Reviewerly/exHarmony huggingface dataset:
Packet Encryption for Securing Real-Time Mobile Cloud Applications

The evolution of smart devices has brought the most confidential data to mobile applications and cloud computing with its processing power is serving the huge processing requirements of these applications. However, these advancements has brought in serious data security concerns, as there is constant shuttling of data from devices to cloud and backforth, and encryption is the most commonly used technique for securing data in transmission. Every encryption technique is breakable, but its efficiency is calculated with the time it can withstand an attack. But in today’s world, as cloud computing comes with almost unlimited computing resources, the present day encryption techniques might become inadequate for securing confidential data in transmission and after transmission. Existing encryption techniques, encrypt the whole data packets in a session using the same session key. If a third party can decrypt a single packet during or after transmission, all the packets in that session can be compromised. This work proposes a packet encryption scheme, where a packet key once used will never be used throughout the session, with minimal latency and maximum security for real-time mobile cloud applications.
Let's Chat in Alipay

Recently, many mobile apps start to incorporate social functions into their design, even for the task-oriented ones, i.e., those designed mainly to help users complete certain tasks. For instance, Taobao, a shopping app, includes online-sharing and instant-messaging functions. However, there is still lack of research on how the users accept and use these social functions. This paper aims to unveil the user requirements on the social functions in task-oriented apps, and accordingly provide design suggestions for app developers. To this end, we conduct semi-structured interviews with 16 participants on how they use instant-messaging functions in three widely-used task-oriented apps, the shopping app Taobao, an online payment app Alipay, and an entertainment app NetEase Cloud Music. Our findings demonstrate that the instant-messaging functions in these apps are not widely accepted, although they benefit user experience and facilitate users' online social activities. We show that both the design and the users' stereotype towards the apps are important reasons. Finally, we suggest several design guidelines.
Deep Learning Based Period Order Detection in Structured Light Three-Dimensional Scanning

Fringe projection profilometry (FPP) is a popular optical 3-dimensional (3D) scanning method. However, existing FPP methods often suffer from the ambiguity problem that only the wrapped phase information can be measured while the true phase information is required for 3D measurement. Although various phase unwrapping methods were suggested to recover the wrapped phase in FPP methods, most of them will fail when the target objects have complex structures. To solve this problem, we propose in this paper to embed the fringe pattern with a set of textural patterns to encode the period order of the true phase information. During the offline phase, a convolutional neural network (CNN) is trained to learn a set of filters that will be activated when they see the code patterns. When the encoded fringe image is captured, the modified morphological component analysis is first performed to extract the code pattern. It is then decoded by the trained CNN to estimate the K-map, which contains the period order of the true phase information. Experimental results show that the proposed method can measure the 3D profile of objects with abrupt jumps in height profile, where the conventional approaches often fail to perform. It also has a much higher computational efficiency due to the effective utilization of GPU by CNN.
Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm

Partial Least Squares (PLS) Mode B is a multi-block method and a tightly coupled algorithm for estimating structural equation models (SEMs). Describing key aspects of parallel computing, we approach the parallelization of the PLS Mode B algorithm to operate on large distributed data. We show the scalability and performance of the algorithm at a very fine-grained level thanks to the versatility of pbdR, a R-project library for parallel computing. We vary several factors under different data distribution schemes in a supercomputing environment. Shorter elapsed times are obtained for the square-blocking factor 16×16 using a grid of processors as square as possible and non-square blocking factors 1000×4 and 10000×4 using an one-column grid of processors. Depending on the configuration, distributing data in a larger number of cores allows reaching speedups of up to 121 over the CPU implementation. Moreover, we show that SEMs can be estimated with big data sets using current state-of-the-art algorithms for multi-block data analysis.
Foresight: Real Time Facial Detection and Recognition Using WebAssembly and Localized Deep Neural Networks

The emergence of facial recognition technology is an appealing solution to address the many present-day needs for verification of identity claims. The application of such technology has become publicly available and has proved its effectiveness as an added layer of security through native applications. Until now, there has been no previous attempt at bringing this solution to a web-based platform supporting real time classification. With frequent reports of websites being exploited and databases being leaked, there is an urgent need for an intelligent security mechanism to overlay the current traditional authentication methods, including usernames and passwords. This paper investigates the possibility of unobtrusive, continuous authentication for web applications based on facial data collected using WebAssembly driven detection algorithms. This novel detection technique has proved the viability to perform real-time image processing on the web with the possibility of achieving near native speeds. This is accompanied with a competitive server side facial recognition rate of 91.67% achieved on the Labeled Faces in the Wild dataset.
Revisiting continuous deployment maturity

