Reviewerly/exHarmony huggingface dataset:
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions Through Perception

Explaining the decisions of models is becoming pervasive in the image processing domain, whether it is by using posthoc methods or by creating inherently interpretable models. While the widespread use of surrogate explainers is a welcome addition to inspect and understand black-box models, assessing the robustness and reliability of the explanations is key for their success. Additionally, whilst existing work in the explainability field proposes various strategies to address this problem, the challenges of working with data in the wild is often overlooked. For instance, in image classification, distortions to images can not only affect the predictions assigned by the model, but also the explanation. Given a clean and a distorted version of an image, even if the prediction probabilities are similar, the explanation may still be different. In this paper we propose a methodology to evaluate the effect of distortions in explanations by embedding perceptual distances that tailor the neighbourhoods used to training surrogate explainers. We also show that by operating in this way, we can make the explanations more robust to distortions. We generate explanations for images in the Imagenet-C dataset and demonstrate how using a perceptual distances in the surrogate explainer creates more coherent explanations for the distorted and reference images.
Identification of strategies over tools for static code analysis

Abstract Static code analysis tools are being increasingly used to improve code quality. The source code’s quality is a key factor in any software product and requires constant inspection and supervision. Static code analysis is a valid way to infer the behavior of a program without executing it. Many tools allow static analysis in different frameworks, different programming languages, and detecting different defects in the source code. Different strategies of using static code analysis tools are often used, and these strategies are not classified. In this paper, an experiment was conducted on different tools and their use in relation to the standard code review cycle. The identified strategies for using static code analysis tools and the steps required to implement them are presented. When using the tool, users should choose one of the identified strategies to implement following the defined steps for successful implementation.
College students' mental health evaluation method based on WLLE and MLP

To address the problems of poor generalization performance, high computational cost and low prediction accuracy of existing methods for evaluation of college students' mental health, we propose a mental health evaluation method based on WLLE (weighted locally linear embedding) and MLP (multilayer perceptron). First of all, by using the cluster sampling method, with SCL90 as the test scale, the mental health data of college students was collected using paper questionnaires and online questionnaires, and the data was pre-processed. Then, the WLLE algorithm is employed for dimensionality reduction of high-dimensional data, and the key features of dataset are extracted. Finally, the nonlinear classifier MLP is used to classify the extracted feature data, and the mental health states of samples to be tested are evaluated. The experimental results prove that compared to the existing mental health early warning methods, the method proposed in this paper can provide higher prediction precision and stronger generalization performance.
Learning Probabilistic Automata Using Residuals

A probabilistic automaton is a non-deterministic finite automaton with probabilities assigned to transitions and states that define a distribution on the set of all strings. In general, there are distributions generated by automata with a non-deterministic structure that cannot be generated by a deterministic one. There exist several methods in machine learning that can be used to approximate the probabilities of an automaton given its structure and a finite number of strings independently drawn with respect to an unknown distribution. In this paper, we efficiently construct a probabilistic automaton from a sample by first learning its non-deterministic structure using residual languages and then assigning appropriate probabilities to the transitions and states. We show that our method learns the structure of the automaton precisely for a class of probabilistic automata strictly including deterministic one and give some experimental results to compare the learned distribution with respect to other methods. To this end, we present a novel algorithm to compute the Euclidean distance between two weighted graphs effectively.
DC-LTM: A Data Collection Strategy Based on Layered Trust Mechanism for IoT

