Reviewerly/exHarmony huggingface dataset:
One-way Explainability Isn't The Message

Recent engineering developments in specialised computational hardware, data-acquisition and storage technology have seen the emergence of Machine Learning (ML) as a powerful form of data analysis with widespread applicability beyond its historical roots in the design of autonomous agents. However -- possibly because of its origins in the development of agents capable of self-discovery -- relatively little attention has been paid to the interaction between people and ML. In this paper we are concerned with the use of ML in automated or semi-automated tools that assist one or more human decision makers. We argue that requirements on both human and machine in this context are significantly different to the use of ML either as part of autonomous agents for self-discovery or as part statistical data analysis. Our principal position is that the design of such human-machine systems should be driven by repeated, two-way intelligibility of information rather than one-way explainability of the ML-system's recommendations. Iterated rounds of intelligible information exchange, we think, will characterise the kinds of collaboration that will be needed to understand complex phenomena for which neither man or machine have complete answers. We propose operational principles -- we call them Intelligibility Axioms -- to guide the design of a collaborative decision-support system. The principles are concerned with: (a) what it means for information provided by the human to be intelligible to the ML system; and (b) what it means for an explanation provided by an ML system to be intelligible to a human. Using examples from the literature on the use of ML for drug-design and in medicine, we demonstrate cases where the conditions of the axioms are met. We describe some additional requirements needed for the design of a truly collaborative decision-support system.
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving

Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue. In this paper, we introduce a fully Visual Attention-based Depth (VADepth) network, where spatial attention and channel attention are applied to all stages. By continuously extracting the dependencies of features along the spatial and channel dimensions over a long distance, VADepth network can effectively preserve important details and suppress interfering features to better perceive the scene structure for more accurate depth estimates. In addition, we utilize geometric priors to form scale constraints for scale-aware model training. Specifically, we construct a novel scale-aware loss using the distance between the camera and a plane fitted by the ground points corresponding to the pixels of the rectangular area in the bottom middle of the image. Experimental results on the KITTI dataset show that this architecture achieves the state-of-the-art performance and our method can directly output absolute depth without post-processing. Moreover, our experiments on the SeasonDepth dataset also demonstrate the robustness of our model to multiple unseen environments.
Crypto Currency

Recently, our Finance Minister Mrs. Nirmala Sitharaman announced that there will be a tax of 30% on income from crypto currencies. For the taxation purpose, crypto currencies have now been included in the definition of Virtual Digital Asset. By choosing this topic we will analysis how this currency will impact economy of India and world if it is recognized by most of the nation of the world or whether this currency is suitable for a large and diverse country like India. Today, crypto currency, led by Bit coin, Lit coin, Ether, etc. are taking the financial world by storm as more people invest and buy these currencies. Despite its growing popularity governments are cracking down on the digital currency because it is decentralized, meaning it has no central authority in the way the Indian government holds authority over the Rupee or Dollar. Therefore, some experts believe crypto poses a threat to central banks and national security.
FastHare: Fast Hamiltonian Reduction for Large-scale Quantum Annealing

Quantum annealing (QA) that encodes optimization problems into Hamiltonians remains the only near-term quantum computing paradigm that provides sufficient many qubits for real-world applications. To fit larger optimization instances on existing quantum annealers, reducing Hamiltonians into smaller equivalent Hamiltonians provides a promising approach. Unfortunately, existing reduction techniques are either computationally expensive or ineffective in practice. To this end, we introduce a novel notion of non-separable~group, defined as a subset of qubits in a Hamiltonian that obtains the same value in optimal solutions. We develop a theoretical framework on non-separability accordingly and propose FastHare, a highly efficient reduction method. FastHare, iteratively, detects and merges non-separable groups into single qubits. It does so within a provable worst-case time complexity of only $O(\alpha n^2)$, for some user-defined parameter $\alpha$. Our extensive benchmarks for the feasibility of the reduction are done on both synthetic Hamiltonians and 3000+ instances from the MQLIB library. The results show FastHare outperforms the roof duality, the implemented reduction method in D-Wave's SDK library, with 3.6x higher average reduction ratio. It demonstrates a high level of effectiveness with an average of 62% qubits saving and 0.3s processing time, advocating for Hamiltonian reduction as an inexpensive necessity for QA.
The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping

Marine habitat mapping is primarily done to monitor and preserve underwater ecosystems. Images captured in a marine environment suffer from acidification, pollutions, waste chemicals, and lighting conditions. Human beings are progressing fast in terms of technology and are also responsible for the degradations of ecosystems, both marine and land habitats. Marine biologists possess a lot of data for the underwater environment, but it is hard to analyze, and the task becomes tiresome. Automating this process would help marine biologists quickly monitor the environment and preserve it. Our research focuses on coral reef classification and two critical aspects, i.e., Image enhancement and recognition of coral reefs. Image enhancement plays an essential role in marine habitat mapping because of the environment in which images are taken. The literature contains many image enhancement techniques for underwater. The authors want to determine whether a single image enhancement technique is suitable for coral reefs. Four image enhancement techniques based on an extensive literature review are selected. We have used DenseNet-169 and MobileNet for image classification. It has been reported that DenseNet-169 has excellent results for coral reefs classification. Histogram techniques combined with DenseNet-169 for classification resulted in higher classification rates.
THOR: Threshold-Based Ranking Loss for Ordinal Regression

In this work, we present a regression-based ordinal regression algorithm for supervised classification of instances into ordinal categories. In contrast to previous methods, in this work the decision boundaries between categories are predefined, and the algorithm learns to project the input examples onto their appropriate scores according to these predefined boundaries. This is achieved by adding a novel threshold-based pairwise loss function that aims at minimizing the regression error, which in turn minimizes the Mean Absolute Error (MAE) measure. We implemented our proposed architecture-agnostic method using the CNN-framework for feature extraction. Experimental results on five real-world benchmarks demonstrate that the proposed algorithm achieves the best MAE results compared to state-of-the-art ordinal regression algorithms.
PREFACE

Abstract 5 th International Conference In Chemical Innovation 2021 (ICCI 2021), took place at the University College TATI (UC TATI) on the 25 th &amp; 26 th of October 2021. UC TATI is located in the east coast of Malaysia, Kemaman, Terengganu. ICCI is a regular scientific meeting that is held biyearly since 2011. ICCI 2021 aims to bring together academic scientists, researchers, technologists, research scholars, and also undergraduate &amp; postgraduate students to exchange and share their experiences and research results on all aspects of sustainable chemical technology, industrial process, chemistry , chemical process &amp; engineering and related areas through a virtual presentation. Due to the impact of COVID-19, with travel restrictions, we organized this conference in virtual mode. During the conference, the conference model was divided into three sessions through Zoom platform, including keynote speeches , invited speeches and oral presentations.There were 6 invited speakers from academic and industrial backgrounds; Prof. Tariq Muneer (Edinburgh Napier University,UK), Prof.Dr. Kamal Zuhairi Zamli (University Malaysia Pahang), Prof Dr. Lim Chee Peng (Deakin University, Australia), Assoc. Prof. Dr. Siti Hamidah (Malaysia-Japan International Institute Of Technology) ,Dato Rozana Saad (Venator Asia Sdn.Bhd) and Kamarul Effendy (Proton Malaysia). Each invited speaker has been given 30 minutes including a question and answers session. List of International Conference In Chemical Innovation 2021 (ICCI 2021) Committee, Advisory Committee ICCI are available in this pdf.
Near-collisions and their Impact on Biometric Security (long)

Biometric recognition encompasses two operating modes. The first one is biometric identification which consists in determining the identity of an individual based on her biometrics and requires browsing the entire database (i.e., a 1:N search). The other one is biometric authentication which corresponds to verifying claimed biometrics of an individual (i.e., a 1:1 search) to authenticate her, or grant her access to some services. The matching process is based on the similarities between a fresh and an enrolled biometric template. Considering the case of binary templates, we investigate how a highly populated database yields near-collisions, impacting the security of both the operating modes. Insight into the security of binary templates is given by establishing a lower bound on the size of templates and an upper bound on the size of a template database depending on security parameters. We provide efficient algorithms for partitioning a leaked template database in order to improve the generation of a master-template-set that can impersonates any enrolled user and possibly some future users. Practical impacts of proposed algorithms are finally emphasized with experimental studies.
Three speakers’ perspectives on experience in Taylor Swift’s selected lyrics from Folklore album

Pop songs are now an important means of expression for the modern generation. Academicians must consider this phenomenon as the object of language studies. This research attempts to analyze three songs in a Grammy-winning album of 2021, Folklore. Song lyrics in Betty, Cardigan, and August, written by Taylor Swift have the quality of narrative poetry (ballads) for having the characteristics of storytelling voiced by a different speaker in each song. The purpose of this research is to show how the varying amounts of transitivity process types in each lyric suggest different perspectives on reality. It uses a stylistic approach to expose the linguistic features that allow the speakers to portray diverse perspectives after going through a difficult relationship. As he apologizes to his partner, the speaker in Betty uses material process significantly to reconstruct the outer experiences. In Cardigan, the speaker emphasizes mental processes, incorporating her personal experience into her knowledge and memory. However, in August, the speaker uses both material and relational processes in equal proportions as she battles with her status in the relationship. Hence, listeners could identify with the various speakers' individual experiences as expressed in the lyrics.
DEBATING THE RELATIONSHIP BETWEEN ACADEMICS AND PRACTITIONERS IN THE HIGHER EDUCATION FIELD

