryptanalysis on Two Pairing-Free Ciphertext-Policy Attribute-Based Encryption Schemes

Ciphertext-policy attribute-based encryption (CPABE) provides fine-grained access control, which is suitable for the modern data sharing model. Lots of CP-ABE schemes constructed over different mathematical structures, such as pairing groups, lattices, etc. Recently, Ding et al. and Wang et al. independently proposed two pairing-free CP-ABE schemes, using only elliptic curve cryptography. However, we found that their schemes are insecure. In this manuscript, we review their schemes and give the corresponding cryptanalysis. We show that, their schemes are vulnerable against collusion attacks.
Dynamic Evaluation Technology of Bridge Based on Multi-source Heterogeneous Data Fusion

Abstract Based on the multi-source and heterogeneous characteristics of massive state sensing data of bridges, this paper establishes a multi-source heterogeneous bridge state sensing data feature extraction and analysis method. A dynamic evaluation model of bridge technology based on multi-source heterogeneous data fusion is proposed. For the first time, the relationship between multi-source perceptual data and bridge infrastructure technology is established, the knowledge graph of bridge infrastructure technology is constructed, and the relationship between various state-aware data and bridge technology is explicitly expressed by the map. This collaborative association is used to dynamically evaluate and predict bridge technology.
Economical Requirements Elicitation Techniques During COVID-19: A Systematic Literature Review

Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements. This phase is cost- and time-intensive, and a project may fail if there are excessive costs and schedule overruns. COVID-19 has affected the software industry by reducing interactions between developers and customers. Such a lack of interaction is a key reason for the failure of software projects. Projects can also fail when customers do not know precisely what they want. Furthermore, selecting the unsuitable elicitation technique can also cause project failure. The present study, therefore, aimed to identify which requirements elicitation technique is the most cost-effective for large-scale projects when time to market is a critical issue or when the customer is not available. To that end, we conducted a systematic literature review on requirements elicitation techniques. Most primary studies identified introspection as the best technique, followed by survey and brainstorming. This finding suggests that introspection should be the first choice of elicitation technique, especially when the customer is not available or the project has strict time and cost constraints. Moreover, introspection should also be used as the starting point in the elicitation process of a large-scale project, and all known requirements should be elicited using this technique.
A computationally efficient symmetric diagonally dominant matrix projection-based Gaussian process approach

Although kernel approximation methods have been widely applied to mitigate the O(n3) cost of the n×n kernel matrix inverse in Gaussian process methods, they still face computational challenges. The 'residual' matrix between the covariance matrix and the approximating component is often discarded as it prevents the computational cost reduction. In this paper, we propose a computationally efficient Gaussian process approach that achieves better computational efficiency, O(mn2), compared with standard Gaussian process methods, when using m≪n data. The proposed approach incorporates the 'residual' matrix in its symmetric diagonally dominant form which can be further approximated by the Neumann series. We have validated and compared the approach with full Gaussian process approaches and kernel approximation based Gaussian process variants, both on synthetic and real air quality data.
Introduction to Cuckoo Search and Its Paradigms: A Bibliographic Survey and Recommendations

Metaheuristic algorithms are found to be more effective and helpful in working out on complex problems (e.g., optimization and mining, etc.). This chapter presents a survey with a brief overview of the cuckoo algorithm, its latest developments, as well as recommendation for the applications in various disciplines. The presented survey analyzes the algorithm search insight, its Lévy flight strategy, and search mechanism efficiency. It was found that despite being capable of solving various complex and multimodal problems researcher further improves cuckoo search algorithm performance. The chapter presents the cuckoo search algorithm, and explains the variants and hybrids of the cuckoo algorithm. Further, the chapter unveils various complex problems of science and engineering where the algorithm can be applied successfully with better convergence and global optimization results.
Muffidgets: Detecting and Identifying Edible Pastry Tangibles on Capacitive Touchscreens

Detecting tangibles on capacitive touchscreen has seen vast attention over the past decade. Current state of the art allows a capacitive touchscreen to detect and identify a number of tangibles based on a unique footprint of interconnected conductive elements on the base of the tangible. In most cases, this conductive material is either metal or some carbon-based conductor, possibly integrated into a 3D-printing process. The choice of conductive material is, however, not limited to these technical elements. In this paper, we showcase how the concept of tangible detection on capacitive touchscreens can be transferred to pastry, creating an ephemeral, edible user interface. The detection and identification of specific pieces of pastry open applications in the area of entertainment, but also food safety.