Reviewerly/exHarmony huggingface dataset:
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation

The generalization gap in reinforcement learning (RL) has been a significant obstacle that prevents the RL agent from learning general skills and adapting to varying environments. Increasing the generalization capacity of the RL systems can significantly improve their performance on real-world working environments. In this work, we propose a novel policy-aware adversarial data augmentation method to augment the standard policy learning method with automatically generated trajectory data. Different from the commonly used observation transformation based data augmentations, our proposed method adversarially generates new trajectory data based on the policy gradient objective and aims to more effectively increase the RL agent's generalization ability with the policy-aware data augmentation. Moreover, we further deploy a mixup step to integrate the original and generated data to enhance the generalization capacity while mitigating the over-deviation of the adversarial data. We conduct experiments on a number of RL tasks to investigate the generalization performance of the proposed method by comparing it with the standard baselines and the state-of-the-art mixreg approach. The results show our method can generalize well with limited training diversity, and achieve the state-of-the-art generalization test performance.
Learning Branching Heuristics for Propositional Model Counting

Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, including many discrete probabilistic inference problems, can be translated into model counting problems to be solved by #SAT solvers. Exact #SAT solvers, however, are often not scalable to industrial size instances. In this paper, we present Neuro#, an approach for learning branching heuristics to improve the performance of exact #SAT solvers on instances from a given family of problems. We experimentally show that our method reduces the step count on similarly distributed held-out instances and generalizes to much larger instances from the same problem family. It is able to achieve these results on a number of different problem families having very different structures. In addition to step count improvements, Neuro# can also achieve orders of magnitude wall-clock speedups over the vanilla solver on larger instances in some problem families, despite the runtime overhead of querying the model.
Bridging the Domain Gap: Improve Informal Language Translation via Counterfactual Domain Adaptation

Despite the near-human performances already achieved on formal texts such as news articles, neural machine translation still has difficulty in dealing with "user-generated" texts that have diverse linguistic phenomena but lack large-scale high-quality parallel corpora. To address this problem, we propose a counterfactual domain adaptation method to better leverage both large-scale source-domain data (formal texts) and small-scale target-domain data (informal texts). Specifically, by considering effective counterfactual conditions (the concatenations of source-domain texts and the target-domain tag), we construct the counterfactual representations to fill the sparse latent space of the target domain caused by a small amount of data, that is, bridging the gap between the source-domain data and the target-domain data. Experiments on English-to-Chinese and Chinese-to-English translation tasks show that our method outperforms the base model that is trained only on the informal corpus by a large margin, and consistently surpasses different baseline methods by +1.12 ~ 4.34 BLEU points on different datasets. Furthermore, we also show that our method achieves competitive performances on cross-domain language translation on four language pairs.
A study on web mining classification and approaches

Due to advancement in technology there is huge rise in structured as well as unstructured data which is growing at a very high pace. There is a great increase in users accessing web and all are sharing and posting data on web due to which the volume of data is increasing and making it complex to extract important information. In currant scenario there is certain need of extraction of potentially useful and valuable information from very large data available on web. This is done by the process of web mining. In this paper we have done the comparison and described the three categories of web mining will helpful in accessing the data from web in efficient way. Further, important parameters in web logs that are used in web usage mining have been described.
Deep Feature Learning Network for Vehicle Retrieval

Vehicle Retrieval plays a very important role for traffic surveillance system which has been being developed in recent decades with innovative methods from traditional approach to deep learning approach. However, there are some challenges of Vehicle Retrieval which still need to be solved such as image resolution, image distortion, occlusion [1]. In this study, we utilized deep feature learning to build a vehicle retrieval system with two main tasks (1) deep feature learning network, (2) vehicle retrieval. Specifically, we adopted Faster R-CNN and SVM for building the feature network generating feature extraction and classification. Then, we used Approximate Nearest Neighbor (ANN) with tree-based approach for indexing feature vectors and searching. The proposed system solved some challenges such as dimensionality reduction of feature vector, improving the retrieval performance based on deep learning. The experimental results have shown that the proposed method is significantly outperforms the state of the art methods for vehicle retrieval on BIT-Vehicle dataset.
Improvement in credit card fraud detection using ensemble classification technique and user data

