Reviewerly/exHarmony huggingface dataset:
Exploring the Limitations of Environment Lighting on Optical See-Through Head-Mounted Displays

Due to the additive light model employed by most optical see-through head-mounted displays (OST-HMDs), they provide the best augmented reality (AR) views in dark environments, where the added AR light does not have to compete against existing real-world lighting. AR imagery displayed on such devices loses a significant amount of contrast in well-lit environments such as outdoors in direct sunlight. To compensate for this, OST-HMDs often use a tinted visor to reduce the amount of environment light that reaches the user's eyes, which in turn results in a loss of contrast in the user's physical environment. While these effects are well known and grounded in existing literature, formal measurements of the illuminance and contrast of modern OST-HMDs are currently missing. In this paper, we provide illuminance measurements for both the Microsoft HoloLens 1 and its successor the HoloLens 2 under varying environment lighting conditions ranging from 0 to 20,000 lux. We evaluate how environment lighting impacts the user by calculating contrast ratios between rendered black (transparent) and white imagery displayed under these conditions, and evaluate how the intensity of environment lighting is impacted by donning and using the HMD. Our results indicate the further need for refinement in the design of future OST-HMDs to optimize contrast in environments with illuminance values greater than or equal to those found in indoor working environments.
A Cordial Sync: Going Beyond Marginal Policies for Multi-agent Embodied Tasks

Autonomous agents must learn to collaborate. It is not scalable to develop a new centralized agent every time a task's difficulty outpaces a single agent's abilities. While multi-agent collaboration research has flourished in gridworld-like environments, relatively little work has considered visually rich domains. Addressing this, we introduce the novel task FurnMove in which agents work together to move a piece of furniture through a living room to a goal. Unlike existing tasks, FurnMove requires agents to coordinate at every timestep. We identify two challenges when training agents to complete FurnMove: existing decentralized action sampling procedures do not permit expressive joint action policies and, in tasks requiring close coordination, the number of failed actions dominates successful actions. To confront these challenges we introduce SYNC-policies (synchronize your actions coherently) and CORDIAL (coordination loss). Using SYNC-policies and CORDIAL, our agents achieve a 58% completion rate on FurnMove, an impressive absolute gain of 25 % points over competitive decentralized baselines. Our dataset, code, and pretrained models are available at https://unnat.github.io/cordial-sync .
A graph-based clustering algorithm for software systems modularization

Clustering algorithms, as a modularization technique, are used to modularize a program aiming to understand large software systems as well as software refactoring. These algorithms partition the source code of the software system into smaller and easy-to-manage modules (clusters). The resulting decomposition is called the software system structure (or software architecture). Due to the NP-hardness of the modularization problem, evolutionary clustering approaches such as the genetic algorithm have been used to solve this problem. These methods do not make much use of the information and knowledge available in the artifact dependency graph which is extracted from the source code. To overcome the limitations of the existing modularization techniques, this paper presents a new modularization technique named GMA (Graph-based Modularization Algorithm). In this paper, a new graph-based clustering algorithm is presented for software modularization. To this end, the depth of relationships is used to compute the similarity between artifacts, as well as seven new criteria are proposed to evaluate the quality of a modularization. The similarity presented in this paper enables the algorithm to use graph-theoretic information. To demonstrate the applicability of the proposed algorithm, ten folders of Mozilla Firefox with different domains and functions, along with four other applications, are selected. The experimental results demonstrate that the proposed algorithm produces modularization closer to the human expert’s decomposition (i.e., directory structure) than the other existing algorithms. The proposed algorithm is expected to help a software designer in the software reverse engineering process to extract easy-to-manage and understandable modules from source code.
Semantic View Synthesis

We tackle a new problem of semantic view synthesis—generating free-viewpoint rendering of a synthesized scene using a semantic label map as input. We build upon recent advances in semantic image synthesis and view synthesis for handling photographic image content generation and view extrapolation. Direct application of existing image/view synthesis methods, however, results in severe ghosting/blurry artifacts. To address the drawbacks, we propose a two-step approach. First, we focus on synthesizing the color and depth of the visible surface of the 3D scene. We then use the synthesized color and depth to impose explicit constraints on the multiple-plane image (MPI) representation prediction process. Our method produces sharp contents at the original view and geometrically consistent renderings across novel viewpoints. The experiments on numerous indoor and outdoor images show favorable results against several strong baselines and validate the effectiveness of our approach.
Color image triple zero-watermarking using decimal-order polar harmonic transforms and chaotic system

