Reviewerly/exHarmony huggingface dataset:
Real-time on-site inspection system for power transmission based on heterogeneous computing

As the need of power supply is tremendously increasing in modern society, the stableness and reliability of the power delivery system are the two essential factors that ensure the power supply safety. With the quick expansion of electricity infrastructures, the failures of power transmission system are becoming more frequent, leading to economic loss and high risk of maintenance work under hazardous conditions. The existing automatic power line inspection utilizes advanced convolutional neural network (CNN) to improve the inspection efficiency, emerging as one promising solution. But the needed computational complexity is high since CNN inference demands large amount of multiplication-and-accumulation operations. In this paper, we alleviate this problem by utilizing the heterogeneous computing techniques to design a real-time on-site inspection system. Firstly, the required computational complexity of CNN inference is reduced using FFT-based convolution algorithms, speeding up the inference. Then we utilize the region of interest (ROI) extrapolation to predict the object detection bounding boxes without CNN inference, thus saving computing power. Finally, a heterogeneous computing architecture is presented to accommodate the requirements of proposed algorithms. According to the experiment results, the proposed design significantly improves the frame rate of CNN-based inspection visual system applied to power line inspection. The processing frame rate is also drastically improved. Moreover, the precision loss is negligible which means our proposed schemes are applicable for real application scenarios.
Cryptoanalysis and enhancement of a binary image encryption system based on interference

In this paper, cryptoanalysis on a binary image encryption system based on interference is conducted. In the cryptosystem under study, the binary plaintext image modulated by a random phase mask (RPM) is separated directly into two phase-only masks (POMs) as private keys. Phase wrapping operation is applied to modulate two separated POMs further for silhouette removal. The plaintext image can be reconstructed by compositing two phase-wrapped POMs. However, since the RPM used in the encryption process is irrelative to the plaintexts, it is possible to retrieve the RPM by a known-plaintext attack (KPA). And then with the help of the retrieved RPM, the information encoded in the arbitrarily given ciphertext can be reconstructed by a ciphertext-only attack (COA). Based on our analysis, a hybrid attack including a KPA and a COA with different constraints is proposed in this study. Besides, the cryptosystem under study can only be used to encode binary plaintexts, which would limit the application of this scheme in the information security. Consequently, an improved cryptosystem in which both binary and gray-scale plaintext images can be encoded is proposed. In addition, the RPM to generate two private keys in the enhanced system is dependent on the plaintexts, which makes the proposed encryption scheme immune to the proposed hybrid attack. The feasibility and effectiveness of the security-enhanced cryptosystem have been validated by numerical simulations.
No‐reference image quality assessment based on multiscale feature representation

The no-reference image quality assessment (NR-IQA) method can evaluate the distortions in an image without the reference image. However, due to the diversity of the image contents and distortion types, it is hard for the existing NR-IQA algorithms to obtain competitive performance on both synthetically and authentically distorted images. To address the problem, a multiscale feature representation-based NR-IQA method that performs well for both synthetic and authentic distortions is proposed. This model consists of two parts: The feature extraction part and the feature fusion part. First, part of the Res2Net-50 network is chosen as the feature extraction part due to its high ability in increasing the range of receptive fields. Then, the feature fusion part consisting of a novel residual block and two fully connected layers is designed to fuse the extracted features and realize the quality score mapping. After a series of stepwise optimization experiments, the most competitive network architecture consisting of the feature extraction part and the feature fusion part is obtained. Comprehensive experiments on the LIVE, TID2013, CSIQ, KADID-10k, KonIQ-10K, and LIVE challenge databases demonstrate that the proposed method can work powerfully on both the synthetic and authentic distortions and also has a strong generalization ability.
SWOT Analysis of Educational Process in Digital Educational Environment of University

The purpose of the study is to identify the features peculiar to the process of educating students in the digital educational environment (hereinafter referred to as DEE) of a university. The article analyses changes in legal documents in the field of education, as a result of which it is determined that the educational process, being a part of the overall teaching/learning process at a university that is aimed at developing well-integrated individuals in the conditions of DEE, provides for integration with digital resources. Using the results of a SWOT analysis, the authors characterise the features of education in a university’s DEE, which accounts for scientific novelty of the study. As a result, it is ascertained that the process of educating students in a university’s DEE involves taking into account new features caused by digitalisation, which bring about corresponding transformations in the choice of content, forms and methods of the educational process.
Advancements in Applications of Quantum Entanglement

