Reviewerly/exHarmony huggingface dataset:
Development a Deterministic Six-State Quantum Protocol(6DP) Using Optisystem Simulator

Quantum Key Distribution (QKD) is a secure communication that uses a quantum encryption protocol in quantum physics using the principle of quantum mechanics. It solves the problems of key distribution between the sender and receiver and uses a special quantum channel to transfer keys in classical computers and also in the field of quantum computing (QC) in the future. Various protocols are designed in quantum key distribution to ensure the security and integrity of data from any surveillance or attack from some eavesdroppers when sending quantum states (photons) through a special quantum communication channel. The analysis of the device is used in the effective simulation process using quantum entanglement technology. The use of OptiSystem software is a simulation application Optical is used in telecommunications. When using for 6DP in distributing quantum keys across the optical fiber channel, quantitative results appear in 6 using networking technology. We improved in the sifted key rate and the QBER rate. Through these results, we believe 6DP is more secure and reliable. For the rest of the protocols in the distribution of quantum keys using quantum entanglement.
Professional Subjective Activity of the Student of Pedagogical University

Currently, researchers in the field of education and upbringing are discovering connections and dependencies that were not previously identified: upbringing and subjectivity, upbringing and personal meanings, upbringing and value choice. The article actualizes the problem of professional subjective activity of a teacher and its impact on the effectiveness of educational activities. The teaching practice existing in pedagogical universities needs to be enriched by the experience of social and scientific activities of students - future teachers. Professional subjective activity of students will create conditions for effective educational activity. The purpose of the article is to analyze the concept of “professional subjective activity” in the context of its significance for the educational activity of a future teacher. Methodology. The study was conducted using a descriptive method by collecting, analyzing and summarizing scientific literature data on the problem under study in order to determine its relevance. The content analysis of scientific articles on the problem of professional subjective activity as conditions for effective educational activity of the future teacher was applied. The concepts of “activity”, “subjectivity”, “professional subjectivity” are considered. In conclusion, it is concluded that the effectiveness of the educator’s educational activity is due to the presence of his professional subjective activity. A generalized definition of the concept of “professional subjective activity” is given.
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language

Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%).
Dryad: Interview with Jen Gibson

In October Jen Gibson started as the new Executive Director for the Dryad Data Repository. I used the opportunity to ask Jen a few questions about Dryad, challenges with data sharing, and ideas about moving Dryad forward. I was particularly interested in the interview as I served on the Dryad Board of Directors from 2013 to 2016.
Sparse Attention with Linear Units

Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants. In this work, we introduce a novel, simple method for achieving sparsity in attention: we replace the softmax activation with a ReLU, and show that sparsity naturally emerges from such a formulation. Training stability is achieved with layer normalization with either a specialized initialization or an additional gating function. Our model, which we call Rectified Linear Attention (ReLA), is easy to implement and more efficient than previously proposed sparse attention mechanisms. We apply ReLA to the Transformer and conduct experiments on five machine translation tasks. ReLA achieves translation performance comparable to several strong baselines, with training and decoding speed similar to that of the vanilla attention. Our analysis shows that ReLA delivers high sparsity rate and head diversity, and the induced cross attention achieves better accuracy with respect to source-target word alignment than recent sparsified softmax-based models. Intriguingly, ReLA heads also learn to attend to nothing (i.e. ‘switch off’) for some queries, which is not possible with sparsified softmax alternatives.
Fine-tuning Neural Language Models for Multidimensional Opinion Mining of English-Maltese Social Data

This paper presents multidimensional Social Opinion Mining on user-generated content gathered from newswires and social networking services in three different languages: English -a high-resourced language, Maltese -a low-resourced language, and Maltese-English -a code-switched language.Multiple fine-tuned neural classification language models which cater for the i) English, Maltese and Maltese-English languages as well as ii) five different social opinion dimensions, namely subjectivity, sentiment polarity, emotion, irony and sarcasm, are presented.Results per classification model for each social opinion dimension are discussed.
New Security Approach for IoT Applications as a Cloud Services

