Reviewerly/exHarmony huggingface dataset:
Securing Cross-App Interactions in IoT Platforms

IoT platforms enable users to connect various smart devices and online services via reactive apps running on the cloud.These apps, often developed by third-parties, perform simple computations on data triggered by external information sources and actuate the results of computation on external information sinks.Recent research shows that unintended or malicious interactions between the different (even benign) apps of a user can cause severe security and safety risks.These works leverage program analysis techniques to build tools for unveiling unexpected interference across apps for specific use cases.Despite these initial efforts, we are still lacking a semantic framework for understanding interactions between IoT apps.The question of what security policy cross-app interference embodies remains largely unexplored.This paper proposes a semantic framework capturing the essence of cross-app interactions in IoT platforms.The framework generalizes and connects syntactic enforcement mechanisms to bisimulation-based notions of security, thus providing a baseline for formulating soundness criteria of these enforcement mechanisms.Specifically, we present a calculus that models the behavioral semantics of a system of apps executing concurrently, and use it to define desirable semantic policies in the security and safety context of IoT apps.To demonstrate the usefulness of our framework, we define static mechanisms for enforcing crossapp security and safety, and prove them sound with respect to our semantic conditions.Finally, we leverage real-world apps to validate the practical benefits of our policy framework.
A Function Emulation Approach for Doubly Intractable Distributions

Doubly intractable distributions arise in many settings, for example, in Markov models for point processes and exponential random graph models for networks. Bayesian inference for these models is challenging because they involve intractable normalizing “constants” that are actually functions of the parameters of interest. Although several computational methods have been developed for these models, each can be computationally burdensome or even infeasible for many problems. We propose a novel algorithm that provides computational gains over existing methods by replacing Monte Carlo approximations to the normalizing function with a Gaussian process-based approximation. We provide theoretical justification for this method. We also develop a closely related algorithm that is applicable more broadly to any likelihood function that is expensive to evaluate. We illustrate the application of our methods to challenging simulated and real data examples, including an exponential random graph model, a Markov point process, and a model for infectious disease dynamics. The algorithm shows significant gains in computational efficiency over existing methods, and has the potential for greater gains for more challenging problems. For a random graph model example, we show how this gain in efficiency allows us to carry out accurate Bayesian inference when other algorithms are computationally impractical. Supplementary materials for this article are available online.
Stochastic AUC Optimization Algorithms With Linear Convergence

Area under the ROC curve (AUC) is a standard metric that is used to measure classification performance for imbalanced class data. Developing stochastic learning algorithms that maximize AUC over accuracy is of practical interest. However, AUC maximization presents a challenge since the learning objective function is defined over a pair of instances of opposite classes. Existing methods circumvent this issue but with high space and time complexity. From our previous work of redefining AUC optimization as a convex-concave saddle point problem, we propose a new stochastic batch learning algorithm for AUC maximization. The key difference from our previous work is that we assume that the underlying distribution of the data is uniform, and we develop a batch learning algorithm that is a stochastic primal-dual algorithm (SPDAM) that achieves a linear convergence rate. We establish the theoretical convergence of SPDAM with high probability and demonstrate its effectiveness on standard benchmark datasets.
Relational Word Embeddings

While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited.In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding.Such strategies may not be optimal, however, as they are limited by the coverage of available resources and conflate similarity with other forms of relatedness.As an alternative, in this paper we propose to encode relational knowledge in a separate word embedding, which is aimed to be complementary to a given standard word embedding.This relational word embedding is still learned from co-occurrence statistics, and can thus be used even when no external knowledge base is available.Our analysis shows that relational word vectors do indeed capture information that is complementary to what is encoded in standard word embeddings.
Cartographic Design for Improved Decision Making: Trade-Offs in Uncertainty Visualization for Tornado Threats

