Reviewerly/exHarmony huggingface dataset:
Learning Household Task Knowledge from WikiHow Descriptions.

Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly rule- and template-based, recent advances in deep learning provide the possibility of acquiring such knowledge directly from natural language sources. As a first step in this direction, we propose a model to learn embeddings for tasks, as well as the individual steps that need to be taken to solve them, based on WikiHow articles. We learn these embeddings such that they are predictive of both step relevance and step ordering. We also experiment with the use of integer programming for inferring consistent global step orderings from noisy pairwise predictions.
A behavioral authentication method for mobile based on browsing behaviors

The passwords for unlocking the mobile devices are relatively simple, easier to be stolen, which causes serious potential security problems. An important research direction of identity authentication is to establish user behavior models to authenticate users. In this paper, a mobile terminal APP browsing behavioral authentication system architecture which synthesizes multiple factors is designed. This architecture is suitable for users using the mobile terminal APP in the daily life. The architecture includes data acquisition, data processing, feature extraction, and sub model training. We can use this architecture for continuous authentication when the user uses APP at the mobile terminal.
Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification

Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients' FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.
Active learning for software engineering

Software applications have grown increasingly complex to deliver the features desired by users. Software modularity has been used as a way to mitigate the costs of developing such complex software. Active learning-based program inference provides an elegant framework that exploits this modularity to tackle development correctness, performance and cost in large applications. Inferred programs can be used for many purposes, including generation of secure code, code re-use through automatic encapsulation, adaptation to new platforms or languages, and optimization. We show through detailed examples how our approach can infer three modules in a representative application. Finally, we outline the broader paradigm and open research questions.
Unsupervised Monocular Depth Estimation Based on Residual Neural Network of Coarse–Refined Feature Extractions for Drone

To take full advantage of the information of images captured by drones and given that most existing monocular depth estimation methods based on supervised learning require vast quantities of corresponding ground truth depth data for training, the model of unsupervised monocular depth estimation based on residual neural network of coarse–refined feature extractions for drone is therefore proposed. As a virtual camera is introduced through a deep residual convolution neural network based on coarse–refined feature extractions inspired by the principle of binocular depth estimation, the unsupervised monocular depth estimation has become an image reconstruction problem. To improve the performance of our model for monocular depth estimation, the following innovations are proposed. First, the pyramid processing for input image is proposed to build the topological relationship between the resolution of input image and the depth of input image, which can improve the sensitivity of depth information from a single image and reduce the impact of input image resolution on depth estimation. Second, the residual neural network of coarse–refined feature extractions for corresponding image reconstruction is designed to improve the accuracy of feature extraction and solve the contradiction between the calculation time and the numbers of network layers. In addition, to predict high detail output depth maps, the long skip connections between corresponding layers in the neural network of coarse feature extractions and deconvolution neural network of refined feature extractions are designed. Third, the loss of corresponding image reconstruction based on the structural similarity index (SSIM), the loss of approximate disparity smoothness and the loss of depth map are united as a novel training loss to better train our model. The experimental results show that our model has superior performance on the KITTI dataset composed by corresponding left view and right view and Make3D dataset composed by image and corresponding ground truth depth map compared to the state-of-the-art monocular depth estimation methods and basically meet the requirements for depth information of images captured by drones when our model is trained on KITTI.
Bayesian multitask learning regression for heterogeneous patient cohorts

Abstract Multitask learning (MTL) leverages commonalities across related tasks with the aim of improving individual task performance. A key modeling choice in designing MTL models is the structure of the tasks’ relatedness, which may not be known. Here we propose a Bayesian multitask learning model that is able to infer the task relationship structure directly from the data. We present two variations of the model in terms of a priori information of task relatedness. First, a diffuse Wishart prior is placed on a task precision matrix so that all tasks are assumed to be equally related a priori. Second, a Bayesian graphical LASSO prior is used on the task precision matrix to impose sparsity in the task relatedness. Motivated by machine learning applications in the biomedical domain, we emphasize interpretability and uncertainty quantification in our models. To encourage model interpretability, linear mappings from the shared input spaces to task-dependent output spaces are used. To encourage uncertainty quantification, conjugate priors are used so that full posterior inference is possible. Using synthetic data, we show that our model is able to recover the underlying task relationships as well as features jointly relevant for all tasks. We demonstrate the utility of our model on three distinct biomedical applications: Alzheimer’s disease progression, Parkinson’s disease assessment, and cervical cancer screening compliance. We show that our model outperforms Single Task (STL) models in terms of predictive performance, and performs better than existing MTL methods for the majority of the scenarios.
The presence of background DNA on common entry points to homes

