Reviewerly/exHarmony huggingface dataset:
Automatic Human Posture Recognition Using Kinect Sensors by Advanced Graph Convolutional Network

This paper proposes a novel automatic posture recognition approach using the skeletal data of human subjects acquired from the Kinect sensors. The acquired skeletal data are used as the input features for training the artificial-intelligence driven recognizer. In this work, we formulate the underlying human-posture recognition problem as the classical multi-classification problem. The graph convolutional network (GCN) is trained to identify the human postures by successive frames through an activity using the Kinect skeletal data (three-dimensional skeletal coordinates). Experimental results using realworld data demonstrate that our proposed GCN leads to a promising classification-accuracy of 92.2% for automatic human-posture recognition. As a result, our proposed novel GCN-based human-posture recognizer greatly outperforms other existing schemes.
Deep Learning Techniques for Multimodal Sentiment Analysis

Multi-modal sentiment analysis, as one of the most cutting-edge studies in artificial intelligence, has achieved multiple achievements and successes on account of the development of deep learning models. Due to the information explosion in social media, the data format is more complex and diverse than ever. Thus, multi-modal sentiment analysis is becoming a popular and promising research field that includes three main research interests: computer vision, natural language processing, and speech recognition. This work first introduces the background of this new technology and its progressive development around social media applications. Next, this paper represents a comprehensive overview of the advanced deep learning models used in multi-modal sentiment analysis. This review highlights several recurrent neural network (RNN) models and graphic neural network (GNN) models in recent studies. Here, we show that GNN based models can solve tasks in other domains such as physics, chemistry, and biology. After that, this work also includes several helpful summaries according to eight different papers. In the end, we illustrate an overall summary of our work. Some future research topics are also given with clear justifications.
Predicting the Risk of Stroke Based on Imbalanced Data Set with Missing Data

How to prevent stroke in daily life and grasp the possibility of illness as early as possible have become important problems. In this paper, we predict the possibility of the disease through the physiological indicators of the at-risk population. In this process, we first dealt with the common problems of missing data and imbalance between classes in medical data sets, and then we combined multiple simple neural networks and XGBoost tree model through ensemble learning methods. After training this strong classifier with the processed data set, we used this model to predict the incidence of stroke and compared it with the results of other classifiers. After several experiments, we got an average accuracy rate of 75.77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and an average specificity of 78.8%, which means that our model can better identify high-risk individuals while ensuring a certain overall accuracy rate. On the whole, our work can quickly derive disease risk through easily accessible physiological indicators, and provide references for early diagnosis and treatment of stroke.
What's a Good Prediction? Challenges in evaluating an agent's knowledge

Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems. However, both constructing and evaluating such models remains an open challenge. The most common approaches to evaluating models is to assess their accuracy with respect to observable values. However, the prevailing reliance on estimator accuracy as a proxy for the usefulness of the knowledge has the potential to lead us astray. We demonstrate the conflict between accuracy and usefulness through a series of illustrative examples including both a thought experiment and empirical example in MineCraft, using the General Value Function framework (GVF). Having identified challenges in assessing an agent's knowledge, we propose an alternate evaluation approach that arises continually in the online continual learning setting we recommend evaluation by examining internal learning processes, specifically the relevance of a GVF's features to the prediction task at hand. This paper contributes a first look into evaluation of predictions through their use, an integral component of predictive knowledge which is as of yet unexplored.
Applying Machine Learning on RSRP-based Features for False Base Station Detection

False base stations – IMSI catchers, Stingrays – are devices that impersonate legitimate base stations, as a part of malicious activities like unauthorized surveillance or communication sabotage. Detecting them on the network side using 3GPP standardized measurement reports is a promising technique. While applying predetermined detection rules works well when an attacker operates a false base station with an illegitimate Physical Cell Identifiers (PCI), the detection will produce false negatives when a more resourceful attacker operates the false base station with one of the legitimate PCIs obtained by scanning the neighborhood first. In this paper, we show how Machine Learning (ML) can be applied to alleviate such false negatives. We demonstrate our approach by conducting experiments in a simulation setup using the ns-3 LTE module. We propose three robust ML features (COL, DIST, XY) based on Reference Signal Received Power (RSRP) contained in measurement reports and cell locations. We evaluate four ML models (Regression Clustering, Anomaly Detection Forest, Autoencoder, and RCGAN) and show that several of them have a high precision in detection even when the false base station is using a legitimate PCI. In our experiments with a layout of 12 cells, where one cell acts as a moving false cell, between 75-95% of the false positions are detected by the best model at a cost of 0.5% false positives.
A Novel Multi-Task Face Super-Resolution Framework Embedding Degraded Augmented GAN Networks

