Reviewerly/exHarmony huggingface dataset:
Addressing the cybersecurity vulnerabilities of advanced nanogrids: A practical framework

The rapid deployment of advanced nanogrids which has enabled utility customers to become energy suppliers, the so-called prosumers with power generation capabilities based on distributed energy resources, has raised issues concerning cybersecurity. The use of the internet of things (IoT) as an enabling technology, along with the bidirectional power flow which demands a bidirectional flow of information between the prosumers and the utility, have paved the way to smarter and more efficient nanogrids, hence the name “advanced nanogrid”. Despite the many advantages of this approach, it makes nanogrids subject to cyber-attacks, providing cybercriminals with the possibility of exploiting nanogrid vulnerabilities as never before. This clearly has an economic and physical impact on the nanogrid operation. In this context, this paper aims to develop a framework to identify assets, assess threats, quantify risks and implement protection mechanisms in future nanogrid developments. The effectiveness of the framework is demonstrated through a cybersecurity analysis of an experimental testbed of an advanced nanogrid based on a photovoltaic (PV) system and controllable loads. The nanogrid includes a blockchain-based energy purchase/sale system, as well as a weather station that sends climatic measurements through a particular communication protocol. This data is used to provide forecasts for making generation/consumption offers. Finally, a cost-benefit analysis has been carried out in which a systematic assessment of the protection mechanisms is proposed, so that a trade-off between the costs of implementing security mechanisms and the losses resulting from unwanted intrusions, can be achieved.
iTourSPOT: a context-aware framework for next POI recommendation in location-based social networks

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ScQ cloud quantum computation for generating Greenberger-Horne-Zeilinger states of up to 10 qubits

In this study, we introduce an online public quantum computation platform, named as ScQ, based on a 1D array of a 10-qubit superconducting processor. Single-qubit rotation gates can be performed on each qubit. Controlled-NOT gates between nearest-neighbor sites on the 1D array of 10 qubits are available. We show the online preparation and verification of Greenberger-Horne-Zeilinger states of up to 10 qubits through this platform for all possible blocks of qubits in the chain. The graphical user interface and quantum assembly language methods are presented to achieve the above tasks, which rely on a parameter scanning feature implemented on ScQ. The performance of this quantum computation platform, such as fidelities of logic gates and details of the superconducting device, are presented.
Detecting Gait Events from Accelerations Using Reservoir Computing

Segmenting the gait cycle into multiple phases using gait event detection (GED) is a well-researched subject with many accurate algorithms. However, the algorithms that are able to perform accurate and robust GED for real-life environments and physical diseases tend to be too complex for their implementation on simple hardware systems limited in computing power and memory, such as those used in wearable devices. This study focuses on a numerical implementation of a reservoir computing (RC) algorithm called the echo state network (ESN) that is based on simple computational steps that are easy to implement on portable hardware systems for real-time detection. RC is a neural network method that is widely used for signal processing applications and uses a fast-training method based on a ridge regression adapted to the large quantity and variety of IMU data needed to use RC in various real-life environment GED. In this study, an ESN was used to perform offline GED with gait data from IMU and ground force sensors retrieved from three databases for a total of 28 healthy adults and 15 walking conditions. Our main finding is that despite its low complexity, ESN is robust for GED, with performance comparable to other state-of-the-art algorithms. Our results show the ESN is robust enough to obtain good detection results in all conditions if the algorithm is trained with variable data that match those conditions. The distribution of the mean absolute errors (MAE) between the detection times from the ESN and the force sensors were between 40 and 120 ms for 6 defined gait events (95th percentile). We compared our ESN with four different state-of-the-art algorithms from the literature. The ESN obtained a MAE not more than 10 ms above three other reference algorithms for normal walking indoor and outdoor conditions and yielded the 2nd lowest MAE and the 2nd highest true positive rate and specificity when applied to outdoor walking and running conditions. Our work opens the door to using the ESN as a GED for applications in wearable sensors for long-term patient monitoring.
SecureBiNN: 3-Party Secure Computation for Binarized Neural Network Inference

