Reviewerly/exHarmony huggingface dataset:
A Study on the Trend of Human Performance Related Events at Nuclear Power Plant in 2019

Events at nuclear power plants are very rare. It is not easy to find lessons with the existing methods of carrying out improvement activities based on lessons learned from the event. In addition, high reliability systems are quite vulnerable to human error. So human performance require periodic management. For the managing human performance, most nuclear power plants identify the weak points and seek to improve human performance. Also, they evaluate and give feedback on human performance levels through periodic monitoring. In this paper, the major causes and upper control limit were selected for trend analysis in human performance related events at nuclear power plants in 2019. In addition, additional analysis was conducted in subjects exceeding the upper control limit. So several insights were discovered and corrective actions will be conducted to improve human performance in nuclear power plant.
KAJIAN ANALISIS DISKRIMINAN MENGUKUR LOYALITAS PELANGGAN TOKO BUKU SUCI KUPANG

Customer Loyalty is an expectation of all business. Many efforts will be done to achieve thatobjectives. Serious problem would be faced is how to investigate the impact of efforts towardimproving Customer Loyalty. Using of Discriminant Analysis is one of the tool which can be solvedthat problem. Without the set of analysis technology, it is impossible to obtain quality decisionrapidly, efficiently and effectively in business.Keywords : Loyalty, Customer Dimensionality, Discriminant Analysis
Convolutional Neural Networks

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Pathway and Future of IoE in Smart Cities: Challenges of Big Data and Energy Sustainability

This chapter deals with the IoT node-level and network energy sustainability and IoE stability aspects, wherein the benefits from node-level and network intelligence are presented. Some field IoT sensing applications, such as in smart cities, pollution monitoring, and agricultural automation, are characterized by high energy consumption, node lifetime criticality, and energy sustainability issues. More discussion on smart meter data compressibility and novel approaches to dealing with big data concerns caused by millions of smart meter installation as a part of smart city monitoring exercise are presented in the chapter. It has been demonstrated how the challenges of big data and energy sustainability are addressed via application-specific unique approaches. The research state-of-the-art and open issues on mitigating big data challenges and energy sustainability have been highlighted. Further, the role of smart grid on uninterrupted IoE operation, the benefits of distributed energy generation through IoE, and the newer challenges of power grid stability/controllability have also been discussed.
An Enhanced Image Encryption Approach using Four Dimension Hyperchaotic Chen map

Digital images needs to be protected and transformed image should be highly unpredictable to the intruder. The transformed image should be immune to all possible cyber attacks and it is very important to encrypt and decrypt with less computational time. To transmit the data in a secured manner with less computational time, an image encryption scheme using four dimension (4D) hyper chaotic chen system is proposed. To enhance security, the diffusion and confusion method is used in the proposed algorithm. In our proposed algorithm, to break the strong correlation with neighboring pixel spiral shuffling and masking pixel values to avoid image leakage achieved using chaotic map. The result analysis such as image histogram, information entropy, correlation, NPCR and UACI analysis assured better performance.
Can the Transformer Learn Nested Recursion with Symbol Masking?

We investigate if, given a simple symbol masking strategy, self-attention models are capable of learning nested structures and generalise over their depth.We do so in the simplest setting possible, namely languages consisting of nested parentheses of several kinds.We use encoder-only models, which we train to predict randomly masked symbols, in a BERTlike fashion.We find that the accuracy is well above random baseline, with accuracy consistently above 50% both when increasing nesting depth and distances between training and testing.However, we find that the predictions made correspond to a simple parenthesis counting strategy, rather than a push-down automaton.This suggests that self-attention models are not suitable for tasks which require generalisation to more complex instances of recursive structures than those found in the training set.
Shape Classification Via Contour Matching Using the Perpendicular Distance Functions

We developed a novel shape descriptor for object recognition, matching, registration and analysis of two-dimensional (2-D) binary shape silhouettes. In this method, we compute the perpendicular distance from each point on the object contour to the line passing through the fixed point. The fixed point is the centre of gravity of a shape. As a geometrically invariant feature, we measure the perpendicular distance function for each line that satisfies the centre of gravity of an object and one of the points on the shape contour. In the matching stage, we used principal component analysis concerning the moments of the perpendicular distance function. This method gives an excellent discriminative power, which is demonstrated by excellent retrieval performance that has been experimented on several shape benchmarks, including Kimia silhouettes, MPEG7 data set.
Applications Of Neuromarketing Sciences On Digital Marketing Models - A Theoretical Frame Work

