Reviewerly/exHarmony huggingface dataset:
Polarization-Based Quantum Key Distribution Encoder and Decoder on Silicon Photonics

Private and secure communication is an indispensable part of the government and individual activities. With the ever-evolving large-scale of quantum computing, traditional public-key cryptography is severely threatened since its security only relies on the computational complexity of certain mathematical functions. Quantum key distribution (QKD), ascribed to its security based on the inviolability of physics laws, provides an absolutely information-secure solution for the future extensive communication encrypting. Herein this Letter, we proposed a simplified and reconfigurable silicon photonics encoder using a pass-block architecture and experimentally demonstrated its performance with a specialized silicon photonics decoder for high-speed quantum key distribution in polarization-based decoy-state BB84 protocol. We achieved an estimated asymptotic secret key rate of 868 kbps with measured quantum bit error rate (QBER) of 0.90% (Z base) and 1.34% (X base) over 20 km emulated fiber link. This work further advances the process of applying QKD using silicon photonics devices into the future secure telecommunication network.
An Efficient Deep Convolutional Neural Network Approach for Object Detection and Recognition Using a Multi-Scale Anchor Box in Real-Time

Deep learning is a relatively new branch of machine learning in which computers are taught to recognize patterns in massive volumes of data. It primarily describes learning at various levels of representation, which aids in understanding data that includes text, voice, and visuals. Convolutional neural networks have been used to solve challenges in computer vision, including object identification, image classification, semantic segmentation and a lot more. Object detection in videos involves confirming the presence of the object in the image or video and then locating it accurately for recognition. In the video, modelling techniques suffer from high computation and memory costs, which may decrease performance measures such as accuracy and efficiency to identify the object accurately in real-time. The current object detection technique based on a deep convolution neural network requires executing multilevel convolution and pooling operations on the entire image to extract deep semantic properties from it. For large objects, detection models can provide superior results; however, those models fail to detect the varying size of the objects that have low resolution and are greatly influenced by noise because the features after the repeated convolution operations of existing models do not fully represent the essential characteristics of the objects in real-time. With the help of a multi-scale anchor box, the proposed approach reported in this paper enhances the detection accuracy by extracting features at multiple convolution levels of the object. The major contribution of this paper is to design a model to understand better the parameters and the hyper-parameters which affect the detection and the recognition of objects of varying sizes and shapes, and to achieve real-time object detection and recognition speeds by improving accuracy. The proposed model has achieved 84.49 mAP on the test set of the Pascal VOC-2007 dataset at 11 FPS, which is comparatively better than other real-time object detection models.
A Conflict-Based Search Framework for Multiobjective Multiagent Path Finding

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. The goal of the problem is thus to find a Pareto-optimal set of solutions instead of a single optimal solution. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the dimensionality of the search space grows exponentially with the number of agents. This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that attempts to address this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to improve its performance. We prove that MO-CBS and its variants can compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms MOM*, a recently developed state-of-the-art multi-objective multi-agent planner. Note to Practitioners—The motivation of this article originates from the need to optimize multiple path criteria when planning conflict-free paths for multiple mobile robots in applications such as warehouse logistics, surveillance, construction site routing, and hazardous material transportation. Existing methods for multi-agent planning typically consider optimizing a single path criteria. This article develops a novel multi-objective multi-agent planner as well as its variants that are guaranteed to find all Pareto-optimal solutions for the problem. We also provide an illustrative example of the algorithm to plan paths for multiple agents that transport materials in a construction site while optimizing both path length and risk. In this example, computing and visualizing a set of Pareto-optimal solutions makes it intuitive for the practitioner to understand the underlying trade-off between conflicting objectives and to choose the most preferred solution for execution based on their domain knowledge.
Large-curvature specular surface phase measuring deflectometry with a curved screen