Abstract Quantum entanglement, an unknown “spooky action” that confused scientists like Einstein, has become one of the research hotspots in quantum mechanics. Based on the technique, applications distributed among diverse fields have appeared, e.g., cryptography, computer science, distant communication, etc, which has attracted interest from researchers in varied fields. This article reviews the basics of manifestations of quantum entanglement, namely quantum key distribution, quantum computation, and quantum teleportation. An overview of milestones is presented, e.g., Bennett-Brassard 1984 protocol, entanglement-based quantum computers, Shor’s and Grover’s algorithm as well as the process of transporting quantum states at an unprecedented speed. Moreover, the implementations of quantum entanglement are demonstrated, evolving these technologies to higher levels. Finally, a brief conclusion and expectations for future developments on entanglement are given.
Nonparametric semi-supervised classification with application to signal detection in high energy physics

Abstract Model-independent searches in particle physics aim at completing our knowledge of the universe by looking for new possible particles not predicted by the current theories. Such particles, referred to as signal , are expected to behave as a deviation from the background , representing the known physics. Information available on the background can be incorporated in the search, in order to identify potential anomalies. From a statistical perspective, the problem is recasted to a peculiar classification one where only partial information is accessible. Therefore a semi-supervised approach shall be adopted, either by strengthening or by relaxing assumptions underlying clustering or classification methods respectively. In this work, following the first route, we semi-supervise nonparametric clustering in order to identify a possible signal. The main contribution consists in tuning a nonparametric estimate of the density underlying the experimental data to identify a partition which guarantees a signal warning while allowing for an accurate classification of the background. As a side contribution, a variable selection procedure is presented. The whole procedure is tested on a dataset mimicking proton–proton collisions performed within a particle accelerator. While finding motivation in the field of particle physics, the approach is applicable to various science domains, where similar problems of anomaly detection arise.
Chaos embedded opposition based learning for gravitational search algorithm

Due to its robust search mechanism, Gravitational search algorithm (GSA) has achieved lots of popularity from different research communities. However, stagnation reduces its searchability towards global optima for rigid and complex multi-modal problems. This paper proposes a GSA variant that incorporates chaos-embedded opposition-based learning into the basic GSA for the stagnation-free search. Additionally, a sine-cosine based chaotic gravitational constant is introduced to balance the trade-off between exploration and exploitation capabilities more effectively. The proposed variant is tested over 23 classical benchmark problems, 15 test problems of CEC 2015 test suite, and 15 test problems of CEC 2014 test suite. Different graphical, as well as empirical analyses, reveal the superiority of the proposed algorithm over conventional meta-heuristics and most recent GSA variants.
Responding to Reality: Evolving Curation Practices and Infrastructure at the University of Illinois at Urbana-Champaign

Objective: The Illinois Data Bank provides Illinois researchers with the infrastructure to publish research data publicly. During a five-year review of the Research Data Service at the University of Illinois at Urbana-Champaign, it was recognized as the most useful service offering in the unit. Internal metrics are captured and used to monitor the growth, document curation workflows, and surface technical challenges faced as we assist our researchers. Here we present examples of these curation challenges and the solutions chosen to address them. Methods: Some Illinois Data Bank metrics are collected internally by within the system, but most of the curation metrics reported here are tracked separately in a Google spreadsheet. The curator logs required information after curation is complete for each dataset. While the data is sometimes ambiguous (e.g., depending on researcher uptake of suggested actions), our curation data provide a general understanding about our data repository and have been useful in assessing our workflows and services. These metrics also help prioritize development needs for the Illinois Data Bank. Results and Conclusions: The curatorial services polish and improve the datasets, which contributes to the spirit of data reuse. Although we continue to see challenges in our processes, curation makes a positive impact on datasets. Continued development and adaptation of the technical infrastructure allows for an ever-better experience for the curators and users. These improvements have helped our repository more effectively support the data sharing process by successfully fostering depositor engagement with curators to improve datasets and facilitating easy transfer of very large files.
Sentence Matching with Deep Self-attention and Co-attention Features