At present, the National Oceanic and Atmospheric Administration is developing new technologies that could offer explicit estimates of the probability that a thunderstorm could produce a tornado up to an hour ahead of the event. Such technologies could radically alter how risk spaces are represented and understood by those who must decide whether or not to take protective action. In addition, there are relatively few studies that examine mapped representations of uncertainty in weather information or the influence of this uncertainty information in weather hazard decision making. To address these gaps, this study presents research subjects with a variety of representations of uncertainty based on the precepts of cartography and information visualization. We propose and test for the existence of three geospatial framing effects that potentially influence subjective estimates of risk: distance from a hazard, warning boundary inclusion or exclusion, and symbolic color coding of uncertainty information. Using a series of computer-aided geographic experiments with a large sample (N = 5,564) of the U.S. population, we find evidence for the existence of each of the three proposed geospatial framing effects. Two of these framings are controlled by the mapmaker—in this case, the weather forecaster—and thus should be considered during the development stages of new products. We discuss the practical implications of the experimental study for current and future tornado warning practices. Key Words: cartography, risk, tornado, uncertainty, visualization.
Artificial Intelligence, Human Evolution, and the Speed of Learning

Stephen Hawking suggests that a living system has the two parts: “a set of instructions that tell the system how to sustain and reproduce itself, and a mechanism to carry out the instructions” (genes and metabolism). On this definition, computer viruses count as living systems as do artificial intelligences. Hawking explains that human evolution has speeded up. While “there has been no detectable change in human DNA”, “the amount of knowledge handed on from generation to generation has grown enormously” (maybe a hundred thousand times as much as in DNA). This signals that we have entered a new stage of evolution—from natural selection based on the Darwinian model of internal transmission to cultural or self-designed evolution based on an accelerated external transmission of information. This paper presents a thought experiment about philosophical and educational consequences of the possible arrival of: (1) Hawking-inspired postdigital human beings created through self-designed evolution quicker than non-tampered (natural) evolution of human intelligence and (2) algorithmic non-carbon-based “living” systems. In our postdigital age, we are slowly but surely taking natural selection into our own hands, and we need to grapple with the pertinent responsibility.
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations

In this paper, a new video super-resolution reconstruction (SRR) method with improved robustness to outliers is proposed. Although the R-LMS is one of the SRR algorithms with the best reconstruction quality for its computational cost, and is naturally robust to registration inaccuracies, its performance is known to degrade severely in the presence of innovation outliers. By studying the proximal point cost function representation of the R-LMS iterative equation, a better understanding of its performance under different situations is attained. Using statistical properties of typical innovation outliers, a new cost function is then proposed and two new algorithms are derived, which present improved robustness to outliers while maintaining computational costs comparable to that of R-LMS. Monte Carlo simulation results illustrate that the proposed method outperforms the traditional and regularized versions of LMS, and is competitive with state-of-the-art SRR methods at a much smaller computational cost.
The Impact of Student Model Updates on Contingent Scaffolding in a Natural-Language Tutoring System

This paper describes an initial pilot study of Rimac, a natural-language tutoring system for physics. Rimac uses a student model to guide decisions about what content to discuss next during reflective dialogues that are initiated after students solve quantitative physics problems, and how much support to provide during these discussions—that is, domain contingent scaffolding and instructional contingent scaffolding, respectively. The pilot study compared an experimental and control version of Rimac. The experimental version uses students’ responses to pretest items to initialize the student model and dynamically updates the model based on students’ responses to tutor questions during reflective dialogues. It then decides what and how to discuss the next question based on the model predictions. The control version initializes its student model based on students’ pretest performance but does not update the model further and assigns students to a fixed line of reasoning level based on the student model predictions. We hypothesized that students who used the experimental version of Rimac would achieve higher learning gains than students who used the control version. Although we did not find a significant difference in learning between conditions, the experimental group took significantly less time to complete the pilot study dialogues than did the control group. That is, the experimental condition led to more efficient learning, for both low and high prior knowledge level learners. We discuss this finding and describe future work to improve the tutor’s potential to support student learning.
Automatic clustering using quantum-based multi-objective emperor penguin optimizer and its applications to image segmentation

In this study, a novel quantum-based multi-objective is proposed using Schrödinger equations. The two new operations namely weighted cluster centroid computation and threshold setting are also introduced to refine cluster centroids. A novel fitness function strategy is also proposed for efficient searching. The proposed technique is compared with various well-known approaches. Experimental outcomes show that the proposed quantum approach outperforms other existing approaches.
Congestion Control for Cloud Gaming Over UDP Based on Round-Trip Video Latency

