Reviewerly/exHarmony huggingface dataset:
A novel spatial phase-unwrapping method based on single-pixel binarized line pattern for real-time 3D measurement

A new spatial phase unwrapping algorithm based on single-pixel binarized line pattern is proposed. The conventional spatial phase unwrapping method produces easily the accumulation and propagation of errors for the wrapped phase in the regions with isolated 2π jumps generated by shadow and sharp changing surfaces. In this paper, the single-pixel binarized (SPB) deformed line pattern extracted by the proposed small window retrieval (SWR) method can determine accurately the corresponding fringe order, then the wrapped phase is unwrapped point-to-point, in which the small window is adaptive for each light line. The measuring accuracy of the proposed method is closed to the quality-guided method with high precision, but it performs simultaneously higher robustness to measure the multi-object. Only one additional SPB line pattern is projected and the phase unwrapping process does not involve a time-consuming point-by-point sorting, so the proposed method can be applied to the real-time 3D measurement. The experimental results verify the feasibility and effectiveness of the proposed method.
Variational Disentanglement for Rare Event Modeling

Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events, and develop a robust prediction arm that joins the merits of the generalized additive model and isotonic neural nets. Results on synthetic studies and diverse real-world datasets, including mortality prediction on a COVID-19 cohort, demonstrate that the proposed approach outperforms existing alternatives.
mTVR: Multilingual Moment Retrieval in Videos

We introduce MTVR, a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21.8K TV show video clips.The dataset is collected by extending the popular TVR dataset (in English) with paired Chinese queries and subtitles.Compared to existing moment retrieval datasets, MTVR is multilingual, larger, and comes with diverse annotations.We further propose mXML, a multilingual moment retrieval model that learns and operates on data from both languages, via encoder parameter sharing and language neighborhood constraints.We demonstrate the effectiveness of mXML on the newly collected MTVR dataset, where mXML outperforms strong monolingual baselines while using fewer parameters.In addition, we also provide detailed dataset analyses and model ablations.Data and code are publicly available at https://github.
An Application of a Pseudo-Parabolic Modeling to Texture Image Recognition

In this work, we present a novel methodology for texture image recognition using a partial differential equation modeling. More specifically, we employ the pseudo-parabolic equation to provide a dynamics to the digital image representation and collect local descriptors from those images evolving in time. For the local descriptors we employ the magnitude and signal binary patterns and a simple histogram of these features was capable of achieving promising results in a classification task. We compare the accuracy over well established benchmark texture databases and the results demonstrate competitiveness, even with the most modern deep learning approaches. The achieved results open space for future investigation on this type of modeling for image analysis, especially when there is no large amount of data for training deep learning models and therefore model-based approaches arise as suitable alternatives.
Research and Application of Interactive Teaching Mode in Senior English Class

Most high school English teaching classrooms are instilling knowledge and filling duck teaching led by teachers, ignoring the subjectivity of students. As a result, it dampens students' enthusiasm for learning. Under the background of the new era curriculum reform concept, the classroom interactive teaching mode exerts its unique advantages, greatly mobilizes the enthusiasm of students in learning, changes the single interactive teaching mode, and enhances the interaction and communication between students and teachers. Therefore, this paper analyzes the research and application of classroom interactive teaching mode in high school English teaching.
A Systematic Literature Review on Load Balancing Algorithms of Virtual Machines in a Cloud Computing Environment

The emergence of cloud computing as computing resources evolved as a new kind of services in the area of communication and information technology from the convergence and evolution of business, technology and methodology perspectives which allows and furnish its users to access the IT resources anywhere and anytime at low cost on pay-per-use basis. Therefore, it should be a kind of non-stop service to its users with adequate fulfillment of the demands made by the users, which are available on the cloud environments. There are many challenges to achieve it and cloud load balancing on virtual machines is one of them. The selected computing resource for the execution of a job does not give guaranty that it would persist throughout the lifecycle of the job. The resources over cloud must be handled and controlled by an appropriate mechanism by setting the priorities on the available cloud resources which are shared among its users. This paper does a systematic literature review on load balancing algorithms for one of a vital cloud resource which is the Virtual Machine over Cloud environment to address these challenges.
A Law of Robustness for Two-Layers Neural Networks

