Reviewerly/exHarmony huggingface dataset:
A Novel-Restraint and Predefined-Time ZNN Model for Time-Dependent Matrix Inversion

The problem of solving complex-valued time-dependent matrix inversion (CVTD-MI) has significant implications for statistics and control theory, and thus it is worthy to be investigated by scholars. Zeroing neural network (ZNN) is an efficient tool for solving time-dependent problems and can be used to get the inverse of complex-variable matrix in disturbed environments. In this paper, a complex-valued fast predefined zeroing neural network (CFPZNN) model is proposed for solving the CVTD-MI problem. The CFPZNN model possesses two considerable properties. In the first place, the predefined time convergence performance ensures that the CFPZNN model can solve the CVTD-MI problems efficiently. In the second place, the CFPZNN model has suprior robustness and can restrain the interference of noise. These two superior points are demonstrated through several sets of numerical simulations. Moreover, the effectiveness and advantages of the proposed CFPZNN model are further demonstrated by citing the widely applied activation functions for comparison.
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop RL methods allow practitioners to instead interactively teach agents through tailored feedback; however, such approaches have been challenging to scale since human feedback is very expensive. In this work, we aim to make this process more sample- and feedback-efficient. We present an off-policy, interactive RL algorithm that capitalizes on the strengths of both feedback and off-policy learning. Specifically, we learn a reward model by actively querying a teacher's preferences between two clips of behavior and use it to train an agent. To enable off-policy learning, we relabel all the agent's past experience when its reward model changes. We additionally show that pre-training our agents with unsupervised exploration substantially increases the mileage of its queries. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods, including a variety of locomotion and robotic manipulation skills. We also show that our method is able to utilize real-time human feedback to effectively prevent reward exploitation and learn new behaviors that are difficult to specify with standard reward functions.
Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging

The article proposes the principles for the development of a fuzzy rule-based physician decision support system n to determine the stages of the most common hepatocellular carcinoma (HCC) among malignant tumors of liver. The stages of HCC, i.e., critical situations, are expressed by different combinations of clinical signs of input data and emerging clinical conditions. These combinations shape the multiplicity of possible situations (critical situations) by forming linguistic rules that are in fuzzy relations with one another. The article presents the task of developing a fuzzy rules-based system for HCC staging by classifying the set of possible situations into given classes. In order to solve the problem, fuzzy rules of clinical situations and critical situations deviated from them are developed according to the possible clinical signs of input data. The rules in accordance with the decision-making process are developed in two phases. In the first phase, three input data are developed: nine rules are developed to determine possible clinical conditions based on the number, size, and vascular invasion of tumor. In the second phase, seven rules are developed based on possible combinations of input data on the presence of lymph nodes and metastases in these nine clinical conditions. At this stage, the rules representing the fuzzification of results obtained are also described. The latter provide an interpretation of results and a decision on related stage of HCC. It also proposes a functional scheme of fuzzy rules-based system for HCC staging, and presents the working principle of structural blocks. The fuzzy rule-based system for HCC staging can be used to support physicians to make diagnostic and treatment decisions
Memory-Efficient Pipeline-Parallel DNN Training

Many state-of-the-art results in domains such as NLP and computer vision have been obtained by scaling up the number of parameters in existing models. However, the weight parameters and intermediate outputs of these large models often do not fit in the main memory of a single accelerator device; this means that it is necessary to use multiple accelerators to train large models, which is challenging to do in a time-efficient way. In this work, we propose PipeDream-2BW, a system that performs memory-efficient pipeline parallelism, a hybrid form of parallelism that combines data and model parallelism with input pipelining. Our system uses a novel pipelining and weight gradient coalescing strategy, combined with the double buffering of weights, to ensure high throughput, low memory footprint, and weight update semantics similar to data parallelism. In addition, PipeDream-2BW automatically partitions the model over the available hardware resources, while being cognizant of constraints such as compute capabilities, memory capacities, and interconnect topologies, and determines when to employ existing memory-savings techniques, such as activation recomputation, that trade off extra computation for lower memory footprint. PipeDream-2BW is able to accelerate the training of large language models with up to 2.5 billion parameters by up to 6.9x compared to optimized baselines.
MOVE STRUCTURES AND THEIR RHETORICAL VERBS OF RESEARCH ARTICLE ABSTRACTS ACROSS ENGLISHES

