Reviewerly/exHarmony huggingface dataset:
The making augmented reality technology-based learning media in molecular hybridization concept

Abstract This study aims to describe the appearance of AR technology-based learning media on the concept of molecular hybridization. Design based research has produced products in the form of AR technology-based learning media on the concept of molecular hybridization. Research shows completing media creation, design development and application creation and analyzing the results of limited trials. This study shows the appearance of learning media AR has several characteristics namely attractive colors and appearance, and interactive and the making of AR technology-based learning media has the potential to be applied to chemical learning in accordance with the concept of molecular hybridization.
Dynamic Attention Aggregation with BERT for Neural Machine Translation

The recently proposed BERT has demonstrated great power in various natural language processing tasks. However, the model does not perform effectively on cross-lingual tasks, especially on machine translation. In this work, we propose three methods to introduce pre-trained BERT into neural machine translation without fine-tuning. Our approach consists of a) a linear-attention aggregation that leverages a parameter matrix to capture the key knowledge of BERT, b) a self-attention aggregation which aims to learn what is vital for input and output, and c) a switch-gate aggregation to dynamically control the balance of the information flowing from the pre-trained BERT or the NMT model. We conduct experiments on several translation benchmarks and substantially improve over 2 BELU points on the IWSLT'14 English - German task with switch-gate aggregation method compared to a strong baseline, while our proposed model also performs remarkably on the other tasks.
Computing Covers Under Substring Consistent Equivalence Relations

Covers are a kind of quasiperiodicity in strings. A string C is a cover of another string T if any position of T is inside some occurrence of C in T. The shortest and longest cover arrays of T have the lengths of the shortest and longest covers of each prefix of T, respectively. The literature has proposed linear-time algorithms computing longest and shortest cover arrays taking border arrays as input. An equivalence relation \(\approx \) over strings is called a substring consistent equivalence relation (SCER) iff \(X \approx Y\) implies (1) \(|X| = |Y|\) and (2) \(X[i:j] \approx Y[i:j]\) for all \(1 \le i \le j \le |X|\). In this paper, we generalize the notion of covers for SCERs and prove that existing algorithms to compute the shortest cover array and the longest cover array of a string T under the identity relation will work for any SCERs taking the accordingly generalized border arrays.
Efficiency –A Layered Model for Authentication

In today's era one of the most prevailing security threat is identity theft. Authentication is an imminent process that tries to resolve the problem by verifying an individual's identity, who wants to access the resources of a system. In view of overwhelming ever-growing use of different authentication scheme their associated cost cannot be ignored. Consequently, it is identified as a requirement for authentication - though it is not a part of the general threat model till now. Moreover, there have been many efforts to estimate the usability of authentication system. However, in particular Efficiency aspect has not been calculated. This paper focuses on importance of Efficiency in password authentication. The efficiency model tries to explain the principles and indicators considered while designing a system for their efficiency. A layered model of Efficiency helps in understanding the different aids required for an efficient authentication system.
Method of Modeling Quantum Computations Using a Hardware Accelerator

During the study, software techniques for optimizing the quantum computer model were considered. The main goal of techniques that increase the efficiency of modeling is to reduce the amount of data that must be processed when modeling quantum gates. The most suitable algorithm for implementing a hardware accelerator was considered and modeled in the experimental part. Algorithms of graph theory or decision diagrams are not very suitable for this task, since their implementation will require the implementation of tensor matrix products, which is more convenient to do on a software simulator. It is also worth noting the increasingly growing interest in modeling quantum computing on special hardware, such as video cards or supercomputers. Therefore, the technique presented in the work is relevant, because it combines software solutions in the field of optimization over the past few years and recently studied methods for modeling quantum computing.
GAPG: a Heuristic Greedy Algorithm for Grouping Storage Scheme in Blockchain

The decentralization and immutability of blockchain make it have a wide application prospect, however, as the number of transactions increases, the storage cost of each node will also increase. So blockchain will be facing the bottleneck of insufficient scalability. Consensus Unit(CU) is one of grouping storage scheme for improving the scalability of blockchain, which organizes different nodes into one cluster and allocates all the blocks of the whole chain to the nodes in each cluster. In this paper, we propose an optimization problem that assigning blockchain data to nodes and minimized the total query cost, this is a variant of generalized basic probability assignment problem, so we propose a heuristic algorithm named GAPG to solve this problem. Numerical results demonstrate that the proposed algorithm is superior to random assignment and greedy algorithms for clusters with a large number of nodes.
Deep Self-Learning Hashing for Image Retrieval

