Reviewerly/exHarmony huggingface dataset:
Sequential recommendation with metric models based on frequent sequences

Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user’s history and his recent actions (sequential dynamics) to provide personalized recommendations. Existing methods capture the sequential dynamics of a user using fixed-order Markov chains (usually first order chains) regardless of the user, which limits both the impact of the past of the user on the recommendation and the ability to adapt its length to the user profile. In this article, we propose to use frequent sequences to identify the most relevant part of the user history for the recommendation. The most salient items are then used in a unified metric model that embeds items based on user preferences and sequential dynamics. Extensive experiments demonstrate that our method outperforms state-of-the-art, especially on sparse datasets. We show that considering sequences of varying lengths improves the recommendations and we also emphasize that these sequences provide explanations on the recommendation.
Can News Help Measure Economic Sentiment? An Application in COVID-19 Times

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Using head mounted display virtual reality simulations in large engineering classes: Operating vs observing

A barrier to using head mounted display (HMD) virtual reality (VR) in education is access to hardware for large classes. This paper compares students’ learning when engaging with an HMD VR simulation as the operator and as the observer, to evaluate whether benefits of HMD VR can be achieved without requiring all students to operate the equipment. Postgraduate engineering students (N = 117) completed a safety hazard identification exercise in a workshop. The performance of students who operated and observed was compared. Results showed that students performed similarly in the exercise that followed the simulation whether they operated HMD VR (n = 33) or observed (n = 84). The finding suggests that educators may be able to use HMD VR simulations in classes with a large enrolment, by reducing the need for investment and management of a large number of sets of HMD VR equipment.&#x0D; Implications for practice or policy:&#x0D; &#x0D; Engineering educators can use HMD VR simulations to teach students about safety in design.&#x0D; Engineering students are able to identify safety hazards in a HMD VR simulation effectively whether they are operating the equipment or observing another student in their group operating the VR equipment.&#x0D; One HMD VR set per student group is sufficient.&#x0D; HMD VR simulations can be used inclusively, even when some students are unable or unwilling to wear the headset.&#x0D; &#x0D;
Spectral Hong–Ou–Mandel Interference between Independently Generated Single Photons for Scalable Frequency‐Domain Quantum Processing

Abstract The photon's frequency degree of freedom, being compatible with mature telecom infrastructure, offers large potential for the stable and controllable realization of photonic quantum processing applications such as the quantum internet. The Hong–Ou–Mandel effect, as a two‐photon interference phenomenon, serves as a central building block for such frameworks. A key element yet missing to enable meaningful frequency‐based implementations as well as scalability in the number of processed photons, is the demonstration of the Hong–Ou–Mandel effect between independently created photons of different frequencies. The experimental implementation of bosonic and fermionic frequency domain Hong–Ou–Mandel interference between independently generated single photons is reported here, with measured visibilities of 74.31% ± 3.56% and 86.44% ± 8.27%, respectively. This is achieved through a scalable photonic frequency circuit that creates two post‐selected pure single photons, which undergo frequency mixing at an electro‐optic phase modulator. The system is on‐the‐fly reconfigurable allowing to probe bosonic and fermionic Hong–Ou–Mandel interference in the same experimental setup. The work demonstrates the versatility of frequency domain processing and its scalability toward higher photon numbers, which enables new quantum gate concepts as well as the establishment of frequency‐based large‐scale quantum networks.
Feasibility of space-based measurement-device-independent quantum key distribution

Abstract The measurement-device-independent (MDI) quantum key distribution (QKD) is considered to be an alternative to overcome the currently trusted satellite paradigm. However, the feasibility of the space-based MDI-QKD remains unclear in terms of the factors: the high-loss uplink between a ground station and a satellite, the limited duration when two ground stations are simultaneously visible, as well as the rigorous requirements for the two-photon interference when performing the Bell-state measurement. In this paper, we present a feasibility assessment of space-based MDI-QKD based on the Micius satellite. Integrated with the orbital dynamics model and atmosphere channel model, a framework is presented to explore the whole parameters space including orbit height, elevation angle, apertures of transceiver and atmospheric turbulence intensity to give the considerations for improving key rates and subsequently provide a relevant parameter tradeoff for the implementation of space-based MDI-QKD. We further investigate the heart of MDI-QKD, the two-photon interference considerations such as the frequency calibration and time synchronization technology against Doppler shift, and the way of performing the intensity optimization method in the dynamic and asymmetric channels. Our work can be used as a pathfinder to support decisions involving as the selection of the future quantum communication satellite missions.
Reprogrammable and high-precision holographic optical addressing of trapped ions for scalable quantum control

