Reviewerly/exHarmony huggingface dataset:
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents

Today, most large-scale conversational AI agents (e.g. Alexa, Siri, or Google Assistant) are built using manually annotated data to train the different components of the system. Typically, the accuracy of the ML models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time consuming. In this paper, we propose a system that leverages user-system interaction feedback signals to automate learning without any manual annotation. Users here tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by errors in ASR, NLU, ER or the application. In some cases, users may not properly formulate their requests (e.g. providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self-learning system automatically detects the errors, generate reformulations and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa-user errors by pooling anonymized data across millions of customers. The proposed self-learning system achieves a win/loss ratio of 11.8 and effectively reduces the defect rate by more than 30% on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self-learning large-scale conversational AI system in production.
Language coverage and generalization in RNN-based continuous sentence embeddings for interacting agents

Continuous sentence embeddings using recurrent neural networks (RNNs), where variable-length sentences are encoded into fixed-dimensional vectors, are often the main building blocks of architectures applied to language tasks such as dialogue generation. While it is known that those embeddings are able to learn some structures of language (e.g. grammar) in a purely data-driven manner, there is very little work on the objective evaluation of their ability to cover the whole language space and to generalize to sentences outside the language bias of the training data. Using a manually designed context-free grammar (CFG) to generate a large-scale dataset of sentences related to the content of realistic 3D indoor scenes, we evaluate the language coverage and generalization abilities of the most common continuous sentence embeddings based on RNNs. We also propose a new embedding method based on arithmetic coding, AriEL, that is not data-driven and that efficiently encodes in continuous space any sentence from the CFG. We find that RNN-based embeddings underfit the training data and cover only a small subset of the language defined by the CFG. They also fail to learn the underlying CFG and generalize to unbiased sentences from that same CFG. We found that AriEL provides an insightful baseline.
An improved transformation between Fibonacci FSRs and Galois FSRs

Feedback shift registers (FSRs), which have two configurations: Fibonacci and Galois, are a primitive building block in stream ciphers. In this paper, an improved transformation is proposed between Fibonacci FSRs and Galois FSRs. In the previous results, the number of stages is identical when constructing the equivalent FSRs. In this paper, there is no requirement to keep the number of stages equal for two equivalent FSRs here. More precisely, it is verified that an equivalent Galois FSR with fewer stages cannot be found for a Fibonacci FSR, but the converse is not true. Furthermore, the total number of equivalent Galois FSRs for a given Fibonacci FSR with n stages is calculated. In order to reduce the propagation time and memory, an effective algorithm is developed to find equivalent Galois FSR and is proved to own minimal operators and stages. Finally, the feasibility of our proposed strategies, to mutually transform Fibonacci FSRs and Galois FSRs, is demonstrated by numerical examples.
Statistical EL is ExpTime-complete

We show that the consistency problem for Statistical EL ontologies, defined by Pe{\~{n}}aloza and Potyka, is ExpTime-hard. Together with existing ExpTime upper bounds, we conclude ExpTime-completeness of the logic. Our proof goes via a reduction from the consistency problem for EL extended with negation of atomic concepts.
Topological Feature Search in Time-Varying Multifield Data

A wide range of data that appear in scientific experiments and simulations are multivariate or multifield in nature, consisting of multiple scalar fields. Topological feature search of such data aims to reveal important properties useful to the domain scientists. It has been shown in recent works that a single scalar field is insufficient to capture many important topological features in the data, instead one needs to consider topological relationships between multiple scalar fields. In the current paper, we propose a novel method of finding similarity between two multifield data by comparing their respective fiber component distributions. Given a time-varying multifield data, the method computes a metric plot for each pair of histograms at consecutive time stamps to understand the topological changes in the data over time. We validate the method using real and synthetic data. The effectiveness of the proposed method is shown by its ability to capture important topological features that are not always possible to detect using the individual component scalar fields.
Neural Baselines for Word Alignment

