Reviewerly/exHarmony huggingface dataset:
Video Face Recognition Using Siamese Networks With Block-Sparsity Matching

Deep learning models for still-to-video FR typically provide a low level of accuracy because faces captured in unconstrained videos are matched against a reference gallery comprised of a single facial still per individual. For improved robustness to intra-class variations, deep Siamese networks have recently been used for pair-wise face matching. Although these networks can improve state-of-the-art accuracy, the absence of prior knowledge from the target domain means that many images must be collected to account for all possible capture conditions, which is not practical for many real-world surveillance applications. In this paper, we propose the deep SiamSRC network that employs block-sparsity for face matching, while the reference gallery is augmented with a compact set of domain-specific facial images. Prior to deployment, clustering based on row sparsity is performed on unlabelled faces captured in videos from the target domain. Cluster centers discovered in the capture condition space (defined by, e.g., pose, scale and illumination) are used as rendering parameters with an off-the-shelf 3D face model, and a compact set of synthetic faces are thereby generated for each reference still based on representative intra-class information from the target domain. For pair-wise similarity matching with query facial images, the SiamSRC exploits sparse representation-based classification with a block structure. Experimental results obtained with the videos from the Chokepoint and COX-S2V datasets indicate that the proposed SiamSRC network can outperform state-of-the-art methods for still-to-video FR with a single sample per person, with only a moderate increase in computational complexity.
Referring Expression Comprehension with Semantic Visual Relationship and Word Mapping

Referring expression comprehension, which locates the object instance described by a natural language expression, gains increasing interests in recent years. This paper aims at improving the task from two aspects: visual feature extraction and language features extraction. For visual feature extraction, we observe that most of the previous methods utilize only relative spatial information to model the visual relationship between object pairs while discarding rich semantic relationship between objects. This makes the visual-language matching difficult when the language expression contains semantic relationship to discriminate the referred object from other objects in the image. In this work, we propose a Semantic Visual Relationship Module (SVRM) to exploit this important information. For language feature extraction, a major problem comes from the long-tail distribution of words in the expressions. Since more than half of the words appear less than 20 times in the public datasets, deep models such as LSTM tend to fail to learn accurate representations for these words. To solve this problem, we propose a word2vec based word mapping method that maps these low frequency words to high frequency words with similar meaning. Experiments show that the proposed method outperforms existing state-of-the-art methods on three referring expression comprehension datasets.
Towards a Better Understanding of Drone Forensics

Unmanned aerial vehicles (drones) have gained increased popularity as their innovative uses continue to expand across various fields. Despite their numerous beneficial uses, drones have unfortunately been misused, through many reported cases, to launch illegal and sometimes criminal activities that pose direct threats to individuals, organizations, public safety and national security. These threats have recently led law enforcement agencies and digital forensic investigators to pay special attention to the forensic aspects of drones. This important research topic, however, remains underexplored. This study aims to further explore drone forensics in terms of challenges, forensic investigation procedures and experimental results through a forensic investigation study performed on a Parrot AR drone 2.0. In this study, the authors present new insights on drone forensics in terms of forensic approaches, access to drone's digital containers and the retrieval of key information that can assist digital forensic investigators establish ownership, recuperate flight data and gain access to media files.
MULTS: A multi-cloud fault-tolerant architecture to manage transient servers in cloud computing

The large-scale utilization of cloud computing resources has led to the emergence of cloud environment reliability as an important issue. In addition, cloud providers are negotiating unreliable virtual machines as a result of exploring unused resources offering them as transient servers - a lower price virtual machine service with resource revocations without user intervention. To increase the availability of transient servers, we propose a multi-cloud fault-tolerant architecture to provide a resilient environment using a scenario-based optimal checkpoint in a scheme to guarantee running processes with reduced user costs. The architecture combines a heuristic to extract information from a case-based reasoning and a statistical model to predict failure events helping to refine fault tolerance parameters. As a result, a cloud environment with better levels of reliability and reduced execution time is provided. Extensive simulations show high levels of accuracy reaching up to 92% survival prediction success rate and a gain of 74,58% of execution time reduction for long running applications. The results are promising, indicating that the proposed architecture can prevent revocation failures under realistic working conditions.
"It Started as a Joke"

