Reviewerly/exHarmony huggingface dataset:
Exploring Contextual Paradigms in Context-Aware Recommendations

Traditional recommendation systems utilise past users' preferences to predict unknown ratings and recommend unseen items. However, as the number of choices from content providers increases, additional information, such as context, has to be included in the recommendation process to improve users' satisfaction. Context-aware recommendation systems exploit the users' contextual information (e.g., location, mood, company, etc.) using three main paradigms: contextual pre-filtering, contextual post-filtering, and contextual modelling. In this work, we explore these three ways of incorporating context in the recommendation pipeline, and compare them on context-aware datasets with different characteristics. The experimental evaluation showed that contextual pre-filtering and contextual modelling yield similar performance, while the post-filtering approach achieved poorer accuracy, emphasising the importance of context in producing good recommendations.
A comparative study of conventional and CNN-based implementations of facial recognition on Raspberry-Pi

This paper experimentally compares two face recognition approaches implemented on Raspberry-Pi in the smart-door system. The first approach is based on Local-Binary Patterns Histograms. The second one utilizes convolutional networks and deep learning. The paper describes the implementations and reports the results in terms of recognition accuracy and time. It shows that the CNN based approach runs faster and achieves better recognition accuracy than LBP even on small library sets and limited resources of Raspberry-Pi.
Annular phase filter based low coherence interferometer for the profile measurement of the transparent ICF shells

Axial resolution improvement in the low coherence interferometry (LCI) with a three-zone annular phase filter is demonstrated. The sidelobes resulting from the super-resolving features of the phase filter can be suppressed by the coherence gate of the LCI. The parameters of the filter are optimized to increase the axial gain GA as large as possible while maintaining an acceptable value of the Strehl ratio S and the transverse gain GT through the numerical simulations. The effect of the actual broadband spectrum and Gaussian amplitude profile of the incident light on the performance of the designed filter is investigated. With the designed filter, the experimental axial gain is 1.25 while the intensity loss is about 5.5 dB with the mirror sample, which is also verified with the dimensional measurements of a transparent inertial confinement fusion (ICF) shell.
Ranking based Question Answering System with a Web and Mobile Application

This work attempts to comprehend the fundamental working of the deep learning models that have been proposed till now in order to arrive at an inventive and accurate ensemble model for handling questions which are either unanswerable or have answers as persistent content from the context. In general cases of Natural Language Processing applications, Long Short-Term Memory and Gated Recurrent Units have shown great performance. With the introduction of Convolutional Neural Networks (which were conventionally used for performing image analysis and object/landmark detection) in the domain of text analysis along with the latest Attention mechanisms substantial progress in this domain was observed. On the SQuAD dataset, these models can learn to train on all the possible varieties of questions that may exist. An ensemble model based on the BERT encoder was also implemented for the Machine Reading Comprehension task.This work presents a ranking based question answering system. The complete system was divided into three modules namely Extension, Question selection Web Service and QA system. Later a chrome extension and a mobile app is developed which presents the above approach.
COVID-19 Pandemic: A Sentiment and Emotional Analysis of Modified Cancellation Policy of Airbnb

COVID-19 has brought the world to a standstill. Almost every country in the world is facing economic crisis. The most affected sector from COVID-19 is the hospitality and tourism industry. The industry has suffered a loss of billions of dollars. For homestays, Airbnb leads the sector with more than 75% market share in homestays. Forced lockdown in almost all countries leads Airbnb modify its cancellation policy. In this paper, we analyse people's sentiments and emotions for the modified cancellation policy. We have used VADER analysis for sentiment analysis and modified BERT for emotional analysis. The results yield that even though maximum people are positive towards Airbnb, still they are angry with the modified policy. The density of anger is more in European and American continent as compared to other continents.
Addressing the Heterogeneity of A Wide Area Network for DNNs

In general, deep neural networks (DNNs) achieve higher accuracy as the amount of training data increases. However, training data are often privacy sensitive, and they may not be collected. There are several methods that leave the training data decentralized in a wide area network and share models. These methods update the models locally based on stochastic gradient descent (SGD) and communicate to aggregate the models. The network bandwidth, training data, and machines are heterogeneous in a wide area network unlike distributed DNNs which use a computer cluster. Due to heterogeneity, the methods using synchronous communication, such as all-reduce SGD, are not suitable, and gossip SGD using asynchronous communication is a dominant method. In this paper, we show that when the network bandwidth is heterogeneous, conventional gossip SGD causes network congestion, and the learning efficiency is not greatly different from the case in which the network bandwidth is homogeneous. We show that the congestion problem can be solved by adjusting the communication frequency, that is, by training multiple times and communicating once. In many works, learning in local nodes and communicating with other nodes are alternated. Furthermore, we propose a warm-up technique to improve the learning efficiency. This proposed technique decreases the amount of communication with nodes that require a long communication time. We verify the effect of the proposed technique in experiments using CIFAR-10 and CIFAR-100.
TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity

Tables are a popular and efficient means of presenting structured information. They are used extensively in various kinds of documents including web pages. Tables display information as a two-dimensional matrix, the semantics of which is conveyed by a mixture of structure (rows, columns), headers, caption, and content. Recent research has started to consider tables as first class objects, not just as an addendum to texts, yielding interesting results for problems like table matching, table completion, or value imputation. All of these problems inherently rely on an accurate measure for the semantic similarity of two tables. We present TabSim, a novel method to compute table similarity scores using deep neural networks. Conceptually, TabSim represents a table as a learned concatenation of embeddings of its caption, its content, and its structure. Given two tables in this representation, a Siamese neural network is trained to compute a score correlating with the tables' semantic similarity. To train and evaluate our method, we created a gold standard corpus consisting of 1500 table pairs extracted from biomedical articles and manually scored regarding their degree of similarity, and adopted two other corpora originally developed for a different yet similar task. Our evaluation shows that TabSim outperforms other table similarity measures on average by app. 7% pp F1-score in a binary similarity classification setting and by app. 1.5% pp in a ranking scenario. The codes and resources introduced here are available at https://github.com/Marhabibi/TabSim.
MCGDM for selection of OSN participants using integration AHP and MOORA methods

Abstract The Olympics of Science National (OSN) is one of the government’s efforts to improve quality of education. The many indicators of measurement and decision-making involved in process of selecting prospective OSN participants required a decision support system for selection of prospective participants to make it easier and transparent. Indicators in this assessment are academic grades, skills grades, class rank and absenteeism. The science department, academic grades and skills scores have mathematical, chemical, physical, biological sub-criteria. There are geography and economic majors in social science. The method used in the decision support system is Analytic Hierarchy Process (AHP) Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) integration based on group or Multi Criteria Group Decision Making (MCGDM) with geometric mean aggregation. Based on research that has been done with a CR less than 0.01, the accuracy obtained is for physics 89.7%, biology 92%, mathematics 90%,chemistry 93.7%, geography 93.8%, and economics 86.6%. The highest accuracy is in Geography
Contactless Human Monitoring: Challenges and Future Direction

Human activity recognition and analysis have a great number of important applications in numerous fields including computer vision, ubiquitous computing, human-computer interactions, healthcare, robotics, and surveillance. Video-based and sensor-based human activity recognition have progressed tremendously in the last two decades. In this chapter, we highlight the major challenges and future research directions in contactless human activity monitoring. More specifically, we will present sensor-level challenges, feature-level challenges, methodological issues, implementation-level aspects, and various application-level challenges.
Canvas Learning Management System Feature Analysis Using Feature-Oriented Domain Analysis (FODA)

Abstract The impact of Covid-19 on the world of education requires the implementation of online learning. Information and computer technology can provide solutions in education through the Learning Management System (LMS). Canvas LMS is a form of LMS that can be utilized by lecturers and students in online learning during the Covid-19 pandemic. In this study, researchers analyzed the features of Canvas LMS using the Feature-Oriented Domain Analysis (FODA) method. Researchers also conducted a mapping of canvas LMS features with the National Standard of Higher Education (SN Dikti). This study showed two stages of validation by Education and LMS experts to verify the results of the analysis. At the end of the course, we recommended a new LMS feature that complies with SN Dikti for Lecturer and Student users.
A distributed source autoencoder of local visual descriptors for 3D reconstruction

This paper presents a local descriptor coding scheme for multicamera surveillance and 3D reconstruction embedding an autoencoder into a traditional distributed source coding strategy. The proposed solution permits shifting most of the computational complexity at the decoder/receiver and exploiting the correlation among descriptors of different cameras (thus reducing the coded bit rate) without increasing the inter-device communication load. Experimental results show that the proposed scheme permits obtaining a satisfying accuracy with respect to the most recent solutions while generating a limited bit rate.
Forecasting the Number of Vehicles in Indonesia Using Auto Regressive Integrative Moving Average (ARIMA) Method

