Reviewerly/exHarmony huggingface dataset:
Development of a mobile application for occupant-centric facility maintenance management

Occupant feedback for facility maintenance management (FMM) is crucial since reactive maintenance relies heavily on the user's inputs and interactions. However, the traditional reporting system lacks location-specific user feedback and timely bi-directional communication offered by, for example, automated chatbots. It results in delays in solving user-concerned issues and discourages occupants from using the traditional reporting system. A social media-based mobile application with location sharing capabilities and automated data processing via natural language processing could enhance the current system's functionality. Also, training the chatbot to identify the issue automatically and speed issuing a new work order offers the potential to solve the problem. This research implements NLP to automate responses to the end-user in human-like language that improves the communication in the facility management.
Predicting Cryptocurrency Price Change Direction from Supply-Side Factors via Machine Learning Methods

Cryptocurrency prices are highly variable. Predicting changes in cryptocurrency price is a hugely important topic to investors and researchers, with much existing research on demand-side factors. The goal of this research project is to design and implement machine learning models to predict future cryptocurrency price change direction based primarily on supply-side factors. Different unsupervised machine learning techniques are used to build the predictive models. These techniques include K Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayesian Classifier, and Random Forest Classifier. A dataset of 10 daily supply-side metrics for three prominent cryptocurrencies (Bitcoin, Ethereum, and Litecoin) at four different time horizons (ranging from one day to 30 days) are used to build and test the machine learning models. The outputs of these models indicate the predicted direction of the price movement over the time horizon (i.e., whether the price would go up or down), not the magnitude of the movement. Experimental results show that predictions were very unreliable for the shorter time spans but very reliable for the longest time spans. The Artificial Neural Network and Random Forest classifiers consistently outperformed the other techniques and achieved a prediction accuracy of over 90% in most models and over 95% in the best models. Experimental results show also that there is no significant difference in predictability between the three prominent cryptocurrencies.
A Taxonomy of Privacy, Trust, and Security Breach Incidents of Internet-of-Things Linked to F(M).A.A.N.G. Corporations

Advancements in technology have brought with it widespread use and utilization of the lnternet-of-Things (IoT); a catch-all moniker identifying the collection of eclectic hardware and software mediums connecting people with small electronic computing and monitoring devices, such as SMART products: television, thermostat, speaker, refrigerator, etc. As personal IoT adaptation has grown in parallel with IoT home integration, so too have IoT concerns related to Privacy, Trust, and the Security risks associated with individual end-user protection, especially from IoT devices linked to Facebook (Meta) (F), Apple (A), Amazon (A), Netflix (N), and Google (G); conversationally identifiable as F(M).A.A.N.G. corporations. As a result, the list of IoT threats, security weaknesses, and vulnerabilities has grown exponentially. Nefarious actors have taken advantage of this growing technology, reaching into homes and offices far beyond established limits set by end-users. This research will use a multiple case study approach to analyze IoT security breach events to identify current Privacy, Trust, and Security risks to end-users associated with IoT devices. Furthermore, this research will also utilize security behavioral research by Anderson and Agarwal to introduce a Safe Computing Practices Model (SCPM) that can be implemented by end-users when integrating IoT devices.
Wearable Sensing and Physical Exercise Recognition

As society ages, it becomes critically important to maintain the physical and mental health of the elderly population. This paper aims at using wearable sensors to automatically recognize three physical exercises (squatting, jumping jacks, flicking back), which are known to assist the elderly with balance, strength and endurance. The system will automatically track and analyze whether the actions are meeting the exercise standards using the wearable sensor and machine learning based recognition techniques. Data were collected from four different positions (chest, wrist, waist, ankle) nine times for ten users with the 3-axis accelerometer sensor with 100 Hz sampling frequency. For the simplicity of testing, each exercise lasted 20 seconds. For the comparative analysis, four kinds of models (decision tree, KNN, SVM, Naïve Bayes) were used to obtain the results of recognition. The study shows that the recognition result is not only related to the location of the sensor position but also related to the classification model. In general, the Decision Tree model performed better than other models for all of the four locations, with an average accuracy of 92.52%.
Recruitment Fraud Detection Method Based on Crowdsourcing and Multi-feature Fusion