Deflectometry has been widely used in topography measurement of the specular surface due to the merits of full-field data acquisition, automatic data processing, high accuracy, and large dynamic range. Existing deflectometry-based methods, such as model phase measuring deflectometry, direct phase measuring deflectometry, and stereo deflectometry, employ a plane screen, so they have a small field view. The characteristics of curved screens can effectively expand the range of the gradient and height field of the measured specular surface. This paper presents a novel deflectometric technique by using a curved screen to measure large-curvature specular surface. In order to obtain a model of the curved screen, a stereo vision method has been proposed to capture fringes displayed on the curved screen. After phase correction along epipolar lines and homologous points matching, the point cloud containing location and phase is obtained. A virtual plane fixed on the screen is assumed to gain posture and position of the screen. The model of the curved screen is transferred to the deflectometric system through the virtual plane. After calibrating the deflectometric system, height of specular objects can be obtained. Simulated and actual experiments on measuring specular objects have been carried out and the results show high accuracy and large field view for large-curvature specular surface of the proposed technique.
Hyperledger Fabric

The success of public blockchains, such as Bitcoin and Ethereum, led to growing interest in Blockchain technology and its application as a distributed system in the most innovative business use cases.
AddGBoost: A gradient boosting-style algorithm based on strong learners

We present AddGBoost, a gradient boosting-style algorithm, wherein the decision tree is replaced by a succession of (possibly) stronger learners, which are optimized via a state-of-the-art hyperparameter optimizer. Through experiments over 90 regression datasets we show that AddGBoost emerges as the top performer for 33% (with 2 stages) up to 42% (with 5 stages) of the datasets, when compared with seven well-known machine-learning algorithms: KernelRidge, LassoLars, SGDRegressor, LinearSVR, DecisionTreeRegressor, HistGradientBoostingRegressor, and LGBMRegressor.
No Humans Here

Many research communities routinely conduct activities that fall outside the bounds of traditional human subjects research, yet still frequently rely on the determinations of institutional review boards (IRBs) or similar regulatory bodies to scope ethical decision-making. Presented as a U.S. university-based fictional memo describing a post-hoc IRB review of a research study about social media and public health, this design fiction draws inspiration from current debates and uncertainties in the HCI and social computing communities around issues such as the use of public data, privacy, open science, and unintended consequences, in order to highlight the limitations of regulatory bodies as arbiters of ethics and the importance of forward-thinking ethical considerations from researchers and research communities.
Higher-order Structure Based Anomaly Detection on Attributed Networks

Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can reflect specific human behavior patterns. Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks. However, due to the lack of an effective mechanism in most existing graph learning methods, these complex interaction patterns fail to be applied in detecting anomalies, hindering the progress of anomaly detection to some extent. In order to address the aforementioned issue, we present a higher-order structure based anomaly detection (GUIDE) method. We exploit attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures, respectively. Moreover, we design a graph attention layer to evaluate the significance of neighbors to nodes through their higher-order structure differences. Finally, we leverage node attribute and higher-order structure reconstruction errors to find anomalies. Extensive experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly outperforms the state-of-art methods.
Word-Region Alignment-Guided Multimodal Neural Machine Translation

We propose word-region alignment-guided multimodal neural machine translation (MNMT), a novel model for MNMT that links the semantic correlation between textual and visual modalities using word-region alignment (WRA). Existing studies on MNMT have mainly focused on the effect of integrating visual and textual modalities. However, they do not leverage the semantic relevance between the two modalities. We advance the semantic correlation between textual and visual modalities in MNMT by incorporating WRA as a bridge. This proposal has been implemented on two mainstream architectures of neural machine translation (NMT): the recurrent neural network (RNN) and the transformer. Experiments on two public benchmarks, English–German and English–French translation tasks using the Multi30k dataset and English–Japanese translation tasks using the Flickr30kEnt-JP dataset prove that our model has a significant improvement with respect to the competitive baselines across different evaluation metrics and outperforms most of the existing MNMT models. For example, 1.0 BLEU scores are improved for the English–German task and 1.1 BLEU scores are improved for the English–French task on the Multi30k test2016 set; and 0.7 BLEU scores are improved for the English–Japanese task on the Flickr30kEnt-JP test set. Further analysis demonstrates that our model can achieve better translation performance by integrating WRA, leading to better visual information use.
A Novel Integration of Face-Recognition Algorithms with a Soft Voting Scheme for Efficiently Tracking Missing Person in Challenging Large-Gathering Scenarios

