Reviewerly/exHarmony huggingface dataset:
EasyUC: Using EasyCrypt to Mechanize Proofs of Universally Composable Security

We present a methodology for using the EasyCrypt proof assistant (originally designed for mechanizing the generation of proofs of game-based security of cryptographic schemes and protocols) to mechanize proofs of security of cryptographic protocols within the universally composable (UC) security framework. This allows, for the first time, the mechanization and formal verification of the entire sequence of steps needed for proving simulation-based security in a modular way: Specifying a protocol and the desired ideal functionality; Constructing a simulator and demonstrating its validity, via reduction to hard computational problems; Invoking the universal composition operation and demonstrating that it indeed preserves security. We demonstrate our methodology on a simple example: stating and proving the security of secure message communication via a one-time pad, where the key comes from a Diffie-Hellman key-exchange, assuming ideally authenticated communication. We first put together EasyCrypt-verified proofs that: (a) the Diffie-Hellman protocol UC-realizes an ideal key-exchange functionality, assuming hardness of the Decisional Diffie-Hellman problem, and (b) one-time-pad encryption, with a key obtained using ideal key-exchange, UC-realizes an ideal secure-communication functionality. We then mechanically combine the two proofs into an EasyCrypt-verified proof that the composed protocol realizes the same ideal secure-communication functionality. Although formulating a methodology that is both sound and workable has proven to be a complex task, we are hopeful that it will prove to be the basis for mechanized UC security analyses for significantly more complex protocols and tasks.
The Use and Abuse of Social Media for Spreading Fake News

This review paper talk about fake news, social media and sets out to explore the relation between fake news and social media and how fake news is becoming a serious threat to civil society.
Sentiment Analysis from Images of Natural Disasters

Social media have been widely exploited to detect and gather relevant information about opinions and events. However, the relevance of the information is very subjective and rather depends on the application and the end-users. In this article, we tackle a specific facet of social media data processing, namely the sentiment analysis of disaster-related images by considering people’s opinions, attitudes, feelings and emotions. We analyze how visual sentiment analysis can improve the results for the end-users/beneficiaries in terms of mining information from social media. We also identify the challenges and related applications, which could help defining a benchmark for future research efforts in visual sentiment analysis.
The effects of anthropomorphic avatars vs. non-anthropomorphic avatars in a jumping game

Avatar identification is a topic of increasingly intense interest. This is largely because avatar identification can promote a wide variety of outcomes: game enjoyment, intrinsic motivation, quality of made artifacts, and more. Yet we still understand very little about how different avatar types affect users. Here, we contribute one of the few highly controlled studies of this nature (N=1074). Specifically, we compare three avatar types in a jumping game: 1) Human (high anthropomorphism), 2) Block-like (low anthropomorphism), and 3) Robot (high anthropomorphism). We find that players randomly assigned to the Robot condition have significantly higher player experience. We find that both Robot and Human conditions lead to higher avatar identification. Finally, using linear hierarchical regression, we find that avatar identification significantly promotes player experience (29.8% variance) and time played (3.5% variance). Our study demonstrates the importance of considering avatar type in designing virtual systems.
Clustering by Search in Descending Order and Automatic Find of Density Peaks

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Clustering by fast search and find of density peaks</i> published on journal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Science</i> in 2014 is a density-based clustering technique, which is not only unnecessary to determine the number of clusters in advance, but also able to recognize the clusters of arbitrary shapes. Due to a manual selection of clustering centers on a decision graph, samples which belong to one cluster may be assigned to two or more clusters and vice versa. On assumption that boundary points which keep comparable densities with cluster centers should be regarded as inner points, we make a new method which not only can find all possible clusters automatically but also can combine those with similarities simultaneously to obtain the final clusters. Unlike clustering by fast search and find of density peaks, we only focus on densities with discarding the relative metric which measures the minimum distance between a cluster center and a point with a higher density. Qualitative and quantitative experimental results on sufficient datasets demonstrate the effectiveness of our method.
Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal

(2019). Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal. International Journal of General Systems: Vol. 48, No. 7, pp. 713-737.
Identification of sarcasm using word embeddings and hyperparameters tuning

Around the world, most of the proposed techniques for the identification of sarcasm either take the utterance in isolation or these methods only perform the categorization of the textual data. Very limited work has been done on how to train or manipulate the various parameters related to textual data so that to improve on the accuracy of the classification method. In this article, we are trying to identify the sarcasm in the textual data using neural networks. We have tried to classify the data using convolutional neural networks (CNN), recurrent neural networks (RNN) and a blend of these techniques to improve accuracy. Our work is not limited to the classification of the sarcastic text, we have also tried to measure the impact of the training data, number of epochs and amount of dropout in the network. The paper also discusses the impact of various embedding on the dataset when converting the same dataset into vectors via different word embeddings. We measured the influence of various parameters on the very large-scale Reddit1 corpus.
Classification of breast and colorectal tumors based on percolation of color normalized images

