Reviewerly/exHarmony huggingface dataset:
Acceleration Method for Learning Fine-Layered Optical Neural Networks

An optical neural network (ONN) is a promising system due to its high-speed and low-power operation. Its linear unit performs a multiplication of an input vector and a weight matrix in optical analog circuits. Among them, a circuit with a multiple-layered structure of programmable Mach-Zehnder interferometers (MZIs) can realize a specific class of unitary matrices with a limited number of MZIs as its weight matrix. The circuit is effective for balancing the number of programmable MZIs and ONN performance. However, it takes a lot of time to learn MZI parameters of the circuit with a conventional automatic differentiation (AD), which machine learning platforms are equipped with. To solve the time-consuming problem, we propose an acceleration method for learning MZI parameters. We create customized complex-valued derivatives for an MZI, exploiting Wirtinger derivatives and a chain rule. They are incorporated into our newly developed function module implemented in C++ to collectively calculate their values in a multi-layered structure. Our method is simple, fast, and versatile as well as compatible with the conventional AD. We demonstrate that our method works 20 times faster than the conventional AD when a pixel-by-pixel MNIST task is performed in a complex-valued recurrent neural network with an MZI-based hidden unit.
On the Connectivity and Giant Component Size of Random K-out Graphs Under Randomly Deleted Nodes

Random K-out graphs, denoted <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{H}(n;K)$</tex> , are generated by each of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> nodes drawing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> out-edges towards <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> distinct nodes selected uniformly at random, and then ignoring the orientation of the arcs. Recently, random K-out graphs have been used in applications as diverse as random (pairwise) key predistribution in ad-hoc networks, anonymous message routing in crypto-currency networks, and differentially-private federated averaging. In many applications, connectivity of the random K-out graph when some of its nodes are dishonest, have failed, or have been captured is of practical interest. We provide a comprehensive set of results on the connectivity and giant component size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{H}(n;K_{n},\gamma_{n})$</tex> , i.e., random K-out graph when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma_{n}$</tex> of its nodes, selected uniformly at random, are deleted. First, we derive conditions for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{n}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> that ensure, with high probability (whp), the connectivity of the remaining graph when the number of deleted nodes is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma_{n}=\Omega(n)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma_{n}=o(n)$</tex> , respectively. Next, we derive conditions for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{H}(n;K_{n}, \gamma_{n})$</tex> to have a giant component, i.e., a connected subgraph with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega(n)$</tex> nodes, whp. This is also done for different scalings of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma_{n}$</tex> and upper bounds are provided for the number of nodes outside the giant component. Simulation results are presented to validate the usefulness of the results in the finite node regime.
Human Character-oriented Animated GIF Generation Framework

Click-through rate (CTR) is a critical metric to boost the popularity of newly published videos on streaming platforms. Humans and human-like characters play a significant role in GIF selection and improving the CTR of the video. This paper proposes a new lightweight method to generate human character-oriented animated GIFs using the end-user device’s computational capabilities. Instead of analyzing full video, the proposed method analyzes the lightweight thumbnail containers to decrease computational complexity in the GIF generation process. Moreover, it uses the segment to generate the GIF and reduced valuable network bandwidth and storage demands in the user end. A feed-forward 2D deep neural network trained on the CelebA dataset is designed to detect humans or humanlike characters and their gender. Experimental evaluations and results performed in 10 full videos showed that the proposed method is 2.34 times more computationally efficient than the SoA approach. The proposed method is designed to support end-user devices with different computational capabilities.
Metadata based need-to-know view in large-scale video surveillance systems

Large-scale video surveillance systems are increasingly seen as the answer to problems concerning public safety, law enforcement, and situational awareness in public places. However, the unauthorized use of personal information derived from video data can be harmful. To preserve privacy, it is important to understand what type of personal information is contained in video surveillance data, and how much of that information is essential for an observer to achieve her authorized purpose. The purpose of the observer is described in terms similar to the information extracted by video surveillance systems, so they can be compared. This helps identify what type of information is better suited to control the flow of information to multiple observers, without compromising the privacy of the individuals. This paper presents a privacy-aware and need-to-know access control framework built on fine-grained data properties, extracted from surveillance data, which must conform to the explicitly defined purpose of the observers.
Action Selection and Execution in Everyday Activities: A Cognitive Robotics and Situation Model Perspective

