Reviewerly/exHarmony huggingface dataset:
A Survey on Humor Detection Methods in Communications

Humor is a part of most of the communications these days. Modeling the humor present in verbal communication is trending recently. Assessing the degree of the humor present in text is a challenging task. Most of the existing studies on humor detection depended on binary classification with respect to the linguistic features. Deep Learning is the best alternative and most prominent technology that is most widely being used in humor detection in verbal communications. Humor Detection needs efficient models and patterns which are not available for all the cases. The researchers in this area are mushrooming day by day. As an effort to boost research and ignite new ideas in this challenging area, this paper explores how deep learning is being used in humor detection. Also reviews various humor detection methodologies which are based on deep learning. This paper presents the critical analysis of the humor detection methodologies and future directions.
Local modeling approach for cross-project defect prediction

Prediction approaches used for cross-project defect prediction (CPDP) are usually impractical because of high false alarms, or low detection rate. Instance based data filter techniques that improve the CPDP performance are time-consuming and each time a new test set arrives for prediction the entire filter procedure is repeated. We propose to use local modeling approach for the utilization of ever-increasing cross-project data for CPDP. We cluster the cross-project data, train per cluster prediction models and predict the target test instances using corresponding cluster models. Over 7 NASA Data sets performance comparison using statistical methods between within-project, cross-project, and our local modeling approach were performed. Compared to within-project prediction the cross-project prediction increased the probability of detection (PD) associated with an increase in the probability of false alarm (PF) and decreased overall performance Balance. The application of local modeling decreased the (PF) associated with a decrease in (PD) and an overall performance improvement in terms of Balance. Moreover, compared to one state of the art filter technique – Burak filter, our approach is simple, fast, performance comparable, and opens a new perspective for the utilization of ever-increasing cross-project data for defect prediction. Therefore, when insufficient within-project data is available we recommend training local cluster models than training a single global model on cross-project datasets.
A Tripartite Game-Theory-Based Cooperation Mechanism for the Electronic Warehouse Receipt Pledge Financing Mode

To explore the electronic warehouse receipt pledge financing cooperation mechanism in the alliance formed between loan enterprises, commercial banks, e-commerce platforms, and logistics enterprises, by constructing a tripartite evolutionary game model, the game equilibrium strategy is then obtained, and the influencing factors of the cooperation mechanism are further simulated. It was found that in this tripartite evolutionary game, the stable strategy combination occurs when the loan enterprise chooses to repay on time, the platform alliance chooses to provide services, and the commercial bank chooses to continue to cooperate. However, heterogeneity exists in the influencing factors for the three-party selection strategy in the game, which is manifested in the fact that the higher the realization rate of the pledge, the more evolution is required in the selection strategy for loan enterprises to continue cooperating. The higher the information service fee, the higher the enthusiasm of the platform alliance in choosing to provide services. Credit levels, loan interest rates, and pledge realization rates are all important factors that affect the choice strategy of commercial banks.
Determination of the Best Employees to Improve Service Quality using the SAW Algorithm

To improve services for the use of services, in this case, the work of Building Cleanliness Maintenance and Installation, or widely known as Cleaning Service. The company always asks service users to provide a performance assessment that the institution has determined for every employee in the service user company. However, currently, the company is experiencing problems related to the performance appraisal due to the assessment still using the conventional method, causing the head of the branch to find it challenging to provide an evaluation of employees as evaluation material. This study tries to create a system that is expected to provide a solution to these problems. The system designed in this study is also a medium to help evaluate employee performance and assist branch unit leaders in making decisions. One method used in this performance appraisal system is the Simple Additive Weighting(SAW) method with the indicator used, including cleanliness, apply 3S, apply 5R, attendance, teamwork, work initiative, and agile at work. The result of this research is that an employee named Harutya gets a score of 0.95, an employee named Arilia receives a score of 0.97, an employee named Ian gets a score of 0.925, and finally, on behalf of Edwin receives a score of 0.920. From the results of the scores above, the employee named Arilia got the highest score, so Harutya was chosen as the best employee.
Fast Connected Component Labelling using Dynamically Reconfigurable Processor (DRP)