With idle games, active withdrawal from the game comprises an essential part of gameplay as players wait for the game state to change over time. This mode of interaction is paradigmatic for the change of roles technologies have in our lives. However, the design elements of idle games are less well understood, particularly from the perspectives of developers. We interviewed six designers of six different popular idle games and inquired into their individual approaches. Via thematic analysis, we refine and expand on existing definitions of idle games as a genre, shed light on ethically charged practices of care in their design, and identify shared core characteristics between the games and processes. We then generate intermediate-level knowledge on the design of idle games. Our work contributes designers' perspectives on idle games and their design to a growing body of literature on the genre.
Secure Deduplication System with Active Key Update and Its Application in IoT

The rich cloud services in the Internet of Things create certain needs for edge computing, in which devices should be able to handle storage tasks securely, reliably, and efficiently. When processing the storage requests from edge devices, each cloud server is supposed to eliminate duplicate copies of repeating data to reduce the amount of storage space and save on bandwidth. To protect data confidentiality while supporting deduplication, some convergent-encryption-based techniques have been proposed to encrypt the data before uploading. However, all these works cannot meet two requirements while preventing brute-force attacks: (i) power-constrained edge nodes should update encryption keys efficiently when an edge node is abandoned; and (ii) the access privacy of edge nodes should be guaranteed. In this article, we propose a novel encryption scheme for secure chunk-level deduplication. Based on this scheme, we present two constructions of the secure deduplication system that support an efficient key update protocol. The key update protocol does not involve any edge node in computational tasks, so that the deduplication system can adopt an active key update strategy. Moreover, one of our constructions, which is called advance construction, can provide access privacy assurances for edge nodes. The security analysis is given in terms of the proposed threat model. The experimental analysis demonstrates that the proposed deduplication system is practical.
Photon Enhanced Interaction and Entanglement in Semiconductor Position-Based Qubits

CMOS technologies facilitate the possibility of implementing quantum logic in silicon. In this work, we discuss a minimalistic modelling of entangled photon communication in semiconductor qubits. We demonstrate that electrostatic actuation is sufficient to construct and control desired potential energy profiles along a Si quantum dot (QD) structure allowing the formation of position-based qubits. We further discuss a basic mathematical formalism to define the position-based qubits and their evolution under the presence of external driving fields. Then, based on Jaynes–Cummings–Hubbard formalism, we expand the model to include the description of the position-based qubits involving four energy states coupled with a cavity. We proceed with showing an anti-correlation between the various quantum states. Moreover, we simulate an example of a quantum trajectory as a result of transitions between the quantum states and we plot the emitted/absorbed photos in the system with time. Lastly, we examine the system of two coupled position-based qubits via a waveguide. We demonstrate a mechanism to achieve a dynamic interchange of information between these qubits over larger distances, exploiting both an electrostatic actuation/control of qubits and their photon communication. We define the entanglement entropy between two qubits and we find that their quantum states are in principle entangled.
Fast Non-Local Neural Networks with Spectral Residual Learning

Effectively modeling long-range spatial correlation is crucial in context-sensitive visual computing tasks, such as human pose estimation and video classification. Enlarging receptive field is popularly adopted in building such non-local deep networks. However, current solutions, including dilation convolution or self-attention based operators, mostly suffer from either low computational efficacy or insufficient receptive field. This paper proposes spectral residual learning (SRL), a novel network architectural design for achieving fully global receptive field. A neural block that implements SRL has three key components: a local-to-global transform that projects some ordinary local features into a spectral domain, compiled operations in the spectral domain, and a global-to-local transform that converts all data back to the original local format. We show its equivalence to conducting residual learning in some spectral domain and carefully re-formulate a variety of neural layers into their spectral forms, such as ReLU or convolutions. The benefits of SRL is three-fold: first, all operations have global receptive field, namely any update affects all image positions. This can extract richer context information in various vision tasks; Secondly, the local-to-global / global-to-local transforms in SRL are defined by bi-linear unitary matrices, which is both computation and parameter economic; Lastly, SRL is a generic formulation, here instantiated by Fourier transform and real orthogonal matrix. We conduct comprehensive evaluations on two challenging tasks, including human pose estimation from images and video classification. All experiments clearly show performance improvement by large margins in comparison with conventional non-local network designs.
Bitcoin adoption