We examine the mechanisms required to handle everyday activities from the standpoint of cognitive robotics, distinguishing activities on the basis of complexity and transparency. Task complexity (simple or complex) reflects the intrinsic nature of a task, while task transparency (easy or difficult) reflects an agent's ability to identify a solution strategy in a given task. We show how the CRAM cognitive architecture allows a robot to carry out simple and complex activities such as laying a table for a meal and loading a dishwasher afterward. It achieves this by using generalized action plans that exploit reasoning with modular, composable knowledge chunks representing general knowledge to transform underdetermined everyday action requests into motion plans that successfully accomplish the required task. Noting that CRAM does not yet have the ability to deal with difficult activities, we leverage insights from the situation model perspective on the cognitive mechanisms underlying flexible context-sensitive behavior with a view to extending CRAM to overcome this deficit.
Partial dependence through stratification

Partial dependence curves (FPD) are commonly used to explain feature importance once a supervised learning model has been fitted to data. However, it is common for the same partial dependence algorithm to give meaningfully different curves for different supervised models, even when the algorithm is applied to the same data. As a result, it is difficult to distinguish between model artifacts and true relationships in the data. In this paper, we contribute metods for computing partial dependence curves, for both numerical (StratPD) and categorical explanatory variables (CatStratPD), that work directly from training data rather than the predictions of a fitted model. Our methods provide a direct estimate of partial dependence, and rely on approximating the partial derivative of an unknown regression function. We investigate settings where contemporary partial dependence methods – including FPD, Accumulated Local Effects (ALE), and SHapley Additive exPlanations (SHAP) methods – give biased results. We demonstrate that our approach works correctly on synthetic data and plausibly on real data sets. This work motivates a new line of inquiry into nonparametric partial dependence that provides robust information about the variables considered in a supervised learning task.
Evaluation of Convolutional Neural Network Architecture for Feasibility Analysis on Fetal Abdomen and Brain Images

This research aims to evaluate the possibilities of fetus ultrasound image classification using machine learning algorithms as normal or abnormal. Most of the earlier research works have produced a high percentage of false-negative classification results—recent research work aimed to reduce the rate of false-negative diagnoses. Also, the number of sonologists for analyzing prenatal ultrasound worldwide is very less and solved by developing an efficient algorithm, which reduces the percentage of false negatives in the diagnosis output. Several earlier research works focused on analyzing fetal abdominal image or fetal head images, making the medical industry use two different diagnostic modules separately. This work aims to design and implement a convolution frame-work named as two Convolution Neural Network (tCNN) model for diagnosing any fetal images. The proposed tCNN model diagnoses the fetal abdominal and fetal brain images and classify them as normal or abnormal. CNN 1 of tCNN performs segmentation and classification based on the acceptance of abdomen circumference and stomach bubble, umbilical vein, and amniotic fluid measurements. CNN 2 shows based on head circumference and head and abdominal circumference, femur, crown-rump, and humerus lengths measured.With clinical validation, an extensive experiment carried out and the results compared with the experts in terms of segmentation accuracy and the obstetric measurements. This paper provides a foundation for future multi-classification research works on diagnosing fetal intracranial abnormalities and differential diagnosis using machine learning algorithms.
Role of Blockchain in Agriculture and Food Sector: A Summary

Agriculture is the backbone of major nations around the world and also a very important sector for the survival of mankind. For a country like India, it contributes immensely and plays a significant role in the overall societal and economical foundation and steadiness. But in spite of all of this, one less focused industry that blockchain has the prospective to transform entirely is agriculture. Even though agriculture is not jargon and attractive as many other sectors such as finance or health care, this very essential and a basic need industry for mankind with lots of issues to be resolved. Blockchain is an innovative and disruptive technology which has a disseminated ledger over a peer-to-peer network for transaction. It provides an insight into inventive approach for information to save, debug, and inculcate transactions and functions and establishing confidence in an open environment in academia and industry by providing renowned and latest technical solutions to traditional problems which are based on this new technology; the security and privacy of blockchains are the center of attraction when deploying blockchain in different applications. The cryptographic aspect is able to provide reliable and secure decentralized solutions. Blockchain has an enormous assortment of applications ahead of cryptocurrencies. The technology is set to drastically renovate a number of industrial sectors like healthcare, financing, and banking. This chapter presents a comprehensive overview and glimpses of the blockchain in agriculture and food sector briefing its applications, case study, and challenges.
Traffic Classification Using Machine Learning