Regarding the problem that false recruitment information is difficult to identify, an online recruitment fraud detection method based on crowdsourcing and multi-feature fusion is proposed. Firstly, the keywords in the recruitment text are extracted through a data crowdsourcing platform; Secondly, a text classification model merged Bidirectional Encoder Representation from Transformers (BERT), Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN) and Attention mechanism is constructed. The model can fuse the contextual features and local features of recruitment text, at the same time assign different weights to the words in the text, and focus on important words; Finally, the keywords extracted by the crowdsourcing platform are integrated into the model to guide its training and highlight the role of keywords in classification. Compared with other network models, the experimental results show that the classification accuracy of the proposed model is increased by 5.3% on average, and can effectively detect online recruitment fraud.
A Survey of Available Information Recovery of Binary Programs Based on Machine Learning

A binary program is an executable file obtained by compiling the source code. In order to reduce the size of software or prevent reverse engineering, software publishers usually only release executable files that have stripped a lot of source information. The rich source-level information is very helpful for software hardening and malware analysis. In recent years, the rapid development of artificial intelligence has promoted the research of binary program information recovery. This paper summarizes the relevant research on effective information recovery of binary programs since 2016. Firstly, the importance of recovering source-level information from binary programs is introduced. Next, the problems are divided into three categories, compiler traceability, signature information recovery and variable information recovery of source program. Finally, based on the latest technology in this field, the future development direction is analyzed.
Generate Lanscape Paintings From Text to Landscape Painting Generation and Redrawing Using Deep Learning Model

In this paper the writer adopts the interdisciplinary research method of landscape art and artificial intelligence, and uses a deep learning model – generative adversarial network to study the generation and redrawing of landscape paintings. Input different types of text information in the test process, so that the model through the interpretation of text to achieve the generation of landscape painting. At the same time, the name of the landscape painting as the input text, with its picture as the initial picture to achieve redrawing. The experimental results were further evaluated from the aspects of image quality and color expression. This model has the potential to be applied to the expression of reference maps, intention maps and the study of garden art and history.
Analysis of Key Technologies of Underground Inspection Robot and Design of Its Manipulator

According to the special underground environment and the research and application status of inspection robot, the key technologies of underground inspection robot such as mobile platform, autonomous navigation and positioning, intelligent control, autonomous charging, background management and online fault diagnosis are discussed. This paper focuses on the design of a special mechanical arm for inspection robot. Combined with the requirements of underground belt conveyor in coal mine, the working space, torque and tension of the mechanical arm, the pressure of gripper and its mechanism realization are calculated, which can add innovative key technology to mine intelligence, and has engineering application value and economic significance.
Dynamic Data Partitioning Strategy Based on Heterogeneous Flink Cluster

As a popular big data stream processing engine, Apache Flink has obvious advantages in stream data processing. The data partitioning strategy of Flink stream processing jobs ignores metrics such as heterogeneous cluster, real-time computational resources of nodes and communication delays between nodes, which can easily cause load imbalance problems. In this paper, we propose a dynamic data partitioning strategy for stream processing jobs based on heterogeneous Flink cluster for adjusting the data load among nodes. The strategy dynamically adjusts the ratio of data processed by each node according to the real-time resource information, communication delay and hardware information of each node, so that the data load among nodes is at a relatively balanced level after multiple rounds of feedback adjustment. The experimental results show that the strategy can effectively improve the throughput and reduce latency of the job
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control

The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics research. However, the computational complexity of GPs has made policy search a highly time and memory consuming process that has not been able to scale to larger problems. In this work, we develop a policy optimization method by leveraging fast predictive sampling methods to process batches of trajectories in every forward pass, and compute gradient updates over policy parameters by automatic differentiation of Monte Carlo evaluations, all on GPU. We demonstrate the effectiveness of our approach in training policies on a set of reference-tracking control experiments with a heavy-duty machine. Benchmark results show a significant speedup over exact methods and showcase the scalability of our method to larger policy networks, longer horizons, and up to thousands of trajectories with a sublinear drop in speed.
Incremental Learning for Enhanced Personalization of Autocomplete Teleoperation

Remote controlling robots without any automated help is difficult due to various limitations. Autocomplete mitigates this difficulty by automatically detecting and completing the intended motions on robots from the input of the user. Such an approach can improve the system performance and reduce the load on the operator. Usually, recognizing intended motions is achieved using pre-trained Deep Learning (DL) models. In this paper, we introduce personalization to the autocomplete teleoperation framework when new operators take over by customizing the autocomplete DL model using incremental learning. Also, we tackle the problem of concept drift that arises in real-life applications; the data distribution of already learned classes may change in unforeseen ways as new observations of these classes come sequentially over time. We create and update an exemplar set using new observations of the classes online so that the model can be trained to adapt to the new observations. Several scenarios have been evaluated to balance the speed of learning with the accuracy of the model, and results demonstrate the effectiveness of the proposed models and their advantage in adapting to the specific operator versus our previous framework: personalization using transfer learning with full feedback.
Robot Learning Physical Object Properties from Human Visual Cues: A novel approach to infer the fullness level in containers

For collaborative tasks, involving handovers, humans are able to exploit visual, non-verbal cues, to infer physical object properties, like mass, to modulate their actions. In this paper, we investigate how the different levels of liquid inside a cup can be inferred from the observation of the movement of the person handling the cup. We model this mechanism from human experiments and incorporate it in an online human-to-robot handover. Finally, we provide a new dataset with human eye+head+hand motion data for human-to-human handovers and human pick-and-place of a cup with three levels of liquid: empty, half-full, and full of water. Our results show that it is possible to model (non-verbal) signals exchanged by humans during interaction and classify the level of water inside the cup being handed over.
Is it Worth to Reason about Uncertainty in Occupancy Grid Maps during Path Planning?

This paper investigates the usefulness of reasoning about the uncertain presence of obstacles during path planning, which typically stems from the usage of probabilistic occupancy grid maps for representing the environment when mapping via a noisy sensor like a stereo camera. The traditional planning paradigm prescribes using a hard threshold on the occupancy probability to declare that a cell is an obstacle, and to plan a single path accordingly while treating unknown space as free. We compare this approach against a new uncertainty-aware planner, which plans two different path hypotheses and then merges their initial trajectory segments into a single one ending in a “next-best view” pose. After this informative view is taken, the planner commits to one of the hypotheses, or to a completely new one if a collision is imminent. Simulations were conducted comparing the proposed and traditional planner. Results show the existence of planning scenarios -like when the environment contains a dead-end, or when the goal is placed close to an obstacle- in which reasoning about uncertainty can significantly decrease the robot's traveled distance and increase the chances of reaching the goal. The new planner was also validated on a real Clearpath Jackal robot equipped with a ZED 2 stereo camera.
Temporally-Aggregating Multiple-Discontinuous-Image Saliency Prediction with Transformer-Based Attention

In this paper, we aim to apply deep saliency prediction to automatic drone exploration, which should consider not only one single image, but multiple images from different view angles or localizations in order to determine the exploration direction. However, little attention has been paid to such saliency prediction problem over multiple-discontinuous-image and none of existing methods take temporal information into consideration, which may mean that the current predicted saliency map is not consistent with the previous predicted results. For this purpose, we propose a method named Temporally-Aggregating Multiple-Discontinuous-Image Saliency Prediction Network (TA-MSNet). It utilizes a transformer-based attention module to correlate relative saliency information among multiple discontinuous images and, furthermore, applies the ConvLSTM module to capture the temporal information. Experiments show that the proposed TA-MSNet can estimate better and more consistent results than previous works for time series data.
Learning to Retrieve Relevant Experiences for Motion Planning

Recent work has demonstrated that motion planners' performance can be significantly improved by retrieving past experiences from a database. Typically, the experience database is queried for past similar problems using a similarity function defined over the motion planning problems. However, to date, most works rely on simple hand-crafted similarity functions and fail to generalize outside their corresponding training dataset. To address this limitation, we propose (FIRE), a framework that extracts local representations of planning problems and learns a similarity function over them. To generate the training data we introduce a novel self-supervised method that identifies similar and dissimilar pairs of local primitives from past solution paths. With these pairs, a Siamese network is trained with the contrastive loss and the similarity function is realized in the network's latent space. We evaluate FIRE on an 8-DOF manipulator in five categories of motion planning problems with sensed environments. Our experiments show that FIRE retrieves relevant experiences which can informatively guide sampling-based planners even in problems outside its training distribution, outperforming other baselines.
Reactive Informative Planning for Mobile Manipulation Tasks under Sensing and Environmental Uncertainty

