Reviewerly/exHarmony huggingface dataset:
Gesture-based human-robot interface for dual-robot with hybrid sensors

Purpose The purpose of this paper is the research of a novel gesture-based dual-robot collaborative interaction interface, which achieves the gesture recognition when both hands overlap. This paper designs a hybrid-sensor gesture recognition platform to detect the both-hand data for dual-robot control. Design/methodology/approach This paper uses a combination of Leap Motion and PrimeSense in the vertical direction, which detects both-hand data in real time. When there is occlusion between hands, each hand is detected by one of the sensors, and a quaternion-based algorithm is used to realize the conversion of two sensors corresponding to different coordinate systems. When there is no occlusion, the data are fused by a self-adaptive weight fusion algorithm. Then the collision detection algorithm is used to detect the collision between robots to ensure safety. Finally, the data are transmitted to the dual robots. Findings This interface is implemented on a dual-robot system consisting of two 6-DOF robots. The dual-robot cooperative experiment indicates that the proposed interface is feasible and effective, and it takes less time to operate and has higher interaction efficiency. Originality/value A novel gesture-based dual-robot collaborative interface is proposed. It overcomes the problem of gesture occlusion in two-hand interaction with low computational complexity and low equipment cost. The proposed interface can perform a long-term stable tracking of the two-hand gestures even if there is occlusion between the hands. Meanwhile, it reduces the number of hand reset to reduce the operation time. The proposed interface achieves a natural and safe interaction between the human and the dual robot.
A total variable-order variation model for image denoising

In this paper, we explore a new variational model based on the fractional derivative and total variation.Due to some metrics, our approach shows great results compared to other competitive models.In particular, deleting the noise and preserving edges, features and corners are headlights to our approach.For the fractional variable-order derivatives, different discretizations were presented to comparison.The theoretical results are validated by the Primal Dual Projected Gradient (PDPG) Algorithm which is well adapted to the fractional calculus.
Lightweight Deep Learning Pipeline for Detection, Segmentation and Classification of Breast Cancer Anomalies

The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods. Results are obtained using the INbreast public database in the context of lesion detection and BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network using ResNet50 is modified to generate the lesion regions proposals followed by a false positive reduction and contour refinement stages while a pre-trained VGG16 network is fine-tuned to classify mammograms. The detection and segmentation stage results show that the cascade configuration achieves a DICE of 0.83 without massive training while the multi-class classification exhibits an MAE of 0.58 with data augmentation.
X*: Anytime Multi-Agent Path Finding for Sparse Domains using Window-Based Iterative Repairs

Real-world multi-agent systems such as warehouse robots operate under significant time constraints -- in such settings, rather than spending significant amounts of time solving for optimal paths, it is instead preferable to find valid collision-free paths quickly, even if suboptimal, and given additional time, to iteratively refine such paths to improve their cost. In such domains, we observe that agent-agent collisions are sparse -- they involve small local subsets of agents, and are geographically contained within a small region of the overall space. Leveraging this insight, we can first plan paths for each agent individually, and in the cases of collisions between agents, perform small local repairs limited to local subspace windows. As time permits, these windows can be successively grown and the repairs within them refined, thereby improving the path quality, and eventually converging to the global joint optimal solution. Using these insights, we present two algorithmic contributions: 1) the Windowed Anytime Multiagent Planning Framework (WAMPF) for a class of anytime planners that quickly generate valid paths with suboptimality estimates and generate optimal paths given sufficient time, and 2) X*, an efficient WAMPF-based planner. X* is able to efficiently find successive valid solutions by employing re-use techniques during the repair growth step of WAMPF. Experimentally, we demonstrate that in sparse domains: 1) X* outperforms state-of-the-art anytime or optimal MAPF solvers in time to valid path, 2) X* is competitive with state-of-the-art anytime or optimal MAPF solvers in time to optimal path, 3) X* quickly converges to very tight suboptimality bounds, and 4) X* is competitive with state-of-the-art suboptimal MAPF solvers in time to valid path for small numbers of agents while providing much higher quality paths.
The Woman Worked as a Babysitter: On Biases in Language Generation