With the advances in deep learning, deep-hashing methods have achieved promising results for image retrieval. However, the problem of the distribution gap between training data and test data remains unsolved. Existing methods rely too much on manually labeled information to construct similarity matrices as supervision signals and focus less on pre-trained networks that can extract semantic information. This limits the generalization performance of the network and produces less discriminative hash codes. In this paper, we propose a novel hashing method, deep self-learning hashing (DSLH), that uses a self-learning strategy with labels constructed using the pre-trained features to enhance the embedded representation of the hash codes. Furthermore, we develop an improved loss function that preserves the similarity of the hash codes while reducing the quantization loss and ensuring the balance of the hash codes. Our analysis and experimental results demonstrate that, compared with recent image-retrieval methods, our method can achieve greater retrieval performance on two benchmark datasets: CIFAR-10 and NUS-WIDE.
Designing a Faculty Chatbot Through User-Centered Design Approach

Conversational agents or chatbots are becoming an increasingly common mean of communication between humans and computers, tackling all aspects of human activity, including the field of education. In this paper a part of ongoing research is presented, with the aim to reveal what kind of interactions with the chatbot in the educational setting would take place. The ultimate goal is to identify informative models and design artifacts that would enable creation of the chatbot which will provide meaningful information to the students of the faculty. The research is performed by following user-centered design approach and engaging the final users in the product lifecycle. For that purpose, students have participated in an online survey and provided examples of the questions they would ask the chatbot about the exams, midterms, office hours, and location of rooms. Participants’ answers were analysed with thematic analysis that revealed nineteen themes of the chatbot user interactions of which nine categories (design artifacts) of the chatbot user interface were novel in educational context. In addition to identifying students’ needs, survey revealed preferences of the students regarding the form and tone of communication with the prospective chatbot.
Security &amp; Privacy Model for Work from Home Paradigm

The outbreak of novel corona-virus pandemic has caused the entire world to initiate a total lockdown and thus, affecting the businesses and other economic activities thus, as a work around several organizations are now capitalizing the work from home paradigm that allows them to continue with their economic activities with certain limitations. However, such a widespread use of the above mentioned paradigm has risked the privacy and security of the underlying computer resources even more. Work from home (WFH) is indeed the need of the hour but is full of loopholes which can be exploited further by those having malicious intentions and when a huge population of the world is following such a paradigm then the threat becomes even more serious. The underlying project aims propose a cyber security model for the work from home paradigm to make it relatively secure. Just like the Biba and Bell-LaPadula models which have been used as a basis for many security practices in general, this project proposes a similar model but specifically meant for the WFH practice as following this paradigm doesn't generally involve a lot of people as compared to the traditional on-site working but the risk surprisingly is more as individuals tend to avoid security practices whilst following `at home' ideology. The model suggests various practices and steps to be taken in order to ensure the confidentiality, integrity and availability of the data with a focus on cyber hygiene.
DC-CAPT: Concurrent Assignment and Planning of Trajectories for Dubins Cars

We present an algorithm for the concurrent assignment and planning of collision-free trajectories (DC-CAPT) for robots whose kinematics can be modeled as Dubins cars, i.e., robots constrained in terms of their initial orientation and their minimum turning radius. Coupling the assignment and trajectory planning subproblems allows for a computationally tractable solution. This solution is guaranteed to be collision- free through the use of a single constraint: the start and goal locations have separation distance greater than some threshold. We derive this separation distance by extending a prior work that assumed holonomic robots. We demonstrate the validity of our approach, and show its efficacy through simulations and experiments where groups of robots executing Dubins curves travel to their assigned goal locations without collisions.
A Lossless Image Compression Algorithm Based On Group Encoding

This paper considers a new lossless image compression algorithm that provides reduction of data redundancy in video processing systems. To achieve the high compression level group encoding approach is proposed. The experimental research shows that proposed algorithm provides 1.33 times better compression quality index than the Run Length Encoding (RLE) algorithm.
Visual Relationship Classification With Negative-Sample Mining

This paper introduces the application of a visual relationship classifier as a standalone system that is meant to be used with external detectors. Through these lens, we propose a training scheme that uses unannotated pairs of objects as negative samples in order to improve precision. The proposed network architecture incorporates common techniques presented in related state-of-the-art solutions with a novel positional encoding scheme. We evaluate the proposed training method and architecture on the Open Images dataset and improve mAP from 34.6% to 78.2% when considering all possible object pairings in each image. For a case where only ground-truth pairs are considered, our method presents a small decrease, from 91.0% to 88.8% mAP.
Slice-Based Super-Resolution Using Light-Weight Network With Relation Loss

