Reviewerly/exHarmony huggingface dataset:
Multilevel Incremental Roadmap Spanners for Reactive Motion Planning

Generating robot motions from a precomputed graph has proven to be an effective approach to solving many motion planning problems. After their generation, roadmaps reduce complex motion planning problems to that of solving a graph-based shortest path. However, generating the graph can involve tradeoffs, such as how sparse or dense to make the graph. Sparse graphs may not provide enough options to navigate around a new obstacle or may result in grossly suboptimal motions. Dense graphs may take too long to search and result in an unresponsive robot. In this paper we present an algorithm that generates a graph with multiple sparse levels- the sparsest level can be searched quickly, while the densest level allows for asymptotically optimal motions. With the paired multilevel shortest path algorithm, after the robot computes an initial solution, it can then incrementally refine the shortest-path as time allows. We demonstrate the algorithms on an articulated robot with 8 degrees of freedom, having them compute an initial solution in a fraction of the time required for a full graph search, and subsequently, incrementally refine the solution to the optimal shortest path from the densest level of the graph.
Adaptive Outcome Selection for Planning with Reduced Models

Reduced models allow autonomous robots to cope with the complexity of planning in stochastic environments by simplifying the model and reducing its accuracy. The solution quality of a reduced model depends on its fidelity. We present 0/1 reduced model that selectively improves model fidelity in certain states by switching between using a simplified deterministic model and the full model, without significantly compromising the run time gains. We measure the reduction impact for a reduced model based on the values of the ignored outcomes and use this as a heuristic for outcome selection. Finally, we present empirical results of our approach on three different domains, including an electric vehicle charging problem using real-world data from a university campus.
Inference of user-intention in remote robot wheelchair assistance using multimodal interfaces

Shared control methodologies have the potential of enabling wheelchair-bound users with limited motor abilities to perform tasks that would usually be beyond their capabilities. Deriving such methodologies in advance is challenging, since they are frequently heavily dependent on unique characteristics of users. Learning Assistance by Demonstration paradigms allow derivation of customized policies by recording how remote human assistants assist particular users. However, for accurate determination of the optimal policies for each user and context, the remote assistant needs to infer the intention of the driver, which is frequently obscured by noisy signals dependent on the user's motor impairment. In this paper, we propose a multimodal teleoperation interface, incorporating map information, haptic feedback and user eye-gaze data, and examine which of these factors are most important for allowing accurate determination of user intention in a simulated tremor experiment. Our study indicates that, for expert assistants, presence of additional haptic and gaze information increases their ability to accurately infer the user's intention, providing supporting evidence for the utility of multimodal interfaces in remote assistance scenarios for Learning Assistance by Demonstration. Our study also reveals strong individual preferences on the different modalities, with large variations of performance occurring depending on whether supplemental eye-gaze or haptic information was given.
Data Augmentation for Historical Documents via Cascade Variational Auto-Encoder

In this paper, we introduce a novel model based on Variational Auto-Encoder (VAE) that is able to find subclasses of categories and generate new samples with fidelity to the subclass in an unsupervised way. In generating characters from historical documents, this model helps generated characters to avoid ambiguity in the case where there are multiple writing styles of one character without being labeled. With this model we augment historical Japanese document dataset to make it more balanced. The model is trained in two steps. In the first step, the model learns the data distribution and learns to map character images into basic shape vectors. In the second step, the model learns to generate new samples conditioned on the basic shape vectors. The generated samples are more robust against intra-class multi-modality. With the usage of augmented dataset, the recognition rate is improved. Ablation study is performed to evaluate the effectiveness of data augmentation.
qCUDA: GPGPU Virtualization for High Bandwidth Efficiency

The increasing demand for machine learning computation contributes to the convergence of high-performance computing and cloud computing, in which the virtualization of Graphics Processing Units (GPUs) becomes a critical issue. Although many GPGPU virtualization frameworks have been proposed, their performance is limited by the bandwidth of data transactions between the virtual machine (VM) and host. In this paper, we present a virtualization framework, qCUDA, to improve the performance of compute unified device architecture (CUDA) programs. qCUDA is based on the virtio framework, providing the para-virtualized driver and the device module for performing the interaction with the API remoting and memory management methods. In our test environment, qCUDA can achieve above 95% of the bandwidth efficiency for most results by comparing it with the native. Also, qCUDA has the features of flexibility and interposition. It can execute CUDA-compatible programs in the Linux and Windows VMs, respectively, on QEMU-KVM hypervisor for GPGPU virtualization.
Lazy Compilation of Variants of Multi-robot Path Planning with Satisfiability Modulo Theory (SMT) Approach

We address variants of multi-robot path planning in graphs (MRPP). We assume robots placed in vertices of an undirected graph with at most one robot per vertex. Robots can move across edges while various problem specific constraints must be satisfied. We introduce a general problem formulation that encompasses known types of robot relocation problems such as multi-robot path planning (MRPP), token swapping (TSWAP), token rotation (TROT), and token permutation (TPERM). We generalize SMT-CBS, a recent solving approach for MRPP based on satisfiability modulo theories (SMT). SMT- CBS compiles MRPP lazily within the SMT framework, starting with the basic model that is refined with a collision resolution constraints whenever collisions between robots occur in the current solution. We show modifications the SMT-CBS algorithm for variants of MRPP and evaluate them experimentally.
Semantic Concept Network and Deep Walk-based Visual Question Answering

Visual Question Answering (VQA) is a hot-spot in the intersection of computer vision and natural language processing research and its progress has enabled many in high-level applications. This work aims to describe a novel VQA model based on semantic concept network construction and deep walk. Extracting visual image semantic representation is a significant and effective method for spanning the semantic gap. Moreover, current research has shown that co-occurrence patterns of concepts can enhance semantic representation. This work is motivated by the challenge that semantic concepts have complex interrelations and the relationships are similar to a network. Therefore, we construct a semantic concept network adopted by leveraging Word Activation Forces (WAFs), and mine the co-occurrence patterns of semantic concepts using deep walk. Then the model performs polynomial logistic regression on the basis of the extracted deep walk vector along with the visual image feature and question feature. The proposed model effectively integrates visual and semantic features of the image and natural language question. The experimental results show that our algorithm outperforms competitive baselines on three benchmark image QA datasets. Furthermore, through experiments in image annotation refinement and semantic analysis on pre-labeled LabelMe dataset, we test and verify the effectiveness of our constructed concept network for mining concept co-occurrence patterns, sensible concept clusters, and hierarchies.
Lithological Mapping using Digital Image Processing Techniques on Landsat 8 OLI Remote Sensing Data in Jahajpur, Bhilwara, Rajasthan

Lithological mapping is important parameters for interpretation, identification and mapping of minerals. Lithological mapping in the study area defines the characteristics of nature of rock types and their association and formation. In this research study Landsat 8 OLI remote sensing data used for lithological mapping of Jahajpur region of Bhilwara super group. The task of lithological mapping completed by analogical and numerical analysis of digital image processing method which involves several digital image processing techniques such as natural band combination, false color composite, principal component analysis, band ratio and minimum noise fraction. The observed result is verified and validated by field survey and published geological survey of India geological map. The observe result have shown complete correlation and similarity with established map.
Partial Undersampling of Imbalanced Data for Cyber Threats Detection

Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack’s behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats.
Online Planning for Autonomous Underwater Vehicles Performing Information Gathering Tasks in Large Subsea Environments

We present an anytime Monte Carlo tree search (MCTS) algorithm to generate real-time, near-optimal search paths in large subsea environments. The MCTS planner continuously builds a tree of the search space until either the allowed time per move is reached or the budget constraint for the search mission is met. In order to improve the performance of the MCTS planner, we propose a novel heuristic action selection policy to determine the value of a leaf node. The proposed heuristic is tailored to problems where making a turn incurs a higher cost than moving straight, such as the case on autonomous underwater vehicles. Through extensive simulations, we show that our heuristic yields a significant performance improvement over a lawnmover path planner - a commonly employed approach in subsea search applications - and over a simple MCTS planner where actions are selected uniformly at random. In our numerical illustrations, we use a real data set abstracted from sonar measurements acquired from the Boston Harbor.
ADAPT-T: An Adaptive Algorithm for Auto-Tuning Worker Thread Pool Size in Application Servers

Modern application servers run on multi-core hardware and these servers use multi-threading to achieve high performance. Performance of such servers is highly dependent on the number of processing threads in the worker thread pool. The optimal thread pool size (that would result in the best performance) depends on workload properties (both the application and the incoming arrival pattern). In this paper, we propose ADAPT-T, a lightweight, adaptive algorithm that can auto-adjust the thread pool size to optimize the latency. ADAPT-T exploits the concave upward property of system performance and optimizes the latency on-line by periodically monitoring the server's behaviour and then adjusts the thread pool size based on monitored statistics. It has a special locking mechanism to minimize the frequent fluctuations that may occur in latency while tuning the thread pool size. We perform extensive experiments under different workload patterns and show that ADAPT-T performs well under a wide range of workload scenarios compared to other policies.
SOOM: Sort-Based Optimizer for Big Data Multi-Query

Mostly, sorting of data is a common operation in many applications, which causes the consumption of resources and thus leads to computation overheads. Regarding the context of Big Data multi-query, the shared sort operations are fairly large, which incur high-cost I/Os whether explicit or implicit. In particular, Big Data multi-query, including aggregation and sort operations, takes long execution time due to reshuffle of the same data multiple times using similar tasks. Therefore, exploiting the sharing data and the sharing sort opportunities of similar tasks can offer the possibility of reusing the previous results to optimize multi-query. For considering sharing data, our previous work, Multi-Query Optimization Using Tuple Size and Histogram (MOTH) system, has been introduced to consider the granularity of the sharing data opportunities among multi-query. However, time overheads regarding redundant data in-network movement (i.e., shuffling time to transfer intermediate data for sort operations) have not been considered. Therefore, the MOTH system has been extended to SOOM (Sort-Based Optimizer over MOTH) system to exploit sharing sort opportunities, including explicit sorts of sort queries and implicit sorts of aggregation queries. The proposed SOOM system consists of two additional modules to exploit sharing sort opportunities, namely query explorer and sort exploiter, which leverage our existing MOTH system to fulfill optimizing multiple aggregation and sort queries. The experimental evaluation has shown that the SOOM system outperforms the naive and the state-of-art techniques regarding query execution time among queries by 45% and 30%, respectively, while introducing maximal intermediate data size reduction by 67% and 61% in average, respectively, over Hadoop-like infrastructures.
Modeling Human Syllogistic Reasoning: The Role of “No Valid Conclusion”

Abstract Syllogistic reasoning, that is the drawing of inferences for categorical‐quantified assertions, is one of the oldest branches of deductive reasoning research with a history exceeding 100 years. In syllogistic reasoning experiments, “No Valid Conclusion” (NVC) is one of the most frequently selected responses and corresponds to the logically correct conclusion for 58% of the syllogistic problem domain. To date, NVC is often neglected in computational models or just treated as a by‐product of the underlying inferential mechanisms such as a last resort when the search for alternatives is exhausted. We illustrate that NVC represents a major shortcoming of current models for human syllogistic reasoning. By introducing heuristic rules for predicting NVC, we demonstrate that simple extensions of the existing models result in substantial improvements in their predictive performances. Our results emphasize the need for better NVC handling in cognitive modeling of human reasoning and provide directions for modelers on how to enhance their approaches.
Key-Frame based Event Recognition in Unconstrained Videos using Temporal Features

Unconstrained videos, captured by inexperienced amateurs, suffer from wide-ranging quality aspects such as poor illumination, considerable camera motion, cluttered background, occlusion and substantial viewpoint variations. Event recognition in these videos is a challenging task in the field of computer vision, serving as potential solution to several practical applications. In this paper, we propose a novel approach for recognizing events in the unconstrained videos that relies on the temporal features of the representative frames (key-frames) in each video. This graph-based key-frame extraction technique exploits the inter-frame temporal variation for the entire video and reduces temporal redundancy amongst consecutive frames. The problem is thereby formulated as a constrained optimization problem, where the temporal distinctness as well as the distance of demarcation amongst the chosen frames is maximized simultaneously. For classification, we propose a model that fuses a deep residual network with the Recurrent Neural Network (RNN) model of Long Short-Term Memory (LSTM) to capture long-term temporal motion patterns. Experimental results reveal better outcome than most of the state-of-the-art methods based on test data from Columbia Consumer Video (CCV), Kodak's consumer video and UCF-101 benchmark datasets. The videos having high content diversity, pose as classification challenges to the research.
Towards a Smart Home for Elder Healthcare

Smart homes play an important role in e-health monitoring, using the various components and devices that intercommunicate with the home system. Thus, it has a huge impact on medical processes, healthcare practices and safety of elder people. Existing e-health monitoring systems do not include optimized techniques which learn the precise patient's behavior to predict future important changes. In this work, we propose a new context-aware system, which monitors daily living activities and evaluates dependency based on the Functional Autonomy Measurement System (SMAF). In fact, our system evaluates a person's dependency, predicts its health condition, detects abnormal situations and risky behavioral changes and offers adapted services.
Association Rules Mining with QAR Data: an Analysis on Unstable Approaches

Stable approach is vital for flight safety, and more attention should be paid in analyzing approaching phase of aircraft stages. This study aims to detect unstable approaches (UA) with the quick access recorder (QAR) data, and analyze association rules between parameters in approaching phase via exploratory data analysis (EDA) and association rule mining techniques. Results show that wind speed should be noticed when aircraft landing at Kunming Changshui airport in January, and high wind speed tend to cause roll, air speed and heading overrun. Moreover, pilots should pay close attention to roll when it exceeds warning value, and make adjustment in time to avoid pitch undergoing the warning value. These findings could be useful in preventing UA incidents and improving flight safety.
Representation Learning Techniques: An Overview

Representation learning techniques, as a paradigm shift in feature generation, are considered as an important and inevitable part of state of the art pattern recognition systems. These techniques attempt to extract and abstract key information from raw input data. Representation learning based methods of feature generation are in contrast to handy feature generation methods which are mainly based on the prior knowledge of expert about the task at hand. Moreover, new techniques of representation learning revolutionized modern pattern recognition systems. Representation learning methods are considered in four main approaches: sub-space based, manifold based, shallow architectures, and deep architectures. This study demonstrates deep architectures are considered as one of the most important methods of representation learning as they cover more general priors of real-world intelligence as a necessity for modern intelligent systems. In other words, deep architectures overcome limitations of their shallow counterparts. In this study, the relationships between various representation learning techniques are highlighted and their advantages and disadvantages are discussed.
Scalable Explanation of Inferences on Large Graphs

Probabilistic inferences distill knowledge from graphs to aid human make important decisions. Due to the inherent uncertainty in the model and the complexity of the knowledge, it is desirable to help the end-users understand the inference outcomes. Different from deep or high dimensional parametric models, the lack of interpretability in graphical models is due to the cyclic and long-range dependencies and the byzantine inference procedures. Prior works did not tackle cycles and make the inferences interpretable. We formulate the explanation of probabilistic inferences as a constrained cross-entropy minimization problem to find simple subgraphs that faithfully approximate the inferences. We prove that the optimization is NP-hard, while the objective is not monotonic and submodular to guarantee efficient greedy approximation. We propose a beam search algorithm to find trees to enhance the explanation interpretability and diversity. To allow efficient search on large and dense graphs without hurting faithfulness, we further propose parallelization and a pruning strategy. We demonstrate superior performance on four networks from distinct applications, comparing favorably to other explanation methods, including LIME.
Reduce, Reuse, Recycle (3Rs) Awareness App: Mobile Learning Application for Supporting Environmental Awareness Initiatives

The reduction of solid wastes through the application of 3Rs (reduce, reuse, and recycle) is important because wastes have a huge negative impact on the natural environment. The 3Rs concept is an effort toward managing solid wastes resulting from human activity as well as protecting the contaminated environment. The concept needs to be introduced to children to enable them cultivate the attitude of caring for the environment. Therefore, a mobile learning application regarding 3Rs has been developed for enhancing 3Rs awareness as an initiative to instill the nature of environmental care in children. On this regard, this study concentrates mainly on developing the prototype of a 3Rs-awareness mobile application. Thirty primary school children were chosen to test the mobile application. Most of the participants were satisfied with the application design and functions. The outcome of the study shows that this application is a promising development to promote environmental awareness.
Characterizing and Balancing the Workloads of Semi-Containerized Clouds

With the development of Cloud computing, many applications are deployed on the Clouds (either public Clouds or private Clouds) for the elastic resource management. A key challenge here is satisfying the Quality-of-Service requirement of online services that requires short response time while maximizing the resource utilization. While applications should be co-located to improve the resource utilization, the co-location results in performance interference between the co-located applications that hurts the QoS of latency-sensitive services. To understand this problem in depth, we characterize and analyze the real business-level application co-location scenario based on the trace of an actual Cloud product released by Alibaba. Our observation shows that the datacenter suffers from severe load imbalance that often results in QoS violation of the online services on the servers with heavy workload. To balance the workload, we propose a scheduling algorithm based on the resource reservation and utilization of containers. Based on the comparison between the experiment and the existing operation results, the proposed algorithm reduces the standard deviation of the load on servers from 5.27 to 1.43 (CPU) and 9.83 to 1.93 (Memory).