We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.
Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management

In the present era, data in any form is considered with greater importance. More specifically, text data has rich and brief information than any other form of data. Extraction and analysis of these data can result in various new findings through text analytics. This has led to applications such as search engines, extraction of product names, sentiment analysis, document classification and few more. Companies are much focused on sentimental analysis to review the positive, negative and neutral comments for their products. Summarization of text is a notable application of Natural Language Processing that reveals the gist of brief documents. Apart from these, on concerning welfare of the society, application based on information extraction can be developed. Handling an emergency situation requires collection of vast information. Extraction of such data can be supportive during disaster management. In order to perceive such task, system must learn the meaning of human languages. To ease the accessibility of text data across language barriers is the primary motive of Natural Language Processing (NLP) systems. The proposed systems has utilized word embedding model, specifically skip gram model to implement the most fundamental task of NLP—entity extraction in social media text. Implementation of N-gram embedding methods paved way for creation of rich context knowledge for the system to handle social media text. Classification of named entities using the proposed system has been carried out using machine learning classifier Support Vector Machine (SVM).
Computation on Encrypted Data using Dataflow Authentication

Abstract Encrypting data before sending it to the cloud protects it against attackers, but requires the cloud to compute on encrypted data. Trusted modules, such as SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program, which becomes part of the trusted code base (TCB), give attackers ample opportunity to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side-channels. Since any larger code base is rife with vulnerabilities, it is not a good idea to outsource entire programs to SGX enclaves. A secure alternative relying solely on cryptography would be fully homomorphic encryption. However, due to its high computational complexity it is unlikely to be adopted in the near future. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data. We introduce the concept of dataflow authentication (DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described above. We implemented DFAuth using a novel authenticated homomorphic encryption scheme, a Java bytecode-tobytecode compiler producing fully executable programs, and an SGX enclave running a small and program-independent TCB. We applied DFAuth to an existing neural network that performs machine learning on sensitive medical data. The transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in 0.86 s.
Risk-aware motion planning for automated vehicle among human-driven cars

We consider the maneuver planning problem for automated vehicles when they share the road with human-driven cars and interact with each other using a finite set of maneuvers. Each maneuver is calculated considering input constraints, actuator disturbances and sensor noise, so that we can use a maneuver automaton to perform higher-level planning that is robust against lower-level effects. In order to model the behavior of human-driven cars in response to the intent of the automated vehicle, we use control improvisation to build a probabilistic model. To accommodate for potential mismatches between the learned human model and human driving behaviors, we use a conditional value-at-risk objective function to obtain the optimal policy for the automated vehicle. We demonstrate through simulations that our motion planning framework consisting of an interactive human driving model and risk-aware motion planning strategy makes it possible to adapt to different traffic conditions and confidence levels.
CaLmi

In today's fast-paced and demanding society, more and more people are suffering from stress-related problems; however, intelligent environments can be equipped with facilities that assist in keeping it under control. This paper presents CaLmi, a system for Intelligent Homes that aims to reduce the stress of its residents by: (a) monitoring its level through a combination of biometric measurements from a wearable device along with information about user's everyday life and (b) enabling the ubiquitous presentation of relaxation programs, which deliver multi-sensory, context-aware, personalized interventions.
Integrated Model of Knowledge Management for Security of Information Technologies: Standards ISO/IEC 15408 and ISO/IEC 18045

The paper presents analysis of existing knowledge management models and justification for introducing an integrated model of knowledge management for both industry and academia.It is proposed to build such a model using well-known standards of IT security -common criteria and methodology for IT security evaluation.The model of knowledge management is elaborated by analysing the content of the relevant elements of standards and establishing the knowledge content that determines the forms of relations between them.The authors propose the application of an architecture of four-factor models towards the formation of knowledge management models in the organization of the information security management system in accordance with the standards of the series ISO/ IEC 27000.
Finding Flaws from Password Authentication Code in Android Apps