While the performance of convolutional neural networks (CNNs)-based single image super-resolution (SISR) has been greatly improved, the enormous parameter sizes and computational complexity of the underlying CNNs make hardware implementation difficult. Recently, several lightweight SISR methods have been developed, but they still do not consider various structural problems that may occur in hardware implementation. To solve this problem, we propose a slice-based SR using light-weight network (LWN) and a slice-based SR using LWN with relation loss (LWNRL). First, LWN(RL) adopts a slice-based architecture to facilitate system-on-chip (SoC) implementation. Second, LWN(RL) avoids global connection modules that are not suitable for SoC implementation, with minimal performance penalty. Finally, we propose a new loss to improve the performance of LWN without additional cost. Experimental results show that LWNRL achieves significant efficiency of SR model. Especially, the larger the resolution or scale factor, the better the performance of LWNRL than the conventional methods.
Hubness-based Sampling Method for Nyström Spectral Clustering

Nyström method is widely used for spectral clustering to obtain low-rank approximations of a large matrix. Sampling is crucial to Nyström method, since selecting the representative sample points that can reflect the data structure is important for obtaining good approximation results. To improve the performance of Nyström based spectral clustering, in this paper, we propose a new sampling method by considering the hubness score of sample points. The data points with the high hubness scores, i.e., appearing frequently in the nearest neighbor lists of other data points, have high probabilities to be selected as the sample points. Taking advantage of the topological property of hubs (i.e., data points with high hubness score), the selected sampling points have close relationships with other data points, thus the proposed method is able to achieve scalable and accurate clustering results. We further design fast computation methods, i.e., local hubness approximated methods, to speed up the sampling process. Experimental results on both synthetic and real-world data sets show that the proposed method not only achieves good performance, but also outperforms other sampling methods for Nyström based spectral clustering.
Enhanced Advanced Encryption Standard with Randomised Round Keys

The Advanced Encryption Standard (AES), is the standard for symmetric encryption of data as per the Federal Information Processing Standard (FIPS) since 2001, and is the most popular encryption standard for securing data. However, the AES is an open encryption algorithm known to all and it is also known to be prone to number of cryptanalysis and brute force attacks. Military networks and applications require a very high degree of security as any loss of data on these will directly impact national security. Hence, the plain AES algorithm is not suitable for use by Military networks and applications. This paper will review the earlier proposals to strengthen the AES, identify the research gap in the efforts and finally propose an enhanced AES with increased randomness and more secure than plain AES. This proposed AES can be safely employed for in military networks and applications. The performance of the enhanced AES with randomised round keys was analysed through implementation in MATLAB software and a comparison with the plain AES. The results show that the proposed enhanced AES will ensure a better security with negligible overheads.
Dual Semantic Relationship Attention Network for Image-Text Matching

Image-Text Matching is one major task in cross-modal information processing. The main challenge is to learn the unified vision and language representations. Previous methods that perform well on this task primarily focus on the region features in images corresponding to the words in sentences. However, this will cause the regional features to lose contact with the global context, leading to the mismatch with those non-object words in some sentences. In this work, in order to alleviate this problem, a novel Dual Semantic Relationship Attention Network is proposed which mainly consists of two modules, separate semantic relationship module and the joint semantic relationship module. With these two modules, different hierarchies of semantic relationships are learned simultaneously, thus promoting the image-text matching process. Quantitative experiments have been performed on MS-COCO and Flickr-30K and our method outperforms previous approaches by a large margin due to the effectiveness of the dual semantic relationship attention scheme.
A ConvLSTM-Combined Hierarchical Attention Network For Saliency Detection

Attention mechanism has been applied in the salient object detection massively. In order to reasonably integrate feature maps at different blocks and focus on the impact between neighbouring pixel concurrently, we propose a novel attention mechanism network, i.e., the ConvLSTM-Combined Hierarchical Attention Network (CHAN), to differentiate the contribution of feature maps of different blocks and focus on the impact between neighbouring pixel at the same time. The spatial attention, combined with ConvLSTM, offers an efficient recurrent mechanism for sequential refinement of the location information of each feature maps. The block-wise attention generates response to different convolutional blocks, and fuses different features reasonably. Experiments show that our saliency model detects salient objects more accurately, thus favorably outperforms most state-of-the-art methods.
Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks

Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks to data with many irrelevant variables often leads to overfitting. In an attempt to circumvent this issue, a prior knowledge pertaining to what features are relevant and their possible feature interactions can be encoded into these networks. In this work, we use decision trees to capture such relevant features and their interactions and define a mapping to encode extracted relationships into a neural network. This addresses the initialization related concerns of fully connected neural networks. At the same time through feature selection it enables learning of compact representations compared to state of the art tree-based approaches. Empirical evaluations and simulation studies show the superiority of such an approach over fully connected neural networks and tree-based approaches.
An empirical study on the characteristics of question-answering process on developer forums

Developer forums are one of the most popular and useful Q&A websites on API usages. The analysis of API forums can be a critical step towards automated question and answer approaches. In this poster, we empirically study three API forums: Twitter, eBay, and AdWords, to investigate the characteristics of question-answering process. We observe that +60% of the posts on all forums were answered with API method names or documentation. +85% of the questions were answered by API development teams and the answers from API development teams drew fewer follow-up questions. Our results provide empirical evidence in future work to build automated solutions to answer developer questions on API forums.
Hybrid multivariate typological model for the banded iron formations from the Bonito mine, Northeastern Brazil

Typological ore delineation is a basic procedure that provides various bases in ore geology on which crucial developments rely on, in particular, those supporting mineral resource evaluation. To deal with several complex issues related to typological delineation of heterogeneous ore bodies, an unusual hybrid multivariate approach, based on a skillful combination of geochemical/geological knowledge and correspondence analysis (CA), was established. This approach was tested on banded iron formations (BIF) in the Bonito mine, Northeastern Brazil, enhancing the available geochemical knowledge by revealing grade combinations related to geological processes causing such deposits. Petrographic studies helped identify four main BIF-types: amphibolitic, hematitic, martitic, and magnetitic itabirites. An exhaustive geochemical database comprising 1,384 BIF samples assayed for Fe2O3, SiO2, Al2O3, P, and Mn grades was established and classified for the aforementioned BIF-types. The methodology provides an updated typological model based on a unique aspect of CA that allows a simultaneous projection of samples and their characteristics onto axes of the same abstract space; this helps validate mathematical results with geological information linked to sample attributes. The BIF typology model was established by applying K-means clustering to factorial scores derived through CA. Hence, the BIF typological model was updated, and new geochemical BIF-types helped interpret some of the main geological processes related to them. Geochemically, the factorial axes could be related to: 1) Iron rich BIF rocks, which are clearly represented on the positive side of Axis 1, comprising almost 50% of the total variance; 2) Manganese and alumina, associated with high silica content, by attachment to Axis 2, which describes a possible and critical terrigenous input.
Privacy Analysis of Query-Set-Size Control

Vast amounts of information of all types are collected daily about people by governments, corporations and individuals. The information is collected, for example, when users register to or use on-line applications, receive health related services, use their mobile phones, utilize search engines, or perform common daily activities. As a result, there is an enormous quantity of privately-owned records that describe individuals' finances, interests, activities, and demographics. These records often include sensitive data and may violate the privacy of the users if published. The common approach to safeguarding user information is to limit access to the data by using an authentication and authorization protocol. However, in many cases the publication of user data for statistical analysis and research can be extremely beneficial for both academic and commercial uses, such as statistical research and recommendation systems. To maintain user privacy when such a publication occurs many databases employ anonymization techniques, either on the query results or the data itself. In this paper we examine and analyze the privacy offered for aggregate queries over a data structures representing linear topologies. Additionally, we offer a privacy probability measure, indicating the probability of an attacker to obtain information defined as sensitive by utilizing legitimate queries over such a system.
Application of Remote Sensing in Detecting Mineralized Zones in the Pan-African Belt of Egypt

Minerals are essential for the ongoing industrial revolution and modern technologies. The increased demands for such resources require an expansion in the ongoing exploration activities, which in turn needs to rely on advanced techniques and methods to speed up the exploration and exploitation processes. Remote sensing is one of the essential tools that are currently being used in the exploration for mineral resources since it minimizes both time and efforts in identifying their locations. Recent developments in the remote sensing instruments have improved the quality of the acquired data in terms of its spectral and spatial resolutions. Additionally, the development in the algorithms and techniques used to analyze the remote sensing data have greatly optimized the information extraction by giving more details not only about the potentially mineralized zones but also about the alteration and secondary minerals distribution within these zones.