Reviewerly/exHarmony huggingface dataset:
DURASIFT

Several important lines of research have been geared toward the ability to search on encrypted data. Techniques such as Symmetric Searchable Encryption (SSE), Private Information Retrieval (PIR), Oblivious RAM (ORAM) and more have been introduced to allow a client to privately query a server for their desired contents. However, most traditional works focus on the setting where the server (or servers) are reliable. On the other hand, research in secure multiparty computation (MPC) and verifiable secret sharing (VSS) have classically solved the issue of how arbitrary computations can be carried out unabated even in the presence of malicious parties. As powerful as they are, they tend to be far less efficient when used in the context of private queries into a database. In this work, we propose a new solution that provides a multi-client, distributed database system that is both private and survivable against server outages, and furthermore allows clients to query a corpus of private documents and receive a sanitized response, while hiding the documents, queries and policies from the servers executing the query, and hiding the policies and redacted documents from the client. Our scheme is built using a combination of secure multiparty computation (MPC) and private information retrieval (PIR), but is highly optimized to be efficient for real-world use on moderate sized databases of unstructured, tagged text documents. Our system supports queries that are boolean formulas of keywords, and as our system is based on MPC and PIR, it does not leak query metadata (e.g., the servers cannot recognize repeated queries for the same document). The system supports multiple owners (who can upload documents and set access policies), multiple clients (who can make queries), and multiple servers who execute the queries.
Summary Embedded Deep Learning Object Detection Model Competition

The embedded deep learning object detection model competition in IEEE MMSP2019 focuses on the object detection for sensing technology in autonomous driving vehicles, which aims at detecting small objects in worse conditions through embedded systems. We provide a dataset with 89,002 annotated images for training and 1,500 annotated images for validation. We test participants' models through 6,000 testing images, which are separated into 3,000 for qualification and 3,000 for finals. There are 87 teams of participants registered this competition and 14 teams submitted the team composition. At last there are nine teams entering the final competition and five teams submitting their final models that can be realized in NVIDIA Jetson TX-2. At the end, only one team's model passed the target accuracy requirement for grading and became the champion of the contest, which the winner is team R.JD.
Poster

Over 8 million users rely on the Tor network each day to protect their anonymity online. Unfortunately, Tor has been shown to be vulnerable to the website fingerprinting attack, which allows an attacker to deduce the website a user is visiting based on patterns in their traffic. The state-of-the-art attacks leverage deep learning to achieve high classification accuracy using raw packet information. Work thus far, however, has examined only one type of media delivered over the Tor network: web pages, and mostly just home pages of sites. In this work, we instead investigate the fingerprintability of video content served over Tor. We collected a large new dataset of network traces for 50 YouTube videos of similar length. Our preliminary experiments utilizing a convolutional neural network model proposed in prior works has yielded promising classification results, achieving up to 55% accuracy. This shows the potential to unmask the individual videos that users are viewing over Tor, creating further privacy challenges to consider when defending against website fingerprinting attacks.
Exploiting User Tagging for Web Service Co-Clustering

We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process of service clustering. Tagging data is inherently uncontrolled, ambiguous, and overly personalized. Two tag recommendation approaches are proposed to improve the tagging data quality and service clustering performance. Comprehensive experiments demonstrate the effectiveness of the proposed framework using a real-world dataset.
Domain-Specific Extensions for an E-Assessment System

E-assessment systems that provide automated feedback are a well-known part of modern education. Extensibility of e-assessment system with respect to domain-specific features is an import aspect of system design. This paper reports on two successful cases in which an existing e-assessment system was extended with domain-specific features.
Teledrone

Outdoor activities are an important way for friends, family, and couples to spend time with one another and share exploration activities. Yet when people become separated by distance it can be difficult if not impossible to participate in outdoor activities together. We propose a design called Teledrone that uses a drone with video conferencing display to create feelings of social presence during shared outdoor activities over distance. With Teledrone, users connect into the video call display and can maneuver the drone to follow a friend or family member during an outdoor activity such as hiking. In this way, Teledrone provides an embodiment for a remote user and can help support spatial awareness.
Building Type Classification from Social Media Texts via Geo-Spatial Textmining