Existing zero-watermarking algorithms basically construct single zero-watermark information for the entire image and hence their efficiency is relatively low. In this paper, a color image triple zero-watermarking algorithm based on decimal-order polar harmonic transforms (DoPHTs) and chaotic system is proposed to achieve triple color image copyright protection, which is robust to geometric attacks. Firstly, the traditional integer-order polar harmonic transforms (IoPHTs) are extended to the decimal-order to construct DoPHTs, which effectively improve the performance of IoPHTs. Secondly, the three forms of DoPHTs, namely, decimal-order polar complex exponential transform (DoPCET), decimal-order polar cosine transform (DoPCT) and decimal-order polar sine transform (DoPST), are used to construct triple zero-watermark information. This algorithm uses geometrically invariant DoPHTs to improve its robustness to geometric attacks and uses the chaotic system to scramble logo image and feature images to enhance its security. The experimental results show that the proposed algorithm is highly robust to common image processing attacks and geometric attacks and is superior to other algorithms.
Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.
A single shuffle is enough for secure card-based computation of any Boolean circuit

Secure computation enables a number of players each holding a secret input value to compute a function of the inputs without revealing the inputs. It is known that secure computation is possible physically when the inputs are given as a sequence of physical cards. This research area is called card-based cryptography. One of the important problems in card-based cryptography is to minimize the number of cards and shuffles, where a shuffle is the most important (and somewhat heavy) operation in card-based protocols. In this paper, we determine the minimum number of shuffles for achieving general secure computation. Somewhat surprisingly, the answer is just one, i.e., we design a protocol which securely computes any Boolean circuit with only a single shuffle. The number of cards required for our protocol is proportional to the size of the circuit to be computed.
Privacy-Preserving Content-Based Image Retrieval Using Compressible Encrypted Images

In this article, we propose a novel content-based image-retrieval (CBIR) scheme using compressible encrypted images called “encryption-then-compression (EtC) images.” The proposed scheme allows us not only to directly retrieve images from visually protected images but to also apply EtC images that can be compressed by using the JPEG standard for the first time. In addition, the sensitive management of secret keys is not required in our framework. The proposed retrieval scheme is carried out on the basis of weighted searching images with MPEG-7-powered localized descriptors (weighted SIMPLE descriptors) combining scalable color descriptor (SCD) or color and edge directivity descriptor (CEDD). Weighted SIMPLE descriptors are extended, and CEDD is also modified to avoid the influence of image encryption. In an experiment, the proposed scheme is demonstrated to have almost no degradation in retrieval performance compared with conventional content-based retrieval methods with plain images under the use of two datasets. In addition, the proposed scheme is shown to outperform conventional privacy-preserving CBIR schemes including state-of-the-art ones in terms of mean average precision (mAP) scores.
A survey of micro-expression recognition

The limited capacity to recognize micro-expressions with subtle and rapid motion changes is a long-standing problem that presents a unique challenge for expression recognition systems and even for humans. The problem regarding micro-expression is less covered by research when compared to macro-expression. Nevertheless, micro-expression recognition (MER) is imperative to exploit the full potential of expression recognition for real-world applications. Recent MER systems generally focus on three important issues: overfitting caused by a lack of sufficient training data, the imbalanced distribution of samples, and robust features for improving the accuracy of recognition. In this paper, we provide a comprehensive survey on MER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature. We then describe the pre-processing in the standard pipeline of an MER system. For the state of the art in MER, we divide the existing novel algorithms into 6 different tasks according to the type of classes and evaluation protocols. Detailed experiment settings and competitive performances for those 6 tasks are summarized in this section. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust MER systems.
Quantum critical detector: amplifying weak signals using discontinuous quantum phase transitions

