Reviewerly/exHarmony huggingface dataset:
VC-VQA: Visual Calibration Mechanism For Visual Question Answering

Visual Question Answering (VQA) is a comprehensive task to answer questions about the visual contents of an image. Recently, a number of studies have pointed out that VQA models tend to be misled by the dataset biases, and rely heavily on the superficial correlations between question and answer, rather than really understanding the visual contents. To address this issue, we propose visual calibration mechanism for VQA(VC-VQA) which extends the conventional VQA model with an additional image feature reconstruction module. The proposed model reconstructs image features based on predicted answer with question and measures the similarity between reconstructed image feature and original image feature, which will guide the VQA model predict the final answer. We evaluate our model on both VQA v1 and VQA v2 datasets, showing that VC-VQA effectively reduces impacts of dataset bias and achieves competitive performance compared to other mainstream methods.
Advanced visualization using image super-resolution method for three-dimensional mobile system

A three-dimensional mobile system with advanced visualization based on image super-resolution method is proposed and implemented. Here, real object information is acquired using three-dimensional scanning, and the three-dimensional visualizations of the object with the improved resolution are displayed on the mobile display with user interaction functionality. Unlike the previous methods, an advanced visualization method displays the resolution-enhanced reconstructed images without the degradation of the quality. From the experimental results, the proposed system presented clearer visualizations of the object, and the quantitative evaluation methods confirm that the proposed system provides the higher-resolution visualizations of the real object without loss of quality on the mobile display.
The identification of doubly stochastic circular image model

In some practical situations, images are set on a circle. For example, images of the facies (thin film) of dried biological fluid, eyes, cut of a tree trunk, etc. Currently, most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. The features of circle images require their consideration in their mathematical models. In this paper, an autoregressive models of homogeneous and inhomogeneous random fields defined on a circle are considered as representations of images with radial or radial-circular structure. In the present paper, autoregressive models of circular images are considered. To represent heterogeneous images with random heterogeneities, «doubly stochastic» models are used in which one or more images control the parameters of the resulting image. Pseudo-gradient algorithms for the modal identification are proposed. The conducted statistical modeling showed that these algorithms give good model identification.
Criminal Behaviour Analysis and Segmentation using K-Means Clustering

Crimes are increasing with a high frequency rates in this new era of world and hence it's a devastating issue that everyone has been experiencing. For finding a patter that can be used for prediction is necessary. The objective of this paper is to understand the concept of data mining and machine learning which can be used for finding criminal patterns and behaviours. The paper is further divided by providing basic differentiation of the clustering techniques used in unsupervised learning. And then after the crime dataset of India that contains record of serious fraud of property in all states and we will apply k-means clustering to find generic patterns. The main reason for this paper is to give a quip thought of how machine learning can be utilized by the law authorization to distinguish, anticipate and illuminate violations by a lot quicker rate.
$$S^3$$Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data

Solving depth estimation with monocular cameras enables the possibility of widespread use of cameras as low-cost depth estimation sensors in applications such as autonomous driving and robotics. However, learning such a scalable depth estimation model would require a lot of labeled data which is expensive to collect. There are two popular existing approaches which do not require annotated depth maps: (i) using labeled synthetic and unlabeled real data in an adversarial framework to predict more accurate depth, and (ii) self-supervised models which exploit geometric structure across space and time in monocular video frames. Ideally, we would like to leverage features provided by both approaches as they complement each other; however, existing methods do not adequately exploit these additive benefits. We present \(S^3\)Net, a self-supervised framework which combines these complementary features: we use synthetic and real-world images for training while exploiting geometric, temporal, as well as semantic constraints. Our novel consolidated architecture provides a new state-of-the-art in self-supervised depth estimation using monocular videos. We present a unique way to train this self-supervised framework, and achieve (i) more than \(15\%\) improvement over previous synthetic supervised approaches that use domain adaptation and (ii) more than \(10\%\) improvement over previous self-supervised approaches which exploit geometric constraints from the real-world data.
Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges

Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face of collision uncertainty minimizes the number of checks before finding the shortest path. However, this may take a prohibitively long time to compute, with no other feasible paths discovered during this period. For many real-time applications, we instead demand strong anytime performance, defined as minimizing the cumulative lengths of the feasible paths yielded over time. We introduce Posterior Sampling for Motion Planning (PSMP), an anytime lazy motion planning algorithm that leverages learned posteriors on edge collisions to quickly discover an initial feasible path and progressively yield shorter paths. PSMP obtains an expected regret bound of Õ(√(SAT)) and outperforms comparative baselines on a set of 2D and 7D planning problems.
Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold

We present a method that finds locomanipulation plans that perform simultaneous locomotion and manipulation of objects for a desired end-effector trajectory. Key to our approach is to consider an injective locomotion constraint manifold that defines the locomotion scheme of the robot and then using this constraint manifold to search for admissible manipulation trajectories. The problem is formulated as a weighted-A* graph search whose planner output is a sequence of contact transitions and a path progression trajectory to construct the whole-body kinodynamic locomanipulation plan. We also provide a method for computing, visualizing, and learning the locomanipulability region, which is used to efficiently evaluate the edge transition feasibility during the graph search. Numerical simulations are performed with the NASA Valkyrie robot platform that utilizes a dynamic locomotion approach, called the divergent-component-of-motion (DCM), on two example locomanipulation scenarios.
Recommendation System Based on Heterogeneous Feature: A Survey

Recommendation systems have become an important field of research in computer science and physics.In recent years, breakthroughs have been achieved in social, biological, and research cooperation networks.With the popularization of big data and deep learning technology development, graph structures are increasingly being used to represent large-scale and complex data in the real world.In this paper, we reviewed the progress made in recommendation systems research in the past 20 years and comprehensively classified recommender systems based on the heterogeneous input features.We introduced layering in the classification of recommendation systems.Furthermore, we proposed a new hierarchical classification model of recommendation systems divided into three layers: feature input, feature learning, and output layers.In the feature learning layer, existing recommendation systems were divided into graph-based, text-based, behavior-based, spatiotemporal-based, and hybrid recommendation systems.Additionally, we provided evaluation index, open-source implementation, experimental comparison and the relative merits for each recommendation method.Subsequently, future development directions of recommendation systems are discussed.
Self-Supervised Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking

Estimating tissue motion is crucial to provide automatic motion stabilization and guidance during surgery. However, endoscopic images often lack distinctive features and fine tissue deformation can only be captured with dense tracking methods like optical flow. To achieve high accuracy at high processing rates, we propose fine-tuning of a fast optical flow model to an unlabeled patient-specific image domain. We adopt multiple strategies to achieve unsupervised fine-tuning. First, we utilize a teacher-student approach to transfer knowledge from a slow but accurate teacher model to a fast student model. Secondly, we develop self-supervised tasks where the model is encouraged to learn from different but related examples. Comparisons with out-of-the-box models show that our method achieves significantly better results. Our experiments uncover the effects of different task combinations. We demonstrate that unsupervised fine-tuning can improve the performance of CNN-based tissue tracking and opens up a promising future direction.
cASpER

During software evolution, code is inevitably subject to continuous changes that are often performed by developers within short and strict deadlines. As a consequence, good design practices are often sacrificed, possibly leading to the introduction of sub-optimal design or implementation solutions, the so-called code smells. Several studies have shown that the presence of code smells makes the source code more change- and fault-prone, reduces productivity, and causes greater rework and more significant design efforts for developers. Refactoring is the practice that developers may use to remove code smells without changing the external behavior of the source code. However, it requires much time and effort and is poorly automated, often leading developers to prefer keeping low-quality code instead of spending time in designing and performing refactoring operations. To mitigate this problem and support developers throughout the process of code smell identification and refactoring, in this paper we present cASpER, a IntelliJ IDEA plugin that provides visual and semi-automatic support for detection and refactoring four different types of code smells.
Loss Surface Modality of Feed-Forward Neural Network Architectures

It has been argued in the past that high-dimensional neural networks do not exhibit local minima capable of trapping an optimisation algorithm. However, the relationship between loss surface modality and the neural architecture parameters, such as the number of hidden neurons per layer and the number of hidden layers, remains poorly understood. This study employs fitness landscape analysis to study the modality of neural network loss surfaces under various feed-forward architecture settings. An increase in the problem dimensionality is shown to yield a more searchable and more exploitable loss surface. An increase in the hidden layer width is shown to effectively reduce the number of local minima, and simplify the shape of the global attractor. An increase in the architecture depth is shown to sharpen the global attractor, thus making it more exploitable.
Development and Investigation of a Motion Planning Algorithm for a Mobile Robot with a Smart Machine Vision System

This study is devoted to the challenges of motion planning for mobile robots with smart machine vision systems. Motion planning for mobile robots in the environment with obstacles is a problem to deal with when creating robots suitable for operation in real-world conditions. The solutions found today are predominantly private, and are highly specialized, which prevents judging of how successful they are in solving the problem of effective motion planning. The purpose of this study: develop and investigate a motion planning algorithm for a mobile robot with a smart machine vision system. The research subject for this article is a motion planning algorithm for a mobile robot with a smart machine vision system. This study provides a review of domestic and foreign mobile robots that solve the motion planning problem in a known environment with unknown obstacles. The following navigation methods are considered for mobile robots: local, global, individual. In the course of work and research, a mobile robot prototype has been built, capable of recognizing obstacles of regular geometric shapes, as well as plan and correct the movement path. Environment objects are identified and classified as obstacles by means of digital image processing methods and algorithms. Distance to the obstacle and relative angle are calculated by photogrammetry methods, image quality is improved by linear contrast enhancement and optimal linear filtering using the Wiener-Hopf equation. Virtual tools, related to mobile robot motion algorithm testing, have been reviewed, which led us to selecting Webots software package for prototype testing. Conclusions have been drawn regarding the concluded research.
Enabling Continual Learning with Differentiable Hebbian Plasticity

Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process. Thus, neural networks that are deployed in the real world often struggle in scenarios where the data distribution is non-stationary (concept drift), imbalanced, or not always fully available, i.e., rare edge cases. We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity (DHP) Softmax layer that adds a rapid learning plastic component (compressed episodic memory) to the fixed (slow changing) parameters of the softmax output layer; enabling learned representations to be retained for a longer timescale. We demonstrate the flexibility of our method by integrating well-known task-specific synaptic consolidation methods to penalize changes in the slow weights that are important for each target task. We evaluate our approach on the Permuted MNIST, Split MNIST and Vision Datasets Mixture benchmarks, and introduce an imbalanced variant of Permuted MNIST - a dataset that combines the challenges of class imbalance and concept drift. Our proposed model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.
Ontology-Based Information Extraction Framework for Academic Knowledge Repository

Extracting required information from the huge academic repository in an efficient way is a tedious job as the repository grows day by day. Getting user satisfaction in searching is the primary concern always. Academic search engines are mostly used by research scholars, scientists, faculty, and students for their academic purposes. The use of ontology in information extraction helps an academic search engine to perform its operation effectively. As the research activities are going on in an extensive pace in all domains, new specialized areas are evolved. This paper proposes a novel framework that incorporates a dynamically evolving ontology that focuses on expanding the existing Computer Science Ontology based on the new findings in existing areas. The Word2Vec model helps to identify new keywords for the expansion of the ontology. The keywords given by the Word2Vec should satisfy a confidence score above a threshold value and only these keywords will be used for updating the Computer Science Ontology. The dynamic semantic web helps a user to retrieve the specific document, and thus provides a high degree of personalization experience.
Open Source Speech Recognition on Edge Devices

Deep learning has revived the field of automatic speech recognition (ASR) in the last ten years and pushed recognition rates into regions on par with humans. Applications like Siri, Amazon Alexa and Google Assistant are very popular, but have inherent privacy problems. In this paper, we evaluate state of the art open source ASR models regarding their usability in a smart speaker without cloud, both in terms of accuracy and runtime performance on cost-effective low power edge devices. We found Kaldi to be the most accurate solution and also among the fastest ones. It runs more than fast enough on an Nvidia Jetson Nano. It is still not on par with commercial cloud services, but getting close to it.
Scheduling Mix-Coflows in Datacenter Networks

Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while the non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely concentrate on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this article, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixCoflow</i> , to schedule deadline coflows to minimize and balance their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixCoflow</i> is able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixCoflow</i> can achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.
A cognitive inspired method for assessing novelty of short-text ideas

In creativity research a typical problem is that of assessing the novelty of ideas or solutions generated by many people to open ended problems. For datasets larger than a few hundreds, human assessment of novelty becomes time consuming and error prone. Existing novelty detection methods such as: distance based text similarity or language model approaches do not work well for small datasets. Moreover, when compared to human novelty ratings, these approaches fail to capture the same cognitive processes or biases. We are proposing a novel cognitive model inspired by a leaky accumulator decision making models for detecting novel ideas from short text. The model is applied on a collection of ideas generated in a group brainstorming experiment. It evaluates an idea term by term and it accumulates surprise and relevance. The final novelty decision is taken at the end of each idea by means of a threshold. An important component of the model is a small domain dataset which is used to evaluate the surprise of a term's context compared to common domain knowledge. The model is compared with other methods: feature based classifiers, tf-idf similarity distance, and pretrained language models (ULMFIT).
Organisational Information Security Maturity Assessment Based on ISO 27001 and ISO 27002

This article proposes a practical methodology for performing information security maturity assessment for organisations which operate an Information Security Management System (ISMS) based on the ISO 27001:2013 standard. The methodology uses a COBIT 5-comparable method to evaluate the maturity level of the security controls and clauses in ISO 27001:2013 and leverages on the guidelines in ISO 27002:2013. It was successfully used in an undisclosed company. Information security professionals can benefit from applying the same methodology or a similar one in organisations of various size and nature. The final product of the assessment is metrics and recommendations for improvement of the ISMS, which can be used for tactical and strategic decision-making, as well as input for organisational information security risk management.
Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images

Survival prediction is a typical task in computer-aided diagnosis with many clinical applications. Existing approaches to survival prediction are mostly based on the classic Cox model, which mainly focus on learning a hazard or survival function rather than the survival time, largely limiting their practical uses. In this paper, we present a Censoring-Aware Deep Ordinal Regression (CDOR) to directly predict survival time from pathological images. Instead of relying on the Cox model, CDOR formulates survival prediction as an ordinal regression problem, and particularly introduces a censoring-aware loss function to train the deep network in the presence of censored data. Experiment results on publicly available dataset demonstrate that, the proposed CDOR can achieve significant higher accuracy in predicting survival time.
An optimal separation of randomized and Quantum query complexity

We prove that for every decision tree, the absolute values of the Fourier coefficients of given order t≥1 sum to at most (cd/t)t/2(1+logn)(t−1)/2, where n is the number of variables, d is the tree depth, and c>0 is an absolute constant. This bound is essentially tight and settles a conjecture due to Tal (arxiv 2019; FOCS 2020). The bounds prior to our work degraded rapidly with t, becoming trivial already at t=√d.
Learning traversability map of different robotic platforms for unstructured terrains path planning

This paper aims to propose an innovative method to obtain the traversability maps of unstructured environments and the best path between two points on the basis of the specific characteristics of the robots that has to perform a given mission. Taken in consideration a robot team that have to traverse an assigned terrain, the peculiar capabilities of each robot are underlined in a dynamic simulation environment and then embedded into a neural network finally used as a robot model for the generation of the traversability maps. On the basis of the obtained results, the best robot within the team (wheeled, legged, hybrid) can be selected. The proposed strategy, together with the obtained simulation results, are presented, carefully analyzed and then compared.