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With this position paper, we want to draw the attention of the community toward theoretical work and practical opportunities that have been overlooked so far regarding concepts, principles, and design knowledge for the user experience of Augmented and Mixed Reality content, I/O devices, interactions, applications, and systems. Despite considerable innovations in commercial products and research prototypes enabling Augmented and Mixed Reality worlds, how to design great user experiences in such worlds has been overall neglected at core, while the information and knowledge currently available to practitioners cover usability aspects mostly. Therefore, we advocate for theoretical foundations of the user experience in Augmented and Mixed Reality and propose several directions for more scientific research in this regard.

We propose new solutions that can efficiently address the problem of malicious memory access from pluggable computer peripherals and microcontrollers embedded within a system-on-chip. This problem represents a serious emerging threat to total-system computer security. Previous work has shown that existing defenses are insufficient and poorly deployed, in part due to performance concerns. In this paper we explore the threat and its implications for system architecture. We propose a range of protection techniques, from lightweight to heavyweight, across different classes of systems. We consider how emerging capability architectures (and specifically the CHERI protection model) can enhance protection and provide a convenient bridge to describe interactions among software and hardware components. Finally, we describe how new schemes may be more efficient than existing defenses.

This companion paper is to support the replication of paper "Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding", which was presented at ACM Multimedia 2020. The supported paper's main purpose was to provide a photogrammetry point cloud-based dataset with hierarchical multilabels to facilitate the area of 3D deep learning. Based on this provided dataset and source code, in this work, we build a complete package to reimplement the proposed methods and experiments (i.e., the hierarchical learning framework and the benchmarks of the hierarchical semantic segmentation task). Specifically, this paper contains the technical details of the package, including file structure, dataset preparation, installation package, and the conduction of the experiment. We also present the replicated experiment results and indicate our contributions to the original implementation.

In this companion paper, we provide the details of the reproducibility artifacts of the paper "Visual Relation of Interest Detection" presented at MM'20. Visual Relation of Interest Detection (VROID) aims to detect visual relations that are important for conveying the main content of an image. In this paper, we explain the file structure of the source code and publish the details of our ViROI dataset, which can be used to retrain the model with custom parameters. We also detail the scripts for component analysis and comparison with other methods and list the parameters that can be modified for custom training and inference.

In our MM'20 paper,, we presented a Kalman filter-based approach for prediction of head motion in 6DoF. The proposed approach was employed in our cloud-based volumetric video streaming system to reduce the interaction latency experienced by the user. In this companion paper, we present the dataset collected for our experiments and our simulation framework that reproduces the obtained experimental results. Our implementation is freely available on Github to facilitate further research.

In this paper we reproduce experimental results presented in our earlier work titled "Describing Subjective Experiment Consistency by $p$-Value P-P Plot" that was presented in the course of the 28th ACM International Conference on Multimedia. The paper aims at verifying the soundness of our prior results and helping others understand our software framework. We present artifacts that help reproduce tables, figures and all the data derived from raw subjective responses that were included in our earlier work. Using the artifacts we show that our results are reproducible. We invite everyone to use our software framework for subjective responses analyses going beyond reproducibility efforts., Comment: Please refer to the original publication: https://dl.acm.org/doi/10.1145/3474085.3477935 Related paper: https://dl.acm.org/doi/abs/10.1145/3394171.3413749 or arXiv:2009.13372

This chapter focuses on cost control by analyzing the intelligent control system of waste paper deinking production line, and finally designs a solution. In the aspect of control index, machine learning has designed a solution that pays less attention to the benefit of a single control loop and focuses on maximizing the overall benefit of the production line. In the aspect of control demand, according to the complexity of waste paper deinking production line, a distributed control solution is designed Finally, according to the dynamic fuzziness in machine learning system, the basic concept of dynamic fuzzy machine learning is constructed by using dynamic fuzzy set The dynamic fuzzy machine learning model is described and the related learning algorithm is given. Finally, a simulation example is given, and satisfactory results are obtained by comparing with the elastic BP algorithm RPROP of neural network.

