Reviewerly/exHarmony huggingface dataset:
On query-to-communication lifting for adversary bounds

We investigate query-to-communication lifting theorems for models related to the quantum adversary bounds. Our results are as follows: 1. We show that the classical adversary bound lifts to a lower bound on randomized communication complexity with a constant-sized gadget. We also show that the classical adversary bound is a strictly stronger lower bound technique than the previously-lifted measure known as critical block sensitivity, making our lifting theorem one of the strongest lifting theorems for randomized communication complexity using a constant-sized gadget. 2. Turning to quantum models, we show a connection between lifting theorems for quantum adversary bounds and secure 2-party quantum computation in a certain honest-but-curious model. Under the assumption that such secure 2-party computation is impossible, we show that a simplified version of the positive-weight adversary bound lifts to a quantum communication lower bound using a constant-sized gadget. We also give an unconditional lifting theorem which lower bounds bounded-round quantum communication protocols. 3. Finally, we give some new results in query complexity. We show that the classical adversary and the positive-weight quantum adversary are quadratically related. We also show that the positive-weight quantum adversary is never larger than the square of the approximate degree. Both relations hold even for partial functions.
Using Blockchain for Optimal and Transparent Resource Allocation

The paper presents, in brief, a proposed blockchain-based architecture for optimal resource allocation, specifically financial resources, in a bounded-setting, called game. The solution touches upon actors involved and roles and rules of engagement, while setting the stage for a more complex proposal for future study. The paper is a review, set as a starting point for a more developed solution, which would allow for financial resources peer-sharing in a bounded setting, with automated functions.
Performance Comparison of Target Tracking Filters in Underwater Multipath Environments

Using time-of-arrivals or time-difference-of-arrivals for the localization and tracking of underwater vehicles is a common approach. Although these two approaches are claimed to have similar performances, the analytical derivations oversee the biases in the delays. Another problem in the underwater tracking is to choose from multiple arrivals which occur due to underwater multipath effects. In this study, ray tracing is used for simulating the complex underwater environment. Nearest neighborhood and probabilistic data association filters are compared for timeofarrival and time-difference-of-arrival measurement models.
NICT’s Neural Machine Translation Systems for the WAT21 Restricted Translation Task

This paper describes our system (Team ID: nictrb) for participating in the WAT'21 restricted machine translation task.In our submitted system, we designed a new training approach for restricted machine translation.By sampling from the translation target, we can solve the problem that ordinary training data does not have a restricted vocabulary.With the further help of constrained decoding in the inference phase, we achieved better results than the baseline, confirming the effectiveness of our solution.In addition, we also tried the vanilla and sparse Transformer as the backbone network of the model, as well as model ensembling, which further improved the final translation performance.
Blockchain utopia and its governance shortfalls

This chapter sows seeds of scepticism about blockchain governance and liberation narratives. It starts off by locating these narratives within a long standing history of utopian thought on technology and law and the recurrent promises of science-driven, technological replacements of law and the State, on and off since the Enlightenment. The discussion then explores the peculiar features of blockchain technology upon which its utopian credentials are said to be grounded. First, it argues that blockchain utopias are, much like their internet predecessors, guilty of technological essentialism by drawing determinist conclusions from an underlying architectural design, and by failing to allow for the functional and technological latencies within that design. It is in their interactions with social domains that those latencies are ‘resolved’ in favour of certain configurations and not others. Such utopian narratives are not harmless when they generate blindfolds about the actual societal concretisation of the technology and its oppressive rather than emancipatory effects. Second, the discussion problematises the claimed virtue of blockchain applications as trustless systems against various conceptions of trust, and argues that blockchain does not and cannot speak to the trust questions at the interface between information and reality, for which society will continue to require trusted third parties to deliver ‘trusted’ value judgements. Furthermore, it is not a foregone conclusion that a society in which trust becomes redundant or less important would in fact be an ideal society. Third, the chapter questions the virtues that lie behind blockchain’s decentralisation theme by arguing that the corporate structure has rather successfully formalised collective action and invariably opted for centralised decision-making for coordination and functional specialisation. Moreover, the decentralisation that blockchain enables within an organisation does not touch upon economic concentration within a market, but it is the latter that presents the real 21st century challenge to the marketplace and democracy.
MODERN EDUCATION - CONDITIONS, CHALLENGES AND PERSPECTIVES