There is paradigm shift in the way consumers make their purchases as social distancing defines the new norm. There is an over reliance on the digital medium and both the consumers and the marketers have started to embrace the same. The customers have become more active in social media networking and the change can be observed not only in their lifestyles but also in their attitudes and perceptions. So it becomes really imperative that this new channel that the marketers are going to rely on has to tailor itself according to the vicissitudes. The digital marketing has brought in transformations in the purchase patterns. Purchases are no longer a group function and neither is it made consciously .This individual exercise is controlled by the brain. So an individual’s neurological elements and his brain becomes the deciders in a purchase activity. There is a call for exploring methods that can entice one’s brain and thereby help a brand to perpetually map itself high up in this pivotal organ. This paper highlight’s the unexplored relationship and the dependency of digital marketing models on neuromarketing sciences. Neuromarketing is an emerging field in marketing. This nascent marketing strategy connects the subconscious and the emotional facet of a consumer to a brand by the application of various neurological multipliers. Of late, neuromarketing has gained a lot of credibility that most of the marketers have started to wake up to the promising calls of this technique and to harness its potentiality in the digital marketing medium. This paper attempts to analyze the applications of neurosciences on the customer engagement models on digital media.
Algorithm selection for software verification based on adversarial LSTM

As a prevalent technique for checking the correctness of software, software verification has achieved a significant progress in the past decades, reaching a point where a large number of verification algorithms and tools are available and sophisticated enough to handle the large-scale industrial software. However, it remains a difficult task to select a suitable verification algorithm or tool for the software at hand, given the fact that the underlying algorithms are diverse and the performance tradeoffs are hard to accurately characterize. In this paper, we study the algorithm selection problem for software verification, and propose a novel algorithm selection model based on the Long Short Term Memory network (LSTM). Our solution employs word2vec to obtain the embedding representation of the code, avoiding constructing the software features manually. We also propose a novel approach to construct the adversarial code examples in order to solve the sparsity and data imbalance problem. The experimental evaluations on the latest available dataset show that our solution improves the prediction accuracy by about 7% compared with the state-of-the-art selection algorithm.
Interaction Estimation in Egocentric Videos via Simultaneous Hand-Object Recognition

We propose an action estimation pipeline based on the simultaneous recognition of the hands and the objects in the scene from an egocentric perspective. From the latest approaches, we have come to the conclusion that the hands are a key element from this point of view. An action consists of the interactions of the hands with the different objects in the scene therefore, the 2D positions of hands and objects are used to compute the object which is more likely to be used. The architecture used for achieving this goal is YOLO, as its prediction speed allows us to predict the actions fluently with good accuracy on the detected objects and hands. After reviewing the available datasets and generators for hand and object detection, different experiments have been conducted. The best results determined by PascalVOC metric have been used in the proposed pipeline.
A Layer-Wise Extreme Network Compression for Super Resolution

Deep neural networks (DNNs) for single image super-resolution (SISR) tend to have large model size and high computation complexity to achieve promising restoration performance. Unlike image classification, model compression for SISR has rarely been studied. In this paper, we found out that DNNs for image classification and SISR have often different characteristics in terms of layer importance. That is, contrary to the DNNs for image classification, the performance of SISR networks hardly decrease even if a few layers are eliminated during inference. This is due to the fact that they typically consist of a bunch of hierarchical and complex residual connections. Based on that key observation, we propose a layer-wise extreme network compression method for SISR. The proposed method consists of: i) reinforcement learning based joint framework for layer-wise quantization and pruning both of which are effectively incorporated into the search space; ii) a progressive preserve ratio scheduling that reflects importance in each layer more effectively, yielding much higher compression efficiency. Our comprehensive experiments show that the proposed method can effectively be applied to the existing SISR networks, thus extremely reducing the model size up to 97% (i.e., 1 bit per weight on average) with marginal performance degradation compared to the corresponding full-precision models.
Generation of Random Binary Sequence Using Adaptive Row–Column Approach and Synthetic Color Image

