Reviewerly/exHarmony huggingface dataset:
On Natural Language Processing Applications for Military Dialect Classification

The authors explore potential Natural Language Processing (NLP) models for the classification of military text. A dataset of military articles were compiled by web scraping public access military documents published by military branches in Australia, Canada, the UK and USA, using web crawler algorithms. Various NLP algorithms were tested and compared to evaluate the classification performance on the military documents to identify the strengths and weaknesses of each model. The top performing model, multiclass BERT, achieved 99.9% accuracy for classification when predicting the military branch for a withheld test set of articles. However BERT did not perform as accurately in the binary classification models when trained on military documents. Overall model performances, training time and the effect of input text length on model performance is compared across all models.
TF-SS Matching: Retrieval of Power Industry Technical Standards via Term Frequency and Semantic Similarity Matching

Power industry technical standard retrieval is an information retrieval problem that aims to search relevant candidate documents according to query phrases or sentences. How to correctly match queries to the corpus and utilize term frequency and semantic features is critical for our task. In this work, we propose a novel solution to these problems based on term frequency (TF) and semantic similarity (SS) matching. Specifically, it consists of three key components: (1) TF matching aims to mine the significance of a particular term within the overall document while filtering out the content without the queries. This way is suitable for accurate retrieval. (2) SS matching is formulated into a deep metric learning framework to jointly learn the sentence representations and semantical embedding metric. (3) Rerank the fusion results from the mentioned TF and SS models to refine the output ranking list. Extensive experiments on the dataset, which contains 38 frequently used technical standards in daily work, prove the effectiveness of our proposed method. Our SS matching achieves comparable results with complex polyencoders. Combined with TF matching, our TF-SS matching can achieve state-of-the-art performance with 80.84% recall, 84% MRR, and 79.82% ROUGE.
Privacy-Preserving Multi-Party Machine Learning for Object Detection

In order to mitigate the privacy threats and resource constraints for real-time object detection applications on edge nodes, we describe an approach to building a distributed multi-party You Only Look Once object detector. We carefully separate out what each device can see to prevent the sharing of sensitive data and model whilst improving prediction results. Privacy, correctness and latency concerns were discussed along the paper showing that the approach does not leak sensitive information, enables the construction of machine learning models that are better than purely local models and where the overall performances are on par with the global predictions resulting from the pooling of all data.
Data sharing by biologists: A comparative study of genome sequence data and lab experiment data

Whether biologists share data depends on data type. This study employed an integrated theoretical framework combining theory of planned behavior and institutional theory to investigate individual motivations, technological resources, and institutional pressures affecting data sharing intentions between biologists depending on whether they were sharing genome sequence data or regular lab experiment data. A series of statistical analyses including principal component factor analysis, hierarchical regression, and independent sample t -test were conducted on a total of 342 survey responses from biologists. The results suggest that biologists' data sharing intentions are influenced by individual motivations, technological resources, and institutional pressures, and that the intensities of those factors are considerably different for genome sequence data and lab experiment data. This study suggests that data sharing by biologists varies depending on the type of data they produce, and that different policies and technological supports should be applied to facilitate the sharing of diverse biological data types. • Biological scientists' data sharing model was developed by integrating institutional theory and theory of planned behavior. • A total of 342 survey responses were used to evaluate the research model with hierarchical regression and t -test. • Biological scientists' data sharing intention is led by institutional pressure, resource, and individual motivation factors. • The levels of those factors are significantly different between genome sequence data and lab experiment data. • Different policies and support need to be applied to encourage biological scientists' data sharing of different data types.
Expectation Maximization Algorithm with Combinatorial Assumption

Expectation maximization (EM) algorithm is a popular and powerful mathematical method for parameter estimation in case that there exist both observed data and hidden data. The EM process depends on an implicit relationship between observed data and hidden data which is specified by a mapping function in traditional EM and a joint probability density function (PDF) in practical EM. However, the mapping function is vague and impractical whereas the joint PDF is not easy to be defined because of heterogeneity between observed data and hidden data. The research aims to improve competency of EM by making it more feasible and easier to be specified, which removes the vagueness. Therefore, the research proposes an assumption that observed data is the combination of hidden data which is realized as an analytic function where data points are numerical. In other words, observed points are supposedly calculated from hidden points via regression model. Mathematical computations and proofs indicate feasibility and clearness of the proposed method which can be considered as an extension of EM.
Conversation Clustering Adaptation for Intent Recognition