In this work, we present a model for building type classification from Twitter text messages (tweets) by employing geo-spatial textmining methods. First, we apply standard text pre-processing methods and convert the tweets into sentence vectors using fastText. For classification, we apply a feedforward network with two fully connected hidden layers and feed the generated sentence vectors as linguistic features. Classification results suggest that the classes are distinguishable to a certain extent with pure text even with unbalanced class distributions and a very small sample size. However, these findings also undermine, that building type classification with pure text data is a challenging task.
Power Transmission Tower CFAR Detection Algorithm Based on Integrated Superpixel Window and Adaptive Statistical Model

In order to detect power transmission tower efficiently in synthetic aperture radar (SAR) images, a new constant false alarm rate (CFAR) detection algorithm based on integrated superpixel window and adaptive statistical model has been proposed. Firstly, the SEEDS algorithm is used to extract superpixels from the SAR image, and the segmented objects are merged into CFAR detection windows. Subsequently, the power transmission towers are detected by the CFAR algorithm with the adaptive statistical model in each window. The experiment results using UAVSAR images prove that the proposed algorithm can achieve good detection performance and suppress false alarms at the same time.
Multimodal mental models: Understanding users’ design expectations for mHealth apps

Employing qualitative structured interviews with mobile health app users, this research describes shared mental models for mHealth and reveals their complexity. The findings uncover prototypical design components common to mental models beyond health apps and suggest that users’ mental models are multimodal, containing distinct and often contradictory dimensions for evaluations of aesthetics and for craftsmanship. The findings also indicate that users’ mental models are informed by experiences with apps from across the mobile landscape. This research suggests that designers of consumer mobile health apps and mobile health interventions should incorporate prototypical or salient features. In doing so, they should index designs to trends across the larger app landscape and innovate the means to balance between multidimensional and conflicting mental models.
Dynamic-stride-net: deep convolutional neural network with dynamic stride

It is crucial to reduce the cost of deep convolutional neural networks while preserving their accuracy. Existing methods adaptively prune DNNs in a layer-wise or channel-wise manner based on the input image. In this paper, we develop a novel dynamic network, namely Dynamic-Stride-Net, to improve residual network with layer-wise adaptive strides in the convolution operations. Dynamic-Stride-Net leverages a gating network to adaptively select the strides of convolutional blocks based on the outputs of the previous layer. To optimize the selection of strides, the gating network is trained by reinforcement learning. The floating point operations per second (FLOPS) is significantly reduced by adapting the strides to convolutional layers without loss of accuracy. Dynamic-Stride-Net reduces the computational cost by 35%-50% with equivalent accuracy of the original model on CIFAR-10 and CIFAR-100 datasets. It outperforms the state-of-the-art dynamic networks and static compression methods.
Toward ‘Suprahuman’ Technology

Drawing upon the findings and experiences of my team’s Research-through-Design practice, I propose that we as a scholarly community pull together (harder) to steer future technological developments toward putting attention on the experiential space between people, deploying computation toward augmenting physical copresence and collective action. Despite long-standing research efforts in HCI and Ubicomp aimed at re-orienting the ship, particularly in recent years, vast commercial internet and mobile-based services have rightfully been critiqued for encouraging some pretty isolating behavior patterns. In the spirit of Suchman’s reframing of our understanding of the human machine interface in her classic text, Human-Machine Reconfigurations: Plans and Situated Actions, and drawing upon the style of and some key ideas from Donna Haraway’s past and recent work [5, 6], I put forward a manifesto that calls for a future of technology that enables the ‘suprahuman’—a productive reweaving of the social fabric between us in ways that include our bodies and the material world, and that privilege bolstering connection and shared action. I respectfully put forward this call to action as fodder for discussion at the Halfway to the Future event.
Load balancing mechanism in the cloud environment using preference alignments and an optimisation algorithm

Cloud computing platforms propose applied directional information technology services to its clients. It provides services according to demands by which shared resources, data, software, and other tools are to be made available based on the client's request. Load balancing is considered as one of the main issues in cloud computing. The quality of service (QoS) factors such as resources application, answering time, process time, scalability, throughput, the durability of the system, and energy consumption are improved by the balanced load. The cloud tasks scheduling and load balancing are considered as an NP-hard issue. Therefore, in this study, a new energy-aware cloud load balancing method is proposed by usage of preference alignments and ant colony optimisation algorithm. This approach aims at balancing the virtual machines workload and energy consumption of cloud data centres and also assures QoS for users. CloudSim simulator is used to confirm the performance of the method. It shows that the proposed method reduces waiting time and energy consumption on a virtual machine.
Extreme Learning Machine Approach for Prediction of Forest Fires using Topographical and Metrological Data of Vietnam

A problem is a well-posed problem if it satisfy, solution existence, solution uniqueness and non- perturbation conditions. Ill-posed problems or inverse problems are the special case of well-posed problem, because inverse of major function does not exist. Forest fire prediction is an inverse problem. Forest are the largest natural resources. Forest fire is a calamity, it is a threat to the entire regime of flora and fauna. Forest fire mitigation is essential because it can devastate biodiversity, wild life and can cause economic loss. In this research work extreme learning machine is used, because it has capability prediction problem solves with better generalization and fast learning speed. ELM is a new approach to be used for forest fire prediction. Presented work predict the forest fire occurrence with the help of topographical and metrological data, with parameters slope, Aspect, Elevation, NDVI, Distance to road, Distance to residential area, Land use, Temperature, Wind speed, Rainfall and forest fire occurrence. The motivation behind this work is to predict the forest fire to provide better way of management for this tragedy. In this research work a relationship is being established between forest fire causing factors and forest fire occurrence using historical data. The used database is already existing data of 540 historical locations of Vietnam. Experiments are conducted on different data partitions of availed data with different activation functions. On the basis of accuracy of model, sigmoid function found to be best and suggested to be used further for forest fire prediction.
Hierarchical fragmented image reassembly using a bundle-of-superpixel representation

We develop a new reassembly pipeline that automatically restores an image from its rips. First, we develop an image fragment descriptor called Bundle-of-Superpixel, which can more effectively support the detection of correlated (adjacent) image fragments and the computation of local pairwise partial matching. We also develop a hierarchical global composition algorithm that iteratively performs the reassembly through a bottom-up manner. We also build a data benchmark for fragmented images and propose a set of quantitative recomposition assessment measures. Through extensive experiments and comparisons, we demonstrate the effectiveness of our proposed algorithms.
node2hash: Graph aware deep semantic text hashing

Semantic hashing is an effective method for fast similarity search which maps high-dimensional data to a compact binary code that preserves the semantic information of the original data. Most existing text hashing approaches treat each document separately and only learn the hash codes from the content of the documents. However, in reality, documents are related to each other either explicitly through an observed linkage such as citations or implicitly through unobserved connections such as adjacency in the original space. The document relationships are pervasive in the real world while they are largely ignored in the prior semantic hashing work. In this paper, we propose node2hash, an unsupervised deep generative model for semantic text hashing by utilizing graph context. It is designed to incorporate both document content and connection information through a probabilistic formulation. Based on the deep generative modeling framework, node2hash employs deep neural networks to learn complex mappings from the original space to the hash space. Moreover, the probabilistic formulation enables a principled way to generate hash codes for unseen documents that do not have any connections with the existing documents. Besides, node2hash can go beyond one-hop connections about directed linked documents by considering more global graph information. We conduct comprehensive experiments on seven datasets with explicit and implicit connections. The results have demonstrated the effectiveness of node2hash over competitive baselines.
Data Driven Development: Challenges in Online, Embedded and On-Premise Software

For more than a decade, data driven development has attracted attention as one of the most powerful means to improve effectiveness and ensure value delivery to customers. In online companies, controlled experimentation is the primary technique to measure how customers respond to variants of deployed software. In B2B companies, an interest for data driven development is rapidly emerging and experiments are run on selected instances of the system or as comparisons of previously computed data to ensure quality, improve configurations and explore new value propositions. Although the adoption of data driven development is challenging in general, it is especially so for embedded systems companies and for companies developing on-premise software solutions. Due to complex systems with hardware dependencies, safety-critical functionality and strict regulations, these companies have longer development cycles, less frequent deployments and limited access to data. In this paper, and based on multi-case study research, we explore the specific challenges that embedded systems companies and companies developing on-premise solutions experience when adopting data driven development practices. The contribution of the paper is two-fold. First, we provide empirical evidence in which we identify the key challenges that embedded systems and on-premise software solutions companies experience as they evolve through the process of adopting data driven development practices. Second, we define the key focus areas that these companies need to address for evolving their data driven development adoption process .
Designing for Play that Permeates Everyday Life: Towards New Methods for Situated Play Design

In this paper we discuss strategies to support our design research agenda of promoting playful engagement within everyday activities and situations. We argue that this agenda is in alignment with the ethos of the third wave of HCI. To support design in this space, we build upon Situated Play Design, an open methodological frame that focuses on uncovering existing manifestations of contextual play as a starting point for designing playful technology. Towards further developing that approach, here we highlight a series of emergent methodological challenges, and speculate tactics to address them. Our contribution is a methodological reflection of how to support situated play design, as well as an invitation for designers to continue envisioning, articulating and sharing new methods in this emerging space.
FFmpeg360 for 360-degree videos

360-degree video streaming is an emerging technology that provides immersive experiences to users. However, it both requires high streaming bandwidth and wastes a significant portion of the bandwidth. To improve bandwidth-efficiency, researchers have proposed oriented projections and FOV rendering. However, existing software and tools are unable to perform these tasks in an online manner under stringent processing latency and throughput constraints.
Learning to Generate 3D Training Data Through Hybrid Gradient

Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform well on real images, because a graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and the placement of the camera. In this work, we propose a new method that optimizes the generation of 3D training data based on what we call gradient. We parametrize the design decisions as a real vector, and combine the approximate gradient and the analytical gradient to obtain the hybrid gradient of the network performance with respect to this vector. We evaluate our approach on the task of estimating surface normal, depth or intrinsic decomposition from a single image. Experiments on standard benchmarks show that our approach can outperform the prior state of the art on optimizing the generation of 3D training data, particularly in terms of computational efficiency.
Science literacy on science technology engineering and math (STEM) learning in elementary schools

Abstract Science in Engineering Technology and Mathematics (STEM) strongly supports the current era of educational development. The component that strongly supports the success of STEM mastery is student literacy towards science concepts. This study aims to reveal the profile of scientific literacy skills that support the achievement of STEM. The method used is qualitative participatory. The participants of this study were 26 grade 6 elementary school students in Bandung. The instrument used is a test of understanding of several technology products around the environment where students live. Triangulation through interviews was conducted to explore students’ answers. The results showed that: (1) there were variations in students’ scientific literacy skills on technology products, (2) the ability of science concepts related to STEM abilities, and (3) the still strong role of learning resources derived from books, the internet and other social media. This study concludes that science skills and STEM literacy of elementary school students in Bandung are still low. This research recommends the need for literacy programs that are united with an integrated teaching and learning process.
Crowdsourcing Approach for Developing Hands-On Experiments in Cybersecurity Education

The operational skills are essential for the cybersecurity practitioners and the hands-on experiments can be utilized to train their practical skills. As malicious attacks are continually changing and new vulnerability appears quickly with the increasing number of new software, it has been a challenging problem to develop hands-on experiments rapidly and continuously which are consistent with realistic threat scenarios. Normally, skilled engineers are familiar with threat scenarios and the attack/defense skills, but they have no motivation to develop hands-on experiments for education. In contrast, the faculties are good at teaching and training but they have no resource or experience to deploy lots of hands-on experiments by themselves. In this paper, we propose a framework called RC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F by adopting the crowdsourcing approach, which acts as a platform for the engineers and the faculties as well as channels between them for resource exchanging. In the framework, we also introduce two incentive models to motivate the participants, where the constructor-oriented Forward Incentive Model (FIM for short) encourages the engineers by the incentive mechanism and the demand-oriented Backward Incentive Model (BIM for short) balances the educational resources for the faculties. We also provide a comprehensive case based on the practical data to analyze the proposed framework. And the results validate that the proposed framework as well as the FIM and BIM models can address the challenge in developing hands-on experiments.