Password authentication is widely used to validate users’ identities because it is convenient to use, easy for users to remember, and simple to implement. The password authentication protocol transmits passwords in plaintext, which makes the authentication vulnerable to eavesdropping and replay attacks, and several protocols have been proposed to protect against this. However, we find that secure password authentication protocols are often implemented incorrectly in Android applications (apps). To detect the implementation flaws in password authentication code, we propose GLACIATE, a fully automated tool combining machine learning and program analysis. Instead of creating detection templates/rules manually, GLACIATE automatically and accurately learns the common authentication flaws from a relatively small training dataset, and then identifies whether the authentication flaws exist in other apps. We collected 16,387 apps from Google Play for evaluation. GLACIATE successfully identified 4,105 of these with incorrect password authentication implementations. Examining these results, we observed that a significant proportion of them had multiple flaws in their authentication code. We further compared GLACIATE with the state-of-the-art techniques to assess its detection accuracy.
Visual Odometry Revisited: What Should Be Learnt?

In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning. Most existing VO/SLAM systems with superior performance are based on geometry and have to be carefully designed for different application scenarios. Moreover, most monocular systems suffer from scale-drift issue.Some recent deep learning works learn VO in an end-to-end manner but the performance of these deep systems is still not comparable to geometry-based methods. In this work, we revisit the basics of VO and explore the right way for integrating deep learning with epipolar geometry and Perspective-n-Point (PnP) method. Specifically, we train two convolutional neural networks (CNNs) for estimating single-view depths and two-view optical flows as intermediate outputs. With the deep predictions, we design a simple but robust frame-to-frame VO algorithm (DF-VO) which outperforms pure deep learning-based and geometry-based methods. More importantly, our system does not suffer from the scale-drift issue being aided by a scale consistent single-view depth CNN. Extensive experiments on KITTI dataset shows the robustness of our system and a detailed ablation study shows the effect of different factors in our system.
Optimization of the multidimensional signal interpolator in a lower dimensional space

Adaptive multidimensional signal interpolators are developed. These interpolators take into account the presence and direction of boundaries of flat signal regions in each local neighborhood based on the automatic selection of the interpolating function for each signal sample. The selection of the interpolating function is performed by a parameterized rule, which is optimized in a parametric lower dimensional space. The dimension reduction is performed using rank filtering of local differences in the neighborhood of each signal sample. The interpolating functions of adaptive interpolators are written for the multidimensional, three-dimensional and two-dimensional cases. The use of adaptive interpolators in the problem of compression of multidimensional signals is also considered. Results of an experimental study of adaptive interpolators for real multidimensional signals of various types are presented.
Improving Natural Language Inference with a Pretrained Parser

We introduce a novel approach to incorporate syntax into natural language inference (NLI) models. Our method uses contextual token-level vector representations from a pretrained dependency parser. Like other contextual embedders, our method is broadly applicable to any neural model. We experiment with four strong NLI models (decomposable attention model, ESIM, BERT, and MT-DNN), and show consistent benefit to accuracy across three NLI benchmarks.
Rabi-resonant and intraband transitions in a multilevel quantum dot system controlled by the cavity-photon reservoir and the electron-photon coupling

The transport properties of a wire-dot system coupled to a quantized single photon with linear polarization in a 3D-cavity is theoretically studied using a quantum master equation. The system is also coupled to a photon reservoir, environment. We interplay between the electron-photon coupling, gγ, and the cavity-reservoir coupling, κ, in three cases: κ<gγ, κ=gγ, and κ>gγ. The current peaks due to intraband transitions between the Rabi-resonant states are formed when the electron-photon coupling is dominant, κ<gγ. The broadening of the current peaks depends on the photon polarization and the geometry of the resonant states. It is remarkable that a current plateau is found when κ≥gγ in which the cavity-reservoir is dominant. In this case, the intraband transitions are diminished and the current peaks are not seen.
Professor Avatar Holographic Telepresence Model

