Reviewerly/exHarmony huggingface dataset:
Factors influencing learning flow of Korean nursing students: A systematic review and meta-analysis

본 연구의 목적은 국내 간호대학생의 학습몰입 관련 요인에 대한 체계적 고찰과 관련된 요인을 규명하기 위함이다. 연구는 PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 지침에 의해 수행되었으며, 국내 간호대학생의 학습몰입과 관련된 문헌 검색은 DBpia, Riss, Kiss, KMbase, KNbase, NDSL을 이용하였다. 연구에서는 최종 35편의 연구가 검토되었으며 메타분석을 위해 R program version 3.5.3을 사용하였고, 각 관련 요인에 대한 총 상관관계 효과크기(ESr)는 Fisher
Gradient Based Activations for Accurate Bias-Free Learning

Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes such as gender, age or race in question. This discriminator is used adversarially to ensure that it cannot distinguish the bias attributes. The main drawback in such a model is that it directly introduces a trade-off with accuracy as the features that the discriminator deems to be sensitive for discrimination of bias could be correlated with classification. In this work we solve the problem. We show that a biased discriminator can actually be used to improve this bias-accuracy tradeoff. Specifically, this is achieved by using a feature masking approach using the discriminator's gradients. We ensure that the features favoured for the bias discrimination are de-emphasized and the unbiased features are enhanced during classification. We show that this simple approach works well to reduce bias as well as improve accuracy significantly. We evaluate the proposed model on standard benchmarks. We improve the accuracy of the adversarial methods while maintaining or even improving the unbiasness and also outperform several other recent methods.
Analysis of Algorithms for Effective Cryptography for Enhancement of IoT Security

The Internet of Things has emerged as one of the most prevalent technologies of the current times, finding its place in a myriad of applications and is widely used for digitization and automation applications such as smart city development, automated monitoring systems, healthcare, energy management and much more. With more devices being connected to the Internet, one of the biggest challenges faced by the Internet of Things surfaces—privacy and security risks. The vulnerability of IoT devices and networks have been brought to light, presenting a threat to the integrity of data. Cryptography has proved itself as a method to secure communication channels and data, as a way to ensure IoT security. In this paper, we aim to compare cryptographic algorithms, namely—AES, DES, RSA and lightweight cryptographic algorithm Fernet, to determine which cryptographic algorithm is the most efficient and secure, and can thereby minimize the risk to data integrity and security in IoT applications.
Medical image retrieval based on deep hashing

The deep hash method has been proved to be the most efficient and approximate nearest neighbor search technique for large-scale image retrieval. However, the existing deep hashing methods have few cases in medical image retrieval, so we propose a deep hash network model for medical image retrieval. Firstly, in order to enhance the feature extraction and learning ability in the network model, we add SENet visual attention mechanism into each residual module of the backbone network ResNet50 for feature extraction. Then the extracted high-dimensional semantic features are added to the Cauchy hash module to generate a compact, centralized hash code, and then complete medical image retrieval. So as to enhance experimental comparison, the proposed method was validated experimentally on NUS-WIDE and MURA data sets. A large number of experiments verify that the proposed method is reliable and can be used for medical image retrieval. Compared with other existing hash algorithms, the retrieval performance of the proposed method has certain improved. The framework of the proposed method is shown in Figure 1.
Beyond GNNs: An Efficient Architecture for Graph Problems

Despite their popularity for graph structured data, existing Graph Neural Networks (GNNs) have inherent limitations for fundamental graph problems such as shortest paths, k-connectivity, minimum spanning tree and minimum cuts. In these instances, it is known that one needs GNNs of high depth, scaling at a polynomial rate with the number of nodes n, to provably encode the solution space, in turn affecting their statistical efficiency. In this work we propose a new hybrid architecture to overcome this limitation. Our proposed architecture that we call as GNNplus networks involve a combination of multiple parallel low depth GNNs along with simple pooling layers involving low depth fully connected networks. We provably demonstrate that for many graph problems, the solution space can be encoded by GNNplus networks using depth that scales only poly-logarithmically in the number of nodes. This also has statistical advantages that we demonstrate via generalization bounds for GNNplus networks. We empirically show the effectiveness of our proposed architecture for a variety of graph problems and real world classification problems.
Concurrent Encryption and Lossless Compression using Inversion Ranks