We propose a quantum critical detector (QCD) to amplify weak input signals. Our detector exploits a first-order discontinuous quantum-phase-transition and exhibits giant sensitivity (χ ∝ N2) when biased at the critical point. We propose a model consisting of spins with long-range interactions coupled to a bosonic mode to describe the time-dynamics in the QCD. We numerically demonstrate dynamical features of the first order (discontinuous) quantum phase transition such as time-dependent quantum gain in a system with 80 interacting spins. We also show the linear scaling with the spin number N in both the quantum gain and the corresponding signal-to-quantum noise ratio during the time evolution of the device. Our work shows that engineering first order discontinuous quantum phase transitions can lead to a device application for metrology, weak signal amplification, and single photon detection.
Real-Time Adaptive Object Detection and Tracking for Autonomous Vehicles

The recent development of deep neural networks attempts to go deeper through the layered architecture to solve complex problems. As expected, the deepening impacts the processing times for inferences in tasks like object classification and localization. This demands solutions for balancing computational resource requirements and extend the versatility of a given model to suit a variety of applications. Often the solutions rely on architectural modifications to ease training times while achieving more accurate results. In this work, we propose a multistage algorithm with an initial stage adopting the YOLOv3 network for object detection and HART approach for object tracking. This is followed by an adaptive post-localization stage shift system taking into consideration the processing times of stage inferences, which are the number of located objects in image sequences. The goal is to change the localization strategy to achieve optimal processing time performance. For evaluation, we present results in terms of precision and processing times in varying traffic conditions. The results demonstrate the effectiveness of the proposed adaptive multistage model in comparison to other real-time state-of-the-art detection strategies, as it achieves a frame rate gain of 49% over YOLOv3 while maintaining competitive task accuracy.
Adaptive regularization with cubics on manifolds

Adaptive regularization with cubics (ARC) is an algorithm for unconstrained, non-convex optimization. Akin to the popular trust-region method, its iterations can be thought of as approximate, safe-guarded Newton steps. For cost functions with Lipschitz continuous Hessian, ARC has optimal iteration complexity, in the sense that it produces an iterate with gradient smaller than $\varepsilon$ in $O(1/\varepsilon^{1.5})$ iterations. For the same price, it can also guarantee a Hessian with smallest eigenvalue larger than $-\varepsilon^{1/2}$. In this paper, we study a generalization of ARC to optimization on Riemannian manifolds. In particular, we generalize the iteration complexity results to this richer framework. Our central contribution lies in the identification of appropriate manifold-specific assumptions that allow us to secure these complexity guarantees both when using the exponential map and when using a general retraction. A substantial part of the paper is devoted to studying these assumptions---relevant beyond ARC---and providing user-friendly sufficient conditions for them. Numerical experiments are encouraging.
Efficient and Secure Location-Based Services Scheme in VANET

In VANET, the privacy of the query becomes a serious concern for location-based services (LBS). An LBS scheme must, also, not breach the location privacy of the user vehicle. In order to protect query privacy of the vehicle user, the existing state-of-the-art schemes either reduce the accuracy of LBS or insert a trusted third party (TTP) between the vehicle user and the location server hosting the LBS scheme. Cryptographic constructs such as public-key encryption, Oblivious Transfer (OT), Private Information Retrieval (PIR), and homomorphic encryption are used to remove the TTP or increase the accuracy of the previous schemes. The problem with these existing schemes is that some of them are either inefficient or insecure. In order to address the security and privacy issues, we propose an efficient privacy-preserving mechanism for protecting the query privacy of the user, information content of the location server, and location privacy-preserving of the vehicle in the LBS scheme for VANET. In this scheme, the vehicle requests for services from the location server without revealing the query content to the location server. The location server sends all Point of Interests (POIs) to the vehicle, but the vehicle can only obtain the requested query content. The query privacy of the user and content privacy of the location server is preserved in the exchange. The proposed variant of the OT extension protocol is computationally and communicationally efficient and uses a lattice-based ring-Learning With Errors (ring-LWE) scheme as a Base-OT.
BEE: a tool for structuring and analyzing bug reports

