Reviewerly/exHarmony huggingface dataset:
Does Using Voice Authentication in Multimodal Systems Correlate With Increased Speech Interaction During Non-critical Routine Tasks?

Multimodal systems offer their functionalities through multiple communication channels. A messenger application may take either keyboard or voice input, and present incoming messages as text or audio output. This allows the users to communicate with their devices using the modality that best suits their context and personal preference. Authentication is often the first interaction with an application. The users' login behavior can thus be used to immediately adapt the communication channel to their preferences. Yet given the sensitive nature of authentication, this interaction may not be representative for the user's inclination to use speech input in non-critical routine tasks. In this paper, we test whether the interactions during authentication differ from non-critical routine tasks in a smart home application. Our findings indicate that, even in such a private space, the authentication behavior does not correlate with the use, nor with the perceived usability of speech input during non-critical task. We further find that short interactions with the system are not indicative of the user's attitude towards audio output, independent of whether authentication or non-critical tasks are performed. Since security concerns are minmized in the secure environment of private spaces, our findings can be generalized to other contexts where security threats are even more apparent.
On validating web information extraction proposals

Many people who have to make informed decisions in today’s always-on culture use information extractors to feed their systems with information that comes from human-friendly documents. Unfortunately, many proposals that validate information extractors have deficiencies that make it difficult to perform homogeneous comparisons, confirm or refute performance hypotheses, or draw unbiased conclusions. Consequently, it is very difficult to select the best-performing proposal on a sound basis. The state-of-the-art validation method overcomes many deficiencies in the previous proposals, but still overlooks the following issues: completeness of the validation datasets, that is, whether they provide a complete set of annotations or not; structure of the information, that is, whether they check the structure of the record instances extracted or just the attribute instances; and, finally, how extractions and annotations are matched. The decisions made regarding the previous issues have an impact on the effectiveness results. In this article, we have exhaustively analysed the literature and we have also highlighted the main weaknesses to tackle. We present a guideline and a method to compute the effectiveness, which complements and enhances the state-of-the-art validation method.
An Optimized Fuzzy-Based Resource Allocation for Cloud Using Secured Tabu Search Technique

Cloud computing provides on-demand storage and high-performance computing services. There are many other types of services that virtual machines (VMs) can provide for all your requests, depending on the service provider’s request for resources. Increasing energy consumption in cloud data centers is a big problem today. Problems with blockchain technology have affected cloud performance. In this work, selective appropriate terms included using the clock scheduling-based stochastic diffusion search (SDS) and optimized fuzzy-based resource allocation are presented.
The Rapidly Exploring Random Tree Funnel Algorithm

This paper shows the feasibility of combining robust motion primitives generated through the Sums Of Squares programming theory with a discrete Rapidly exploring Random Tree algorithm. The generated robust motion primitives, referred to as funnels, are then employed as local motion primitives, each with its locally valid Linear Quadratic Regulator (LQR) controller, which is verified through a Lyapunov function found through a Sum Of Squares (SOS) search in the function space. These funnels are then combined together at execution time by the Rapidly-exploring-Random-Tree (RRT) planner, and is shown to provide provably robust traversal of a simulated forest environment. The experiments benchmark the RRT-Funnel algorithm against an RRT algorithm which employs a maximum distance to the nearest obstacle heuristic in order to avoid collisions, as opposed to explicitly handling uncertainty. The results show that employing funnels as robust motion primitives outperform the heuristic planner in the experiments run on both algorithms, where the RRT-Funnel algorithm does not collide a single time, and creates shorter solution paths than the benchmark planner overall, although it takes a significantly longer time to find a solution.
User perceptions and use of authentication methods: insights from youth in Mexico and Bosnia and Herzegovina

Purpose This paper aims to address the user perspective about usability, security and use of five authentication schemes (text and graphical passwords, biometrics and hardware tokens) from a population not covered previously in the literature. Additionally, this paper explores the criteria users apply in creating their text passwords. Design/methodology/approach An online survey study was performed in spring 2019 with university students in Mexico and Bosnia and Herzegovina. A total of 197 responses were collected. Findings Fingerprint-based authentication was most frequently perceived as usable and secure. However, text passwords were the predominantly used method for unlocking computer devices. The participants preferred to apply personal criteria for creating text passwords, which, interestingly, coincided with the general password guidelines, e.g. length, combining letters and special characters. Originality/value Research on young adults’ perceptions of different authentication methods is driven by the increasing frequency and sophistication of security breaches, as well as their significant consequences. This study provided insight into the commonly used authentication methods among youth from two geographic locations, which have not been accounted for previously.
Vision based Fall Detection using Optimized Convolutional Neural Network