A large number of Internet of Things (IoT) devices such as sensor nodes are deployed in various urban infrastructures to monitor surrounding information. However, it is still a challenging issue to collect data in a low-cost, high-quality, and reliable manner through IoT technique. Although the recruitment of mobile vehicles (MVs) to collect urban data has proved to be an effective method, most existing data collection systems lack a trust detection mechanism for malicious terminal nodes and malicious vehicles, which should lead to security vulnerabilities in practice. This paper proposes a novel data collection strategy based on a layered trust mechanism (DC-LTM). The strategy recruits MVs as data collectors of the sensor nodes based on the data value in the city, evaluates the trustworthiness of the data reported by the nodes, and records the results to the cloud data center. Furthermore, in order to make the data collection system more efficient and trust mechanism more reliable, we introduce unmanned aerial vehicles (UAVs) dispatched by data centers to actively verify the core sensor node data and use the core sensor data as baseline data to evaluate the credibility of the vehicles and the trust value of the whole network sensor nodes. Different from the previous strategies, UAVs adopts the DC-LTM method to obtain the node data while actively obtaining the trust value of MVs and nodes, which effectively improves the quality of data acquisition. Simulation results show that the mechanism effectively distinguishes malicious vehicles that provide false data in exchange for payment and reduces the total cost of system recruitment payments. At the same time, the proposed incentive mechanism encourages vehicle to complete the evaluation task and improves the accuracy of node trust evaluation. The recognition rates of false data attacks and flooding attacks as well as the recognition error rate of normal nodes are 100%, 98.9%, and 3.9%, respectively, which improves the quality of system data collection as a whole.
Multi objective optimization model and algorithm of logistics service transaction matching on blockchain platform

The logistics service transaction matching of the blockchain platform is an important part of the transaction. Firstly, the evaluation index system of the logistics service transaction satisfaction degree in the blockchain platform is constructed. According to the evaluation index of the logistics service users and providers, the actual situation of the logistics service transaction and the principle of maximizing the satisfaction degree of the logistics service users and providers, we set the expectation range of both sides and do data standardization, design the calculation method of satisfaction information amount for different expectation data types, build a multi-objective optimization model for logistics service transaction matching on blockchain platform, and seek the matching solution that makes all service users and providers the most satisfaction. Considering the large amount of data in blockchain, the concurrent design of NSGA-II algorithm based on Hadoop is carried out. Through the calculation of the example, in the matching of multi-objective optimization model of logistics service transaction matching on the blockchain platform, when the users and providers of logistics service are more satisfied about the indicators with each other, and then it gets more satisfactory matching results.
Development of Automated Methods for the Domain Ontology Population with the Help of a Virtual Assistant

In the paper, we consider the use of a virtual assistant with a natural language interface for the task of population of the OWL ontology of computer equipment products based on the information provided on the websites of online stores. The practical value of the work is to provide the user with the opportunity to search for the required products simultaneously on several sites based on a search query described in natural language.
The analysis of university teachers’ readiness to implement digital technologies in the educational process (based on a survey of the teaching staff of Foreign Languages Department)

The purpose of this research is to study the teaching staff opinion on the use of digital technologies in the process of teaching foreign languages at a technical university. In the context of education transformation the question of finding and applying new interactive learning technologies becomes especially relevant. The teachers of Foreign Languages Department at Rostov State University pay great attention to this problem. The use of modern mobile applications in foreign language classes for teaching various types of speech activity shows positive results. In particular, the Mind Maps technology has been tested by us in class while teaching lexical topics corresponding to the steering documents of specialties. Its use in the educational process facilitates the perception and memorization of lexical material by students, promotes the development of speech skills, and makes the learning process exciting. It was found that in the course of working on the creation of Mind Maps, students with a high level of digital skills are more successful in overcoming the language barrier, striving to expand the scope and diversify the structure of Mind Maps. The results of a survey of teachers on the effectiveness of using this method in teaching foreign languages as well as the readiness to use digital techniques while working with students are presented. The positive and negative aspects of introducing digital methods into the educational process, the interest of senior teachers to use modern technologies and their active integration into the learning process are revealed. The results can be applied by university professors and school teachers in the process of teaching foreign languages and other courses. The research methods were the method of static description of the material, polling, analysis, comparison and generalization of scientific information.
Design of Barcode Recognition System Based on YOLOV5

