Reviewerly/exHarmony huggingface dataset:
Application of $$\mathscr {T}$$-Direct Codes in Multiple-Rate Codes

A straightforward extension of linear codes with complementary dual is the class of $$\mathscr {T}$$ -Direct codes. A systematic construction of $$\mathscr {T}$$ -Direct codes along with the coding scheme, capable of handling variable-length q-ary user messages, is proposed. By inducing constituent codes, constructed using a self-dual basis of extension field $$\mathbb {F}_{q}$$ , construction of $$\mathscr {T}_\mathbb {K}$$ -Direct codes over $$\mathbb {F}_{q}$$ is defined. It is shown that the constituent codes of a $$\mathscr {T}_\mathbb {K}$$ -Direct code are equipped with the necessary properties required for handling the variable-rate feature in a multiple-rate code. In the proposed coding scheme, q-ary user messages of lengths 1, 2, $$\ldots $$ , $$\mathscr {T}_\mathbb {K}$$ can be encoded and decoded efficiently, providing variable information rates $$\mathscr {R} = \frac{k}{n^2}, \frac{k+1}{n^2}, \ldots , \frac{k\mathscr {T}_\mathbb {K}}{n^2}$$ . Furthermore, the encoding and decoding scheme for distinct as well as non-distinct constituent codes is illustrated with the help of an example.
Classification

Generally classification is about predicting a label whereas regression is usually used to predict a quantity. Classification models approximate a mapping function from inputs to produce a class or category. Logistic regression is used instead of linear regression for binary classification problems. It produces a value ranging from 0 to 1, which can be interpreted as a probability that an event occurred. Decision Trees, Random Forests, and Neural Networks can also be used for classification tasks, similar to that in regression. Support vector machines calculate a hyperplane that maximizes the distance between classes. Naive Bayes uses Bayes’ Theorem to predict classes under the assumption of independence. Similarly, Data processing techniques can further improve classification performance as well.
Visual classification of dumpsters with capsule networks

Abstract Garbage management is an essential task in the everyday life of a city. In many countries, dumpsters are owned and deployed by the public administration. An updated what-and-where list is in the core of the decision making process when it comes to remove or renew them. Moreover, it may give extra information to other analytics in a smart city context. In this paper, we present a capsule network-based architecture to automate the visual classification of dumpsters. We propose different network hyperparameter settings, such as reducing convolutional kernel size and increasing convolution layers. We also try several data augmentation strategies, as crop and flip image transformations. We succeed in reducing the number of network parameters by 85% with respect to the best previous method, thus decreasing the required training time and making the whole process suitable for low cost and embedded software architectures. In addition, the paper provides an extensive experimental analysis including an ablation study that illustrates the contribution of each component in the proposed method. Our proposal is compared with the state-of-the-art method, which is based on a Google Inception V3 architecture pretrained with Imagenet. Experimental results show that our proposal achieves a 95.35% accuracy, 2.35% over the previous best method.
Human Fall Detection Analysis with Image Recognition Using Convolutional Neural Network Approach

Human falling may cause injuries and sometimes may lead to deadly conditions. Therefore, in recent decade, the systems used for monitoring of human falling and non-falling are receiving attention among research community for its diversified features and social benefits. These systems solve the problem of falling and gets activated to avert the likely incident with an alarm message, and uses fall recognition classifiers. System helps to identify the human in the intended regions, and classifiers are trained using the information available in the images. The lack of massive scale datasets and human errors limits the generalization of models in terms of robustness and efficiency to invisible regions. In the proposed work, an automatic fall detection using deep learning is modeled using dataset of falling and non-falling images. The sensitive information available in the original images is kept secure and private to maintain the safety and protection by the presented work. The experiments were conducted using real-world fall datasets having both types of human images, i.e., falling and non-falling, and the results obtained clearly indicate system enhancement for falling and non-falling image recognition using convolutional neural network (CNN) algorithm and achieving higher accuracy and reduced loss with a trained dataset which finds the optimal performance from real-time environments.
Deep learning versus the human visual system for detecting motion blur in radiography