During the last few years, the rapid immersion of information and communication technology has made an enormous change in the whole society, not lеаving the education process behind as its primary and most essential segment. The internet has become a tool for communication, sharing experiences, a multi-cultural bridge for overcoming cultural, ethnic, social and intellectual differences. It is a basic medium for bridging the obstacles and challenges, which is impossible in the non-digital world. The internet has found its place in the educational system as its inevitable and sustainable assistant. Dealing with the fast pace of transition from a traditional into a contemporary, multimedia classroom, the teacher is obliged to moderate and transform his/her pedagogical competences with a new approach technical literacy and fluency. Using this new approach is a demanding process, forcing the teacher to gain special skills and upgrade professionally.ICT tools need to be complementary with the national curriculum, aims and objectives of the course and its potential outcomes. Thus the teachers are challenged to use their full accessibility and utilize them in the everyday teaching and learning process.The developmental changes impose the need for constant replenishment of knowledge and development of skills and abilities, hence one need to learn during their whole life. The issue of expectations of teachers and determination the quality of education represents a complex pedagogical issue because it contains a series of interconnected and influential aspects and factors. When the above will build social, economic, philosophical, cultural heritage and experience of society itself, among other things, increases the importance and responsibility on the issue of expectations of teachers to develop and achieve quality education.
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks

In order to safely deploy Deep Neural Networks (DNNs) within the perception pipelines of real-time decision making systems, there is a need for safeguards that can detect out-of-training-distribution (OoD) inputs both efficiently and accurately. Building on recent work leveraging the local curvature of DNNs to reason about epistemic uncertainty, we propose Sketching Curvature for OoD Detection (SCOD), an architecture-agnostic framework for equipping any trained DNN with a task-relevant epistemic uncertainty estimate. Offline, given a trained model and its training data, SCOD employs tools from matrix sketching to tractably compute a low-rank approximation of the Fisher information matrix which characterizes which directions in the weight space are most influential on the predictions over the training data. Online, we estimate uncertainty by measuring how much perturbations orthogonal to these directions can alter predictions at a new test input. We apply SCOD to pre-trained networks of varying architectures on several tasks, ranging from regression to classification. We demonstrate that SCOD achieves comparable of better OoD detection performance with lower computational burden relative to existing baselines.
Collaborative Edmodo in writing: A conceivable course of fusion

A problem that still persists in Collaborative Writing is the lack of use of relevant technology to counter lessened interaction and learning participations in developing writing skills effectively. To offset these difficulties, this study examines the use of Edmodo on students' collaborative writing. This study used a quasi-experimental study of 56 students, grade 10, Gresik, Indonesia, with two assigned classes of experimental and the control group, each containing of 28 students. The results of this study reveal that there is a significant influence of Edmodo on Collaborative writing, evidenced by the results of sig. (2-tailed) is 0,000 (lower than 0.05). Edmodo has proven to be a dependable means when merged with a Collaborative Writing strategy, and has also been attested to reassure student participation and interaction. Suggestions and further research basis are also presented, especially to innovative scholars as treasured opportunities for accompanying enquiry to pay more courtesy to the progress of Collaborative Writing which is increasingly unlocked to always re-join hearty users’ wants.&#x0D; &#x0D; &#x0D; Key Words : Collaborative, Writing, Edmodo,Technology, Course of Fusion
Context-Aware Dialogue Response Generation Integrating Causal Relation

This is an extension from a selected paper from JSAI2020. In order to generate more content-rich responses, as well as avoid irrelevant or monotonous ones, some researchers set off to focus on dialogue generation models in which causal relation is also taken into consideration. However, it is hard to distinguish whether there is causal relation between speaker’s and responder’s utterances from dialogue automatically, which makes collecting such utterance pairs even harder. In this paper, we first propose a transfer learning method to learn causality features from web contents such as Wikipedia dataset and pre-train a causality classifier model. Subsequently, the causality classifier model is taken advantages into collecting utterance pairs containing causality, which is used in the re-ranking method of seq2seq generation model instead of rule-based one, to improve the dialogue diversity and context coherency. An evaluation is conducted to show that the proposed method improves diversity and coherency of dialogues generation from three aspects: 1. The causality classifier model used for detecting dialogue pairs is confirmed by three famous word embedding methods and two famous corpus. 2. The causality generation model improves response diversity, which is evaluated by automatic dialogue evaluation metrics. 3. The causality generation model improves dialogue context coherency, which is evaluated by human observes.
Experiments with Contextualized Word Embeddings for Keyphrase Extraction