Abstract High-precision, individually programmable manipulation of quantum particles is crucial for scaling up quantum information processing (QIP) systems such as laser-cooled trapped-ions. However, restricting undesirable “crosstalk” in optical manipulation of ion qubits is fundamentally challenging due to micron-level inter-ion separation. Further, inhomogeneous ion spacing and high susceptibility to aberrations at UV wavelengths suitable for most ion-species pose severe challenges. Here, we demonstrate high-precision individual addressing ( λ = 369.5 nm) of Yb + using a reprogrammable Fourier hologram. The precision is achieved through in-situ aberration characterization via the trapped ion, and compensating (to λ /20) with the hologram. Using an iterative Fourier transformation algorithm (IFTA), we demonstrate an ultra-low (&lt;10 −4 ) intensity crosstalk error in creating arbitrary pair-wise addressing profiles, suitable for over fifty ions. This scheme relies on standard commercial hardware, can be readily extended to over a hundred ions, and adapted to other ion-species and quantum platforms.
An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations.We have designed our datasets named LRD200 and LRD100, which have been used for training and classification.The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment.The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database.Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person.With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively.A facial deflection of about 30˚ on either side from the front face showed an average face recognition precision of 72.25% -81.85%.This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera.It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.
CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k-fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.
Sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-Based Approach

In the past few years, retail market industries have taken a broad form to sell the products online and also to give the opportunity to customers to provide their valuable feedbacks, suggestions and recommendations. The aim of this paper is to provide an automatic comment analyzer. And propose an automatic comment analyzer and classification system which can determine the polarity of the customer comments collected from Amazon and Flipkart data domains effectively. This system should be able to process the large number of reviews. It should categorize the comments as positive, negative and neutral classes using five prime supervised learning classifiers such as NB, LR, SentiWordNet, RF and KNN. The paper also discusses their experimental results and challenges found. Therefore, this study shows the maximum usage of feature extraction, positive–negative sentiment, Amazon web source, mobile phone for a large set of reviews in the existing algorithms. It included the preliminary definitions, information extraction and retrieval aspects, role of machine learning, and the comment mining. The comment analysis and classification described comment polarity, orientation, subjectivity detection, comment analysis, summarization and classification. The classification of comment analysis techniques explained various lexicons and supervised algorithms along with the essential used-based concerns.
Quantitative Assessment of Cybersecurity Risks for Mitigating Data Breaches in Business Systems

The evaluation of data breaches and cybersecurity risks has not yet been formally addressed in modern business systems. There has been a tremendous increase in the generation, usage and consumption of industrial and business data as a result of smart and computational intensive software systems. This has resulted in an increase in the attack surface of these cyber systems. Consequently, there has been a consequent increase in the associated cybersecurity risks. However, no significant studies have been conducted that examine, compare, and evaluate the approaches used by the risk calculators to investigate the data breaches. The development of an efficient cybersecurity solution allows us to mitigate the data breaches threatened by the cybersecurity risks such as cyber-attacks against database storage, processing and management. In this paper, we develop a comprehensive, formal model that estimates the two components of security risks: breach cost and the likelihood of a data breach within 12 months. The data used in this model are taken from the industrial business report, which provides the necessary information collected and the calculators developed by the major organizations in the field. This model integrated with the cybersecurity solution uses consolidated factors that have a significant impact on the data breach risk. We propose mathematical models of how the factors impact the cost and the likelihood. These models allow us to conclude that results obtained through the models mitigate the data breaches in the potential and future business system dynamically.
Augmented reality for enabling un- and under-trained individuals to complete specialty construction tasks

