Reviewerly/exHarmony huggingface dataset:
‘Technology is Everywhere, we have the Opportunity to Learn it in the Valley’: The Appropriation of a Socio-Technical Enabling Infrastructure in the Moroccan High Atlas

Abstract This paper describes the appropriation processes involved in establishing a socio-technical enabling infrastructure in a valley in the High Atlas of Morocco. We focus on the challenges of co-establishing such an intervention in a rural/mountainous region that is already undergoing a process of continuous development and profound transformation. We reflect upon the changes and unforeseen appropriation by our local partners and inhabitants in the valley of a computer club primarily used as an informal learning centre for school children. We followed an ethnographic approach and combined research perspectives from both socio-informatics and anthropology. This paper sheds light on what a successful cooperation and intervention in this kind of challenging environment can look like. It does this by taking seriously competing expectations, fragile infrastructural foundations and the socio-cultural context. Despite the challenges, the intervention managed to lead to the establishment of a socio-technical enabling infrastructure that plays a particularly valuable role in local educational endeavours and that is now moving towards supporting other members of the community. The paper thus provides insights regarding what has to be considered to create a mutually beneficial cooperation with all relevant stakeholders as well as a sustainable intervention.
Arabic Aspect-Based Sentiment Analysis: A Systematic Literature Review

Recently sentiment analysis in Arabic has attracted much attention from researchers. A modest number of studies have been conducted on Arabic sentiment analysis. However, due to the vast increase in users’ comments and reviews on social media and e-commerce websites, the necessity to detect sentence-level and aspect-level sentiments has also increased. The aspect-based sentiment analysis has emerged to detect sentiments at the aspect level. Few studies have attempted to perform aspect-based sentiment analysis on Arabic texts because Arabic natural language processing is a challenging task and because of the lack of available Arabic annotated corpora. In this paper, we conducted a systematic review of the methods, techniques, and datasets employed in aspect-based sentiment analysis on Arabic texts. A total of 21 articles published between 2015-2021 were included in this review. After analysing these articles, we found a lack of annotated datasets that can be used by researchers. In addition, the used datasets were limited to few fields. This review will serve as a foundation for researchers interested in Aspect-Based Sentiment Analysis, it will assist them in developing new models and techniques to tackle this task in the future.
UAV Swarm Confrontation Using Hierarchical Multiagent Reinforcement Learning

With the development of unmanned aerial vehicle (UAV) technology, UAV swarm confrontation has attracted many researchers’ attention. However, the situation faced by the UAV swarm has substantial uncertainty and dynamic variability. The state space and action space increase exponentially with the number of UAVs, so that autonomous decision-making becomes a difficult problem in the confrontation environment. In this paper, a multiagent reinforcement learning method with macro action and human expertise is proposed for autonomous decision-making of UAVs. In the proposed approach, UAV swarm is modeled as a large multiagent system (MAS) with an individual UAV as an agent, and the sequential decision-making problem in swarm confrontation is modeled as a Markov decision process. Agents in the proposed method are trained based on the macro actions, where sparse and delayed rewards, large state space, and action space are effectively overcome. The key to the success of this method is the generation of the macro actions that allow the high-level policy to find a near-optimal solution. In this paper, we further leverage human expertise to design a set of good macro actions. Extensive empirical experiments in our constructed swarm confrontation environment show that our method performs better than the other algorithms.
A Multi-Dimensional Evaluation of Synthetic Data Generators

Synthetic datasets are gradually emerging as solutions for data sharing.Multiple synthetic data generators have been introduced in the last decade fueled by advancement in machine learning and by the increased demand for fast and inclusive data sharing, yet their utility is not well understood.Prior research tried to compare the utility of synthetic data generators using different evaluation metrics.These metrics have been found to generate conflicting conclusions making direct comparison of synthetic data generators very difficult.This paper identifies four criteria (or dimensions) for masked data evaluation by classifying available utility metrics into different categories based on the measure they attempt to preserve: attribute fidelity, bivariate fidelity, population fidelity, and application fidelity.A representative metric from each category is chosen based on popularity and consistency, and the four metrics are used to compare the overall utility of four recent data synthesizers across 19 datasets of different sizes and feature counts.The paper also examines correlations between the selected metrics in an attempt to streamline synthetic data utility.
Parameter estimation in quantum sensing based on deep reinforcement learning