Integration IoT applications and cloud services that are appearance in the pioneer IT solution, is a most important issue in the current research. IoT is a preferable technology that is following different ways in produce and lunch about things (sensors, devices or machines) such that communicated together under an advanced network. Also, cloud computing (CC) has the new approach for IT resource management and the new platforms for service development, when a broad access network is available. Therefore, IoT and cloud computing (CC) should be considered as two critical paradigms about IT resources, services and things under large access interconnections and ubiquitous networkaccess. Aggregation of cloud computing (CC) and IoT technologies, which we write CCIoT as its acronyms, may be a revolution in the future of the IT utilization, so much different from which we have seen in the past decades. This fact is happened when their multi research lines will be succeeded. In this regard, we need to response to the main research parts: 1) advanced smart things in the CCIoT terminals, 2) decentralized and live communication interof things, 3) unlimited resource with access facilitated, and also 4) high security where is divided in the four subjects: data access and protection, things management, secure CCIoT interconnection protocols for devices, and services monitoring. In this research we will study CCIoT security aspects for deterministic problems. At first we have review and select a best configuration for an IoT application where hosted to the cloud platform. Then we redefine our security problem where is confined for some functions. For improve these functions we implement new proposed methods where it maybe cover one or New Security Approach for IoT Applications as a Cloud Services . Ali Hussein Shamman Wael Jabbar abed Al-nidawi Zaid Farooq Salih Saba Kamil Jaafar Al-wassiti Almustaqbal University College1.2, Universiti Kebangsaan Malaysia3,4 alial-safi@mustaqbal-college.edu.iq Dr.WaelAl-nidawi@mustaqbal-college.edu.iq zaid.iphone0@gmail.com p103496@siswa.ukm.edu.my ISSN (Printed): 2314-7350 ISSN (Online): 2231-8852 2 Journal of Advanced Computer Science and Technology Research, Vol 10, No1, March 2020, 1..-20. more security threat. The result of our work will be comparing to the other ways and carefully reported. Result of this research may be useful in the part of proposed secure configuration of Cloud and IoT when they are provide common service and also improve security functions resistance.
TRISH - a Triggerless Random Interleaved Sampling Hardware

We present a sampling hardware that can be used as a demonstration system for the triggerless random interleaved sampling method. The hardware is capable of acquiring sample-trains of lengths 2, 3 and 4 with a programmable inter-sample distance. Each sample-train consists of samples which originate from separate ADC channels. The whole system is supervised by a single board computer, Raspberry Pi, which also forms a communication interface for the system. The paper proposes a calibration method, which allows for reduction of the interchannel vo ltage offsets, equalization of the voltage gains in the individual ADC channels, as well as equalization of the inter-sample time distances.
Formation of "soft" skills in the educational university space while teaching foreign languages

The article analyses the appropriate methodological approaches in which the ideas of forming and developing of flexible skills can be applied in seminars and tutorials in such disciplines as "Foreign language", "Foreign language in the professional sphere", "Business foreign language" implementing at the university. The formation and development of flexible skills in such disciplines on the one hand, as an independent learning goal, involves the development of a range of skills necessary for the implementation of various types of activities, and on the other hand, as a means of developing foreign-language communicative competence, increasing the students level of foreign-language socialization. Technologies for the development of critical thinking and problem-based learning, project activities, learning in collaboration, case studies, a rotational model of mixed learning. can be applied for the formation and development of flexible skills at the university.
When Worlds Collide: Protecting Physical World Interests against Virtual World Malfeasance

If a virtual-world-game character is cast upon real-world property without the consent of the landowner, inducing or encouraging players to trespass, is the virtual-world creator liable for damages? The United States Supreme Court has recognized that digital technology presents novel issues, the resolution of which must anticipate its further rapid development. It is beyond dispute that protective legislation will be unable to keep up with rapidly evolving technology. The burden of anticipating and addressing issues presented by emerging technologies will ultimately fall upon the businesses responsible for generating them. This duty was most notably adopted by the creators of Pokemon Go in settlement of nuisance and trespass claims brought by a nationwide class seeking injunctive relief from the placement of virtual Pokestops and Pokemon Gyms (“Gyms”) on real property. This article is the first to address this landmark settlement and proposes that future developers and creators seeking to avoid similar liability exposure implement selfregulatory practices, such as Value Sensitive Design, to create human values-based frameworks within which they can create and advance technologies. The societal need and social impact of such selfregulation is clearly illustrated by emerging litigation seeking to hold virtual-world actors responsible for real-world consequences utilizing common law tort theories. In the absence of legislation, as case law develops, self-regulatory frameworks like Value Sensitive Design are essential to create constructs within which creators can develop technologies that consider human values, address civic concerns, and avoid lawsuits, while still achieving commercial and technological objectives.
Layanan Library and Knowledge Center (LKC) Bina Nusantara University di Era Pandemi Covid-19