Financial fraud a serious problem in banking system. Credit card fraud growing with increasing Internet usage, as it becomes very simple to collect user data and do fraud transaction. Fortunately, all records including fraud and legit transactions are present in the financial record. Improved data mining techniques are now capable to find solutions for such outlier detections. Financial data freely available in many sources, but this data has some challenges like,1) the profile of legit and fraudulent behavior changes constantly, 2) there a class imbalance problem in dataset, because less than 3% transaction are fraud, 3) transaction verification latency also one more problem. All this data issues are handled using pre-processing techniques like cleaning and reduction. Main aim of this research to find out, output attribute is Fraud, with better time complexity. To this end, K-means, Random Forest and J48 algorithm used, and its accuracy rates are compared to find best fit pre-processing and machine learning algorithm. It observed that accuracy rate of Random Forest 93.8% when both global and local dataset used.
Submodel Decomposition Bounds for Influence Diagrams

Influence diagrams (IDs) are graphical models for representing and reasoning with sequential decision-making problems under uncertainty. Limited memory influence diagrams (LIMIDs) model a decision-maker (DM) who forgets the history in the course of making a sequence of decisions. The standard inference task in IDs and LIMIDs is to compute the maximum expected utility (MEU), which is one of the most challenging tasks in graphical models. We present a model decomposition framework in both IDs and LIMIDs, which we call submodel decomposition that generates a tree of single-stage decision problems through a tree clustering scheme. We also develop a valuation algebra over the submodels that leads to a hierarchical message passing algorithm that propagates conditional expected utility functions over a submodel-tree as external messages. We show that the overall complexity is bounded by the maximum tree-width over the submodels, common in graphical model algorithms. Finally, we present a new method for computing upper bounds over a submodel-tree by first exponentiating the utility functions yielding a standard probabilistic graphical model as an upper bound and then applying standard variational upper bounds for the marginal MAP inference, yielding tighter upper bounds compared with state-of-the-art bounding schemes for the MEU task.
Visual Relation Detection using Hybrid Analogical Learning

Visual Relation Detection is currently one of the most popular problems for visual understanding. Many deep-learning models are designed for relation detection on images and have achieved impressive results. However, deep-learning models have several serious problems, including poor training-efficiency and lack of understandability. Psychologists have ample evidence that analogy is central in human learning and reasoning, including visual reasoning. This paper introduces a new hybrid system for visual relation detection combining deep-learning models and analogical generalization. Object bounding boxes and masks are detected using deep-learning models and analogical generalization over qualitative representations is used for visual relation detection between object pairs. Experiments on the Visual Relation Detection dataset indicates that our hybrid system gets comparable results on the task and is more training-efficient and explainable than pure deep-learning models.
Argumentation schemes as a way of arguments studies

The paper investigates argument (argumentation) schemes that are used in the theory of argumentation for analyzing everyday reasoning. Such schemes should be understood as structures representing the most general types of argument. Today they are studied not only from theoretical perspectives but also in relation to their application in education and information technologies. Argumentation schemes allow us to identify, produce and evaluate real arguments, as well as critically assess discussions. Despite their popularity, argumentation schemes face some problems. The current presentation introduces the concept of schemes, describes their possible classifications, analyzes strengths and limitations in various approaches. However, the main focus of the paper is on the direction in which the conception of an argument scheme is being developed. I argue that the new stage of its development can be correlated with the birth of a dynamic style of argument in­vestigation that is opposed to a static version. A similar dichotomy can be observed in logic that stands for two types of formality: as pertaining to forms and as pertaining to rules.
Audio Speed Changer Application: Advancing Students’ Listening Comprehension

As a receptive skill, listening is very influential for students' language development. Unfortunately, it often becomes the most challenging skill in learning a foreign language for students. One of the obstacles experienced by students of the English Study Program of STAIN Majene is the speed of spoken native language. This research aims to advance students' listening comprehension by using Audio Speed Changer (ASC). This research applied classroom action research of Kammis and Taggart model. It used four phases, namely planning, implementation, observation, and reflection. There were three instruments used in this research, namely listening test, observation sheet, and interview. The test was used to measure students' achievement. An observation sheet was used to identify speed development. An interview was used to gain information about students' responses to ASC application. There were two cycles in this research. The success of a cycle was determined by using two criteria as follow: (1) students' mean score had to be higher than 75 points in the last phase of a cycle; (2) there should be at least 80% of students who got at least 75 points for their final scores. This research found that students' scores dominated the poor category in the first cycle, but it reduced 35.32 points of its percentage in the second cycle. Students' scores in the second cycle dominated enough categories. ASC application was able to overcome one of the students' problems in listening comprehension.
Teachers’ understanding towards concepts and processes of children's literacy

This study aims to describe the understanding and process of implementing literacy in early childhood. The method used in this study is a qualitative descriptive method. Data collection techniques were used through observation, interviews, and documentation. The subjects were Early Childhood Education (ECE) teachers in Lhokseumawe. Ten teachers were selected using purposive sampling technique from five kindergartens; Pertiwi Kindergarten, Bunayya, Safiatuddin and Ukhtani. The results showed that: (1) the understanding of ECE teachers in Lhokseumawe interpreted the concept of literacy as a basic skill that children must have in reading, writing, and speaking. Introduction of literacy for early childhood as initial preparation to master reading, writing and speaking skills so that early childhood becomes more prepared to face education at the next level, Elementary School or Madrasah Ibtidaiyah, (2) The process of implementing literacy in ECE institutions is generally carried out in various forms of play activities and children's daily habit and using various forms of methods and media or educational toys.
Automated system to measure Tandem Gait to assess executive functions in children

As mobile technologies have become ubiquitous in recent years, computer-based cognitive tests have become more popular and efficient. In this work, we focus on assessing motor function in children by analyzing their gait movements. Although there has been a lot of research on designing automated assessment systems for gait analysis, most of these efforts use obtrusive wearable sensors for measuring body movements. We have devised a computer vision-based assessment system that only requires a camera which makes it easier to employ in school or home environments. A dataset has been created with 27 children performing the test. Furthermore, in order to improve the accuracy of the system, a deep learning based model was pre-trained on NTU-RGB+D 120 dataset and then it was fine-tuned on our gait dataset. The results highlight the efficacy of proposed work for automating the assessment of children’s performances by achieving 76.61% classification accuracy.
Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding

Visual context provides grounding information for multimodal machine translation (MMT). However, previous MMT models and probing studies on visual features suggest that visual information is less explored in MMT as it is often redundant to textual information. In this paper, we propose an object-level visual context modeling framework (OVC) to efficiently capture and explore visual information for multimodal machine translation. With detected objects, the proposed OVC encourages MMT to ground translation on desirable visual objects by masking irrelevant objects in the visual modality. We equip the proposed with an additional object-masking loss to achieve this goal. The object-masking loss is estimated according to the similarity between masked objects and the source texts so as to encourage masking source-irrelevant objects. Additionally, in order to generate vision-consistent target words, we further propose a vision-weighted translation loss for OVC. Experiments on MMT datasets demonstrate that the proposed OVC model outperforms state-of-the-art MMT models and analyses show that masking irrelevant objects helps grounding in MMT.
Deep Gaussian Processes: A Survey

Gaussian processes are one of the dominant approaches in Bayesian learning. Although the approach has been applied to numerous problems with great success, it has a few fundamental limitations. Multiple methods in literature have addressed these limitations. However, there has not been a comprehensive survey of the topics as of yet. Most existing surveys focus on only one particular variant of Gaussian processes and their derivatives. This survey details the core motivations for using Gaussian processes, their mathematical formulations, limitations, and research themes that have flourished over the years to address said limitations. Furthermore, one particular research area is Deep Gaussian Processes (DGPs), it has improved substantially in the past decade. The significant publications that advanced the forefront of this research area are outlined in their survey. Finally, a brief discussion on open problems and research directions for future work is presented at the end.
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey

In this survey, we first introduce the background of popular sensors used for self-driving, their data properties, and the corresponding object detection algorithms. Next, we discuss existing datasets that can be used for evaluating multi-modal 3D object detection algorithms. Then we present a review of multi-modal fusion based 3D detection networks, taking a close look at their fusion stage, fusion input and fusion granularity, and how these design choices evolve with time and technology. After the review, we discuss open challenges as well as possible solutions. We hope that this survey can help researchers to get familiar with the field and embark on investigations in the area of multi-modal 3D object detection.
Design and Fabrication of Wheelchair CUM Stretcher

The wheelchair and stretcher are very widely used in hospitals, airports, train stations, shopping malls, etc. This design here is a modified wheelchair stretcher as needed. This machine converts the wheelchair into a stretcher. The chair transforms into a stretcher when the levers are operated. The stretchers can be detached from the main frame according to the convenience of the patient and doctors can make it easier Access to the patient with less effort and transport The folding mechanism enables a large number of stretchers arranged in chair form to be accommodated in a comparatively smaller space. in hospitals, patients have to be moved from a wheelchair to a stretcher, from a stretcher to a bed, from a bed to a wheelchair or vice versa; which creates unsafe conditions for patients. A wheelchair stretcher is required to facilitate mobility for the disabled patient.
Virtual and Augmented Reality for increasing the awareness of current scientific research

&amp;lt;p dir=&amp;quot;ltr&amp;quot;&amp;gt;Virtual reality (VR) devices allow the exploration of 3D data in a fully immersive fashion and make it possible to create a powerful engagement experience and a direct interaction with current scientific data to learn more about astronomy in Education and Public Outreach (E&amp;amp;PO) activities. In 2019 the INAF Osservatorio Astronomico di Palermo (INAF-OAPa) launched &amp;lt;em&amp;gt;3DMAP-VR&amp;lt;/em&amp;gt; (3-Dimensional Modeling of Astrophysical Phenomena in Virtual Reality; Orlando et al. 2019, RNAAS 3, ID.176), a project for visualizing 3D results of astrophysical (magneto)-hydrodynamic (MHD) simulations, through VR equipments. The models, uploaded on the Sketchfab portal (a platform widely used to publish and share 3D models and VR contents), received a very positive feedback from the scientific community and the general public.&amp;amp;#160;&amp;amp;#160;Here we will show some of the scenes produced in the framework of 3DMAP-VR&amp;amp;#160; to describe astrophysical phenomena. More specifically, we will focus our attention on MHD simulations describing the interaction of exoplanets (https://skfb.ly/6QYtC) with their host stars, and on artististic views of exoplanets which are based on information extracted from multi-wavelength observations, such as in the case of exoplanets 55 Cancri (https://skfb.ly/6R6Pt) and Wasp-76b (https://skfb.ly/6QZHF). Moreover, the 3DMAP-VR project team used augmented reality to produce informative videos to explore the characteristics of some of these models, published on &amp;lt;em&amp;gt;media.inaf.it&amp;lt;/em&amp;gt; and &amp;lt;em&amp;gt;edu.inaf.it&amp;lt;/em&amp;gt;. These E&amp;amp;PO products not only allowed the public to understand the astrophysical phenomena but they have stimulated great synergy between the outreach team and the astronomers, and between researchers and the public.&amp;lt;/p&amp;gt;
Digital Image Watermarking using Chaotic Encryption and Arnold Transform

Internet has caused an extraordinary increase in the transfer and sharing of digital data like text, videos, images, audio, etc. over it. However, with the advent of modern access technology, multimedia data is more prone to security risks as data can be modified or redistributed without prior permission. Chaotic encryption-based blind digital image watermarking technique applicable to both grayscale and colour images. Discrete cosine transform (DCT) is used before embedding the watermark in the host image. Arnold transform is used in addition to chaotic encryption to add double-layer security to the watermark. Three different variants of the proposed algorithm have been tested and analysed. The simulation results show that the proposed scheme is robust to most of the image processing operations like joint picture expert group compression, sharpening, cropping, and median filtering. To validate the efficiency of the proposed technique, the simulation results are compared with certain state-of-art techniques.
Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability

Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In this paper, we describe a planner that uses richer information about agents’ knowledge to improve upon QDec-FP. With this change, the planner not only scales up to larger problems with more objects, but it can also support signaling, where agents signal information to each other by changing the state of the world.
Using T˜-Transformation for Solving Tank and Heating System Equations

In this article, a fuzzy Tarig evolve (T-n-transform) is implemented. Similar theorems and properties have been proven. To explain the technique of this fuzzy transform in differential equations, examples in real life are presented. This study shows the applicability of this interesting fuzzy transform for solving differential equations with constant coefficients also for its computational power. It is desirable to use it as a new technique, to not only solve “nonlinear fractional differential equations", and to analyze prelocal system information. Moreover, significant theorems are presented to explain the properties of T˜-transform as well as a suggested method is validated with two reality examples.
Data integrity and security of digital signatures on electronic systems using the digital signature algorithm (DSA)

The digital signature generation process begins with the creation of a public key and a private key. A public key is generated and published to verify the signature and calculate the hash value of the received document. At present, in the very fast development of information technology, quantum computers have emerged the ability to solve very large and complex amounts of data calculated by qubits, which when compared to quantum computers can work 10 minutes to work on a process that takes 1025 years on a computer. Therefore, the research focuses on how electronic signatures on documents have a reliable security system. The Digital Signature Algorithm (DSA) is a key algorithm used for digital signatures, which uses the Secure Hash Algorithm (SHA-1) to convert messages into message digest and parameters based on the ElGamal signature algorithm. The author also shows an example of digital signature encryption and decryption process by taking any numbers p = 59419 and q = 3301 to prove that the message can be formed and verified its authenticity.