Genre-based studies on the move structure of research article (RA) abstracts have established variations across cultures. However, previous studies included at most two countries for comparisons of abstracts written by native against non-native speakers. With the advent of World Englishes, it is deemed more practical to examine abstracts across Englishes to determine the writing conventions of the L1, L2 and EFL speakers of English. Consequently, the present study is a structural move analysis of RA abstracts focusing on the macro-structural moves across the Englishes and the lexical verbs employed used in each move. It examined 36 RA abstracts from linguistics and language and education fields, consisting of 12 abstracts each from the Inner, Outer and Expanding Circles of English by Kachru (1992). Each abstract was segmented into moves using the Five-Move Model of Santos (1996), which includes moves: (1) Situating the Research (STR); (2) Presenting the Research (PTR); (3) Discussing the Methodology (DTM); (4) Summarizing the Findings, (STF), and (5) Discussing the Research (DTR). The study found that the Inner Circle of English has the structure: Abstract  (STR) + PTR + DTM + (STF) + (DTR). The Outer Circle has the structure: Abstract  (STR) + PTR + (DTM) + STF + DTR. The Expanding Circle has the structure: Abstract  (STR) + PTR + DTM + (STF) + (DTR). The formulaic structures of abstract moves revealed that the only common move across Englishes is PTR. Following the approach of Musa et al. (2015), the lexical verbs realizing the purpose of each of the rhetorical moves were listed in order to come up with lists of rhetorical verbs which can be used in structuring an RA abstract. The study concludes with implications for academic writing instruction that calls for future abstract analyses that are world Englishes-inspired.
An Implementation of Hybrid Enhanced Sentiment Analysis System using Spark ML Pipeline: A Big Data Analytics Framework

Today, we live in the Big Data age.Social networks, online shopping, mobile data are main sources generating huge text data by users.This "text data" will provide companies with useful insight on how customers view their brand and encourage them to make business strategies actively in order to maintain their trade.Hence, it is essential for the enterprises to analyse the sentiments of social media big data to make predictions.Because of the variety and existence of data, the study of sentiment on broad data has become difficult.However, it includes opensource Big Data platforms and machine learning techniques to process large text information in real-time.The advancement in fields including Big Data and Deep Learning technology has influenced and overcome the traditional restrictions of distributed computing.The primary aim is to perform sentiment analysis on the pipelined architecture of Apache Spark ML to speed upward the computations and improve machine efficiency in different environments.Therefore, the Hybrid CNN-SVM model is designed and developed.Here, CNN is pipeline with SVM for sentiment feature extraction and classification in ML to improve the accuracy.It is more flexible, fast and scalable.In addition, Naive Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression classifiers have been used to measure the efficiency of the proposed system on multi-node environment.The experimental results demonstrate that in terms of different evaluation metrics, the hybrid sentiment analysis model outperforms the conventional models.The proposed method makes it convenient for effective handling of big sentiment datasets.It would be more beneficial for corporations, government and individuals to improve their great value.
Key Distribution Scheme for Optical Fiber Channel Based on SNR Feature Measurement

With the increase in the popularity of cloud computing and big data applications, the amount of sensitive data transmitted through optical networks has increased dramatically. Furthermore, optical transmission systems face various security risks at the physical level. We propose a novel key distribution scheme based on signal-to-noise ratio (SNR) measurements to extract the fingerprint of the fiber channel and improve the physical level of security. The SNR varies with time because the fiber channel is affected by many physical characteristics, such as dispersion, polarization, scattering, and amplifier noise. The extracted SNR of the optical fiber channel can be used as the basis of key generation. Alice and Bob can obtain channel characteristics by measuring the SNR of the optical fiber channel and generate the consistent key by quantization coding. The security and consistency of the key are guaranteed by the randomness and reciprocity of the channel. The simulation results show that the key generation rate (KGR) can reach 25 kbps, the key consistency rate (KCR) can reach 98% after key post-processing, and the error probability of Eve’s key is ~50%. In the proposed scheme, the equipment used is simple and compatible with existing optic fiber links.
Word centrality constrained representation for keyphrase extraction

To keep pace with the increased generation and digitization of documents, automated methods that can improve search, discovery and mining of the vast body of literature are essential. Keyphrases provide a concise representation by identifying salient concepts in a document. Various supervised approaches model keyphrase extraction using local context to predict the label for each token and perform much better than the unsupervised counterparts. Unfortunately, this method fails for short documents where the context is unclear. Moreover, keyphrases, which are usually the gist of a document, need to be the central theme. We propose a new extraction model that introduces a centrality constraint to enrich the word representation of a Bidirectional long short-term memory. Performance evaluation on two publicly available datasets demonstrate our model outperforms existing state-of-the art approaches. Our model is publicly available at https://github.com/ZHgero/keyphrases_centrality.git.
Breaking the Deadly Triad with a Target Network