The paper proposes SecureBiNN, a novel three-party secure computation framework for evaluating privacy-preserving binarized neural network (BiNN) in semi-honest adversary setting. In SecureBiNN, three participants hold input data and model parameters in secret sharing form, and execute secure computations to obtain secret shares of prediction result without disclosing their input data, model parameters and the prediction result. SecureBiNN performs linear operations in a computation-efficient and communication-free way. For non-linear operations, we provide novel secure methods for evaluating activation function, maxpooling layers, and batch normalization layers in BiNN. Communication overhead is significantly minimized comparing to previous work like XONN and Falcon. We implement SecureBiNN with tensorflow and the experiments show that using the Fitnet structure, SecureBiNN achieves on CIFAR-10 dataset an accuracy of 81.5%, with communication cost of 16.609MB and runtime of 0.527s/3.447s in the LAN/WAN settings. More evaluations on real-world datasets are also performed and other concrete comparisons with state-of-the-art are presented as well.
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models

Most of the Chinese pre-trained models adopt characters as basic units for downstream tasks. However, these models ignore the information carried by words and thus lead to the loss of some important semantics. In this paper, we propose a new method to exploit word structure and integrate lexical semantics into character representations of pre-trained models. Specifically, we project a word’s embedding into its internal characters’ embeddings according to the similarity weight. To strengthen the word boundary information, we mix the representations of the internal characters within a word. After that, we apply a word-to-character alignment attention mechanism to emphasize important characters by masking unimportant ones. Moreover, in order to reduce the error propagation caused by word segmentation, we present an ensemble approach to combine segmentation results given by different tokenizers. The experimental results show that our approach achieves superior performance over the basic pre-trained models BERT, BERT-wwm and ERNIE on different Chinese NLP tasks: sentiment classification, sentence pair matching, natural language inference and machine reading comprehension. We make further analysis to prove the effectiveness of each component of our model.
Tokenization of Real Estate Assets Using Blockchain

Blockchain technology is one of the key technologies that have revolutionized various facets of society, such as the banking, healthcare, and other critical ecosystems. One area that can harness the usage of blockchain is the real estate sector. The most lucrative long-term investment is real estate, followed by gold, equities, mutual funds, and savings accounts. Nevertheless, it has administrative overheads such as lack of transparency, fraud, several intermediaries, title issues, paperwork, an increasing number of arbitrations, and the lack of liquidity. This paper proposes a framework that uses blockchain as an underlying technology. With the aid of blockchain and the suite of tools, it supports many of these problems that can be alleviated in the real estate investment ecosystem. These include smart contracts, immutable record management, tokenization, record tracking, and time-stamped storage. Tokenization of real estate lowers the entry barrier by fixing liquidity and interoperability and improving the interaction between various stakeholders.
View-Aware Salient Object Detection for $360^{\circ }$ Omnidirectional Image

Image-based salient object detection (ISOD) in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> scenarios is significant for understanding and applying panoramic information. However, research on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> ISOD has not been widely explored due to the lack of large, complex, high-resolution, and well-labeled datasets. Towards this end, we construct a large scale <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> ISOD dataset with object-level pixel-wise annotation on equirectangular projection (ERP), which contains rich panoramic scenes with not less than 2K resolution and is the largest dataset for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> ISOD by far to our best knowledge. By observing the data, we find current methods face three significant challenges in panoramic scenarios: diverse distortion degrees, discontinuous edge effects and changeable object scales. Inspired by humans' observing process, we propose a view-aware salient object detection method based on a Sample Adaptive View Transformer (SAVT) module with two sub-modules to mitigate these issues. Specifically, the sub-module View Transformer (VT) contains three transform branches based on different kinds of transformations to learn various features under different views and heighten the model's feature toleration of distortion, edge effects and object scales. Moreover, the sub-module Sample Adaptive Fusion (SAF) is to adjust the weights of different transform branches based on various sample features and make transformed enhanced features fuse more appropriately. The benchmark results of 20 state-of-the-art ISOD methods reveal the constructed dataset is very challenging. Moreover, exhaustive experiments verify the proposed approach is practical and outperforms the state-of-the-art methods.
Combination of GRU and CNN Deep Learning Models for Sentiment Analysis on French Customer Reviews Using XLNet Model

