Reviewerly/exHarmony huggingface dataset:
How economic freedom reflects on the Bitcoin transaction network

Based on the assumption that economic freedom facilitates the growth of traditional economic sectors, this study seeks to understand the role it plays in fintech sector. For this purpose, by adopting the social network analysis methodological approach, it considers the geographical network of Bitcoin transactions as a proxy to measure the national level of fintech development and compares it with a set of economic freedom indicators related to the top 70 world economies. The analysis revealed significant relationships between the performed network centrality measures and national levels of economic freedom. In particular, as confirmed by the implemented multilevel regression models, high levels of freedom to trade internationally combined with a restrained value of inflation and low administrative requirements can be considered as determining factors for fintech development. The study also showed a large number of Bitcoin transactions conducted in those countries characterized by fewer capital controls and restrictions, a result that feeds suspicions of illegal behavior, such as money laundering or terrorism financing, carried out through the use of cryptocurrencies. Findings of this research might be strategic for fintech entrepreneurship and policymakers interested in designing policies that aim to foster innovative sectors while ensuring the legality of financial flows.
Sneaking Physical Exercise into Sedentary Work Life: Design Explorations of Ambient Reminders in Opportune Moments

Prolonged sitting at work can have a negative effect on health, which can be mitigated by engaging in compensatory physical activity. By following a constructive-design-research approach, we developed two interactive prototypes of ambient reminders for encouraging physical exercise in opportune moments at the workplace. To explore the perceived usefulness of the concepts, we conducted a user study with office workers. We report on our lessons learned about what makes a moment during daily work opportune for reminding and conducting a physical exercise, of how to identify these moments with sensing technology, and how to provide unobtrusive but effective notifications during these moments.
Many-to-One Gesture-to-Command Flexible Mapping Approach for Smart Teaching Interface Interaction

Mapping between gestures and semantics is inherently challenging, especially in the aspect of defining meaningful gesture semantics that are easy to understand and remember. To address this challenge, we put forward an intent-driven approach in this paper, which is based on users' interaction intent. We implemented a many-to-one flexible mapping between multiple gestures and one semantic object. We also designed and evaluated a domain-specific gestural input technique for classroom use by considering the intent of the user. The main contributions of this work consist of: (1) a mapping between multiple gestures and one semantics and (2) an intelligent and natural interacting interface model for a three-dimensional (3D) user interfaces (UIs). The proposed algorithm is evaluated with five frequently used gestures and applied to the UIs system. The experimental results demonstrate that the gesture-based algorithm performs well and can substantially reduce the memory loads of the user.
Cognitive Science and Artificial Intelligence for Human Cognition and Communication

HAS broad horizons, which covers different characteristics of cognition.Moreover, cognitive science is an interdisciplinary, scientific study of human reasoning, emotions, language, perception, attention, and memory.However, artificial intelligence (AI) explores the design of computers and software that would be capable of intelligent behavior.The integration of cognitive science and AI offers a deep understanding of human cognition and communication.In addition, the creative and technical skills apply the knowledge in AI solutions and applications in engineering psychology.This special section guest editorial illustrates the connections between cognitive science and AI that examines human performance and design of engineering psychology.The earlier studies have revealed powerful principles, methodologies, and algorithms prompting in human self-examination and perception for building AI systems that better match human performance.Thus, this special section mainly focuses on the convergence of cognitive science
The Accountability Crisis In Advertising and Marketing

Once upon a time a new medium emerged that captured the attention of audiences. The inventor had great dreams for the medium: It would become a powerful tool for teaching and learning, making illiteracy a thing of the past. Instead of waiting for others to describe events, and put their own
Efficient Priors for Scalable Variational Inference in Bayesian Deep Neural Networks

Stochastic variational inference for Bayesian deep neural networks (DNNs) requires specifying priors and approximate posterior distributions for neural network weights. Specifying meaningful weight priors is a challenging problem, particularly for scaling variational inference to deeper architectures involving high dimensional weight space. Based on empirical Bayes approach, we propose Bayesian MOdel Priors Extracted from Deterministic DNN (MOPED) method to choose meaningful prior distributions over weight space using deterministic weights derived from the pretrained DNNs of equivalent architecture. We empirically evaluate the proposed approach on real-world applications including image classification, video activity recognition and audio classification tasks with varying complex neural network architectures. The proposed method enables scalable variational inference with faster training convergence and provides reliable uncertainty quantification.
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards

Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations. Meta-imitation learning is a promising approach towards enabling agents to learn a new task from one or a few demonstrations by leveraging experience from learning similar tasks. In the presence of task ambiguity or unobserved dynamics, demonstrations alone may not provide enough information; an agent must also try the task to successfully infer a policy. In this work, we propose a method that can learn to learn from both demonstrations and trial-and-error experience with sparse reward feedback. In comparison to meta-imitation, this approach enables the agent to effectively and efficiently improve itself autonomously beyond the demonstration data. In comparison to meta-reinforcement learning, we can scale to substantially broader distributions of tasks, as the demonstration reduces the burden of exploration. Our experiments show that our method significantly outperforms prior approaches on a set of challenging, vision-based control tasks.
Data Acquisition Technologies and System for Automating, Record-Keeping and Managing Water Supply Processes

The authors analyzed metered data acquisition technologies and methods; demonstrated architectural solutions, communication technologies and the distributed telemetering system design technique; demonstrated possibilities of using modern acquisition and analysis tools to elaborate recommendations on the optimal mode of use of technical objects.
Comparative analysis of Naïve Bayes, K Nearest Neighbor and C.45 method in weather forecast

Abstract Weather forecast in an area is unpredictable. This is due to the fact that human factors cannot predict it. The weather forecast is by applying data mining using the algorithm Naive Bayes, K-nearest Neighbor (K-NN), and C.45. Bayesian Classification is a statistical classification method that is useful for the process of determining the probability of a class membership. KNN Algorithm is a classification algorithm based on the similarity between one data and another data. C4.5 algorithms is an easy-to-use classification method interpreted. The best level of accuracy between the three algorithms can be determined by comparison. Comparison of algorithm aims to get the algorithm that is considered accurate, precision, recall and f-measure to make a prediction of a problem. the results of the comparison of the k-Nearest Neighbor, Naïve Bayes and C4.5 classification algorithms used in weather prediction case studies stating that the KNN classification algorithm is a classification algorithm that has the highest accuracy with k = 7 and fold = 5 in predicting the weather compared to Naïve Bayes classification algorithm with fold = 3 and C.45 which reached 71.58% followed by C.45 with fold = 20 having an accuracy of 69.83%. and finally Naïve Bayes 68.77%.
Robust data cleaning methodology using online support vector regression for ultra-short baseline positioning system

Ultrashort baseline (USBL) acoustic positioning system is a significant navigation means for human occupied vehicle due its simple structure, convenient operation, and large-scale-maneuver capacity. In order to improve the quality of USBL raw data effectively and efficiently, a robust data cleaning methodology using Online Support Vector Regression (OSVR) is proposed to deal with measurement outliers and missing values. In this study, we applied sliding-window samples to train the OSVR model for online time series prediction and then utilized the obtained one-step ahead prediction to detect and replace outliers or supplement missing values. The experimental results of the online test show that the proposed methodology can satisfy the requirement of real-time navigation and acquire consecutive and consistent positioning data for USBL. In comparison with the raw data, the root mean square error results in longitude and latitude are reduced by 91.75% and 85.53%, respectively. In addition, such methodology outperforms other data cleaning algorithms based on Least Square (LS) and kernel recursive LS.
Robust multidepth object encryption based on a computer-generated hologram with a cascaded structure

A robust, asymmetric, multidepth, three-dimensional object encryption scheme based on a computer-generated Fresnel hologram in the cascaded fractional Fourier domain is proposed. A layer-based Fresnel transform is used to generate a computer-generated hologram, which is then decomposed into two phase-only masks with a random phase distribution using matrix composition and decomposition methods. Encryption is implemented by using the created phase-only masks in two cascaded fractional Fourier transform domains, and a pair of private keys is generated in the encryption process. The cryptosystem is asymmetric and possesses high resistance against various potential attacks, such as brute-force, chosen-plaintext, known-plaintext, and ciphertext-only attacks. The simulation results and cryptanalysis confirmed the feasibility and effectiveness of the proposed encryption scheme.
Accelerating generalized linear models with MLWeaving: a one-size-fits-all system for any-precision learning

Learning from the data stored in a database is an important function increasingly available in relational engines. Methods using lower precision input data are of special interest given their overall higher efficiency. However, in databases, these methods have a hidden cost: the quantization of the real value into a smaller number is an expensive step. To address this issue, we present ML-Weaving, a data structure and hardware acceleration technique intended to speed up learning of generalized linear models over low precision data. MLWeaving provides a compact in-memory representation that enables the retrieval of data at any level of precision. MLWeaving also provides a highly efficient implementation of stochastic gradient descent on FPGAs and enables the dynamic tuning of precision, instead of using a fixed precision level during learning. Experimental results show that MLWeaving converges up to 16 x faster than low-precision implementations of first-order methods on CPUs.
Comparison of Fixation Methods for Preservation Cytology Specimens of Cell Block Preparation Using 10% Neutral Buffer Formalin and 96% Alcohol Fixation in E-cadherin and Ki-67 Immunohistochemical Examination

Cytological and molecular examinations are among the most important examinations in cancer diagnosis. 96% alcohol is a fixative solution commonly used by clinicians for cytological samples because of its accessibility and affordability. Cellblock preparation from cytology specimen may increase morphology detail and may be used for further biomarker analysis. E-cadherin is an adhesion protein expressed in the cell membrane of most carcinoma. Ki67 is a protein expressed in nuclei of malignant cells that used as a proliferation marker.This study was designed to investigate the effect of fixation duration in 96% alcohol on protein preservation for immunohistochemistry (IHC) evaluation compared to 10% neutral buffered formalin (NBF) as the gold standard.Twenty-five fine-needle aspiration biopsy (FNAB) specimen diagnosed as carcinoma were fixed in 10% NBF and 96% alcohol for 1 hour, 6 hours, 24 hours, 48 hours and 72 hours. Cell blocks preparation were made from those 6 groups of specimens. E-cadherin and Ki67 IHC were done to cell blocks section and evaluated. The data were statistically analysed using the Friedman test with p-value < 0.05 of a significant level.There were significant differences between E-cadherin and Ki67 expression in cell block preparation from 96% alcohol-fixed cytology specimen for 1 hour, 6 hours, 24 hours, 48 hours and 72 hours to 10% NBF (p = 0.0001).The result indicated that 96% alcohol is not suitable as a fixative solution for cell block preparation in E-cadherin and Ki-67 IHC examination.
An Architecture for Blockchain based Peer to Peer Energy Trading

Peer to peer energy trading (P2P ET) is expected to be one of the key elements of the next generation power systems. Existing centralized grid systems face several challenges to support P2P ET such as single point of failure, communication overhead, security and privacy problems. Blockchain based P2P ET has been suggested by many researchers as a possible solution to the aforementioned problems. In this paper, we propose P2P ET architecture that allows performing secure energy trading without compromising system efficiency. The proposed architecture consists of three layers: Blockchain layer, Off-Blockchain communication layer and Physical layer. We describe the components of each layer along with interactions that exist between them to achieve the overall objectives. Moreover, we explain how the proposed architecture can achieve a better performance.
Standing on the Feet of Giants — Reproducibility in AI

A recent study implies that research presented at top artificial intelligence conferences is not documented well enough for the research to be reproduced. My objective was to investigate whether the quality of the documentation is the same for industry and academic research or if differences actually exist. My hypothesis is that industry and academic research presented at top artificial intelligence conferences is equally well documented. A total of 325 International Joint Conferences on Artificial Intelligence and Association for the Advancement of Artificial Intelligence research papers reporting empirical studies have been surveyed. Of these, 268 were conducted by academia, 47 were collaborations, and 10 were conducted by the industry. A set of 16 variables, which specifies how well the research is documented, was reviewed for each paper and each variable was analyzed individually. Three reproducibility metrics were used for assessing the documentation quality of each paper. The findings indicate that academic research does score higher than industry and collaborations on all three reproducibility metrics. Academic research also scores highest on 15 out of the 16 surveyed variables. The result is statistically significant for 3 out of the 16 variables, but none of the reproducibility metrics. The conclusion is that the results are not statistically significant, but still indicate that my hypothesis probably should be refuted. This is surprising, as the conferences use double‐blind peer review and all research is judged according to the same standards.
Protecting GANs against privacy attacks by preventing overfitting.

Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable approach to share data without releasing the original dataset. It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared. However, recent work has shown that the GAN models and their synthetically generated data can be used to infer the training set membership by an adversary who has access to the entire dataset and some auxiliary information. Current approaches to mitigate this problem (such as DPGAN) lead to dramatically poorer generated sample quality than the original non–private GANs. Here we develop a new GAN architecture (privGAN), where the generator is trained not only to cheat the discriminator but also to defend membership inference attacks. The new mechanism provides protection against this mode of attack while leading to negligible loss in downstream performances. In addition, our algorithm has been shown to explicitly prevent overfitting to the training set, which explains why our protection is so effective. The main contributions of this paper are: i) we propose a novel GAN architecture that can generate synthetic data in a privacy preserving manner without additional hyperparameter tuning and architecture selection, ii) we provide a theoretical understanding of the optimal solution of the privGAN loss function, iii) we demonstrate the effectiveness of our model against several white and black–box attacks on several benchmark datasets, iv) we demonstrate on three common benchmark datasets that synthetic images generated by privGAN lead to negligible loss in downstream performance when compared against non–private GANs.
Performance Evaluation of Single Board Computer for Hadoop Distributed File System (HDFS)