With the increasing presence of NLU tools such as automatic dialogue systems, achieving high accuracy of intent recognition in chatbots becomes an especially important problem to tackle. This issue cannot be solved without sufficient training data, but the scarcity of labelled training data often poses a major challenge to the development of real-life chatbots. Therefore, methods utilizing unlabelled data resources have been recently gaining interest. One of most notable approaches is the use of pre-trained encoders based on language models. Trained for general purposes, they benefit from further domain adjustments. In our work we offer an approach that can increase the model’s accuracy for text classification, which can serve as an alternative for standard methods of domain adaptation. Our approach consists of a combination of methods: a clustering approach, similar to intent induction; an encoder domain adaptation on a cluster classification task, similar to intent recognition using unlabelled data; and model fine-tuning on labelled datasets. In this approach unlabelled data becomes complementary to labelled data, reducing the time needed for corpus building. We evaluate our approach on: 1) the public WebApp dataset and 2) a demanding real-life banking domain dataset, achieving 0.97 and 0.93 accuracy respectively. This approach, called Conversation Clustering Adaptation (CCA), when applied to an encoder, increases the accuracy of intent recognition up by to 12.4pp and exceeds current state-of-the-art methods while benefiting from the use of additional training data. We share our code at https://github.com/michal-lew/cca.
Distributed HALS Algorithm for NMF based on Simple Average Consensus Algorithm

Nonnegative Matrix Factorization (NMF) is an efficient dimensionality reduction method for nonnegative data. Recently, a distributed algorithm has been proposed for multiple agents in a network to execute the hierarchical alternating least squares algorithm, which is well known as a fast computation method for NMF. However, the average consensus algorithm used there requires each agent to store the entire history of the values of its variables until the complete average consensus is reached, which increases the memory usage and computational cost. In this paper, we propose to replace the complicated average consensus algorithm with a simple one, and show through simulations that this replacement does not degrade the quality of the result if the values of the hyper-parameters are properly chosen.
Digital forensic investigation methodology for Storage Space: Based on the <scp>NIST</scp> digital forensic process

For forensic examiners, investigating the RAID systems of servers has been challenging, as the examiners must reconstruct multiple high-capacity disks to identify digital evidence. Software-based RAID systems' use has been released for personal use, so examiners should consider RAID systems even when they investigate personal computers. Although there is a high probability that the software-based RAID will become a target of crime, there has been little research into digital forensic methodology for software-based RAID, as exemplified by Storage Space. In this paper, we introduce details about the structure of Storage Space found through reverse engineering. Storage Space was analyzed by applying the digital forensic process of NIST. We explain how to reconstruct a virtual disk configured using Storage Space and develop an automated tool to do so. To evaluate our study and the tool developed, we created an experimental scenario and describe in detail the forensic procedure and technical methods for the analysis of Storage Space. Our research can be used as the basis of forensic investigations for Storage Space.
Scala Type System

The two fundamental design considerations of a programming language are static versus dynamic typing and strong versus weak typing. Types in a programming language are checked at compile time and can be inferred by a compiler. Scala is a strong and statically typed language with a unified type system.
Privacy-Preserving Top-K Nearest Keyword Search Queryies over Encrypted Graph Data

Under the cloud storage and cloud computing environment, searchable encryption has been extensively studied over the past years. However, most searchable encryption schemes are focusing on solving keyword search queries over encrypted textual data, and there only exists very few searchable encryption schemes that support top-k nearest keyword search queries for graph structured data. The top-k nearest keyword search is a wildly used query type in many application areas, such as social network and web graphs. Given a graph, a top-k nearest keyword search query can return the k nearest neighbors of the querying vertex with a specific keyword. The existing scheme uses 2-hop labeling and order-preserving encryption to achieve shortest distance computations over encrypted graphs. However, the scheme cannot return the distance between the querying vertex and the k nearest neighbors in the query result. This work solves the above problem using homomorphic encryption and secure integer comparison protocol, and propose a graph encryption scheme supporting top-k nearest keyword search. We provide detailed security analysis for the proposed scheme, and prove that the scheme achieves CQA2 security.
Data Transmission Method of a Wireless Digital Microwave Communication System Based on the Zigzag Permutation Algorithm

