Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence

Abstract Artificial intelligence (AI) holds the keys to unlocking a future of unconceivable prosperity for humankind: it may dramatically boost the performance of economies and provide unprecedented opportunities for citizens, companies, and the public sector. It may also advance our ability to address humanity’s challenges, such as providing effective tools for disaster control, a weapon against climate change, or a cure for diseases like cancer. But AI also conceals the potential for a future at the other end of the spectrum. A future where citizens of dystopian societies undergo permanent monitoring for the way they behave or could behave. Where discriminatory treatment is a norm and fundamental rights are systematically ignored. A society where the gap between the haves and have-nots becomes unbridgeable. The role of public policy in the AI space is thus extremely important. Public policy can create the conditions for humanity to get the most out of AI while steering it away from the dystopian future, popularly depicted in science fiction books or films, by designing appropriate ethical and legal frameworks to protect society’s core values.
Cybersecurity

Abstract Cybersecurity policies aim to ensure that digital technologies are safe and resilient. It is impossible to guarantee that technology is fully cyber-incident proof. However, it is possible and desirable to increase technologies’ resilience to incidents and malicious attacks to minimize the risk of using technology. For this reason, cybersecurity is an essential ingredient of any public strategies geared to fostering the digital economy: lowering risk implies lowering the economy’s expected costs and increasing the likelihood of adoption through greater trust. Cyber-risk can entail enormous costs for the economy, businesses, and ordinary users alike. However, markets do not autonomously converge to the optimal level of investment in cybersecurity from a social point of view, and public policy is needed to create the right incentives and steer all actors to contribute to the safety of the cyberspace.
Political Meritocracy in China

Abstract This chapter distinguishes between the ideal and the reality of political meritocracy in China and asks what’s necessary to reduce the gap between the two. The first section lays out the theoretical case for justifying political hierarchies between rulers and citizens in large-scale political communities (such as China) that do not use periodic elections to select rulers. The second section asks if it is possible to limit abuses of power in a political meritocracy, with a focus on the contemporary Chinese political context. I conclude that more democratic practices and values short of competitive elections at the top will be necessary to save the political system, though current trends point in the wrong direction.
The Sharing Economy

Abstract Nothing better represents the meaning of digital disruption than the “sharing economy”. Online sharing platforms allow owners of under-used assets (for example, a car, a house, or even a specific knowledge or expertise) to compete with established businesses, offering services of comparable, if not enhanced, quality and exploit their assets in a profitable way. Users can access services often supplied by non-professional “peers” at advantageous conditions compared to those offered by professionals. The resulting impact on markets and legacy businesses can be wide and significant, though not always positive: it may concern the environment, labour markets, and society at large, with important implications for policy-making. This chapter provides an overview of the sharing economy and analyses its main features.
On the Statistical Learning Analysis of Rain Gauge Data over the Natuna Islands

Located in the middle of South China Sea with distance more than 700 m to nearby main lands, Natuna Islands settings remain the focus of scientific conversation. This article presents state-of-the-art statistical learning methods for analyzing rain gauge data over the Natuna Islands. By using shape preserving piecewise cubic interpolation, we managed to interpolate 671 null values from the daily precipitation data. Dominant periodicity analysis of daily precipitation signals using Lomb-Scargle Power Spectral Density shows annual, intraseasonal, and interannual precipitation patterns over the Natuna Islands. Unsupervised anomaly analysis using the Isolation Forest algorithm shows there are 146 anomaly daily precipitation data points. We also conducted an experiment to predict the accumulation of monthly precipitation over the Natuna Islands using the Bayesian structural time series algorithm. The results show that the local linear trend with seasonality model is able to model the value of accumulated monthly precipitation for a twelve-month prediction horizon. The work presented here has profound implications for rainfall observations in this area.
Market Value and Agents Benefits of Enhanced Short-Term Solar PV Power Generation Forecasting

Renewable energy sources such as PV solar or wind power are intermittent and non-dispatchable. Massive integration of these resources into the electric mix poses some challenges to meeting power generation with demand. Hence, improving power generation forecasting has raised much interest. This work assesses the market value of enhanced PV solar power generation forecasting. Then, we analyse the different agents present in the electricity system. We link the studied agents to the proposed market values based on both analyses. Improving the accuracy of RES forecasting has massive potential as the sector grows and new agents arise. It can have reactive values like reducing imbalances or proactive values such as participating in intraday markets or exercising energy arbitrage. However, accurate forecasting can also lead to opportunistic values that can be exploited by malicious agents if they are not adequately regulated.
Thumbnail Secret Image Sharing in Cloud Storage