The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In this paper, we investigate the target network as a tool for breaking the deadly triad, providing theoretical support for the conventional wisdom that a target network stabilizes training. We first propose and analyze a novel target network update rule which augments the commonly used Polyak-averaging style update with two projections. We then apply the target network and ridge regularization in several divergent algorithms and show their convergence to regularized TD fixed points. Those algorithms are off-policy with linear function approximation and bootstrapping, spanning both policy evaluation and control, as well as both discounted and average-reward settings. In particular, we provide the first convergent linear Q-learning algorithms under nonrestrictive and changing behavior policies without bi-level optimization.
Automated Nudity Recognition using Very Deep Residual Learning Network

The exponentially growing number of pornographic material has brought many challenges to the modern daily life, particularly where children and minors have unlimited access to the internet. In Malaysia, all local and foreign films should obtain the suitability approval before distribution or public viewing, and this process of screening visual contents of all the TV channels imposes a huge censorship cost to the service providers such as Unifi TV. To leverage this issue, this paper proposes to use an emerging model of Deep Learning (DL) techniques called Residual Learning Convolutional Neural Networks (ResNet), in order to automate the process of nudity detection in visual contents. The pre-trained ResNet model, with hundred and one layers, was utilized to perform transfer learning and solve a new binary classification problem of nudity versus non-nudity. The performance of the proposed model is evaluated based on a newly created dataset comprising more than 4k samples of nudity and non-nudity images. After conducting experiments on the nudity dataset, the deep learning method succeeded to achieve the best performance of 70.42% in term of F-score, 84.04% in term of accuracy, and 93.72% in term of AUC .
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses.This inspired recent research on few-shot WSD using meta-learning.While such work has successfully applied meta-learning to learn new word senses from very few examples, its performance still lags behind its fully-supervised counterpart.Aiming to further close this gap, we propose a model of semantic memory for WSD in a meta-learning setting.Semantic memory encapsulates prior experiences seen throughout the lifetime of the model, which aids better generalization in limited data settings.Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork.We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce (e.g.one-shot) scenarios and produces meaning prototypes that capture similar senses of distinct words.
Test for Detection of Weak Graphic Passwords in Passpoint Based on the Mean Distance between Points

This work demonstrates the ineffectiveness of the Ripley’s K function tests, the distance to the nearest neighbor, and the empty space function in the Graphical Authentication scenario with Passpoint for the detection of non-random graphical passwords. The results obtained show that none of these tests effectively detect non-random graphical passwords; the reason for their failure is attributed to the small sample of the spatial pattern in question, where only the five points of the graphical password are analyzed. Consequently, a test based on mean distances is proposed, whose experiments show that it detects with good efficiency non-random graphical passwords in Passpoint. The test was designed to be included in the Graphical Authentication systems with Passpoint to warn the user about a possibly weak password during the registration phase, and in this way, the security of the system is increased.
Big Data Full-Text Search Index Minimization Using Text Summarization

An efficient full-text search is achieved by indexing the raw data with an additional 20 to 30 percent storagecost. In the context of Big Data, this additional storage space is huge and introduces challenges to entertainfull-text search queries with good performance. It also incurs overhead to store, manage, and update the largesize index. In this paper, we propose and evaluate a method to minimize the index size to offer full-text searchover Big Data using an automatic extractive-based text summarization method. To evaluate the effectivenessof the proposed approach, we used two real-world datasets. We indexed actual and summarized datasets usingApache Lucene and studied average simple overlapping, Spearman’s rho correlation, and average rankingscore measures of search results obtained using different search queries. Our experimental evaluation showsthat automatic text summarization is an effective method to reduce the index size significantly. We obtained amaximum of 82% reduction in index size with 42% higher relevance of the search results using the proposedsolution to minimize the full-text index size.
Advances in artificial neural networks as a disease prediction tool