Abstract The number of vehicles in Indonesia increases significantly from year to year. This has an impact on various aspects such as traffic jams, air pollution, traffic accidents etc. The purpose of this study is to obtain the best model for forecast the number of cars and the number of motorcycles in the next 11 years. For the purpose, ARIMA method was used. Using the historical data of the number of cars and the number of motorcycles from 2001 to 2019, the best model for forecasting the number of cars and the number of motorcycles is ARIMA (1,1,0) and ARIMA (2,1,2), respectively. The models have MAPE of 7.01% and 7.24% for cars and motorcycles, respectively.
Modern Arabic speech corpus for Text to Speech synthesis

there are hardly any open access large single speaker corpora that could be effectively used to build a Text-to-Speech system, especially for Arabic being a low-resourced language. Thus, the aim of this paper is to present a new open access single speaker corpus. It is by far the largest Arabic speech resource suitable for building text-to-speech systems. The released corpus consists of more than 16-hours audio files aligned with their corresponding phonetic transcription in Buckwalter format, and the orthographic text transcripts representing 81, 000 words fully diactritized. The corpus design was determined by different factors among which is the coverage of the most frequent lemmas, this latter being a common unit in most of the Arabic words. The corpus is freely available for download from http://oujda-nlp-team.net/en/corpora/speech-corpus-1-0/.
VSRN: Visual-Semantic Relation Network for Video Visual Relation Inference

Video visual relation inference refers to the task of automatically detecting the relation triplets between the observed objects in videos with the form of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${ &lt; subject, predicate, object&gt;}$ </tex-math></inline-formula> , which requires correctly labeling each detected object and their interaction predicates. Despite the recent advances in image visual relation detection using deep learning techniques, relation inference in videos remains a challenging topic. On one hand, since the introduction of temporal information, it needs to model the rich spatio-temporal visual information for objects and videos. On the other hand, wild videos are often annotated with incomplete relation triplet tags and a few of them are semantically overlapped. However, previous methods adopt hand-crafted visual features extracted from the trajectories, describing local appearance characteristics of isolated objects. And they treat the problem as a multi-class classification task, which makes the relation tags mutually exclusive. To address the above issues, we propose a novel model, termed Visual-Semantic Relation Network (VSRN). In this network, we leverage three-dimensional convolution kernel to capture spatio-temporal features, and encode global visual features in videos through pooling operation on each time slice. Moreover, the semantic collocations between objects are also incorporated so as to obtain comprehensive representations of the relationships. For relation classification, we treat the problem as a multi-label classification task and regard each tag to be independent to predict various relationships. Additionally, we modify commonly used evaluation metric, video-wise recall, to a pair-wise metric (Roop) for testing the performance of models in predicting multiple relationships for the object pairs, Extensive experimental results on two large-scale datasets demonstrate the effectiveness of our proposed model which significantly outperforms the previous works.
Smart Culture Lens: An Application That Analyzes the Visual Elements of Ceramics

The Smart Culture Lens developed in this study is an application developed by utilizing the visual element classification system of ceramic and AI technology. The user can analyze the visual elements of the ceramic photo taken with a smartphone and search for similar ceramics related to each visual element. For this development, as a first step, visual elements such as color, form, material, and pattern were defined as criteria for classifying ceramic appearance, and the visual element classification system of ceramics was organized. In the second step, 19,610 images of 7,346 ceramics were collected through museum visit photography and web, and a database was built by annotating these images with a visual element classification system. In the third step, representative object detection models, Faster R-CNN and Mask R-CNN were trained based on a ceramic classification system. Through those trained object detection models, visual elements and masks of the input image were recognized, and representative colors of the area were extracted using the k-means algorithm through the recognized masks. The performance of the trained object detection models (Average precision of Form / Material / Pattern 1st-level category = 0.87 / 0.89 / 0.72) shows that the amount of collected data and the established classification system are useful. Finally, by applying the above development results, a mobile application called 'Smart Culture Lens' was developed, and the usefulness of this application was confirmed through a user experience test. This study combines AI technology into cultural heritage so that people can intuitively explore artifacts from a new perspective, which differs from traditional artifact exploring methods. All the detailed processes of this development will be a guide to how to apply AI technology to the cultural heritage.
Robust LCEKF for Mismatched Nonlinear Systems with Non-Additive Noise/Inputs and Its Application to Robust Vehicle Navigation

