Reviewerly/exHarmony huggingface dataset:
Safe and efficient imitation learning by clarification of experienced latent space

Behavioral cloning from observation (BCO) allows the robot to learn the policy without the expert's action information. However, it requires a few interactions with the environment to infer expert's action with risk of robot failures. In addition, BCO assumes that the inferred action is of accurate, causing wrong and inefficient updates of the policy. Both problems can be resolved by outlier detection whether the faced state is experienced or not. This paper addresses such outlier detection mechanisms using variational autoencoder (VAE) to improve safety and efficiency of the standard BCO. For the first safety problem, we suppose that the expert's demonstrations only visited the safe states, and then, VAE is learned by the expert's state data to detect inexperienced and dangerous scenes. For the second efficiency problem, another VAE is trained with the state data safely collected by the imitator's policy to detect the scenes where the inferred actions are not accurate. In handwriting robot experiments, the proposed mechanisms succeeded in improving the standard BCO in terms of both the safety (roughly 64%) and the efficiency (roughly 44%). The high versatility of the proposed mechanisms is verified from learning various alphabets.
Allocation and Tracking of Public Funds using Blockchain

Blockchain is a technology that helps create and maintain a cryptographically secure, shared, and distributed ledger (a database) for transactions. It is a public ledger to which everyone has access but control of the ledger does not lie with a central authority. One can get transparency, accountability and trust by using blockchain to perform digital transactions. It is an enabling technology for individuals as well as companies to collaborate while ensuring that trust and transparency are maintained throughout the process. Considering these characteristics, blockchain can be used for tracking the flow of public funds assigned to a project. The paper explores the uses of blockchain technology in real-life commercial applications and proposes a method that will be used to allocate and track public funds using blockchain technology.
Semantic Segmentation of 3D Point Cloud Based on Spatial Eight-Quadrant Kernel Convolution

In order to deal with the problem that some existing semantic segmentation networks for 3D point clouds generally have poor performance on small objects, a Spatial Eight-Quadrant Kernel Convolution (SEQKC) algorithm is proposed to enhance the ability of the network for extracting fine-grained features from 3D point clouds. As a result, the semantic segmentation accuracy of small objects in indoor scenes can be improved. To be specific, in the spherical space of the point cloud neighborhoods, a kernel point with attached weights is constructed in each octant, the distances between the kernel point and the points in its neighborhood are calculated, and the distance and the kernel points’ weights are used together to weight the point cloud features in the neighborhood space. In this case, the relationship between points are modeled, so that the local fine-grained features of the point clouds can be extracted by the SEQKC. Based on the SEQKC, we design a downsampling module for point clouds, and embed it into classical semantic segmentation networks (PointNet++, PointSIFT and PointConv) for semantic segmentation. Experimental results on benchmark dataset ScanNet V2 show that SEQKC-based PointNet++, PointSIFT and PointConv outperform the original networks about 1.35–2.12% in terms of MIoU, and they effectively improve the semantic segmentation performance of the networks for small objects of indoor scenes, e.g., the segmentation accuracy of small object “picture” is improved from 0.70% of PointNet++ to 10.37% of SEQKC-PointNet++.
Cervical cancer prediction using stacked ensemble algorithm with SMOTE and RFERF

With the tremendous advancement in machine learning and deep learning, organizations are using numerous algorithms for analyzing the huge amount of data to come up with insights which contains meaningful out comes. Especially in medical health care systems, machine learning is being used widely to forecast and treat the illness at early in stage. Cervical cancer is one of such diseases which can be diagnosed if identified in early stage, but the symptom-less nature of the disease has become challenging for the researchers and practitioners to predict the disease at early stage. There are numerous factors which will prone to cervical cancer risk in women. In this study we have proposed a stacked ensemble technique which takes heterogenous base learners and a meta learner for predicting the cervical cancer from various risk factors, SMOTE has been used for data Balancing and RFE with Random Forest used for feature extraction through which we have got better accuracy than the existing methods. We have also identified top 8 features which impact the advancement of the classification model.
Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning

Abstract Neural models command state-of-the-art performance across NLP tasks, including ones involving “reasoning”. Models claiming to reason about the evidence presented to them should attend to the correct parts of the input while avoiding spurious patterns therein, be self-consistent in their predictions across inputs, and be immune to biases derived from their pre-training in a nuanced, context- sensitive fashion. Do the prevalent *BERT- family of models do so? In this paper, we study this question using the problem of reasoning on tabular data. Tabular inputs are especially well-suited for the study—they admit systematic probes targeting the properties listed above. Our experiments demonstrate that a RoBERTa-based model, representative of the current state-of-the-art, fails at reasoning on the following counts: it (a) ignores relevant parts of the evidence, (b) is over- sensitive to annotation artifacts, and (c) relies on the knowledge encoded in the pre-trained language model rather than the evidence presented in its tabular inputs. Finally, through inoculation experiments, we show that fine- tuning the model on perturbed data does not help it overcome the above challenges.
ZSS Signature Based Data Integrity Verification for Mobile Edge Computing