Containers are a mainstream virtualization technique commonly used to run stateful workloads over persistent storage. In multi-tenant hosts with high utilization, resource contention at the system kernel often leads to inefficient handling of the container I/O. Assuming a distributed storage architecture for scalability, resource sharing is particularly problematic at the client hosts serving the applications of competing tenants. Although increasing the scalability of a system kernel can improve resource efficiency, it is highly challenging to refactor the kernel for fair access to system services. As a realistic alternative, we isolate the storage I/O paths of different tenants by serving them with distinct clients running at user level. We introduce the Danaus client architecture to let each tenant access the container root and application filesystems over a private host path. We developed a Danaus prototype that integrates a union filesystem with a Ceph distributed filesystem client and a configurable shared cache. Across different host configurations, workloads and systems, Danaus achieves improved performance stability because it handles I/O with reserved per-tenant resources and avoids intensive kernel locking. Danaus offers up to 14.4x higher throughput than a popular kernel-based client under conditions of I/O contention. In comparison to a FUSE-based user-level client, Danaus also reduces by 14.2x the time to start 256 high-performance webservers. Based on our extensive experience from building and evaluating Danaus, we share several valuable lessons that we learned about resource contention, file management, service separation and performance stability.

Function-as-a-Service (FaaS) has raised a growing interest in how to "tame" serverless to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional "serverful" computing. To help in this task, we propose MLLess, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLess implements two key optimizations: a significance filter and a scale-in auto-tuner, and leverages them to specialize model training to the FaaS model. Our results certify that MLLess can be 15X faster than serverful ML systems [24] at a lower cost for ML models (such as sparse logistic regression and matrix factorization) that exhibit fast convergence.

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.

The paper titled “Are Automated Debugging Techniques Actually Helping Programmers?” was published in the proceedings of the International Symposium on Software Testing and Analysis (ISSTA) in 2011, and has been selected to receive the ISSTA 2021 Impact Paper Award. The paper investigated, through two user studies, how developers used and benefited from popular automated debugging techniques. The results of the studies provided (1) evidence that several assumptions made by automated debugging techniques did not hold in practice and (2) insights on limitations of existing approaches and how these limitations could be addressed. In this talk, we revisit the original paper and the work that led to it. We then assess the impact of that research by reviewing how the area of automated debugging has evolved since the paper was published. Finally, we conclude the talk by reflecting on the current state of the art in this area and discussing open issues and potential directions for future work.

Our eventual goal regarding this study is to support students with developing paper-writing skill. In order to achieve this goal, we have been trying to find characteristic features of good papers through analyzing educational and other kinds of data. We take conference papers as target data and suppose full/regular papers are good because they are chosen as reviewers evaluate them more valuable to be presented in the conference than other papers. In our series of study, we have been surveying the differences of full and short papers. In this paper, we aim to investigate further differences of them by taking different analysis method. We have been using the numbers of occurrences of each word in full/short papers as the data for analysis. In this paper we use the numbers of full/short papers that contain the word instead of the total numbers of occurrences of words. We define a new index of a word which shows how likely it is used in full or short papers. We discuss its effectiveness by applying it to an experiment of distinguishing full and short papers.

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.

Storm and flood disasters such as typhoons and torrential rains are becoming more intense and frequent. The national government and municipalities must respond to such natural disasters as soon as possible. When the scale of damage is large; however, it takes much time to investigate the severity of damage, and the initial response can be delayed. If we could precisely and rapidly estimate the severity of damage for each city at an early stage, the national government would be able to better support the municipalities, and consequently respond quickly to help citizens. In this paper, we propose a novel approach to estimate the severity of disaster damage within a short time period after a disaster occurs by exploiting real-time population data generated from cellular networks. First, we investigate the relationship between real-time population data and the severity of damage. Then, we design a Graph Convolutional Networks for Disaster Damage Estimation, called D2E-GCN, which fully exploits the directed and weighted characteristics of human mobility graph. We conduct an offline evaluation on real-world datasets including two typhoons that hit Japan. The evaluation results show that the proposed method outperforms baseline methods which do not consider the graph structure of cities, and the proposed method can estimate the severity of damage approximately 48 hours after typhoons passed. Moreover, we find the experimental insight that the estimation performance can be significantly affected by the graph construction method for GCN models.

In this paper, we demonstrate how to conduct OD analysis based on big taxi trajectory data with XStar in an efficient manner. XStar, originally developed by the first author, is a standalone software system dedicated to trajectory-data users with little programming skills and affordable computing devices. Since its release in Jan. 2019, it has received downloads of over 4000 by May 2021.

Blind natural video quality assessment (BVQA), also known as no-reference video quality assessment, is a highly active research topic. In our recent contribution titled "Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features" published in ACM Multimedia 2020, we proposed a two-level video quality model employing statistical temporal features and spatial features extracted by a deep convolutional neural network (CNN) for this purpose. At the time of publishing, the proposed model (CNN-TLVQM) achieved state-of-the-art results in BVQA. In this paper, we describe the process of reproducing the published results by using CNN-TLVQM on two publicly available natural video quality datasets.

Deploying context management systems at a global scale comes with a number of challenges and requirements. We argue that the hypermedia model and the agent-oriented paradigm help achieve the vision of Context-as-a-Service. We categorize challenges according to context processing concerns and use a scenario to exemplify how the proposed architectural principles help overcome the challenges.

The author claimed for the artifact associated with his paper the following ACM Reproducibility badges:(1) Artifact Available,(2) Artifact Evaluated-Functional,(3) Results Reproduced. After an in-depth review process, we agree to assign all the requested badges as we found it to meet the following requirements:i) it is uploaded on a persistent repository, accessible via a DOI; ii) it is well documented, consistent with the presented data, complete of all the necessary software sources and packages, and exercisable; iii) it is exhaustive in the reproduction of all the relevant data of the paper. Some curves in some reproduced plots are truncated, due to the computational limits imposed by the short-term deadline of the review process. Nevertheless, the overall trends are respected, and the curves are supporting the paper's claims.

Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision how interfaces can bring definitions of technical terms and symbols to readers when and where they need them most. We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols. A usability study showed that the tool helps researchers of all experience levels read papers. Furthermore, researchers were eager to have ScholarPhi's definitions available to support their everyday reading., 18 pages, 17 figures, 2 tables. To appear at the 2021 ACM CHI Conference on Human Factors in Computing Systems. For associated video, see https://youtu.be/yYcQf-Yq8B0. v2 changes: expanded discussion of design process and implementation; improved figure design. v3 changes: fixed typo in cell of Table 2; updated HEDDEx and Schwarz-Hearst accuracy in Section 5.3

Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.

New discoveries in science are often built upon previous knowledge. Ideally, such dependency information should be made explicit in a scientific knowledge graph. The Keystone Framework was proposed for tracking the validity dependency among papers. A keystone citation indicates that the validity of a given paper depends on a previously published paper it cites. In this paper, we propose and evaluate a strategy that repurposes rhetorical category classifiers for the novel application of extracting keystone citations that relate to research methods. Five binary rhetorical category classifiers were constructed to identify Background, Objective, Methods, Results, and Conclusions sentences in biomedical papers. The resulting classifiers were used to test the strategy against two datasets. The initial strategy assumed that only citations contained in Methods sentences were methods keystone citations, but our analysis revealed that citations contained in sentences classified as either Methods or Results had a high likelihood to be methods keystone citations. Future work will focus on fine tuning the rhetorical category classifiers, experimenting with multiclass classifiers, evaluating the revised strategy with more data, and constructing a larger gold standard citation context sentence dataset for model training.