This paper introduces BEE, a tool that automatically analyzes user-written bug reports and provides feedback to reporters and developers about the system's observed behavior (OB), expected behavior (EB), and the steps to reproduce the bug (S2R). BEE employs machine learning to (i) detect if an issue describes a bug, an enhancement, or a question; (ii) identify the structure of bug descriptions by automatically labeling the sentences that correspond to the OB, EB, or S2R; and (iii) detect when bug reports fail to provide these elements. BEE is integrated with GitHub and offers a public web API that researchers can use to investigate bug management tasks based on bug reports. We evaluated BEE's underlying models on more than 5k existing bug reports and found they can correctly detect OB, EB, and S2R sentences as well as missing information in bug reports. BEE is an open-source project that can be found at <a>https://git.io/JfFnN</a>. A screencast showing the full capabilities of BEE can be found at <a>https://youtu.be/8pC48f_hClw</a>.
Data Sanitization to Reduce Private Information Leakage from Functional Genomics

The generation of functional genomics datasets is surging, because they provide insight into gene regulation and organismal phenotypes (e.g., genes upregulated in cancer). The intent behind functional genomics experiments is not necessarily to study genetic variants, yet they pose privacy concerns due to their use of next-generation sequencing. Moreover, there is a great incentive to broadly share raw reads for better statistical power and general research reproducibility. Thus, we need new modes of sharing beyond traditional controlled-access models. Here, we develop a data-sanitization procedure allowing raw functional genomics reads to be shared while minimizing privacy leakage, enabling principled privacy-utility trade-offs. Our protocol works with traditional Illumina-based assays and newer technologies such as 10x single-cell RNA sequencing. It involves quantifying the privacy leakage in reads by statistically linking study participants to known individuals. We carried out these linkages using data from highly accurate reference genomes and more realistic environmental samples.
XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization

The ability to correctly model distinct meanings of a word is crucial for the effectiveness of semantic representation techniques.However, most existing evaluation benchmarks for assessing this criterion are tied to sense inventories (usually WordNet), restricting their usage to a small subset of knowledge-based representation techniques.The Word-in-Context dataset (WiC) addresses the dependence on sense inventories by reformulating the standard disambiguation task as a binary classification problem; but, it is limited to the English language.We put forward a large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages from varied language families and with different degrees of resource availability, opening room for evaluation scenarios such as zero-shot cross-lingual transfer.We perform a series of experiments to determine the reliability of the datasets and to set performance baselines for several recent contextualized multilingual models.Experimental results show that even when no tagged instances are available for a target language, models trained solely on the English data can attain competitive performance in the task of distinguishing different meanings of a word, even for distant languages.XL-WiC is available at https://pilehvar.github.io/xlwic/.
A Digital Image Confidentiality Scheme Based on Pseudo-Quantum Chaos and Lucas Sequence

Several secure image encryption systems have been researched and formed by chaotic mechanisms in current decades. This work recommends an innovative quantum color image encryption method focused on the Lucas series-based substitution box to enhance the competence of encryption. The suggested encryption technique has more excellent key space and significant confidentiality. The chaotic system, along with the substitution box, exhibits additional complicated dynamical behavior, sufficient arbitrariness, and uncertainty than all others focused on just chaotic models. Theoretical and simulation assessments show that the offered image encryption performs admirably, its traditional equivalents in terms by efficiency in terms of statistical analysis.
Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing via Geo-Indistinguishability

Nowadays, vehicles have been increasingly adopted in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location to servers to facilitate the service utilities. Traditional location privacy protection mechanisms cannot be applied to vehicle-based SC since they assume workers’ mobility on a 2-dimensional plane without considering the network-constrained mobility features of vehicles. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) over road networks. Our objective is to design a location obfuscation strategy to minimize the quality loss due to obfuscation with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">geo-indistinguishability</i> satisfied. Considering the computational complexity of VLP, by resorting to discretization, we first approximate VLP to a linear programming problem that can be solved by well-developed approaches. To further improve the time-efficiency, we conduct constraint reduction for VLP by exploiting key features of geo-indistinguishability in road networks and problem decomposition based on VLP’s constraint structure. Finally, we carry out both trace-driven simulation and real-world experiments, where our experimental results demonstrate the superiority of our approach over a known state-of-the-art location obfuscation strategy in terms of both quality-of-service and privacy.