Word alignments identify translational correspondences between words in a parallel sentence pair and is used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems , or to perform quality estimation. In most areas of natural language processing, neural network models nowadays constitute the preferred approach, a situation that might also apply to word alignment models. In this work, we study and comprehensively evaluate neural models for unsupervised word alignment for four language pairs, contrasting several variants of neural models. We show that in most settings, neural versions of the IBM-1 and hidden Markov models vastly outperform their discrete counterparts. We also analyze typical alignment errors of the baselines that our models overcome to illustrate the benefits-and the limitations-of these new models for morphologically rich languages.
Trios: Stylistic Rendering of 3D Photos

3D photography has emerged as a medium that provides an immersive dimension to 2D photos. We present Trios, an interactive mobile app that combines the vividness of image-based artistic rendering with 3D photos by implementing an end-to-end pipeline for their generation and stylization. Trios uses Apple’s accelerated image-processing APIs and dedicated Neural Engine for depth-generation and learning-based artistic rendering. The pipeline runs at interactive frame rates and outputs a compact video, which can easily be shared. Thus, it serves as a unique interactive tool for digital artists interested in creating immersive artistic content.
Android Application for Finding Local Jobs

Abstract: The development of Technology has prompted a reality where everybody is associated just by means of PCs, cell phones and tablets. Subsequently has caused world to appear to be more modest. That one creation that rolled out an extraordinary improvement in the loves of individuals are PDAs. The variable that makes cell phone a savvy gadget are portable applications. Different applications fill different needs as per the need. Inspite of extensive variety of utilization accessible, not many areas actually need utilizing this innovation. One such areas is opportunity in requests for employment of limited scope industry. The conventional approach to advising position in these areas are through news papers or releases. Consequently this, project points planning an application for the gig searchers and suppliers of the limited scale industry. Another area that need are local area level association's. As people group develops keeping up with the record of the individuals gets dreary. At present little local area keep up with records of their individuals like individual informations, phone records, and so on mannually in a register. Every one of these data could be only a tick away when put togther in one application. This undertaking points in planning one such application.
Use of Cryptography and Signing for Network Security

Abstract: With technological advancements along with tremendous generation and storage of sensitive data, which can be cryptographic keys, passwords or other data that can be crucial for operation of an organization, thereis a need to secure this sensitive data startingfrom its creation, its transfer from one place to another and its final place of storage. Thiscan be done with the help of cryptography and cryptographic algorithms that can help secure this entire process. With cryptography, it is possible to securely transfer sensitive data to ensure that it cannot be read or tampered by a third party. Cryptography and its related algorithms can also be used to secure the network for safe transfer and storage of sensitive data. In this paper, starting with a brief introduction to cryptography concepts and some networkingprotocols, we shall discuss and compare usage of an algorithm such as AES, hashing and importance of Digital Certificates and Signing operation along with encryption for better security.
Learning to Detect Boundary Information for Brain Image Segmentation

Abstract MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to 5.3% compared to the state-of-the-art models) in detecting and segmenting brain tissue images.
Perancangan Website Yayasan Panti Asuhan Mekar Lestari

The development of information technology has brought many positive impacts on society. One of the most felt impacts is the use of the website. Websites can help provide information about an organization. The organization can be a profit oriented organization or a non profit oriented organization. One organization that requires the use of a website is the Mekar Lestari Orphanage Foundation. The Mekar Lestari Orphanage Foundation is located in the Rawa Buntu area, Tangerang. The Mekar Lestari Orphanage Foundation houses about 80 orphans. The Mekar Lestari Orphanage Foundation has always relied on donations from the surrounding community for their daily needs and activities at the orphanage. The Mekar Lestari Orphanage Foundation so far in recording donations and orphanage activities as well as managing orphans still uses the manual method, so it is difficult to provide information to donors to find out activities in the orphanage, as well as the limited information that can be known by the public about the orphanage. With the design of the Mekar Lestari Orphanage Foundation's website, it can help the community (donors) in making donations, as well as regarding various activities and data regarding orphans sheltered by the Mekar Lestari Orphanage Foundation. This website design will use the PHP and HTML programming languages. It is hoped that this website will make it easier for people (donors) to make donations, and help the Mekar Lestari Orphanage Foundation to manage and provide information related to activities at the Mekar Lestari Orphanage Foundation.
Research and Application of Smart Classroom in Surgical Practice Teaching

