Reviewerly/exHarmony huggingface dataset:
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages

How do typological properties such as word order and morphological case marking affect the ability of neural sequence models to acquire the syntax of a language? Cross-linguistic comparisons of RNNs' syntactic performance (e.g., on subject-verb agreement prediction) are complicated by the fact that any two languages differ in multiple typological properties, as well as by differences in training corpus. We propose a paradigm that addresses these issues: we create synthetic versions of English, which differ from English in one or more typological parameters, and generate corpora for those languages based on a parsed English corpus. We report a series of experiments in which RNNs were trained to predict agreement features for verbs in each of those synthetic languages. Among other findings, (1) performance was higher in subject-verb-object order (as in English) than in subject-object-verb order (as in Japanese), suggesting that RNNs have a recency bias; (2) predicting agreement with both subject and object (polypersonal agreement) improves over predicting each separately, suggesting that underlying syntactic knowledge transfers across the two tasks; and (3) overt morphological case makes agreement prediction significantly easier, regardless of word order.
Certifiably Robust Interpretation in Deep Learning

Deep learning interpretation is essential to explain the reasoning behind model predictions. Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks. Although gradient-based saliency maps are popular methods for deep learning interpretation, recent works show that they can be vulnerable to adversarial attacks. In this paper, we address this problem and provide a certifiable defense method for deep learning interpretation. We show that a sparsified version of the popular SmoothGrad method, which computes the average saliency maps over random perturbations of the input, is certifiably robust against adversarial perturbations. We obtain this result by extending recent bounds for certifiably robust smooth classifiers to the interpretation setting. Experiments on ImageNet samples validate our theory.
Modern Application of Internet of Things in Healthcare System: Review

The recent advancement in information and Communication Technology has led to the emergence of the Internet of Things (IoT). In the modern medical system, the use of IoT technology has brought so much convenience to doctors and patients, because IoT is suitable for a variety of medical fields (such as real-time monitoring, patient information management, and health care management). Human body sensor network (BSN) technology is one of the core technologies that is being used in the development of IoT in the medical system. This technology is characterized by a little power supply and lightweight wireless sensor nodes that can be used to monitor patients. The research paper highlights the main requirements of modern medical systems based on BSN.
Input-output characteristics of single-mode cavity with multiple coherently coupled artificial atoms

In previous cavity quantum electrodynamics (QED) systems, atomic gas is usually treated as single atoms, thereby resulting in phenomena such as Rabi splitting, and single-photon blocking. Benefiting from the development of superconducting circuit QED, the superconducting quantum interference devices (SQUIDs) can be regarded as artificial atoms, and the detuned coupling of them through LC harmonic oscillators will constitute an equivalent coherent coupling between artificial atoms. According to this, we study the effect of multiple artificial atoms with coherent coupling on the input and output of a single-mode cavity, and analyze the transmission spectrum from the perspective of decorated state. We find that single-mode cavities containing multiple artificial atoms with coherent coupling have significantly different transmittances from cavities containing single atoms, the transmission spectra of which are correlated with the coherent coupling coefficients between the artificial atoms, and the coupling coefficients between the cavity modes and the artificial atoms, and we also find that both the cavity mode leakage rate and the artificial atom decay rate are related to each other. And as the number of artificial atoms increases, the number of transmission peaks does not increase, and there are only three transmission peaks at most. In order to explain the law of this transmission spectrum, we quantize both artificial atoms and cavity modes, and calculate the eigenvalues and eigenstates of the whole in a single quantum case. In principle, if there are several particles, they will form several decorative states, and there will theoretically appear several transmission peaks. However, we find that there are some decorated states that do not contain the photonic component and thus do not contribute to the transmission peak. From the specific form of these decorated states, many of them have the property of many-body entanglement. Therefore, using such a single-mode cavity containing multiple coherently coupled artificial atoms, we can construct the required many-body entangled state by simply inputting weak monochromatic light, and at the same time, we can sense the forms of multi-body entanglement states in the cavity through the change of transmittance.
An Approach for Encrypted Exchange of Information in Corporate Networks Based on Tcl/Tk