Bitcoin is the most successful cryptocurrency with more than half of the market capitalization of all more than 2,000 currently existing cryptocurrencies. In recent years, there have been several high-profile hacks and scams that resulted in billions of stolen funds. In this paper, we focus on the impact of Bitcoin cybersecurity and privacy characteristics on its adoption. A survey (N = 152) has been conducted among users and non-users of Bitcoin in Slovenia to test the proposed research model. The results suggest that in addition to known factors (i.e., usefulness, ease of use and subjective norm) trust into Bitcoin security also influences Bitcoin adoption. The results however show no support for the influence of perceived threat of Bitcoin scams or Bitcoin anonymity on Bitcoin adoption.
Notary

Notary is a new hardware and software architecture for running isolated approval agents in the form factor of a USB stick with a small display and buttons. Approval agents allow factoring out critical security decisions, such as getting the user's approval to sign a Bitcoin transaction or to delete a backup, to a secure environment. The key challenge addressed by Notary is to securely switch between agents on the same device. Prior systems either avoid the problem by building single-function devices like a USB U2F key, or they provide weak isolation that is susceptible to kernel bugs, side channels, or Rowhammer-like attacks. Notary achieves strong isolation using reset-based switching, along with the use of physically separate systems-on-a-chip for agent code and for the kernel, and a machine-checked proof of both the hardware's register-transfer-level design and software, showing that reset-based switching leaks no state. Notary also provides a trustworthy I/O path between the agent code and the user, which prevents an adversary from tampering with the user's screen or buttons.
Learning Target Dynamics While Tracking Using Gaussian Processes

Tracked targets often exhibit common behaviors due to influences from the surrounding environment, such as wind or obstacles, which are usually modeled as noise. Here, these influences are modeled using sparse Gaussian processes that are learned online together with the state inference using an extended Kalman filter. The method can also be applied to time-varying influences and identify simple dynamic systems. The method is evaluated with promising results in a simulation and a real-world application.
Deep Learning-Based Morphological Taggers and Lemmatizers for Annotating Historical Texts

Part-of-speech tagging, morphological tagging, and lemmatization of historical texts pose special challenges due to the high spelling variability and the lack of large, high-quality training corpora. Researchers therefore often first map the words to their modern spelling and then annotate with tools trained on modern corpora. We show in this paper that high quality part-of-speech tagging and lemmatization of historical texts is possible while operating directly on the historical spelling. We use a part-of-speech tagger based on bidirectional long short-term memory networks (LSTMs) [11] with character-based word representations and lemmatize using an encoder-decoder system with attention. We achieve state-of-the-art results for modern German morphological tagging on the Tiger corpus and also on two historical corpora which have been used in previous work.
Multi-Frame RGB/NIR Imaging for Low-Light Color Image Super-Resolution

We propose a method for super-resolution (SR) of low-resolution (LR) color images taken in low-light scenes. Our method is based on the multi-frame SR technique, which reconstructs a high-resolution color image by fusing multiple LR images that were taken at different camera positions so as to alter the alignment between scene details and pixels. Previous multi-frame SR methods have implicitly assumed that the LR images could be captured with less noise and blur. However, images taken in low-light scene will contain high level of noise and motion blur, making it difficult to achieve high quality SR. To overcome these problems, we utilize a single sensor that captures red, green, blue (RGB) and near-infrared (NIR) information. We capture multiple raw images of a low-light scene using our RGB/NIR single sensor with an NIR flash unit. Because a NIR flash can provide sufficient illumination for low-light scenes, the structural scene information that, contributes to an effective SR process, can be captured with high quality. With the help of the NIR information, we jointly perform deblurring, denoising and SR of LR RGB/NIR raw images. Our experiments demonstrate the effectiveness of the proposed method using synthetic and real RGB/NIR raw images.
Security analysis and application of Chebyshev Chaotic map in the authentication protocols

Due to the quick expansion and fast growth of computer networks, remote user's authentication and key agreement protocol is required. Chaotic maps play a very important role in the construction of authentication and key agreement protocol. Recently, many of the key agreement protocols are proposed based on chaotic maps and it can generate secure session key as well as resist known-key attacks. In this paper, it has been illustrated that most of the existing schemes cannot establish secure communication. Moreover, chaotic map-based authentications schemes have been found vulnerable to identity guessing, password guessing, impersonation, stolen smart card, and violation of session key attacks. Although some of the protocol has advantages of low computation cost but they lacks to attain security attributes. Moreover, these schemes do not support verified session key security with a minimum number of messages exchanged. Therefore, a chaotic map-based authentication scheme is essential to overcome existing weaknesses.
Well-Posed Geoscientific Visualization Through Interactive Color Mapping

Scientific visualization aims to present numerical values, or categorical information, in a way that enables the researcher to make an inference that furthers knowledge. Well-posed visualizations need to consider the characteristics of the data, the display environment and human visual capacity. In the geosciences, visualizations are commonly applied to spatially varying continuous information or results. In this contribution we make use of a suite of newly written computer applications which enable spatially varying data to be displayed in a performant graphics environment. We present a comparison of color-mapping using illustrative color spaces (RGB, CIELAB). The interactive applications display the gradient paths through the chosen color spaces. This facilitates the creation of color-maps that accommodate the non-uniformity of human color perception, producing an image where genuine features are seen. We also take account of aspects of a dataset such as parameter uncertainty. For an illustrative case study using a seismic tomography result, we find that the use of RGB color-mapping can introduce non-linearities in the visualization, potentially leading to incorrect inference. Interpolation in CIELAB color space enables the creation of perceptually uniform linear gradients that match the underlying data, along with a simply computable metric for color difference, ∆E. This color space assists accuracy and reproducibility of visualization results. Well-posed scientific visualization requires both 'visual literacy' and 'visual numeracy' on an equal footing with clearly written text. It is anticipated that this current work, with the included color-maps and software, will lead to wider usage of informed color-mapping in the geosciences.
Optimality Assessment of Memory-Bounded ConvNets Deployed on Resource-Constrained RISC Cores

A cost-effective implementation of Convolutional Neural Nets on the mobile edge of the Internet-of-Things (IoT) requires smart optimizations to fit large models into memory-constrained cores. Reduction methods that use a joint combination of filter pruning and weight quantization have proven efficient in searching the compression that ensures minimum model size without accuracy loss. However, there exist other optimal configurations that stem from the memory constraint. The objective of this work is to make an assessment of such memory-bounded implementations and to show that most of them are centred on specific parameter settings that are found difficult to be implemented on a low-power RISC. Hence, the focus is on quantifying the distance to optimality of the closest implementations that instead can be actually deployed on hardware. The analysis is powered by a two-stage framework that efficiently explores the memory-accuracy space using a lightweight, hardware-conscious heuristic optimization. Results are collected from three realistic IoT tasks (Image Classification on CIFAR-10, Keyword Spotting on the Speech Commands Dataset, Facial Expression Recognition on Fer2013) run on RISC cores (Cortex-M by ARM) with few hundreds KB of on-chip RAM.
Implementation of Haversine Formula on Location Based Mobile Application in Syiah Kuala University

Syiah Kuala University (Unsyiah) is one of the leading public universities in Sumatera where thousands of students are accepted and graduated every year. This means, that Unsyiah is visited by many people from various regions and backgrounds. Most visitors find it difficult to locate the building they want to visit. This is due to the lack of information regarding the buildings situated around Unsyiah. Based on this problem, an application is developed to solve the problem. The application built is an Android-based application by using the Location Based Service method. This application is useful as a guide map for visitors to reach the location and explore Unsyiah. The distance computation between two points is calculated using Haversine Formula. The Android based application is called Unsyiah Guidance Map and a web-based application is developed for administrator to insert building data. The application was successfully tested with blackbox testing. The usability testing for the application was using the System Usability Scale (SUS) method. The SUS score obtained was 84.36 that falls into B grade; this means that the application is acceptable and easy to use. The distance calculations tested 10 times using Haversine Formula and Google Maps Distance. These tests yielded an average difference value of 0.088 meters. With the help of SPSS software, it shows that both Haversine Formula and Google Maps Distance do not have significant differences. It can be concluded that Haversine Formula that was used on the nearest distance computation has small difference compared to Google Maps Distance.
ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine

In this study, it is aimed to develop a user-friendly, interactive web software for association rules mining. In the developed software, among the association rule methods; Filtered Associatior, Apriori, Frequent Pattern Growth, Predictive Apriori, Generalized Sequential Patterns, HotSpot, Tertius algorithms are used. In addition, association rules algorithms have certain limitation(s) regarding the structure of the data set. Therefore, preprocess menu in the software includes missing value assignment and variable type conversion, methods. In order to evaluate the association rules, support and confidence criteria are present in the software. However, it is not always possible to distinguish interesting and important rules only according to criteria of support and confidence. Therefore, in the proposed software; leverage, lift and conviction criteria are also included. A medical application is performed by using association rules mining, and the experimental results are evaluated based on the outputs of the developed software.