It is becoming increasingly relevant to understand how much DNA is present on, and who might contribute to, samples collected from surfaces frequently targeted for DNA sampling in criminal investigations. This study investigates the presence of such DNA, also termed background DNA (bDNA), on common entry points in five homes occupied by known individuals. Samples were collected from internal and external windowsills and edges within bedrooms and living rooms, as well as front and rear door handles. The findings of this study contribute to our current knowledge on the prevalence of bDNA in the home and will assist scientists in making informed decisions on sampling strategy.
Gamma-Reduction in Type Theory of Acyclic Recursion

In this article, we introduce Moschovakis higher-order type theory of acyclic recursion Larλ. We present the potentials of Larλ for incorporating different reduction systems in Larλ, with corresponding reduction calculi. At first, we introduce the original reduction calculus of Larλ, which reduces Larλ-terms to their canonical forms. This reduction calculus determines the relation of referential, i.e., algorithmic, synonymy between Larλ-terms with respect to a chosen semantic structure. Our contribution is the definition of a (γ) rule and extending the reduction calculus of Larλ and its referential synonymy to γ-reduction and γ-synonymy, respectively. The γ-reduction is very useful for simplification of terms in canonical forms, by reducing subterms having superfluous λ-abstraction and corresponding functional applications. Typically, such extra λ abstractions can be introduced by the λ-rule of the reduction calculus of Larλ.
Clothing-integrated RFID-based Interface for Human-Technology Interaction

In this paper, we present a new type of passive ultra-high frequency (UHF) radio frequency identification (RFID)-based user interface for human-technology interaction. Our system is controlled with a shirtsleeve-integrated electro-textile antenna. The other part of the system consists of small identification (ID) ring antennas, each with a unique ID, embedded into our environment. Each ID ring has an RFID integrated circuit (IC) and can thus be “activated” by placing the shirtsleeve next to the specific ID ring. The shirtsleeve-integrated antenna has bands going around the wrist, thus increasing the read range even when the ring is behind the wrist. This cost-effective and maintenance-free solution can be seamlessly integrated into our everyday clothing, as well as into furniture, textiles, and items around us. The presented first prototype of this system, which can be used when sitting by a table, allows wireless controlling of technology by simple hand movements. The achieved read ranges are very promising, allowing activation of ID rings from distances of around 0.5-1 meters from a mobile RFID reader placed on a table.
Comparative Analysis between Error-State and Full-State Error Estimation for KF-Based IMU/GNSS Integration against IMU Faults

Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation effciency. This study will be extended to the integrity monitoring of multisensor systems.
Using Honeypots to Catch Adversarial Attacks on Neural Networks

Deep neural networks are known to be vulnerable to adversarial attacks.Numerous efforts have focused on defenses that either try to patch holes intrained models, or try to make it difficult or costly to compute adversarialexamples that exploit holes. In our work, we explore a counter-intuitiveapproach of creating a trapdoor-enabled defense. Unlike prior works that try topatch or disguise vulnerable points in the model, we intentionally injecttrapdoors, user-induced weaknesses in the classification manifold that, likehoneypots, attract attackers searching for adversarial examples. Attackers'algorithms naturally gravitate towards our trapdoors, leading them to produceattacks that are similar to the trapdoors in the feature space. These areeasily identified through similarity of neuron activation signatures comparedto those of trapdoors. In this paper, we introduce trapdoors and describe animplementation of a trapdoor-enabled defense. First, we analyze trapdoors as adefense. We prove that not only do trapdoors successfully shape the computationof adversarial attacks, but also that resulting attack inputs will have featurerepresentations very similar to those of inputs with trapdoors. Second, weexperimentally show that by protecting models with trapdoors, we can detect,with high accuracy, adversarial examples generated by state-of-the-art attacks(Projected Gradient Descent, optimization-based CW, Elastic Net, BPDA), withnegligible impact on the classification of normal inputs. These resultsgeneralize across classification domains, including image, facial, andtraffic-sign recognition. We also evaluate and validate trapdoors' robustnessagainst multiple strong adaptive attacks.
Implementation of Various Load-Balancing Approaches for Cloud Computing Using CloudSim

