Reviewerly/exHarmony huggingface dataset:
A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases

Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One popular way to solve the KB-QA problem is to make use of a pipeline of several NLP modules, including entity discovery and linking (EDL) and relation detection. Recent success on KB-QA task usually involves complex network structures with sophisticated heuristics. Inspired by a previous work that builds a strong KB-QA baseline, we propose a simple but general neural model composed of fixed-size ordinally forgetting encoding (FOFE) and deep neural networks, called FOFE-net to solve KB-QA problem at different stages. For evaluation, we use two popular KB-QA datasets, SimpleQuestions and WebQSP, and a newly created dataset, FreebaseQA. The experimental results show that FOFE-net performs well on KB-QA subtasks, entity discovery and linking (EDL) and relation detection, and in turn pushing overall KB-QA system to achieve strong results on all datasets.
SANQ: A Simulation Framework for Architecting Noisy Intermediate-Scale Quantum Computing System

To bridge the gap between limited hardware access and the huge demand for experiments for Noisy Intermediate-Scale Quantum (NISQ) computing system study, a simulator which can capture the modeling of both the quantum processor and its classical control system to realize early-stage evaluation and design space exploration, is naturally invoked but still missing. This paper presents SANQ, a Simulation framework for Architecting NISQ computing system. SANQ consists of two components, 1) an optimized noisy quantum computing (QC) simulator with flexible error modeling accelerated by eliminating redundant computation, and 2) an architectural simulation infrastructure to construct behavior models for evaluating the control systems. SANQ is validated with existing NISQ quantum processor and control systems to ensure simulation accuracy. It can capture the variance on the QC device and simulate the timing behavior precisely (&lt;1% and 10% error for various real control systems). Several potential applications are proposed to show that SANQ could benefit the future design of NISQ compiler, architecture, etc.
Contingent Payments on a Public Ledger: Models and Reductions for Automated Verification

We study protocols that rely on a public ledger infrastructure, concentrating on protocols for zero-knowledge contingent payment, whose security properties combine diverse notions of fairness and privacy. We argue that rigorous models are required for capturing the ledger semantics, the protocol-ledger interaction, the cryptographic primitives and, ultimately, the security properties one would like to achieve. Our focus is on a particular level of abstraction, where network messages are represented by a term algebra, protocol execution by state transition systems (e.g. multiset rewrite rules) and where the properties of interest can be analyzed with automated verification tools.We propose models for: (1) the rules guiding the ledger execution, taking the coin functionality of public ledgers such as Bitcoin as an example; (2) the security properties expected from ledger-based zero-knowledge contingent payment protocols; (3) two different security protocols that aim at achieving these properties relying on different ledger infrastructures; (4) reductions that allow simpler term algebras for homomorphic cryptographic schemes. Altogether, these models allow us to derive a first automated verification for ledger-based zero-knowledge contingent payment using the Tamarin prover. Furthermore, our models help in clarifying certain underlying assumptions, security and efficiency tradeoffs that should be taken into account when deploying protocols on the blockchain.
The model of a dynamic university library based on the leading query services

The university libraries are analyzed as an integrated system of elements with various characteristics. Within any system, any structural changes will inevitably impact its every component. The authors describe the mechanism for leading query and corresponding services. They also define the term “dynamic university library” which is the traditional or networked user information services with any media based on the leading query services. The dynamic library services are classified by the library specialization (library, research and academic services) and by information delivery method (traditional, online traditional and the leading query services). The core collection structure comprises three equally-weighted elements: the basic, intermediary, and intellectual one. The scheme of useable library operation is revealed. Along with sources ordered, the libraries can offer a series of additional services: reviews, author’s bibliography, citation data, subject bibliographies, etc. Based on the suggested classification, the core structure and operation scheme, the model of a dynamic university library is designed.
Multimedia: Different Processes