Mobile edge computing (MEC), which merits in reducing response time by executing services in close proximity to end devices, has recently emerged as one of promising solutions for mobile services and hence attracted increasing research. Edge nodes usually pre-download parts of private data which are stored in the cloud to enable end devices' fast access, which mitigates overburden of the centralized cloud. However, malicious attackers or unreliable services providers may corrupt with those private data on edge servers. To this end, how to validate data integrity in MEC environment safely and efficiently has become a crucial problem. In this paper, we propose a ZSS Signature based Data Integrity Verification solution for MEC (ZSDIV-MEC), which supports privacy protection and public auditing by introducing a third party auditor (TPA) and the employment of ZSS signature scheme. In the proposed ZSDIV-MEC, system architecture is described followed with a detailed verification protocol, which takes full consideration of verification for data on three cases including single edge, multiple edges, and a joint of multiple edges and the center cloud. Performance analysis of the protocol including feasibility, security, privacy, and dynamicity is discussed respectively. Experimental results in comparison with baseline schemes demonstrate that our solution has better performance in data verification and computation overhead.
MILR: Mathematically Induced Layer Recovery for Plaintext Space Error Correction of CNNs

The increased use of Convolutional Neural Networks (CNN) in mission-critical systems has increased the need for robust and resilient networks in the face of both naturally occurring faults as well as security attacks. The lack of robustness and resiliency can lead to unreliable inference results. Current methods that address CNN robustness require hardware modification, network modification, or network duplication. This paper proposes MILR a software-based CNN error detection and error correction system that enables recovery from single and multi-bit errors. The recovery capabilities are based on mathematical relationships between the inputs, outputs, and parameters(weights) of the layers; exploiting these relationships allows the recovery of erroneous parameters (iveights) throughout a layer and the network. MILR is suitable for plaintext-space error correction (PSEC) given its ability to correct whole-weight and even whole-layer errors in CNNs.
Exact Reactive Receding Horizon Motion Planning for Aerial Vehicles

This article presents a motion planning strategy that integrates a reactive receding horizon local trajectory generation methodology with a navigation function based global planner to guarantee that a quadrotor type mav navigating in partially-known cluttered environment can reach its goal. The environment is partially known in the sense that there can be obstacles which are not a priori known to be included in the navigation function construction and used by the global planner. These obstacles are avoided reactively at the level of the local planner, which reacts to sensing information within the Field of View (fov) of the onboard sensors and keeps generating dynamically feasible safe trajectories along the segments generated by the global planner. The proposed motion planning strategy combines the advantages of global and reactive aerial navigation, producing locally optimal (in a minimum-jerk sense) vehicle motion. The effectiveness of the planner is demonstrated through realistic ros-Gazebo simulations.
A Generic Blockchain Framework to Secure Decentralized Applications

Blockchain technology is gaining popularity in industries and governments for information monitoring, distribution, and tracking. Thanks to the built-in security properties, blockchain provides security integrity to various decentralized applications (dApps) involving distributed operations including supply chain, healthcare, banking, internet of things (IoT), and networking. In this paper, we propose a generic blockchain framework (GBF) for applying two blockchains to the dApp systems, one to establish trust and the other to use the trust for securing applications. GBF provides a generally applicable framework and addresses the foundational questions of the blockchain objectives, participants, and the underlying distributed consensus protocol in use. We apply GBF to various case studies from the recent blockchain research to show its effectiveness and generality. We also prototype GBF using smart contract and experiment on CloudLab for preliminary evaluations focusing on the application-general metrics. We propose GBF to facilitate blockchain/dApp research and development by providing the initial framework and enable the preliminary analyses so that the decentralized applications with specific aims can build on GBF.
Cultural Weight-Based Fish School Search: A Flexible Optimization Algorithm For Engineering

Many real-life engineering applications are optimization problems. To find the best configuration of variables to minimize costs and maximize efficiency are typically used engineering software as CAD, CAE and CAM. In this context, Machine Learning can be used to automate and improve this type of application. This despite, those searches not seldomly evoke unreliable areas and suggest risky solutions. Because of inaccuracies, volatility, unfeasibility, and specificities of real environments, the easy incorporation of cultural practices (i.e. normative, situational, domain and historic knowledge) and as well as the production of multiple acceptable solutions for a problem is always welcome, especially in Engineering. The present article put forward a hybridization of a multi-modal algorithm (Weight-Based Fish School Search - wFSS) with Cultural Algorithms' belief space. New cwFSS is able to guide the optimization also considering normative knowledge from experts, technical literature and problem domain readily available knowledge to prevent the incorporation of constraints into the fitness function. We also evaluated the use of temporal knowledge to guide the simulation. The proposed method was tested in a thermal power plant efficiency optimization and compared with standard wFSS and the Niching Migratory Multi-Swarm Optimizer (NMMSO), winner of CEC'2015 niching competition. As results, cwFSS has outperformed at times NMMSO about time, fitness and variability, as well as traditional wFSS about time, stability, safeness and variability of the multimodal solutions. By avoiding penalties, the appropriation of a priori search directly into the search can effectively and elegantly help better support for engineering decisions.
A Solution to Covid-19: Detection and Recognition of Faces with Mask

In this COVID-19 crisis, wearing masks is necessary and no longer an option to the general public. To follow the strict directives given by the government, the businesses have to implement a cost-effective approach to ensure that all its employers wear a face mask and help to control the spread of coronavirus. The proposed solution consists of an automatic face mask detection system that eliminates the need of an employee at the entrance. The working model detects a face mask in every person by analysing each frame of the video and alert through security mail when the mask is not detected. Our proposed system is designed using the Convolution Neural Network (CNN) model for mask detection and image subtraction technique to recognise the faces with mask. The scope of the project pertains to avoid the entry of unauthorized people into an organization by recognizing the face despite the presence of a mask. The model shows an accuracy of 99.82% on a custom dataset. It is an effective protection step to impede the transmission of the novel coronavirus.
Specific Emitter Identification Based on Two Residual Networks

In order to solve the scarcity of labeled samples in actual combat scenarios makes it difficult to converge the deep learning model in a complex electromagnetic environment. This paper combines the residual learning model of residual network and complex-valued residual network, we proposes a feature fusion that makes full use of a method that has a small amount labeled data. First, we need input reprocessed data into two residual learning networks. And then, we regard the data features extracted by the two residual networks as the real and imaginary parts of the classifier to train the complex network classifier. Finally, Using signal data of the radiation source to be identified to verify the effectiveness of the algorithm. Experiments have proved that compared with the baseline method, this method could improve the recognition effect of communication radiation sources under the condition of little labeled samples.
Using Ultrasonic Haptics Within an Immersive Spider Exposure Environment to Provide a Multi-Sensorial Experience

A proof of concept virtual reality system is presented that integrates ultrasonic feedback sensations to provide a demonstrative virtual reality exposure therapy environment containing multiple scenarios with virtual spiders. This system and environment were utilised to conduct a study containing 35 participants with the goal of investigating the effect the environment could have on their level of anxiety. This level of anxiety was measured in three different forms: changes in frontal asymmetry analysis of EEG data, changes in skin conductance levels and subjective units of distress. The Fear of Spiders Questionnaire was used to determine which participants in the study reported to be moderately afraid of spiders. For these participants all three measurement forms for anxiety showed statistically significant increases in a comparison between baseline and scenarios with the virtual spiders. A statistically significant correlation between scores on the Fear of Spiders Questionnaire and changes in anxiety shows the system to have had a greater effect on the anxiety levels of those who were more afraid of spiders, than those who were not. There was also a statistically significant correlation discovered between immersion and increase in anxiety, highlighting the significance of immersion in future virtual reality exposure therapy applications.
Safe instance screening for primal multi-label ProSVM

As an important model in multi-label learning, ProSVM considers two problems simultaneously: one is to distinguish the relevant labels from the irrelevant labels of instances, and the other is to rank the relevant labels. Thus it produces better generalization ability in comparison with other multi-label models. However, ProSVM leads to high computational cost when the label space is enormous since it employs the label-pairs instead of the instances in the training process. To effectively deal with this defect, a safe screening rule is proposed to speed up the learning process, named PSSR. Different from the existing safe screening rule, our screening method is constructed based on solving the primal problem of ProSVM rather than the dual one. So it can address large-scale problems. To the best of our knowledge, it is the first safe screening rule for the primal problem in multi-label learning. Experimental results on eight benchmark datasets demonstrate the superiority of PSSR. Finally, our PSSR is applied to the clinical data of diabetic patients, and obtain better performance.
DiLizium: A Two-Party Lattice-Based Signature Scheme