Percolation is a fractal descriptor that has been applied recently on computer vision problems. We applied this descriptor on 58 colored histological breast images, and 165 colored histological colorectal images, both stained with Hematoxylin and Eosin, in order to extract features to differentiate between benign and malignant cases. The experiments were also performed over normalized images, aiming to analyze the influence of different color normalization techniques on percolation-based features and whether they can provide better classification results. The feature sets obtained from the application of the method on the original images and on the normalized images with three different techniques were tested using 12 different classifiers. We compared the obtained results with other relevant methods in the area and observed significant contributions, with AUC rates above 0.900 in both normalized and non-normalized images. We also verified that color normalization does not contribute to the classification of breast tumors when associated with percolation features. However, color normalized images from the colorectal tumor’s dataset provided better results than the original images.
Book data grouping in libraries using the k-means clustering method

Abstract Clustering is a process of sorting out a data set to become separate cluster groups and each has similarities, and aims to group the data into one cluster. This research aims to group the book information which is contained in Universitas Prima Indonesia, by using K-means clustering method. On this K-means clustering algorithm, the variables used as input are : NIM, Name, Book Title and Author. The output produced consists of 3 clusters, those are (C1) the most frequently borrowed book, (C2) book that is often borrowed, and (C3) book that is rarely borrowed. With the use of this K-means Clustering method, the final result obtained consists of member of cluster 1 as many as 19 members, member of cluster 2 as many as 22 members, and member of cluster 3 as many as 19 members. The information of grouping this book data can be used by the library. In the case of the selection of books that must be added to the library and to minimize the books that are rarely borrowed so as not to cause a buildup of books that are rarely borrowed, so there is a space for books to be added into the library.
Model-Based Requirement Engineering to Support Development of Complex Systems

About 90% of the innovations on today’s vehicles are driven by electrics/electronics or software functions. This shifts the focus from a component-oriented perspective to a function-oriented view in engineering. At the same time, descriptions of systems and subsystems are required in order to coordinate collaboration with suppliers. These different views on the system under development essentially affect requirements engineering. On the one hand, information have to be provided, e.g., for requirement specifications for single systems. On the other hand, requirements have to be defined for functions realized on different systems and subsystems. This paper presents a concept for model-based requirement engineering supporting to gather and provide requirements needed for different views within the automotive development process. The concept presented is based on five partial models representing the emerging automotive system from an abstract concept (use cases) to a solution (system structure). Each of the partial models, namely use cases, functions, function realization, system structure and product structure are described with regard to required artifacts and their relations based on object-oriented modeling. These artifacts and relations serve for the allocation of requirements or can be specified by existing requirements. The proposed concept support different views upon the vehicle system and allow to generate structured, consistent documents, like requirements specifications for single systems within the development process. At the same time, the impact of changes concerning single requirements, use cases, functions or systems can be analyzed. The application of the proposed concept is highlighted using the example of an electric vehicle.
An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.
Study on Fashion Image Retrieval Methods for Efficient Fashion Visual Search

Fashion image retrieval (FIR) is a challenging task, which requires searching for exact items accurately from massive collections of fashion products based on a query image. Despite recent advances, FIR still has limitations for application to real-world visual searches. The main reason for this is not only the trade-off between the model complexity and performance, but also the common nature of fashion images captured under uncontrolled circumstances (e.g. varying viewpoints and lighting conditions). In particular, fashion images are vulnerable to shape deformations and suffer from inconsistency between the user's query images and refined product images. Moreover, multiple fashion objects can be present simultaneously within a single image. In this paper, we considered an FIR method that is optimized for the fashion domain. We investigated training strategies and deep models to improve the retrieval performance. The experimental results on three benchmarks from DeepFashion dataset show that considered methods could achieve the significant improvements compared to the previous FIR methods.
Implementation of Certainty Factor Method for Expert System

Abstract PC computer is a flexible electronic device and can receive and process data quickly and accurately, and can also store data for a long time. There is a time when the PC hardware is damaged. So that it annoys the user. In connection with the problems that occur, the researchers aim to build an Expert System application to diagnose damage to PC computers using the Certainty Factor method by using Visual Studio 2010 applications and using Microsoft Access 2013 as a database. Applications that are built can be an alternative in knowing the type of damage and provide alternative solutions in handling damage to PC computer hardware.
A comparison of calibrated and intent-aware recommendations

Calibrated and intent-aware recommendation are recent approaches to recommendation that have apparent similarities. Both try, to a certain extent, to cover the user's interests, as revealed by her user profile. In this paper, we compare them in detail. On two datasets, we show the extent to which intent-aware recommendations are calibrated and the extent to which calibrated recommendations are diverse. We consider two ways of defining a user's interests, one based on item features, the other based on subprofiles of the user's profile. We find that defining interests in terms of subprofiles results in highest precision and the best relevance/diversity trade-off. Along the way, we define a new version of calibrated recommendation and three new evaluation metrics.
Vision2Sensor