Based on the concept of explanation sheets, we present an approach to make spreadsheets easier to understand and thus easier to use and maintain. We identify the notion of explanation soundness and show that explanation sheets which conform to simple rules of formula coverage provide sound explanations. We also present a practical evaluation of explanation sheets based on samples drawn from widely used spreadsheet corpora and based on a small user study.In addition to supporting spreadsheet understanding and maintenance, our work on explanation sheets has also uncovered several general principles of explanation languages that can help guide the design of explanations for other programming and domain-specific languages., This work is partially supported by the National Science Foundation under the grant CCF-1717300. Work financed by European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization COMPETE 2020 Programme and by National Funds through the Portuguese funding agency FCT project POCI-01-0145-FEDER-016718 and NOVA LINCS UID/CEC/04516/2013, and by FLAD project 233/2014.

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining., to appear in ISSTA 2021

FT-Matrix is a typical vector-SIMD architecture that refines the cooperation between scalar and vector units. This approach is widely used in digital signal processing, high-performance computing, and artificial intelligence, among other fields. FT-Matrix currently adopts C vector extension as the main programming model, improving the utilization efficiency of SIMD by providing explicit vector extension API. Moreover, it is difficult to efficiently transplant parallel programs (OpenCL, CUDA) adopted by users. This paper proposes an automatic mapping and code optimization method for OpenCL kernels on FT-Matrix architecture. The proposed approach solves these challenges by means of work item coalescing, slicing and rotation, and instruction-level code optimization. Preliminary results show that our method can achieve high performance and good hardware utilization for OpenCL kernels, as well as decreasing the programming difficulty on FT-Matrix.

Over 100 fatalities and more than 8000 injuries are reported on average every day in the US caused by motor vehicle accidents. In order to provide drivers a safer travel plan, we present a machine learning powered risk profiler for road segments using geo-spatial data. We built an end-to-end pipeline to extract static road features from map data and combined them with other data such as weather and traffic patterns. Our approach proposes novel methods for data pre-processing and feature engineering using statistical and clustering methods. Our model achieves significant performance improvement for risk prediction using hyper-parameter optimization (HPO) and the open source AutoGluon library to optimize the ML model. Finally, an enduser visualization interface is developed in the form of interactive maps. The results indicate 31% improvement in model performance compared to baseline when model is applied to a new geo location. We tested this approach on six major cities in the US. The findings of this research will provide users a tool to quantitatively assess accident risk at road segment level.

Trusted execution as offered by Intel's Software Guard Extensions (SGX) is considered as an enabler to protect the integrity and confidentiality of stateful workloads such as key-value stores and databases in untrusted environments. These systems are typically long running and require extension mechanisms built on top of dynamic loading as well as hot-patching to avoid downtimes and apply security updates faster. However, such essential mechanisms are currently neglected or even missing in combination with trusted execution. We present sgx-dl, a lean framework that enables dynamic loading of enclave code at the function level and hot-patching of dynamically loaded code. Additionally, sgx-dl is the first framework to utilize the new SGX version 2 features and also provides a versioning mechanism for dynamically loaded code. Our evaluation shows that sgx-dl introduces a performance overhead of less than 5% and shrinks application downtime by an order of magnitude in the case of a database system.

We explored the paper augmentation technique where a tablet computer is placed under the paper to modify its content appearance and create the illusion of a static object appearing perceptually dynamic. Our approach is based on the Deformation Lamps (DL) technique commonly used with front projection. However, we applied it to rear projection, which removes the need for a fixed hardware setup or handling and wearing dedicated hardware. The key challenge was to ensure the appropriate mixing of light reflecting from the paper (to see what is printed on the paper) and the light passing though the paper (to see what the tablet screen is projecting) since (i) a tablet screen is a relatively weak light source when being used in rear projection and (ii) commonly, paper has high opacity so the content from one side is not visible on the other side. In order to achieve the appropriate mixing of light, we reduced the opacity of the paper by perforating it (thus allowing more light to pass through), and modified the algorithm for generating dynamic luminance animation by adding gamma and brightness correction. The outcomes provide a guide on how to modify the paper and the algorithm in order to achieve the DL effect with rear projection using a tablet computer.

