Reviewerly/exHarmony huggingface dataset:
Random compact Gaussian kernel: Application to ELM classification and regression

Abstract Extreme learning machine (ELM) kernels using random feature mapping have recently gained a lot of popularity because they require low human supervision. However, the superiority in the mapping mechanism of ELM kernels is accompanied by a higher computation cost, rendering the kernel learning algorithms hard to tackle large scale learning tasks. On the other hand, the implicit mapping used in the conventional Gaussian kernel is computationally cheaper than the explicit computation of the ELM kernel, but requires trivial human intervention. This paper proposes to merge both properties by defining a new kernel, the random compact Gaussian (RCG) kernel. The random feature mapping property enables RCG kernel to save parameters selection time, while the implicitly mapping property enables RCG kernel to save kernel calculation time. The proposed kernel works by scaling one kernel parameter in the conventional Gaussian kernel to multiple kernel parameters, and generating all parameters randomly based on a continuous probability distribution. We prove that the RCG kernel is a Mercer kernel. The kernel is calculated implicitly before seeing the training samples and used to train ELMs. The experiments on 25 binary classification and regression benchmark problems show that the RCG kernel typically outperforms other competitive kernels. Compared to ELM kernel, RCG kernel not only achieves the better generalization performance on most datasets, but also needs much less kernel calculation cost. In addition, the sensitivity analysis of the kernel parameters of k -fold cross validation is conducted and the results show that the RCG kernel is robust and stable for repeated trials.
Prostat kanseri tahmini için çeşitli denetimli makine öğrenimi tekniklerinin karşılaştırılması

Prostate cancer is a kind of cancer that is seen worldwide and causes death of many people.Early diagnosis of cancer helps patients during the treatment phase.For this reason, cancer prediction is very crucial, according to the symptoms seen in the patient.One of the biggest problems in medicine is diagnosing diseases.The absence of certain definitive rules for the evaluation of symptoms of prostate cancer and the low rate of prediction of the diagnostic methods currently in effect made this study essential.It is thought that machine learning methods can be effective for the solution of the problems where there are no specific and definite rules and the factors affecting the event can be predicted.With this awareness, various solutions are developed by computer-aided systems.In this paper, we compare and discuss the performance of different supervised machine learning algorithms (i.e., k-nearest neighbor, support vector machines, random forest, logistic regression, linear regression, Naive Bayes, linear discrimination analysis, linear classification, multi-layer perceptron and deep neural network) for prostate cancer prediction.In this study, an open-access online prostate cancer data which consists of observations of 100 patients is used.The main intention is to evaluate the correctness in classifying data with respect to effectiveness and efficiency of each algorithm in terms of precision, recall, AUC, F1-Score, accuracy.The accuracy of the methods may vary according to the training and test data.In order to obtain more stable results, each algorithm was run more than ten times and their five best performances were recorded.The results show that multi-layer perceptron (MLP) can result in high prediction accuracy that is better compared to other approaches.Experimental results show that MLP gives the highest accuracy (97%) with the lowest error rate (0.03).The MLP classifier outperformed the other algorithms used in this study and is one of the best studies ever reported in the literature in terms of accuracy, AUC and F1 score performance criteria.As a result, we can say that if the computer is trained with machine learning methods based on patient information, it can be clinically useful with high accuracy in predicting cancer.In this way, an unnecessary biopsy of the patient can be prevented.
Random Histogram Forest for Unsupervised Anomaly Detection

Roughly speaking, anomaly detection consists of identifying instances whose features significantly deviate from the rest of input data. It is one of the most widely studied problems in unsupervised machine learning, boasting applications in network intrusion detection, healthcare and many others. Several methods have been developed in recent years, however, a satisfactory solution is still missing to the best of our knowledge. We present Random Histogram Forest an effective approach for unsupervised anomaly detection. Our approach is probabilistic, which has been proved to be effective in identifying anomalies. Moreover, it employs the fourth central moment (aka kurtosis), so as to identify potential anomalous instances. We conduct an extensive experimental evaluation on 38 datasets including all benchmarks for anomaly detection, as well as the most successful algorithms for unsupervised anomaly detection, to the best of our knowledge. We evaluate all the approaches in terms of the average precision of the area under the precision-recall curve (AP). Our evaluation shows that our approach significantly outperforms all other approaches in terms of AP while boasting linear running time.
SMPLy Benchmarking 3D Human Pose Estimation in the Wild