Purpose: The necessity of image retakes is initially determined on a preview monitor equipped with an operating system; therefore, some image blurring is only noticed later, on a high-resolution monitor. The purpose of this study is to investigate blur detection performance on radiographs via a deep learning approach compared with human observers. Approach: A total of 99 radiographs (blurry 57, nonblurry 42) were independently observed and rated by six observers using preview and diagnostic liquid crystal displays (LCDs). The deep convolution neural network (DCNN) was trained and tested using ninefold cross-validation. The average areas under the ROC curves (AUCs) were calculated for each observer with LCDs and by stand-alone DCNN for each test session and then statistically tested using a 95% confidence interval. Results: The average AUCs were 0.955 for stand-alone DCNN and 0.827 and 0.947 for human observers using preview and diagnostic LCDs, respectively. The DCNN revealed a high performance for image motion blur on digital radiographs (sensitivity 94.8%, specificity 96.8%, and accuracy 95.6%), along with the capability to detect a slight motion blur that was overlooked by human observers with a preview LCD. There were no cases of motion blur overlooked by the stand-alone DCNN, of which some were incorrectly recognized as nonblurry by human observers. Conclusions: The deep learning-based approach was capable of distinguishing slight motion blur that was unnoticeable on a preview LCD, and thus, is expected to aid the human visual system for detecting blurred images in the initial review of digital radiographs.
Research on the Relationship between Intelligent Analysis and Weight of Keywords in English Test Questions

With the rapid development of educational informationization, Internet of Things, and other technologies, English education has been paid special attention to, and all aspects such as educational model, learning behavior, teaching philosophy, and teaching evaluation have been greatly influenced by educational informationization. Based on the experience of practical education, this paper explores and studies the connotation and characteristics of English test questions and its influence and application on modern English test questions. This paper constructs a systematic method to extract keywords from English test questions. It perfects the fair and reasonable index of English keywords, establishes the weight system, discusses the relationship of keywords, and conducts academic research on vocabulary, word frequency and word position, emphatically adopts BayesNet algorithm to extract keywords, and realizes the evaluation system of English test keywords based on intelligent analysis and weight relationship. The results show that (1) selecting the calculation method and weight relationship suitable for the text system to carry out intelligent analysis, the weight ratio exceeds 65%; that is, the text keyword retrieval is successful. (2) The average accuracy (%), average recall (%) and average <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -measure (%) in weighted names are almost less than 70%. Only the BayesNet algorithm has 72.3% weight analysis in keyword extraction in reading comprehension. (3) KEA algorithm, PAT TERR algorithm, and BayesNet algorithm take 0-2.8 s, 0-2.6 s, and 0-2.1 s, respectively, and the BayesNet algorithm takes the shortest time. The calculation time of users is greatly saved. (4) According to the calculation results of CPT model, the sum of the weights of the three algorithms is equal to 1, and the BayesNet algorithm is dominant in extracting keywords with a weight analysis of 0.529 in verb translation.
Modelling and Simulation of Building-Integrated Photovoltaics (BIPV) Installations in Swinburne University

Governments and organizations around the globe are increasingly searching for ways to reduce pollution from their sectors, such as greenhouse emissions, with a significant focus on the use and implementation of sustainable RE generation. A large portion of renewable energy (RE) work has been devoted to Photo-Voltaic (PV) systems using solar energy to generate electricity to provide both electrical and thermal charges as the abundance, free and clean characteristics of this RE source cause no disturbance or carbon emissions to the environment. Building Integrated Photovoltaic (BIPV) systems have developed tremendously in Malaysia in recent years as an application of PV technology and have been shown to help buildings partially fulfil the load as a feasible RE generation technology. In this paper, the efficiency and feasibility of BIPV application in Swinburne University of Technology Sarawak is investigated. Weather details such as air temperature, relative humidity, daily solar radiation, and the earth temperature in the university will be collected and analyzed. Different BIPV solar power systems are designed and simulated technically through modelling and simulation on related software. The analysis revealed that Feed in BIPV system is consider as the most promising way to achieve the target of saving. The maximum yearly consumption of the building will be 320000 kWh. By considering the BIPV system is connected to the grid, there will be extra 39715 kWh of production being fed back to the grid each year which gives an earning of RM11915 each year. Besides, the investment ratio will be 33.9% and the payback period for the entire BIPV system will be 14 years.
FoodChain: A Food Delivery Platform Based on Blockchain for Keeping Data Privacy

Marketplace systems for delivery food retain valuable sensitive data besides charging high fees for each order. On the other hand, Blockchain has being adopted as a distributed database to reduce costs and remove intermediary actors. We have developed FoodChain that is a blockchain based system, decentralizes the food delivery solution and keeps data in public ledgers by ensuring the privacy of sensitive data. Thus, customers, restaurants and couriers can verify and track their sensitive data that are stored in the blockchain. The interactions that occur directly between the involved actors are tracked by smart contracts, which in turn, may contain rules and conditions about the order delivery agreement. Moreover, all sensitive data is encrypted before being sent to the blockchain and can be decrypted using asymmetric encryption. The proposed demo presents the following FoodChain features: smart contracts running on Fantom testnet blockchain, an API to connect with a public database and three web-based decentralized applications (DApp).
Crowd Characterization in Surveillance Videos Using Deep-Graph Convolutional Neural Network