The security of a communication model is defined by the strength of the encryption and key generation algorithm. To ensure security, in block cipher techniques, a complex computation process is used for encryption. But, in stream cipher techniques, complex key generation techniques are used. This paper proposes a novel and complex key generation algorithm for stream cipher techniques that generate 15,72,864-bit key from a synthetic color image using a pattern-based bit extraction technique. A segmented form of a generated key can be used as a dynamic key in block cipher techniques. The proposed algorithm randomly uses eight patterns to extract bits from a synthetic color image to generate the key. The generated key’s randomness is tested using the NIST statistical analysis tool and compared with the keys generated from existing techniques. The key length and keyspace are compared with existing methods.
Secure Efficient Federated KNN for Recommendation Systems

K-nearest neighbors (KNN) has been successfully used for recommendation, but querying neighbors of high quality is nearly impossible when the feature space is small or has limited training data. However, due to privacy requirements and government policies, directly transferring data from one data owner to another is not workable. Therefore, we propose a novel KNN approach, secured federated KNN (SF-KNN), that takes privacy requirements into consideration and builds a federated model to gain global neighbors with joint parties, in order to improve the model performance. Specifically, it empowers the parties to train high-quality models with little data. More importantly, it makes cross-domain training possible. We implement SF-KNN on Euclidean and cosine metrics using user-based and item-based methods. In our experiment, we evaluate the proposed SF-KNN on three data sources, MovieLens, Netflix, and Amazon, and several diverse domains, movies, books, clothes, jewellery and food, by comparing it against various baselines. The experiment results indicate that SF-KNN is able to learn more precise neighbors than a local KNN trained by parties individually. In general, it outperforms the local KNN on all of the datasets, reaching 15% average accuracy gain on the Euclidean metric and 8% of it on the cosine metric when simulating 10 parties across all data sources.
What to Select: Pursuing Consistent Motion Segmentation from Multiple Geometric Models

Motion segmentation aims at separating motions of different moving objects in a video sequence. Facing the complicated real-world scenes, recent studies reveal that combining multiple geometric models would be a more effective way than just employing a single one. This motivates a new wave of model-fusion based motion segmentation methods. However, the vast majority of models of this kind merely seek consensus in spectral embeddings. We argue that a simple consensus might be insufficient to filter out the harmful information which is either unreliable or semantically unrelated to the segmentation task. Therefore, how to automatically select valuable patterns across multiple models should be regarded as a key challenge here. In this paper, we present a novel geometric-model-fusion framework for motion segmentation, which targets at constructing a consistent affinity matrix across all the geometric models. Specifically, it incorporates the structural information shared by affinity matrices to select those semantically consistent entries. Meanwhile, a multiplicative decomposition scheme is adopted to ensure structural consistency among multiple affinities. To solve this problem, an alternative optimization scheme is proposed, together with a proof of its global convergence. Experiments on four real-world benchmarks show the superiority of the proposed method.
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering

Natural language processing often faces the problem of data diversity such as different domains, themes, styles and so on.Therefore, a single language model (LM) is insufficient to learn all knowledge from diverse samples.To solve this problem, we firstly propose an autoencoding topic model with mixture prior (mATM) to perform clustering for the data, where the clusters defined in semantic space describe the data diversity.Having obtained the clustering assignment for each sample, we develop the ensemble LM (En-sLM) with the technique of weight modulation.Specifically, EnsLM contains a backbone which is adjusted by a few modulated weights to fit for different sample clusters.As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.EnsLM can be trained jointly with mATM with flexible LM backbone.We evaluate the effectiveness of both mATM and EnsLM on different language understanding and generative tasks.
Analysis of the Female Fishermen's Labor and Perception

The proportion and role of female fishermen in the fisheries industry is being emphasized. However, the labor situation of female fishermen was not clearly understood. Therefore, it is difficult to establish a policy for female fishermen. Also, the reality of the policy has declined. In this study, the actual condition of female fishermen was analyzed. Housework, fishing work, and perceptions of female fishermen were summarized. As a result of the survey, female fishermen do not appreciate their status. The decision-making power in fishing activities tends to be concentrated on the husband. Double labor, such as domestic chores and fishing labor, is a major factor hindering the activities of female fishermen. Policy attention is required to improve the status of female fishermen.
Recurrent network-based hybrid acoustic model for Automatic Speech Recognition