Ensuring information security is crucial for corporations with a complex, geographically distributed structure at many levels: large banks, multinational and state-owned companies.Often the corporate networks of such organizations are built with equipment from different generations and from different manufacturers, which significantly complicates the process of managing the IT system.The article presents the elements of the security system used to protect information in a distributed corporate network.Various elements (firewalls, cryptographic algorithms, anti-virus programs, etc.) and company products, which are the main units in building corporate security systems, are described.Special attention is paid to public key cryptography and a software implementation Coder 1.0 (developed on Tcl/Tk) is proposed, implementing encrypted data exchange inside of corporate networks and between corporate networks.
Multimedia Fusion Privacy Protection Algorithm Based on IoT Data Security under Network Regulations

This study provides an in-depth analysis and research on multimedia fusion privacy protection algorithms based on IoT data security in a network regulation environment. Aiming at the problem of collusion and conspiracy to deceive users in the process of outsourced computing and outsourced verification, a safe, reliable, and collusion-resistant scheme based on blockchain is studied for IoT outsourced data computing and public verification, with the help of distributed storage methods, where smart devices encrypt the collected data and upload them to the DHT for storage along with the results of this data given by the cloud server. After testing, the constructed model has a privacy-preserving budget value of 0.6 and the smallest information leakage ratio of multimedia fusion data based on IoT data security when the decision tree depth is 6. After using this model under this condition, the maximum value of the information leakage ratio of multimedia fusion data based on IoT data security is reduced from 0.0865 to 0.003, and the data security is significantly improved. In the consensus verification process, to reduce the consensus time and ensure the operating efficiency of the system, a consensus node selection algorithm is proposed, thereby reducing the time complexity of the consensus. Based on the smart grid application scenario, the security and performance of the proposed model are analyzed. This study proves the correctness of this scheme by using BAN logic and proves the security of this scheme under the stochastic prediction machine model. Finally, this study compares the security aspects and performance aspects of the scheme with some existing similar schemes and shows that the scheme is feasible under IoT.
A New Approach for Target Capability Assessment and Selection in Complex Situations

In modern information warfare, medium and long-range weapons with high strike precision are often used to ensure stable, accurate, and ruthless conditions. However, the target system often has multiple target units and which target units to specifically attack becomes the primary issue. This study constructs a target combat system network based on the complex network theory and maximum entropy principle, establishes a key target selection model based on this network, and then determines the use of medium and long-range weapons to strike the key target unit in the enemy combat system network. The key target selection model consists of four parts. The first is a value model of the node itself constructed by comprehensively considering the relationship between the target and the war, the importance of the target in the system, and the threat of the target. The second is a node network value model based on the potential field theory. The third is to construct a cascading failure model of the target system through the definition of triples (results of strike action, target unit status, and failure influence relationship between target units). Fourth, with the optimal cost-effectiveness ratio as the objective function, the evaluation criterion function model is established. Case scenarios and simulation experiments illustrate the rationality and effectiveness of the method which can provide effective solutions for commanders to select key targets.
W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy Videos

Deep Neural Networks are increasingly used in video frame interpolation tasks such as frame rate changes as well as generating fake face videos. Our project aims to apply recent advances in Deep video interpolation to increase the temporal resolution of fluorescent microscopy time-lapse movies. To our knowledge, there is no previous work that uses Convolutional Neural Networks (CNN) to generate frames between two consecutive microscopy images. We propose a fully convolutional autoencoder network that takes as input two images and generates upto seven intermediate images. Our architecture has two encoders each with a skip connection to a single decoder. We evaluate the performance of several variants of our model that differ in network architecture and loss function. Our best model out-performs state of the art video frame interpolation algorithms. We also show qualitative and quantitative comparisons with state-of-the-art video frame interpolation algorithms. We believe deep video interpolation represents a new approach to improve the time-resolution of fluorescent microscopy.
Probabilistic Error Detection Model for Knowledge Graph Refinement

Knowledge graphs are widely used in information queries. They are built using triples from knowledge bases, which are extracted with varying accuracy levels. Accuracy plays a key role in a knowledge graph, and knowledge graph construction uses several techniques to refine and remove any inaccurate triples. There are many algorithms that have been employed to refine triples while constructing knowledge graphs. These techniques use the information about triples and their connections to identify erroneous triples. However, these techniques lack in effective correspondence to human evaluations. Hence, this paper proposes a machine learning approach to identify inaccurate triples that correspond to actual human evaluations by injecting supervision through a subset of crowd-sourced human evaluation of triples. Our model uses the probabilistic soft logic’s soft truth values and an empirical feature, the fact strength, that we derived based on the triples. We evaluated the model using the NELL and YAGO datasets and observed an improvement of 12.56% and 5.39% in their respective precision. In addition, we achieved an average improvement of 4.44% with the F1 scores, representing a better prediction accuracy. The inclusion of the fact strength augmented the modeling precision by an average of 2.13% and provided a higher calibration. Hence, the primary contribution of this paper is the proposal of a model that effectively identifies erroneous triples, aligning with high correspondence to actual human judgment
Analysis of CNN Architectures for Human Action Recognition in Video