Health monitoring and tracking using Human Activity Recognition is a burgeoning study area, especially when it comes to detecting falls in the elderly. Falls can result in injuries, bodily injury, fractures, and other problems. Visual surveillance&#x0027;s major goal is to replace traditional video surveillance with intelligent visual surveillance. Different feature extraction algorithms were developed to better detect the fall. Convolutional Neural Network (CNN) in Fall Detection with the Optical Flow algorithm is used to extract features to find the fall detection. Designing CNN architecture for classification purposes without overfitting is a challenging task. The goal of this work is to avoid overfitting in training by automatically optimizing the CNN architecture with fine tuned dropout rate. This helps to increase the efficiency of the model. Four different CNN architecture are developed to predict fall detection. The UP-FALL Detection Dataset is used to test the performance of the four models. The experimental results show that second model produced high accuracy compared with other models.
Seeking Human Help to Manage Plan Failure Risks in Semi-Autonomous Mobile Manipulation

Abstract We present a framework for identifying, communicating, and addressing risk in shared-autonomy mobile manipulator applications. This framework is centered on the capacity of the mobile manipulator to sense its environment, interpret complex and cluttered scenes, and estimate the probability of actions and configuration states that may result in task failures, such as collision (i.e., identifying “risk”). If the threshold for acceptable risk is exceeded, a remote operator is notified and presented with timely, actionable information in which the person can quickly assess the situation and provide guidance for the robot. This framework is demonstrated with a use case in which a mobile manipulator performs machine tending and material handling tasks.
An Improved Method Research on Graphics and Image Processing System

In the era of high-speed information, with the popularization of network and the rapid development of multimedia communication technology, human society has entered a key new stage of development. Human productivity and living style have been significantly altered as a result of social informatization. The field of computer image processing has also made significant strides recently. Photoshop has evolved into a must-have piece of software for those who work in computer graphics and image processing on a regular basis. It contributes to increased job productivity in the processing of computer visuals and photos because of its extensive functionality, user-friendliness, and humanization. Photoshop is a graphics and image processing technology programme that is very efficient. This software’s image processing function is quite strong, and it is capable of producing graphics and image processing of the highest quality. This technology is widely used in practical work. Professionals are required to operate skillfully and grasp the technology accurately. Photoshop is widely used in current graphics and image processing. While meeting the relevant processing requirements of users, it is also used in various occasions related to image processing.
AudiWFlow: Confidential, collusion-resistant auditing of distributed workflows

We discuss the problem of accountability when multiple parties cooperate towards an end result, such as multiple companies in a supply chain or departments of a government service under different authorities. In cases where a fully trusted central point does not exist, it is difficult to obtain a trusted audit trail of a workflow when each individual participant is unaccountable to all others. We propose AudiWFlow, an auditing architecture that makes participants accountable for their contributions in a distributed workflow. Our scheme provides confidentiality in most cases, collusion detection, and availability of evidence after the workflow terminates. AudiWFlow is based on verifiable secret sharing and real-time peer-to-peer verification of records; it further supports multiple levels of assurance to meet a desired trade-off between the availability of evidence and the overhead resulting from the auditing approach. We propose and evaluate two implementation approaches for AudiWFlow. The first one is fully distributed except for a central auxiliary point that, nevertheless, needs only a low level of trust. The second one is based on smart contracts running on a public blockchain, which is able to remove the need for any central point but requires integration with a blockchain.
Planning to chronicle: Optimal policies for narrative observation of unpredictable events