Paper-based digital microfluidic biochips (P-DMFBs) have recently attracted great attention for its low-cost, in-place, and fast fabrication. This technology is essential for agile bio-assay development and deployment. P-DMFBs print electrodes and associate control lines on paper to control droplets and complete bio-assays. However, P-DMFBs have following issues: 1) control line interference may cause unwanted droplet movements, 2) avoiding control interference degrades assay performance and routability, 3) single layer fabrication limits routability, and 4) expensive ink cost limits low-cost benefits of P-DMFBs. To solve above issues, this work proposes an interference-free design methodology to design P-DMFBs with fast assay speed, better routability, and compact printing area. The contributions are as follows: First, we categorize control interference into soft and hard. Second, we identify only soft interference happens and propose to remove soft control interference constraints. Third, we propose an interference-free design methodology. Finally, we propose a cost-efficient ILP-based fluidic design module. Experimental results show proposed method outperforms prior work [14] across all bio-assay benchmarks. Compared to previous work, our cost-optimized designs use only 47%~78% area, gain 3.6%~16.2% more routing resources, and achieve 0.97x~1.5x shorter assay completion time. Our performance-optimized designs can accelerate assay speed by 1.05x~1.65x using 81%~96% printed area.

Paper-based digital microfluidic biochips (P-DMFBs) have emerged as a safe, low-cost, and fast-responsive platform for biochemical assays. In P-DMFB, droplet manipulations are executed by the electrowetting technology. In order to enable the electrowetting technology, pattern arrays of electrodes and control lines are coated on paper with a hydrophobic Teflon film and dielectric parylene-C film. Different from traditional DMFBs, the manufacturing of P-DMFBs is efficient and inexpensive since the electrodes and control lines are printed on photo paper with an inkjet printer. Active paper-based hybridized chip (APHC) is a type of P-DMFBs that has open and closed part. APHC enjoys more convenience than common P-DMFBs since it has no need to fabricate and maintain the micro gap between glass and paper chip, which requires highly delicate treatments. However, the pattern rails of electrodes in APHCs are denser than traditional P-DMFBs, which makes existing electrode routing algorithm fail in APHCs. To deal with the challenge in electrode routing of APHCs, this paper proposes a multi-commodity network flow-based routing algorithm, which simultaneously maximizes the routability and minimizes the total wire length of control lines. The multi-commodity flow model can utilize the pin-sharing between electrodes, which can improve routability and reduce the detour of routing lines. Moreover, the activation sequences of electrodes are considered, which guarantees that the bioassay will not be interfered with after pin-sharing. The proposed method achieves a 100% successful routing rate on real-life APHCs while other electrode routing method cannot solve the electrode routing of APHCs successfully.

Using machine learning (ML) services, both service customers and providers need to monitor complex contractual constraints of ML service that are strongly related to ML models and data. Therefore, establishing and monitoring comprehensive ML contracts are crucial in ML serving. This paper demonstrates a set of features and utilities of the QoA4ML framework for ML contracts.

The current demand for apps has greatly moved the software development market. Due their massive distribution and use by thousands of different users, special attention needs to be paid to the interfaces design and the ways of interaction with these apps. An efficient way to evaluate these aspects in advance is prototyping. However, prototyping is often overlooked because of the additional cost it has put into the app development process and the time that may require it. The aim of this paper is to present a prototyping process for apps based on the paper construction of interface drafts and their interactions. Later these drafts are captured by the camera of a smartphone, generating a version of the app that can be used by the client to evaluate the interactions. This process also generates a digital version of the project based on a Domain Specific Language (DSL), whereby this prototype can be refined, generating, if necessary, a new executable version for evaluation or a new paper model to be presented and modified by the client. The validation of the proposal is done through a case study, in which an application is modeled on paper through a stencil for Android interface. Thus, generating the digital version of the project as well as the codes that implement the screens and the navigation structures between them.