Aiming at the problem that the redundancy of redundant coded data packets in data transmission affects the performance of long-distance communication, a data transmission method of a wireless digital microwave communication system is proposed based on a zigzag replacement algorithm. A wireless digital microwave communication multichannel network was built to improve the maximum polling capacity of the system under the same system refresh cycle. The sampling nodes in the network system are reasonably clustered, the idea of token communication is introduced into the cluster, and a multichannel invisible token transmission mechanism is designed to reduce the transmission times of data communication. The channel coding and decoding scheme is established based on the zigzag replacement algorithm to reduce the redundancy of redundant coded data packets. The available information of the node is transmitted downward through a DIO message to realize the data transmission of wireless digital microwave communication. Taking the test distance of 5000 m as an example, the packet loss rate of the data transmission method designed in this paper is 9.3%, which is 4.8% and 5.9% lower than that of the communication system based on dynamic label information update and spatial autocorrelation, respectively. Therefore, it can still maintain stable communication performance in long-distance communication.
Overfitting

The main purpose of machine learning is to learn the real model of the data from the training set, so that it can perform well on the unseen test set. We call this the generalization ability. Generally speaking, the training set and the test set are sampled from the same data distribution. The sampled samples are independent of each other, but come from the same distribution. We call this assumption the independent identical distribution (i.i.d.) assumption.
Scene Classification of Remote Sensing Images Based on Deep Network Transferability and Image Complexity

In order to solve the low efficient problem of the remote sensing image scene classification, a classification method is proposed which is based on deep network transferability (DNT) and image complexity (IC). Firstly, the pre-trained deep networks are sorted by their transferability. Then, the scenes of the image datasets are sorted by their complexity. Finally, the scenes are classified by the suitable networks according to the deep network transferability and image complexity. The test results in the commonly used remote sensing dataset show that, the classification accuracy of the proposed method is similar to the GoogLeNet network, and the classification time is less than the one of the VGG16 network , thus the classification efficiency is improved obviously. As this method can choose different deep networks suitable for the image complexity, so the classification efficiency is much better.
Technical Guidelines for the Creation and Deployment of 360° Video-Based Virtual Reality (VR) Reusable Learning Objects (RLOs)

Medical education has become more challenging, with a lack of resources and clinical environments where students can prepare for their clinical practice. New technologies are being used to fill this gap in education, with 360° videos providing a viable and effective solution. However, the creation of such resources has not been well documented, which forces educators to face the same setbacks during this development process. This paper presents technical guidelines for the creation of a Virtual Reality (VR) application using 360° videos with medical content for an undergraduate-level course. The guidelines are presented through the various decisions that were taken during the development process of the application, providing the rationale behind each one and their necessity. The guidelines validate the effective design of a technologically enhanced learning tool as an addition to the existing methods used in medical education.
Extracting Software Design from Text: A Machine Learning Approach

Extracting software design components from text is a goal of many research works. Utilizing Machine learning techniques for that goal can improve the accuracy of this process instead of using traditional Natural Language Processing (NLP) methods. In this paper, the proposed approach uses machine learning techniques to extract classes and attributes from plain text (e.g. software requirements documents) through two consequent classifiers, the first one classifies each word into a class or not using predefined features, then, the second classifier starts to classify words into an attribute or not. Finally, dependency parsing is used to define a set of rules applied to the document given the extracted classes and attributes to relate the attributes to the classes. One of the contributions of this paper is the created dataset in its final pre-processed form that could make it easier to use in the software design field in the future.
Next-Generation Teacher Competencies

The chapter examines next-generation teacher competencies based on a research study that involved a national level consultative workshop for teachers. Data was obtained from 120 workshop participants through crowd-sourcing data collection technique. As a part of qualitative data, focus group discussion (FGD) with 15 senior high-school teachers selected through snowball-sampling technique was also conducted. The study revealed a diagnostic competency balance scorecard to measure competency levels of teachers, proposed a competency development cycle, and suggested strategies to develop next-generation teacher competencies.
Comparing The Effects of Face-to-Face and Online English Lounges On Students’ Speaking Ability and Attitude Toward Foreign Teachers