With the advancement of science and technology as well as the rapid development of next generation information technologies, such as big data and artificial intelligence, smart classrooms have emerged as the times have demanded. Adopting innovative teaching modes in smart classrooms allows the realization of various teaching techniques that cannot be achieved in traditional classrooms. Especially for some courses in medical schools, due to changes in the medical environment and the improvement of patients’ awareness of medical rights and interests, the traditional teaching methods are becoming more restricted. For example, in clinical surgery, due to the increasing number of medical students in recent years and requirements for strict aseptic principle adherence in operating rooms, practical teaching has encountered great restrictions, thus preventing it from meeting the needs of students. In order to solve this problem, this research constructs a smart classroom teaching model for surgical practice teaching based on hardware equipment, such as smart classrooms, interconnected surgical mobile broadcast equipment, and intelligent medical simulators. Through this teaching model, the teaching effect and quality are further analyzed, laying a foundation for smart teaching in future medical courses.
An Augmented Reality Maintenance Assistant with Real-time Quality Inspection on Handheld Mobile Devices

Abstract With the advances of industry 4.0, augmented reality (AR) devices are being deployed across the manufacturing sector to enhance worker perception and efficiency. AR is often used to deliver spatially relevant work instructions on mobile devices for maintenance procedures on the factory floor. In these situations, workers use their mobile devices to view instructions in the form of 3D animations and annotations that directly overlay the equipment being maintained. Workers then follow the AR instructions and must ultimately rely on their own judgement and knowledge of the procedure as they progress from step to step. An AR assistant that could validate each stage of the procedure in real time and provide the worker with feedback on any observed errors would ensure that each maintenance procedure is completed successfully. This work presents a mobile, quality inspection system for AR maintenance procedures that is capable of assessing the maintenance task in real time. The system is designed for deployment on handheld mobile devices and can thus manage the challenges inherent to performing quality inspection with a non-fixed vision system. This work enumerates four essential qualities of mobile quality inspection tools and outlines some of the challenges encountered during the development of such a system. In the end, testing established that the system could provide adequate assistance for capturing inspection images, accurately process the captured images using machine vision, and generate detailed feedback from the quality inspection in a timely manner.
Early diagnosis of breast cancer using multiparametric ultrasound and hybrid technologies

Breast cancer currently occupies a leading place in the structure of morbidity and mortality of the female world population. Despite the improvement of methods of diagnosis and treatment of this pathology, more than 2,1 million new cases are detected annually. All these facts make us look for more informative methods of diagnosing breast cancer. The purpose of the study is to evaluate the effectiveness of a new improved algorithm of the diagnosis of one of the rarest forms of intracystic breast cancer. 153 patients with suspected cancer in breast cyst were examined in the study. Compression elastography, shear-wave elastography and fine needle aspiration were included in the standard traditional examination algorithm according to our improved method with using an ozone-oxygen mixture. We have obtained data on lower sensitivity and specificity of the traditional diagnostic complex of examination of patients with suspected intracystic breast cancer in comparison with the improved algorithm.
A New Modified MARS Cryptosystem Based on Niho Exponent with an Enhanced S-Box Generation