In this paper, we compare the performance of Single Board Computer (SBC) cluster between using local storage and using Network Attached Storage (NAS) as Hadoop Distributed File System (HDFS) data storage. We build a Hadoop cluster with one master node and four slave nodes. The node used is SBC with Micro SD Card as local storage. For benchmarking, we use terasort and testDFSIO programs that available on Hadoop. The results indicate that the performance of SBC for the Hadoop framework increased by using NAS. The processing time needed to run a terasort program is faster with NAS than local storage. The greater the data processed, the greater the percentage of processing time. And the comparison of read speed using both is almost the same but write speed when using NAS is almost twice faster than using local storage. We also use the Performance Analysis Tool (PAT) to measure the performance of SBC. PAT collects CPU performance metrics on SBC when running a terasort program. The results show the CPU utilization of SBC is better to use NAS than local storage.
Application of Improved Convolutional Neural Network in Image Classification

Because the LeNet-5 convolutional neural network is not accurate in classifying images with complex texture features, in order to improve the accuracy, this paper proposes a multi-convolution neural network. First, on the basis of the LeNet-5 network, a convolutional layer and a pooling layer are added to deepen the depth of the network. Secondly, the first two pooling layers are propagated backward and connected to the last pooling layer through convolution, so as to make full use of the low-level features extracted by the network and effectively combining the low-level features and high-level features extracted by the network. Finally, use the Adam optimizer to adjust and update the parameters of the network. Experimental results on the Cats vs. Dogs, Cifar-10 and Fer2013 datasets show that the multi-convolution neural network has good classification ability on complex texture feature datasets.
Adversarial Training Methods for Boltzmann Machines

A Restricted Boltzmann Machines (RBM) is a generative Neural Net that is typically trained to minimize KL divergence between data distribution P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data</sub> and its model distribution P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RBM</sub> . However, minimizing this KL divergence does not sufficiently penalize an RBM that place a high probability in regions where the data distribution has a low density, and therefore, RBMs always generate blurry images. In order to solve this problem, this paper extends the loss function of RBMs from KL divergence to adversarial loss and proposes an Adversarial Restricted Boltzmann Machine (ARBM) and an Adversarial Deep Boltzmann Machine (ADBM). Different from the other RBMs, an ARBM minimizes its adversarial loss between the data distribution and its model distribution without explicit gradients. Different from traditional DBMs, an ADBM minimizes its adversarial loss without a layer-by-layer pre-training. In order to generate highquality color images, this paper proposes an Adversarial Hybrid Deep Generative Net (AHDGN) based on an ADBM. The experiments verify that the adversarial loss can be minimized in our proposed models, and the generated images are comparable with the current state-of-the-art results.
Privacy-Awareness Fair Contract Signing Protocol Based on Blockchain

With the rise of network technology in recent years, e-commerce has occupied an important part in trade activities. How to ensure fair transactions between untrustworthy parties is a current research hotspot. This paper combines RSA blind signature and blockchain technology to design a fair contract signing protocol based on blockchain privacy protection. The RSA blind signature generates a contract blind signature, which guarantees the sensitive details of the transaction contract; the blockchain technology provides a decentralized trusted third party for the contract, ensuring fair and equitable transactions. After analysis, the protocol has the characteristics of correctness, unforgeability, blindness, and untraceability.
The Effect of Real-Time Sensing of a Window on Energy Efficiency, Comfort, Health and User Behavior

Sensing technologies integration in buildings has grown rapidly. Most of them are connected to platforms for monitoring data and notifying anomalies, but few are integrated within building elements (either for data collection or response). These technologies help regulate HVAC, or detect building systems’ failures, but few enable passive sustainable strategies or space maintenance. It is possible to enhance building responsive operation by gathering granular data on passive systems’ operation and space occupation by setting a building component, that houses a sensor network. That is, if plugged into Building Information Models, it permits adaptation to unusual climate conditions or abrupt space use [Linked with the following correlated research projects:
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models

Probabilistic topic modeling is a common first step in crosslingual tasks to enable knowledge transfer and extract multilingual features. Although many multilingual topic models have been developed, their assumptions about the training corpus are quite varied, and it is not clear how well the different models can be utilized under various training conditions. In this article, the knowledge transfer mechanisms behind different multilingual topic models are systematically studied, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.