The covid-19 pandemic has succeeded in changing the order of life, one of which is in the field of libraries. This also changes the services carried out in the BINUS University library. The BINUS University Library has made several adjustments to some of its services. Services that are usually done in person and face-to-face are now done online. This online service is also an example of using computers as a medium for communication. This computer-mediated communication is reflected in several services and innovations carried out by the BINUS University library. This article aims to describe the services and innovations carried out by the BINUS University library. The method used is direct interview and literature study. The services provided by the BINUS University library are asynchronous, that is, they are carried out in a different time and space, and synchronous, that is, they are carried out in the same time and space. The results of the study show that several services and innovations have been carried out, such as reference clinics, cross-service services, document provision, and plagiarism checking, which are now also carried out online. Circulation services in the form of a book delivery system have also been improved during the pandemic. The BINUS University library has also made several innovations in its services in the pandemic era. These innovations include the payment of fines through Go-Pay and QRIS BCA, a virtual tour library service, and libtalks.
The Role of Village Governments in Reducing the Risk of Flood Disaster Through Disaster Preparedness Villages

This study aims to identify and describe the role of the village government in reducing flood risk through the Disaster Alert Village in Kademangan Village, Mojoagung Jombang. The type of research is descriptive qualitative research. Data collection techniques using observation techniques, interviews, and documentation. The technique of determining the informant uses a purpose sampling technique. Data analysis was carried out using data reduction techniques, data presentation, and drawing conclusions. The results showed that the role of the Kademangan village government in reducing flood risk through the Disaster Alert Village, namely: the government's role as a regulator by making regulations to reduce flood risk in Kademangan village by forming an organization Disaster Alert Village. The government's role as a dynamizer is a driving force to move the community to participate in overcoming problems or obstacles that occur by realizing the success of the goals to be achieved. The role of the government as a facilitator is to provide facilities for communities affected by floods.
Imitation with Neural Density Models

We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward. Our approach maximizes a non-adversarial model-free RL objective that provably lower bounds reverse Kullback–Leibler divergence between occupancy measures of the expert and imitator. We present a practical IL algorithm, Neural Density Imitation (NDI), which obtains state-of-the-art demonstration efficiency on benchmark control tasks.
LibGuides: LI855 Elementary Nonfiction Collection: Home

Nonfiction collection for Fruit Valley Elementary School. Created for LI855, Collection Development and Management.
Hybrid Features Enabled Adaptive Butterfly Based Deep Learning Approach for Human Activity Recognition

Human activity recognition framework is facing many challenges and issues that are promoting the development of a new activity recognition system to improve accuracy under realistic conditions. Therefore, efficient human activity detection is proposed in this paper. At first, the input video is converted into frames then the keyframes are selected using a structural similarity measure. Next, the local and global features are selected from the video frames. Finally, the selected features are fed to the classifier for identifying human activity. In the proposed method, the traditional deep learning algorithm is improved by applying optimization techniques. Adaptive Monarch Butterfly Optimization algorithm (AMBO) is used to improve the performance of the traditional deep learning algorithm. Monarch Butterfly Optimization Algorithm (MBO) is a population-based natural inspired algorithm. It mimics the foraging and the social behavior of the butterflies. The optimal parameters are selected for activity detection. The proposed method is computed based on accuracy, sensitivity, and specificity and implemented using the MATLAB platform. The experimental results show activity recognition accuracy improved to 96%.
Styling and Theming a Web App