One important class of applications entails a robot scrutinizing, monitoring, or recording the evolution of an uncertain time-extended process. This sort of situation leads to an interesting family of active perception problems that can be cast as planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to. The distinguishing characteristic of this setting is that the robot has influence over what it captures via its sensors, but exercises no causal authority over the process evolving in the world. As such, the robot’s objective is to observe the underlying process and to produce a “chronicle” of occurrent events, subject to a goal specification of the sorts of event sequences that may be of interest. This paper examines variants of such problems in which the robot aims to collect sets of observations to meet a rich specification of their sequential structure. We study this class of problems by modeling a stochastic process via a variant of a hidden Markov model and specify the event sequences of interest as a regular language, developing a vocabulary of “mutators” that enable sophisticated requirements to be expressed. Under different suppositions on the information gleaned about the event model, we formulate and solve different planning problems. The core underlying idea is the construction of a product between the event model and a specification automaton. Using this product, we compute a policy that minimizes the expected number of steps to reach a goal state. We introduce a general algorithm for this problem as well as several more efficient algorithms for important special cases. The paper reports and compares performance metrics by drawing on some small case studies analyzed in depth via simulation. Specifically, we study the effect of the robot’s observation model on the average time required for the robot to record a desired story. We also compare our algorithm with a baseline greedy algorithm, showing that our algorithm outperforms the greedy algorithm in terms of the average time to record a desired story. In addition, experiments show that the algorithms tailored to specialized variants of the problem are rather more efficient than the general algorithm.
An Automated Vulnerability Assessment Approach for WebAPI that Considers Requests and Responses

In recent years, WebAPIs are being published to allow external users to use Web services. On the other hand, the number of attacks that exploit WebAPI vulnerabilities is increasing. In order to prevent damage caused by abusing WebAPI vulnerabilities, the OWASP (Open Web Application Security Project) has published a guideline (OWASP API Security Top 10) describing the 10 vulnerabilities with the highest security risk in WebAPIs. However, the guideline does not describe the methods of attack and detailed countermeasures. Therefore, there are some vulnerabilities that are difficult to detect with existing WebAPI vulnerability assessment tools. In this paper, we propose a vulnerability assessment method that automatically obtains the WebAPI reference and repeatedly analyzes the requests through WebAPI and their responses. The proposed vulnerability assessment method will enable vulnerability assessment for vulnerability items that are difficult to be detected by existing vulnerability assessment tools among vulnerability items described in the guideline (OWASP API Security Top 10).
Ten Questions for a Theory of Vision

By and large, the remarkable progress in visual object recognition in the last few years has been fueled by the availability of huge amounts of labelled data paired with powerful, bespoke computational resources. This has opened the doors to the massive use of deep learning, which has led to remarkable improvements on new challenging benchmarks. While acknowledging this point of view, in this paper I claim that the time has come to begin working towards a deeper understanding of visual computational processes that, instead of being regarded as applications of general purpose machine learning algorithms, are likely to require tailored learning schemes. A major claim of in this paper is that current approaches to object recognition lead to facing a problem that is significantly more difficult than the one offered by nature. This is because of learning algorithms that work on images in isolation, while neglecting the crucial role of temporal coherence. Starting from this remark, this paper raises ten questions concerning visual computational processes that might contribute to better solutions to a number of challenging computer vision tasks. While this paper is far from being able to provide answers to those questions, it contains some insights that might stimulate an in-depth re-thinking in object perception, while suggesting research directions in the control of object-directed action.
Compound Aspect Extraction by Augmentation and Constituency Lattice

Aspects are opinion targets to extract in aspect-based sentiment analysis. While existing methods can already produce satisfactory extraction results, they suffer when faced with compound aspect terms, typically phrase-level aspect terms that have inner structure and occur infrequently in the training set. This issue can be mainly attributed to the scarcity of training examples targeting compound aspect terms and by the neglect of the syntactic structure of a sentence in the modeling process. In this article, we aim to cope with compound aspect extraction by a two-stage hybrid approach. First, we introduce a conditional generation method for data augmentation in a masked sequence-to-sequence framework, which is controllable to preserve original aspects while generating a new sentence. Second, we propose a constituency lattice structure that is induced from the constituency-based parse tree of a sentence. Experimental results on two review datasets show that this approach can greatly improve the effect of compound aspect extraction.
Authentic News Prediction in Machine Learning using Passive AggressiveAlgorithm

Due to increase of users in the online social media platform in the past few years, fake news has become a serious downside issue and are spreading like wildfire. By the inevitable widespread of fake news, humans are incapable of detecting whether the news is genuine or fake. In order to restrain many researches have been done for automatic detection of counterfeit news in Machine Learning to help people identify and avoid fake news. The proposed techniques are implemented using Natural Language Processing (NLP) Algorithm with classifier namely Passive-Aggressive with Sci-kit Python Library packages. The outcome is text-based. Here the use of Machine learning algorithm technique is put forward in classifying the news articles. A Machine Learning Algorithm Namely Passive - Aggressive Classifier (PAC) is trained. Framework like Flask is used for web development. Python packages like NumPy, Pandas, Sci-kit, NLTK, are used to train the machine. The outcome is based on URL-Search. After training and testing its implementation, a meaningful accuracy in detection has been achieved.
Continuous verification of system of systems with collaborative MAPE-K pattern and probability model slicing

