Reviewerly/exHarmony huggingface dataset:
OceanWATERS Lander Robotic Arm Operation

Ocean Worlds Autonomy Testbed for Exploration Research and Simulation (OceanWATERS) is an open-source simulator for developing onboard autonomy software for robotic exploration of ocean worlds, such as Europa, Enceladus, and Titan, built on the Robot Operating System (ROS) and Gazebo simulation environment. Inevitable ground communication delays increase demand for a high degree of autonomy during excavation, collection and transfer of samples to scientific instruments for in-situ analysis. This paper offers a detailed discussion of the robotic arm design and operation for such autonomous surface exploration, taking as reference the Europa Lander mission. The lander arm, which is designed primarily to acquire icy surface and subsurface samples within the arm's workspace, is a 6-degree-of-freedom manipulator with two end effectors: a sample excavation tool and a trenching end-effector. The robotic arm's modes and operations can be summarized as follows: stowed arm, intended as the lander arm default configuration characterized by zero-power consumption; un-stowed arm, target arm configuration after its first deployment; selection and deployment of the end -effector to use next; guarded move, to detect ground level at the desired trenching location; drill ice using the grinder; dig trench at a particular location using the scoop; deliver sample to the sample transfer dock; discard redundant samples. The motion planning tool used for the lander arm is MoveIt, a ROS package. MoveIt uses sampling-based planning and collision checking libraries to determine safe paths. The Rapidly Exploring Random Trees* (RRT*) has been chosen as default planning algorithm as it provides optimal plans with an exponential speed and is guaranteed to find a solution, if feasible solutions exist. Furthermore, this work quantifies and discusses the energy requirements for excavating and collecting samples. In OceanWATERS, force feedback from the terrain, which influences the arm dynamics, is modelled using a discrete element method (DEM) simulation. The DEM and Gazebo software run in parallel and communicate through a co-simulation plugin. This paper presents an analysis and comparison of three DEM open source software (YADE, ESyS-Particle, Project Chrono) for implementation in OceanWATERS and motivates the choice of YADE as most suitable candidate.
TPE: A Hardware-Based TLB Profiling Expert for Workload Reconstruction

We propose TPE, a hardware-based framework to perform workload execution forensics in microprocessors. Specifically, TPE leverages custom hardware instrumentation to capture the operational profile of the Translation Lookaside Buffer (TLB), as well as process these information off-line through multiple machine learning and/or deep learning approaches, in order to identify the executed processes and reconstruct the workload. Unlike software-based forensics methods implemented at the operating system (OS) or hypervisor level, whose data logging and monitoring mechanisms may be compromised through software attacks, TPE is implemented directly in hardware and, therefore, provides innate immunity to software tampering. A prototype of TPE is demonstrated in Linux on two representative architectures, i.e., 32-bit ×86 and 64-bit RISC-V, implemented in the Simics and Spike simulation environment respectively. Experimental results using the Mibench workload benchmark suite reveal favorable process identification accuracy at low logging rate, which corroborates the effectiveness and the generalizability of TPE.
Medical Image Segmentation by using Active Flow Model with Simulated Annealing

This study suggested the active flow model with simulated annealing as the effective segmentation method for medical image. The technique proposed in this study emulated the previous active flow model by applying the simulated annealing to adjust the α parameters. The technique's success was examined through the samples of synthesis, CT, and ultrasound images. The samples were tested with three different texture segmentation models including the traditional active contour model, active flow model, and active flow model with simulated annealing. The results showed that the active flow model with simulated annealing achieved better segmentation on CT image, yet lower precision on synthesis and ultrasound images. Accordingly, the technique needs improvement in flexibility for α parameter adjustment.
Using Sankey diagrams to visualize drag and drop action sequences in technology-enhanced items

We propose a Sankey diagram-based visualization method to illustrate test-takers' action sequences in technology-enhanced, drag-and-drop items. This method extends existing studies focusing only on initial or last attempt(s) of answer formulation, and extracts, at the item level, comprehensive information of test-takers' drag-and-drop actions. Using the process data from three mathematical items in the National Assessment of Educational Progress, we exemplify how to develop Sankey diagrams, discover frequent actions at each stage of problem solving, deduce test-takers' response strategies, and discuss the relationship among domain (i.e., mathematics) knowledge, response strategy, and item performance. The applications and future extensions of this method can contribute to process-based problem-solving research in educational assessments.
Discovery of Real World Context Event Patterns for Smartphone Devices Using Conditional Random Fields

Mobile applications are Event Driven Systems that react to user events and context events (e.g. changes in network connectivity, battery level, etc.) The large number of context events complicate the testing process. Context events may modify several context variables (e.g. screen orientation, connectivity status, etc.) that affect the behavior of an application. This work examines a data set of real-world context changes on Android phones. We collect every context event that occurs on the mobile devices of 58 Android users over 30 days to identify complex relationships and patterns. This work uses Machine Learning (ML) techniques including Conditional Random Fields (CRFs) and Deep Neural Networks (DNNs) to predict sequence labels for context events. These techniques are compared to Majority Baseline (MB). The trade-offs among these methods reveal that CRF is the most effective technique for sequence prediction/labeling of the data-set. Future work may apply the data collection strategy and ML techniques to domains for emerging technologies in areas such as Internet of Thing, smartwatches, and autonomous vehicles.
Oversampling by a Constraint-Based Causal Network in Medical Imbalanced Data Classification

A key challenge of oversampling in medical imbalanced data classification is that the generation of new minority samples often neglects rich causal dependencies among features, with each being responsible for disease diagnosis. This leads us to define a constraint-based approach that generates new samples by explicitly discovering and leveraging the inherent local causal variability of features under a global view. Our approach employs causal Markov property to construct a causal network that explicitly characterizes these unique causal configurations of a particular disease as a variable number of nodes and links. By perturbing those learned causal features from majority class, we synthesize new samples in the territory of minority space. An additional sample selection estimator is introduced to choose the most representative samples. Empirical evaluations on four medical datasets suggest our approach significantly outperforms the state-of-the-art methods.
Hardware Implementation of the Reverse Conversion RNS-WNS on FPGA

The Residue Number System is a modern powerful tool for solving a number of specialized tasks: digital signal processing, cryptography, increasing reliability, accelerating computations, etc. The effectiveness of its use largely depends on the solution of the problem of reducing the delay and area costs for the reverse conversion of numerical data to the weighted number system. The paper considers the main methods of reverse conversion from the residue number system to the weighted number system. Their hardware implementation on FPGA and a comparative analysis are presented.
Secure Distributed Applications the Decent Way

Remote attestation (RA) authenticates code running in trusted execution environments (TEEs), allowing trusted code to be deployed even on untrusted hosts. However, trust relationships established by one component in a distributed application may impact the security of other components, making it difficult to reason about the security of the application as a whole. Furthermore, traditional RA approaches interact badly with modern web service design, which tends to employ small interacting microservices, short session lifetimes, and little or no state.
On Constructing A Better Correlation Predictor For Prnu-Based Image Forgery Localization

Localizing image forgeries is one of the key topics in multimedia forensics. Among many image forgery localization techniques, the one based on the photo-response non-uniformity (PRNU) noise has attracted substantial attention because of its capability of localizing forgeries regardless of the type of forgery. However, despite the devoted efforts to improving the performance of PRNU-based forgery localization, there remain challenges to be overcome, especially for detecting subtle forgeries in PRNU-attenuated regions due to complex image content. In this work, we investigate the feasibility and effectiveness of convolutional neural networks (CNN) in predicting PRNU correlations under complex backgrounds for more accurate forgery localization. The experimental results on 20 cameras and 200 realistic forgery images show that significant improvement in correlation prediction and forgery localization can be achieved even with a light-weight CNN model. The robustness of different correlation predictors against JPEG compression is also evaluated.
Deep Learning with Real-Time Inference for Human Detection in Search and Rescue

Human casualties in natural disasters have motivated technological innovations in Search and Rescue (SAR) activities. Difficult access to places where fires, tsunamis, earthquakes, or volcanoes eruptions occur has been delaying rescue activities. Thus, technological advances have gradually been finding their purpose in aiding to identify and find the best locations to put available resources and efforts to improve rescue processes. In this scenario, the use of Unmanned Aerial Vehicles (UAV) and Computer Vision (CV) techniques can be extremely valuable for accelerating SAR activities. However, the computing capabilities of this type of aerial vehicles are scarce and time to make decisions is also relevant when determining the next steps. In this work, we compare different Deep Learning (DL) imaging detectors for human detection in SAR images. A setup with drone-mounted cameras and mobile devices for drone control and image processing is put in place in Ecuador, where volcanic activity is frequent. The main focus is on the inference time in DL learning approaches, given the dynamic environment where decisions must be fast. Results show that a slim version of the model YOLOv3, while using less computing resources and fewer parameters than the original model, still achieves comparable detection performance and is therefore more appropriate for SAR approaches with limited computing resources.
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries

As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, "Mondegreen", to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts.
Explainable Fraud Detection for Few Labeled Time Series Data

Fraud detection technology is an important method to ensure financial security. It is necessary to develop explainable fraud detection methods to express significant causality for participants in the transaction. The main contribution of our work is to propose an explainable classification method in the framework of multiple instance learning (MIL), which incorporates the AP clustering method in the self-training LSTM model to obtain a clear explanation. Based on a real-world dataset and a simulated dataset, we conducted two comparative studies to evaluate the effectiveness of the proposed method. Experimental results show that our proposed method achieves the similar predictive performance as the state-of-art method, while our method can generate clear causal explanations for a few labeled time series data. The significance of the research work is that financial institutions can use this method to efficiently identify fraudulent behaviors and easily give reasons for rejecting transactions so as to reduce fraud losses and management costs.
The Integration Mechanism of Multimedia Computer Technology and College English Education

In today's information age, multimedia computer and network teaching have incomparable advantages in improving students' knowledge level, cultivating students' information literacy and comprehensive practical ability. Giving full play to the advantages of digitalization, networking, intelligence and multimedia in English teaching, taking computers as tools and networks as resources, has brought about important changes in English teaching methods and created a lively classroom atmosphere. The use of multimedia computer technology in college English teaching can overcome the disadvantages existing in the current college English teaching, such as common education, neglecting the cultivation of students' language use ability, etc., realize student-centered, change teachers' dominant position, and promote college English teaching to a new level. Based on the analysis of the problems in traditional English teaching, this paper discusses the integration method of multi-purpose teaching and college English, in order to provide reference for the reform of college English education and teaching.
Efficient Sketch Recognition Via Compact Spatial Embedding Graph Neural Networks

Sketches are descriptive, high-level visual media in many systems and applications. However, current methods for sketch recognition are mainly based on large neural networks, which have millions of parameters and are too cumbersome to be deployed on edge devices. Besides, convolutional neural networks are not optimal, since many areas in sketches are blank and without any information. Hence, this paper aims at designing an efficient network that maintains the state-of-the-art accuracy. Our solution is a novel graph neural network that utilizes densely connected grouped convolutions on the graph representation of sketches. It allows us to extract and aggregate spatio-temporal features efficiently. Moreover, a compact spatial embedding module is introduced to explore spatial contexts, and consequently facilitates recognition. In this way, our network is small (about 10% parameters of ResNet- 18) and efficient (inference speed in 800 ~ 2,000 FPS), meanwhile has the competitive accuracy on the QuickDraw dataset.
Characteristics Performance Prediction of PV Panel Using Cuckoo Search Algorithm

Abstract This paper focuses on prediction and evaluation the correlation factors of PV panel characteristics in order to determine the implicit correlation forms for the efficiency and temperature of the system under Iraq climate conditions. Implicit correlation forms consisted on the ambient temperature, solar radiation, wind speed and humidity. Cuckoo search algorithm was employed as an intelligent optimization method to predict the correlation factors of each variable under different evaluation method which are, mean square error (MSE), integrate absolute error (IAE) and integrate square error (ISE). The results appeared that the proposed method was succeeded to predict both of the PV panel temperature and efficiency with MSE evaluation method compared with other methods. Where, the lowest MSE was recorded lower the 1 % for each characteristic.
Regression shrinkage and selection variables via an adaptive elastic net model

Abstract In this paper, a new method of selection variables is presented to select some essential variables from large datasets. The new model is a modified version of the Elastic Net model. The modified Elastic Net variable selection model has been summarized in an algorithm. It is applied for Leukemia dataset that has 3051 variables (genes) and 72 samples. In reality, working with this kind of dataset is not accessible due to its large size. The modified model is compared to some standard variable selection methods. Perfect classification is achieved by applying the modified Elastic Net model because it has the best performance. All the calculations that have been done for this paper are in R program by using some existing packages.
Real-World Development of a Cleaning CDPR for Primary Lamella Sedimentation Tanks

Lamella sedimentation tanks are more efficient than conventional sedimentation tanks in treating sewage water because of their low footprint and high efficiency by the inclusion of inclined plates. However, regular cleaning is required for maintaining the efficiency of preventing clogging resulting from sludge accumulation. As the task is repetitive and dangerous, human cleaning is less preferable while no automatic solution is ready in the market. As a result, an automated solution is developed using a Cable Driven Parallel Robot (CDPR) to carry cleaning tools within the tank, due to its advantages of large workspace capability and simple mechanical set-up. In this paper, we introduce the application of lamella tank cleaning using CDPR, including the robot design, setup, operation and cleaning results. The development and results show the potential and maturity of using the developed CDPR system for real-world and longer-term uses.
An Improved Dynamic E-Commerce Recommendation System

Recommender system is a strategy in e-commerce, which recommends items based on the user’s interest. It has the ability to anticipate whether a specific user would incline toward a thing or not based on the user's profile. Recommender systems are useful for both e-commerce service provider and users. So, it should be required for a recommendation system to provide most preferable items to the user’s interest. In this presented work the recommendation system provide recommendations on the user’s interest. In this context the web usage mining and the content mining with additional factors are proposed. The proposed recommendation system computes the user product purchasing habits, product popularity, and user’s web navigational behavior. The product purchasing habit is calculated using the previously purchased product price and the current product price range. Similarly, the product popularity is calculated by the number of users provide the positive reviews about the target product. Additionally, web navigational behavior is computed using the web usage data for the user self and the other users who are watching the same product. In order to combine the calculated factors, the weight is computed and higher weights are used to recommend the suitable product. The experiments are performed with the implemented system and compared with the traditional system. The comparative performance study of the system in terms of precision, recall, and memory consumption demonstrate the proposed technique is efficient and accurate in comparison with the available traditional data model.
Show, Rethink, And Tell: Image Caption Generation With Hierarchical Topic Cues

Current state-of-the-art approaches for image captioning mainly apply the encoder-decoder framework with attention mechanisms, most of which ignore interactions between different types of image features and perform attention operations only once per word. The mentioned problems limit the captioning model’s capability to capture sufficient information to generate high-quality captions. By contrast, humans often rethink to polish up descriptions by re-focusing on more correct and important information, which is hard to capture at first glance. In this paper, we introduce a novel topic-guided captioning model to imitate such a human’s rethinking process by modeling interactions between visual and hierarchical semantic features of topics. To the best of our knowledge, we are the first to effectively consider hierarchical semantic features as guidance to facilitate visual attention, achieving human-like rethinking for captioning. Extensive experiments on the MS COCO dataset show that our proposed model achieves superior performance over state-of-the-art methods.
Robust Re-weighting Prototypical Networks for Few-Shot Classification

Prototypical networks (ProtoNet) inferences the class of each probe sample by its nearest prototype representation among all classes. Since ProtoNet learns the prototype representation from only a few labeled samples of each class, it is effective for few-shot classification and has been widely applied in computer vision, natural language processing and many other fields. However, ProtoNet suffers from a non-robustness deficiency, i.e., the prototype representation of a certain class will be seriously drifted if some labeled samples are unluckily corrupted. To overcome this deficiency, we propose a robust re-weighting prototypical networks (RRPNet) model to adaptively highlight the critical samples in learning the prototype representations. In particular, like ProtoNet, RRPNet utilizes the arithmetic mean to compute the prototypes, unlike ProtoNet, it incorporates a re-weighting mechanism to set a weight for each summation item. The weight reduces the influences of noisy samples in learning the prototypes, and thus makes RRPNet some-how more robust to noisy samples. We propose an adaptive weighting scheme in this paper. Several experimental results on both MiniImageNet and TieredImageNet datasets show that RRPNet is superior to the representative ProtoNet methods for few-shot image classification.