Abstract The training method that uses predefined degradation methods to provide training data for deep convolutional neural networks suffers from overfitting and image distortion when performing face super-resolution reconstruction. In this paper, we propose a multi-task facial super-resolution reconstruction framework embedded in a degraded augmented GAN network. Compared with the commonly used method of generating low-quality face images through pixel interpolation, this paper inversely embeds a generative adversarial network composed of residual coding blocks in the network. Through the feature learning ability of the network, the feature fitting of the face images collected in the natural scene is performed to generate training data that can more fully represent the real noise in the natural scene, thereby improving the noise reduction and image reconstruction capabilities of the network. In order to take full advantage of the task-specific features, the semantic features of the dataset are extracted through a pre-trained facial organ semantic detection framework and the data is used to add face semantic information in the super-resolution reconstruction network. The experimental results show that the model proposed in this study can achieve super-resolution reconstruction of ultra-low-resolution face images simply and efficiently, and achieve super-resolution face images with lower visual quality than other advanced methods with lower model complexity. It solves the problem of single style and distortion of face reconstruction images caused by the training method of the data set obtained by the usual predefined degradation.
Deep Learning for Automatic Detection of Qualitative Features of Lecturing

Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students’ works or even providing directions to the teachers on how to enhance the lectures. This research explores how an academic lecture can be assessed automatically by quantitative features. First, we prepare a set of qualitative features based on teaching practices and then annotate the dataset of academic lecture videos. We then show how these features could be detected automatically using machine learning and computer vision techniques. Our results show the potential usefulness of our work.
Students’ Motivation in English Learning amid Covid-19 Pandemic: A Systemic Functional Linguistics Perspective

The pandemic of covid-19 has had a negative impact on schooling quality around the world. The lack of student enthusiasm to learn using digital platforms is one issue leading to the low quality of education. Several prior types of research have looked into how students perceive their motivation to learn in the face of pandemic covid-19. However, modal analysis to reveal students' motivation through their written personal views on learning during the epidemic of Covid-19 remains a relatively unexplored area. This research aims to expose the students’ motivation to learn during the outbreak of covid-19 using modality theory. 20 English Department students, Faculty of Language and Arts, University of HKBP Nommensen were involved in this research. The students were assigned to write an essay about their experiences and opinions of learning amid the covid-19 outbreak. The essay consists of approximately 250 words. The research was conducted in a mixed method. The results showed that the most modality used in the text is probability 39.82%, afterwards frequency 27.60%, obligation 17.19%, and inclination 15.39%, respectively. A medium level or category dominated each type of modality. Regarding polarity, 159 (71.95%) of modal words were in the positive range, and the rest of the modality 62 (28.05%) were located in the negative range. This demonstrates that students' enthusiasm to learn throughout the pandemic was characterized as medium and highly positive. The findings were relatively different from the previous research on the same topic. Most of the research revealed low student motivation amid the covid-19 pandemic. However, this research showed different results that the EFL students were still motivated to study their lessons amid the covid-19 pandemic. The results bring consequences to the future educational research to consider using linguistic perspectives in the research method, especially the application of Systemic Functional Linguistics to reveal the meanings of the students’ perspectives on certain educational issues.
Separating Sensor Anomalies From Process Anomalies in Data-Driven Anomaly Detection

Data-driven anomaly detection over time series data is studied from the perspective of separating data anomalies&#x2014;corresponding to sensor failures&#x2014;from process anomalies&#x2014;that arise from equipment or operational failures. A semi-supervised approach is proposed that utilizes two predictive models trained on non-anomalous data using two different sensor groups as inputs, and a nested hypothesis test to reliably classify data or process anomalies. Conditions are derived on choice of sensor groups to guarantee reliable detection, and a case study is presented to demonstrate the proposed classification approach.
Medical Cyber-Physical Systems Enabled with Permissioned Blockchain

Global health security concerns have gained vast importance in recent times with outbreak of COVID-19. Today, the growing interdependence among countries and states has effected into accelerated growth of pandemics. A global need for rugged medical systems on a common platform is deemed today. Pandemics will not stop, they will resurrect again, they will happen irrespective till such times the medical world attains a disease less world in future. But till then, we can attempt to decelerate the pandemics growth enabled with new generation technologies. Medical cyber-physical systems are marred by a number of challenges and this paper proposes a model to negate these identified challenges enabled on multichain blockchain platform that imparts peculiar blockchain characteristics to the network of effected systems. The proposed model also enables to share encrypted data on select blockchain nodes granted defined access controls with proven encryption algorithms.
Error Mitigation of Grover’s Quantum Search Algorithm