Sentiment analysis is the task of detecting opinions of people from text using techniques of natural language processing. It is critical in assisting businesses in actively improving their company strategy and better understanding client feedback on their products. Recently, the researchers have shown that deep learning models, namely convolutional neural network (CNN), recurrent neural networks (RNNs), and contextualized transformer-based word embeddings, give hopeful results for extracting sentiment from text. Withal, bidirectional RNN utilizes two directions of RNN to better extract long-term dependences, CNN has the benefit of high-level features extracting, and it may not examine a sequence of correlations efficiently. In addition, the transformer-based word embeddings are the computational resources needed to fine-tune to solve the problem of overfitting on small datasets. For that, we propose in this work a combination of different RNNs models [e.g., long short-term memory (LSTM), bidirectional LSTM, and gated recurrent unit (GRU)] and CNN, using different word embeddings (MultiFiT, XLNet, and CamemBERT). The experimental results show that the combination of GRU and CNN with XLNet has an apparent improvement upon the state of the art on three French datasets: 1) French Amazon Customer Reviews, 2) AlloCiné Dataset, and 3) French Twitter Sentiment Analysis, with 96.5%, 90.1%, and 89.6% accuracies in decreasing order, respectively.
Tradeoff Options for Bipartite Graph Partitioning

Web connectivity graphs and similar linked data such as inverted indexes are important components of the information access systems provided by social media and web search services. The Bipartite Graph Partitioning mechanism of Dhulipala et al. [KDD 2016] relabels the vertices of large sparse graphs, seeking to enhance compressibility and thus reduce the storage space occupied by these costly structures. Here we develop a range of algorithmic and heuristic refinements to Bipartite Graph Partitioning (BP) that lead to faster computation of space-reducing vertex orderings whilst continuing to apply the same broad algorithmic paradigm. Using a range of web graph and information retrieval system index data as test cases, we demonstrate an implementation that executes up to approximately four times faster than the baseline implementation we commenced with, while holding compressibility approximately constant. We have also improved the asymptotic execution time of BP by replacing a sorting step by a customized median-finding step.
Prospectivity mapping of carbonatite-associated iron oxide deposits using an integration process of ASTER and Sentinel-2A multispectral data

Carbonatites and associated rocks are commonly known to host precious commodities, including rare earth elements (REE), niobium, and phosphates. They form ring-shaped complexes frequently found as small occurrences in hard-to-reach areas, making their exploration a challenging task using conventional methods. Remote sensing has been increasingly regarded as a time- and cost-saving method to map and identify new potential zones, meeting the growing need for mineral resources. In this study, we use multispectral data by combining information from ASTER and Sentinel 2A sources to delineate and map carbonatite-associated REE- and Nb-enriched iron oxide deposits. The Moroccan Oulad Dlim massif, at the NW margin of the west African craton, where carbonatite occurrences are reported, was selected as a study area. A fuzzy gamma approach was adopted to combine information from several image processing techniques, including mineral indices, principal component analysis (PCA) and minimum noise fraction (MNF). A concentration-area (C-A) fractal model is applied together with the prediction-area (P-A) plot for objective anomalies classification and prediction ability assessment for each fuzzy integrated map. Subsequently, a prospective map that predicts and defines potential areas was produced through the integration of Sentinel- and ASTER-derived evidential maps. Validation was carried out via field surveys coupled with in-situ portable X-Ray Fluorescence analysis and laboratory mineralogical characterization. The Results were consistent with field data, reaching a prediction rate of 90%. They reveal the importance of the investigated structures as potential sites for REE, Nb, and phosphates. These outcomes are encouraging and provide evidence that data fusion (ASTER-Sentinel 2A) is an effective and helpful approach for geological exploration programmes.
Robust Object Detection Using Knowledge Graph Embeddings

Object recognition for the most part has been treated as a one-hot problem that assumes classes to be discrete and unrelated. In this work, we challenge the prevalence of the one-hot approach in closed-set object detection. We evaluate the error statistics of learned class embeddings from a one-hot approach with knowledge embeddings that inherit semantic structure from natural language processing or knowledge graphs, as widely applied in open world object detection. Extensive experimental results on multiple knowledge-embeddings as well as distance metrics indicate that knowledge-based class representations result in more semantically grounded misclassifications while performing on par compared to one-hot methods on the challenging COCO and Cityscapes object detection benchmarks. We generalize our findings to multiple object detection architectures by proposing a knowledge-embedded design knowledge graph embedded (KGE) for keypoint-based and transformer-based object detection architectures.
Risk and Exposure of XAI in Persuasion and Argumentation: The case of Manipulation