Crowd behavior is a natural phenomenon that can provide valuable insight into the crowd characterization process. Modeling the visual appearance of a large crowd gathering can reveal meaningful information about its dynamics. Parametric modeling can be used to develop efficient and robust crowd monitoring systems. A crowd can be structured or unstructured based on the organization. In this article, crowd characterization has been mapped to a graph classification problem to classify movements based on order parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> ), active force components, and steadiness (Reynolds number). The graphs are constructed from the motion groups obtained using an active Langevin framework. These graphs are processed using a deep graph convolutional neural network for crowd characterization. For experimentation, we have prepared a dataset comprising of videos from popular publicly available datasets and our own recorded videos. The proposed framework has been compared with the latest deep learning-based frameworks in terms of accuracy and area under the curve (AUC). We have obtained a 4%–5% improvement in accuracy and AUC values over the existing frameworks. The insights obtained from the proposed framework can be used for better crowd monitoring and management.
LLQA - Lifelog Question Answering Dataset

Recollecting details from lifelog data involves a higher level of granularity and reasoning than a conventional lifelog retrieval task. Investigating the task of Question Answering (QA) in lifelog data could help in human memory recollection, as well as improve traditional lifelog retrieval systems. However, there has not yet been a standardised benchmark dataset for the lifelog-based QA. In order to provide a first dataset and baseline benchmark for QA on lifelog data, we present a novel dataset, LLQA, which is an augmented 85-day lifelog collection and includes over 15,000 multiple-choice questions. We also provide different baselines for the evaluation of future works. The results showed that lifelog QA is a challenging task that requires more exploration. The dataset is publicly available at https://github.com/allie-tran/LLQA .
Entertainment Advisor Using Sentiment Analysis

Sentiment or opinion mining analyses include the study of people's written opinions, feelings, attitudes, and emotions. It has been one of the most active areas of research in natural language processing and text mining in recent years. Due to its importance for business and society, research has also moved to management science and social sciences beyond computer science. This article presents an Entertainment Advisor system that uses Sentiment Analysis to calculate the Sentiment Polarity and Subjectivity of Movies or TV shows by reading its reviews. The proposed system is a recommender system, which recommends based on the sentiment polarity. The features provided in the proposed system would let users write their reviews. These reviews would be directly stored in a text file and accessible to all the users for anonymous view. The recommendation is made based on the polarity of all the reviews. The proposed recommender system also allows users to select a particular review and calculate its polarity for demonstration and better understanding.
Studying the Diagnostic Effectiveness of English Education Based on Rule Mining and Question Type Association Analysis

The greatest difficulty in implementing diagnostic assessment lies in the tracking, recording, real-time feedback, and correction of English learners’ learning process, and these difficulties are difficult to be solved when human beings (teachers or students) are the assessment subjects. To this end, this paper uses data mining to build a question type association model, machine learning to build an English education prediction model, and finally adds the existing knowledge point association model to the system to obtain a diagnostic evaluation model. Based on the diagnostic evaluation model, a question association rule-based algorithm is designed and implemented and validated in three aspects: grouping time, test recommendation, and performance improvement. Based on the requirements analysis, the authors designed and implemented a diagnostic assessment subsystem using the diagnostic assessment model and the related paper grouping algorithm and added it to the university English diagnostic practice system. The diagnostic assessment model for English education proposed in this paper can accurately evaluate the learning status of English learners, dynamically diagnose the learning obstacles of English learners, and effectively provide better practice guidance and test recommendations for English learners’ learning status and knowledge-based question type obstacles.
Classification From Positive and Biased Negative Data With Skewed Labeled Posterior Probability

The binary classification problem has a situation where only biased data are observed in one of the classes. In this letter, we propose a new method to approach the positive and biased negative (PbN) classification problem, which is a weakly supervised learning method to learn a binary classifier from positive data and negative data with biased observations. We incorporate a method to correct the negative influence due to a skewed confidence, which is represented by the posterior probability that the observed data are positive. This reduces the distortion of the posterior probability that the data are labeled, which is necessary for the empirical risk minimization of the PbN classification problem. We verified the effectiveness of the proposed method by synthetic and benchmark data experiments.
Python for Data Analysis