Mobile and wearable sensing devices are pervasive, coming packed with a growing number of sensors. These are supposed to provide direct observations about user activity and context to intelligent systems, and are envisioned to be at the core of smart buildings, towards habitat automation to suit user needs. However, much of this enormous sensing capability is currently wasted, instead of being tapped into, because developing context recognition systems requires substantial amount of labeled sensor data to train models on. Sensor data is hard to interpret and annotate after collection, making it difficult and costly to generate large training sets, which is now stalling the adoption of mobile sensing at scale. We address this fundamental problem in the ubicomp community (not having enough training data) by proposing a knowledge transfer framework, Vision2Sensor, which opportunistically transfers information from an easy to interpret and more advanced sensing modality, vision, to other sensors on mobile devices. Activities recognised by computer vision in the camera field of view are synchronized with inertial sensor data to produce labels, which are then used to dynamically update a mobile sensor based recognition model. We show that transfer learning is also beneficial to identifying the best Convolutional Neural Network for vision based human activity recognition for our task. The performance of a proposed network is first evaluated on a larger dataset, followed by transferring the pre-trained model to be fine-tuned on our five class activity recognition task. Our sensor based Deep Neural Network is robust to withstand substantial degradation of label quality, dropping just 3% in accuracy on induced degradation of 15% to vision generated labels. This indicates that knowledge transfer between sensing modalities is achievable even with significant noise introduced by the labeling modality. Our system operates in real-time on embedded computing devices, ensuring user data privacy by performing all the computations in the local network.
An Efficient Differential Privacy Logistic Classification Mechanism

The logistic model is a very elementary and important model in the field of machine learning. In this article, an efficient differential privacy logistic classification mechanism is proposed. The proposed mechanism is better than object function perturbation mechanism in terms of running time and accuracy. Regarding accuracy, the proposed mechanism's accuracy is almost the same as the no differential privacy (non-dp) mechanism, and the proposed mechanism is better than that of the object function perturbation mechanism in both the test accuracy and the train accuracy. As for the running time of the training model, the proposed mechanism is better than the object function mechanism and is the same as the non-dp mechanism.
Explainable Argumentation for Wellness Consultation

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a 'good' explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.
High throughput implementation of SHA3 hash algorithm on field programmable gate array (FPGA)

Cryptographic hash function is an essential element in sensitive communications, such as banking, military and health. It ensures secure communication by checking data integrity, storing passwords and other important roles. Keccak hash function (i.e. SHA3) is the best one in terms of resistance against recent cryptanalysis attacks as well as of hardware performance. However, an efficient improvement in terms of hardware performance is always needed, such as increasing speed or decreasing area consumption. In this paper, we have focused on improving the speed (throughput) of Keccak hash algorithm by proposing a new design which is based on decreasing the number of clock cycles needed to produce a hash value. Consequently, we could achieve 33.35 Gbps as a highest achieved throughput. However, a decrease in terms of maximum frequency has been noticed. Our design has been implemented in Xilinx Virtex5 and Virtex6 FPGA device, and has been compared to recent published implementations.
Capacity of blockchain based Internet-of-Things: Testbed and analysis

Abstract An integration of Internet-of-Things (IoT) and blockchain becomes increasingly important to secure IoT data in an anti-tampering manner. Challenges arise from the immense scale of IoT and the resultant impact of network partitioning on blockchain. We design a new testbed to evaluate the impact, where resource-limited IoT devices, acting as light nodes, are an integral part of a blockchain. Our testbed is built on the Ethereum platform with non-trivial modifications on key modules. The partitioning of IoT is emulated by probabilistically dropping blocks travelling among the miners. We also propose a new discrete-time Markov chain model to validate our testbed and analyze the impact of block mining rates and network conditions on the capacity of public blockchains. The model is first formed to be non-ergodic with an infinite state space. By exploiting the eventual consistency property of blockchain, the model is collapsed to be ergodic and approximated with a finite state space and significantly improved tractability. Both the testbed and analysis reveal the blockchain capacity can be improved by accelerating the block mining rates which, however, increases stale blocks. Our analysis provides an asymptotic upper bound for the blockchain capacity.
High-Precision Vision Sensor Method for Dam Surface Displacement Measurement

Dam surface displacement measurement can monitor the safety of dam operation and provide early warning for dam accidents. This paper proposes a method for measuring the displacement of the dam surface based on double vision sensors. Mathematical morphology is combined with the Roberts edge detection operator to complete the sub-pixel extraction of the marker edge. The method makes the edge of the marker in the image smoother compared with the Zernike moment sub-pixel edge detection algorithm. The edge gray center of gravity method is proposed to realize the positioning of the marker, which improves the precision of the vision measurement method. Additionally, this paper proposes the design of a test system and describes the experiments conducted in this study. The results revealed that the proposed method can achieve millimeter-level measurement accuracy and has the advantages of simple operation, short measurement period, online monitoring, and low cost, which satisfies the requirements for measuring the dam surface displacement.
An echo state network architecture based on quantum logic gate and its optimization

Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor's 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance.