Reviewerly/exHarmony huggingface dataset:
High-precision video extensometer based on a simple dual field-of-view telecentric imaging system

We present a novel dual field-of-field (dual-FOV) video extensometer for high-precision tensile/compressive strain measurement in material testing. The present dual-FOV video extensometer, based on the combined use of a bilateral/object-space telecentric imaging system and a cube prism, can orthogonally capture two separated FOVs on the sample surface. For any two gauge points selected in the two separated FOVs, the gauge length can be theoretically computed from the systematic parameters of the imaging system. By tracking the movement of the gauge points using advanced subset-based digital image correlation (DIC) algorithm, temporal changes of the gauge length, and consequently the average strains of the test sample can be obtained. Due to the enlarged gauge length, logitudinal strains can be measured with higher sensitivity and precision compared with previously reported video extensometers that use only a telecentric lens. The precision, accuracy, and practicability of the proposed video extensometer are validated by real tensile tests.
Linear approximation fuzzy model for fault detection in cyber-physical system for supply chain management

The paper’s goal is to propose an IoT assisted Cyber-Physical System with a fault detection technique using a fuzzy algorithm for the supply chain management (SCM). Mathematical models are important for detecting faults. Devicescan contribute to device vulnerabilities. Propertiesof the components in an embedded application can identify defective components in cyber systems of SCM. In this paper linear approximation Boolean fuzzifier model can detect faults in cyber systems of SCM. The rough-set approximation principle with the fuzzy membership functions not only eliminates the ambiguity in the detection method, whereas it helps to classify faulty components in IoT assisted Cyber-Physical System.
Hardware-Based Domain Virtualization for Intra-Process Isolation of Persistent Memory Objects

Persistent memory has appealing properties in serving as main memory. While file access is protected by system calls, an attached persistent memory object (PMO) is one load/store away from accidental (or malicious) reads or writes, which may arise from use of just one buggy library. The recent progress in intra-process isolation could potentially protect PMO by enabling a process to partition sensitive data and code into isolated components. However, the existing intra-process isolations (e.g., Intel MPK) support isolation of only up to 16 domains, forming a major barrier for PMO protections. Although there is some recent effort trying to virtualize MPK to circumvent the limit, it suffers large overhead. This paper presents two novel architecture supports, which provide 11 - 52 × higher efficiency while offering the first known domain-based protection for PMOs.
A Survey of Methodology in Self-Adaptive Systems Research

Major research venues on autonomic and self-adaptive systems have been active for 16 years, exploring and building on the seminal vision of autonomic computing in 2003. We study the current trajectory and progress of the research field towards this vision, surveying the research questions that are asked by researchers and the methodological practice that they employ in order to answer these questions. We survey contributions under this lens across the three main venues for primary research in autonomic and self-adaptive systems work: ICAC, SASO, and SEAMS. We examine the last three years of contributions from each venue, totaling 210 publications, to gain an understanding of the dominant current research questions and methodological practice - and what this shows us about the progress of the field. Our major findings include: (i) most research questions still focus one level below the highest autonomy level vision; (ii) methodological practice is split almost evenly between real-world experiments and simulation; (iii) a high level of positive results bias exists in publications; and (iv) there are low levels of repeatability across most contributions.
Deep Multiple Instance Learning for Landslide Mapping

In this letter, a novel neural network (CDMI-Net) that combines change detection and multiple instance learning (MIL) is proposed for landslide mapping. After obtaining a score map of landslides provided by the network, the final binary map is generated by fast postprocessing. The benefits of the proposed method are threefold. First, using the MIL framework, the network is trained only by the scene-level samples and it reduces the need for pixel-level samples. Second, a change-detection network architecture using a two-stream U-Net with shared weights is designed to learn the deep features of the landslide from the two-period aerial images, reducing the false-positive results. Third, integrating a gated attention-based pooling layer and a fast level-set evolution algorithm can finally produce the pixel-level results. Experimental results show that the proposed CDMI-Net achieves comparable and even better performance on the testing image pairs than all other methods and has great potential for the landslide mapping application.
Privacy-Aware Autonomous Valet Parking: Towards Experience Driven Approach