Abstract Parameter estimation is a pivotal task, where quantum technologies can enhance precision greatly. We investigate the time-dependent parameter estimation based on deep reinforcement learning, where the noise-free and noisy bounds of parameter estimation are derived from a geometrical perspective. We propose a physical-inspired linear time-correlated control ansatz and a general well-defined reward function integrated with the derived bounds to accelerate the network training for fast generating quantum control signals. In the light of the proposed scheme, we validate the performance of time-dependent and time-independent parameter estimation under noise-free and noisy dynamics. In particular, we evaluate the transferability of the scheme when the parameter has a shift from the true parameter. The simulation showcases the robustness and sample efficiency of the scheme and achieves the state-of-the-art performance. Our work highlights the universality and global optimality of deep reinforcement learning over conventional methods in practical parameter estimation of quantum sensing.
An Optimized Framework for Breast Cancer Classification Using Machine Learning

Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.
Entanglement Purification and Protection in a Superconducting Quantum Network

High-fidelity quantum entanglement is a key resource for quantum communication and distributed quantum computing, enabling quantum state teleportation, dense coding, and quantum encryption. Any sources of decoherence in the communication channel, however, degrade entanglement fidelity, thereby increasing the error rates of entangled state protocols. Entanglement purification provides a method to alleviate these nonidealities by distilling impure states into higher-fidelity entangled states. Here we demonstrate the entanglement purification of Bell pairs shared between two remote superconducting quantum nodes connected by a moderately lossy, 1-meter long superconducting communication cable. We use a purification process to correct the dominant amplitude damping errors caused by transmission through the cable, with fractional increases in fidelity as large as 25%, achieved for higher damping errors. The best final fidelity the purification achieves is 94.09±0.98%. In addition, we use both dynamical decoupling and Rabi driving to protect the entangled states from local noise, increasing the effective qubit dephasing time by a factor of 4, from 3 to 12 μs. These methods demonstrate the potential for the generation and preservation of very high-fidelity entanglement in a superconducting quantum communication network.
Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation

Session-based recommendation aims to predict a user's next action based on previous actions in the current session. The major challenge is to capture authentic and complete user preferences in the entire session. Recent work utilizes graph structure to represent the entire session and adopts Graph Neural Network to encode session information. This modeling choice has been proved to be effective and achieved remarkable results. However, most of the existing studies only consider each item within the session independently and do not capture session semantics from a high-level perspective. Such limitation often leads to severe information loss and increases the difficulty of capturing long-range dependencies within a session. Intuitively, compared with individual items, a session snippet, i.e., a group of locally consecutive items, is able to provide supplemental user intents which are hardly captured by existing methods. In this work, we propose to learn multi-granularity consecutive user intent unit to improve the recommendation performance. Specifically, we creatively propose Multi-granularity Intent Heterogeneous Session Graph which captures the interactions between different granularity intent units and relieves the burden of long-dependency. Moreover, we propose the Intent Fusion Ranking module to compose the recommendation results from various granularity user intents. Compared with current methods that only leverage intents from individual items, IFR benefits from different granularity user intents to generate more accurate and comprehensive session representation, thus eventually boosting recommendation performance. We conduct extensive experiments on five session-based recommendation datasets and the results demonstrate the effectiveness of our method.
Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Changes in the ungulate population density in the wild has impacts on both the wildlife and human society. In order to control the ungulate population movement, monitoring systems such as camera trap networks have been implemented in a non-invasive setup. However, such systems produce a large number of images as the output, hence making it very resource consuming to manually detect the animals. In this paper, we present a new dataset of wild ungulates which was collected in Latvia. Moreover, we demonstrate two methods, which use RetinaNet and Faster R-CNN as backbones, respectively, to detect the animals in the images. We discuss the optimization of training and impact of data augmentation on the performance. Finally, we show the result of aforementioned tune networks over the real world data collected in Latvia.
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions. While recent advances made AI generation of questions from stories possible, the fully-automated approach excludes parent involvement, disregards educational goals, and underoptimizes for child engagement. Informed by need-finding interviews and participatory design (PD) results, we developed StoryBuddy, an AI-enabled system for parents to create interactive storytelling experiences. StoryBuddy's design highlighted the need for accommodating dynamic user needs between the desire for parent involvement and parent-child bonding and the goal of minimizing parent intervention when busy. The PD revealed varied assessment and educational goals of parents, which StoryBuddy addressed by supporting configuring question types and tracking child progress. A user study validated StoryBuddy's usability and suggested design insights for future parent-AI collaboration systems.
Transit search: An optimization algorithm based on exoplanet exploration