Cloud computing is an Internet based technology which empowers its user by providing access to an offsite huge distributed virtualized and scalable infrastructure via Internet. This infrastructure is composed of a number of hosts which may be homogeneous or heterogeneous in configuration are networked together to enable network storage, computing, services to the users. During high demands of users the workload on infrastructure increases which may impact the performance in cloud environment due to load imbalance. To overcome such issues the concept of load balancing arises. It is a methodology which works towards to equalize the load among the available resources in cloud surroundings. The role of load balancing is vital in cloud computing surroundings as it is responsible for maintaining the Quality of Services (QoS) in terms of optimized usage of resources, limiting the operational cost, making energy efficient system and increasing scalability. Testing of the load balancing algorithms in real cloud environs is a bottleneck due to resource wastage and lack of time. Simulation provides an aid to developers or researchers to provide a feedback by mimicking the actual environment. The paper deals with simulation based analysis of different distributed and centralized load balancing algorithms implemented in CloudSim.
Voice-Driven Modeling: Software Modeling Using Automated Speech Recognition

Voice-driven programming allows engineers to alleviate physical discomfort, pain, and injury. It also has the potential to be faster than typing and assist those with disabilities. While there are a number of solutions to voice-driven programming, Model-Driven Engineering (MDE) has yet to exploit this non-conventional but high-potential approach to software development. Standard convention in MDE practice involves creating software models using a traditional mouse and keyboard combination, or whiteboard sketch hardware. In this position paper, we introduce our vision and ideas for a Voice-Driven Modeling (VDM) approach. Our vision involves a framework that includes 3 phases: Speech Processing, Natural Language Processing, and Context Specific Modeling. We describe these 3 phases in this paper, which others can apply in their attempts to realize VDM. We additionally include our research plans for developing a VDM solution targeted at Simulink models and our early proof of concept capable of implementing several example commands. We establish the pertinence of this work through a survey that finds negligible work on VDM and highlights the potential impact this can have on the field of MDE as a whole. Specifically, it is our position that it can have a positive impact on modelers in general, modelers with disabilities, and domain experts not familiar with modeling. It is our hope that this work helps fuel research in this area, allowing for a new way to develop software models.
Learning by Unsupervised Nonlinear Diffusion

This paper proposes and analyzes a novel clustering algorithm that combines graph-based diffusion geometry with techniques based on density and mode estimation. The proposed method is suitable for data generated from mixtures of distributions with densities that are both multimodal and have nonlinear shapes. A crucial aspect of this algorithm is the use of time of a data-adapted diffusion process as a scale parameter that is different from the local spatial scale parameter used in many clustering algorithms. We prove estimates for the behavior of diffusion distances with respect to this time parameter under a flexible nonparametric data model, identifying a range of times in which the mesoscopic equilibria of the underlying process are revealed, corresponding to a gap between within-cluster and between-cluster diffusion distances. These structures can be missed by the top eigenvectors of the graph Laplacian, commonly used in spectral clustering. This analysis is leveraged to prove sufficient conditions guaranteeing the accuracy of the proposed \emph{learning by unsupervised nonlinear diffusion (LUND)} procedure. We implement LUND and confirm its theoretical properties on illustrative datasets, demonstrating the theoretical and empirical advantages over both spectral clustering and density-based clustering techniques.
Overcoming economic stagnation in low-income communities with programmable money

Purpose The purpose of this study hopes to encourage further research into the topic of local currencies, as well as specific research into increasing the efficiency of these systems through the use of blockchain technology. There is currently a lack of available research into the topic, which poses barriers for those who wish to study it, so the paper provides a general overview to be used as a starting point for those wishing to broaden their knowledge of local currency systems while also introducing a working implementation of a proof-of-concept for the proposed system. Design/methodology/approach A literature review of available studies on local currencies is conducted to provide an overview of the current systems and their shortcomings. Subsequently, blockchain technology is briefly introduced and an Ethereum-based model is proposed, which helps overcome the problems identified. The section exploring the Ethereum-based model draws code written by the author to simulate the features. Findings The paper concludes that blockchain technology can significantly help improve efficiency, transparency and security of local currency systems, while also helping cut costs associated with the implementation of a complementary monetary system. In the medium-term, local currency systems will most likely use a blockchain protocol as the underlying technology for the network. Originality/value Local currencies are an understudied topic by itself and the intersection between them and blockchain is a nearly non-existent research space. Thus, the paper takes a multidisciplinary approach, aiming to bridge the fields of computer science and economics to provide a foundation for further research.
Geochemistry of the Lake Chad Tributaries Under Strongly Varying Hydro-climatic Conditions

