Reviewerly/exHarmony huggingface dataset:
Investigating Paraphrasing-Based Data Augmentation for Task-Oriented Dialogue Systems

With synthetic data generation, the required amount of human-generated training data can be reduced significantly. In this work, we explore the usage of automatic paraphrasing models such as GPT-2 and CVAE to augment template phrases for task-oriented dialogue systems while preserving the slots. Additionally, we systematically analyze how far manually annotated training data can be reduced. We extrinsically evaluate the performance of a natural language understanding system on augmented data on various levels of data availability, reducing manually written templates by up to 75% while preserving the same level of accuracy. We further point out that the typical NLG quality metrics such as BLEU or utterance similarity are not suitable to assess the intrinsic quality of NLU paraphrases, and that public task-oriented NLU datasets such as ATIS and SNIPS have severe limitations.
Deep Learning Trends and Inspired Systems in Natural Language Processing

Text data is rapidly becoming a commonplace entity. Social media forms a nebula of such data that is easily accessible to common people and researchers. For corporate and other businesses, its surveys and company mails that provide them with the data. So it is not a surprise for the field of Natural Language Processing to witness a consistent rise in research and insights and more specifically in Natural Language Understanding (NLU) by precepting syntax, structure and sentences together. Recent advancements in representation learning methodologies have also unlocked understanding of text greatly by exploiting a common and overlooked point of view, i.e., attention. This research chapter will explore the recent trends in deep learning and the inspired systems in this area by charting the insights and inspiration that have come to build our current state-of-the-art models and architecture. This work also presents a summary of similarities and contrasts of various such models to conclude on this evolution of deep learning in NLP.
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time

Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era in game learning and deep reinforcement learning. While the achievements of AlphaGo and AlphaZero - playing Go and other complex games at super human level - are truly impressive, these architectures have the drawback that they require high computational resources. Many researchers are looking for methods that are similar to AlphaZero, but have lower computational demands and are thus more easily reproducible. In this paper, we pick an important element of AlphaZero - the Monte Carlo Tree Search (MCTS) planning stage - and combine it with temporal difference (TD) learning agents. We wrap MCTS for the first time around TD n-tuple networks and we use this wrapping only at test time to create versatile agents that keep at the same time the computational demands low. We apply this new architecture to several complex games (Othello, ConnectFour, Rubik's Cube) and show the advantages achieved with this AlphaZero-inspired MCTS wrapper. In particular, we present results that this agent is the first one trained on standard hardware (no GPU or TPU) to beat the very strong Othello program Edax up to and including level 7 (where most other learning-from-scratch algorithms could only defeat Edax up to level 2).
Robo-Advising Under Rare Disasters

Robo-advisors provide investors with automated portfolio management services, and their growth is unprecedented. They performed less well during the COVID-19 pandemic than they had done during the non-crisis period. That less satisfactory performance was due to the fact that rare disasters are highly unlikely to occur and yet have a huge impact on financial markets. To improve the performance and learning efficiency of Robo-advising, this paper develops a novel learning framework to tackle rare disaster events. This framework integrates importance sampling with the reinforcement learning algorithm. Instead of sampling transition probability from a ground-truth probability distribution, we sample it from a proposal distribution, where the event of interest occurs more frequently. The proposed algorithm is validated by data covering both the 2008 financial crisis and the COVID-19 pandemic, showing superior performance over benchmarked methods. The estimated quarterly return of the robo-advisor portfolio using the proposed algorithm’s optimal policy is 0.512\%, significantly higher than both the benchmarked policy and the average quarterly return, which are -0.639\% and -14.55\% respectively. This is attributed to the intended learning about rare disasters so robo-advisors sharply lower the position on risky assets in advance. The proposed algorithm is model-free and reduces the variance of value estimates through importance sampling. In addition to methodological contributions, our study contributes to the growing literature on robo-advising by taking rare events into account.
Efficient Population Based Hyperparameter Scheduling for Medical Image Segmentation

The training hyperparameters (learning rate, augmentation policies, e.t.c) are key factors affecting the performance of deep networks for medical image segmentation. Manual or automatic hyperparameter optimization (HPO) is used to improve the performance. However, manual tuning is infeasible for a large number of parameters, and existing automatic HPO methods like Bayesian optimization are extremely time consuming. Moreover, they can only find a fixed set of hyperparameters. Population based training (PBT) has shown its ability to find dynamic hyperparameters and has fast search speed by using parallel training processes. However, it is still expensive for large 3D medical image datasets with limited GPUs, and the performance lower bound is unknown. In this paper, we focus on improving the network performance using hyperparameter scheduling via PBT with limited computation cost. The core idea is to train the network with a default setting from prior knowledge, and finetune using PBT based hyperparameter scheduling. Our method can achieve 1%–3% performance improvements over default setting while only taking 3%–10% computation cost of training from scratch using PBT.
OnlyCaps-Net, a Capsule only Based Neural Network for 2D and 3D Semantic Segmentation