Throughout the last decade, utilization of machine learning has seen a sharp rise in fields such as computing, transportation, engineering, and medicine. Artificial neural networks (ANNs) have demonstrated increased application due to their versatility and ability to learn from large datasets. The emergence of electronic health records has propelled healthcare into an era of personalized medicine largely aided by computers. This review summarizes the current state of ANNs as a predictive tool in medicine and the downfalls of reliance on a self-adjusting computer network to make healthcare decisions. Medical ANN studies can be grouped into three categories - diagnosis, classification, and prediction, with diagnostic studies currently dominating the field. However, recent trends show prediction studies may soon outnumber the remaining categories. ANN prediction studies dominate in fields such as cardiovascular disease, neurologic disease, and osteoporosis. Neural networks consistently show higher predictive accuracy than industry standards. But several pitfalls are preventing mainstream adoption. Clinicians often rely on situational pearls to make complex healthcare decisions, ANNs often do not account for intuitive variables during their analysis. Instead, ANNs rely on incomplete patient data and ‘black box’ computing to make decisions that are not completely transparent to the end-user. This has led to ‘runaway’ networks that may ultimately make inaccurate and harmful decisions. This review emphasizes the extensive potential of machine learning in medicine and the obstacles that must be overcome to utilize its full potential.
Accelerating Text Communication via Abbreviated Sentence Input

Typing every character in a text message may require more time or effort than strictly necessary.Skipping spaces or other characters may be able to speed input and reduce a user's physical input effort.This can be particularly important for people with motor impairments.In a large crowdsourced study, we found workers frequently abbreviated text by omitting midword vowels.We designed a recognizer optimized for expanding noisy abbreviated input where users often omit spaces and mid-word vowels.We show using neural language models for selecting conversational-style training text and for rescoring the recognizer's n-best sentences improved accuracy.On noisy touchscreen data collected from hundreds of users, we found accurate abbreviated input was possible even if a third of characters was omitted.Finally, in a study where users had to dwell for a second on each key, sentence abbreviated input was competitive with a conventional keyboard with word predictions.After practice, users wrote abbreviated sentences at 9.6 wordsper-minute versus word input at 9.9 words-perminute.
Optical front-end for a quantum key distribution cubesat

Recent advances in pointing and tracking capabilities of small satellite platforms have enabled adoption of capabilities such as high-resolution Earth Observation (EO), inter-satellite laser communications and, more recently, quantum communications. Quantum communications requires unusually narrow optical beams and tight pointing performance (on the order of ten microradians) to close an inherently brightness-limited quantum link. This limit is due to quantum communication protocols such as quantum key distribution and teleportation requiring individual quantum states to be transmitted with photon number restrictions. We examine an opportunity to combine quantum communications with laser communications in sharing an optical link. We discuss a combined quantum and laser communication terminal capable of performing space-to-ground entanglement-distribution and high data rate communications on a 12U CubeSat with a 95mm beam expander and an 60 cm aperture optical ground telescope. Photon pairs produced by the quantum terminal are entangled in polarization so the polarization must be maintained throughout the optical link. We discuss active and passive compensation methods in space and polarization reference frame correction using a polarized reference beacon at the ground station. The combined quantum and laser communication terminal approach enables secure communications over an optical channel with rates of 100 Mbps and sub-nanosecond time transfer.
Developing a Graphical User Interface for an Artificial Intelligence-Based Voice Assistant

Artificial intelligence machineries have been extensively active in human life in recent times. Self-governing devices are enhancing their way of interacting with both human and devices. Contemporary vision in this topic can pave the way for a new process of human-machine interaction in which users will get to know how people can understand human language, adapting and communicating through it. One such tool is voice assistant, which can be incorporated into many other brilliant devices. In this article, the voice assistant will receive the audio from the microphone and then convert that into text, later with the help of ‘pyttsx3', and then the text response will be converted into an audio file; then the audio file will be played. The audio is processed using the voice user interface (VUI). This article develops a functional intelligent personal assistant (IPA) and integrates it with a graphical user interface that can perform mental tasks such as ON/OFF of smart applications based on the user commands.
Research on the Application of Blockchain Technology in the Field of Corporate Governance

The Annual General Meeting of Shareholders is considered to be a boring and mandatory annual ceremony. At present, its information, forum, decision-making functions, and voting procedures are all flawed. As a new technology, the introduction of blockchain can greatly reduce the cost of shareholder voting and company organization costs, improve the company’s decision-making efficiency and the transparency of voting and elections, and effectively solve the problems existing in the current annual general meeting of shareholders. The research in this paper provides an intelligent solution for the traditional inefficiency of corporate governance.