Every year, new Convolutional Neural Network (CNN) architectures appear to deal with different problems in the activity of image and video recognition. These architectures usually work along the ImageNet dataset for looking for the best performance of the CNNs without taking into account the video task where they are used. This can represent a problem if the task is Human Action Recognition (HAR) in video, since the CNN architectures are pre-trained with an image dataset that can practically contain any object, while HAR problem requires consecutive frames of people doing actions. To prove the idea that using CNNs pre-trained on an image dataset does not always achieve the best performance on a video dataset and that, therefore, it is worth comparing the performance of different CNNs under similar circumstances for the HAR problem, this work proposes an analysis between eight different CNN architectures. Each one of the CNN was exclusively trained with RGB images, which were extracted from the frames of the different classes of videos of HMDB51 dataset. To make the classification of an activity in video, we average the predictions taking into account the successes. We also made some ensembles with the best performance CNNs to measure the improvement in accuracy. Our results suggest that Xception is a strong baseline model that could be used by the community to make their comparisons of their proposals more robust.
Sentiment Analysis of COVID19 Reviews Using Hierarchical Version of d-RNN

In recent years understanding person’s sentiments for catastrophic events has been a major subject of research. In recent times COVID19 has raised psychological issues in people’s minds across world. Sentiment analysis has played significant role in analysing reviews across wide array of real-life situations. With constant development of deep learning based language models, this has become an active investigation area. With COVID19 pandemic different countries have faced several peaks resulting in lockdowns. During this time people have placed their sentiments in social media. As review data corpora grows it becomes necessary to develop robust sentiment analysis models capable of extracting people's viewpoints and sentiments. In this paper, we present a computational framework which uses deep learning based language models through delayed recurrent neural networks (d-RNN) and hierarchical version of d-RNN (Hd-RNN) for sentiment analysis catering to rise of COVID19 cases in different parts of India. Sentiments are reviewed considering time window spread across 2020 and 2021. Multi-label sentiment classification is used where more than one sentiment are expressed at once. Both d-RNN and Hd-RNN are optimized by fine tuning different network parameters and compared with BERT variants, LSTM as well as traditional methods. The methods are evaluated with highly skewed data as well as using precision, recall and F1 scores. The results on experimental datasets indicate superiority of Hd-RNN considering other techniques
Emotional Similarity Word Embedding Model for Sentiment Analysis

We propose a method for constructing a dictionary of emotional expressions, which is an indispensable language resource for sentiment analysis in the Japanese. Furthermore, we propose a method for constructing a language model that reproduces emotional similarity between words, which to date has yet not been considered in conventional dictionaries and language models. In the proposed method, we pretrained sentiment labels for the distributed representations of words. An intermediate feature vector was obtained from the pre-trained model. By learning an additional semantic label on this feature vector, we can construct an emotional semantic language model that embeds both emotion and semantics. To confirm the effectiveness of the proposed method, we conducted a simple experiment to retrieve similar emotional words using the constructed model. The results of this experiment showed that the proposed method can retrieve similar emotional words with higher accuracy than the conventional word-embedding model.
DIGITAL IMAGE ENCRYPTION USING LAPLACE TRANSFORM AND LFSR

In the world of rapid evolution of exchanging digital data, data security is essential to protect data from the unauthorized parities. With the broad use of digital images of various fields, it is important to preserve the confidentiality of image’s data from any unauthorized access. Cryptography is a technique that assists in the development of such algorithms for security purpose. In this paper, key is generated using a random number generator based on Linear Feedback Shift Register (LFSR) and Laplace Transformation.
A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset

Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale data is challenging. In this work, we propose a sequence classification deep model using depth video data for scenarios when the video data are limited. Unlike summarizing the frame contents of each frame into a single class, our method can directly classify a depth video, i.e., a sequence of depth frames. Firstly, the proposed system transforms an input depth video into three sequences of multi-view temporal motion frames. Together with the three temporal motion sequences, the input depth frame sequence offers a four-stream representation of the input depth action video. Next, the DenseNet121 architecture is employed along with ImageNet pre-trained weights to extract the discriminating frame-level action features of depth and temporal motion frames. The extracted four sets of feature vectors about frames of four streams are fed into four bi-directional (BLSTM) networks. The temporal features are further analyzed through multi-head self-attention (MHSA) to capture multi-view sequence correlations. Finally, the concatenated genre of their outputs is processed through dense layers to classify the input depth video. The experimental results on two small-scale benchmark depth datasets, MSRAction3D and DHA, demonstrate that the proposed framework is efficacious even for insufficient training samples and superior to the existing depth data-based action recognition methods.
The role of Smart Technologies in Managing Customer Knowledge: A Survey Study

Contemporary organizations in the era of the knowledge economy have taken care of managing customer knowledge to achieve efficiency and effectiveness in the work entrusted to them, and at the same time, recent developments in information technology have pushed organizations to acquire smart technologies, which help them deal with customers intelligently to find smart solutions that achieve the goals of both the client and the organization. In this scientific study, we touched on the role of smart technologies in managing customer knowledge through the main question: What is the role of smart technologies in managing customer knowledge? by using the method of critical review to review a set of studies related to the variables of the study. The results of the study showed that smart technologies contribute to the success of managing customer knowledge and smart technologies enable managing customer knowledge anywhere, anytime, and they also affect the customer experience. The current study found that customer knowledge is the basis for gaining competitive advantage, and through its management helps in gaining customers and building databases in organizations, the study has recommended organizations to use smart technologies in customer knowledge management to meet the wishes and needs of their customers, and that requires the support of the leadership of the organization with all resources, It is also recommended that researchers and specialists in the field of knowledge management and managing customer relationships pay more attention to addressing research areas extensively in smart solutions that contribute to the support of customer knowledge management.
More Creativity and Less Restriction: About the Relationship between Translation and Copyright

This article discusses the importance of adjusting the attitude toward the copyright of translation, taking the articles in United States Copyright Law and the Berne Convention as the reference. By looking into various incidents and examples in translation across different languages and areas, and among licensed and voluntary translator communities, the article discusses the significant amount of creative labor and intellectual work in the translation and why less restriction from the original work’s copyright can lead to quality improvement in translation. The article concludes that creativity takes an influential role throughout the translation process, and an environment with fewer restrictions from the copyright holder of the original work can boost the average quality of the translations. Hence, society can fully benefit from U.S. copyright law’s core purpose of promoting “the Progress of Science and useful Arts.”
Science fiction as the element of current management education

International scientific conference «The study of topical issues of modern society from the perspective of history, political science, sociology and philosophy» : conference proceedings (August 30–31, 2022. Riga, the Republic of Latvia). Riga, Latvia : «Baltija Publishing», 2022. 121 pages.
Analysis of Reading Interest of Santri Madrasah Aliyah Pondok Pesantren Ummusshabri as an Effort to Improve Information Literacy in Kendari City

This study examines the Analysis of Reading Interest of Santri Madrasah Aliyah Pondok Pesantren Ummusshabri as an Effort to Improve Information Literacy in Kendari City. This research approach is qualitative by using research methods or in-depth interviews. This research was conducted in depth and detail on a particular institution with objective observations, in this case the researcher wanted to see how the development of the interest of the students of Madrasah Aliyah at the Ummusshabri Islamic Boarding School as an effort to increase information literacy in the city of Kendari. Qualitative descriptive research is a method in the status of human groups, an object with the aim of making descriptive, systematic, factual and accurate pictures or paintings about the facts or phenomena being investigated (Sevilla, 1993). Based on the results of the study showing the development of reading interest in Madrasah Aliyah students, the Ummusshabri Islamic boarding school as an effort to increase information literacy in Kendari City, the Ummusshahabri Islamic boarding school has a literacy program that is run especially in Madrasah Aliyah, which has a special day to read books and twice a week to visit library, this is done to increase the reading interest of the students, where the reading development of the students of Madrasah Aliyah Ummusshabri Islamic Boarding School from year to year has not yet increased, this is in accordance with the results of the informant's perception that quantitatively or counting numbers check more deeply but from time to time.