Reviewerly/exHarmony huggingface dataset:
Improving the efficiency of the steganographic method of data hiding with the application of iterative functions and noise addition

The development of computer and digital technology contributes to the growth of information flows transmitted through open and closed communication channels. In many cases, this information is confidential, financial, or commercial in nature and is of value to its owners. This requires the development of mechanisms to protect information from unauthorized access. There are two fundamental areas of secure data transmission over the open communication channels – cryptography and steganography. The fundamental difference between them is that cryptography hides from others the content of the message, and steganography hides the very fact of the message transmission. This paper is devoted to steganographic methods of data concealment, which are less researched than cryptographic, but have significant potential for use in a variety of applications. One of the important characteristics of most methods is their effectiveness. In general, efficiency is assessed in the context of solving specific problems. However, the most common criteria for the effectiveness of steganographic methods are the amount of hidden data and the method of transmitting the secret key to the receiving party, which will not allow the attacker to intercept it. Because media files make up a significant portion of network traffic, a digital image is chosen as the stegocontainer. It is proposed to determine the coordinates of the embedding location on the basis of iterative functions. The advantage of their use is the compactness of the description of the coordinates of the pixels in which the data will be hidden. In addition, it is proposed to use the Diffie-Gellman algorithm to transfer the parameters of iterative functions to the receiving side. This method of key distribution makes the steganographic method less vulnerable to being stolen by an attacker. The second performance criterion is the amount of hidden data. The paper found that the moderate addition of multiplicative noise makes it possible to increase the amount of hidden data without significantly reducing the visual quality of the stegocontainer. To analyze the distortions in the image-stegocontainer, which are due to the influence of noise and modification of the lower bits of pixels, the method of a quantitative assessment of visual quality is used, which is based on the laws of visual perception. Keywords: steganographic data hiding; hiding efficiency; iterative functions; Diffie-Gelman algorithm.
A No-Reference Perception Based Metric for Detection of Packet Loss Induced Artifacts in Videos

Objective quality measures are usually concerned with overall degradation in quality, disregarding impairments and artifacts such as those due to packet loss error concealment. Unfortunately, it is particularly hard to model due to its unpredictable nature and content-dependency. We propose a human perception based no-reference framework utilizing motion-compensated frame interpolation and colour gradients to identify and localize packet loss impaired regions in video frames. The unique and principal contribution of this work is the attempt to mimic the human visual system throughout the process instead of the traditional approach of trying to replicate the mean opinion score.
Genetic Optimisation of C++ Applications

Software developers sometimes use inefficient data structures or library interfaces without considering the potential impact they may have during the runtime of a program. This is due to the significant effort required to research and evaluate possibly more eﬃcient alternatives. Consequently, there is a need for tooling to automate the design space exploration. Our proposed code optimisation solution, called Artemis++, tries to address this issue with automatic exploration and transformation of data structures to optimise software performance. In preliminary testing on three mainstream C++ libraries, we have observed improvements up to 16.09%, 27.90%, and 2.74% for CPU usage, runtime and memory, respectively.
Edwin P. Hollander (1927–2020).

Memorializes Edwin P. Hollander (1927-2020). Hollander was a major figure in the study of group and organizational leadership. He received his BS from Case Western Reserve University in 1948, his MS in psychometrics, working with Robert Thorndike in 1950, and his PhD in social psychology from Columbia University in 1952. After serving as a naval aviation psychologist during the Korean War, Ed began his academic career at Carnegie Mellon, initiating work on leadership that led to a 20-year research program at the State University of New York at Buffalo where he was also founding director of the doctoral program in social/organizational psychology and provost of social sciences. In 1989, he became the City University of New York Distinguished Professor of Psychology at Baruch College until his retirement in 1999. During his career, he had many visiting appointments, including the Tavistock Institute, Harvard, Oxford, the National Academy of Sciences, and Istanbul University. Hollander also served as president of the Eastern Psychological Association and American Psychological Association (APA) Division 1, APA's Board of Social and Ethical Responsibility for Psychology (BSERP), and on the Governing Council of the International Society of Political Psychology. He received a distinguished career award from the International Association of Applied Psychology, the International Leadership Association, the Network of Leadership Scholars, the New York Academy of Sciences, and the Center for Creative Leadership. His landmark books, including Leaders, Groups, and Influence (1964) and Leadership Dynamics (1978), positioned leadership as an influence process occurring at the nexus of leader, followers, and organizational context. Ed emphasized the importance of an engaged followership to the study of leadership, rather than focusing primarily on the leader. He is perhaps best known for the idiosyncrasy credit model, a framework that details how follower perceptions affect leader behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Injection-Constrained State Estimation

In applications of state estimation involving data assimilation over a spatial region, it is often convenient, and sometimes necessary, to confine the state correction to a prescribed subspace of the state space that corresponds to the measurement location. This is the injection-constrained state-estimation problem, where the injection of the output error is constrained to a specified subspace of the state space. Unlike full-state output-error injection, which is the dual of static full-state feedback, constrained output-error injection is the dual of static output feedback. To address the injection-constrained state-estimation problem, this paper develops the injection-constrained unscented Kalman filter (IC-UKF) and the injection-constrained retrospective cost filter (IC-RCF). The performance of these filters is evaluated numerically for linear and nonlinear state-estimation problems in order to compare their accuracy and determine their suboptimality relative to full-state output-error injection. As a benchmark test case, IC-UKF and IC-RCF are applied to the viscous Burgers equation for state and parameter estimation.
x4 Super-Resolution Analysis of Magnetic Resonance Imaging based on Generative Adversarial Network without Supervised Images

Magnetic resonance imaging (MRI) is widely used in clinical medical auxiliary diagnosis. In acquiring images by MRI machines, patients usually need to be exposed to harmful radiation. The radiation dose can be reduced by reducing the resolution of MRI images. This paper analyzes the super-resolution of low-resolution MRI images based on a deep learning algorithm to ensure the pixel quality of the MRI image required for medical diagnosis. It then reconstructs high-resolution MRI images as an alternative method to reduce radiation dose. This paper studies how to improve the resolution of low-dose MRI by 4 times through super-resolution analysis based on deep learning technology without other available information. This paper constructs a data set close to the natural low-high resolution image pair through degenerate kernel estimation and noise injection and constructs a two-layer generated countermeasure network based on the design ideas of ESRGAN, PatchGAN, and VGG-19. The test shows that our method is better than EDSR, RCAN, and ESRGAN in comparing non-reference image quality evaluation indexes.
Holo-Sensory Materiality

Holo-Sensory Materiality explores the overlapping relationships between augmented reality and human sensory experience. In order to do so, it employs Microsoft HoloLens technology as a primary decision-making tool in the design workflow, and wearable bio-sensors to evaluate the human experience. The exploration of these technological overlaps offers an expanded mode of inquiry relative to a host of architectural potentials, including the dialogue with augmentation and a deeper sense of conceptual development. The presented project uses Fologram to overlay a full-scale hologram in a physical artspace as a digital projection. Its purpose is to engage people with multi-sensory experiences that force humans to re-evaluate and re-perceive our material world.
The Internet in the Territory of Literature: Analysis of Blocks Dedicated to the Poetry of I. Yusupov

In this paper, the issues of Internet technologies and the integration of literature are studied by analyzing the blocks dedicated to the poetry of I. Yusupov. The introduction part of the paper reveals the relevance of the topic and the technologies and tools used. The website, Facebook page, telegram channels and YouTube channel dedicated to the poetry of I. Yusupov were analyzed on the basis of the following criteria: rating by the number of visitors, attendance, distribution of visitors by age categories, by country, by operating systems, amount of publications, relative activity by content type, dynamics of number of subscribers and views and so on. Based on these analyzes, appropriate recommendations and conclusions have been developed.
Label Distribution Learning by Exploiting Feature-Label Correlations Locally

Label distribution learning (LDL) is a novel learning paradigm that predicts the degree of representation of multiple labels to an instance. Existing algorithms use all features to predict label distribution. However, each label is often related to part of the features, hence considering other irrelevant features may lead to deviation in both instance searching and model prediction. In this paper, we propose a new LDL algorithm by exploiting the local correlation between features and labels (LDL-LCFL). The main idea is to exploit the local correlations between features and labels, which will be used in the improved k NN algorithm for prediction. Experiments were conducted on eight well-known label distribution data sets with four distance measurements and two similarity measurements. Results show that compared with nine popular LDL methods, our algorithm's prediction ranking is superior.
Wiper Arm Recognition Using YOLOv4

Quantity control is as important as quality control for those products manufactured in the mass production phase. However, there is scarce work implementing deep learning methods in the manufacturing line, especially in wiper arm recognition. This paper proposed a deep learning-based wiper arm recognition for the windshield wiper manufacturer to reduce human error and workforce requirement in quantity control. The proposed method applied the state-of-the-art YOLOv4 object detection algorithm. Our proposed method able to achieve 100% in terms of precision, recall, F1-score, and mean average precision. Moreover, the proposed method can make correct predictions under several conditions: object occlusion, different scales of objects, and different light environments. In term of speed, the proposed method can be predicted up to 30.55 fps when using a moderate GPU.
DSAMT: Dual-Source Aligned Multimodal Transformers for TextCaps

When generating captions for images, previous caption methods tend to consider the visual features of the image but ignore the Optical Character Recognition (OCR) in it, which makes the generated caption lack text information in the image. By integrating OCR modal as well as visual modal into caption prediction, TextCaps task is aimed at producing concise sentences recapitulating the image and the text information. We propose Dual-Source Aligned Multimodal Transformers (DSAMT), which utilize words from two sources (object tags and OCR tokens) as the supplement to vocabulary. These extra words are applied to align caption embedding and visual embedding through randomly masking some tokens in caption and calculating the masked token loss. A new object detection module is used in DSAMT to extract image visual features and object tags on TextCaps. We additionally use BERTSCORE to evaluate our predictions. We demonstrate our approach achieves superior results compared to state-of-the-art models on TextCaps dataset.
Teachers’ Insights on the Development of Personal Competence of Pupils with Visual Impairments in Music Schools

According to the legal acts of the European Union and Lithuania regulating the aims and peculiarities of the organization of education, non-formal education and learning and educational assistance for pupils with special educational needs, scientific research of Lithuanian and foreign authors, it can be stated that musical activity for pupils with special educational needs benefits revealing and strengthening of their personal competence. However, the research on development of personal competence through non-formal and formal education, complemented with music and art programs for children with special needs, especially students with visual impairments, is insufficient.&#x0D; The aim of the research is to reveal teachers’ insights on the development of personal competence of pupils with visual impairments in music schools. To reveal the aim of this work and to reach its goals qualitative research based on qualitative content analysis is used. The methods to collect information are: semi-structured interviews and monitoring.&#x0D; Personal competence can be defined as corresponding components, that make up three types of skills: self-understanding (well-being, self-expression, independence), self-evaluation (self-assessment, self-esteem, self-confidence), self-control (self-regulation, decision-making, responsibility, sociability). The results of the semi-structured interview with 5 teachers suggest that purposeful development of personal competence of pupils with visual impairments can help to create and implement already existing social skills, to strengthen and expand them with practical experience. Music schools have an opportunity to educate pupils with visual impairments by individualizing curricula, enabling them to disclose their talents and meeting their individual needs for self-expression, nurturing innate powers, preparing for autonomous life, understanding common values, which will later be used as a framework for their future lives.
Was this the real Web? Quantitative overview of the Polish… ccTLD Internet Archive data (1996–2001)

This article is an attempt to build a quantitative panorama of the Polish country code top-level domain (ccTLD) in the years 1996–2001 on the basis of data generously provided by the Internet Archive. The purpose of analyzing over 72 million captures is to show that these resources have limited potential in reconstructing the early Polish Web. The availability of historical Web resources and tools for their easy exploration in no way determines their potential value and usefulness in research, even if we do not have access to alternative sources. Czy to był prawdziwy Web? Ilościowy przegląd polskiej domeny krajowej w zbiorach Internet Archive (1996–2001) Artykuł przedstawia ilościowy opis zasobów polskiej domeny krajowej (country code top-level domain, ccTLD) z lat 1996–2001, dostępnych w zbio­rach Wayback Machine, archiwum Webu prowadzonym przez Internet Archive. Celem analizy ponad 72 mln archiwizacji (captures) jest wykaza­nie, że zasoby te mają ograniczony potencjał w rekonstruowaniu polskiego wczesnego Webu. Dostępność historycznych zasobów WWW i narzędzi do ich łatwej eksploracji w żaden sposób nie przesądza o ich potencjalnej wartości i przydatności w badaniach, nawet jeśli nie mamy dostępu do al­ternatywnych źródeł.
HUBUNGAN ANTARA KEBIASAAN LITERASI TERHADAP KETERAMPILAN BERBICARA DI KELAS 3 MIN 1 PRINGSEWU

Abstract&#x0D; The problem in this study is that there are still students who have not been fluent in reading, and the lack of interest of students in this regard so that middle-class students in the level of development of the child still do not master the material at school. This goal is to find out the relationship between the habits of literacy towards speaking skills in class 3 MIN 1 Pringsewu. This type of research is quantitative research. Data collection techniques using questionnaires ( Questionnaries ) observation, interviews. The population in this study amounted to 217 students. The sample in this study were 25 students with cluster techniques at MIN 1 Pringsewu sub-district Pringsewu district. Data were analyzed using regression tests. With the known value of tarithmetic = 3,263 with a significant value of 0,580 &lt; 0,05, then H0 is accepted which means there is a ( significant ) effect of trust ( x ) variable on the participant variable ( Y ).&#x0D; &#x0D; Keyword : Related Literacy, Speaking Skills&#x0D; &#x0D; Abstrak &#x0D; &#x0D; Masalah dalam penelitian ini adalah masih adanya siswa yang belum lancar dalam membaca, dan kurangnya minat siswa dalam hal tersebut sehingga siswa tingkat kelas menengah tingkat perkembangan anak tersebut masih belum menguasai materi di sekolah. Tujuan ini untuk mengetahui hubungan antara kebiasaan literasi terhadap keterampilan berbicara di kelas 3 MIN 1 Pringsewu. Jenis penelitian ini adalah penelitian kuantitatif, teknik pengumpulan data menggunakan angket ( kuesioner ) observasi, wawancara. Populasi dalam penelitian ini berjumlah 217 siswa. Sampel dalam penelitian ini adalah 25 siswa dengan teknik cluster di MIN 1 Pringsewu Kecamatan Pringsewu Kabupaten Pringsewu. Data dianalisis menggunakan uji regression. Dengan diketahui nilai t hitung = 3,263 dengan nilai siginifikan 0,580 &lt; 0,05, maka H0 ditolak dan H1 diterima yang berarti ada pengaruhnya yang nyata ( signifikan ) variable trust ( x ) terhadap variable partisipan ( Y ).&#x0D; Kata kunci : Keterkaitan Literasi, Keterampilan Berbicara
Synthesis of Line of Sight Angle Coordinate Filter on the Basis of Interactive Multi-Model Evaluation Algorithm

On the basis of the tracking multi-loop target angle coordinate system, the article has selected and proposed a interactive multi-model adaptive filter algorithm to improve the quality of the target phase coordinate filter. In which, the 3 models selected to design the line of sight angle coordinate filter; Constant velocity (CV) model, Singer model and constant acceleration model, characterizing 3 different levels of maneuverability of the target. As a result, the evaluation quality of the target phase coordinates is improved because the evaluation process has redistribution of the probabilities of each model to suit the actual maneuvering of the target. The structure of the filters is simple, the evaluation error is small and the maneuvering detection delay is significantly reduced. The results are verified through simulation, ensuring that in all cases the target is maneuvering with different intensity and frequency, the line of sight angle coordinate filter always accurately determines the target angle coordinates.
ESP: A NEEDS ANALYSIS OF MARITIME ENGLISH AT POLIMARIN

As an archipelagic country, Indonesia has long been known for its marine potential. This potential comes with new challenges, one of them being language problem. In this regard, English shows its prominent role as the international language to bridge communications. This research provided a needs analysis to identify the “necessities”, “lacks”, and “wants” in relation with English usage in maritime sector, specifically for those conducting internship at international ships. A qualitative approach and a descriptive design were applied in this research. The respondents consisted of 19 students of Polimarin who had completed internship at international ships, selected through total sampling technique. Their responses were collected by a questionnaire, complemented by interview. The collected data were then processed through a model consisting of four stages; data collection, data reduction, data display, and conclusion drawing. The “necessities” analysis revealed speaking and vocabulary as the most needed language skill and component. The “lacks” analysis showed that the syllabus used pays attention to English language skills and components evenly and that some respondents were underprepared before doing internship. The “wants” analysis came up with the simple present tense and the simple past tense as the most wanted tenses to master, pronouns as the most wanted grammatical feature and passive voice as the least wanted, all language functions evenly wanted, and group discussion as the most preferred learning activity. In conclusion, syllabus revision is due to improve the quality of English learning by accommodating the students’ “necessities”, “lacks”, and “wants”. Key words:ESP, Maritime English, Needs Analysis
The Influence of Blockchain Technology Factors on the Four Aspects of Organizational Behavior

Blockchain technology, as appeared in several studies, is perceived to improve organizational performance. Historically, previous research indicated that technology is one of four essential aspects of organizational behavior. However, the research on the impact of Blockchain technology's influence on organizational behavior is in the early phase. This qualitative research with bibliography technique is facilitated by Publish and Perish tools aiming to explore the essential factors of Blockchain Technology that influence four essential factors (people, structure, environment, and technology) of Organizational Behavior including confidentiality, reliability, and authenticity of data. In this frame, the research collected 34 selected papers — the 16 essential factors found in this study impacted the behavioral organization directly. The study result is essential for the industry that will implement Blockchain to prepare the strategy and provide a clear picture of Blockchain technology's impact on industry behavioral organization
Image Stitching with Locally Shared Rotation Axis

We consider the problem of stitching image sequences with cameras undergoing pure rotational motion. We leverage the assumption of a locally constant rotation axis, i.e., neighboring frames have a shared but unknown rotation axis. This assumption holds in many common image capturing scenarios, e.g., panoramic sweeping motions. Using this additional constraint, we develop techniques for three-view camera rotation estimation; a minimal solver for the two-view estimation with a known rotation axis; and a globally optimal robust estimator for the two-view case. We show on publicly available datasets that the proposed methods lead to camera rotation estimation superior to the state-of-the-art in terms of accuracy with comparable run-time. The source code will be made available.