The probability of losing vulnerable companions, such as children or older ones, in large gatherings is high, and their tracking is challenging. We proposed a novel integration of face-recognition algorithms with a soft voting scheme, which was applied, on low-resolution cropped images of detected faces, in order to locate missing persons in a challenging large-crowd gathering. We considered the large-crowd gathering scenarios at Al Nabvi mosque Madinah. It is a highly uncontrolled environment with a low-resolution-images data set gathered from moving cameras. The proposed model first performs real-time face-detection from camera-captured images, and then it uses the missing person's profile face image and applies well-known face-recognition algorithms for personal identification, and their predictions are further combined to obtain more mature prediction. The presence of a missing person is determined by a small set of consecutive frames. The novelty of this work lies in using several recognition algorithms in parallel and combining their predictions by a unique soft-voting scheme, which in return not only provides a mature prediction with spatio-temporal values but also mitigates the false results of individual recognition algorithms. The experimental results of our model showed reasonably good accuracy of missing person's identification in an extremely challenging large-gathering scenario.
The race to save the Internet from quantum hackers

The quantum computer revolution could break encryption — but more-secure algorithms can safeguard privacy. The quantum computer revolution could break encryption — but more-secure algorithms can safeguard privacy.
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge

Assessing the mitotic count has a known high degree of intra- and inter-rater variability. Computer-aided systems have proven to decrease this variability and reduce labeling time. These systems, however, are generally highly dependent on their training domain and show poor applicability to unseen domains. In histopathology, these domain shifts can result from various sources, including different slide scanning systems used to digitize histologic samples. The MItosis DOmain Generalization challenge focused on this specific domain shift for the task of mitotic figure detection. This work presents a mitotic figure detection algorithm developed as a baseline for the challenge, based on domain adversarial training. On the challenge’s test set, the algorithm scored an F $$_1$$ score of 0.7183. The corresponding network weights and code for implementing the network are made publicly available.
Secure Key Exchange Against Man-in-the-Middle Attack: Modified Diffie-Hellman Protocol

One of the most famous key exchange protocols is Diffie-Hellman Protocol (DHP) which is a widely used technique on which key exchange systems around the world depend. This protocol is simple and uncomplicated, and its robustness is based on the Discrete Logarithm Problem (DLP). Despite this, he is considered weak against the man-in-the-middle attack. This article presents a completely different version of the DHP protocol. The proposed version is based on two verification stages. In the first step, we check if the pseudo-random value α that Alice sends to Bob has been manipulated! In the second step, we make sure that the random value β that Bob sends to Alice is not manipulated. The man-in-the-middle attacker Eve can impersonate neither Alice nor Bob, manipulate their exchanged values, or discover the secret encryption key.
Synchronously scrambled diffuse image encryption method based on a new cosine chaotic map

We present a new cosine chaotic mapping proved by chaos theory test and analysis such that the system has good cryptography properties, wide chaos range, simple structure, and good sensitivity to initial value, and the mapping can meet the needs of chaotic image encryption. Based on the cosine chaotic system, we propose a new encryption method. First, according to the cyclic characteristics of the mapping, the cyclic information wave is simulated. Second, the quasi-Doppler effect is used to synchronously scramble and diffuse the image to obfuscate the original pixel. Finally, the XOR diffusion of image pixels is carried out by information wave to further enhance the encryption effect. Simulation experiment and security analysis show that the algorithm has good security, can resist the common attack mode, and has good efficiency.
Understanding Screen Relationships from Screenshots of Smartphone Applications