We describe a network congestion control mechanism for cloud gaming (CG) platforms based on the user datagram protocol (UDP). To minimize the contribution of the downstream transmission delay to the total end-to-end latency in the interaction-perception loop, we first define the round-trip video latency (RTVL) and develop a congestion model. Based on them, we design and implement an adaptation strategy that detects the early stages of congestion to prevent high values of RTVL and network bufferbloat, thus avoiding packet losses. Using data measured from the network, our strategy modifies the target output bitrate of the video encoder to throttle down or upto the data flow sent by the server to the client. In the presence of sudden downstream channel capacity drops of over 40%, our algorithm reactively manages to satisfy the key CG requirements for interactive games by entirely avoiding the packet losses and keeping the RTVL below 100 ms. In reasonably stable network conditions, our algorithm proactively keeps exploring for higher bitrates and building a “network state dictionary,” due to which it achieves an effective downstream channel capacity use of ~95%.
Enhanced Multiqubit Phase Estimation in Noisy Environments by Local Encoding

The first generation of multi-qubit quantum technologies will consist of noisy, intermediate-scale devices for which active error correction remains out of reach. To exploit such devices, it is thus imperative to use passive error protection that meets a careful trade-off between noise protection and resource overhead. Here, we experimentally demonstrate that single-qubit encoding can significantly enhance the robustness of entanglement and coherence of four-qubit graph states against local noise with a preferred direction. In particular, we explicitly show that local encoding provides a significant practical advantage for phase estimation in noisy environments. This demonstrates the efficacy of local unitary encoding under realistic conditions, with potential applications in multi-qubit quantum technologies for metrology, multi-partite secrecy and error correction.
A privacy preserved and credible network protocol

The identities of packet senders and receivers are treated as important privacy information in communication networks. Any packet can be attributed to its sender for evaluating its credibility. Existing studies mainly rely on third-party agents that contain the packet sender’s identity to ensure the sender’s privacy preservation and credibility. In this case, packet senders run the risk that their privacy might be leaked by the agent. To this end, this paper proposes a Privacy Preserved and Credible Network Protocol (PCNP), which authorizes the agent to hide the identities of senders and receivers, while guaranteeing the credibility of a packet. The feasibility of the PCNP deployment is analyzed, and its performance is evaluated through extensive experiments.
lifeXplore at the Lifelog Search Challenge 2019

First held in Yokohama, Japan in 2018, the Lifelogging Search Challenge (LSC) is an interactive lifelogging content retrieval competition, where international teams are faced with the task of finding specific moments within a large collection of multimodal lifelogging data. As one of the successfully participating LSC2018 teams (2nd place), we present our improved lifeXplore system for the second iteration of this challenge. Based on diveXplore, an interactive video retrieval system that has been developed for the annual Video Browser Showdown, the lifeXplore system similarly operates on shot-segmented video data, while maintaining browseable feature maps and offering customizable similarity search, yet also introducing additional features such as metadata filtering and interactive day inspection. Alongside several smaller functionality and usability improvements, the LSC2019 version of the system in particular includes combinable filters, geolocation-based filtering and automatic feature map browsing in order to improve a user's searching and browsing experience.
An accurate and efficient multi-category edge detection method

Edge detection plays an important role in image processing. With the development of deep learning, the accuracy of edge detection has been greatly improved, and people have more requirements for edge detection tasks. Most edge detection algorithms are binary edge detection methods, but there are usually multiple categories of edges in an image. In this paper, we present an accurate multi-category edge detection network, the richer category-aware semantic edge detection network (R-CASENet). In order to make full use of convolutional neural network’s powerful feature expression capabilities, we attempt to use more information from feature maps for edge feature extraction and classification. Using the ResNet101 network as the backbone, firstly we merge the building blocks in different composite blocks and down-sample to obtain the feature maps. Then we fuse the feature maps in different composite blocks to obtain the final fused classifier. Experimental results show that R-CASENet can achieve state-of-the-art performance on the large SBD dataset. Furthermore, to get precise one-pixel width edges, we also propose an edge refinement network (ERN) structure. The proposed scheme is an end-to-end method and the proposed ERN can reduce redundant points and improve computational efficiency, especially for further image processing.
Enlarged representation of peripersonal space in pregnancy