In this article, a novel astrophysics-inspired meta-heuristic optimization algorithm, namely Transit Search (TS) is proposed based on a famous exoplanet exploration method. More than 3800 planets have been detected using transit technique by the database of the space telescopes. Transit is a method that has shown more potential than the second well-known successful method (radial velocity) with 915 discovered planets until 2022 March. It is difficult to detect the planets because of their small dimension in the cosmos scale. Due to the high efficiency of the transit method in astrophysics and its capabilities, it has been used to formulate an optimization technique for this research. In the transit algorithm, by studying the light received from the stars at certain intervals, the changes in luminosity are examined and if a decrease in the amount of the received light is observed, it indicates that a planet passes from the star front. In order to evaluate the capability of the proposed algorithm, 73 constrained and unconstrained problems are considered and the results have been compared with 13 well-known optimization algorithms. This set of examples includes a wide range of types of problems including mathematical functions (28 high-dimensional and 15 low-dimensional problems), CEC functions (10 problems), constrained mathematical benchmark problems (G01–G13), as well as 7 constrained engineering problems. The results indicated that the overall average error for the proposed algorithm is the lowest amount for the benchmark problems in comparison with the other efficient algorithms
Observation of Two-Mode Squeezing in a Traveling Wave Parametric Amplifier

Traveling wave parametric amplifiers (TWPAs) have recently emerged as essential tools for broadband near quantum-limited amplification. However, their use to generate microwave quantum states still misses an experimental demonstration. In this Letter, we report operation of a TWPA as a source of two-mode squeezed microwave radiation. We demonstrate broadband entanglement generation between two modes separated by up to 400 MHz by measuring logarithmic negativity between 0.27 and 0.51 and collective quadrature squeezing below the vacuum limit between 1.5 and 2.1 dB. This work opens interesting perspectives for the exploration of novel microwave photonics experiments with possible applications in quantum sensing and continuous variable quantum computing.
Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey

Object detection and tracking play a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems. Although current computer vision technologies can provide satisfactory object detection results in occlusion-free scenarios, the perception performance of onboard sensors is inevitably limited by the range and occlusion. Owing to the flexible location and pose for sensor installation, infrastructure-based detection, and tracking systems can enhance the perception capability of connected vehicles; as such, they have quickly become a popular research topic. In this survey paper, we review the research progress for infrastructure-based object detection and tracking systems. Architectures of roadside perception systems based on different types of sensors are reviewed to show a high-level description of the workflows for infrastructure-based perception systems. Roadside sensors and different perception methodologies are reviewed and analyzed with detailed literature to provide a low-level explanation for specific methods followed by Datasets and Simulators to draw an overall landscape of infrastructure-based object detection and tracking methods. We highlight current opportunities, open problems, and anticipated future trends.
AuCPace: Efficient verifier-based PAKE protocol tailored for the IIoT

Increasingly connectivity becomes integrated in products and devices that previously operated in a stand-alone setting. This observation holds for many consumer applications in the so-called "Internet of Things" (IoT) as well as for corresponding industry applications (IIoT), such as industrial process sensors. Often the only practicable means for authentication of human users is a password. The security of password-based authentication schemes frequently forms the weakest point of the security infrastructure. In this paper we first explain why a tailored protocol designed for the IIoT use case is considered necessary. The differences between IIoT and the conventional Internet use-cases result in largely modified threats and require special procedures for allowing both, convenient and secure use in the highly constrained industrial setting. Specifically the use of a verifier-based password-authenticated key-exchange (V-PAKE) protocol as a hedge against public-key-infrastructure (PKI) failures is considered important. Availability concerns for the case of failures of (part of) the communication infrastructure makes local storage of access credentials mandatory. The larger threat of physical attacks makes it important to use memory-hard password hashing. This paper presents a corresponding tailored protocol, AuCPace, together with a security proof within the Universal Composability (UC) framework considering fully adaptive adversaries. We also introduce a new security notion of partially augmented PAKE that provides specific performance advantages and makes them suitable for a larger set of IIoT applications. We also present an actual instantiation of our protocol, AuCPace25519, and present performance results on ARM Cortex-M0 and Cortex-M4 microcontrollers. Our implementation realizes new speed-records for PAKE and X25519 Diffie-Hellman for the ARM Cortex M4 architecture.
A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance

Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients' discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings).The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models.For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.
Sequence-to-Sequence Knowledge Graph Completion and Question Answering

Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embedding vectors.These methods have recently been applied to KG link prediction and question answering over incomplete KGs (KGQA).KGEs typically create an embedding for each entity in the graph, which results in large model sizes on real-world graphs with millions of entities.For downstream tasks these atomic entity representations often need to be integrated into a multi stage pipeline, limiting their utility.We show that an off-the-shelf encoder-decoder Transformer model can serve as a scalable and versatile KGE model obtaining state-of-the-art results for KG link prediction and incomplete KG question answering.We achieve this by posing KG link prediction as a sequence-tosequence task and exchange the triple scoring approach taken by prior KGE methods with autoregressive decoding.Such a simple but powerful method reduces the model size up to 98% compared to conventional KGE models while keeping inference time tractable.After finetuning this model on the task of KGQA over incomplete KGs, our approach outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning. 1