All graphical user interfaces are comprised of one or more screens that may be shown to the user depending on their interactions. Identifying different screens of an app and understanding the type of changes that happen on the screens is a challenging task that can be applied in many areas including automatic app crawling, playback of app automation macros and large scale app dataset analysis. For example, an automated app crawler needs to understand if the screen it is currently viewing is the same as any previous screen that it has encountered, so it can focus its efforts on portions of the app that it has not yet explored. Moreover, identifying the type of change on the screen, such as whether any dialogues or keyboards have opened or closed, is useful for an automatic crawler to handle such events while crawling. Understanding screen relationships is a difficult task as instances of the same screen may have visual and structural variation, for example due to different content in a database-backed application, scrolling, dialog boxes opening or closing, or content loading delays. At the same time, instances of different screens from the same app may share some similarities in terms of design, structure, and content. This paper uses a dataset of screenshots from more than 1K iPhone applications to train two ML models that understand similarity in different ways: (1) a screen similarity model that combines a UI object detector with a transformer model architecture to recognize instances of the same screen from a collection of screenshots from a single app, and (2) a screen transition model that uses a siamese network architecture to identify both similarity and three types of events that appear in an interaction trace: the keyboard or a dialog box appearing or disappearing, and scrolling. Our models achieve an F1 score of 0.83 on the screen similarity task, improving on comparable baselines, and an average F1 score of 0.71 across all events in the transition task.
Ātea Presence— Enabling Virtual Storytelling, Presence, and Tele-Co-Presence in an Indigenous Setting

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Developing, evaluating, and</b> disseminating IT research prototypes for and with indigenous partners is both challenging and rewarding. In conjunction with our domain expert collaborators, Te Rau Aroha Marae [Bluff, Aotearoa/New Zealand (NZ)] and our academic colleagues at the Universities of Waikato and Canterbury, we are implementing a mixed reality telepresence system to connect a diasporic Māori community to their historical, cultural, and geographic mātauranga (knowledge). In this article, we describe our project, tea Presence, which is guided by the principles of partnership, participation, and protection. We describe the design and evaluation of the system developed, the collaborative process we undertook with Te Rau Aroha Marae, and our Māori academic colleagues and report on lessons learned along the way.
Design of a Gabor Filter-Based Image Denoising Hardware Model

The intervention of noise into images during data acquisition and transmission is inevitable. Hence, the denoising of such affected images is essential in order to have effective image analysis where it needs image filtering. The Gabor filter is widely adapted in various image processing applications for feature extraction, texture analysis, pattern analysis, etc. The Gabor-based filtering technique adopted in work is aimed for image filtering in order to extract edges. The design of a low-power portable system deploys hardware accelerators to achieve high performance per watt in feature extraction and edge detection. In this paper, an image denoising hardware accelerator model is mapped from the Gabor filter function. Moreover, hardware models for realizing various parameters involved in the Gabor function are also presented. A MATLAB model for the proposed denoising hardware accelerator is simulated and performance is measured in terms of the peak-signal-to-noise ratio, mean square error, histograms and compared with algorithm level performance reported in the literature. It is observed that the proposed hardware architecture model showed better performance compared to the mathematical models reported in the literature. However, the key limitation is the degradation of hardware performance due to a truncation or rounding of the sample’s word length.
Revisiting man-in-the-middle attacks against HTTPS

A man-in-the-middle (MitM) attack enables threat actors to position themselves in a conversation between two parties. It can be used to eavesdrop on, or impersonate, either of the parties and may enable the perpetrator to steal personal information, including login credentials, payment card data and account details. By leveraging the hijacked information, the attacker can perform an unsanctioned password change, commit identity theft, authorise money transfers, and so on. This article re-examines MitM against HTTPS by both briefly referring to its constituents and assessing its feasibility on modern browsers. We show that under certain circumstances, specific variations of MitM can be effective on all mainstream browsers using cheap, pocket-sized hardware, open-source software and a script-kiddie level of understanding.
OneLabeler: A Flexible System for Building Data Labeling Tools

Labeled datasets are essential for supervised machine learning. Various data labeling tools have been built to collect labels in different usage scenarios. However, developing labeling tools is time-consuming, costly, and expertise-demanding on software development. In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios. The framework consists of common modules and states in labeling tools summarized through coding of existing tools. OneLabeler supports configuration and composition of common software modules through visual programming to build data labeling tools. A module can be a human, machine, or mixed computation procedure in data labeling. We demonstrate the expressiveness and utility of the system through ten example labeling tools built with OneLabeler. A user study with developers provides evidence that OneLabeler supports efficient building of diverse data labeling tools.