This paper explores the use of contextualized word embeddings for keyphrase extraction, framed as a sequence labeling task. It compares multiple configurations of neural network architectures that build on top of a transformer encoder. The baseline model is obtained by fine-tuning the BERT/SciBERT transformers. Four architectures are presented and evaluated. Each of them is using a different configuration of LSTM and/or multi-head attention modules. We propose a custom loss function and use it to train all architectures with the exception of the baseline. The paper also documents the differences observed when building on top of either the original word embeddings (obtained from training on a language modelling task) or the fine-tuned embeddings of the baseline model. Finally, we present a case study on the behaviour of the attention mechanisms during inference, highlighting the most interesting findings.
Automated Design (AutoDes): Current Trends and Future Research Directions

The book has presented current trends and state-of-the-art advancements in the automated design of machine learning and search algorithms. In this context, we define automated design(AutoDes) to include automated algorithm/approach configuration, composition, and selection. This chapter provides a conclusion to the book by bringing together these contributions, highlighting different focus areas, and setting the agenda for future research directions. This is presented in terms of reusability in automated design, explainable automated design, computational costs, theoretical aspects, automated design standardization, and semi-automated design.
Textual Representations for Crosslingual Information Retrieval

In this paper, we explored different levels of textual representations for cross-lingual information retrieval. Beyond the traditional token level representation, we adopted the subword and character level representations for information retrieval that had shown to improve neural machine translation by reducing the out-of-vocabulary issues in machine translation. We found that crosslingual information retrieval performance can be improved by combining search results from subwords and token level representation.Additionally, we improved the search performance by combining and re-ranking the result sets from the different text representations for German, French and Japanese.
The SEnDIng project Data Science and Internet of Things professionals’ training

Data Science (DS) and Internet of Things (IoT) are currently among the key drivers of skills and competences required by the IT market. As a skills’ gap is projected in the DS and IoT domains, substantial effort is required by training providers for the upskilling of IT workforce. The SEnDIng project aims to address the skills’ gap of Data Scientists and IoT engineers by developing and delivering two learning outcomes-oriented, modular VET programmes. Trainings will be delivered into three phases: e-learning, face-to-face and work-based learning. During the self-paced online training, trainees will be upskilled on DS or IoT, face-to-face training will cultivate their transversal skills and work-based learning will allow trainees to apply the acquired skills on realistic case studies in their workplace with mentoring support. Upon successful completion of the training, participants will go through the certification which is designed and developed by SEnDIng and is aligned with the NQFs, EQF and ECVET.
Where Do the Metals Go?

Volcanic eruptions spread harmful metals in the environment. Now the biggest study to date details exactly where they end up.
FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task

In this paper, we describe our end-to-end multilingual speech translation system submitted to the IWSLT 2021 evaluation campaign on the Multilingual Speech Translation shared task.Our system is built by leveraging transfer learning across modalities, tasks and languages.First, we leverage generalpurpose multilingual modules pretrained with large amounts of unlabelled and labelled data.We further enable knowledge transfer from the text task to the speech task by training two tasks jointly.Finally, our multilingual model is finetuned on speech translation taskspecific data to achieve the best translation results.Experimental results show our system outperforms the reported systems, including both end-to-end and cascaded based approaches, by a large margin.In some translation directions, our speech translation results evaluated on the public Multilingual TEDx test set are even comparable with the ones from a strong text-to-text translation system, which uses the oracle speech transcripts as input.
Designing Instruction and Professional Development to Support Augmented Reality Activities

Advanced technologies are changing the educational and organizational landscape. Technologies such as augmented reality are providing professionals access to technology-enhanced activities that promote greater acquisition of new concepts through immersive learning experiences. Prior research conducted on augmented reality has resulted in findings that demonstrate numerous benefits associated with its use including increasing learner levels of motivation, content knowledge, and critical and problem-solving skills. These tools have been implemented at all levels of education and across a range of professional settings. This article will explore how the inclusion of these tools provide employees access to cutting-edge technologies that promote skill growth and improve efficacy in their professional responsibilities and how fog computing has the capability enhance this technology.
Hybrid inner-outer algorithm for solving real-world mechanical optimization problems

Abstract In the real world, the problems mostly are complex; more precisely, the problems generally are nonlinear or large scale other than if it was mandatory to resolve it under certain constraints, and that is common in engineering design problems. Therefore, the complexity of problem plays a critical role in determining the computational time and cost. Accordingly, a novel algorithm called inner-outer array is proposed in this paper. It depends on the design of parameters and then tolerance design as one of design of experiment stages. In this work, the inner-outer algorithm is used to solve real-world optimization problems to choose the preferable feasible regions of the entire search domain. Numerical results are documented and compared based on four well-known constrained mechanical engineering issues. It can be concluded that the performance of inner-outer algorithm is good to optimize constrained engineering problems, but it still needs some enhancements in the future work.
Performance Evaluation of Semi-Global Approaches for the Dense Image Matching of Stereo Satellite Images

In this study, the performance of semi-global methods for satellite stereo image matching was investigated. The performance of semi-global methods for the well-known Middlebury and KITTI sets has been the subject of many studies. Applications using satellite images may have different difficulties compared to close-range images. RVLSatStereo was used as the dataset. With this study, the similarities between SGM and MGM algorithms, in which areas they had difficulties and in which areas they gave good results were examined. As far as we know, the general performance evaluation of the semi-global methods has not been done with the BAD 2.0 metric in satellite images containing areas with different characteristics.
Investigation Study on Secured Data Communication with Blockchain and IOT in Green Cloud Computing

Green cloud computing is used for utilization of computer resources in well-organized way. Internet of Things (IoT) is a developing model through heterogeneous technologies with smart objects that are seamlessly connected to the Internet. Blockchain technology in green cloud provides the secrecy and preserves the control system in energy-efficient manner for real-time applications. Blockchain extracts the information from IoT devices and accumulate the records to preserve the transparency at different locations. Different blockchain methods were discussed to provide secured communication in green computing. But, the existing techniques failed to improve the security level and reduce the energy consumption during data communication. In order to address these problems, an IoT-based least square orthogonal regressive whirlpool cryptographic hash blockchain (IoT-LSORWCHB) method is introduced in green computing. The key aim of IoT-LSORWCHB method is to collect and analyze the patient data with blockchain for performing the secured data communication in green cloud environment. Simulation is carried out on factors such as data confidentiality level, processing time, and energy consumption with respect to number of patient data.
Complaint Detection and Classification of Customer Reviews

In a world where competition and technology usage increase consistently, customer satisfaction has become important for companies. In this study, the customer reviews, obtained from the results of the surveys that are made via different channels, are analyzed and when a problem is detected, a quick solution is aimed. For the complaint detection and classification on the customer reviews process, long short-term memory, which is a recurrent neural network, is applied. A data set from the tourism industry is labelled to carry out the proposed method. The results retrieved on performing the method on the data, which is relatively larger than the similar works in literature, are acceptable and the proposed model works in real-time.
Collection Management Basics, 7th ed.

"Collection Management Basics, 7th ed.." Journal of Electronic Resources in Medical Libraries, 18(2-3), pp. 173–174
What’s Decidable About (Atomic) Polymorphism?

Due to the undecidability of most type-related properties of System F like type inhabitation or type checking, restricted polymorphic systems have been widely investigated (the most well-known being ML-polymorphism). In this paper we investigate System Fat, or atomic System F, a very weak predicative fragment of System F whose typable terms coincide with the simply typable ones. We show that the type-checking problem for Fat is decidable and we propose an algorithm which sheds some new light on the source of undecidability in full System F. Moreover, we investigate free theorems and contextual equivalence in this fragment, and we show that the latter, unlike in the simply typed lambda-calculus, is undecidable.
A CONFIGURABLE MINING APPROACH FOR LEARNING SERVICES CUSTOMIZATION

There is no doubt that this age is the age of data and technology. Moreover, there is tremendous development in all fields. The personalized material is a good approach in the different fields. It provides a fit material that matches the styles of readers. It supports readers in various reading domains. This research paper aims to support students in the educational system. Additionally, the research paper designs to increase education values for students. Furthermore, the research paper builds the smart appropriate materials through Egyptian Knowledge Banking (EKB) based on the learner question. The Egyptian Knowledge Bank (EKB) is a rich platform for data. The research paper is implemented in the faculty of Commerce and Business Administration, Business Information System program (BIS) at Helwan University, Egypt.
Data-Driven Modeling of Switched Dynamical Systems via Extreme Learning Machine

In this work, a data-driven modeling framework of switched dynamical systems under time-dependent switching is proposed. The learning technique utilized to model system dynamics is Extreme Learning Machine (ELM). First, a method is developed for the detection of the switching occurrence events in the training data extracted from system traces. The training data thus can be segmented by the detected switching instants. Then, ELM is used to learn the system dynamics of subsystems. The learning process includes segmented trace data merging and subsystem dynamics modeling. Due to the specific learning structure of ELM, the modeling process is formulated as an iterative Least-Squares (LS) optimization problem. Finally, the switching sequence can be reconstructed based on the switching detection and segmented trace merging results. An example of the data-driven modeling DC-DC converter is presented to show the effectiveness of the developed approach.