Connected-component labelling is a process of assigning a unique label to all pixels in the image which are connected. This technique is essential for distinguishing different objects in the image, and prerequisite for image analysis and object recognition. Most of the existing CCL algorithms where implemented using serial processors. The processing power required to assign unique labels to each connected component is very high in serial processors. To meet the requirement of the real-time image processing applications, parallel connected component labelling techniques are used. Parallel connected component labelling technique divides the image into different regions and local labels can be assigned parallelly during first pass. In the second pass, local labels are merged to generate global labels. The goal of this paper is to implement the parallel CCL method on a reconfigurable computing architecture to evaluate the performance. Due to the ability to change hardware architecture during algorithm execution, reconfigurable computing platforms offer best performance per watt ratio, though there are implementation restrictions. We implemented parallel CCL algorithm using a dynamically reconfigurable architecture available in the market and compared against a CPU implementation and observed 3 times faster execution.
Measuring the results of pedagogical research

The article is devoted to the relevant for modern humanities of the ratio of quantitative and qualitative research methods in the pedagogical field. Based on the separation of «nature sciences» and «cultural sciences» by V. Windelband and G. Rickert, the authors urge the scientific community to understand their difference at the present stage.&#x0D; Based on a comparative analysis of the objectives, object, subject and hypotheses of research (primarily thesis) in psychology and pedagogy, the specificity of each scientific discipline is revealed, which mediates specific tools to verify the validity and effectiveness of the results obtained. The most controversial issues include the use of mathematical statistics methods in pedagogical research, which, due to the ontology of the studied phenomena, do not adequately cover the observed changes, therefore, are not able to describe them. The most inadequate quantitative methods are used in the sphere of upbringing and adult education, where the processes of reflection, conception, self-determination and the construction of life scenarios in accordance with one’s own convictions take precedence, and the factors of life and prospects cannot be ignored. It is proposed at the stage of creating a pedagogical research program to pay special attention to the definition of the ontology of the phenomenon under study, in accordance with the chosen methodology, to fix the ratio «goal - means of achieving it - diagnostics / monitoring of the process - performance assessment», which will allow answering the question about the ratio / adequacy of quantitative and qualitative methods-proofs of its implementation. In addition, it is necessary to support the institution of dialogue platforms for scientific communities, within which representatives of different subject areas would come to a consensus on this issue and keep it in research and expert work.
Design of Variational Autoencoder for Generation of Odia Handwritten Numerals in CNN Model

Generative models have fascinated attention of researchers as it learns the important features from the trained data to generate the structures similar to the data provided for training. Autoencoders are the basic building components of generative models. In this work, we have designed a variational autoencoder to generate a large number of data to support the generative adversarial network model. The encoder, as well as the decoder of the proposed model, is designed using convolutional layers. The proposed method is verified on IIT Bhubaneswar Odia handwritten database. The encoder generates the feature vectors with the probability distribution of each category in latent space that follows the Gaussian distribution. It is verified that the decoder recognizes the features due to proper training. The generated images are quite similar to original data that validate the proposed VAE is well-generative. To measure the performance of the model, loss is calculated using binary cross-entropy along with Kullback–Leibler divergence loss. The proposed model is trained with Adam optimizer.
The selection of burglary cases based on multidimensional features and <scp>PageRank</scp>

Abstract With the rapid development of urbanization, a series of burglary cases occurred frequently. It is very important to push suspicious cases according to the top rank in order to improve the ability of intelligent concatenation cases. This article proposes a method of selecting burglary cases based on multidimensional features and PageRank, which could analyze the fact description of burglary cases, extract the multidimensional features of cases and perform feature representation to obtain the feature vector of each case. Then it constructed the network through the similarity between the multidimensional features and used PageRank to calculate the importance of each case for ranking. At last, the algorithm is verified based on the real data of burglary cases. The experimental results showed that the robustness test is better than the other two methods. And the proposed method is more effective and can be better applied to the sorting of burglary cases.
Inductive Multi-view Multiple Clusterings

The typical clustering algorithms concern only one clustering result, which ignore the other alternative patterns of intricate real world data. As a result, approaches that exploit diverse clusterings in the same dataset have been studied in the past decade. Most previous multiple clusterings approaches are developed for single view datasets. Existing multi-view approaches address multiple clusterings problem in a transductive manner, which can not be generalized to new data. To solve these issues, this paper presents an inductive multi-view multiple clusterings (IMVMC) framework. Specifically, IMVMC aligns the heterogeneous views at first, then generates multiple common subspaces by optimizing a group of encoder deep networks. Finally, it introduces a diversity control term to minimize the redundancy among these subspaces. For purpose of improving the robustness of the model, IMVMC further introduce a regular term to constrain model parameters. Experiments on benchmark datasets verify the effectiveness of IMVMC in capturing multiple clusterings of high diversity and quality.
Reading Gauges Using Computer Vision

Analogue gauges are widely used in process industries. The current method of reading analogue gauges is completely human dependent which involves risk, time and often introduces errors. This calls for an alternative method that is proposed in the paper. The aim of the paper is to demonstrate the use of computer vision in reading analogue gauge parameters using image processing techniques [1]. The results will be available in digital platforms helping in safer, quicker as well as accurate diagnostics and maintenance. A camera is used to capture the gauge image or video and the QR code corresponding to the gauge. In each image or video frame, the QR code will be used to access the database to get the gauge-related information. Using this information from the database, we will detect the needle position with respect to the extreme values using radial lines and calculating average pixel values along them. The current reading is interpolated using the knowledge of gauge scale (liner, square-law, etc.). Experiments show that the proposed method works very well for dial pointer identification of gauges with different shapes and different pointer colours [2]. However, the method fails to read gauges accumulating fog. The maximum uncertainty in the determination of the pointer is less than the human eye can discriminate.
Dynamic Neural Face Morphing for Visual Effects

In this work we present a machine learning approach for face morphing in videos, between two or more identities. We devise an autoencoder architecture with distinct decoders for each identity, but with an underlying learnable linear basis for their weights. Each decoder has a learnable parameter that defines the interpolating weights (or ID weights) for the basis which can successfully decode its identity. During inference, the ID weights can be interpolated to produce a range of morphing identities. Our method produces temporally consistent results and allows blending different aspects of the identities by exposing the blending weights for each layer of the decoder network. We deploy our trained models to image compositors as 2D nodes with independent controls for the blending weights. Our approach has been successfully used in production, for the aging of David Beckham in the Malaria Must Die campaign.
A Correlative Analysis of Course Grades as Related to Curricular Prerequisite Structure and Inter-Class Topic Dependencies

This Research-to-Practice Work in Progress paper presents an analysis of student performance in courses in light of the prerequisite structure of those courses and the degree of topic-level dependencies between the courses. Prerequisite relationships, especially long sequences of relationships, are the largest contributing factor to curricular complexity, a measure of the ease of progression through a degree. While the effect of prerequisites on curricular complexity can be quantitatively measured, the relationship of student performance between the courses in a course sequence and the topic-level dependency between the courses have not been studied. Here we study the correlation in course grades in an electrical engineering curriculum. Additionally, we quantify the topic-level dependencies between courses by building a hierarchy of topics through the electrical engineering curriculum. These preliminary analyses indicate that there are likely other strong factors that affect class grades beyond specific course competencies and that the prerequisite structure of the curriculum may not necessarily accurately reflect a relationship in terms of course competencies.
Deep Learning Based Spatiotemporal Human Action Recognition and Localization System

Spatiotemporal human action localization system is a field in computer vision and is of interest for real-world applications implemented in smart surveillance cameras, such as to improve public security, monitor patients' activities, or even detect any early symptoms of certain diseases. The system presented in this thesis followed the YOWO machine learning architecture reference, which was proposed by Köpüklü etc. (2019). YOWO extracts both spatial and temporal information. Bounding box regression and action classification can be done end-to-end. This aims to generate output faster compare to other state-of-the-art approaches. The implementation of this system is trained and tested with J-HMDB and NTU RGB+D datasets. Using certain specifications of machine defined, the system is just able to process video at 0.75seconds per frame with an accepted accuracy value. However, the system succeeds in increasing the human action localization accuracy from the YOWO reference with an accuracy of 41.6% to 43.84%.The result of the experiments shows that the modified architecture is able to improve the accuracy of YOWO. However, it slows down the frame rate of video processing.
Robust top-down and bottom-up visual saliency for mobile robots using bio-inspired design principles

Modern camera systems in robotics tend to pro-duce overwhelming amounts of visual information due to their high resolutions and high frame rates. This raises a fundamental question of how robots should focus attention on a region of the visual scene, and how they should process information in the periphery. This is particularly an issue for mobile robots, where the computational resources of low-power embedded computing boards tend to be much less than for workstations. In this paper, we look to biological design in the primate brain for inspiration on how to solve this problem. We develop a novel computational fusion of bottom-up and top-down visual saliency information. The bottom-up saliency is produced using standard colour, intensity, and motion image processing methods. The top-down saliency is produced using a deep convolutional neural network for object detection and recognition, with foveated images for computational efficiency. Regions of attention are obtained using a computational model of the basal ganglia, thought to be involved in optimal decision making. The model of the basal ganglia is based on the multi-hypothesis sequential probability ratio test (MSPRT). The visual saliency scheme is evaluated on omnidirectional video feed highlighting a proximity to human behaviour.
Cryptanalysis of Double-trapdoor Hash Function and Multi-Trapdoor Hash Function Schemes