Abstract In this paper, based on the research focus of the current logistics field, a deep learning based express single-side bar code extraction algorithm is designed. This algorithm can recognize the original image and read out the information of the package. The algorithm is divided into two parts: bar code localization algorithm and bar code recognition algorithm. To avoid high delay, we use the YOLOV5S network to complete the barcode localization algorithm. First, the YOLOV5S algorithm is used to frame out the barcode, and the bounding Box is cut off. Then, OpenCV's method is used to obtain the deflection Angle of the image. Finally, affine transformation is used to correct the images with deflection. The bar code recognition algorithm is to use ZBAR algorithm to decode the bar code and output the decoded content in the form of string, and finally realize the location and recognition of the bar code on the single side of the express. Finally, through the test of the data set, it can be seen from the experimental results that this paper has accomplished the goal well.
News text classification based on hybrid model of Bidirectional Encoder Representation from Transformers and Convolutional Neural Network

Abstract At present, with the rapid development of Internet technology, text data show massive characteristics. Network media and new media platforms have become an important part of journalism and communication, and network news has become one of the important sources of information. In order to meet the needs of online news reading users and improve the efficiency of personalized news recommendation by content distribution platform, it is urgent to effectively manage and utilize online news. Based on this, this paper studies and implements the algorithm for news text classification, and the goal is to build a classification model for news text with higher classification accuracy. In this paper, BERT and CNN algorithm are combined to classify news texts. The core idea is to send BERT as the embedding layer into the CNN model. The experimental results show that this method is better than the simple BERT model or the simple CNN model.
Cocktail Glass Network: Fast Depth Estimation Using Channel to Space Unrolling

Depth-estimation from a single input image can be used in applications such as robotics and autonomous driving. Recently, depth-estimation networks with UNet encoder/decoder structures have been widely used. In these decoders, operations are repeated to gradually increase the image resolution, while decreasing the channel size. If the upsampling operation at a high magnification can be processed at once, the amount of computation in the decoder can be dramatically reduced. To achieve this, we propose a new network structure, i.e., a cocktail glass network. In this network, convolution layers in the decoder are reduced, and a novel fast upsampling method is used that is known as channel-to-space unrolling, which converts thick channel data into high-resolution data. The proposed method can be easily implemented using simple reshaping operations; therefore, it is suitable for reducing the depth-estimation network. Considering the experimental results based on the NYU V2 and KITTI datasets, we demonstrate that the proposed method reduces the amount of computation in the decoder by half, while maintaining the same level of accuracy; it can be used in both lightweight and large-model-capacity networks.
Developing and Integrating an Augmented Reality App for Teaching and Learning About Enzymes

In this chapter, we report on the development of an augmented reality (AR) app for teaching the working of enzymes . The aim of the study presented was to investigate how teachers and developers can work together in creating technology for education, in this case AR for teaching biology. In the project, four teachers from two secondary schools participated in a Lesson Study (LS) team with educators and developers of teacher training institutes and a professionals specializing in creating applications for virtual and augmented reality. We report on the process of development, the resulting app and lessons as well as on the first experiences in the classroom. The main conclusion on the process concerns the integration between lesson design and app development. Basic preparations on acquainting teachers with the use of AR and its content need to be made before starting the design and development cycle. Regarding the app itself, we identified improvements on the way AR can be a more integrated part of the learning activities.
Gradient Local Binary Pattern For Convolutional Neural Networks

Convolutional neural networks(CNNs) have achieved a performance significantly superior to traditional machine learning methods. However, in the traditional machine learning methods, the feature extraction algorithms are compelling and beneficial for CNNs. This paper introduces the classic feature extraction algorithm gradient local binary pattern(GLBP) to the CNNs. More specially, the GLBP extractor weights will be fixed into the $3\times 3$ sized kernels to construct the GLBP layer to replace the first layer of CNNs. In the GLBP layer, the features extracted by the GLBP kernels will concate or add to the feature process by the convolutional kernels. Through extensive experiments, we demonstrated that the GLBP layer could efficiently improve CNNs performance. When training on the ImageNet dataset, the ResNet18 with GLBP layer obtained 1.19% Top-1 accuracy improvement and 0.87% Top-5 accuracy improvement, respectively.
Understanding Insider Attacks in Personalized Picture Password Schemes