The dynamic growth of the worldwide network resulted in ever-increasing interconnections at all levels of the Internet. Network congestion and packet loss are one of the main problems faced due to internet traffic. Previously many methods and tools were used to overcome this problem. Machine learning is an upcoming field which has wide range of scope which is yet to be explored. Using machine learning outstanding results have been observed. In this paper classification of malicious and non-malicious packets and future probabilities using machine learning are don on the basis of naïve Bayesian and svm classifier.
Research on Sentiment Analysis and Entity Recognition of COVID-19 Based on Multi-task Sentiment Analysis Model in Artificial Intelligence

Text sentiment analysis is an important branch of artificial intelligence. After the outbreak of COVID-19, a lot of highly emotional texts were flooded on the Internet. In the task of entity-level sentiment analysis for major public health events, there is often a lack of large-scale labeled corpus and a mature model that can effectively deal with medical and health texts. A multi-task sentiment analysis model based on BERT was proposed, which combined sentiment analysis and entity recognition. This model is used to extract sentiment information of Weibo comments related to COVID-19 and judge whether the entities in the text are targeted by emotions. The composition layer was constructed by combining the emotional information and entity information, and the loss function was designed to integrate the multi-task into the same model. More than 5000 Weibo text data about COVID-19 were preprocessed and annotated to form an entity-level corpus. Finally, several common methods were used to compare with the proposed method. Several comparison indexes were used to evaluate the model. The experimental results showed that the proposed model had better overall performance than other models.
Cross-Lingual Text Classification of Transliterated Hindi and Malayalam

Transliteration is very common on social media, but transliterated text is not adequately handled by modern neural models for various NLP tasks. In this work, we combine data augmentation approaches with a Teacher-Student training scheme to address this issue in a cross-lingual transfer setting for fine-tuning state-of-the-art pre-trained multilingual language models such as mBERT and XLM-R. We evaluate our method on transliterated Hindi and Malayalam, also introducing new datasets for benchmarking on real-world scenarios: one on sentiment classification in transliterated Malayalam, and another on crisis tweet classification in transliterated Hindi and Malayalam (related to the 2013 North India and 2018 Kerala floods). Our method yielded an average improvement of +5.6% on mBERT and +4.7% on XLM-R in F1 scores over their strong baselines. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
SYN2020: A New Corpus of Czech with an Innovated Annotation

The paper introduces the SYN2020 corpus, a newly released representative corpus of written Czech following the tradition of the Czech National Corpus SYN series. The design of SYN2020 incorporates several substantial new features in the area of segmentation, lemmatization and morphological tagging, such as a new treatment of lemma variants, a new system for identifying morphological categories of verbs or a new treatment of multiword tokens. The annotation process, including data and tools used, is described, and the tools and accuracy of the annotation are discussed as well.
A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data

The Industry 4.0 revolution is impacting manufacturing companies, which need to adopt more data intelligence processes in order to compete in the markets they operate. In particular, quality control is a key manufacturing process that has been addressed by Machine Learning (ML), aiming to improve productivity (e.g., reduce costs). However, modern industries produce a tiny portion of defective products, which results in extremely unbalanced datasets. In this paper, we analyze recent big data collected from a major automotive assembly manufacturer and related with the quality of eight products. The eight datasets include millions of records but only a tiny percentage of failures (less than 0.07%). To handle such datasets, we perform a two-stage ML comparison study. Firstly, we consider two products and explore four ML algorithms, Random Forest (RF), two Automated ML (AutoML) methods and a deep Autoencoder (AE), and three balancing training strategies, namely None, Synthetic Minority Oversampling Technique (SMOTE) and Gaussian Copula (GC). When considering both classification performance and computational effort, interesting results were obtained by RF. Then, the selected RF was further explored by considering all eight datasets and five balancing methods: None, SMOTE, GC, Random Undersampling (RU) and Tomek Links (TL). Overall, competitive results were achieved by the combination of GC with RF.
Improved Graph Methods for Table Layout Understanding