This chapter introduces the basics of styling and theming a web app. First, it introduces styling an HTML web page. Subsequently, it acquaints you with the Cascade Styling Sheet (CSS) and calls a CSS code inside the architecture of a Dash web app. Following that, it presents the Bootstrap technology, including an approach to universally set the theme of a Dash web app by employing default theme templates, including those from an external source. Following that, it familiarizes you with a technique of setting the layout of a web app using the dash_bootstrap_components library.
All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Similarity measures are a vital tool for understanding how language models represent and process language. Standard representational similarity measures such as cosine similarity and Euclidean distance have been successfully used in static word embedding models to understand how words cluster in semantic space. Recently, these measures have been applied to embeddings from contextualized models such as BERT and GPT-2. In this work, we call into question the informativity of such measures for contextualized language models. We find that a small number of rogue dimensions, often just 1-3, dominate these measures. Moreover, we find a striking mismatch between the dimensions that dominate similarity measures and those which are important to the behavior of the model. We show that simple postprocessing techniques such as standardization are able to correct for rogue dimensions and reveal underlying representational quality. We argue that accounting for rogue dimensions is essential for any similarity-based analysis of contextual language models.
Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space

Estimating Kullback Leibler (KL) divergence from samples of two distributions is essential in many machine learning problems. Variational methods using neural network discriminator have been proposed to achieve this task in a scalable manner. However, we noted that most of these methods using neural network discriminators suffer from high fluctuations (variance) in estimates and instability in training. In this paper, we look at this issue from statistical learning theory and function space complexity perspective to understand why this happens and how to solve it. We argue that the cause of these pathologies is lack of control over the complexity of the neural network discriminator function and could be mitigated by controlling it. To achieve this objective, we 1) present a novel construction of the discriminator in the Reproducing Kernel Hilbert Space (RKHS), 2) theoretically relate the error probability bound of the KL estimates to the complexity of the discriminator in the RKHS space, 3) present a scalable way to control the complexity (RKHS norm) of the discriminator for a reliable estimation of KL divergence, and 4) prove the consistency of the proposed estimator. In three different applications of KL divergence : estimation of KL, estimation of mutual information and Variational Bayes, we show that by controlling the complexity as developed in the theory, we are able to reduce the variance of KL estimates and stabilize the training
A Call to Develop Standards for Those Delivering ‘Research Practice’ Training

The nature of the research endeavour is changing rapidly and requires a wide set of skills beyond the research focus. The delivery of aspects of researcher training ‘beyond the bench’ is met by different sections of an institution, including the research office, the media office and the library. In Australia researcher training in open access, research data management and other aspects of open science is primarily offered by librarians. But what training do librarians receive in scholarly communication within their librarianship degrees? For a degree to be offered in librarianship and information science, it must be accredited by the Australian Library and Information Association (ALIA), with a curriculum that is based on ALIA’s lists of skills and attributes. However, these lists do not contain any reference to key open research terms and are almost mutually exclusive with core competencies in scholarly communication as identified by the North American Serials Interest Group and an international Joint Task Force. Over the past decade teaching by academics in universities has been professionalised with courses and qualifications. Those responsible for researcher training within universities and the material that is being offered should also meet an agreed accreditation. This paper is arguing that there is a clear need to develop parallel standards around ‘research practice’ training for PhD students and Early Career Researchers, and those delivering this training should be able to demonstrate their skills against these standards. Models to begin developing accreditation standards are starting to emerge, with the recent launch of the Centre for Academic Research Quality and Improvement in the UK. There are multiple organisations, both grassroots and long-established that would be able to contribute to this project.
Word-Level Coreference Resolution

Recent coreference resolution models rely heavily on span representations to find coreference links between word spans. As the number of spans is O(n2) in the length of text and the number of potential links is O(n4), various pruning techniques are necessary to make this approach computationally feasible. We propose instead to consider coreference links between individual words rather than word spans and then reconstruct the word spans. This reduces the complexity of the coreference model to O(n2) and allows it to consider all potential mentions without pruning any of them out. We also demonstrate that, with these changes, SpanBERT for coreference resolution will be significantly outperformed by RoBERTa. While being highly efficient, our model performs competitively with recent coreference resolution systems on the OntoNotes benchmark.
Attention-based Contrastive Learning for Winograd Schemas.

Self-supervised learning has recently attracted considerable attention in the NLP community for its ability to learn discriminative features using a contrastive objective. This paper investigates whether contrastive learning can be extended to Transfomer attention to tackling the Winograd Schema Challenge. To this end, we propose a novel self-supervised framework, leveraging a contrastive loss directly at the level of self-attention. Experimental analysis of our attention-based models on multiple datasets demonstrates superior commonsense reasoning capabilities. The proposed approach outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones.