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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.

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

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.

Natural disasters have significant economic and social impacts. Among them, health disasters affect education possibly more than others. From the ongoing COVID-19 pandemic, we can see it has negative impacts on students’ learning. It appears to affect students from different age groups differently. Students in primary and secondary education seem to suffer more than their counterparts in higher education. In this literature review, we attempt to understand the impacts of COVID-19 pandemic on students’ learning.

Edge computing promises to bring computation close to the end-users to support emergent applications such as virtual reality. However, the computational capacity at the edge of the network is currently limited. To become a pervasive paradigm, edge computing needs highly dispersed decentralized deployments, that, contrary to cloud, cannot benefit from economies of scale. In this situation, crowdsourcing appears attractive - there are plenty of computing devices at the disposal of the general public, and these devices are located exactly where computing power is needed the most - at the edge of the network. Crowdsourcing has been a success maker for scientific computing projects, e.g., SETI@home, or distributed ledger systems empowering decentralized finance. However, as of now, there is no crowdsourced system that addresses the needs of edge computing. In this position paper, we aim to identify the causes of this shortcoming, analyze the potential ways to overcome it, and outline future directions.

Existing approaches to cyber security in the automotive sector are not fit to deliver the resilience required for safe mass deployment of advanced driving functions and intelligent mobility services. This paper promotes an innovative approach to operational cyber resilience, the CyRes methodology, which aims to enable robust and resilient engineering practices in this sector from design to manufacture to operation. CyRes is based on three principles: engineered differences; detecting, understanding and acting on cyber events; and proactive updates. The aim of this short paper is to raise awareness of the problems and the many intellectual challenges in this particular sector. CyRes is an exciting opportunity for engineers and computer scientists to transfer widely studied, mature methods, such as those developed by the runtime verification community and the self-adaptive systems community, for cyber resilience.

What if things had a voice? What if we could talk directly to things instead of using a mediating voice interface such as an Alexa or a Google Assistant? In this paper, we share our insights from talking to a pair of boots, a tampon, a perfume bottle, and toilet paper among other everyday things to explore their conversational capabilities. We conducted Thing Interviews using a more-than-human design approach to discover a thing’s perspectives, worldviews and its relations to other humans and nonhumans. Based on our analysis of the speculative conversations, we identified some themes characterizing the emergent qualities of people’s relationships with everyday things. We believe the themes presented in the paper may inspire future research on designing everyday things with conversational capabilities at home.

The large-scale Bluetooth low energy (BLE) location-based services (LBS) are challenging due to the requirement of additional Bluetooth beacons, which inevitably incur tremendous hardware and maintenance cost. To alleviate this issue, this work presents WiBeacon which repurposes ubiquitously deployed WiFi access points into virtual beacons via cross-technology communication (CTC). WiBeacon only requires moderate software updates in APs, thus enabling fast deployment with zero additional hardware and also low maintenance cost via the remote Internet access. We implement it on COTS WiFi APs and evaluate it in various scenarios including a real-world commercial BLE LBS application as the pilot study. During this two-week pilot study, our WiBeacon provides reliable LBS, e.g., as robust as conventional BLE beacons, for 512 users with 150 types of smartphones. The full paper of this work [2] was published in MobiCom 2021.

In industrialized wine production, brewing and aging are two key steps. These two processes require the liquors to be bottled for a long time, sometimes more than ten years. The liquor is vulnerable and highly susceptible to microbial contamination during storage, causing undetectable deterioration. During the production process, wineries control the indoor temperature and carbon dioxide concentration to slow down other microorganisms' reproduction speed. These methods, however, do not prevent pathogenic microorganism growth. Currently, microbial culture methods are not suitable for real-time liquor quality monitoring in wineries. Therefore, we have designed a lightweight monitoring system called Lili, which uses light signals to monitor real-time liquor quality changes. Lili detects the changes in surface tension and absorption spectrum caused by microbial metabolites and growth during deterioration. Lili employs eight LEDs and one photodiode to achieve fine-grained surface tension and absorption spectrum measurements. By analyzing these changes, Lili realizes real-time quality monitoring. In this paper, the characteristic offset degree measurement and the absorption spectrum dimension expansion are two critical technologies. In addition, we implemented countermeasures against ambient light noise and sloshing interference. Lili's surface tension and absorption spectrum measurement errors are only 0.89 mN/m and 2.4%, respectively, making it useful to identify the contamination duration, microorganism content and microorganism composition. These two data points can be used to determine potential issues with liquor quality when the liquor becomes health-threatening or even just contaminated, with an accuracy of 97.5%.