Developers of applications relying on cryptographic libraries can easily make mistakes in their use. Popular dynamic languages such as JavaScript make testing or verifying such applications particularly challenging. In this paper, we present our ongoing work toward a methodology for automatically checking security properties in JavaScript code. Our main idea is to attach security annotations to values that encode properties of interest. We illustrate our idea using examples and, as an initial step in our line of work, we present a formalization of security annotations in a statically typed lambda calculus. As next steps, we will translate our annotations to a dynamically typed formalization of JavaScript such as λJS and implement a runtime checked type extension using code instrumentation for full JavaScript.

A Trusted Execution Environment (TEE) provides an isolated environment for sensitive workloads. However, the code running in the TEE may contain vulnerabilities that could be exploited by attackers and further leveraged to corrupt the TEE. The increasing buggy code inside the TEE concerns the security of the entire TEE. In this position paper, we present the challenges towards securing trusted execution environments and potential mitigation.

IPIN Indoor Positioning and Indoor Navigation is the top academic conference, this paper intends to address the developing history of IPIN conference papers from a bibliometrical perspective in order to analyze and grasp the status and trend of IPIN conference papers as a whole. 891 records were retried from the Web of Science and used as the dataset in this study. Bibliometrical methods were the employed to conduct an analysis from the aspects including annual publication numbers, research institutions, core authors, and keywords. The core authors and keywords were analyzed diachronically. We especially focus on the evolution of concepts in the papers of IPIN. It has been found that the papers in IPIN is at a stable developing stage. There are mainly 8 research areas, including indoor positioning system, human bodies, coarse model, laser spot, etc. Taken together with our data indicating, it can be found that since 2015, using big data and machine learning to solve problems is a new research hotspot in IPIN field. And 20% of the keywords have been used throughout the whole developing stage. This study provides a way to identify the emerging and dying concepts of a field from massive knowledge concepts, which could be of great significance to the development of IPIN research.

Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach or an application is much more complicated. Many publications leverage multiple modalities to convey their findings and spread their ideas - they include pseudocode, tables, images and diagrams in addition to text, and often make publicly accessible their implementations. It is important to be able to represent and query them as well. We utilize RDF Knowledge graphs (KGs) to represent multimodal information and enable expressive querying over modalities. In our demo we present an approach for extracting KGs from different modalities, namely text, architecture images and source code. We show how graph queries can be used to get insights into different facets (modalities) of a paper, and its associated code implementation. Our innovation lies in the multimodal nature of the KG we create. While our work is of direct interest to DL researchers and practitioners, our approaches can also be leveraged in other scientific domains.

To support the replication of "Instance of Interest Detection", which was presented at MM'19, this companion paper provides the details of the artifacts. Instance of Interest Detection (IOID) aims to provide instance-level user interest modeling for image semantic description. In this paper, we explain the file structure of the source code and publish the details of our IOID dataset, which can be used to retrain the model with custom parameters. We also provide a program for component analysis to help other researchers to do experiments with alternative models that are not included in our experiments. Moreover, we provide a demo program for using our model easily.

This companion paper supports the experimental replication of paper "Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network", which is presented at ACM Multimedia 2019. We provide the software package for replicating the implementation of Multi-Layered Comparison Network (MCN), as well as the Polyvore-T dataset and baseline methods compared in the original paper. This paper contains the guides to reproduce the experiment results including outfit compatibility prediction, outfit diagnosis and automatic outfit revision.

We propose a typesafe abstraction to tensors (i.e. multidimensional arrays) exploiting the type-level programming capabilities of Scala through heterogeneous lists (HList), and showcase typesafe abstractions of common tensor operations and various neural layers such as convolution or recurrent neural networks. This abstraction could lay the foundation of future typesafe deep learning frameworks that runs on Scala/JVM.