Cloud-based storage systems are the norm in our interconnected society, although there remain a number of research and operational challenges relating to the security of such systems. One ongoing research challenge is the design of efficient and secure query authentication mechanism for cloud-based storage systems, in the sense that data users can verify the authenticity and integrity of the retrieved data from the cloud servers. In 2017, Chandrasekhar and Singhal proposed a query authentication protocol for cloud-based storage systems, designed with efficiency and scalability in mind [IEEE Transactions on Services Computing, 10(4):520-533]. In this comment, we demonstrate that their protocol is not secure as claimed, due to weaknesses in two core building blocks of the protocol, namely: the double-trapdoor variant and the multi-trapdoor variant of the trapdoor hash function. By revealing this weakness, we hope future protocol designers can avoid similar mistake in their work.
Face Recognition Using Neural Network Technique and Binary Pattern Methods

This paper presents the act of face recognition using neural network techniques and binary pattern methods. This is aimed at devising a technique for robotic control via facial expression. The Matlab environment was used in this work to provide accurate processing of image and facial expression categorization using a neural network. By using the microcontroller board of basic stamp 2 (BS2), the wireless transmitter circuit was built in this study. Also, the receiver part was designed with a decoder and BS2 microcontroller and interfaced with MATLAB by serial port communication. The major function of BS2 is to remain for convinced typescript from MATLAB and process it to confirm the command required to be wirelessly transmitted. Through serial port to BS2, the four characters were sent by MATLAB, with one quality for every emotion detected. The gestures and facial expressions provide intuitional cues for interpersonal communication. Local Binary Pattern (LBP) features were introduced for texture analysis and recently have been introduced to represent faces in facial image analysis. The most important properties of LBP features are their computational simplicity and their tolerance to illumination changes. LBP features can be derived very fast in a single scan through the raw image and are within the low-dimensional feature space for texture analysis; they are applied as a local feature extraction method in facial expression recognition. This work introduced facial expression examination algorithms for motionless images and efficient extension to the sequence of images. The results showed that the classification accuracy achieved was 41% for static expression images and 86.31% for series of images; this is significantly higher than the accuracy of other standard image processing and recognition techniques.
Evaluation of Sparse Binary Word Vectors in Chinese-English Machine Translations

We study the problem of language representation for automatic processing. Based on the idea of sparse lifting, our work projects dense word vectors into a higher-dimensional space and then generates sparse binary representations for words. In this way, our model combines the benefits from both one-hot representations and the distributional word vectors and provides a general representation scheme for automatic natural language processing. In an empirical study on Chinese to English bidirectional machine translations, the proposed model clearly demonstrates its advantage in downstream applications.
Speech Signal Processing Using Consonant-Vowel Location Detection

Phoneme identification plays a major role in the field of speech processing. Currently, machine learning-based methods are the mainstream, but for devices that cannot incorporate heavy processing, we proposed a non-machine learning method to detect consonant-vowel positions using consonant classification criteria of voicelessness, Place of articulation, and Manner of articulation. The proposed method is composed of Fourier transform and simple operation and considered to be fully applicable to existing digital signal processors. These methods are implemented in MATLAB/Simulink.
Adaptive attention encoder for attribute graph embedding

In recent years, unsupervised graph representation learning based on graph encoders has made significant progress. However, the existing methods have three shortcomings. First, the existing method based on graph convolutional network (GCN) cannot distinguish between different neighbor nodes. Second, these methods ignore the actual distribution of graph data. Finally, most existing embedding algorithms focus on restoring the feature matrix or adjacency matrix, which is not necessarily the best choice. Considering the above problems, an adaptive attention adversarial variational graph autoencoder (AAAVGE) is proposed. Its core ideas are (1) Use graph attention to distinguish the weights of different neighbor nodes. (2) Use the adversary strategy to guide the embedding vector to obey the true distribution of data. (3) Use an adaptive learning strategy to train the encoder. Link prediction experiments on four public benchmark data sets show that our proposed AAAVGE can significantly improve the performance of graph embedding compared with the mainstream graph embedding methods.