It is well known that the standard state estimation technique performance is particularly sensitive to perfect system knowledge, where the underlying assumptions are: (i) Process and measurement functions and parameters are known, (ii) inputs are known, and (iii) noise statistics are known. These are rather strong assumptions in real-life applications; therefore, a robust filtering solution must be designed to cope with model misspecifications. A possible way to design robust filters is to exploit linear constraints (LCs) within the filter formulation. In this contribution we further explore the use of LCs, derive a linearly constrained extended Kalman filter (LCEKF) for systems affected by non-additive noise and system inputs, and discuss its use for model mismatch mitigation. Numerical results for a robust tracking and navigation problem are provided to show the performance improvement of the proposed LCEKF, with respect to state-of-the-art techniques, that is, a benchmark EKF without mismatch and a misspecified EKF not accounting for the mismatch.
A Muti-Field Feature Interaction Convolutional Neural Network for Resume Recommendation

Resume-job matching is one of the most important steps in recruitment chain. In big companies like Tencent, we routinely spend thousands of hours searching for potential candidates through internal and external recruiting platforms every day, which is tedious and passive. Besides, the traditional job descriptions are difficult to cover various hiring scenarios with surging diversified and personalized needs. To this end, in this paper, we propose an end-to-end deep neural network for active resume recommendation without dependence on job requirements. The key idea is to model latent preferences of recruiters by learning interactions of multi-field features with convolutional neural network. Based on real behavior data generated by the internal recruiting website, the experimental results show that our model improves resume click-through rate against a series of state-of-the-art methods. In this way, we find an effective approach to lower the time budgets for our recruiters.
A Study on the Causes of Garbage Collection in Java for Big Data Workloads

Garbage collection in Java is critical to the performance of Big Data applications. Our study brings forth the causes that invoke the 3 Java garbage collectors for Big Data and non Big Data workloads. We conclude that allocation failure occurs for about 31-99 per cent of the total garbage collector execution time and is the most common cause for garbage collection in Big Data workloads. In case of Dacapo workloads, we observe that the prominent cause for garbage collection is system gc calls which occur for about 40-99 per cent of the total garbage collector execution time.
Continuous time quantum walks on graphs: Group state transfer

We introduce the concept of group state transfer on graphs, summarize its relationship to other concepts in the theory of quantum walks, set up a basic theory, and discuss examples. Let X be a graph with adjacency matrix A and consider quantum walks on the vertex set V(X) governed by the continuous time-dependent unitary transition operator U(t)=exp(itA). For S,T⊆V(X), we say X admits "group state transfer" from S to T at time τ if the submatrix of U(τ) obtained by restricting to columns in S and rows not in T is the all-zero matrix. As a generalization of perfect state transfer, fractional revival and periodicity, group state transfer satisfies natural monotonicity and transitivity properties. Yet non-trivial group state transfer is still rare; using a compactness argument, we prove that bijective group state transfer (the optimal case where |S|=|T|) is absent for almost all τ. Focusing on this bijective case, we obtain a structure theorem, prove that bijective group state transfer is "monogamous", and study the relationship between the projections of S and T into each eigenspace of the graph. Group state transfer is obviously preserved by graph automorphisms and this gives us information about the relationship between the setwise stabilizer of S⊆V(X) and the stabilizers of certain vertex subsets F(S,t) and I(S,t). The operation S↦F(S,t) is sufficiently well-behaved to give us a topology on V(X); this is simply the topology of subsets for which bijective group state transfer occurs at time t. We illustrate non-trivial group state transfer in bipartite graphs with integer eigenvalues, in joins of graphs, and in symmetric double stars. The Cartesian product allows us to build new examples from old ones.
Use of blockchain technology for Academic Certification in Higher Education Institutions

Blockchain (BC) technology promises great new technological benefits in different areas such as agriculture, health, government and education, however, there are some challenges that need to be overcome. In the field of education, this technology makes it possible to eliminate bottlenecks in some processes, in addition, it provides greater security in the transactions carried out and protection of the information of the institutions. This article presents a proposal for the application of BC technology in the field of Higher Education, specifically in academic certification, in which the main actors are identified, and the process to implement this technology, benefits and challenges in the case of academic certification are explained. This case provides valuable help in understanding the precise and coherent functioning of BC technology and its contribution to higher education.