The construction industry is facing a severe labor shortage that is threatening the performance of projects around the world. Advanced technologies may be able to alleviate the effects of this labor cliff. Specifically, Augmented Reality (AR) has been shown to enhance the performance of current industry professionals completing different construction tasks and is also being explored as a learning tool for students and technicians alike. This research studies if AR can be used as a tool to enable untrained individuals to complete construction tasks. Three groups were identified for this research: construction professionals, construction students, and individuals with no construction experience. All three groups completed two construction tasks with AR, including the assembly of prefabricated electrical conduit and the layout of electrical devices in a room. The results show that all three groups completed the task in statistically similar times; however, the work of individuals with no construction experience had significantly lower accuracy during the electrical device layout task. These results suggest that construction companies may be able to leverage untrained individuals to perform certain construction tasks with AR, enabling trained and experienced professionals to focus on more challenging tasks.
Signal recovery based on optoelectronic reservoir computing for high speed optical fiber communication system

Signal recovery effects of a channel equalization method based on optoelectronic reservoir computing (RC) are investigated for actual high speed optical fiber communication systems. The RC is implemented numerically by using a Mach–Zehnder intensity modulator with an optoelectronic delay feedback loop. Two optical communication systems with different transmission distances and receivers based on 25G-class optics are built in-lab. 50-Gb/s non-return-to-zero (NRZ) and four-level pulse amplitude modulation (PAM4) signals transmitted by the two systems are recovered by using optoelectronic RC and different combinations of feed-forward equalizer (FFE) with decision feedback equalizer (DFE) and maximum likelihood sequence estimation (MLSE), respectively. Under the same conditions, optoelectronic RC is better than FFE&DFE, and far superior to FFE&MLSE in the equalization performances. When the receiver is a positive–intrinsic–negative or avalanche photodiode , for the NRZ signal, compared with 45-tap FFE and 9-tap DFE, the sensitivity is increased by ∼ 3.2/3.4 dB; for the PAM4 signal, the sensitivity is improved by ∼ 4.9/4.6 dB compared with 123-tap FFE and 9-tap DFE. These experimental results show that the optoelectronic RC has excellent performances for signal recovery of different modulation formats and transmission systems. • Channel equalization method based on optoelectronic reservoir computing (RC). • The signals to be recovered from actual high speed optical fiber communication systems. • Better equalization performance than that of the FFE&DFE and FFE&MLSE algorithms. • Lower system complexity and implementation cost.
A Practice for Object Detection Using YOLO Algorithm

When we look at images or videos, we can easily locate and identify the objects of our interest within moments. Passing on this intelligence to computers is nothing but object detection - locating the object and identifying it. Object Detection has found its application in a wide variety of domains such as video surveillance, image retrieval systems, autonomous driving vehicles and many more. Various algorithms can be used for object detection but we will be focusing on the YoloV3 algorithm. YOLO stands for "You Only Look Once". The YOLO model is very accurate and allows us to detect the objects present in the frame. YOLO follows a completely different approach. Instead of selecting some regions, it applies a neural network to the entire image to predict bounding boxes and their probabilities. YOLO is a single deep convolutional neural network that splits the input image into a set of grid cells, so unlike image classification or face detection, each grid cell in YOLO algorithm will have an associated vector in the output that tells us if an object exists in that grid cell, the class of that object, the predicted bounding box for that object. The model here is progressive so it learns more over time, increasing its prediction accuracy over time. The way the model works is that it makes many predictions in one frame and decides to use the most accurate prediction, thus discarding the other. The predictions are made randomly, so if the model feels like there is an object in the frame which is of a very small pixel it will take that also into consideration. To make it more precise and clearer, the model simply creates bounding boxes around everything in the frame, it would make predictions for each box and pick the one with the most confidence score. All this is done in a small-time frame, thus showing why this specific model is the best to use in a real time situation.
Development of Android-Based Mathematics Learning Game on the Topic of Congruence and Similarity