In recent years, the amount of data has increased explosively, which has spawned the large-scale development of cloud storage. Increasingly, individuals and enterprises store images in cloud space. The storage security of the cloud is generally guaranteed by encryption, but this can no longer meet the needs of image management and protection. In order to realize the management and loss tolerance of images, this paper proposes a thumbnail secret image sharing method. The proposed scheme combines the advantages of thumbnail-preserving encryption (TPE) and secret image sharing (SIS) with different meaningful shadows. Thumbnails can realize the visual management of stored images, and secret image sharing can realize the perfect security of stored images. The proposed scheme realizes the confidentiality, integrity, and availability of images, which are three elements of information security. Compared with TPE, our scheme not only realizes the visual management of images but also achieves loss tolerance and perfect security. Compared with SIS with different meaningful shadows, our scheme will greatly improve the sharing efficiency and reduce the consumption of computing resources. In this paper, the theoretical analysis and security proof of the proposed scheme are presented. In addition, we also conduct sufficient experiments and comparative explanations.
A novel Internet of Things based fall detection system in smart home

In the field of motion monitoring in smart home, the 5G technology can be applied to Internet of Things systems for facilitating our daily life. In this paper, a comprehensive study on the fall detection system based on 5G network is presented. Starting with analyzing the moving stability, a wearable foot pressure measurement system is devised. Furthermore, the center of pressure during moving is computed by using the pressure data. Besides, the signal transmitting security issue is also considered. The physical layer security authentication and the cross-layer encryption are employed and integrated within the security strategy. As a case study, we evaluate the proposed method on fall detection tasks in smart home and the experimental results establish strong evidence of a satisfying performance.
Polynomial-time trace reconstruction in the smoothed complexity model

In the trace reconstruction problem , an unknown source string x ∈ {0, 1} n is sent through a probabilistic deletion channel which independently deletes each bit with probability δ and concatenates the surviving bits, yielding a trace of x . The problem is to reconstruct x given independent traces. This problem has received much attention in recent years both in the worst-case setting where x may be an arbitrary string in {0, 1} n [DOS19, NP17, HHP18, HL20, Cha21a, Cha21b] and in the average-case setting where x is drawn uniformly at random from {0, 1} n [PZ17, HPP18, HL20, Cha21a, Cha21b]. This paper studies trace reconstruction in the smoothed analysis setting, in which a “worst-case” string x worst is chosen arbitrarily from {0, 1} n , and then a perturbed version x of x worst is formed by independently replacing each coordinate by a uniform random bit with probability σ . The problem is to reconstruct x given independent traces from it. Our main result is an algorithm which, for any constant perturbation rate 0 &lt; σ &lt; 1 and any constant deletion rate 0 &lt; δ &lt; 1, uses poly( n ) running time and traces and succeeds with high probability in reconstructing the string x. This stands in contrast with the worst-case version of the problem, for which \(\text{exp}(\tilde{O}(n^{1/5})) \) is the best known time and sample complexity [Cha21b]. Our approach is based on reconstructing x from the multiset of its short subwords and is quite different from previous algorithms for either the worst-case or average-case versions of the problem. The heart of our work is a new poly( n )-time procedure for reconstructing the multiset of all O (log n )-length subwords of any source string x ∈ {0, 1} n given access to traces of x .
Character-based Joint Word Segmentation and Part-of-Speech Tagging for Tibetan Based on Deep Learning

Tibetan word segmentation and POS tagging are the primary tasks of Tibetan natural language processing. Most of existing methods of Tibetan word segmentation and POS tagging are based on rules and statistics, which need manual construction of features. In addition, the joint mode has shown stronger capabilities for word segmentation and POS tagging and have received great interests. In this paper, we propose Bi-LSTM+IDCNN+CRF structures, a simple yet effective end-to-end neural network model, for joint Tibetan word segmentation and POS tagging. We conduct step-by-step and joint experiments on the Tibetan datasets. The results demonstrate that the performance of the Bi-LSTM+IDCNN+CRF model is the best regardless of the step-by-step or joint mode. We obtain state-of-the-art performance in the joint tagging mode. The F1 score of the word segmentation task reached 92.31%, and the F1 score of the POS tagging task reached 81.26%.
A Study of Extracting Causal Relationships from Text