For secure and efficient transmission or storage, data files are commonly compressed and encrypted. In this work, we introduce a cost-effective encryption method of files as a built-in component of a lossless compression algorithm, thus avoiding the added cost of employing two separate processes. We have shown in earlier studies that preprocessing data with Burrows-Wheeler Transformation followed by Inversion Ranking transformation in advance of the utilization of an entropy coder resulted in an extremely effective general-purpose lossless compression technique [1], [2]. During the compression process, we encrypt the frequency vector of the Inversion Ranking transformation and transmit it along with the compressed data. Since the frequency vector is required for decompression, no further encryption is necessary to secure the compressed file, see Figure 1.
A low-complexity destriping method for lossless compression of remote-sensing data

Remote sensing are widely used in applications including geoexploration, topographic mapping and weather forecasting, producing vast amounts of multi and hyper-spectral image data that need to be compressed [1]. The data acquisition process often leads to artifacts in the form of stripes with unpredictable positions and amplitudes [2]. The stripes deteriorate the smoonthless of the original image, causing challenges for high-ratio lossless compression. This motivates us to propose a split-and-compress framework. Rather than direct compression, we split (decompose) the image into a smooth part and a sparse remainder (capturing the stripes and artifacts alike) and compress the two parts separately. The decomposition is achieved using a fast, robust statistics based method with linear computational complexity on the number of pixels.
Deep Learning for Part of Speech (PoS) Tagging: Konkani

This is the first time that an experiment using deep learning has been attempted with a Konkani language data set. For this study, over 100,000 PoS tagged Konkani sentences were used. The f-scores for deep learning are 90.73% for training data and 71.43% for test data. These results are better than the ones previously reported for Konkani. We have provided a list of references of PoS tagging for Indian languages specifically using deep learning to place our research in perspective.
Study on the Current Use of Field Data Analysis Results to Improve Utilization

Purpose: We aim to analyze the reason for the poor utilization of field data analysis results and suggest an approach to improve it.BRMethods: We analyzes the surveys conducted by the Defense Agency for Technology and Quality between 2019 and 2020 and interviews with experts and working-level officials.BRResults: The main causes for the poor utilization of field data analysis results include a lack of trust in the raw data and the preprocessing methods used, lack of existing results for the desired weapon systems, limited practical application of the results, limited access to raw data and electrical data, and limited understanding of RAM.BRConclusion: Alternatives should be used to improve the utilization of field data analysis results, for example, the institution of a Joint Committee for Data Acquisition and Preprocessing, collection of manufacturers’ data and sensor data, expansion of target systems, and provision of a greater number of training seminars.
Reformulasi Sastra Piwulang sebagai Alternatif Baru Model Pendidikan Karakter di Indonesia

Character education is the main basis for complete human development. For the Indonesian people, character education is a manifestation of the nation's conscience that must be understood through the existence of daily life experiences. In the repertoire of Javanese literature there is a genre of piwulang literature that was born because of the practical need for character education in the Javanese court environment in the 19th century. The values ​​of character education that were built at that time continue to be embedded in the life of the Javanese people to this day. This fact would be a strong reason for the effort to reformulate piwulang literature as a new alternative model of character education in Indonesia. The approach used in this research is a text phenomenological approach with van Dijk's theory of critical discourse analysis about discourse and its social cognition relations. From the results of a study of piwulang literary texts, it is concluded that the syntactic structure of didactic texts leads to strengthening the sense of taste which in Javanese terms is referred to as "rasa sejati". The essence of taste values ​​include: bekti (respect), tresna (love), and andhap asor (polite). The teaching formulation is in the knowledge of things related to ala-becik, adat-waton, and manners. These three are the essence of character education that is very relevant to be applied today.&#x0D; Keywords: Character education; Serat Wulangreh; critical discourse
MPEG-G Reference-Based Compression of Unaligned Reads Through Ultra-Fast Alignments

With the widespread application of next generation sequencing technologies, the volume of sequencing data became comparable to that of big data domains. The compression of sequencing reads (nucleotide sequences, quality values, read names), in both raw and aligned data, is a way to alleviate bandwidth, transfer, and storage requirements of genomics pipelines. ISO/IEC MPEG-G standardizes the compressed representation (i.e. storage and streaming) of structured, indexed sets of genomic sequencing data for both raw and aligned data. For the latter, reference-based compression is a strategy used to compress nucleotide sequences of sequencing reads by using alignment information to a reference sequence, which can be used to represent nucleotide sequences by storing the starting position of the alignment on the reference sequence, and the differences between the reference and the actual read. This general scheme is implemented in different ways by genomic data compressors, such as DeeZ, Quip, and CRAM, which apply to aligned reads.
System Design and Implementation of Assistive Device for Hearing Impaired People