Named entity recognition has been extensively studied in the past decade. The state-of-the-art models, trained on general text such as Wikipedia articles and newsletters, have achieved F_1>0.90. Entity types are focused on people, location, organization, etc. However, entity recognition from domain-specific text, in particular research papers, is still challenging. In this paper, we perform a comparative study of sequence tagging (ST) methods on this task using a manually curated corpus from biomedical papers on Lyme disease. Each model we compare consists of a ST and a non-ST classification component. In this pilot study, we freeze the non-ST classifier to study how the ST component performs with variants of the conditional random field (CRF) and bidirectional long short-term memory (BiLSTM). The results shed light on the importance of pre-trained word embeddings such as ELMo and the residual unit. The attention mechanism and enriched features do not seem to boost the performance in recognizing entity mentions and their positions, which is likely to be caused by the relatively small training sample. We plan to improve the model by increasing the training corpus size and trying different combinations of features.

Biological systems exhibit extraordinary levels of diversity. Embodied behaviours are the emergent property of many loosely-coupled parallel processes, from the microscopic level, e.g. materials, to more abstracted levels, e.g. organs and limbs. We summarise our investigations into using a synthetic methodology, i.e. an understanding-by-building approach, for designing the complex interactions of falling paper shapes. By studying how simple systems such as a falling paper shape behave, we can analyse the specific characteristics of the interaction between morphology and the environment, and how this leads to programmable non-trivial behaviours. We present current results and discuss the implications on the future of design in robotics.

Nowadays, recommendation systems play an indispensable role in many fields, including e-commerce, finance, economy, and gaming. There is emerging research on publication venue recommendation systems to support researchers when submitting their scientific work. Several publishers such as IEEE, Springer, and Elsevier have implemented their submission recommendation systems only to help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an effective recommendation system for paper submission. With the input data (the title, the abstract, and the list of possible keywords) of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize deep learning models to build an efficient recommendation engine for the proposed system. Finally, we present the User Interface (UI) and the architecture of our paper submission recommendation system for later usage by researchers.

Verification techniques have been applied to the design of secure protocols for decades. However, relatively few efforts have been made to ensure that verified designs are also implemented securely. Static code verification techniques offer one way to bridge the verification gap between design and implementation, but require substantial expertise and manual labor to realize in practice. In this short paper, we propose black-box runtime verification as an alternative approach to extend the security guarantees of protocol designs to their implementations. Instead of instrumenting the complete protocol implementation, our approach only requires instrumenting common cryptographic libraries and network interfaces with a runtime monitor that is automatically synthesized from the protocol specification. This lightweight technique allows the effort for instrumentation to be shared among different protocols and ensures security with presumably minimal performance overhead.

The development and evaluation of prototypes is an important part of game development. This study aimed to establish whether the form factor, either paper or an iPad, would affect children's ratings of a prototype game. The 42 participants were aged between 7 and 11 and used the Fun Toolkit to measure user experience. The results showed that the form factor had little influence over their overall ratings of the game concept. However, the children rated the aesthetics higher on the paper version compared to the iPad, despite the graphics being identical, suggesting a potential effect of the form factor on this aspect of the prototype. Participants were also asked if they would pay to download the game as a measure of engagement, but their comments indicated that parental controls might influence the results when evaluating monetary transactions with children.

In this study, we evaluate a new electronic mobile application, PGA (Programming Grading Assistant). It was designed to make grading paper-based exams easier for graders and professors. Not only does it facilitate grading, but PGA also provides students with more meaningful (semantic) feedback than non-PGA grading. We outline the PGA grading process, the execution of a user study, and the results from that study that prove the effectiveness of PGA technology. Other results indicate that PGA awarded grades are equivalent to red-pen awarded grades and that PGA grades are more consistent across the board.