Discovering causal knowledge is an important aspect of much scientific research and such findings are often recorded in scholarly articles. Automatically identifying such knowledge from article text can be a useful tool and can act as an impetus for further research on those topics. Numerous applications, including building a causal knowledge graph, making pipelines for root cause analysis, discovering opportunities for drug discovery, and overall, a scalable building block towards turning large pieces of text into organized information can be built following such an approach. However, it requires robust methods to identify and aggregate causal knowledge from a large set of articles. The main challenge in designing new methods is the absence of a large labeled dataset. As a result, existing methods trained on existing datasets with limited size and variations in linguistic pattern, are unable to generalize well on unseen text. In this paper, we explore multiple unsupervised approaches, including a reinforcement learning-based model that learns to identify causal sentences from a small set of labeled sentences. We describe and discuss in detail our experiments for each approach to further encourage exploration of methods that can be re-utilized for different tasks as well, as opposed to simply exploring a supervised learning process which although superior in performance lacks the versatility to be re-purposed for slightly different tasks. We evaluate our methods on a custom-created dataset and show unique techniques to extract cause-effect relationships from the English language.
Neural Machine Translation for Aymara to Spanish

There are many native languages in Latin America, over the decades the number of speakers was reduced by the strong influence of the Spanish language. There is a continuous concern for the preservation of these languages, such as: Aymara, Quechua, Guaraní. To create Neural Machine Translator (NMT) models, there is no data set of translations from the native language Aymara - Spanish. Therefore, this document presents a data set of conversations in native Aymara language and the respective translations into Spanish. The first translation tests with the seq2seq model are also carried out. The first initial results are promising, considering that it is the first application of Natural Language Processing (NLP) and translation machine for the native Aymara language.
Hard Samples Make Difference: An Improved Training Procedure for Video Action Recognition Tasks

In recent years, the Computer Vision community is witnessing slow shift in Computer Vision tasks from CNN to Attention-based architectures like Transformers which have attained top accuracy on the major recognition tasks. Historically, most of the research works concentrated on image level recognition task as basic ones. However there is still huge room for improvements when moving towards video level tasks. Generally, video models are built on top of image level architectures with attention mechanism or 3D-CNN layers that are globally interconnected across spatial and temporal dimensions. While recent studies of Transformers advocated more on architectural changes, CNN based approaches focus more on best practice of training neural networks which led to a better speed-accuracy trade-off. These best practices are yet to be developed for video recognition tasks. The major contribution of this paper is a novel approach for video-model training. This approach is based on unsupervised hard samples mining to focus training on them rather than on whole dataset. By incorporating this approach into training pipeline of Video Swin Transformer we achieved state-of-the-art accuracy on Kinetics-400 dataset (84.4% and 96.5% for Top-1 and Top-5 accuracy, respectively).
Extractive Text Summarization for Turkish: Implementation of TF-IDF and PageRank Algorithms

Due to the massive amount of information available on the web, reaching the desired content has become more and more difficult. Automatic text summarization helps to solve the problem by minimizing the document size while keeping its core information. In this study, two extractive single document automatic text summarization systems for Turkish are presented which implement the statistical-based TF-IDF algorithm as well as the combination of TF-IDF with the graph-based PageRank algorithm. The study aims to reveal the usability and effectiveness of these algorithms for Turkish documents. Moreover, the results of the TF-IDF implementation and the hybrid approach are compared using the co-selection measures, precision, recall, and F-score. In the evaluation phase, the system-generated summaries are categorized and tested based on their word sizes and the predetermined thresholds and compared against the human-generated summaries. The results indicate that the hybrid system performs better than the TF-IDF system even in lower thresholds, and also both systems are inclined to improve average F-scores in higher threshold generated summarization.
Facial Gesture Recognition for Drone Control

In recent years personal unmanned aerial systems (UAS) or drones have become part of everyday life. In this paper the design and implementation of an intelligent drone controller system that can be used for drone flight navigation in real time through recognition of facial gestures is presented. To do this, the drone’s pilot wears an Electroencephalogram (EEG) headband containing three silver/silver chloride (Ag-AgCl) electrodes placed on the pilot’s frontal cortex. The drone commands implemented in this project were: move up, move down, and move forward and backwards. The corresponding facial gestures for these commands were: raising eyebrows, hard eye blinking, clenching the user’s teeth, and rest position. The EEG signals associated with each facial expression were classified via an artificial neural network (ANN). To acquire the EEG signals, the Cyton board (8-channel biosensing board from OpenBCI) was used. Matlab software was used for denoising and feature extraction of the EEG signals, and for the design and training of the ANN. To be able to classify the EEG signals, feature extraction methods were performed. For the feature extraction, three statistical quantities were computed from each facial gesture collected from the subjects. The statistical parameters were: the standard deviation, root mean square, and mode. Then, the Neural Net Pattern Recognition tool from Matlab was used for the implementation and training of the ANN. After the ANN model was created, the output of the ANN was used to control a small drone. Results of the ANN training yielded a 95.5% accuracy in the classification of the facial gestures. Finally, the intelligent drone controller system was tested with the drone in real time, proving that our initial goal was met.
Mobile Application for After School Pickup Solution: Malaysia Perspectives