Predicting 3D human pose from images has seen great recent improvements. Novel approaches that can even predict both pose and shape from a single input image have been introduced, often relying on a parametric model of the human body such as SMPL. While qualitative results for such methods are often shown for images captured in-the-wild, a proper benchmark in such conditions is still missing, as it is cumbersome to obtain ground-truth 3D poses elsewhere than in a motion capture room. This paper presents a pipeline to easily produce and validate such a dataset with accurate ground-truth, with which we benchmark recent 3D human pose estimation methods in-the-wild. We make use of the recently introduced Mannequin Challenge dataset which contains in-the-wild videos of people frozen in action like statues and leverage the fact that people are static and the camera moving to accurately fit the SMPL model on the sequences. A total of 24,428 frames with registered body models are then selected from 567 scenes at almost no cost, using only online RGB videos. We benchmark state-of-the-art SMPL-based human pose estimation methods on this dataset. Our results highlight that challenges remain, in particular for difficult poses or for scenes where the persons are partially truncated or occluded.
ORGANIZATIONAL CITIZENSHIP BEHAVIOR ROLE IN MEDIATING THE EFFECT OF TRANSFORMATIONAL LEADERSHIP ON ORGANIZATIONAL PERFORMANCE IN GOLD INDUSTRY OF SAUDI ARABIA

This paper mainly aims at investigating the mediating effect of organizational citizenship behaviour in the relationship between transformational leadership behaviour and organizational performance as well as the mediating effect of organizational citizenship behaviour in the relationship between psychological ownership, working environment, employee involvement, incentives, and employee motivation, and organizational performance. To achieve the objectives of the study, quantitative research approach was applied through a questionnaire specifically designed to collect primary information from the samples of the study, which consisted of (250) managers in gold shops in Jaddah. The collected data was analysed using SPSS 20.0 and Smart-PLS 3. The results showed that organization citizenship behaviour mediates the relationship between transformational leadership behaviour, psychological ownership and incentives, and organizational performance in the Saudi Arabian gold industry. Also, organization citizenship behaviour has no mediating effect on the relationship between working environment, employee involvement, employee motivation, and organizational performance in the Saudi Arabian gold industry. At the end of this study, the researcher recommended the need to encouraging the practice of organizational citizenship behaviours by developing a set of special regulations and instructions, and consider organizational citizenship behaviours as one of the important criteria in the annual performance appraisal models for employees, in addition to that, the quantitative method was adopted in this research to test research hypotheses, thus, future studies can rely on other methods qualitative or case study to verify the results of this research.
Application Research of Key Frames Extraction Technology Combined with Optimized Faster R-CNN Algorithm in Traffic Video Analysis

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real-time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi-time scale dual-stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R-CNN is proposed, and the Faster R-CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high-resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R-CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory.
Impact of Information and Communication Technology in the Indian Education System During COVID-19

Knowledge has become power for every nation in the 21st century. Information and communication become important sources for the transformation of any human civilization. Lack of information affects society and development. Evolution plays an important role in biological development, but the information will enhance the social development of the human being. In the digital era, information and communication technology (ICT) guides in various areas like education, army, business, etc. Here, in this chapter, the authors represent some of the facts of ICT in the field of education in India. During COVID-19, Indian education system faces lots of challenges, and ICT plays a major role to cope with these problems. The work of ICT is to distribute education in every field without any prioritization.
Improving Amharic Speech Recognition System Using Connectionist Temporal Classification with Attention Model and Phoneme-Based Byte-Pair-Encodings

Out-of-vocabulary (OOV) words are the most challenging problem in automatic speech recognition (ASR), especially for morphologically rich languages. Most end-to-end speech recognition systems are performed at word and character levels of a language. Amharic is a poorly resourced but morphologically rich language. This paper proposes hybrid connectionist temporal classification with attention end-to-end architecture and a syllabification algorithm for Amharic automatic speech recognition system (AASR) using its phoneme-based subword units. This algorithm helps to insert the epithetic vowel እ[ɨ], which is not included in our Grapheme-to-Phoneme (G2P) conversion algorithm developed using consonant–vowel (CV) representations of Amharic graphemes. The proposed end-to-end model was trained in various Amharic subwords, namely characters, phonemes, character-based subwords, and phoneme-based subwords generated by the byte-pair-encoding (BPE) segmentation algorithm. Experimental results showed that context-dependent phoneme-based subwords tend to result in more accurate speech recognition systems than the character-based, phoneme-based, and character-based subword counterparts. Further improvement was also obtained in proposed phoneme-based subwords with the syllabification algorithm and SpecAugment data augmentation technique. The word error rate (WER) reduction was 18.38% compared to character-based acoustic modeling with the word-based recurrent neural network language modeling (RNNLM) baseline. These phoneme-based subword models are also useful to improve machine and speech translation tasks.
Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter

Understanding predictions made by Machine Learning models is critical in many applications. In this work, we investigate the performance of two methods for explaining tree-based models: 'Tree Interpreter (TI)' and 'SHapley Additive exPlanations TreeExplainer (SHAP-TE)'. Using a case study on detecting anomalies in job runtimes of applications that utilize cloud-computing platforms, we compare these approaches using a variety of metrics, including computation time, significance of attribution value, and explanation accuracy. We find that, although the SHAP-TE offers consistency guarantees over TI, at the cost of increased computation, consistency does not necessarily improve the explanation performance in our case study.
Privacy-preserving location data stream clustering on mobile edge computing and cloud

The advancements in positioning technologies have led to the emergence of various location-based services, resulting in a drastic increase in location-based data generation, producing big-data. Location data are often linked with user privacy, as they can reveal sensitive information such as the places visited by a person. Moreover, most location-based services involve resource-constrained devices, needing lightweight data processing approaches. Due to these reasons, privacy and efficiency have been two of the primary components of location-based data processing. The existing approaches do not study both issues in the same setting. Consequently, current methods fail to provide efficient privacy preservation solutions towards location-based data stream processing. To address these issues, we investigate the effective integration of edge computing, cloud computing and differential privacy for location-based data clustering, which is an essential area in service recommendations (e.g. recommending the closest hotels to a particular location). In the proposed setup, we use local differential privacy to ensure user privacy. Next, we apply edge-based clustering on the differentially private input data using mobile edge devices. Next, the centroids of the clusters are collected at a cloud server to generate final clustering in a privacy-preserving manner. Our experiments show that the proposed approach provides maximum accuracy of 90% on lower privacy budgets (e.g. ɛ = 0.45-0.5).
Customer Service Chatbots: A Qualitative Interview Study into the Communication Journey of Customers

The current qualitative interview study describes the communication journey of customers who wish to contact companies, and their evaluation of chatbot communication within this journey. Interviews were conducted with a sample (N = 24) that was varied in terms of gender, age, educational level and household composition. Experiences with nine customer service chatbots were included. The analysis focuses on three stages in the journey: first, customers' prior expectations when contacting a company; second, their experiences during chatbot conversations, and third, their final conclusions about under which conditions customer service chatbots should be implemented, and the consequences of chatbot communication for customers' company perceptions. Implications for research and practice are discussed.
Toxic Comment Detection using LSTM

While online communication media acts as a platform for people to connect, collaborate and discuss, overcoming the barriers for communication, some take it as a medium to direct hateful and abusive comments that may prejudice an individual's emotional and mental well being. Explosion of online communication makes it virtually impossible for filtering out the hateful tweets manually, and hence there is a need for a method to filter out the hate-speech and make social media cleaner and safer to use. The paper aims to achieve the same by text mining and making use of deep learning models constructed using LSTM neural networks that can near accurately identify and classify hate-speech and filter it out for us. The model that we have developed is able to classify given comments as toxic or nontoxic with 94.49% precision, 92.79% recall and 94.94% Accuracy score.
Analysing Multimodal Communication and Persuasion in Populist Radical Right Political Blogs

This chapter discusses how populist radical right politicians utilise political blogs for political communication and persuasion. Applying a critical discursive and visual rhetorical analytical approach on a case example of a Finnish populist radical right-wing political blog, the chapter shows that use of digital and visual communicative tools allows politicians to express negative views about immigrants and minorities without expressing an explicit personal opinion, thus avoiding coming across as xenophobic or prejudiced. The chapter discusses the social and political implications of the political blog discourse and draws attention to the importance of the social media for the electoral fortunes of populist radical right-wing parties. Finally, it encourages discursive researchers in the future to pay analytic attention to various non-verbal, especially visual, forms of online political communication and persuasion.
3D surface reconstruction of small height object based on thin structured light scanning