Abstract Our ability to maintain a coherent bodily self despite continuous changes within and outside our body relies on the highly flexible multisensory representation of the body, and of the space surrounding it: the peripersonal space (PPS). The aim of our study was to investigate whether during pregnancy - when extremely rapid changes in body size and shape occur - a likewise rapid plastic reorganization of the neural representation of the PPS occurs. We used an audio-tactile integration task to measure the PPS boundary at different stages of pregnancy. We found that in the second trimester of pregnancy and postpartum women did not show differences in their PPS size as compared to the control group (non-pregnant women). However, in the third trimester the PPS was larger than the controls’ PPS and the shift between representation of near and far space was more gradual. We therefore conclude that during pregnancy the brain adapts to the sudden bodily changes, by expanding the representation of the space around the body. This may represent a mechanism to protect the vulnerable abdomen from injury from surrounding objects.
Peekaboo Cam

The home is a rich context for design research to study things and the Everyday. However, home is also a place of utmost privacy for most people. To better understand this context through an observational artifact without impacting privacy, we designed the Peekaboo cam that enables inhabitants to control their data release actively or passively. The Peekaboo cam is an observational research camera with a coverable lens. We validate in a field study in two homes for 14 days. The resulting photo streams provide qualitative insights on Everyday things in transition. We suggest four design guidelines for observational artifacts for home ecologies.
Dynamic Path Planning Based on Improved D* Algorithms of Gaode Map

To solve the problem of long-distance path planning for outdoor robots, an improved $D^{\ast}$ algorithm combing with Gaode map based on vector model is proposed. Specifically, the global static path to the target point is planned through the Gaode Map Open Platform, and is divided into path sub-nodes. The dynamic $D^{\ast}$ algorithm of heuristic function h(n) is improved under the vector model, and the long-distance path planning is realized by node iterations. The simulation is carried out on Gazebo and Rviz platforms. Results show that compared with the traditional $D^{\ast}$ algorithm, the running time of the robot is reduced by 32.8%, the number of corners is reduced by 64.6%, the number of dead zones is reduced by 64.3%, and the success rate of the planned path to the target point is greatly improved, which has high feasibility.
Agile Project Management in Libraries: Creating Collaborative, Resilient, Responsive Organizations

Libraries must continually adapt to meet the ever-changing needs of users. Doing so requires the ability to be flexible and respond quickly. This is a case study of how The George Washington University Libraries rethought its approach to collection development using a cross-functional, team-based strategy that leveraged the benefits of Agile project management. Adopting an Agile approach to collection development maximized the impact of the library's financial resources and enabled staff to develop new skills. This project underscores the value of collaboration, agility, and experimentation in libraries.
An Area-Decomposition Based Approach for Cooperative Tasking and Coordination of UAVs in a Search and Coverage Mission

This paper develops a low-computation area-decomposition based approach for tasking and planning for a team of Unmanned Aerial Vehicles (UAVs) involved in a search mission. For an area of interest captured as a convex polygon, a minimum bounding rectangle is calculated and decomposed into several partitions, which are assigned to different UAVs based on their flying and sensing capabilities. A novel Mixed Integer Linear Programming (MILP) based technique is developed to automatically task the UAVs to sweep the area of interest. If each individual UAV follows the created sweeping pattern and visit the generated waypoints, applying the developed method, it can be guaranteed that the entire area of interest is searched. The developed algorithm is applied to tasking and coordination of multiple UAVs searching an area on a farm and the implementation details and results are provided.
Securing Firmware in Internet of Things using Blockchain

As of today, most of the Internet of Things (IoT) devices work in a centralized environment. With IoT devices having to execute thousands of operations per second, it has become difficult for centralized systems to handle such platforms. Also, factors like operational and maintenance cost of these server machines and danger of DoS(Denial of Service) attack on such servers has threatened the practitioners to come up with a more distributed way of storing the contents. With its decentralized and privacy preserving features, blockchain technology fits well into the scheme of things. This mitigates the quintessential challenges faced in most of the centralized systems. In this paper, we are focusing on the new firmware update scheme to perform the update operations securely. We propose a blockchain based solution for managing firmware updates in IoT. Improper management of devices and distribution of firmware updates from the device vendor could sabotage the IoT ecosystem. The objective of this proposed scheme is to verify and distribute the firmware binaries securely to the IoT device deployed by the device vendor. The PUSH-based method is used for a firmware update in which verification of firmware is done by Hash Chain. It preserves the integrity of firmware by linking the latest version information by previous versions information with the help of a smart contract mechanism.