The topic includes four main themes: (1) The Collaborative Work in Cloud Storage Services: The collaborative work is seen as a force for the individual and community. It, in the field of education, expresses the interaction among students of individual differences who work within collaborative aims and skills to achieve a specific aim. In addition, cloud storage predicts a tremendous change in the way information is stored and applications are run. That is, instead of storing information and running programs on PCs, everything will be hosted in a cloud that can be accessed anywhere and processed by addition or deletion collaboratively. (2) Computer- supported collaborative learning environment (CSCL): Collaborative learning is an umbrella term for a variety of educational approaches involving joint intellectual effort by students, or students and teachers together. It is based on the idea that learning is naturally a social act in which the participants talk among themselves. A group of students engaged in collaborative learning works together to achieve shared goals. (3) Mobile learning: Mobile learning is a term that has been used widely in different places all over the world. it has been encouraged to be used in higher education institutions because of a set of factors such as the availability of mobile phones, their ability to motivate students, and the freedom and privacy they provide to share information. Mobile learning is defined as E-learning that uses mobile devices or learning connected to a mobile device, Laouris & Eteokleous. (4) Open-Source Learning Management Systems: The integration of many Educational technologies in education have been widely promoted for their potential to enrich, enhance and extend student-learning experiences. Hence, pioneer educational establishments all over the world try to benefit of these technologies as much as possible to convey knowledge resources to both of the learner and teacher in least time, effort and cost. One of these educational technology tools which has been prominent in the field of education and technology integration is Learning Management Systems known as LMS.
A FRAMEWORK FOR COLLABORATIVE AND ACTIVE LEARNING FOR ENHANCING STUDENT ENGAGEMENT

The developing technology has influenced and enhanced the life around us, so the use of the digital resources must not be hindered to serve the educational, teaching and learning sectors, in the modern times more emphasis is laid on the ways of education, relating to the teaching and learning techniques or methods used in the institutions, colleges or universities, on how the learning and teaching methods be enhanced so the students learn more comfortably and in a better way suiting their capacity or learning demands.As well, it will help the teachers to convey the topic concepts in a more relevant way to the students, helping them understand better and learn in an effective way.For this concept of enhancement in teaching and learning, many steps are been taken around the globe, many methods are been used like for instance the flip teaching technique, where the students are required to go through the learning material provided by the teacher to further explore the concepts, which are explained during the classes.Other methods include research-based learning and many more, but in the current course of time, individual traits of students when it comes to learning is often neglected, due to which the students find it difficult to understand and learn better, due to each student being unique and having various personalities and mindsets.A framework must be devised to enhance the earning ability of students based on their personalities.Modern technological tools must be used to support in the direction of teaching based on an individual's personalities and mindsets.Various groups can be formed of the students who fall into a certain category, teaching methods must be devised and quality checks must be ensured.Their learning outcomes must be measured based on their teaching methods implemented by the teachers.
Gadgetku.id application as a Solution to Facilitate the Fulfillment of All Gadget Needs today (case study: area Tangerang Banten)

The gadget is a sophisticated item that can present a variety of news media, social networking, hobbies, and even entertainment. The presence of gadgets today makes some people become dependent. If the gadget is damaged, they will repair the gadget itself or bring it to the service place. The problem in this study is the difficulty of finding information about the place of seller of gadget components and looking for a trusted service place, the nearest location and affordable prices. To overcome this problem, the author designed an application called Gadgetku.Id used a qualitative research method consisting of data collection, data analysis and system design. In this application have 2 main services, namely service and shop. The purpose of designing this application is to be able to assist users in finding a trusted service place, the nearest location, safe, scheduled and without the need to go to the service place and help to find gadget needs through the shop feature.
An effective and efficient algorithm for ranking web documents via genetic programming

We propose an effective and efficient algorithm for ranking web documents, called CombGenRank. This algorithm introduces a novel selection criterion in the classical genetic programming paradigm, which already proved to be effective for supporting web document ranking, called elitism. Extensive experimental results conducted on top of well-known web document collections confirm the benefits deriving from our proposed approach. This algorithm is motivated by search services based on cloud computing.
The Power of One Qubit in Machine Learning

Kernel methods are used extensively in classical machine learning, especially in the field of pattern analysis. In this paper, we propose a kernel-based quantum machine learning algorithm that can be implemented on a near-term, intermediate scale quantum device. Our proposal is based on estimating classically intractable kernel functions, using a restricted quantum model known as deterministic quantum computing with one qubit. Our method provides a framework for studying the role of quantum correlations other than quantum entanglement for machine learning applications.
Price Prediction of Chili Commodities in Bandung Regency Using Bayesian Network