Driverless parking, an influential application of Mobility as a Service (MaaS) model, is one of the clear early benefits for autonomous vehicles, given often narrow spaces and multiple potential hazards (such as pedestrians stepping out from in between other vehicles). In recent years, real momentum has been building up for designing automated parking models for vehicles. However, in such an autonomous parking design, location privacy and identity privacy issues are always overlapping due to the improper sharing of data. Most existing studies barely investigate and poorly address such privacy issues. Motivated by this, we develop (and evaluate) an experience-driven, secure and privacy-aware framework of parking reservations for automated cars. Our idea of using differential privacy with zero-knowledge proof provides both security and privacy guarantees to users. Furthermore, the performance of the developed model is enhanced by exploiting reinforcement learning approach such that the utility of the system and the parking reservation rate can be maximized. Extensive evaluation demonstrates the superiority of the proposed model.
A Survey on Privacy Issues in Mobile Social Networks

Mobile Social Networks (MSNs) are a category of social networks that have features of both traditional OSNs and Location-Based Social Networks (LBSNs). MSNs provide users with an opportunity to explore social media, connect with people nearby, or with those sharing similar interests. The ease with which people can connect and use these networks to obtain information, share user-centric data, and request services on the go through their mobile devices as long as they have access to the internet has made MSNs omnipresent. Due to the widespread nature of MSNs and the lack of effective privacy-preserving architecture makes them a target for several attacks by adversaries and the users of the networks are completely vulnerable to these privacy breaches. With the advancements in technology, the number of attacks and their possible solutions is complex and expansive. Therefore, there is a need to properly categorize them to understand the intricacies of different attacks and the concepts used by researchers to devise different solutions. This survey on privacy issues in MSNs serves as a way to provide an introduction to MSNs. We categorize privacy in MSN on the basis of the aspect they effect and summarize the different threats in each category. Furthermore, we provide an elaborate classification of the various privacy-preserving solutions proposed in recent research works, under each category of privacy. We also review different datasets and data generation tools that can be used by future researchers in this field.
Learning-Driven Interference-Aware Workload Parallelization for Streaming Applications in Heterogeneous Cluster

In the past few years, with the rapid development of CPU-GPU heterogeneous computing, the issue of task scheduling in the heterogeneous cluster has attracted a great deal of attention. This problem becomes more challenging with the need for efficient co-execution of tasks on the GPUs. However, the uncertainty of heterogeneous cluster and the interference caused by resource contention among co-executing tasks can lead to the unbalanced use of computing resource and further cause the degradation in performance of computing platform. In this article, we propose a two-stage task scheduling approach for streaming applications based on deep reinforcement learning and neural collaborative filtering, which considers fine-grained task division and task interference on the GPU. Specifically, the Learning-Driven Workload Parallelization (LDWP) method selects an appropriate execution node for the mutually independent tasks. By using the deep Q-network, the cluster-level scheduling model is online learned to perform the current optimal scheduling actions according to the runtime status of cluster environments and characteristics of tasks. The Interference-Aware Workload Parallelization (IAWP) method assigns subtasks with dependencies to the appropriate computing units, taking into account the interference of subtasks on the GPU by using neural collaborative filtering. For making the learning of neural network more efficient, we use pre-training in the two-stage scheduler. Besides, we use transfer learning technology to efficiently rebuild task scheduling model referring to the existing model. We evaluate our learning-driven and interference-aware task scheduling approach on a prototype platform with other widely used methods. The experimental results show that the proposed strategy can averagely improve the throughout for distributed computing system by 26.9 percent and improve the GPU resource utilization by around 14.7 percent.
Deep learning based wiretap coding via mutual information estimation

Recently, deep learning of encoding and decoding functions for wireless communication has emerged as a promising research direction and gained considerable interest due to its impressive results. A specific direction in this growing field are neural network-aided techniques that work without a fixed channel model. These approaches utilize generative adversarial networks, reinforcement learning, or mutual information estimation to overcome the need of a known channel model for training. This paper focuses on the last approach and extend it to secure channel coding schemes by sampling the legitimate channel and additionally introduce security constraints for communication. This results in a mixed optimization between the mutual information estimate, the reliability of the code and its secrecy constraint. It is believed that this lays the foundation for flexible, generalizable physical layer security approaches due to its independence of specific model assumptions.
SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing

Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.
A recommendation algorithm based on fine-grained feature analysis

Most existing recommender methods have insufficient power to capture or recover fine-grained features of products and user preferences. Knowledge graphs contain considerable information about products and mutual relationships in the world. To analyze the fine-grained features during the process of recommendation, we propose a knowledge-aware collaborative learning framework (KACL), which is the first work to combine users’ historical reviews with knowledge graphs. First, we use a named-entity recognition (NER) system to recognize the named entities that correspond to item features in the unstructured reviews. After that, we use an entity-linking (EL) system to map entities which identified in the first step to the corresponding entity in Wikipedia. Next, we constructed a sub-graph that depends on the extracted entity and the related ones and embedded the sub-graph into a low latent vector space as pretrain item embedding through the knowledge graph embedding model TransR. In the next step, we will fine-tune item embedding through deep learning built by 4 levels of relu and combine collaborative information with pretrain item embedding. And in accordance with the user review behavior, the item embedding will be averagely poled to express the user embedding, we will put the item embedding and the user embedding into the classification model. Finally, we use the KACL to integrate collaborative filtering with entity representation and make recommendations. Experiments are performed on real-world datasets to evaluate the effectiveness of our new method. The results show that the analysis of fine-grained features based on a knowledge graph helps our KACL improve recommender accuracy compared with the selected state-of-the-art models.
Exploring Programmers' API Learning Processes: Collecting Web Resources as External Memory

Modern programming frequently requires the use of APIs (Application Programming Interfaces). Yet many programmers struggle when trying to learn APIs. We ran an exploratory study in which we observed participants performing an API learning task. We analyze their processes using a proposed model of API learning, grounded in Cognitive Load Theory, Information Foraging Theory, and External Memory research. The results provide support for the model of API Learning and add new insights into the form and usage of external memory while learning APIs. Programmers quickly curated a set of API resources through Information Foraging which served as external memory and then primarily referred to these resources to meet information needs while coding.
Towards Designing Conversational Agents for Pair Programming: Accounting for Creativity Strategies and Conversational Styles

Established research on pair programming reveals benefits, including increasing communication, creativity, self-efficacy, and promoting gender inclusivity. However, research has reported limitations such as finding a compatible partner, scheduling sessions between partners, and resistance to pairing. Further, pairings can be affected by predispositions to negative stereotypes. These problems can be addressed by replacing one human member of the pair with a conversational agent. To investigate the design space of such a conversational agent, we conducted a controlled remote pair programming study. Our analysis found various creative problem-solving strategies and differences in conversational styles. We further analyzed the transferable strategies from human-human collaboration to human-agent collaboration by conducting a Wizard of Oz study. The findings from the two studies helped us gain insights regarding design of a programmer conversational agent. We make recommendations for researchers and practitioners for designing pair programming conversational agent tools.
Testing, filtering, and insinuating: Matching and attunement of emoji use patterns as non-verbal flirting in online dating

This study adds to research on digital interactions by exploring the role of emoji use patterns in online dating among young adults. Focus group data suggest emojis play a similar role as non-verbal behaviors do in an offline context, where coordination is used to ascertain mutual interest. The data shows how the degree of synchronization in emoji use patterns (such as frequency, types, and style) is used as a feedback tool to assess a chat partner's romantic fit. Attunement of emoji use patterns between the partners is also used to signal and gage interest, for example by using suggestive emojis to “test” the other partner's interest and intentions, or to establish a shared mood. Conversely, a lack of reciprocity is experienced as awkward or wrong and can produce insecurity and doubt. To understand this back-and-forth process of mutual suggestion and attunement, we propose a modified version of Collins’ interaction ritual model. In this model, we highlight the importance of shared interpretative schemas and interaction scripts, which set normative expectations for how emoji patterns can be used to build sexual or romantic chemistry.
A Historiography of Human–Computer Interaction

This essay examines the state of the history of human–computer interaction (HCI) from multiple fields of study. In many ways, there is a lack of consensus about what we mean by not only HCI but also computer interfaces. I find that the different definitions and approaches found primarily in history and media studies offer many different avenues for scholars to continue exploring how humans and computers relate to each other. A theoretical background on the topic is provided and then historical case studies concerning both computer interfaces and the embodied use of computers are discussed to understand what conversations are held in common and what areas are still left to be explored.
Agile Stage‐Gate Management (ASGM) for physical products