Picture passwords, which require users to complete a picture-based task to login, are increasingly being embraced by researchers as they offer a better tradeoff between security and memorability. Recent works proposed the use of personalized familiar pictures, which are bootstrapped to the users’ prior sociocultural activities and experiences. However, such personalized approaches might entail guessing vulnerabilities by people close to the user (e.g., family members, acquaintances) with whom they share common experiences within the depicted familiar sceneries. To shed light on this aspect, we conducted a controlled in-lab eye-tracking user study (n = 18) focusing on human attack vulnerabilities among people sharing common sociocultural experiences. Results revealed that insider attackers, who share common experiences with the legitimate users, can easily identify regions of their selected secrets. The extra knowledge possessed by people close to the user was also reflected on their visual behavior during the human attack phase. Such findings can drive the design of assistive security mechanisms within personalized picture password schemes.
Nonexistence of some ternary linear codes

We prove the nonexistence of some ternary linear codes of dimension 6, which implies that n3(6,d)=g3(6,d)+2 for d=48,49,66,67,149,150, where g3(k,d)=∑i=0k−1⌈d/3i⌉ and nq(k,d) denotes the minimum length n for which an [n,k,d]q code exists. To prove the nonexistence of a putative code through projective geometry, we introduce some proof techniques such as i-Max and i-Max-NS to rule out some possible weights of codewords.
Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records

Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred.We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter.We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing "STEM" has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952).In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR.
What to Do When Requirements Are Changing All the Time?

In our earlier work we sketched an approach to developing software systems. The goal of this paper is to further illustrate the applicability and use of that approach. Via the practical example of the development of a control system, we illustrate the applicability of our approach to another type of system (other than the usual information system) and the manoeuvrability (‘agility’) of our textual System Sequence Descriptions. We discuss how to deal with situations where requirements are changing all the time (‘agility’ during requirements analyses). We also want to sketch the mental process of going from a simple, naïve software solution towards various more subtle ones, probably inspired/ guided by brainstorms with customers. In this case, it even ends up in a generic system (so, not for one particular user organization only).
The Hierarchical Decision Model of cybersecurity risk assessment

This paper studies the application of the Analytical Hierarchy Process in the assessment of cybersecurity risk and suggests a model based on eight criteria of cybersecurity and three cybersecurity alternatives. The main goal is to present to cybersecurity experts a hierarchical model that provides the confidentiality, integrity and availability of security systems. It can be used to improve cybersecurity in information systems and receive the cost-benefit inquiries.
Automatic Trimap Generation by a Multimodal Neural Network

In many of the existing alpha matting implementations, an intermediate representation called a trimap needs to be created manually. To automate the process, we propose a generic neural network for trimap generation based on saliency map detection. Our model multi-modally learns a saliency map and a trimap simultaneously. Because of this structure, the network focuses on reducing the error of the trimap especially within the areas with high salience. We used both the saliency map detection dataset and the alpha matting dataset to achieve accuracy in extracting subjects from natural images and generating trimaps. Experiments showed that our model could generate trimaps that are almost identical to manually generated ones. The method can also be easily combined with existing alpha matting algorithms.
Research on the Application of AI Anchors in TV Media under the Background of Artificial Intelligence

research-article Research on the Application of AI Anchors in TV Media under the Background of Artificial Intelligence Share on Author: Yi Wang Sejong University, South Korea Sejong University, South KoreaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 238Pages 1–4https://doi.org/10.1145/3469213.3470671Online:18 August 2021Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Comado: Communication System for Ambient Connection between Distance Locations

Current video call systems can facilitate formal conversation, yet they lack the ability to provide users with an informal sense of presence. To this end, we propose Comado, a communication system that creates an ambient connection using online interactive haptic sharing. Outside of face-to-face communication, Comado provides haptic feedback to the desk to convey a sense of presence. From preliminary results, we plan to further improve the design of Comado to be deployed in the wild.