This study was conducted to determine to what extent the English Caf&#x00E9; Program affected the students&#x2019; speaking ability and their attitude toward foreign teachers as well as the significant differences in the effects between the program carried out face-to-face and online as perceived by the participants in this study. This is a descriptive-comparative survey research. Frequency analysis, mean comparison analysis, t-test, and correlation analysis were performed. Mean values show that as perceived by the respondents, the program, implemented face-to-face and online, improve the students&#x2019; speaking ability and attitude towards foreign teachers to a great extent. Computations for significant difference show that there is no significant difference in the perceptions of the respondents. In the correlation analyses, in both face-to-face English Cafe and online English Caf&#x00E9; programs, students&#x2019; speaking ability came out to be significantly positively correlated to their attitude towards foreign teachers. The results also reveal that there is a significant negative correlation between students&#x2019; speaking ability and teachers&#x2019; length of service only in the face-to-face English Caf&#x00E9; program. There was no correlation between students&#x2019; speaking ability and their attitude towards foreign teachers and teachers&#x2019; educational qualification in both face-to-face and online English Caf&#x00E9; programs.
An ASR N-Best Transcript Neural Ranking Model for Spoken Content Retrieval

Spoken Content Retrieval (SCR) using ASR transcripts is in-creasingly important for multimedia content archives. How-ever, SCR is often impacted by ASR errors. In recent years novel neural ranking methods for information retrieval (IR) have achieved improved search effectiveness over established methods. In this study, we examine neural ranking methods in SCR. Specifically we introduce two new neural ranking methods designed for use with errorful ASR transcripts. In the first, we train a neural ranking model using both man-ual transcripts and ASR transcripts or manual transcripts and artificially created ASR transcripts. In the second, we use an ASR N-best extension of two existing neural ranking methods: Deep Relevance Matching Model (DRMM) and Position-Aware Convolutional Recurrent Matching (PACRR). We report two sets of SCR experiments which evaluate our neural ranking methods. In the first, we use ASR transcripts to retrieve documents from an archive of spoken instruction videos. In the second, we examine a known-item search on a collection of user-generated spoken videos. We find that training the neural ranking model on both ASR and man-ual transcripts improves Mean Reciprocal Rank (MRR). The N-best extension of PACRR is particularly effective in both experiments in comparison to a standard BM25 IR model.
A Text Statistics Method for Professional Teaching Resources Based on Different Features and Big Data

With the continuous improvement of the digitization of resources, the construction of teaching resources has gradually received attention. To study the real utilization of teaching resources, the text statistics method of professional teaching resources based on different characteristics and big data is studied. First, the text statistics data of professional teaching resources are collected, and the text statistics algorithm of professional teaching resources is designed based on the different characteristics and big data. Second, a text statistics platform for teaching resources was constructed, the weight of text statistics for teaching resources was improved, and the text statistics of professional teaching resources were realized. Experiments proved that the design of professional teaching resources text statistics methods based on difference characteristics and big data is faster., It is time-saving and has certain application value.
Kernel ridge reconstruction for anomaly detection: general and low computational reconstruction

Autoencoders (AEs) have been widely used for anomaly detection because models trained to reconstruct a normal data are expected to have a higher reconstruction error for anomalous data than that for normal data, and the higher error is adopted as a criterion for identifying anomalies. However, the high capacity of AEs is sometimes able to reconstruct anomalous data even when trained only on normal data, which leads to overlooked anomalies. To remedy this problem, we propose a kernel ridge reconstruction (KRR) approach for general, high-performance, and low computational anomaly detection. KRR replaces the non-linear decoder network of the AE with a linear regressor, which uses the weighted sum of training normal data for reconstruction, and thus prevents the reconstruction of anomalous data. We also reveal the desired property of the encoder for KRR to achieve high anomaly detection performance and propose an effective training algorithm to realize such property by instance discrimination and feature decorrelation. In addition, KRR reduces the computational cost because it replaces the non-linear decoder network with a linear regressor. Our experiments on MNIST, CIFAR10, and KDDCup99 datasets prove its applicability, high performance, and low computational cost. In particular, KRR achieved an area under the curve (AUC) of 0.670 with 12 millions multiply-accumulate operations (MACs) on the CIFAR10 dataset, outperforming a recent reconstruction-based anomaly detection method (MemAE) with a 1.1-fold higher AUC and 0.291 as many MACs.