In this paper we address mobile manipulation planning problems in the presence of sensing and environmental uncertainty. In particular, we consider mobile sensing manipulators operating in environments with unknown geometry and uncertain movable objects, while being responsible for accomplishing tasks requiring grasping and releasing objects in a logical fashion. Existing algorithms either do not scale well or neglect sensing and/or environmental uncertainty. To face these challenges, we propose a hybrid control architecture, where a symbolic controller generates high-level manipulation commands (e.g., grasp an object) based on environmental feedback, an informative planner designs paths to actively decrease the uncertainty of objects of interest, and a continuous reactive controller tracks the sparse waypoints comprising the informative paths while avoiding a priori unknown obstacles. The overall architecture can handle environmental and sensing uncertainty online, as the robot explores its workspace. Using numerical simulations, we show that the proposed architecture can handle tasks of increased complexity while responding to unanticipated adverse configurations.
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model

Visual servoing enables robotic systems to perform accurate closed-loop control, which is required in many applications. However, existing methods require either precise calibration of the robot kinematic model and cameras or use neural architectures that require large amounts of data to train. In this work, we present a method for unsupervised learning of visual servoing that does not require any prior calibration and is extremely data-efficient. Our key insight is that visual servoing does not depend on identifying the veridical kinematic and camera parameters, but instead only on an accurate generative model of image feature observations from the joint positions of the robot. We demonstrate that with our model architecture and learning algorithm, we can consistently learn accurate models from less than 50 training samples (which amounts to less than 1 min of unsupervised data collection), and that such data-efficient learning is not possible with standard neural architectures. Further, we show that by using the generative model in the loop and learning online, we can enable a robotic system to recover from calibration errors and to detect and quickly adapt to possibly unexpected changes in the robot-camera system (e.g. bumped camera, new objects).
UFO Depth: Unsupervised learning with flow-based odometry optimization for metric depth estimation

We propose an efficient method for unsupervised learning of metric depth estimation from a single image in the context of unconstrained videos captured from UAVs. We combine the accuracy of an analytical solution based on odometry with the power of deep learning. First, we show how to correct the noisy odometric measurements by optimizing the alignment between the derotated optical flow and the projected linear speed in the image. Then, we detail an analytical depth estimation method based on optical flow and corrected camera velocities. Subsequently, the improved depth and camera veloc-ities obtained analytically are used, as additional cost terms, for training our novel unsupervised learning architecture for metric depth estimation. We extensively test on a recent UAV dataset, which we significantly extend by adding completely novel scenes. We outperform by significant margins different kinds of state-of-the-art approaches, ranging from analytical and unsupervised solutions to transformer-based architectures that require heavy computation and pre-training. The resulting algorithm could be deployed on embedded devices, being a good candidate for practical robotics use cases, such as obstacle avoidance and safe landing for UAV s.
Stable 3D Human Pose Estimation in Low- Resolution Videos with a Few Views

We discuss the problem of 3D pose estimation for multi-view videos. With previous frame-by-frame multi-view methods, it has been difficult to achieve stable estimation under challenging settings such as low-resolution or with only a few views. Temporal approaches are effective ways of addressing such problems, but enforcing temporal consistency with neigh-boring frames sometimes damages the precision of the results. We propose a temporal approach with selective corrections based on the observation that errors in the frame-by-frame approach are concentrated under certain adverse conditions. Our method evaluates the confidence of the frame-by-frame results and compensates for the inaccurate keypoints with temporal information while retaining the accurate keypoints. In our experiments on the CMU Panoptic dataset customized for low-resolution and a few views, we reported 32.98 mm for MPJPE and 98.64% for 3D-PCK@150. Compared to the state-of-the-art method, our method improved MPJPE by 1.14 mm and corrected 16 % of incorrect keypoints.