&lt;p&gt;In general, students' math skills in Indonesia are low. An effort must be made to get students to enjoy learning mathematics so that their mathematical abilities improve. The rapid development of smartphone technology provides an opportunity for use in mathematics learning. This study aims to develop an Android-based mathematics learning game for junior high school students on the topic of congruence and similarity. The research method used in this study is the Plomp development method with three stages: preliminary research, the prototyping stage, and the assessment phase. The subjects of this study were 56 seventh-grade students from the State Junior High School (SMPN 1) Wagir Malang. Six students participated in small group trials and 50 students participated in field trials. The results showed that the Android-based learning game on the topic of congruence and similarity had been successfully developed. The game media have been validated by experts with a score of 3.36, which shows that the media created are valid. The practicality tests in the small group trial resulted in a score of 3.50, while the field trial resulted in a score of 3.51. The effectiveness tests carried out in the small group trial resulted in a score of 3.36, while the field trial results had a score of 3.38. These results indicate that the learning games developed are practical and effective for use in mathematics learning.&lt;/p&gt;
Reachability analysis of deep ReLU neural networks using facet-vertex incidence

Deep Neural Networks (DNNs) are powerful machine learning models for approximating complex functions. In this work, we provide an exact reachability analysis method for DNNs with Rectified Linear Unit (ReLU) activation functions. At its core, our set-based method utilizes a facet-vertex incidence matrix, which represents a complete encoding of the combinatorial structure of convex sets. When a safety violation is detected, our approach provides backtracking which determines the complete input set that caused the safety violation. The performance of our method is evaluated and compared to other state-of-the-art methods by using the ACAS Xu flight controller and other benchmarks.
Subpixel Matching Using Double-Precision Gradient-Based Method for Digital Image Correlation

Digital image correlation (DIC) for displacement and strain measurement has flourished in recent years. There are integer pixel and subpixel matching steps to extract displacement from a series of images in the DIC approach, and identification accuracy mainly depends on the latter step. A subpixel displacement matching method, named the double-precision gradient-based algorithm (DPG), is proposed in this study. After, the integer pixel displacement is identified using the coarse-fine search algorithm. In order to improve the accuracy and anti-noise capability in the subpixel extraction step, the traditional gradient-based method is used to analyze the data on the speckle patterns using the computer, and the influence of noise is considered. These two nearest integer pixels in one direction are both utilized as an interpolation center. Then, two subpixel displacements are extracted by the five-point bicubic spline interpolation algorithm using these two interpolation centers. A novel combination coefficient considering contaminated noises is presented to merge these two subpixel displacements to obtain the final identification displacement. Results from a simulated speckle pattern and a painted beam bending test show that the accuracy of the proposed method can be improved by four times that of the traditional gradient-based method that reaches the same high accuracy as the Newton–Raphson method. The accuracy of the proposed method efficiently reaches at 92.67%, higher than the Newton-Raphon method, and it has better anti-noise performance and stability.
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks

Invertible neural networks (INNs) have been used to design generative models, implement memory-saving gradient computation, and solve inverse problems. In this work, we show that commonly-used INN architectures suffer from exploding inverses and are thus prone to becoming numerically non-invertible. Across a wide range of INN use-cases, we reveal failures including the non-applicability of the change-of-variables formula on in- and out-of-distribution (OOD) data, incorrect gradients for memory-saving backprop, and the inability to sample from normalizing flow models. We further derive bi-Lipschitz properties of atomic building blocks of common architectures. These insights into the stability of INNs then provide ways forward to remedy these failures. For tasks where local invertibility is sufficient, like memory-saving backprop, we propose a flexible and efficient regularizer. For problems where global invertibility is necessary, such as applying normalizing flows on OOD data, we show the importance of designing stable INN building blocks.
Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms

Differential privacy is fast becoming the gold standard in enabling statistical analysis of data while protecting the privacy of individuals. However, practical use of differential privacy still lags behind research progress because research prototypes cannot satisfy the scalability requirements of production deployments. To address this challenge, we present Chorus, a framework for building scalable differential privacy mechanisms which is based on cooperation between the mechanism itself and a high-performance production database management system (DBMS). We demonstrate the use of Chorus to build the first highly scalable implementations of complex mechanisms like Weighted PINQ, MWEM, and the matrix mechanism. We report on our experience deploying Chorus at Uber, and evaluate its scalability on real-world queries.