Cybersecurity is now more important than ever, and so is education in this field. However, the cybersecurity domain encompasses an extensive set of concepts, which can be taught in different ways and contexts. To understand the state of the art of cybersecurity education and related research, we examine papers from the ACM SIGCSE and ACM ITiCSE conferences. From 2010 to 2019, a total of 1,748 papers were published at these conferences, and 71 of them focus on cybersecurity education. The papers discuss courses, tools, exercises, and teaching approaches. For each paper, we map the covered topics, teaching context, evaluation methods, impact, and the community of authors. We discovered that the technical topic areas are evenly covered (the most prominent being secure programming, network security, and offensive security), and human aspects, such as privacy and social engineering, are present as well. The interventions described in SIGCSE and ITiCSE papers predominantly focus on tertiary education in the USA. The subsequent evaluation mostly consists of collecting students' subjective perceptions via questionnaires. However, less than a third of the papers provide supplementary materials for other educators, and none of the authors published their dataset. Our results provide orientation in the area, a synthesis of trends, and implications for further research. Therefore, they are relevant for instructors, researchers, and anyone new in the field of cybersecurity education. The information we collected and synthesized from individual papers are organized in a publicly available dataset., Comment: ACM SIGCSE 2020 conference, 7 pages, 3 figures, 1 table

We present Self-powered Paper INterfaces (SPIN) combining folding paper creases with triboelectric nanogenerator (TENG). Embedding TENG into paper creases, we developed a design editor and set of fabrication techniques to create paper-based interfaces that power sensors and actuators. Our SPIN design editor enables users to design their own crease pattern by changing parameters, embed power generating modules into the design, estimate total power generation, and export the files. Then following the fabrication instructions, users can cut and crease materials, and assemble them to build their own interfaces. We employ repetitive push-and-pull based embodied interactions with the mechanism of paper creases and demonstrate four application examples that show new expressive possibilities applying different scales of embodied interactions.

While there are several frameworks for CNN inference on mobile GPUs, they do not achieve real-time processing for the most of the CNNs that aim at reasonable accuracy since they all employ kernel-by-kernel execution model and do not effectively support INT8 quantization yet. In this paper, we reveal that mobile GPUs suffer from large kernel launch overhead unlike server GPUs, and then propose an on-device deep learning inference framework that can achieve real-time inference of CNNs on mobile GPUs by removing kernel launch overhead and by effectively exploiting INT8 quantization. We have evaluated the proposed framework with a state-of-the-art CNN based face detector (RetinaFace), and observed up to 2.01X of speedup compared to ARM Compute Library (ACL) on a commodity smartphone.

This paper proposes a method that analyzes the tendency of the paper for the originality detection of papers. This method is aiming at the behavior of idea plagiarism. The plagiarism detection task can be divided into three steps which are retrieval of candidate source document set, selection of source document set and post processing. The texts are modeled by the topic model and then are divided into several topic blocks by combining the topic modeling with the classical text segmentation method TextTiling. Then, the topic blocks which may be the source block of plagiarism are filtered out by calculating topic similarities among these topic fragments. On this basis, topic words of the topic block to be measured are extracted and its idea tendency aiming at each topic word are analyzed by the sentiment analysis method based on the dictionary. And further the source topic block set is filtered out based on the idea tendency Finally, a method of evaluating the original degree of the text is given for the intelligent plagiarism detection by the fusion of topic similarity and the result of the analysis of idea tendencies. Through experimental verification, this method can effectively evaluate the originality of the paper and has a good accuracy.

A device that can measure the amount of paper, display it, and announce it by voice is designed. The hardware part of the device consists of two metal plates, a capacitor voltage detection circuit and a single-chip microcomputer; the software part uses the least square method for linear fitting, which reduces the amount of data required for system self-calibration, and uses statistical principles to reduce linear errors, which improves the accuracy of discriminating the number of sheets of paper.

With the spread of electronic devices such as smartphones and tablets, the transfer of electronic information such as images and videos has become commonplace. To exchange electronic information using e-mail or social network services a sender needs to know the recipient's contact information. However, if the recipient is a newly-encountered person or an ad hoc partner, a significant number of them are reluctant to exchange contact details. To address this issue, we propose an interaction method for transferring electronic information by tearing a sheet of paper. This is an approach that utilizes the fact that when a piece of paper is torn into two pieces, the features of the torn edges of the two pieces match.