Chili is one of the agricultural commodities consumed by Indonesian people. Market data in recent years show that chili prices tend to fluctuate as supply and demand changes. One of the impacts of chili price changes for farmers is the production cost is higher than the selling price. In addition to supply and demand changes, the weather is also indicated as a factor of price changes due to the weather being considered by farmers to grow chili. Price prediction is needed to determine the condition of chili prices in the future to help farmers in making decisions to plant at the right time. One method that can be used to make prediction is Data Mining classification method. In this paper, Bayesian network algorithm was used as Data Mining classification method to predict the price of chili commodity in Bandung Regency based on weather information and classified the price into economic class and not economic class. The result shows that the prediction model obtained by the Bayesian Network gives a system’s performance for precision and recall that is 1 and 0.94 respectively with average accuracy of 85.5% in classifying the price.
Depth Map Super-Resolution via Multilevel Recursive Guidance and Progressive Supervision

With the development of deep learning, image super-resolution has made great breakthroughs. However, compared with a color image, the performance of depth map super-resolution is still poor. To address this problem, multilevel recursive guidance and progressive supervised network (MRG-PS) is proposed in this paper. First, a multilevel recursive guidance architecture is presented to extract features of a color stream and depth stream, in which the depth stream is guided by the color features at each level. Second, a progressive supervision module is developed to supervise the multilevel recursion to obtain depth residual information on different levels. Finally, a residual fusion and construction strategy is designed to fuse all residual information and reconstruct the high-resolution depth map. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.
Reassessing the Efficacy of Foreign Aid and Grants in Poverty Reduction in Nigeria

This study re-examines the efficacy of foreign aid and grants on poverty reduction in Nigeria using time series data covering the period of 1999 to 2017. To perform the inquiry, the paper focus on disaggregated data for the analysis of foreign aid and grants, that is, technical cooperation grants (TCG), official development assistance (ODA) and other grants. Grounded on the Great Big Push theoretical framework, the study tested the marginal effect of TCG, ODA and other grants on poverty incidence in Nigeria using the Autoregressive Distributed Lag (ARDL) bounds testing approach. The empirical findings show that TCG and ODA have positive but insignificant impact on national poverty incidence in the short-term horizon; however, in the long-term, the effect of TCG and ODA on poverty incidence is negative. This finding suggests that the plausibility of poverty reduction policy based on external aid and grants is contestable. This affirms the argument that externally nominated solutions in form of foreign aid and grants do not connect with locally defined problems in poverty reduction.
Modeling of professional activities of future teachers in master training conditions

The article describes the state of the problem under study on scientific and pedagogical literature and normative documents, specifies the essence of the key concepts of the research, specifies the peculiarities of preparation of future teachers for the professional activity in the conditions of the master's degree.The peculiarities of preparation of future teachers for the professional activity in the conditions of the magistracy through its modeling are substantiated. It is noted, that simulation of the process of professional training for the activities of future teachers provides the basis for answering the question: how to build a learning process, in order to increase the qualities of the personality of the teacher of higher education, sufficient for professional activities. Principal significance is the question of the essence of the profession of a teacher of higher education, the content and competence of his/her duties, the culture of his/her pedagogical influence, his/her values and ideals, and professionalism.It is determined, that preparation of future teachers for the professional activity in the conditions of the magistracy is a process of organization of educational, scientific and pedagogical activity of a master student during his/her studies in the magistracy, aimed at forming his/her readiness for the activity of a teacher of higher education. The purpose of preparing future teachers for the professional activity in the conditions of the magistracy is to form the personality of the future specialist, to ensure his/her professional development and pedagogical skills, professional competence, professionally significant qualities, as well as the ability to carry out a scientific research.The model of preparation of future teachers for the professional activity in the conditions of the master's degree based on the implementation of organizational and methodical conditions is presented
Analysis Effect of Cloud Cover, Wind Speed, and Water Temperature to BOD and DO Concentration Using QUAL2Kw Model (Case Study In Winongo River, Yogyakarta)

Winongo river is one of the rivers in the province of D.I. Yogyakarta that included in the category of contaminated. the research aims to learn on the effect variation of cloud cover, wind speed, and water temperature on BOD and DO concentrate in order to choose water quality management strategy at Winongo river. This research conducting a simulation with variating the cloud cover variables (0%, 69%, and 88%), wind velocity (0 m/second, 0,211 m/second and 1,22 m/second), and so the water temperature air (24,2°C, 28,14°C, and 30,6°C). The modeling result shows that cloud cover variables don’t affect the DO-BOD concentrate at Winongo river (sig = 0). While the wind velocity variables only have a small effect on the changed of DO and BOD at Winongo river (sig &gt; 0,05). And for water temperature variables had the significant effect on the concentration changed of DO (sig &lt; 0,05) and have a small effect on the changed of BOD at Winongo river (sig &gt; 0,05). The DO and BOD management strategy can be done by making of riparian buffer strips or planting vegetation on the riverbank Winongo, build a WWTP Communal in every district along the river Winongo
Level set distribution model of nested structures using logarithmic transformation