Since their introduction by Sabour et al., capsule networks have been extended to 2D semantic segmentation with the introduction of convolutional capsules. While extended further to 3D semantic segmentation when mixed with Convolutional Neural Networks (CNNs), no capsule-only network (to the best of our knowledge) has been able to reach CNNs' accuracy on multilabel segmentation tasks. In this work, we propose OnlyCaps-Net, the first competitive capsule-only network for 2D and 3D multi-label semantic segmentation. OnlyCaps-Net improves both capsules' accuracy and inference speed by replacing Sabour et al. squashing with the introduction of two novel squashing functions, i.e. softsquash or unitsquash, and the iterative routing with a new parameter free single pass routing, i.e. unit routing. Additionally, OnlyCaps-Net introduces a new parameter efficient convolutional capsule type, i.e. depthwise separable convolutional capsule.
Research on Course Evaluation Index Selection Based on Decision Tree Algorithm

High-quality course teaching is the goal pursued by modern universities. The traditional course evaluation system has the characteristics of multiple indexes and fuzzy boundaries between indexes, which has defects such as redundancy and even invalid indexes. In order to improve the evaluation efficiency and enhance the user experience of the evaluation subject, this paper proposed a course evaluation index selection method based on decision tree. The course evaluation data of a university was selected as the research data, including10 indexes and 632 courses with a total of 138,635 records. After the preprocessing operations of summarizing and averaging on the research data, the discrete data was obtained by K-means clustering algorithm, and the corresponding classification label was obtained for each course. Then, a classification model was constructed based on decision tree algorithm C4.5. Two of the 10 indexes are filtered by the decision tree. The experimental results showed that the accuracy of our model reached 90.5%. Therefore, the method proposed could effectively improve the reliability of the course evaluation system.
AUGraft: Graft New API Usage into Old Code

Software libraries are essential in software development process. When a library evolves, client applications that rely on the library APIs are supposed to migrate their code to utilize new or updated features. Code migration is a challenging task, as developers need to spend time and effort searching for and understanding alternate API usage. Some tools collected code migration instances by mining software repositories and guided old code to evolve according to heuristic migration rules. Most of them focused on statement level modification and relied on a high degree of matching between deprecated API usage and migration instances, thus the applicable scenarios were limited. We propose a new approach, AUGraft, which automatically grafts new API usage into old code by matching and editing fine-grained abstract syntax tree elements. By searching 1,881 projects on FDroid, AUGraft collected 442 unique migration instances with Android SDK, of which 418 are correct. We applied AUGraft on 15 projects and got 367 migration results, of which 294 usages are useful, and 225 usages in them are migrated perfectly. We demonstrate AUGraft can improve the utilization of migration instances with high code migration accuracy.
A Defense against JavaScript Object-Based Fingerprinting and Passive Fingerprinting

Browser fingerprinting is a mechanism that identifies and collects the attributes of a device and each of these attribute values are combined to generate a unique identifier for the related device which can be used for tracking purposes. There are quite a lot of API which allows a web application to access these attributes of the device, to identify the uniqueness of each device. Therefore websites are increasingly adopting fingerprinting techniques as a way to track users and their activities online. Unlike cookie-based tracking, browser fingerprinting is not regulated and happens silently without user knowledge, posing a serious threat to online user privacy. To counteract this privacy threat, this work aims at proposing a novel anti-fingerprinting browser extension that uses the technique of API normalization against JavaScript Object-based fingerprinting and Passive Fingerprinting. It helps the users to mask their unique attribute values with more generic values which enables them to easily blend in with the crowd and become less unique. We collected generic values for each attribute for spoofing and experimentally verified the effectiveness of our extension.
Preview of an Enterprise Product Architecture (EPA) to Support a Modular Open Systems Approach (MOSA)

The potential for significant MOSA benefits appears unachieved to date. Recently, the requirement to use a MOSA has been made statutory and regulatory (Ref. 1-5). Previous work (Ref. 6) suggested that a customer-controlled component architecture is a necessary activity to achieve MOSA technical and business benefits across multiple programs. This paper extends that premise while previewing the concept of an Enterprise Product Architecture (EPA) for use in an acquisition environment where there is a requirements organization is contractually separated from the development organization. The requirements organization is composed of an enterprise that is responsible for multiple subordinate programs, and each subordinate program is responsible for developing major systems with their suppliers. Industry product line approaches developed the concept of an EPA (Ref. 7). This paper explores how an EPA can be used to set an enterpriselevel MOSA to increase opportunities to achieve expected MOSA benefits in the context of a multi-tiered acquirer with multiple programs using different contracts with different suppliers. This paper is a preview for a more detailed version of this concept planned for a future publication. The technical approach previewed in this paper leads to a product line strategy for the enterprise focused on software and data. This paper defines key terms used for the development and application of an Enterprise Product Architecture (EPA). Products to be acquired would be accomplished through the interaction of the subordinate programs and their supplier(s). The development of the EPA begins with the establishment of functional boundaries. Interfaces between functional boundaries will be defined as a result of operational analysis that recognizes how different functional groupings interact to accomplish operational behaviors. Interfaces between functional groupings will be defined at a logical level to enable the incorporation of diverse products to accomplish requirements in the functional grouping. This paper previews the use of three independent categories of functional product groupings: Mission, Utility, and Infrastructure. EPA development includes the establishment of functional groupings that enable capabilities to be agnostic of the underlying infrastructure, hence the reason for the separately categorized Infrastructure functional groupings. Utility functional groupings represent a class of applications that provide operational functionality used by more than one Mission functional grouping. The Utility functional groupings facilitate integration and orchestration of Mission functions. Mission functional groupings represent the rest of the non-infrastructure capabilities needed for the programs to accomplish their operations.
Land Administration System using Hyperledger Fabric

The land administration system in various developing countries like India addresses issues like insufficient, fraudulent, or misrepresented data. As the system consists of an intermediary between seller and buyer, generally known as a broker, this middle man makes arrangements for the required physical documents needed for the agreement as proof of property. The broker ensures that all attributes related to the property are noted down and registered by the authorized government office. The records are stored in various departments according to the land administration system, this makes the process timeconsuming, unsynchronized, and easily immune to tampering of data. Due to this, there remains no accountability to manage the information’s authenticity which also leads to incomplete verification. This paper aims to develop a land administration system using blockchain that helps to increase transparency and maintain the integrity of the system, making sure that all the transactions in the network are endorsed by all the peers involved. As blockchain is immutable, the history of records cannot be changed as records get permanently linked to the network, thereby streamlining documentation and record-keeping. Thus, with the help of permissioned enterprise blockchain like hyperledger fabric, a tamper-proof and forge-proof peer-to-peer network is leveraged.
A Comparison on Dental X-ray Segmentation based on Fuzzy Clustering for ADIS System

Image segmentation in image processing is the method by which images are divided into different segments. Many methods are available and one of the commonly used method are fuzzy-based systems. This paper ignites light into different fuzzy clustering methods, used for the segmentation of dental radiographs. Fuzzy C-means (FCM), Semi-Supervised fuzzy clustering with spatial constraints (SSFC-SC) and Fuzzy Level Set Algorithm (SFC-LM) are discussed in this paper for the automated dental radiograph segmentation. The three algorithms give different performance rate based on time and accuracy. These algorithms are evaluated against a real dental radiograph dataset available, and this set contains panoramic and periapical radiographs. The proposed study help in identifying the strengths and weakness of the algorithms. From the comparisons, it is inferred that the SSFC-SC based systems fair better than its counterparts.
Breast Cancer Detection Using Different Classification and Ensemble Methods

Breast cancer causes the cells in the breast to grow in an uncontrolled manner. Countless research has been conducted to enhance the timely diagnose of breast cancer, which expands the likelihood of survival and hence the survival rates. Mammography photographs were the focus of the majority of the investigation. Early detection of cancer is frequently delayed because symptoms are frequently not visible during the early stages of cancer. Mammography scans, on the other hand, can sometimes produce false positives, putting the patient health in danger. Different techniques, which are simpler to implement and perform better with different data sets, are needed to develop more reliable and safer forecasts. Healthcare data is huge and unstructured. Machine learning has long been the methodology of choice in breast cancer pattern classification and forecast modelling because of its distinct benefits based on feature identification from a comprehensive breast cancer dataset. The study evaluates and compares the results of seven different ML algorithms based on different performance metrics.
Papr Readr Bot: A Conversational Agent to Read Research Papers

Reading research papers can be a tedious and lonely task. We present Papr Readr Bot, a (chat)bot which aims to assist researchers in reading papers more effectively and with less cognitive effort by providing paper summaries, answering questions about the paper, extracting figures from the paper, taking notes, and generate citation. Papr Readr Bot demonstrates and provides hands-on experiences of various deep learning-based skills that can be integrated in useful and social conversational assistants for reading related contexts. We open-sourced Papr Readr Bot at https://github.com/michellefxl/papreadr
How to Design User-Centered Decision Support Systems in Public Budgeting? Guidelines and a Web-Based Prototype With First Insights From a Mixed-Methods Study

Due to increasing complexity and high political importance, public budgeting should be technically supported in the best way possible. In this paper, the use of a decision support prototype is explored to assist stakeholders in the context of public budgeting, to address the questions of what information is needed to facilitate decisions, and how to design and embed such a system. A user-centered approach was chosen, including a context analysis, qualitative interviews, and a shortened design sprint. From the insights gained, design implications as well as two hands-on application scenarios were derived. Based on this, a high-fidelity prototype was developed with modern web technologies. Afterwards, a summative evaluation was conducted in an interactive and cohesive online survey where participants could interact directly with the embedded prototype. The results show that the majority of participants expect such a system to have a positive impact on decision-making during budget preparations. Furthermore, most of the defined design requirements were fulfilled and the usability as well as the visual aesthetics of the prototype, were evaluated in a positive manner. The design implications provide a profound basis for further research and design iterations.
Correction of radial distortion for fish eye lens camera model using code V

Abstract This work proposes a design of compensation for radial distortion of the barrel and pincushion distortion on picture geometry, both of which damage the image by utilizing the fisheye design model in the code v tool as the basis for the compensation. In order to prevent the picture's form from degrading, this model was constructed using image angle height mapping. As a result, there are two stages to this paper. The first step is to create a fish eye design model that has a field of vision (FOV) of 180 degrees by selecting the most appropriate optical design specification and design parameters. The second step is an examination of the design model utilizing several Code V choices, such as 2D image simulation (ISM), modulation transfer function, spot diagram, and distortion grid. Radial distortion compensation suggests the projection mapping model as a solution to this problem; this model also incorporates the stereographic approach. The compensation outcome is displayed by the 2D picture simulation using the most advanced quantitative measurements and state-of-the-art methods.
WeHub: The Social Center of Smart Homes

There is relatively little discussion on the strengthening of interpersonal relationships as a potential benefit of Smart Homes. The social nodes of our households remain largely unaffected by digital support to this day. We propose the design concept WeHub – a flexible tabletop device that enables, supports and enhances a multitude of social practices in order to foster wellbeing in private living contexts. Based on our vision of integrating an interactive table as a central smart furniture-device hybrid into homes, we describe the design of two prototypes that each focus on different factors: I. technical feasibility and II. the envisioned user experience.
Trust Based Resolving of Conflicts for Collaborative Data Sharing in Online Social Networks

Twenty-first century, the era of Internet, social networking platforms like Facebook and Twitter play a predominant role in everybody’s life. Ever increasing adoption of gadgets such as mobile phones and tablets have made social media available all times. This recent surge in online interaction has made it imperative to have ample protection against privacy breaches to ensure a fine grained and a personalized data publishing online. Privacy concerns over communal data shared amongst multiple users are not properly addressed in most of the social media. The proposed work deals with effectively suggesting whether or not to grant access to the data which is co-owned by multiple users. Conflicts in such scenario are resolved by taking into consideration the privacy risk and confidentiality loss observed if the data is shared. For secure sharing of data, a trust framework based on the user’s interest and interaction parameters is put forth. The proposed work can be extended to any data sharing multiuser platform.
Implementation and Evaluation of a Multilingual Search Pilot in the Europeana Digital Library

Europeana, a digital library that aggregates content from libraries, archives and museums from all around Europe, offers search functionality using the metadata of more than 62 million objects. However, in most cases, this data is only available in one language, while users come from countries with different languages. Europeana's strategy for the improvement of multilingual experiences includes the design and implementation of a multilingual information retrieval system based on the translation of queries and metadata to English. As a first development in this context, we have implemented a pilot applying query translation to English for the Spanish version of the website in order to surface results that have English metadata associated with them. We conducted an evaluation to assess the performance of this pilot and identify issues. The good performance rates observed allowed us to take the pilot to production, and the issues identified led to a list of specific actions, which should be addressed to the extent possible before the application of a wider multilingual information retrieval system.
Machine-Learning Basics

In Chap. 1 , we discussed how machine-learning algorithms differ from traditional algorithms. This chapter will introduce some machine-learning terminologies as well as some details about the working of ML algorithms.