Reviewerly/exHarmony huggingface dataset:
Detecting Transparency of Glasses with Capsule Networks Based on Deep Learning

Object detection applications started to take their places in our lives with the improvements in technology and artificial intelligence. In daily life, some applications such as license plate detection, optical character recognition become indispensable. In parallel with ongoing technological developments today, the technologies which soon will be a part of our daily life such as detection of suspicious situations through security cameras and autonomous cars, are improving rapidly. The studies in the object detection area are have generally focused on opaque objects. The number of studies on transparent objects is very limited. In this study, a system which is trained by the dataset that contains transparent and non-transparent glasses, is used to detect transparency of these glasses. Recently introduced capsule networks are used to develop a proposed system. To compare the obtained results, LeNet, AlexNet, and ResNet are trained and tested with the same dataset. When the result of the study is evaluated, it was seen that CapsNet got better results on classification accuracy than other deep learning methods which are used in this study. The obtained test accuracy, precision, recall, and F-score values for CapsNet are 91.58%, 91.61%, 91.58%, and 91.56%, respectively. The best obtained test accuracies for LeNet, AlexNet, and ResNet are 84.58%, 88.58%, and 87.58%, respectively. Thus, AlexNet got the second-best result on classification accuracy. While the ResNet took third place, LeNet stayed behind all of them.
Exploring Self-Supervised Graph Learning in Literature-Based Discovery

Literature-Based Discovery (LBD) aims to find associations, as one or more connecting concepts, of two “unrelated” concepts hidden in the literature. The most recent studies fulfilled the task by learning the representations of the concepts and using the representations’ similarities to decide whether a connecting concept is a reasonable one. However, these approaches cannot handle “complex” associations with multiple connecting concepts properly. To address this issue, we propose a neural network model LBDSetNet which can assign a “credibility” score to a plausible association with either one or more connecting concepts. By unifying both the literature and the candidate associations as bags of concepts, we can generate “less credible” literature and train LBDSetNet by enforcing it to distinguish the generated and original literature, overcoming the lack of labeled associations. We also propose a new double-margin cost function for better model training by generating “more credible” documents. We experiment to show that our model can find “complex” associations effectively and efficiently. Comparative experiments reveal that the LBDSetNet solution performs significantly better than the previously proposed models on “simple” associations. The double margin cost function also proves its advantage in model training.
Empowering Interactive Robots by Learning Through Multimodal Feedback Channel

This paper introduces the first workshop on “Empowering Interactive Robots by Learning Through Multimodal Feedback Channel” organized at the 23rd ACM International Conference on Multimodal Interaction in Montreal, Canada. This workshop aims to bring together researcher from different disciplines to stimulate a discussion on how multimodality can become key to the emerging field of interactive machine learning.
Impact of detecting clinical trial elements in exploration of COVID-19 literature

The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature. Although semi-structured information resulting from concept recognition and detection of the defining elements of clinical trials (e.g. PICO criteria) has been commonly used to support literature search, the contributions of this abstraction remain poorly understood. In this study, we compare the results retrieved by a standard search engine with those filtered using clinically-relevant concepts and their relations. Most importantly, with an analysis based on the TREC-COVID dataset, we find that the relational concept selection filters the original retrieved collection in a way that decreases the proportion of unjudged documents and increases the precision, which means that the user is likely to be exposed to a larger number of relevant documents.
Knowledge Discovery of the Delays Experienced in Reporting COVID-19 Confirmed Positive Cases Using Time to Event Models

Survival analysis techniques model the time to an event where the event of interest traditionally is recovery or death from a disease. The distribution of survival data is generally highly skewed in nature and characteristically can include patients in the study who never experience the event of interest. Such censored patients can be accommodated in survival analysis approaches. During the COVID-19 pandemic, the rapid reporting of positive cases is critical in providing insight to understand the level of infection while also informing policy. In this research, we introduce the very novel application of survival models to the time that suspected COVID-19 patients wait to receive their positive diagnosis. In fact, this paper not only considers the application of survival techniques for the time period from symptom onset to notification of the positive result but also demonstrates the application of survival analysis for multiple time points in the diagnosis pathway. The approach is illustrated using publicly available data for Ontario, Canada for one year of the pandemic beginning in March 2020.
Android-Based Hotel Room Reservation Service System

The hotel is a commercial building that provides facilities between lodging, luggage services, food and drink services and facilities for the use of hotel items (lodging). Limited room reservation information usually makes it difficult for the public or the user to find out. In this study the authors made an Android-based hotel room reservation system. This system is designed with the discussion of android studio programming, with this system it is very helpful for the public to find out information and help the hotel to make it easier to process about empty rooms. The presence of this Android-based reservation service system at sapphire home hotel is expected to provide convenience for the public to make room reservations online.
Using natural language processing methods to improve the applicability of altmetrics in academic evaluation

To improve the applicability of altmetrics in academic evaluation, we need a more comprehensive understanding of how academic papers were mentioned online. Although the content of mentions can be obtained through APIs on many platforms, it should rely on artificial intelligence technology to efficiently analyze and identify connections from a huge amount of data. In view of this, this study proposes a method that based on natural language processing to generate optimized altmetric counts.
Gullibility Theory and Ponzi Scheme Participation among University Students in Malaysia

Financial fraud can be broadly defined as an intentional act of deception involving financial transactions for purpose of personal gain. Many fraud cases involve complicated financial transactions included the Ponzi scheme which is conducted by 'white-collar criminals' such as business professionals with specialized knowledge and criminal intent. In the 1920s, Charles Ponzi engaged with the Ponzi scheme which also known as money flows from the bottom up. The continuous issues of the Ponzi scheme in Malaysia, especially the involvement of students as victims has become the major problem to look forward to the factors that influence university students to this investment fraud. This paper explores the gullibility factors of individuals' extent of exposure to a Ponzi scheme among Bumiputera university students in Malaysia. This study conducts face-to-face interviews and observations with Bumiputera university students in Malaysia through purposive sampling. Findings show that the victims involved in the Ponzi scheme are gullible, meaning that their level of trustworthiness is high, based on the four elements of Gullibility theory (situation, cognition, personality, and state). This study will benefit the regulators, enforcement agencies, and universities to further understand the reasons why the students are involved in Ponzi schemes. It also benefits the body of knowledge about the characteristics of Ponzi scheme companies that are considered people of choice. Future researchers should investigate the methods to minimize future investors' participation, especially the students and losses in fraudulent investments.
Evolutionary Generation of Test Case for Deep Neural Network Based on Coverage Guidance

In the past few years, Deep Neural Network(DNN) has made great progress. However, it is difficult to guarantee that DNN-based applications can get satisfactory results. Testing is an effective technology to improve the accuracy and robustness of DNN, in which test case generation is an important task. In this paper, we combine the differential evolution algorithm and coverage criterion which is stronger than neuron coverage to generate test cases for DNN. This method uses the prediction loss of DNN and coverage criterion as the fitness function to generate small perturbations at the pixel level between different channels of the image. Then the generated perturbations are added to the image to construct a test case. Theoretically, the test cases generated by this method can not only make the neural network produce misclassification, but also achieve higher coverage for neurons in the DNN, so that defects in DNN can be detected more comprehensively.
Automated Decision Systems: Why Human Autonomy is at Stake

For more than 50 years, humans have been using model- and data-based support systems in decision making with the hope that system supported decisions are not only better, but also more objective and fairer (i.e. more efficient and less biased). With data-based Artificial Intelligence (AI) systems, this hope is revived. AI systems are to support physicians in diagnosing illnesses, or assist managers when recruiting personnel. Furthermore, AI systems could even replace humans when deciding whether or not applicants receive a loan, an insurance policy or can lease a car. Even governments and public institutions are already making use of such automated decision systems including such diverse applications as setting bail in legal proceedings, risk assessment in youth welfare services or eligibility for state assistance to the unemployed and access to health services. The results of the current applications of automated decision systems are, however problematic. In this essay I examine the question of how automated decision systems impact the autonomy of humans and what requirements are to be placed on automated decision systems in order to protect individuals and society.
Feature Selection for Privileged Modalities in Disease Classification

Multimodal data allows supervised learning while considering multiple complementary views of a problem, improving final diagnostic performance of trained models. Data modalities that are missing or difficult to obtain in clinical situations can still be incorporated into model training using the learning using privileged information (LUPI) framework. However, noisy or redundant features in the privileged modality space can limit the amount of knowledge transferred to the diagnostic model during the LUPI learning process. We consider the problem of selecting desirable features from both standard features which are available during both model training and testing, and privileged features which are only available during model training. A novel filter feature selection method named NMIFS+ is introduced that considers redundancy between standard and privileged feature spaces. The algorithm is evaluated on two disease classification datasets with privileged modalities. Results demonstrate an improvement in diagnostic performance over comparable filter selection algorithms.
Mechanism Designs for Solar Tracking

In this paper, mechanism design for solar trackers is discussed in terms of serial and parallel architectures that are analyzed to characterize the feasible performance of mechanism solutions that can fully track the sun during its diurnal east-to-west and seasonal north-south motions. Their main operational characteristics are discussed as design criteria and used to compare existing solutions. New mechanism designs are outlined as examples of the proposed guidelines.
Uncertainty-Aware Self-paced Learning for Grammatical Error Correction

Recently, pre-trained language models have gained dramatic progress on grammatical error correction (GEC) task by fine-tuning on a small amount of annotated data. However, the current approaches ignore two problems. On the one hand, the GEC datasets suffer from annotation errors which may impair the performance of the model. On the other hand, the correction difficulty varies across sentences and the generating difficulty of each token within a sentence is inconsistent as well. Therefore, hard and easy samples in GEC task should be treated differently. To address these issues, we propose an uncertainty-aware self-paced learning framework for GEC task. We leverage Bayesian deep learning to mine and filter noisy samples in the training set. Besides, we design a confidence-based self-paced learning strategy to dynamically adjust the loss weights of hard and easy samples. Specifically, we measure the confidence score of the model on the samples at the token-level and the sentence-level, and schedule the training procedure according to the confidence scores. Extensive experiments demonstrate that the proposed approach surpasses the baseline model by 2.0+ point of \(F_{0.5}\) scores on several GEC datasets and proves the effectiveness of our approach.
Automatically Detecting Textual Content in High-Resolution Images

Classifying targets in satellite images is a nontrivial task which requires dealing with a large number of undesirable elements such as clouds, building shadows and other unexpected objects. Among these, a commonly found element refers to artificially inserted post-processing objects like textual content, as the added text usually takes the form of watermarks, sensor specifications, street and place location names, etc. Manually selecting text segments is tedious, time-consuming, and requires the familiarity with image editing tools to precisely delineate these writing areas. Therefore, in this paper, a new automatic approach for detecting textual elements in satellite images is presented. Our approach combines cartoon-texture decomposition, thresholding-based rules, morphological operations, and connected component analysis into a fully automated and concise framework. Experiments on real satellite images and comparisons against well-established text detection methods demonstrate the high accuracy and low false-positive rate achieved by our approach when detecting textual content.
Comparison of Two Frameworks for Analyzing Longitudinal Data

Under the random design of longitudinal data, observation times are irregular, and there are mainly two frameworks for analyzing such kind of longitudinal data. One is the clustered data framework and the other is the counting process framework. In this paper, we give a thorough comparison of these two frameworks in terms of data structure, model assumptions and estimation procedures. We find that modeling the observation times in the counting process framework will not gain any efficiency when the observation times are correlated with covariates but independent of the longitudinal response given covariates. Some simulation studies are conducted to compare the finite sample behaviors of the related estimators, and a real data analysis of the Alzheimer’s disease study is implemented for further comparison.
DA-BERT: Enhancing Knowledge Selection in Dialog via Domain Adapted BERT with Dynamic Masking Probability

One of the most challenging tasks in Knowledge-grounded Task-oriented Dialog Systems (KTDS) is the knowledge selection task, which aims to find the proper knowledge snippets to handle user requests. This paper proposes DA-BERT to employ pre-trained BERT with domain adaptive training and newly proposed dynamic masking probability to deal with knowledge selection in KTDS. Domain adaptive training minimizes the domain gap between the general text data BERT is pre-trained on and the dialog-knowledge joint data; and dynamic masking probability enhances the training in an easy-to-hard mode. Experimental results on the benchmark dataset show that our proposed training method outperforms the state-of-the-art models with large margins across all the evaluation metrics. Moreover, we analyze the bad case of our method and recognize several typical errors in the bad case set to facilitate further research in this direction.
RDMMFET: Representation of Dense Multimodality Fusion Encoder Based on Transformer