Despite the increasing concerns on sustainability issues worldwide, IoT devices are still being designed unsustainably, and often end up as e-waste in landfill after a very short lifespan. This state of affairs is alarming, as we expect hundreds of billion connected devices in a few years, and calls for solutions to help maximizing the usable lifetime of IoT systems. In this paper, we review this problem in detail, and argue that legacy devices using outdated wireless technologies could make use of cross-technology communication to directly interact with newer IoT products, thereby increasing their durability. We also summarize our recent efforts in this domain and highlight how they could help in designing sustainable IoT systems.

5G offers a slew of new features and capabilities to support a whole gamut of new applications. On the other hand, 5G new radio (NR), especially, high-band mmWave radio, also poses new challenges, as shown by recent measurement studies of commercial 5G services. In order to effectively support new classes of application such as extra low-latency and/or high-bandwidth applications, we argue that truly cross-layer network-application integration that exposes application semantics to enable 5G and beyond 5G (B5G) networks to make intelligent decisions, e.g., for dynamic radio resource allocation, is needed. Unfortunately the existing 5G flow-based framework is inadequate to support such cross-layer integration. We therefore advocate a software-defined, fine-grained QoS framework. We use ultra-high resolution (UHR) volumetric video streaming as a use case and conduct very preliminary experiments to demonstrate the potential benefits of the proposed framework. This position paper serves as a strawman to call for new intelligent architectural designs for B5G networks and next-generation wireless systems.

Healthy online discourse is becoming less and less accessible beneath the growing noise of controversy, mis- and dis-information, and toxic speech. While IR is crucial in detecting harmful speech, researchers must work across disciplines to develop interventions, and partner with industry to deploy them rapidly and effectively. In this position paper, we argue that both detecting online information disorders and deploying novel, real-world content moderation tools is crucial in promoting empathy in social networks, and maintaining free expression and discourse. We detail our insights in studying different social networks such as Parler and Reddit. Finally, we discuss the joys and challenges as a lab-grown startup working with both academia and other industrial partners in finding a path toward a better, more trustworthy online ecosystem.

When faced with an information need, different users are likely to issue different queries. A renewed interest in these user query variations has resulted in a number of collections, tasks, and studies which utilize multiple queries for each topic. The most commonly applied technique for generating query variations is to show a short backstory to a pool of crowdworkers, and ask each of them to provide a keyword query that they would expect to provide more information pertaining to the backstory. In this short paper we explore whether the length of the backstory and the mode in which the backstory is conveyed to crowdworkers affect the resulting queries. Our experiments demonstrate that both the length of the backstory and the mode in which the backstory is delivered influence the resultant query variations; and that there is a consequent downstream implication in terms of forming the judgment pools necessary to assess systems in the presence of query variations.

There are ethical concerns surrounding how youth interact with biowearable technology and the potential effects it has on their psychological and physiological health. We need to give youth the tools to critically reflect and explore ethical issues surrounding biowearables in order for them to make informed decisions about how they interact with them. To address this, we developed the Biotech Ethics cards as part of a critical making workshop. They are a set of design cards designed to scaffold critical reflection during a critical making workshop where youth prototype a biowearable from a kit. We focus this short paper on the requirements, initial design and revisions we made after studying card use in our workshop. We identified key design elements that are important in the cards and that may generalize to the design of other card sets meant to be integrated into a critical making process.

Background: Software engineering research articles should make precise claims regarding their contribution, so that practitioners can decide when they might be interested and researchers can better recognize (1) whether the given research is valid, (2) which published works to use as stepping stones for their own research (and which not), and (3) where additional research is required. In particular, articles should spell out what assumptions were made at each research step. Question: Can we identify recurring patterns of assumptions that are not spelled out? Method: This is a position paper. It formulates impressions, but does not present concrete evidence. Results: Assumptions that are wrong or assumptions that are risky and not explicit threaten the integrity of the scientific record. There are several recurring types of such assumptions. The frequency of these problems is currently unknown. Conclusion: The software engineering research community should become more conscious and more explicit with respect to the assumptions that underlie individual research works.

High-Performance Computing (HPC) is at an inflection point in its evolution. General-purpose architectures approach limits in terms of speed and power/energy, requiring the development of specialized architectures to deliver accelerated performance. Additionally, the arrival of new user communities and workloads---including machine learning, data analytics, and quantum simulation---increases the breadth of application characteristics we need to support, putting pressure on the complexity of the architectural portfolio. At the same time, data movement has been identified as a main culprit of energy waste, pushing hardware designers towards a tighter integration of the different technologies. The resulting integrated systems offer great opportunities in terms of power/performance tradeoffs, but also lead to challenges on the software side. In this position paper, we highlight the trends leading us to integrated systems and describe their substantial advantages over simpler, single accelerated designs. Further, we highlight its impact on the corresponding software stack and its challenges and impact on the user. This introduces a different way to design, program and operate HPC systems, and ultimately the need to drop some long-held dogmas or believes in HPC systems.

The aim of the SPICE project is to build social cohesion, both between and within citizen communities, by developing tools and methods to support citizen curation. We define citizen curation as a process in which cultural objects are used as a resource by citizens to develop their own personal interpretations. Within communities, citizens can use their interpretations to build a representation of themselves and their shared perspective on culture. Interpretations can also be used to support social cohesion across groups. In this short position paper we outline the methodologies and technologies needed to be built in order to build a recommender system of cultural objects that will implement these goals of social cohesion and inclusion.

Despite the discussions over the national artificial intelligence (AI) strategy of Turkey since 2019, the report has not yet been published. This makes it difficult to comprehend Turkey's national AI targets and strategies. Digital Transformation Office (DTO) was established as an inter-institutional coordination unit to lead digital transformation (including AI). This study analyzes tweets, official website and YouTube videos of the Digital Transformation Office based on the principles in World Economic Forum's 2019 White Paper, “A Framework for Developing a National Artificial Intelligence Strategy”. The main motivation of the research is to reveal Turkey's main concerns and priorities on the AI agenda. Despite the initial efforts and investments on AI, the inadequate evaluation of social and economic structures together with technological infrastructure indicates that Turkey has been experiencing long-term uncertainty in AI strategy.

There is no doubt that the body of knowledge spanned by the computing disciplines has gone through an unprecedented expansion, both in depth and breadth, over the last century. In this position paper, we argue that this expansion has led to a crisis in computing education: quite literally the vast majority of the topics of interest of this conference are not taught at the undergraduate level and most graduate courses will only scratch the surface of a few selected topics. But alas, industry is increasingly expecting students to be familiar with emerging topics, such as neuromorphic, probabilistic, and quantum computing, AI, and deep learning. We provide evidence for the rapid growth of emerging topics, highlight the decline of traditional areas, muse about the failure of higher education to adapt quickly, and delineate possible ways to avert the crisis by looking at how the field of physics dealt with significant expansions over the last centuries.

Redirected walking (RDW) allows users to navigate a large virtual world in a small physical space. At this time, if the applied redirection is below the detection threshold, the human hardly notice. However, some papers reported that users perceived changes in curvature gain even when redirections smaller than the detection threshold were applied. This means that the change in curvature gain caused human perception. Therefore, in this paper, we identified a threshold for the change in curvature gain, which was found to be 3.06°/m. Further experiments using different variation methods for variations in curvature gain will follow.

Over the last decades, different kinds of design guides have been created to maintain consistency and usability in interactive system development. However, in the case of spatial applications, practitioners from research and industry either have difficulty finding them or perceive such guides as lacking relevance, practicability, and applicability. This paper presents the current state of scientific research and industry practice by investigating currently used design recommendations for mixed reality (MR) system development. We analyzed and compared 875 design recommendations for MR applications elicited from 89 scientific papers and documentation from six industry practitioners in a literature review. In doing so, we identified differences regarding four key topics: Focus on unique MR design challenges, abstraction regarding devices and ecosystems, level of detail and abstraction of content, and covered topics. Based on that, we contribute to the MR design research by providing three factors for perceived irrelevance and six main implications for design recommendations that are applicable in scientific and industry practice.

This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the encoder-decoder model based on Transformer. To train a highly accurate end-to-end model, we need to prepare a large image-to-text paired dataset for the target language. However, it is difficult to collect this data, especially for resource-poor languages. To overcome this difficulty, our proposed method utilizes well-prepared large datasets in resource-rich languages such as English, to train the resource-poor encoder-decoder model. Our key idea is to build a model in which the encoder reflects knowledge of multiple languages while the decoder specializes in knowledge of just the resource-poor language. To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language's dataset and the resource-rich language's dataset to learn language-invariant knowledge for scene text recognition. The proposed method also pre-trains the decoder by using the resource-poor language's dataset to make the decoder better suited to the resource-poor language. Experiments on Japanese scene text recognition using a small, publicly available dataset demonstrate the effectiveness of the proposed method., Accept as short paper at ACM MMAsia 2021

Online teaching and learning became everyday practice during the Covid-19 pandemic in education worldwide. There is a strong belief that it will remain part of common practice even in post-pandemic times. Therefore, it is important to systematically measure the user experience from the perspective of key stakeholders. In this paper, we report findings of a large scale multi-faceted usability evaluation study of tools widely used for synchronous online teaching and learning (Zoom vs competitors). The study was conducted by three independent groups of experts, who are presenting the results of the study and an analysis of the findings in this paper. The experts performed a comparative evaluation of Zoom alongside other tools used for online synchronous teaching and triangulated the findings of the study using alternative evaluation methods (personas, heuristics, interviews, etc.). The study highlights the findings of the three evaluation studies and produces a list of suggestions for improvements of the Zoom online teaching/learning platform. We also discuss the contribution of different evaluation methods in the final recommendations.

This paper describes an interactive art installation, placed at a central location in Athens, that utilises live data from environmental sensors and maps them to visual, auditory, and tactile / kinaesthetic elements. The installation in question is multisensory in nature, since it employs ubiquitous computing technologies, various sensors, virtual reality (VR) interfaces, multi-channel audio displays and interactive light structures. Furthermore, a web app can be used as an additional data source, since users can provide a subjective evaluation of their emotional response towards stimuli originating from the city. The emphasis of this paper is on the role of the installation as an interface between the public and the city itself; thus, the interactive components of the installation are described in more detail.

The ultimate challenge that arises in 6G and 5G and beyond networks is how the mobile networks of the future will be able to cope with the constantly increasing number of devices. In Multiuser Multiple-Input and Multiple-Output (mu-MIMO), the base station basically serves many users at the same time using the same frequency. The topic considered in this paper is the spectral performance in Downlink mu-MIMO. This paper's goal is to improve the achieved spectral efficiency of the communication system, as well as the achieved reliability of the communication link. An algorithm based on downlink scheduling and the resource allocation is proposed to achieve maximum spectral use of the system. Considering user's demands, this paper's mechanism is able to allocate the potential user of the queue to the "best antenna" based on its demands and distance. That provokes a larger number of devices that can be supported, always according to the specifications stated in IEEE 802.11ax.

This paper reports findings from a systematic review of research on women’s health in Latin America within the field of Human-Computer Interaction (HCI). With a particular focus on digital intervention involving menopause, reproductive health, and cancer, we conduct a systematic review to inquire about the current state of digital health in the context of Latin America. We employed PRISMA 1 to conduct the review and identified 1285 records of studies that conducted with Latin American women and addressed any of the aforementioned life events or conditions (n=1285). We screened and assessed these records for eligibility and included 8 studies which are presented in this paper (n=8). We present a meta-analysis of the included studies which includes the type of intervention, country, study size, methods, condition addressed, year, authors, and technologies. We conclude sharing preliminary insights and proposing next steps for the advancement of women’s health HCI interventions in Latin America.

An approach to evaluate vehicular networks is to make use of simulators. A simulation engine allows the vehicular network to control the traffic simulation on run time. In this regard, researchers can assess their vehicular network models under realistic scenarios (e.g., highway, urban, rural). However, setting up these traffic mobility scenarios may result in a time-consuming task requiring the definition of several elements: (i) the road map, (ii) the traffic elements (e.g., traffic lights), (iii) the type of vehicles, and (iv) vehicle traces/routes. This paper focuses on facilitating this process, taking advantage of a well-known open-source traffic simulator called SUMO (Simulation of Urban MObility). Even though the SUMO simulator builds in several facilities to generate traffic mobility scenarios, it requires a considerable learning time. This paper proposes an open-source traffic simulator SUMO extension that encapsulates most SUMO facilities in an easy-to-use traffic generation tool called SUMO-based Traffic Mobility Generation Tool (STGT). The STGT tool facilities the scenario generation avoiding the learning curve of the SUMO framework, and provides performance statistics of the generated vehicular movements. The STGT tool copes with the whole traffic simulation process, including a graphical user interface for novice users and a command-line interface for advanced users.

Tactile Internet (TI) refers to the transmission of touch and the real-time control of applications like the remote control for teleoperation of machines, drones or vehicles. Traditional TCP and UDP protocols are not suitable for these applications, because they are mainly designed for content delivery or non real-time interactive applications. Some ideas from transport protocols for real-time multimedia like RTP are closer to Tactile Internet; however, they do not satisfy requirements of low latency and high reliability. In this paper, we present the Multi-connection Tactile Internet Protocol (MTIP), a novel transport protocol on top of the Internet Protocol to support the reliability and latency for Internet applications over multi-homed devices connected to several wireless networks. In order to provide that service, MTIP is based on the combination of sequence numbers and timestamps in packets, enhanced with global clocks and context awareness. The paper focuses on building a state machine based model of MTIP to prove the correct behaviour of the protocol using the SPIN tool to verify a number of relevant correctness properties.

The evolution and innovation of technology within society has caused many changes and challenges for computing education. However, the root cause of these challenges are often misunderstood, or neglected. Therefore, this paper deals with the question of identifying the contextual factors that create the challenges which exist in K-12 computing education. To do this, the perspectives of 32 English college employees were considered, with the challenges and contextual factors identified being mapped to Biggs 3P Model of classroom learning. Through this process, in addition to a short comparison between English and Finnish education systems and policy, the 3P Model was re-framed to include the fourth P of Policy consisting of ‘government priorities’, and ‘regulatory bodies’. Findings indicate the extent to which contextual factors influence the challenges and effectiveness of computing education, and how many of these factors are interlinked, despite ranging from macro-environment trends and policy to an individual student’s knowledge of programming. Together, these findings raise important philosophical questions of what computing education is, and what the goals of computing education should be. The answers to these questions have significant implications for practice, which practitioners and policy makers must consider for the design of any computing program of study, while the amended 3P Model can be used to identify the contextual factors which may influence the effectiveness of such programs. Furthermore, this paper contributes to the limited body of research concerning English college education, and more specifically perspectives regarding computing education in this context.

This paper discusses an approach to optimizing the performance of an end-to-end data-driven control approach for building energy management. The proposed approach, designed for systems that exhibit non-stationary behavior, involves two primary components: (1) performance degradation detection, followed by (2) relearning a set of data-driven models of the system to update the controller policy using a reinforcement learning approach. The overall control framework involves a large hyperparameter space that has to be tuned for "optimal" performance. In this paper, we analyze the sensitivity and robustness to a small set of relevant hyperparameters that have a significant impact on the overall performance. We study the performance in terms of the accuracy of the derived data-driven models that support relearning and the speed of convergence of the reinforcement learning controller.

Identifying water end-uses from household meter readings yields valuable insights of commercial and environmental value for both customers and water providers. But smart water meter data may expose sensitive information about the activities of metered households. This paper considers the case where a water provider wishes to publish a database of household water meter traces to be used by water analysts for research and planning purposes. We consider the risks to privacy should an adversary gain access to this database, with the threat of uniquely identifying a household of interest and exposing their water use activities. Previous work shows that the effectiveness of privacy protection techniques is strongly domain and application dependent, but few privacy studies have considered smart water metering. This paper introduces a framework for activity-aware privacy protection for databases of household smart water meters and evaluates its effectiveness using real-world and synthetic datasets. We found that privacy-protection is strongly dependent on the type of activity. Aggregating readings protects households from disclosure of small scale and common activities. For individualistic, infrequent activities such as garden watering or clothes washing, we found that data-aware sampling of households and seasons offers the best protection.

Edge computing is rapidly changing the IoT-Cloud landscape. Various testbeds are now able to run multiple Docker-like containers developed and deployed by end-users on edge devices. However, this capability may allow an attacker to deploy a malicious container on the host and compromise it. This paper presents a dataset based on the Linux Auditing System, which contains malicious and benign container activity. We developed two malicious scenarios, a denial of service and a privilege escalation attack, where an adversary uses a container to compromise the edge device. Furthermore, we deployed benign user containers to run in parallel with the malicious containers. Container activity can be captured through the host system via system calls. Our time series auditd dataset contains partial labels for the benign and malicious related system calls. Generating the dataset is largely automated using a provided AutoCES framework. We also present a semi-supervised machine learning use case with the collected data to demonstrate its utility. The dataset and framework code are open-source and publicly available.

Given black-box access to the prediction API, model extraction attacks can steal the functionality of models deployed in the cloud. In this paper, we introduce the SEAT detector, which detects black-box model extraction attacks so that the defender can terminate malicious accounts. SEAT has a similarity encoder trained by adversarial training. Using the similarity encoder, SEAT detects accounts that make queries that indicate a model extraction attack in progress and cancels these accounts. We evaluate our defense against existing model extraction attacks and against new adaptive attacks introduced in this paper. Our results show that even against adaptive attackers, SEAT increases the cost of model extraction attacks by 3.8 times to 16 times.