This chapter will introduce Ethics in algorithm development and common tools that are used for data analysis. Ethics is an important consideration in developing Artificial Intelligence algorithms. The outputs of computers are derived from the data that are provided as input and the algorithms developed by Artificial Intelligence developers who must be held accountable in their analysis. Tools such as Numpy, Pandas, and Matplotlib are covered to aid in the data analysis process. Numpy is a powerful set of tools to work with complete data lists efficiently. Pandas is a package designed to make working with “relational” or “labeled” data both easy and intuitive. Finally, Matplotlib is a powerful Python plotting library used for data visualization.
Definition and types of age of a professional

The article is devoted to the problem of the formation and implementation of a professional at different stages of life, professional and work path, which is relevant in practical and theoretical terms. The purpose of the article is to define and substantiate the tiered structure of individual professionalization. The formal basis for identifying the levels of professionalization of a person was various types of age: the age of the life path, the age of the labor path and the age of the professional path. The substantive basis is the specific patterns and neoplasms characteristic of each age of the professional. The article analyzes the specificity of a person's professional path, which is considered as a process and result of the development of the subject of a specific professional activity. The stages of the professional path and the tasks solved by a person at this level have been identified. It has been established that the professional path is a polycyclic education and is implemented in accordance with the principles of specialization and efficiency. The article defines the specifics of the work path, as a process and result of the development of the subject of labor. It was revealed that the work way is a non-professional education. It is realized through specific types of professional activity, but is not limited to them. The labor path is a monocyclic education, but it consists of cycles of the professional path corresponding to the types of professions and specialties that a person owns. The article analyzes the psychological content of a person's life path, the components of which are the labor and professional path. It has been determined that the path of life acts as a psychological context for solving problems of the labor and professional path. It is the basis for the formation of an attitude towards professionalization and influences the definition of its life meaning. It is shown that the successful implementation of the labor and professional path has a positive effect on the content of the path of life, expands the repertoire of the types of his life, increases his vitality and resilience.
Accelerating Distributed Machine Learning in Disaggregated Architectures with Flexible Optically Interconnected Computing Resources

We introduce an optically interconnected disaggregated architecture for GPU resources and demonstrate a 3× increase in GPU utilization and up to 73.2% acceleration of application runtime for distributed machine learning workloads.
Secure and Evaluable Clustering Based on a Multifunctional and Privacy-Preserving Outsourcing Computation Toolkit

Although tremendous revolution has been made in the emerging cloud computing technologies over digital devices, privacy gradually becomes a big concern in outsourcing computation. Homomorphic encryption has been proposed to facilitate the preservation of data privacy while computational tasks being executed on ciphertext. However, many existing studies only support limited homomorphic calculation functions which barely satisfy complex computing tasks such as machine learning with massive computing resources and rich types of function. To address this problem, a novel multifunctional and privacy-preserving outsourcing computation toolkit is proposed in this paper, which supports several homomorphic computing protocols including division and power on ciphertext of integers and floating point numbers. Specifically, we first implement the homomorphic mutual conversion protocol between integer and floating point ciphertext to balance the efficiency and feasibility, considering the high-precision ciphertext operation on floating point numbers costs 100x computational overhead than that on integers. Second, we implement a homomorphic K-means algorithm based on our proposed toolkit for clustering and design the homomorphic silhouette coefficient as the evaluation index, thereby providing an informative cluster assessment for local users with limited resources. Then, we simulate the protocols of our proposed toolkit to explore the parameter sensitivity in terms of computational efficiency. Last, we report security analysis to prove the security of our toolkit without privacy leakage to unauthorized parties. Comprehensive experiments further demonstrate the efficiency and utility of our toolkit.
Type-II Fuzzy-Based Guava Fruit Image Enhancement

AbstractType-II fuzzy is used in the suggested work to improve the visibility of the guava fruit image. Type-II fuzzy is quite good in dealing with uncertainty. The guava image is divided into dark and bright parts using fuzzy partitioning with S-shaped and Z-shaped membership functions. Fuzzy sure entropy utilized to select the optimal threshold values for visibility enhancement. Proposed scheme provides improved version of guava images. Experimental results demonstrate that proposed method in terms of visual and quantitative analysis that image enhancement results has less number of saturated pixels percentage, visible edges and color gradients has better performance.KeywordsGuava fruitEnhancementType-II fuzzyVisibility