Grover’s quantum search algorithm delivers quadratic speedup over classical counterparts for finding an item in unstructured database. But the accuracy of the algorithm degrades as the number of qubits are increased. Noises of various types affect the accuracy of the Grover’s algorithm. The imperfect measurement on the qubit results in measurement errors appears to be one of the factors which limit the scalability of near-term quantum devices/algorithms. This paper is focused on the implementation of measurement error mitigation technique on Grover’s algorithm up to 4-qubits. The measurement error mitigation model for Grover’s algorithm is developed and implemented on the real-time quantum computer. The accuracy of Grover’s algorithm up to 4-qubits with and without measurement errors is evaluated and compared. The results indicate that measurement error mitigation technique mitigates the measurement errors of the Grover’s algorithm and improves its accuracy.
A Script-Driven Approach to Mapping Satellite-Derived Topography and Gravity Data Over the Zagros Fold-and-Thrust Belt, Iran

Abstract Integrated geophysical mapping benefits from visualizing multi-source datasets including gravity and satellite altimetry data using 2D and 3D techniques. Applying scripting cartographic approach by R language and GMT supported by traditional mapping in QGIS is presented in this paper with a case study of Iranian geomorphology and a special focus on Zagros Fold-and-Thrust Belt, a unique landform of the country affected by complex geodynamic structure. Several modules of GMT and ’tmap’ and ’raster’ packages of R language were shown to illustrate the efficiency of the console-based mapping by scripts. Data sources included high-resolution raster grids of GEBCO/SRTM, EGM-2008, SRTM DEM and vector geologic layers of USGS. The cartographic objective was to visualize thematic maps of Iran: topography, geology, satellite-derived gravity anomalies, geoid undulations and geomorphology. Various cartographic techniques were applied to plot the geophysical and topographic field gradients and categorical variations in geological structures and relief along the Zagros Fold-and-Thrust Belt. The structures of Elburz, Zagros, Kopet Dag and Makran slopes, Dasht-e Kavir, Dasht-e Lut and Great Salt Desert were visualized using 3D-and 2D techniques. The geomorphometric properties (slope, aspect, hillshade, elevations) were modelled by R. The study presented a series of 11 new maps made using a combination of scripting techniques and GIS for comparative geological-geophysical analysis. Listings of R and GMT scripting are provided for repeatability.
A Pairing-Free Key Policy Weighted Attributed-Based Encryption

Attribute-based encryption (ABE) is a promising cryptographic paradigm, which can implement fine-grained access control over encrypted data. Due to its feature, ABE is naturally suitable for realizing secure data sharing in clouds. However, there are few ABE paradigms can be applied in real scenarios due to both high complexity and weak security. In this paper, we propose a practical paradigm to simplify access policy and to further improve both computational efficiency and security. A weighted access policy is built to provide rich expressiveness with a simple structure. The weight of each attribute can be redefined so that flexibility is guaranteed. On top of that, our paradigm is with shorter ciphertext and shorter private key. The overall security is reduced to a stronger Hashed Diffie-Hellman assumption. Performance analysis demonstrates that the proposed paradigm is efficient to securely achieve data sharing in cloud computing. Simulation experiment shows that (1) encryption time overhead of PKP-WABE is almost just 10\% of BSW07 and AC17; (2) decryption time overhead of PKP-WABE outperforms AC17 when number of attributes is less than 20. Thus both encryption and decryption of the proposed paradigm gets very close to the theoretical minimum time complexity.
Memory Protections

This chapter covers issues relating to protection of the main memory, typically DRAM, but also NVRAM in the near future. It overviews threats against main memory, dividing them into active and passive attacks. It then discusses mechanisms which can help ensure confidentiality, integrity, and access pattern protections for the main memory. It finally presents assumption relating to main memory protections.
JATS Superhighway

Tag sets change over time. Tag set designers manage a complex system where everything is connected to everything else and new user requirements continue to surface. Tag set users manage complex systems to create, manage, and archive documents. Users strongly resist backward-incompatible change, so as JATS has grown we have made compromises in the design to meet new requirements while maintaining backward-compatability. We think it is time to consolidate redundant models, remove deprecated items, and generally reduce confusion. Can we guide users towards a new, backwards incompatible version in a way that they'll find palatable? We have a plan. We're going to extend the JATS 1.x schema so that it contains the new, 2.0 models in addition to the old models. Then we'll make an "Onramp" subset of 1.x that has the deprecated items removed. Documents valid against the onramp subset of 1.x will also be valid against 2.0.
Cooperative Task Assignment and Path Planning via an $$A^*$$-Market-Based Algorithm