Aiming at the problem of 3D surface reconstruction of small height objects, a method based on the scanning principle with thin structured light is proposed. The laser fringe is produced by a laser light source. It scans the surface of small height object under the control of a precise motion control system, and is integrated with a stereo light microscope and two cameras to form a complete structured light profilometry system. This method is very suitable for 3D surface reconstruction of small height objects. In order to deal with the phenomenon of "cracking" and local uneven brightness in microscope fringe image, the fringe image sequence is captured under different camera exposure parameters, and the quality of fringe image is improved by image fusion. The location of pixels on the center line of laser stripes is detected by the Loess local second-order fitting method, and the positions of these pixels are smoothed and predicted by using the Lowess local linear fitting method. The deformation of the laser stripe center curve is calculated by constructing a baseline, and the 3D surface reconstruction of the object with small height is realized in the image space. Taking the small characters on the surface of Chinese coins and human hair as test samples, their 3D surfaces are constructed by using the method proposed in this paper. The reconstructed 3D surface shapes are highly consistent with the real 3D surface shapes of objects.
GMDN: A lightweight graph-based mixture density network for 3D human pose regression

3D human pose estimation from 2D detections is an ill-posed problem because multiple solutions may exist due to the inherent ambiguity and occlusion. In this paper, we propose a novel graph-based mixture density network (GMDN) to tackle the 2D-to-3D human pose estimation problem. We formulate the 2D joint locations of the human body as a graph, and thus the pose estimation task can be redefined as a graph regression problem. Additionally, we present a novel graph convolutional operation with the incorporation of structural knowledge about human body configurations to assist with reasoning of the structural relations implied in the human bodies. Furthermore, we employ mixture density networks to formulate the 3D human poses as a multimodal distribution. The presented GMDN is lightweight with only 0.30M parameters, and the experimental results demonstrate that it achieves state-of-the-art performance.
Is It Better to Be Objectively Wrong or Subjectively Right?

ABSTRACT For many years, archaeologists have relied on Munsell Soil Color Charts (MSCC) as tools for standardizing the recording of soil and sediment colors in the field and artifacts such as pottery in the lab. Users have identified multiple potential sources of discrepancy in results, such as differences in inter-operator perception, light source, or moisture content of samples. In recent years, researchers have developed inexpensive digital methods for color identification, but these typically cannot be done in real time. Now, a field-ready digital color-matching instrument is marketed to archaeologists as a replacement for MSCC, but the accuracy and overall suitability of this device for archaeological research has not been demonstrated. Through three separate field and laboratory trials, we found systematic mismatches in the results obtained via device, including variable accuracy against standardized MSCC chips, which should represent ideal samples. At the same time, the instrument was consistent in its readings. This leads us to question whether using the “subjective” human eye or the “objective” digital eye is preferable for data recording of color. We discuss how project goals and limitations should be considered when deciding which color-recording method to employ in field and laboratory settings, and we identify optimal procedures.
Temporal LiDAR Frame Prediction for Autonomous Driving

Anticipating the future in a dynamic scene is critical for many fields such as autonomous driving and robotics. In this paper we propose a class of novel neural network architectures to predict future LiDAR frames given previous ones. Since the ground truth in this application is simply the next frame in the sequence, we can train our models in an self-supervised fashion. Our proposed architectures are based on FlowNet3D and Dynamic Graph CNN. We use Chamfer Distance (CD) and Earth Mover's Distance (EMD) as loss functions and evaluation metrics. We train and evaluate our models using the newly released nuScenes dataset, and characterize their performance and complexity with several baselines. Compared to directly using FlowNet3D, our proposed architectures achieve CD and EMD nearly an order of magnitude lower. In addition, we show that our predictions generate reasonable scene flow approximations without using any labelled supervision.
Monocular Depth Estimation Based on Deep Learning:A Survey

Monocular depth estimation relied on RGB images is an important ill posed problem in ithe system of computer vision. Recently, people use the method of deep learning to discuss this problem Most of the existing monocular depth estimation algorithms relied on convolution neural network. Depth estimation based on 2D images has important applications in image segmentation, 3D object detection, robot navigation, object tracking and autonomous driving. This paper gives a brief overview of this problem, reviews, evaluates and discusses the monocular depth estimation algorithms relied on deep learning, and looks forward to the direction of further research in the face of some challenges.