Recently, there have been significant advances in document layout analysis and, particularly, in the recognition and understanding of tables and other structured documents in handwritten historical texts. In this work, a series of improvements over current techniques based on graph neural networks are proposed, which considerably improve state-of-the-art results. In addition, a two-pass approach is also proposed where two graph neural networks are sequentially used to provide further substantial improvements of more than 12 F-measure points in some tasks. The code developed for this work will be published to facilitate the reproduction of the results and possible improvements.
A Method of Product Selection Based on Online Reviews

With the rapid development of information technology and market economy, global e-commerce platform develops rapidly. Recently, online reviews are widely available on e-commerce platforms to express customers’ experience of products. When ranking alternative products based on online reviews, how to make full use of the information in online reviews to represent the sentiment analysis results of online reviews is an important prerequisite for decision analysis. To this end, we propose a method for measuring the time utility and support utility of online reviews. Then a method for representing the sentiment analysis results of online reviews in the form of linguistic distribution is proposed. In addition, in view of the attributes and their weights being unknown, we propose a method for extracting product attributes from online reviews by using the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm; and the objective weights of attributes are determined through the Criteria Importance through Intercriteria Correlation (CRITIC) method. Additionally, in order to highlight the differences between the alternatives, the roulette wheel selection algorithm is first used to randomly select product attributes. Then the alternative products can be ranked by the extended Multi-Attributive Border Approximation area Comparison (MABAC) method with mixed information. Finally, we illustrate the applicability of the proposed method through a case study of selecting a 5G mobile phone and simulation experiment.
RTNet: An End-to-End Method for Handwritten Text Image Translation

Text image recognition and translation have a wide range of applications. It is straightforward to work out a two-stage approach: first perform the text recognition, then translate the text to target language. The handwritten text recognition model and the machine translation model are trained separately. Any transcription error may degrade the translation quality. This paper proposes an end-to-end leaning architecture that directly translates English handwritten text in images into Chinese. The handwriting recognition task and translation task are combined in a unified deep learning model. Firstly we conduct a visual encoding, next bridge the semantic gaps using a feature transformer and finally present a textual decoder to generate the target sentence. To train the model effectively, we use transfer learning to improve the generalization of the model under low-resource conditions. The experiments are carried out to compare our method to the traditional two-stage one. The results indicate that the performance of end-to-end model greatly improved as the amount of training data increases. Furthermore, when larger amount of training data is available, the end-to-end model is more advantageous.
NEAT: A Resilient Deep Representational Learning for Fault Detection Using Acoustic Signals in IIoT Environment

Fault diagnostics involving the Internet-of-Things (IoT) sensors and edge devices is a challenging task due to their limited energy and computational capabilities. Another challenge concerning IoT sensors or devices is the incursion of noise when used in an industrial environment. The noisy samples affect the decision support system that could lead to financial and operational losses. This article proposes a noisy encoder using artificial intelligence of things (NEAT) architecture for fault diagnosis in IoT edge devices. NEAT combines autoencoders and Inception module to co-train the clean and noisy samples for solving the said problem. Experimental results on benchmark data sets reveal that the NEAT architecture is noise resilient in comparison to the existing works. Furthermore, we also show that the NEAT architecture has lightweight characteristics as it yields a lower number of parameters, weight storage, training, and testing times that support its real-life applicability in an Industrial IoT environment.
Private Linear Transformation: The Joint Privacy Case

In this paper, we introduce the problem of Private Linear Transformation (PLT). This problem includes a single (or multiple) remote server(s) storing (identical copies of) <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> messages and a user that wants to compute <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> linear combinations of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> -subset of these messages by downloading the minimum amount of information from the server(s) while protecting the privacy of the entire set of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> messages. This problem generalizes the private information retrieval and private linear computation problems. In this work, we focus on the single-server case. For the setting in which the coefficient matrix of the required <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> linear combinations generates a Maximum Distance Separable (MDS) code, we characterize the capacity for all parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K, D, L$</tex> , where the capacity is defined as the supremum of all achievable download rates. In addition, we present lower and/or upper bounds on the capacity for the settings with non-MDS coefficient matrices and the settings with a prior side information.