We initiate the study of the inherent tradeoffs between the size of a neural network and its robustness, as measured by its Lipschitz constant. We make a precise conjecture that, for any Lipschitz activation function and for most datasets, any two-layers neural network with k neurons that perfectly fit the data must have its Lipschitz constant larger (up to a constant) than n/k where n is the number of datapoints. In particular, this conjecture implies that overparametrization is necessary for robustness, since it means that one needs roughly one neuron per datapoint to ensure a O(1)-Lipschitz network, while mere data fitting of d-dimensional data requires only one neuron per d datapoints. We prove a weaker version of this conjecture when the Lipschitz constant is replaced by an upper bound on it based on the spectral norm of the weight matrix. We also prove the conjecture in the high-dimensional regime n≈d (which we also refer to as the undercomplete case, since only k≤d is relevant here). Finally we prove the conjecture for polynomial activation functions of degree p when n≈dp. We complement these findings with experimental evidence supporting the conjecture.
Cryptanalysis of Start Code-Based Encryption Method for HEVC

We propose a cryptanalysis for MPEG high efficiency video coding bitstream encryption based on the start code. Today, most digital video data are transmitted over an open Internet network in an encrypted state for security and privacy. In general, video services must guarantee real-time communications; therefore, it is necessary to reduce the computational complexity required to encrypt video data. Consequently, many selective encryption algorithms that selectively encrypt a highly sensitive part of the entire video data have been studied. Recently, we proposed a selective encryption algorithm based on the start code for high efficiency video coding, which dramatically reduces the computational complexity of encryption. In this paper, we studied the cryptanalysis of the encryption algorithm based on the start code and found a security vulnerability for a ciphertext-only attack. The proposed method presents a method to restore the encrypted first byte of the network abstraction layer unit header by analyzing the unencrypted high efficiency video coding bitstream. The first byte of the network abstraction layer unit header can be restored using the unencrypted parts, such as the order, syntax element, and length of the network abstraction layer unit. The experimental results demonstrated the security vulnerability by breaking the start code based encryption algorithm that is difficult to recover using the brute-force attack with only one to six recovery attempts using the proposed ciphertext-only attack.
TOWARDS LEARNING LOW-LIGHT INDOOR SEMANTIC SEGMENTATION WITH ILLUMINATION-INVARIANT FEATURES

Abstract. Semantic segmentation models are often affected by illumination changes, and fail to predict correct labels. Although there has been a lot of research on indoor semantic segmentation, it has not been studied in low-light environments. In this paper we propose a new framework, LISU, for Low-light Indoor Scene Understanding. We first decompose the low-light images into reflectance and illumination components, and then jointly learn reflectance restoration and semantic segmentation. To train and evaluate the proposed framework, we propose a new data set, namely LLRGBD, which consists of a large synthetic low-light indoor data set (LLRGBD-synthetic) and a small real data set (LLRGBD-real). The experimental results show that the illumination-invariant features effectively improve the performance of semantic segmentation. Compared with the baseline model, the mIoU of the proposed LISU framework has increased by 11.5%. In addition, pre-training on our synthetic data set increases the mIoU by 7.2%. Our data sets and models are available on our project website.
EOG – Based Computer Control System for People with Mobility Limitations

Computer usage is an essential task that is required in almost every aspect of life today.As technology develops, this task becomes more and more necessary in the broader area.This task, which is quite simple for healthy people, has difficulties for people with muscular diseases or limited mobility.It is essential to use a computer not to have problems in their daily lives and adaptions.In this study, a computer control system was designed so that people with diseases such as ALS and paralysis can control computers easily.This system controls the mouse cursor according to the signal obtained by using the person's EOG signals.In this way, such people can control the computer without using their hands.In the study, the obtained EOG signals were amplified with an instrumentation amplifier and filtered digitally, thus it is ensured that the system can perform successfully.The designed system has great ease of use as it is a microcontroller-based and portable system and has a small size.In addition to the hardware, the embedded software necessary for the system's control has also been developed.With this designed system, people with limited mobility will easily use computers with only glasses in their daily lives.
The Implementation of Grammarly In Error Analysis Implementasi Grammarly Dalam Error Analysis