School pickup is a daily routine for most parents during the school term. The use of private vehicles as daily commute to transport students to and from schools has caused school-related traffic congestion and air pollution problems. The after school waiting bay of most schools in urban areas is normally very crowded with students waiting for transport to travel home. Teachers on duty and school student council members are assigned to monitor and control the crowded situation. Some schools implement drive-thru pickup procedure in which the teacher on duty need to be very attentive and observe the cars approaching the bay for pickup in order to queue up the students at the pickup bay. With the outbreak of pandemic COVID-19, the teachers not only need to observe the vehicles heading to the pickup bay but to ensure the students obey social distancing norms. To alleviate after school pickup related problems, a mobile application for after school pickup is proposed. With this application, the school teachers would be able to obtain a list of pickup requests from the parents via the mobile application and queue up the students accordingly with safe social distancing at the pickup bay. This can ease the crowded situation, minimize parents’ waiting time at the pickup lane, and reduce air pollution problem caused by vehicle engines that are running while waiting at the pickup lane.
Improving Public Services Accessibility Through Natural Language Processing: Challenges, Opportunities and Obstacles

The adoption of AI in the public sector processes and operations is already showing significant benefits, improving for example their efficiency and effectiveness in delivering services. In this direction, the EU-funded easyRights project explores the application of NLU techniques to improve service accessibility and in particular to extract from administrative documents an effective and step-wise description of the user experience. The project is devoted to easing the understanding of service procedures and improving the experience for the service users. The easyRights project especially aims at easing the access to public services to immigrants so targeting a special users’ category whose social fragility is further challenged by bureaucratic complexity. The present paper delineates the work done by applying NLU techniques to service-related administrative documents in four European cities. The first part describes the NLU system intended to play the role of a pathway generator and further outlines the basic architecture of the pathway composed of four key descriptors for each step, namely the “what”, “when”, “where”, and “how”. In the second part, the article discusses the initial outputs of the experiments related to a total of eight services developed in four pilot projects. Evidence of the key obstacles encountered is therefore discussed. Finally, the paper critically reflects on the general value of the achievements and findings and opens up some crucial questions related to the relevant lessons learnt from the side of the public authority and their preparedness to fully exploit the potential benefits from the adoption of AI solutions.
Evaluating Suitability of a Blockchain Platform for a Smart Education Environment

Recently, blockchain technology has been used in diverse systems and applications beyond cryptocurrencies. With features such as data immutability and a decentralized structure, blockchain technology is increasingly being adopted in various governance use cases where trust and security are essential concerns, particularly in smart campus areas. Education or learning is a field that offers several opportunities to integrate blockchain into its systems. However, there is a luck of studies into which blockchain platforms, based on consensus algorithms, are most suitable for adopting into a smart education environment. Further study is required to guide education informatics researchers in selecting and developing a suitable blockchain platform for experimenting with their applications. This paper will evaluate which consensus algorithm and blockchain platform are appropriate to apply to such an environment based on its quality requirements.
Intelligent Bixby Recommender Systems

In today’s society, there is an explosion of data, which brings with it the inherent challenge of dealing with this data. One such issue is analyzing data and making personalized suggestions of utterances to the users before any query is issued to the voice assistant. It should be possible to recommend the most relevant set of queries for quick access in advance to the user. These set of queries should be based on what the user might want to ask the voice assistant at a particular time based on the context such as the location, occasion and other features. Currently, ‘Bixby’ does not have a feature to recommend utterances to the users based on their demographics and usage patterns. Handling implicit data is problematic since it is difficult to analyze the user’s preferences. In this study, we analyze our strategy of recommending personalized utterances to users based on similar user profiles registered with ‘Bixby’ by taking into account several characteristics of the user such as the current context (time, place, occasion, etc.), demographics, utterances, and the frequency with which the utterances are recorded.