This paper discusses the design, implementation and development of assistive device for hearing impaired. Sign language is one of the oldest and most natural form of language for communication, but since most people does not know sign language. Finding interpreters for different group of people is difficult. To treat the deaf people as one among the society, a user-friendly assistive device is necessary. This paper provides the details about the implementation of standalone interpreter using transfer learning for finger spelling-based American Sign Language using Raspberry Pi. A Graphical User Interface (GUI) is created and tested for establishing a two-way communication to convert text into sign language. A language barrier is created using an assign language structure that is different from normal text. Hence, the users will now depend on the vision-based communication that will be able to bring normal people, deaf and mute people on the same grounds of interaction. There is a need to build a possible sign language translator, which can take communication in sign language and translate them into written and oral language. Such a translator that uses neural networks for finger spelling-based American Sign Language would greatly lower the barrier for many deaf and mute individuals to be able to better communicate with others in day-to-day interactions. In proposed method, hand gesture is first passed through a filter, and after the filter has applied, the gesture is passed through a classifier which predicts the class of the hand gestures. This method provides 95.7% accuracy for the 26 alphabets and 0, 1, 2 numerical.
Fragmented Central Affinity Approach for Reducing Ambiguities in Dataset

It is always a known fact that the role of data and its purity is very crucial in the data mining. The key role of data in the data mining is related from decision-making. It is well-known fact that if data are impure, then result will be a false picture. This crucial stage is also known as the ambiguities in datasets. Anomalous or irregular value in database is solitary of the biggest problems faced in data analysis and in data mining applications. Data preprocessing for the data mining is a key phase which is crucial place where ambiguities of database can be reduce or remove. The present study proposed an algorithm which tries to solve the problem related to an anomalous and irregular values, i.e., outliers, inliers, and missing values from a real-world imbalanced database. The study projected is based on the fragmented central affinity approach for reducing ambiguities in dataset.
The Effect of Creative Problem Solving Program for Pre-Early Childhood Special Teacher on their Creativity and Self-Efficacy

The purpose of this study was to investigate the effect of a creative problem solving program on the creativity and self-efficacy of pre-primary special education teachers. For this purpose, 15 students who took the ‘Creative Problem Solving Program’, an non-curricular program and special education for children at N University located in G city, were targeted. The creative problem-solving program consisted of a process of recognizing problems using various creative thinking techniques for 15 sessions, generating ideas, and then planning and implementing them. To analyze the effects of pre-primary special education teachers on creativity and self-efficacy, a paired t-test was conducted to compare the differences between the pre-test and the post-test of the creative problem-solving program. As a result, after the creative problem solving program, there was a significant improvement in the creativity(t=-8.897,p&lt;.001) and self-efficacy(t=-4.602,p&lt;.001) of the pre-primary special education teachers. These results show that the creative problem-solving program affects the confidence or belief in creativity and self-competence, including the creative flexibility and alternative solving skills of pre-primary special education teachers. Through this, it is thought that the creativity program using creative problem-solving methods will be helpful in the application of curricular and non-curricular curriculum for pre-primary special education teachers.&#x0D;
Considerations for Rules and Regulations to Improve Effectiveness of Using Field Data Analysis Results

Purpose: To improve the effectiveness of using field data analysis results, we study the current rules and regulations established for South Korea.BRMethods: We investigate the current laws and regulations related to reliability of weapons systems. Additionally, we review documents such as the RAM, CBM plus guidebook, and RAM-C published by the US Department of Defense.BRResults: The current laws and regulations regarding field data analysis results of weapons systems are examined in detail.BRConclusion: To enhance the effectiveness of using the aforementioned field data analysis results, after reviewing the currently available regulations/rules, we clarify additional regulations/rules required in the defense field for South Korea.
Unexpected collaborations: A science librarian and a reference archivist co-teaching

Libraries and archives are often grouped together as knowledge organizations with some shared goals and purposes, and in academic institutions this is no different. They might compete for the same resources or be managed by the same administrative teams; however, how much collaboration is there between libraries and archives in academia? Some might suppose that there is consistent collaboration and interconnected priorities and projects which foster that collaboration. Those librarians providing support for historians or history departments may have strong working relationships with reference archivists, but what about those working in other areas of librarianship? Would there be opportunity, for example, for an academic science librarian to partner with a reference archivist to elucidate the treasures hidden within archives relevant to scientists? We can definitively answer that question from our own personal experience. Since 2018, the authors, a science librarian and reference archivist, have worked together to introduce those beyond history departments the opportunities for archival research. In this column, we describe how we started working together, our various collaborative efforts over the years, and what we have learned along the way.
Temporal Reasoning of English Text Documents