In this paper, we propose DiLizium: a new lattice-based two-party signature scheme. Our scheme is constructed from a variant of the Crystals-Dilithium post-quantum signature scheme. This allows for more efficient two-party implementation compared with the original but still derives its post-quantum security directly from the Module Learning With Errors and Module Short Integer Solution problems. We discuss our design rationale, describe the protocol in full detail, and provide performance estimates and a comparison with previous schemes. We also provide a security proof for the two-party signature computation protocol against a classical adversary. Extending this proof to a quantum adversary is subject to future studies. However, our scheme is secure against a quantum attacker who has access to just the public key and not the two-party signature creation protocol.
Saving Mr. Water: anthropomorphizing water promotes water conservation

Anthropomorphism refers to the assignment of human-like attributes to otherwise non-human entities. In the pro-environmentalism literature, there is indication that anthropomorphizing nature can promote green behaviors. The current research examines how anthropomorphizing water can promote water conservation behaviors via environmental locus of control—one primary impediment to people acting in an environmentally-friendly fashion. A randomized controlled field trial was carried out across three apartment buildings in Melbourne, Australia, from July 2018 to June 2019. At the end of the first six months, which served as our baseline, half of the households had received monthly water conservation tips within an e-mail newsletter format with water anthropomorphized; the other half received the same tips without anthropomorphism. In the latter six months, households in the experimental group consumed fewer liters of water than those in the control group. Our end-of-year survey revealed that anthropomorphizing water increased responding households’ internal environmental of control, which then encouraged water conservation behaviors. A follow-up laboratory experiment replicated the results within a controlled atmosphere. Both theoretical contributions and practical implications are discussed.
EFL Vocabulary Learning Using a Learning Analytics-based E-book and Recommender Platform

Learning vocabulary is a crucial but challenging activity for English as a foreign language learners, and computer-assisted language learning can facilitate this process. Moreover, e-learning is attracting a great deal of attention as a new technology to bring educational support which traditional learning systems cannot provide. Recommender systems as its implementation have been subject to discussion. In this study, we propose a comprehensive learning analytics-based platform for efficient vocabulary learning, including an e-book reader and a book/quiz recommender. The system on this platform estimates learners' knowledge based on their activities and brings personalized recommendation and its bases to the learners. Also, this platform provides teachers with visualized feedback regarding the recommendation and students' engagement in learning.
Designing a Simple Fiducial Marker for Localization in Spatial Scenes Using Neural Networks

The paper describes the process of designing a simple fiducial marker. The marker is meant for use in augmented reality applications. Unlike other systems, it does not encode any information, but it can be used for obtaining the position, rotation, relative size, and projective transformation. Also, the system works well with motion blur and is resistant to the marker's imperfections, which could theoretically be drawn only by hand. Previous systems put constraints on colors that need to be used to form the marker. The proposed system works with any saturated color, leading to better blending with the surrounding environment. The marker's final shape is a rectangular area of a solid color with three lines of a different color going from the center to three corners of the rectangle. Precise detection can be achieved using neural networks, given that the training set is very varied and well designed. A detailed literature review was performed, and no such system was found. Therefore, the proposed design is novel for localization in the spatial scene. The testing proved that the system works well both indoor and outdoor, and the detections are precise.
A Novel Robust Classification Method for Ground-Based Clouds

Though the traditional convolutional neural network has a high recognition rate in cloud classification, it has poor robustness in cloud classification with occlusion. In this paper, we propose a novel scheme for cloud classification, in which the convolutional neural networks are used for feature extraction and a weighted sparse representation coding is adopted for classification. Three such algorithms are proposed. Experiments are carried out using the multimodal ground-based cloud dataset and the results show that in the case of occlusion, the accuracy of the proposed methods can be much improved over the traditional convolutional neural network-based algorithms.
Group of components detection in engineering drawings based on graph matching

Computer-aided design (CAD) applications are paramount tools to inspect piping and instrumentation diagrams (P&ID) that represent the structure and functionality of oil and gas facilities. These tools allow engineers to navigate along the P&IDs and facilitate their inspection and maintenance tasks. Usually, these applications allow the user to search components for their identity or label. For instance, searching component ID−2354 or the components with Valve label. Nevertheless, sometimes, engineers wish to search for a group of components that has a specific structure or similar to it. For example, they want to detect the appearances of structures that include a valve check connected to two general valves and a butterfly valve. This paper proposes a method, based on the graph matching theory, that solves this problem. The computational complexity to detect the appearance of a component in a P&ID is linear with respect to the number of components whereas the computational complexity to detect similar structures is exponential. For this reason, heuristic algorithms have to be used. The aim of this work is to add this functionality to a CAD application.