The phenomenon of cooperation among independent systems to achieve common goals has been growing. In this regard, the concept of system of systems (SoS), wherein numerous independent systems cooperate with each other, has been proposed. The key characteristic of an SoS is the operational and managerial (O/M) independence of each constituent system (CS). Each CS of a collaborative SoS with high O/M independence provides different levels of internal-knowledge sharing and is entitled to voluntary participation in the SoS (i.e., dynamic reconfiguration). To increase goal-achievement rate, we need to verify SoS considering the knowledge-sharing and dynamic reconfiguration constraints.
Microtomographic Analysis of a Palaeolithic Wooden Point from the Ljubljanica River

A rare and valuable Palaeolithic wooden point, presumably belonging to a hunting weapon, was found in the Ljubljanica River in Slovenia in 2008. In order to prevent complete decay, the waterlogged wooden artefact had to undergo conservation treatment, which usually involves some expected deformations of structure and shape. To investigate these changes, a series of surface-based 3D models of the artefact were created before, during and after the conservation process. Unfortunately, the surface-based 3D models were not sufficient to understand the internal processes inside the wooden artefact (cracks, cavities, fractures). Since some of the surface-based 3D models were taken with a microtomographic scanner, we decided to create a volumetric 3D model from the available 2D tomographic images. In order to have complete control and greater flexibility in creating the volumetric 3D model than is the case with commercial software, we decided to implement our own algorithm. In fact, two algorithms were implemented for the construction of surface-based 3D models and for the construction of volumetric 3D models, using (1) unsegmented 2D images CT and (2) segmented 2D images CT. The results were positive in comparison with commercial software and new information was obtained about the actual state and causes of the deformation of the artefact. Such models could be a valuable aid in the selection of appropriate conservation and restoration methods and techniques in cultural heritage research.
Research and Application of Intelligent Layout Design Algorithm for 3D Pipeline of Nuclear Power Plant

The pipeline layout design of nuclear power plant is to find the optimal route to meet the objectives and constraints in the 3D routing space. However, due to the intensive equipment and complex structure of the nuclear power plant, various types of pipeline systems, complex layout constraints, and a large number of pipelines, even for experienced designers, pipeline layout is a difficult and time-consuming task. In order to solve the problem of the automatic layout of pipeline in 3D routing space of nuclear power plant, a pipeline automatic routing method combining Dijkstra algorithm in large space and improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><msup><mrow /><mi>∗</mi></msup></math> algorithm in local space is proposed in this paper. Firstly, the method identifies the key vertices of each room in the nuclear power plant, constructs the topological routing map, and determines the preliminary passage area of the pipeline through the traditional Dijkstra algorithm. Secondly, the space of the layout area is divided into 3D grids, and then the items in the area are identified and preprocessed. Finally, the 3D pipeline routing environment is established through AABB-OBB hybrid collision detection technology. On this basis, the improved method of A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mrow /><mi>∗</mi></msup></math> evaluation function is given to satisfy the pipeline layout constraints and improve the search efficiency. Through experiments, the effectiveness of this method is proved. This method can quickly and automatically route the nuclear power pipelines that meet the requirements of the engineering, which greatly improves the efficiency of 3D pipeline layout design for the nuclear power plant.
Exchangeable and sampling-consistent distributions on rooted binary trees

Abstract We introduce a notion of finite sampling consistency for phylogenetic trees and show that the set of finitely sampling-consistent and exchangeable distributions on n -leaf phylogenetic trees is a polytope. We use this polytope to show that the set of all exchangeable and sampling-consistent distributions on four-leaf phylogenetic trees is exactly Aldous’ beta-splitting model, and give a description of some of the vertices for the polytope of distributions on five leaves. We also introduce a new semialgebraic set of exchangeable and sampling consistent models we call the multinomial model and use it to characterize the set of exchangeable and sampling-consistent distributions. Using this new model, we obtain a finite de Finetti-type theorem for rooted binary trees in the style of Diaconis’ theorem on finite exchangeable sequences.