The Lake Chad Basin (LCB) is one of the main endorheic basins in the world and has undergone large-level and surface variations during the last decades, particularly during the Sahelian dry period in the 1970s and the 1980s. The Chari–Logone River system covers 25% of the LCB but accounts for up to 82% of the Lake Chad water supply. The aim of this study is to investigate the dissolved phase transported by the Chari–Logone system, in order (1) to elucidate the origin and the behavior of major elements and the weathering processes in the watershed; (2) to estimate the total dissolved flux, its variability over the last decades and the driving factors. To do so, samples were collected monthly between January 2013 and November 2016 at three representative sites of the basin: in the Chari River in "Chagoua," in the Logone River in "Ngueli" just before the confluence of both rivers, and at a downstream site in "Douguia," 30 km after the confluence. Concentrations in major elements displayed significant seasonal variations in the Chari and Logone waters. At the seasonal time scale, the comparison between the concentrations of chemical elements and the flow rates showed a hysteresis loop. This hysteresis behavior corresponds to a variable contribution over time of two water bodies, fast surface water, and slow groundwater, the latter carrying higher concentrations and Ca/Na ratio, which may result from the contribution of pedogenic carbonate weathering to the dominant signature of silicate weathering. At the annual time scale, similar average concentrations are observed in the Chari and Logone Rivers, despite contrasted annual runoff. In addition, an interannual stability of ionic concentrations was observed in the Chari–Logone River during the flood regime, both during the years covered by our monitoring (2013–2016) and during the pre-drought period (1969, 1972 and 1973). This situation corresponds to a chemostatic behavior, where the annual river discharge is the main factor controlling the interannual variation of chemical fluxes.
Non-contact method of thickness measurement for a transparent plate using a laser auto-focus scanning probe

In this paper we propose a novel non-contact method of thickness measurement for a transparent plate using a laser auto-focus scanning (LAFS) probe. Through the analysis of the focus error signal (FES) from the actual specimen’s upper and lower surface reflectances, a measurement model is introduced to find the best focus position on the corresponding surface of the transparent plate. Thus, the thickness of a specimen can be obtained. Herein, the LAFS probe measurement characteristics by auto-searching the FES method can be analyzed, which can then realize non-contact measurement and focus detection of micro-displacements. Therefore, we have successfully designed a low-cost, high-precision auto-focusing thickness measurement system with linear piezoelectric nano-positioning. To verify the proposed method, some transparent specimens were evaluated, and the thicknesses were rapidly and successfully measured. Experiments confirm the validity of the proposed system, and the results show that the method is effective for the thickness measurement of transparent plates.
A secure data protection technique for healthcare data in the cloud using homomorphic encryption and Jaya–Whale optimization algorithm

Cloud computing has become a powerful mechanism for initiating secure communication among users. The advancements in the technology lead to provide various services, like accessing network, resources, and platform. However, handling large datasets and security are major issues in the cloud systems. Hence, this paper proposes a technique, namely, Jaya–Whale Optimization (JWO), which is the integration of Jaya algorithm and Whale optimization algorithm (WOA) and adapts homomorphic encryption for initiating secure data transmission in the cloud. The original data are preserved by generating Data Protection (DP) coefficient using the proposed JWO algorithm. In the proposed algorithm, the fitness is calculated based on privacy and utility parameters for selecting the optimal solution. Also, the sanitized data are generated by EXORing the Key Information Product (KIP) matrix and key vector. Finally, the data owner provides the key to the users for retrieving the original data from the sanitized data. The experimentation is carried out using Cleveland, Hungarian, and Switzerland datasets in terms of BD, accuracy, and fitness and the analysis shows that the proposed JWO provides superior performance in terms of BD, accuracy, and fitness parameters with values 0.720, 0.822, and 0.722.