&lt;p&gt;English is still considered as a difficult thing to learn, both for students and teachers. That is because English is still regarded as a foreign language and is rarely used in daily conversation. The difficulty in mastering English is found not only in the speaking aspect but also in the writing element. In examining student writing (error analysis), lecturers at the Akademi Maritim Nusantara Cilacap used a program called Grammarly. This study aims to see the implementation of Grammarly in helping lecturers make Error Analysis. Researchers found that Grammarly was enough to help lecturers in conducting error analysis. Researchers then classify the types of errors found in student writing using Grammarly. From 7 (seven) student writings that were used as data sources in this study, there were 9 (nine) types of errors that were made in the writing of the seven students. The most common errors were errors with spelling (39%) and punctuation (40.3%). Although Grammarly is proven to help the work of lecturers in conducting Error Analysis, Grammarly still cannot find sentences that have errors semantically quickly.&lt;/p&gt;&lt;p&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;
Weakly Supervised Pre-Training for Multi-Hop Retriever

In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question.The main steps of this process are sub-question detection, document retrieval for the subquestion, and generation of a new query for the final document retrieval.However, building a dataset that contains complex questions with sub-questions and their corresponding documents requires costly human annotation.To address the issue, we propose a new method for weakly supervised multi-hop retriever pretraining without human efforts.Our method includes 1) a pre-training task for generating vector representations of complex questions, 2) a scalable data generation method that produces the nested structure of question and subquestion as weak supervision for pre-training, and 3) a pre-training model structure based on dense encoders.We conduct experiments to compare the performance of our pre-trained retriever with several state-of-the-art models on end-to-end multi-hop QA as well as document retrieval.The experimental results show that our pre-trained retriever is effective and also robust on limited data and computational resources.
Financial development and income inequality: a nonlinear econometric analysis of 21 African countries, 1990-2019

From 1990 to 2019, this study examines the nonlinear dynamic impact of financial development on income inequality in an unconventional policy regime in a panel of 21 African countries. More importantly, we use Panel Smooth Transition Regression to extend the existing debate on this subject, with roots back to the seminal work of G-J and many others, and add a twist by distinguishing between a conventional (1990–1999) and unconventional policy regime (2000–2019), as well as the threshold level at which financial development reduces inequality. Our baseline results will be supported by the Generalized Method of Moments. The PSTR model was chosen because it can account for features that dynamic panel techniques cannot, such as endogeneity, homogeneity, cross-country variability, and time instability within the model. We found evidence of a non-linear effect between the two variables, with the threshold found to be 21.90% of GDP, below which financial development reduces inequality in Africa, and this confirms the U-shape in unconventional policy regimes and the G-J in conventional policy regimes. Unconventional monetary policies were found to trigger the financial-inequality relationships. The focal policy recommendation is that the financial sector be given adequate consideration and recognition by, inter alia, implementing appropriate financial reforms, developing an adequate investment strategy, and maintaining spending on science and technology investment in African countries below the threshold. Again, when implementing unconventional monetary policies in African countries, extreme caution is required.
A Dataset and Baselines for Multilingual Reply Suggestion

Reply suggestion models help users process emails and chats faster.Previous work only studies English reply suggestion.Instead, we present MRS, a multilingual reply suggestion dataset with ten languages.MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch.Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks.We build a generation model and a retrieval model as baselines for MRS.The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages.MRS is publicly available at https://github.com/zhangmozhi/mrs. Multilingual Reply SuggestionAutomated reply suggestion is a useful feature for email and chat applications.Given an input message, the system suggests several replies, and users may click on them to save typing time (Figure 1).This feature is available in many applications including Gmail, Outlook, LinkedIn, Facebook Messenger, Microsoft Teams, and Uber.Reply suggestion is related to but different from open-domain dialog systems or chatbots (Adiwardana et al., 2020;Huang et al., 2020).While both are conversational AI tasks (Gao et al., 2019), the goals are different: reply suggestion systems help the user quickly reply to a message, while chatbots aim to continue the conversation and focus more on multi-turn dialogues.Ideally, we want our model to generate replies in any language.However, reply suggestion models require large training sets, so previous work mostly
Event Ticketing Accounting Information System using RFID within the COVID-19 Fitness Etiquettes