Introduced into theaters in the 1860s, Pepper’s Ghost startled theatergoers with an effect that allowed live people or objects to materialize into the scene. The illusion of a ghost is an actor located forward of and below the stage floor. The glass illustrates the reflection of the offstage “ghost,” while the leftmost “ghost” simulates what the audiences see. Modern versions of this effect consist of a completely new way of projecting video to create the illusion of life-size, full-color, moving images but projected as 2D images into a set. The mind of the audience creates the 3D illusion. This technology enables a new line of communication, which is called “holographic telepresence” that delivers a life-sized holographic experience in real time, enabling to connect more effectively and make an impact on audiences. The technology reduces expenses and saves on time travel. This project identified the parameters for correct setup of holographic telepresence, so that future users will be able to replicate and use with ease. The project used action research, which provides fast and effective solutions. The results demonstrated that it was possible that by defining the parameters and a guide for setting up a holographic telepresence.
Applying CMMI Best Practices to Improve Processes

Today more and more organizations are demanding quality software products. To cover this demand, companies that provide computer solutions focus on the quality of their processes to generate value in a competitive market. This article presents the case study of an educational institution that decides to use the best practices of CMMI to improve the areas of management processes of requirements, verification and validation. The results of the improvement process show that the practices were improved, the defects decreased and the customer satisfaction was improved.
Decomposing Textual Information For Style Transfer

This paper focuses on latent representations that could effectively decompose different aspects of textual information.Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality.We validate these methods with several state-of-the-art textual style transfer methods.Higher quality of information decomposition corresponds to higher performance in terms of bilingual evaluation understudy (BLEU) between output and human-written reformulations.
Implementing confidential transactions with lattice techniques

The notion of confidential transactions plays a central role in ensuring the confidentiality of transaction amounts in a block-chain-based cryptocurrency. Past researches have confirmed that the access policy of transaction amounts influences the anonymity of a cryptocurrency. Most of the current techniques of confidential transactions are based on the discrete logarithm problem. Because of the threats from quantum algorithms, clients are eager to enjoy security guarantees in a post-quantum scenario. Lattices are an ideal source of hardness, due to its exclusive worst-case to the average-case phenomenon. Nevertheless, the distinctions between the discrete logarithm problem and lattice problems build an obstacle in the way of borrowing the ideas from the former to the latter directly. In this study, inspired by the notion of commitments to polynomials and zero-knowledge arguments of knowledge for the inhomogeneous short integer solution problem, the authors give an approach to implement confidential transactions using lattice techniques.
Homotopic Approach for Robot Allocation Optimization Coupled With Path Constraints

This letter investigates a special task allocation problem with constraints of path planning. The path planning process involved is defined as the follow-up step of task allocation. The allocation problem is coupled with path constraints, which will affect the utility of allocation solution. A homotopic approach featuring two homotopy primitives is proposed to resolve the complexity caused by the coupling between task allocation and path planning. Reference paths generated by the homotopic approach are transformed to desired task bundles for robots via transformation functions. A distributed task allocation architecture with market mechanism is employed to make consensus on task allocation solution in multi-robot. Simulation results validate the performance of the proposed methods comparing with conventional allocation methods.
Training Anfis System with Moth-Flame Optimization Algorithm

Adaptive Neuro Fuzzy Inference System (ANFIS) is an adaptive network that can use the computation and learning abilities of artificial neural network together with the inference feature of fuzzy logic.The ANFIS system, which is used in the solution of many problems such as classification and estimation of deep learning applications, meets the needs in many different areas such as modeling, control, and parameter estimation.In recent years, heuristic methods have been used for the training of this network, which requires initial and result parameters by its structure.Moth-Flame Optimization Algorithm (MFO) is one of the current heuristic methods modeled by the influence of the spiral movement of the moths towards the light source.In this study, the MFO algorithm was used for the first time for the optimization of initial and result parameters in the ANFIS system.In the determination of parameters, nonlinear system identification, time series estimation, classification problems were tried to be solved.When the results obtained for the ANFIS trained with the known heuristic methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA) and the results of ANFIS trained by the MFO were examined, it was observed that the MFO had lower error values.