Visual question answering (VQA) is the natural language question-answering of visual images. The model of VQA needs to make corresponding answers according to specific questions based on understanding images, the most important of which is to understand the relationship between images and language. Therefore, this paper proposes a new model, Representation of Dense Multimodality Fusion Encoder Based on Transformer, for short, RDMMFET, which can learn the related knowledge between vision and language. The RDMMFET model consists of three parts: dense language encoder, image encoder, and multimodality fusion encoder. In addition, we designed three types of pretraining tasks: masked language model, masked image model, and multimodality fusion task. These pretraining tasks can help to understand the fine-grained alignment between text and image regions. Simulation results on the VQA v2.0 data set show that the RDMMFET model can work better than the previous model. Finally, we conducted detailed ablation studies on the RDMMFET model and provided the results of attention visualization, which proves that the RDMMFET model can significantly improve the effect of VQA.
Learning Factors Knowledge Tracing Model Based on Dynamic Cognitive Diagnosis

This paper mainly studies the influence of dynamic cognitive diagnosis on personalized learning. Considering the influence of knowledge correlation factors and human brain memory factors on learning activities, a knowledge tracing model integrating learning factors is proposed. Firstly, based on the exercise-knowledge association information, the model maps learners and exercises to the knowledge space with clear meaning. Then, the evolution process of learners’ knowledge learning is quantitatively modeled in the knowledge space by integrating the classical learning curve and forgetting curve theory of pedagogy. On the other hand, considering the influence of topic semantics in the learning process, a knowledge tracing model integrating topic semantics is proposed in this paper. Firstly, the model designs a dynamic enhanced memory network to store the common information of knowledge and describes the learners’ dynamic mastery of knowledge. Secondly, the depth representation method of exercise resources is proposed to mine the text personality information and integrate it into the process of learners’ knowledge change modeling. Through a large number of experiments on exercise records, it is verified that the proposed model has accurate prediction performance and knowledge tracing interpretability.
An Improved Group Signature Scheme with VLR over Lattices

For group signatures (GS) supporting membership revocation, verifier-local revocation (VLR) mechanism is the most flexible choice. As a post-quantum secure cryptographic counterpart of classical schemes, the first dynamic GS-VLR scheme over lattices was put forward by Langlois et al. at PKC 2014; furthermore, a corrected version was shown at TCS 2018. However, both designs are within Bonsai trees and featuring bit-sizes of group public-key and member secret signing key proportional to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="normal">log</mi> <mtext> </mtext> <mi>N</mi> </math> where N is the group size; therefore, both schemes are not suitable for a large group. In this paper, we provide an improved dynamic GS-VLR over lattices, which is efficient by eliminating a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="script">O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi mathvariant="normal">log</mi> <mtext> </mtext> <mi>N</mi> </mrow> </mfenced> </math> factor for both sizes. To realize the goal, we adopt a more efficient and compact identity-encoding technique. At the heart of our new construction is a new Stern-type statistical zero-knowledge argument of knowledge protocol which may be of some independent cryptographic interest.
From Illusions to Beyond-Real Interactions in Virtual Reality

Despite recent advances in technology, current virtual reality (VR) experiences have many limitations. When designing VR interactions, we can leverage the unique affordances of this virtual medium and our ability to programmatically control the renderings to not only overcome these limitations, but also to create new interactions that go beyond the replication of the real world. In my dissertation, I seek to answer the following research questions: How can we utilize the unique affordances that VR offers to overcome the current limitations of this technology? How can we go even further and design mixed reality interactions that leverage these affordances to extend our experiences in the real world? In my work, I approach movement-based VR interactions from a sensorimotor control perspective, carefully considering the plasticity and limits of human perception. To answer the first research question, I explore various visuo-haptic illusions to overcome the limitations of existing haptic devices. In my ongoing work, I am building tools that help researchers and practitioners design and evaluate novel and usable mixed reality interactions that have no real-world counterparts.
Combining SWAPs and Remote CNOT Gates for Quantum Circuit Transformation

Quantum computers offer enormous speed advantages over their classical counterparts. Still, optimization on quantum circuits is necessary to further increase their potential. Additionally, physical realizations of quantum computers place restrictions on quantum circuits, regarding the available quantum gates. In order to satisfy these restrictions, non-native gates need to be expressed as an equivalent cascade of natively available quantum gates which induces a mapping overhead. Two complementary approaches to this problem are to move around the qubits (using SWAP gates) or to apply so-called remote gates, i.e. pre-computed cascades of native gates which keep the qubit placement.In this paper, we explore how combinations of movements and remote gates can be employed to reduce the required overhead regarding the number of native gates as well as the circuit depth. We also discuss ways to find out which qubits to address with the movements in order to optimize these metrics. Our general evaluation is supplemented by evaluations on two IBM quantum computer architectures to show how quantum circuits can be optimized by the presented patterns.