Abstract. In this article we present findings from focus groups (FGs) consisting of academics and practitioners in the higher education (HE) field from different parts of the world, who debated their perceptions of the current state of the art in the HE field. The main findings, which call for further research, as well as activities of academics, practitioners and policymakers at various levels, are as follows: 1) there is a gap between basic academic research and applied research (with the latter often not meeting the appropriate conceptual level), which needs to be closed; 2) this gap is expressed also in the global context of the HE field; 3) multidisciplinarity, interdisciplinarity, transdisciplinarity, methodological quality of both basic and applied research need to be developed and 4) scholars’, practitioners’ and governments’ (policymakers’) responsibilities need to be addressed. Keywords: higher education (HE), research, academics, practitioners, government, policy
Naturalistic Causal Probing for Morpho-Syntax

Probing has become a go-to methodology for interpreting and analyzing deep neural models in natural language processing. However, there is still a lack of understanding of the limitations and weaknesses of various types of probes. In this work, we suggest a strategy for input-level intervention on naturalistic sentences. Using our approach, we intervene on the morpho-syntactic features of a sentence, while keeping the rest of the sentence unchanged. Such an intervention allows us to causally probe pre-trained models. We apply our naturalistic causal probing framework to analyze the effects of grammatical gender and number on contextualized representations extracted from three pre-trained models in Spanish: the multilingual versions of BERT, RoBERTa, and GPT-2. Our experiments suggest that naturalistic interventions lead to stable estimates of the causal effects of various linguistic properties. Moreover, our experiments demonstrate the importance of naturalistic causal probing when analyzing pre-trained models.
PSYCHOLOGICAL ASPECTS OF PROFESSIONAL PRIMARY CLASS TEACHER TRAINING

Professional training of a primary school teacher is considered to he many-sided. This article touches upon psychological aspects o f this problem. Analyzing the main conditions for development of a teacher as a professional the authors stresses on the following: personal orientation on a concrete pupil, psychological competence and self-management of a primary class teacher.
Using Blockchain to Track Government Fund Distribution

All along, a blockchain is a making synopsis of records, called blocks, related utilizing cryptography. Each block contains a hash of the past transaction, a timestamp, and transaction information. By plan, a blockchain is invulnerable to alteration of the data. In this, we propose a design to follow holds appointed to the public authority as they cross via the general power operation at per step. This framework utilizes blockchain improvement to remain mindful of straightforwardness and security at each development as the assets push forward. This design permits a bare essential record with all connected clients in the chain to exchange information on a piece of restricted data. The framework utilizes encryption to secure unexpected information including hash values to keep a square of exchanges a chain way that is given and checked by each center point expected to assert the conversation and save the information in an immediate plan inside the public power. The design ponders a full-check, secure and exact asset task, and overall arranging framework to assist with shaping an ethical government system.
Cost-Effectively Detecting, Preventing and Mitigating Cyber Threats to Nuclear Energy Systems

Cost-effectively detecting, preventing, and mitigating cyber threats are a concern for most organizations, especially those in critical infrastructure sectors. The electrical and mechanical equipment for nuclear power plants is very important to nuclear safety and dependent upon computer-based equipment, appropriate standards and practices for the development and testing of computer hardware and software shall be established and implemented throughout the service life of the system, and in particular throughout the software development cycle. Security systems do not need more tools, they just need more rules, because fighting new threats with more tools just adds complexity and more degrees of freedom; that these new tools always bring on board. It is time to rethink our approach to Cyber Security.
Generalized Wigner-Yanase Skew Information and the Affiliated Inequality

A family of skew information quantities is obtained, in which the well-known Wigner-Yanase skew information and quantum Fisher information stand as special cases. A transparent proof of convexity of the generalized skew information is given, implying a simple proof of the Wigner-Yanase-Dyson conjecture. We find in this work an exact skew information inequality for qubit system, which may regard as the information counterpart of the uncertainty relation. A lower bound for generalized skew information of a pair of incompatible observables in arbitrary dimension and also the upper bound for qubit system are achieved.
Assessing the Prevalence of Benevolence in Response to Online Toxicity on Reddit: A First Step

When toxicity arises in the context of online discourse, how common is a benevolent response?We analyzed 10,963,849 comments posted to the discussion site Reddit in January 2016.Of these, 75,269 comments were classified as toxic using Perspective API on four toxicity metrics.A group of 792 online volunteers re-rated the toxicity of a random sample of 14,856 of these comments.They then read a direct reply to each comment and rated this reply on benevolence (e.g., how much the reply demonstrated understanding of the content, empathy for the commenter, and used a polite/helpful tone).We estimated that of the 8591 comments which were verified by our raters as toxic, 37.8% of replies were rated as above the scale midpoint in benevolence, suggesting that replying with understanding, empathy and politeness in the face of toxicity may not be as rare online as we might think.The average benevolence of these replies was 3.16 where 1 = not at all benevolent and 6 = very much (SD = 1.32).In future research, we plan to train a machine learning algorithm using these benevolence ratings with the hopes of classifying larger sets of toxic comment-reply pairs from Reddit, with the aim of further understanding the prevalence of benevolence in response to online toxicity.
Impact Analysis of Emerging Semantic Communication Systems on Network Performance

With the paradigm shift from Shannon’s legacy, semantic communication (SC) is emerging as one of the promising next-generation communication technologies. The new paradigm in communication technology allows the meaning of transmitted messages to be successfully delivered to a receiver. Hence, the semantic communication focuses on the successful delivery of transmitted messages such as human language communication. In order to realize such new communication, both transmitter and receiver should share the same background knowledge with each other. Recently, several researchers have developed task-specific SC systems by exploiting astonishing achievements in deep learning, which can allow the same knowledge to be shared between them. However, since such SC systems are specialized to handle specific applications, not all users can be serviced by the SC systems. Therefore, a network will face a coexistence of an SC system and a traditional communication (TC) system. In this paper, we investigate how introducing emerging SC systems affects the performance of the TC system from a network perspective. For analysis, we consider the signal-to-noise ratio (SNR) differently for the user served by an SC system and the user served by a TC system. Then, by using two different SNR equations, we formulate a max-min fairness problem in the coexistence of SC and TC systems. Via extensive numerical results, we compare the network performance of TC and SC users with and without SC systems, and then confirm that SC systems are indeed a promising next-generation communication alternative.
Aplikasi Sistem Informasi Dan Pemetaan Daerah Pariwisata Berbasis Web

Increase public awareness of regional potential, this tourism information system includes tourism development and website-based tourism. Tourism is a broad term that refers to various tourism activities that are supported by various facilities and services provided by the community, business world, government, and local government. Aceh tourism has a lot of tourism potential, but few people realize it. Social media, blogs and websites are all used to promote tourism in Aceh. However, there is no system that can store all the data and display a map of the location of attractions and all other aspects of tourism. This is necessary because there is no system that facilitates the acquisition of knowledge and tourist destinations. This encourages to build an information system that can provide complete information to the public and tourists who want to visit Aceh. The results of this study show tourist attractions, lodging and restaurants and can be a consideration for choosing tourist attractions in Aceh. In addition, the system can display various information such as name, address, description, tourist destination, and route.
A Webometric Study of Selected International Library Association Websites An Evaluative Study

The official website of any organisation provides authentic information about it. The current study is a novel attempt to explore international library association websites and assess those websites based on web content evaluation and webometric parameters. Several library websites have been analysed by researchers. However, the same has not been attempted regarding the library association website at the international level. International library associations indexed by American Library association’s website were taken as the sample. Data were collected using a link analysis tool from https://moz.com/ which helps in determining Domain Authority, Page Authority, in-links, and out-links; Google was used for the links. Official websites of the library associations were visited for the website evaluation with a well-structured checklist having 19 criteria. International Association of Law Libraries is found to have the highest simple web impact factor and external web impact factor. In contrast, the Association of Jewish Libraries has the highest internal web impact factor. All the websites examined in this study are hosted under the .org domain. The study revealed that 7 out of 8 (87.5 %) international library association websites’ Domain Authority and Page Authority is below 50. Essential information like customer service, currency, and FAQ are not available on the websites. The results of this study will help the library associations in re-structuring and better maintenance of their website.
Existence and Consistency of the Maximum Pseudo \b{eta}-Likelihood Estimators for Multivariate Normal Mixture Models

Robust estimation under multivariate normal (MVN) mixture model is always a computational challenge. A recently proposed maximum pseudo \b{eta}-likelihood estimator aims to estimate the unknown parameters of a MVN mixture model in the spirit of minimum density power divergence (DPD) methodology but with a relatively simpler and tractable computational algorithm even for larger dimensions. In this letter, we will rigorously derive the existence and weak consistency of the maximum pseudo \b{eta}-likelihood estimator in case of MVN mixture models under a reasonable set of assumptions.