Sentence matching refers to extracting the semantic relation between two sentences which is widely applied in many natural language processing tasks such as natural language inference, paraphrase identification and question answering. Many previous methods apply a siamese network to capture semantic features and calculate cosine similarity to represent sentences relation. However, they could be effective for overall rough sentence semantic but not sufficient for word-level matching information. In this paper, we proposed a novel neural network based on attention mechanism which focuses on learning richer interactive features of two sentences. There are two complementary components in our model: semantic encoder and interactive encoder. Interactive encoder compares sentences semantic features which are encoded by semantic encoder. In addition, semantic encoder considers the output of interactive encoder as supplementary matching features. Experiments on three benchmark datasets proved that self-attention network and cross-attention network can efficiently learn the semantic and interactive features of sentences, and achieved state-of-the-art results.
Siamese recurrent neural networks for the robust classification of grid disturbances in transmission power systems considering unknown events

The automated identification and localisation of grid disturbances is a major research area and key technology for the monitoring and control of future power systems. Current recognition systems rely on sufficient training data and are very error-prone to disturbance events, which are unseen during training. This study introduces a robust Siamese recurrent neural network using attention-based embedding functions to simultaneously identify and locate disturbances from synchrophasor data. Additionally, a novel double-sigmoid classifier is introduced for reliable differentiation between known and unknown disturbance types and locations. Different models are evaluated within an open-set classification problem for a generic power transmission system considering different unknown disturbance events. A detailed analysis of the results is provided and classification results are compared with a state-of-the-art open-set classifier.
Features of Use of Paradigmatic Approach in Research on History of Education and Pedagogical Thought

The article identifies the features and substantiates the algorithm for using the paradigmatic approach in research on the history of education and pedagogical thought. In this regard, the concept of “pedagogical paradigm” is clarified, which is considered as a theoretical position or theory accepted by the scientific community as a standard, a model for solving research problems and capable of justifying and implementing a holistic model of education. It is shown that in the course of competition between different approaches to solving pedagogical problems the so-called binary oppositions are formed – polar points of view, and the opposition between them reflects the genesis of pedagogical theory within certain chronological frameworks. Attention is paid to quantization, which is the deployment of phases of the pedagogical paradigm. On this basis, the algorithm has been defined, i.e. the sequence of actions when applying the paradigmatic approach in research on the history of education and pedagogical thought, including: analysis of philosophical and pedagogical concepts, ideas, positions that led to qualitative changes in public education policy, theory and practice of education during the period under analysis; identification within the pedagogical concepts of binary oppositions – polar points of view on the essence of education, the criteria for selecting the content of education, forms and methods of educational activities; determining the impact of new pedagogical paradigms on mass educational practice.
Unsupervised Clustering of Comments Written in Albanian Language

Now-a-days, social media and communications in social media have become very important for services providers and those play a key role in service quality improvement as well as in decision making.The services consumers' discussions usually are written in their local languages and extracting important knowledge sometimes is very hard and problematic.In this field the natural language processing techniques are helpful, but different languages have their specifics and difficulties, and some languages are not prosperous enough in the techniques and methods on NLP, especially the local speaking of the language.In this scientific paper, we have tried to solve such a problem for the Albanian language spoken in Kosovo.Namely, for a dataset of the comments, written in Albanian language in Kosovo (local speaking), collected from the social media, by use of unsupervised clustering techniques, to make clustering regarding the topic of discussion in the comment.In this research, the different techniques of text feature extraction (vectorization and others) and clustering algorithms (K-means, Spectral, Agglomerative, etc.), are used with the idea to find and define more appropriate techniques for the Albanian language.In this paper are shown the results of the conducted experiments as well as discussions about what to use in case of the Albanian language and other languages similar or in group with Albanian (those which have a weak NLP).
User Experience Design (UXD) Competency Model: Identifying Well-Rounded Proficiency for User Experience Designers in the Digital Age

Twentieth-century principles of designMost college-level design programs in the United States trace their curricular heritage and pedagogical practices to the Bauhaus and other modernist efforts to improve the function and appearance of messages, products, and environments.The typical location of design study in departments or colleges of art emphasized the visual qualities of objects, mastery of craft, and individual authorship as core values.And the trade-based history of design encouraged resistance to the development of theory and substantive study across disciplines for much of the twentieth century.The principles guiding design practice in the decades following World War II were those of an industrial economy.Design was improved through a linear, causal process of incremental change that identified root causes of friction and eliminated problems one at a time.Constraints defining the scope of design work were known, mostly limited to physical objects and their production, and perceived to be consistent in their influence over time.A primary role for design was to reduce the number of interacting variables in the problem context; to simplify complexity, not only in form, but also among competing priorities that required resolution.The stopping point of the design process was "almost perfect."The design of new editions or related objects repeated the development process for each variation.Strategically, design was at the cosmetic end of top-down decision-making and appearance differentiated companies in the marketplace.Communication strategists viewed people in broad demographic terms, targeting them as passive consumers of segmented marketing, not through substantive differences in the qualities of their experiences.Alistair Parvin describes design of this era as something done to and for people, not with or by people.The content of design education mirrored these principles and the production technologies of the time.Students' college introduction to design was typically through a series of abstract formal investigations, deferring discussions of audience and meaning until after mastering a limited visual vocabulary presumed to be universally appropriate to any message or context.Typographic competencies were built through carefully scaffolded assignments of ascending scale-from letter to word, paragraph to page, and finally to document.The study of typography was separated from the study of image, integrated only in later applications and rarely linked to investigations of language or changing demands on readers.College textbooks reinforced this simple-to-complex sequence of content, long after software asked designers for simultaneous decisions about typeface, point size, line spacing, margins, and column width and style before the designer could set a single letterform.
Quantum Rauch-Tung-Striebel smoothed state

Smoothing is a technique that is used to estimate the state of a system using measurement information both prior and posterior to the estimation time. Two notable examples of this technique are the Rauch-Tung-Striebel and Mayne-Fraser-Potter smoothing techniques for linear Gaussian systems, both resulting in the optimal smoothed estimate of the state. However, when considering a quantum system, classical smoothing techniques can result in an estimate that is not a valid quantum state. Consequently, a different smoothing theory was developed explicitly for quantum systems. This theory has since been applied to the special case of linear Gaussian quantum (LGQ) systems, where, in deriving the LGQ state smoothing equations, the Mayne-Fraser-Potter technique was utilized. As a result, the final equations describing the smoothed state are closely related to the classical Mayne-Fraser-Potter smoothing equations. In this paper, I derive the equivalent Rauch-Tung-Striebel form of the quantum state smoothing equations, which further simplify the calculation for the smoothed quantum state in LGQ systems, and I provide insight into the dynamics of the smoothed quantum state. This form of the LGQ smoothing equations brings to light a property of the smoothed quantum state that was hidden in the Mayne-Fraser-Potter form, namely the nondifferentiability of the smoothed mean. By identifying the nondifferentiable part of the smoothed mean, I am then able to derive a necessary and sufficient condition for the quantum smoothed mean to be differentiable in the steady-state regime.
Typology emerges from simplicity in representations and learning

We derive well-understood and well-studied subregular classes of formal languages purely from the computational perspective of algorithmic learning problems. We parameterise the learning problem along dimensions of representation and inference strategy. Of special interest are those classes of languages whose learning algorithms are necessarily not prohibitively expensive in space and time, since learners are often exposed to adverse conditions and sparse data. Learned natural language patterns are expected to be most like the patterns in these classes, an expectation supported by previous typological and linguistic research in phonology. A second result is that the learning algorithms presented here are completely agnostic to choice of linguistic representation. In the case of the subregular classes, the results fall out from traditional model-theoretic treatments of words and strings. The same learning algorithms, however, can be applied to model-theoretic treatments of other linguistic representations such as syntactic trees or autosegmental graphs, which opens a useful direction for future research.
Extraction and evaluation of formulaic expressions used in scholarly papers

Formulaic expressions, such as ‘in this paper we propose’, are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is ‘showing the aim of this paper’. Thus, resources of formulaic expressions, such as a dictionary, that could be looked up easily would be useful. However, forms of formulaic expressions can often vary to a great extent. For example, ‘in this paper we propose’, ‘in this study we propose’ and ‘in this paper we propose a new method to’ are all regarded as formulaic expressions. Such a diversity of spans and forms causes problems in both extraction and evaluation of formulaic expressions. In this paper, we propose a new approach that is robust to variation of spans and forms of formulaic expressions. Our approach regards a sentence as consisting of a formulaic part and non-formulaic part. Then, instead of trying to extract formulaic expressions from a whole corpus, by extracting them from each sentence, different forms can be dealt with at once. Based on this formulation, to avoid the diversity problem, we propose evaluating extraction methods by how much they convey specific communicative functions rather than by comparing extracted expressions to an existing lexicon. We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence. Experimental results show that the proposed extraction method achieved the best performance compared to other existing methods.