This paper addresses the problem of cooperative task assignment and path planning. A market-based algorithm is designed to achieve the multi-robot task assignment (MRTA) task consisting of independent subtasks and cooperative subtasks executed by two robots. Simultaneously an $$A^*$$ algorithm is designed to achieve the optimal path for each robot under the consideration of robot utilization rate. The proposed $$A^*$$ -market-based algorithm is verified by an industrial example, that is, the coordinated welding task. It is shown that the performance of our algorithm is better than the baseline of the use of the genetic algorithm with the shortest total distance.
Blockchain Framework for Data Storage and Security

In this age of sensitive information where knowledge is power, our data has become an invaluable resource. Easy and public access to information makes it vulnerable to adulteration. The ever-increasing cases of cyber-attacks are causing organizations to spend an exorbitant amount on security. General cloud storage systems are considered efficient for data storage and sharing, but it has multiple limitations such as data centralization, data leakage, and high maintenance cost. Data centralization makes it a hotspot for cyber-attacks, making it prone to data outflows and tampering. An effective alternative is a decentralized system. With advancements in information technology and cyber security, the need for authenticity and verification is highly sought after, making blockchain technology an extremely indispensable tool in the hands of many organizations and enterprises. It solves the problem of security by encrypting its data storing it as a “Hash” or encoded data so that only the user with the key can access the data. The finance sector is being overwhelmed by the ever increasing reliability of blockchain and the implementation of its framework and architecture. Blockchain is a distributed ledger system across a network of users. The blockchain technology, being decentralized, is proposed as a disseminated and diffused or distributed approach which is testified to decipher and decode the security requirements of the new digital era as well as serve as a platform and a jump pad for advancements in various other fields such as Internet of Things (IoT), data storage, biometric security, healthcare facilities, smart grids, and many more. This chapter begins with a basic outline of all you need to know about blockchain, an upcoming evolutionary technology that ameliorates the world of data decentralization and security. It further explains its use, features, areas of implementation, architecture, as well as its limitations that are detrimental to be cognizant of how the blockchain system works/is implemented. It then explores P2P networks and interplanetary file systems (IPFS) followed by its current use cases in the form of Filecoin. Next, the chapter explores the privacy and security aspects of the blockchain. It showcases some of the faults of blockchain ledgers like Sybil and discusses some techniques that are implemented to fix them. It discusses bitcoin, one of the most secure blockchain architecture to date, and describes private and public keys, peer to peer network, hash.&amp;nbsp; chained storage, digital signature, and consensus algorithms implemented by bitcoin to prevent any fraudulent transactions from taking place.
A Review of Efficient Mobility Techniques for Privacy and Secured Cloud Computing Architectures for Infrastructure as a Service

Cloud computing has since been turned into the most transcendental growth. This creative invention provides forms of technology and software assistance to companies. Cloud computing is a crucial concept for the distribution of information on the internet. Virtualization is a focal point for supporting cloud resources sharing. The secrecy of data management is the essential warning for the assurance of computer security such that cloud processing will not have effective privacy safety. All subtleties of information relocation to cloud stay escaped the clients. In this review, the effective mobility techniques for privacy and secured cloud computing have been studied to support the infrastructure as service.
Political Affiliation and Human Mobility Under Stay-at-Home Orders: A Difference-in-Difference Analysis with County and Time Fixed Effects

Background In late March 2020, state and local governments across the country issued stay-at-home directives to slow the spread of COVID-19. However, divergent messages from political parties on the severity of COVID-19 and differing levels of support of these social distancing measures have potentially prompted differential behaviors across political groups. This study examines state-level partisan differences in changes in human mobility during stay-at-home orders. Methods Aggregated and de-identified large-scale human mobility data was collected from Cuebiq, a mobility insights platform, to measure the fraction of users that staying at home. A difference-in-difference analysis was performed to evaluate the changes in human mobility before and after stay-at-home orders were implemented by state political afflictions. Results Before March 19th, 2020, there was a 0.82% difference (SE=0.003, p&lt;0.001) between the percent of users staying at home in republican states versus democratic states. Difference-in-difference analysis revealed that on average, democratic states experienced a 4.11% (SE = 0.006, p&lt;0.001) greater increase in the percent of users that stayed at home compared to republican states pre-post the implementation of stay-at-home orders. States that were not issued state-wide stay-at-home orders were excluded from the study. Conclusion Evidence supports the differential changes in adherence to stay-at-home orders by state political affiliation. These results suggest that political messaging may be a strong factor in influencing social distancing behaviors.