We present a qualitative study of Agile Stage‐Gate Management (ASGM),: a hybrid new product development methodology that combines Agile and Stage‐Gate Management (SGM) approaches for the coordination of new product development. When applied to software projects, Agile is expected to deliver reduced development times, improved resource utilization, and greater financial success. We examine whether ASGM practitioners realize similar outcomes in a sample of global firms developing complex electro‐mechanical products (e.g., automobile components, railway propulsion systems, and medical devices). Our grounded theory approach articulates an understanding of ASGM through extensive interviews of experienced professionals. Our thematic analysis supports many expected benefits (i.e., speed to market, innovation enabling), but also does not encourage others, and reveals new pitfalls that deserve recognition (i.e., resource inefficiency). ASGM is not a panacea for all product development. Overall, physical product firms adopting this method can expect reduced development times and higher levels of innovation but will expend more resources to complete development projects, but a dichotomy exists. Physical product developers using ASGM experience a negative impact on project resource efficiency due to the need for dedicated resources, frequent product demonstrations, and duplicative management structures.
Measuring User Experience Quality of Voice Assistants

Virtual digital assistants are predicted to experience enormous growth in enterprise applications worldwide. Popular voice assistants are integrated into many devices and systems. Measuring the quality of user experience (UX) must, therefore, also be of great interest to evaluate and improve voice assistants. In this article, three scales for measuring the UX aspects of voice assistance systems are presented, which, in combination with a new questionnaire concept, allow these measurements. It is shown with which method the scales have been constructed and analysed, especially for systems with voice user interfaces. The results are three new scales for voice communication that can be combined with the modular questionnaire concept of the UEQ+.
Application of deep learning and target visual detection in english vocabulary online teaching

The corpus software has many functions, such as keyword retrieval, context co-occurrence, word list generation and word frequency statistics. It can quickly and accurately provide various corpus and information, such as word-formation collocation, context, word frequency and so on. In this paper, the author analyzes the application of deep learning and target visual detection in English vocabulary online teaching. Deep learning is a kind of machine learning algorithm which includes multi-layer non-linear mapping and tries to obtain high-level abstract representation of data. By extracting features from information, the identifiable components in the image can be extracted. The results show that the application of corpus in College English vocabulary teaching can promote students’autonomous use of corpus in English vocabulary learning. The simulation experiment improves the performance of the system by choosing parameters, and the classification accuracy is more than 90%. Corpus can enable students to learn real and natural language and master natural collocation. At the same time, corpus can help students understand the semantic and pragmatic norms of words in communication and recognize the characteristics of register variants. Future research can use Map-reduce technology to accelerate the training process, save training time and test more hyperparameters.
Differentially private 1R classification algorithm using artificial bee colony and differential evolution

Classification is an important topic in data mining field. Privacy preserving classification is a substantial subtopic that aims to perform classification of private data with satisfactory accuracy, while allowing sensitive information leakage at minimal level. Differential privacy is a strong privacy guarantee that determines privacy leakage ratio by using ϵ parameter; and enables privacy of individuals whose sensitive data are stored in a database. There exist some differentially private implementations of well-known classification algorithms such as ID3, random tree, random forests, Naïve Bayes, SVM, logistic regression, k-NN etc. Although One Rule (1R) is a simple but powerful classification algorithm, any implementation of differentially private 1R classification algorithm has not been proposed in the literature to our best knowledge. Motivated by this gap, first we propose a differentially private 1R classification algorithm (DP1R), then improve its performance by using metaheuristics that are differential evolution (DE) and artificial bee colony (ABC) in this study. Additionally, we also apply DE and ABC to improve performance of differentially private Naïve Bayes classifier and compare with DP1R. Moreover, DP1R is compared with the state-of-the-art differentially private algorithms such as differentially private SVM, differentially private logistic regression, differentially private ID3, and differentially private random tree on nine publicly available UCI datasets. The experimental results demonstrate that DP1R is an efficient classifier that has very similar accuracy to differentially private SVM which has the best accuracy results, however with respect to running time comparison of the methods, DP1R has the best performance among the all methods.