Because maximizing stadium attendance demand is of utmost importance, for both sports economists and sport management researchers, understanding the potential determinants of such demand better has become a priority in the last decades. Here, conducting a systematic scoping review, we map this previous research in terms of its characteristics, its nature, and its volume, thus offering a concise perspective on what has been previously explored, and, more importantly, what remains to be analyzed in the future. Intriguingly, we observe a lack of studies exploring data generated in both niche and women's sports, as well as in most emerging markets. Further, the field has not yet established the use of disaggregated stadium attendance data, despite notable potential methodological pitfalls. Keywords attendance, demand, football/soccer, major league baseball (MLB), major league soccer (MLS), national basketball association (NBA), national football league (NFL), national hockey league (NHL), outcome uncertainty, spectator sports For executives operating in the field of professional sports, maximizing stadium attendance demand is of utmost importance. On the one hand, generating revenue
Medical Image Enhancement to Extract Brain Tumors from CT and MRI images

Always MRI and CT Medical images are noisy so that preprocessing is necessary for enhance these images to assist clinicians and make accurate diagnosis. Firstly, in the proposed method uses two denoising filters (Median and Slantlet) are applied to images in parallel and the best enhanced image gained from both filters is voted by use PSNR and MSE as image quality measurements. Next, extraction of brain tumor from cleaned images is done by segmentation method based on k-mean. The result shows that the proposed method is giving an optimal solution due to denoising method which is based on multiple filter types to obtain best clear images and that is leads to make the extraction of tumor more precision best.
Learning Term Embeddings for Lexical Taxonomies

Lexical taxonomies, a special kind of knowledge graph, are essential for natural language understanding. This paper studies the problem of lexical taxonomy embedding. Most existing graph embedding methods are difficult to apply to lexical taxonomies since 1) they ignore implicit but important information, namely, sibling relations, which are not explicitly mentioned in lexical taxonomies and 2) there are lots of polysemous terms in lexical taxonomies. In this paper, we propose a novel method for lexical taxonomy embedding. This method optimizes an objective function that models both hyponym-hypernym relations and sibling relations. A term-level attention mechanism and a random walk based metric are then proposed to assist the modeling of these two kinds of relations, respectively. Finally, a novel training method based on curriculum learning is proposed. We conduct extensive experiments on two tasks to show that our approach outperforms other embedding methods and we use the learned term embeddings to enhance the performance of the state-of-the-art models that are based on BERT and RoBERTa on text classification.
Stimulating polarization switching dynamics in mid-infrared quantum cascade lasers

A unique feature of quantum cascade lasers relies on the ultrafast carrier relaxation lifetime that occurs on a picosecond time scale. Furthermore, the very sharp electronic transitions among the conduction-band states (subbands) lead to specific selection rules, which in theory exclude the TE-polarized light emission. Under cross-polarization reinjection, we stimulate the polarization switching dynamics in such an intersubband device. The mid-infrared modulation signal is phase-shifted between the TM mode and the TE mode, with a typical microsecond time scale different from pure time-delay dynamics. We also prove that both the frequency and the duty cycle of the modulated pattern can be slightly tuned by varying the characteristics of the reinjected light. These results reveal the possibility of favoring the TE polarization in a quantum cascade laser and generating a square-wave modulation with cross-polarization reinjection.
Multi-View Representation Learning with Manifold Smoothness

Multi-view representation learning attempts to learn a representation from multiple views and most existing methods are unsupervised. However, representation learned only from unlabeled data may not be discriminative enough for further applications (e.g., clustering and classification). For this reason, semi-supervised methods which could use unlabeled data along with the labeled data for multi-view representation learning need to be developed. Manifold information plays an important role in semi-supervised learning, but it has not been considered for multi-view representation learning. In this paper, we introduce the manifold smoothness into multi-view representation learning and propose MvDGAT which learns the representation and the intrinsic manifold simultaneously with graph attention network. Experiments conducted on real-world datasets reveal that our MvDGAT can achieve better performance than state-of-the-art methods.
Comparative analysis of first-order optimization algorithms

Deep Learning is a popular and emerging research area showing great contribution in various fields. Advancement in Deep Learning during the last few decades makes it the most prominent field with lots of new possibilities for applying in the various new areas. Recently Deep Neural Network has become more complex and deeper which requires more generalize optimization algorithms. This paper is a systematic comparative study of Deep Learning having focus on some popular and most commonly used first-order optimization algorithms. It covers varieties of first-order optimization methods used for improving training accuracy and trim down training time.