Temporal expression recognition and normalization (TERN) is promising research area in the field of natural language processing. Automatic temporal information extraction system is in great demand due to rapid growth of digitized information on the Internet. Lot of research has been done in temporal information extraction. But due to variety of temporal expression present in the textual data, interpretation needs more efforts. It is important to understand nature of expression, and then, its interpretation ways can be defined. For example, “on the same day,” it requires information about previous sentences in the text to define reference time rather than considering document creation time as reference time statically. In this paper, a normalization system is proposed that dynamically chose the reference time from text rather than relying on traditional approach of selecting a document creation date as reference time. The system is evaluated on gold standard dataset like WikiWar, AQUAINT, and TempEval-2 datasets.
Automatic Segmentation of Red Blood Cells from Microscopic Blood Smear Images Using Image Processing Techniques

Navya, K. T. Das, Subhraneil Prasad, KeerthanaHuman blood is a very effective parameter to detect, diagnose and rectify ailments of the human body. Complete blood count (CBC) is a method to clinically obtain a statistical measure of blood and its related parameters, i.e., red blood cells (RBCs), white blood cells (WBCs), platelets, hemoglobin concentration to name a few. This helps to determine the physical state of the subject. For further diagnosis, peripheral blood smear, a thin layer of blood smeared on a microscope slide and stained using various staining methods is examined for the morphology of the cells by the pathologists. However, manual inspection of smear images is tedious, time-consuming, and laboratorian-dependent. Although there are certain software-based approaches to tackle the problem, most of them are not robust for all staining methods. Thus, the need is to create an automated algorithm that will work for different staining types, thereby alleviating both the aforementioned drawbacks. This work aims to create an automatic method of segmenting and counting RBCs from blood smear images using image processing techniques to help diagnose RBC-related disorders. In the proposed method, the images are first preprocessed, i.e., standardized to a uniform color and illumination profile using contrast enhancement, adaptive histogram equalization followed by Reinhard stain normalization algorithms. WBCs and platelets are extracted in HSI color space and subtracted from the original image to retain only RBCs. Thereafter using morphological operations and active contour segmentation algorithms, a count of total RBCs were obtained even for overlapped cells in the microscopic blood smear image. The proposed method achieved counting accuracy of 89.6% for 150 images.
Providing Physical Layer Security for IoTs in the Last Mile

Communication security is one of the top security challenges for connected devices. Different from other links such as backhaul, the last mile technology also depends on the requirements of end users. Wireless technologies are generally selected for the mobility of users and ease of use. However, wireless medium has an open nature and thus wireless links are more prone to physical layer attacks compared to their wired counterparts. Moreover, simple end devices have constrained resources in both hardware and software, and it is not always feasible to apply conventional cryptographic approaches to provide security. We turn to chaos theory to provide security for simple devices at physical layer. The FM-DCSK and FM-CSK transmission system are built and implemented in the proposed secure communication system. The information message is embedded in wideband random-like signals, making the message remain covert. Transmission security is achieved by using the initial conditions and spreading factor as keys. To guard against active attacks, procedures for dynamic adjustment of initial conditions and other parameters are proposed. The scheme's cost effective features include the simplicity of communication setup and the low power consumption in generating and controlling the chaos signal. The sensitivity to initial condition and complex dynamic feature of chaotic function make it a promising approach for physical layer security.
Beyond Learning Features: Training a Fully-Functional Classifier with ZERO Instance-Level Labels

We attempt to train deep neural networks for classification without using any labeled data. Existing unsupervised methods, though mine useful clusters or features, require some annotated samples to facilitate the final task-specific predictions. This defeats the true purpose of unsupervised learning and hence we envisage a paradigm of `true' self-supervision, where absolutely no annotated instances are used for training a classifier. The proposed method first pretrains a deep network through self-supervision and performs clustering on the learned features. A classifier layer is then appended to the self-supervised network and is trained by matching the distribution of the predictions to that of a predefined prior. This approach leverages the distribution of labels for supervisory signals and consequently, no image-label pair is needed. Experiments reveal that the method works on major nominal as well as ordinal classification datasets and delivers significant performance.