As an essential cryptological element, symmetric-key block ciphers have long been utilized to offer information security. Even though they were created to provide data confidentiality, their adaptability grants them to be utilized in the creation of various cryptological techniques, including message authentication protocols, hash functions, and stream cryptograms. MARS is a symmetric shared-key block cryptosystem that supports 128-bit blocks and keys with sizes ranging from 128 to 448 bits. The cryptographic cores of MARS come in a variety of rounds, each constructed to take benefit of the robust outcomes in order to enhance security and performance over earlier ciphers. The MARS cipher is given a new function in this work that uses the operations ROT, XOR, NOP, INV, hash 512, Quotient, and MOD for improving the technique of the cipher. The goal of our modification is attaining a superior confusion level whilst retaining the MARS cryptosystem’s differential and linearity aspects.
Image Processing and Analysis for Preclinical and Clinical Applications

Preclinical and clinical imaging aims to characterize and measure biological processes and diseases in animals [...]
EG-TransUNet: Enhanced and Guided U-Net with Transformer for Biomedical Image Segmentation

Abstract Although methods based on convolutional neural network have improved the performance of biomedical image segmentation, to meet the precision requirements of medical imaging segmentation task, the medical image segmentation method based on the deep learning still need to solve the following problems: 1) It is difficult to extract the discriminative feature of the lesion region in medical images during the encoding caused by variable sizes and shapes; 2) It is difficult to fuse the spatial and semantic information of the lesion region effectively during the decoding caused by redundant information and semantic gap. In this paper, we propose a novel U-Net variant architecture called EG-TransUNet, which is able to improve the feature discrimination at the level of spatial detail and semantic location using progressive enhancement module (PEM) and channel spatial attention (CSA) based on self-attention and effectively fuse the spatial and semantic information using semantic guidance attention (SGA). The proposed EG-TransUNet allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on EG-TransUNet demonstrate that the method advances the performance on five publicly available segmentation datasets, and also, is more generalizable as compared to state-of-the-art methods.
Analysis on the Influencing Factors and Training Strategies of Young Talents in Hospitals under the Background of Big Data

Under the background of big data, young talents in hospitals across my country are constantly striving to exert their personal scientific research and technical capabilities in terms of scientific research performance. At the same time, it also exposed many problems that need to be solved urgently. Under the background of the new era, each hospital needs to exert and increase its training in terms of scientific research and technology research and the use of young talents. By building a scientific platform and establishing an effective scientific research performance evaluation management system, the backbone of outstanding young medical talents can be realized. In the context of big data, this paper studies and analyzes the influencing factors of hospital young talent scientific research and performance management. After the preliminary data collection work, the chi-square verification was carried out on the survey data of young medical scientific research talents. It is confirmed that the research conclusions based on big data have certain basis for improving the scientific research management level of each hospital based on the research conclusions, thus providing a certain theoretical basis for enhancing the scientific research performance of young talents in the hospital.
DA-UNet: Deep Supervision and Atrous Inception-based U-Net Combining CRF for Automatic Liver Segmentation from CT

Abstract Due to low contrast and the blurred boundary between liver tissue and neighboring organs sharing similar intensity values, the problem of liver segmentation from CT images has not yet achieved satisfactory performance and remains a challenge. To alleviate these problems, we introduce deep supervision (DS) and atrous inception (AI) technologies with conditional random field (CRF) and propose three major improvements that are experimentally shown to have substantive and practical value. First, we replace the encoder's standard convolution with the residual block. Residual block can increase the depth of the network. Second, we provide AI module to connect the encoder and decoder. AI allows us to obtain multi-scale features. Third, we incorporate the DS mechanism into the decoder. This helps to make full use of information of the shallow layers. In addition, we employ the Tversky loss function to balance the segmented and non-segmented regions and perform the further refinement with a dense CRF. Finally, we extensively validate the proposed method on three public databases: LiTS17, 3DIRCADb, and SLiver07. Compared to the state-of-the-art (SOTA) methods, the proposed method achieved increased segmentation accuracy for the livers with low contrast and the fuzzy boundary between liver tissue and neighboring organs and is, therefore, more suited for automatic segmentation of these livers.