In the last decades, Artificial intelligence (AI) systems have been increasingly adopted in assistive (possibly collaborative) decision-making tools. In particular, AI-based persuasive technologies are designed to steer/influence users’ behaviour, habits, and choices to facilitate the achievement of their own - predetermined - goals. Nowadays, the inputs received by the assistive systems leverage heavily AI data-driven approaches. Thus, it is imperative to have transparent and understandable (to the user) both the process leading to the recommendations and the recommendations. The Explainable AI (XAI) community has progressively contributed to “opening the black box”, ensuring the interaction’s effectiveness, and pursuing the safety of the individuals involved. However, principles and methods ensuring the efficacy and information retain on the human have not been introduced yet. The risk is to underestimate the context dependency and subjectivity of the explanations’ understanding, interpretation, and relevance. Moreover, even a plausible (and possibly expected) explanation can lead to an imprecise or incorrect outcome or its understanding. This can lead to unbalanced and unfair circumstances, such as giving a financial advantage to the system owner/provider and the detriment of the user. This paper highlights that the sole explanations - especially in the context of persuasive technologies - are not self-sufficient to protect users’ psychological and physical integrity. Conversely, explanations could be misused, becoming themselves a tool of manipulation. Therefore, we suggest characteristics safeguarding the explanation from being manipulative and legal principles to be used as criteria for evaluating the operation of XAI systems, both from an ex-ante and ex-post perspective.
Conditional self-attention generative adversarial network with differential evolution algorithm for imbalanced data classification

Imbalanced data classification is an important research topic in real-world applications, like fault diagnosis in an aircraft manufacturing system. The over-sampling method is often used to solve this problem. It generates samples according to the distance between minority data. However, the traditional over-sampling method may change the original data distribution, which is harmful to the classification performance. In this paper, we propose a new method called Conditional Self-Attention Generative Adversarial Network with Differential Evolution (CSAGAN-DE) for imbalanced data classification. The new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority data. In CSAGAN-DE, the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority class. Then, the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification performance. Several experiments are conducted to evaluate the performance of the new CSAGAN-DE method. The results show that the new method can efficiently improve the classification performance compared with other related methods.
Exploring the reviews of Google Maps to assess the user opinions about public libraries

Purpose In present networked society, almost all the entities are present on Google Maps. Google Maps has become the first preference for the users for searching and retrieving information about any place. Google Maps provides an opportunity for a user to post ratings and reviews for an entity, including public libraries about their facilities and services, etc. This study explores how users of public libraries in Delhi (India) use Google Maps to share their opinions on library facilities and services and to assess whether Google Maps have the potential to act as an interactive feedback system between users and the authorities. Design/methodology/approach The Google Maps platform was used as a data source. To classify reviews into distinct categories, a content analysis was performed. Further Sentiment analysis of reviews was executed to highlight the positive, negative and neutral aspects of selected libraries. Moreover, reviews, endorsements and ratings of library products and services were also checked. Findings The research reveals that the culture of posting online reviews is increasing day by day and most of the reviews are posted in the English language, with Non Local Guides as the most prolific reviewers. In the reviews, the total number of topics discussed were 685, of which (70.51%) contained positive sentiments, (15.32%) were negative and (14.1%) were neutral reviews. The majority of the negative topics were discussed among the staff category, followed by facilities. Environment and children's areas received positive feedback with five-star ratings and most of them were marked helpful by other users. Users also loved technology, location and collection. The study concluded that Google Maps can be used as a new interactive feedback tool that library authorities can use to connect with users, replacing traditional suggestion boxes and online forms. Research limitations/implications The research is limited to a single platform (Google Maps) and the capital city of India (Delhi) and cannot be generalized across platforms and regions. Moreover, such libraries were taken under the scanner of this study which possessed more than 50 reviews. Originality/value The study will help to identify the shortcomings of the Delhi public libraries through Google Maps platform and help them to devise effective strategies to improve their management in view of suggestions and complaints.