Speech is a key means of communication. Nowadays, speech is becoming a more common, if not standard, interface to technology. This will be seen within the trend of technology changes over the years. Increasingly, voice is employed to regulate programs, appliances and private devices within homes, cars, workplaces, and public spaces through smartphones and residential assistant devices using Amazon's Alexa, Google's Assistant and Apple's Siri, and other proliferating technologies. This is often achievable with the help of Automatic Speech Recognition (ASR). Automatic Speech Recognition is a process that accurately translates spoken utterances into text. These technologies enable machines to reply correctly and reliably to human voices and supply useful and valuable services. As communicating with computer is quicker using voice instead of using keyboard, so people will prefer such system. Communication among the person is dominated by speech, therefore it’s natural for people to expect voice interfaces with computer. This can be accomplished by developing speech to text which allows computer to translate voice request and dictation into text. The three models in traditional ASR system are acoustic model, language model and lexicon model. The challenges involved in Automatic Speech Recognition are different styles of speech, environment which include background noise and also accent of speaker. To mitigate these challenges, deep learning models are utilized. The main idea is to analyses features of input audio signals such as spectrogram and MFCC and to develop cutting edge deep learning models. The proposed end-to-end model achieved an error rate of 0.60 on Librispeech dataset.
Classical Optimization Algorithms

This chapter briefly introduces the most widely used traditional optimization algorithms, including the gradient based method and its variants, basic methods for constrained optimization, pattern search for non-differentiable or black-box optimization problems, and deterministic global optimization methods.
Pola komunikasi organisasi pustakawan-guru dalam pemberian layanan prima perpustakaan

Introduction. The purpose of this study is to examine the organizational communication patterns between librarians and teachers in providing quality library services. &#x0D; Data Collection Method. This is a qualitative research with a case study approach with librarians and teachers as samples. Data collection techniques were interviews, observations, and literature study.&#x0D; Data Analysis. Data analysis techniques were conducted by reducing data, presenting data, and then making conclusions or data verification. &#x0D; Results and Discussion. The results of this study indicate that a well-established organizational communication pattern between librarians and teachers consists of task coordination, problem-solving, information sharing, conflict resolution, and building togetherness or relationships. All of them supported excellent services. &#x0D; Conclusion. Librarian who implement a good horizontal organizational communication model can provide high-quality services to teachers who need information, which is beneficial to the library and the school as an organization. The recommendation of this study is that schools should be able to treat librarians as key people in organizing ideas and information resources, rather than as only as academic supporters.
Appraisal of Electronic Evidence in Nigerian Courts

The impact of technology and technological advancements on our world today cannot be wished away. To a large extent, events all over the world are shaped by the radical advancement of technology. The digital revolution has dramatically altered the methods of creating and storing documents today. Given that technology has very much become an integral feature of our world and country today, it is naturally expected that the laws should be revolutionary in coming to terms with and embracing the dynamics of technology. Nigeria in her laws have evolved with the ever-changing dynamics of technology is the permission of the admissibility of evidence generated from electronic sources. The Evidence Act, 2011 provides for the admissibility of documents generated from electronic sources such as computers and also enumerates the conditions that must be fulfilled in order for electronic evidence to be admitted. This papers gives an appraisal of the attitude of courts in Nigeria towards electronically generated evidence.
Generalized Adversarially Learned Inference.

Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions of image and latent vector pairs. We generalize these approaches to incorporate multiple layers of feedback on reconstructions, self-supervision, and other forms of supervision based on prior or learned knowledge about the desired solutions. We achieve this by modifying the discriminator's objective to correctly identify more than two joint distributions of tuples of an arbitrary number of random variables consisting of images, latent vectors, and other variables generated through auxiliary tasks, such as reconstruction and inpainting or as outputs of suitable pre-trained models. We design a non-saturating maximization objective for the generator-encoder pair and prove that the resulting adversarial game corresponds to a global optimum that simultaneously matches all the distributions. Within our proposed framework, we introduce a novel set of techniques for providing self-supervised feedback to the model based on properties, such as patch-level correspondence and cycle consistency of reconstructions. Through comprehensive experiments, we demonstrate the efficacy, scalability, and flexibility of the proposed approach for a variety of tasks. The appendix of the paper can be found at the following link: https://drive.google.com/file/d/1i99e682CqYWMEDXlnqkqrctGLVA9viiz/view?usp=sharing