In this study, we propose a method for constructing a multishape statistical shape model (SSM) for nested structures such that each is a subset or superset of another. The proposed method has potential application to any pair of shapes with an inclusive relationship. These types of shapes are often found in anatomy, such as the brain surface and ventricles. The main contribution of this paper is to introduce a new shape representation called log-transformed level set function (LT-LSF), which has a vector space structure that preserves the correct inclusive relationship of the nested shape. In addition, our method is applicable to an arbitrary number of nested shapes. We demonstrate the effectiveness of the proposed shape representation by modeling the anatomy of human embryos, including the brain, ventricles, and choroid plexus volumes. The performance of the SSM was evaluated in terms of generalization and specificity ability. Additionally, we measured leakage criteria to assess the ability to preserve inclusive relationships. A quantitative comparison of our SSM with conventional multishape SSMs demonstrates the superiority of the proposed method.
A Binary Ant Lion Optimisation Algorithm Applied to the Set Covering Problem

The study and understanding of algorithms that solve combinatorial problems based on swarm intelligence continuous metaheuristics, is an area of interest at the level of basic and applied science. This is due to the fact that many of the problems addressed at industrial level are of a combinatorial type and a subset no less than these are of the NP-hard type. In this article, a mechanism of binarization of continuous metaheuristics that uses the concept of the percentile is proposed. This percentile concept is applied to the An Lion optimization algorithm, solving the set covering problem (SCP). Experiments were designed to demonstrate the importance of the percentile concept in the binarization process. Subsequently, the efficiency of the algorithm is verified through reference instances. The results indicate that the binary Ant Lion Algorithm (BALO) obtains adequate results when evaluated with a combinatorial problem such as the SCP.
Robust part‐based visual tracking via adaptive collaborative modelling

Discriminative correlation filter-based tracking algorithms have recently shown impressive performance on benchmark data sets. However, visual tracking is still a challenging task in the case of partial occlusions, irregular deformations and so on. In this study, the authors intend to solve these issues by introducing the adaptive collaborative model into part-based tracking. First, instead of a simple linear superposition, the collaborative strategy they proposed combines the template model and colour-based model adaptively and relies on the strengths of both to promote the accuracy. Second, we utilise the voting strategy to figure out the final object position from reliable parts, and the motion information is used in evaluation for reliable parts to enable the tracker to be robust in various situations. Third, the authors utilise a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, they introduce a dimensionality reduction method to limit the computational complexity of the tracker. Abundant experiments demonstrate that the tracker performs superiorly against several advanced algorithms on both the Online Tracking Benchmark (OTB) 2013 and OTB2015 data sets while maintaining the high frame rates.
Well-Foundedness in Weighted Argumentation Frameworks

We revise classical properties of Abstract Argumentation Frameworks in presence of weights on attacks. We focus on the notion of well-foundedness originally provided by P. M. Dung in his pioneering work. We generalise it by considering sequences of Set-maximal Attack sets, instead of a plain sequence of arguments: such sets include all the arguments attacking a previous set in the sequence. By using a parametric framework based on an algebraic structure, we are able to study different proposals of weighted defence in the literature, and consequently relate their well-foundedness. We generalise such a property to any weighted defence, but also to original Dung’s defence. Finally, we provide conditions for the uniqueness of the preferred and existence of the stable extensions.
Bayesian Optimized Continual Learning with Attention Mechanism

Though neural networks have achieved much progress in various applications, it is still highly challenging for them to learn from a continuous stream of tasks without forgetting. Continual learning, a new learning paradigm, aims to solve this issue. In this work, we propose a new model for continual learning, called Bayesian Optimized Continual Learning with Attention Mechanism (BOCL) that dynamically expands the network capacity upon the arrival of new tasks by Bayesian optimization and selectively utilizes previous knowledge (e.g. feature maps of previous tasks) via attention mechanism. Our experiments on variants of MNIST and CIFAR-100 demonstrate that our methods outperform the state-of-the-art in preventing catastrophic forgetting and fitting new tasks better.