Background: Achieving a steady stream of small releases and employing practices such as continuous deployment requires maturity in company processes. Maturity models provide one approach for companies to pinpoint areas of improvement by providing a position and hints to reflect on. Incorporating maturity models with agile software development and continuous deployment has its challenges, though. Aims: The focus of the study is in understanding the evolution of software processes towards continuous deployment in an industry organization over time when a maturity model is used as a yardstick in evaluation. Method: An embedded case study by design, the study utilizes and replicates a survey on the state of software projects in a large Finnish software company, Solita. The survey was initially conducted in 2015 with responses from 35 projects and now replicated in 2017 with responses from 43 projects. Both quantitative and qualitative approaches for survey responses are used in the analysis. Results: Maturity of software processes in the case company show improvement in deployment and in monitoring, albeit short of statistical significance. Technological advances in the application of cloud computing have likely spurred development in these areas. Capability in processes related to test automation and quality has not changed much in two years. Conclusions: Maintaining maturity in software processes requires constant attention as impressions on process quality can gradually diminish. Projects which are built on a compatible technology stack have a greater chance in achieving continuous deployment and thus being more mature. Customer preferences also make a difference in the ability to reach certain maturity levels.
The Role of Attention Mechanism and Multi-Feature in Image Captioning

Up to now, caption generation is still a hard problem in artificial intelligence where a textual description must be generated for a given image. This problem combines both computer vision and natural language processing. Generally, the CNN - RNN is a popular architecture in image captioning. Currently, there are many variants of this architecture, where the attention mechanism is an important discovery. Recently, deep learning methods have achieved state-of-the-art results for this problem. In this paper, we present a model that generates natural language descriptions of given images. Our approach uses the pre-trained deep neural network models to extract visual features and then applies an LSTM to generate captions. We use BLEU scores to evaluate our model performance on Flickr8k and Flickr30k dataset. In addition, we carried out a comparison between the approaches without attention mechanism and attention-based mechanism.
User's profile ontology-based semantic model for personalized hotel room recommendation in the web of things

The hotel industry is not exempt from technology disruption and will only keep its competitiveness through strategy [3]including adjusting to the new customers demands, reacting to competitors' innovations and taking advantage of the new technological developments. Personalization can enable an organization to customize its offerings at the individual level. This paper seeks to design and evaluate a novel Web of Things personalization model that effectively utilizes information about a customer's preferences to create a personalized service space.
Iterative Autoencoding and Clustering for Unsupervised Feature Representation

Unsupervised feature representation is a challenging problem in machine learning and computer vision. Since manual labels are unavailable for training, it is difficult to reduce the gap between learned features and image semantics. This paper proposes an iterative autoencoding and clustering approach, which consists of an autoencoding sub-network and a classification sub-network, for unsupervised feature representation. On one hand, the autoencoding sub-network maps images to features. On the other hand, using the features generated by the autoencoding sub-network, the classification sub-network maps the features to classes and estimates pseudo labels by clustering the features simultaneously. Through iterations between the feature representation and the pseudo-labels-supervised classification, the gap between features and image semantics is reduced. Experimental results on handwritten digits recognition and objects classification prove that the proposed approach achieves state-of-the-art performance compared with existing methods.
Software/Hardware Co-design of the Microprocessor for the Serial Port Communications

The eight-bit stack processor architecture is proposed, which is designed for the FPGA implementation. The microprocessor with this architecture has small hardware costs, reduced software amount, and ability to add up to hundred new user instructions to its instruction set. The microprocessor architecture is adapted for programming the serial port communications and is able to perform the data stream parsing.
Sensorless Synchronous Motors Classification Using Random Forest and Linear Support Vector Classifier

Automation system become increasingly complex and it needed to be monitored actively. Due to wide variance tasks to be done not suprising that the complexity problem occurs. The failure of the system can impact to economic loss for a company. For this purpose, this paper compare the methods to classify sensorless synchronous motors task to investigate the accuracy and precision of random forest classification and linear Support Vector Classifier (SVC). With cross-validation, the result show that the average accuracy is 99.816% with random forest and 66.04% with linear SVC. As the conclution, random forest classification is more precise than SCV method in this field.
Image Quality Enhancement in C Programming

In today's technologically advanced and highly digitalized world, huge amounts of raw images are being captured on a daily basis. Processing these images to extract useful information and/or manipulating them is a herculean task in itself. Image enhancement has been an important facet in image processing ever since images were captured on photographic paper using silver chlorides and other photosensitive chemicals. Earlier converting negatives took more than eight hours straight. Now with the advent of computers and digital images we can do this in milliseconds. One such problem was to enhance an image. In this paper, efforts have been made for performing digital image enhancement by using Histogram Equalization and Bilinear Interpolation to enhance the contrast and scaling of the image, so that, one can say at a cursory glance that the image quality has improved. The C programming language is a good algorithm description format as it is a widely used language and one can understand the basics of image processing and how image enhancement is done at a pixel level. This paper is intended to serve as a reference for developers and programmers who are keen on learning about image manipulation by directly working with the pixel information.
Hyper-heuristic Image Enhancement (HHIE): A Reinforcement Learning Method for Image Contrast Enhancement

Conventional contrast enhancement methods such as histogram equalization (HE) have not obtained acceptable results on many different low-contrast images and they also cannot automatically handle various images. These problems are a result of specifying parameters manually for the sake of producing high-contrast images. We proposed an automatic image contrast enhancement on Hyper-heuristic. In this study, simple exploiters are proposed to improve the contrast of current image. To select these exploiters appropriately, reinforcement learning is proposed. This selection is based on the functional history of these exploiters. Having multi aim of preserving brightness, retaining the shape features of the original histogram, and controlling on the rate of over-enhancement are the achievements of the proposed method. These objectives are suitable for the application of consumer electronics. By this simulation results, it has been shown that in terms of visual assessment, absolute mean brightness error (AMBE) and peak signal-to-noise (PSNR) of the proposed method are superior to literature methods.
Insights in Experimental Data through Intuitive and Interactive Statistics

It is not unusual for empirical scientists, who are often not specialists in statistics, to have only limited trust in the statistical analyses that they apply to their data. The claim of this course is that an improved human-computer interaction with statistical methods can be accomplished by providing a simple mental model of what statistics does, and to support this model through well-chosen visualizations and interactive exploration. In order to support this proposed approach, an entirely new program for performing interactive statistics, called ILLMO, was developed. This course will use examples of frequent statistical tasks such as hypothesis testing, linear regression and clustering to introduce the key concepts underlying intuitive and interactive statistics.
Local Coupled Extreme Learning Machine Based Image Watermarking using DCT in YCbCr Space

Recently developed single layer feed forward neural network model (SLFN): local coupled extreme learning machine (LC-ELM) based color image watermarking in DCT domain is designed in the proposed scheme. LC-ELM is used to handle non-linear regression analysis by considering the image watermarking problem as a regression problem. Firstly, the RGB image is transformed into YCbCr to obtain high de-correlation in YCbCr space to accomplish good imperceptibility and robustness. After transformation, DCT is used on the selected blocks depending on the randomness measurement of Y component. The features extracted from the selected blocks helps in making image dataset and embedding the watermark using trained LC-ELM. The experimental results performed on color images confirmed the generalization performance of LC-ELM in addition to imperceptibility and robustness.
Semi-Siamese Network for Content-Based Video Relevance Prediction

The intractable “cold-start” problem often encountered in existed video recommendation systems when a new video is coming with minimal users' feedback since most existing recommendation algorithms heavily rely on the users' implicit feedbacks. This paper presents a novel idea for solving the “cold start” problem by analyzing the video content itself. The Harmonic Sampling is designed in our work for utilizing the rank information automatically during the sampling procedure, and a Semi-Siamese network is proposed for overcoming the asymmetric training samples. The proposed method demonstrated its effectiveness in dealing with “cold-start” problem, achieving superior performance over the Content-based Video Relevance Prediction Dataset.
Acoustic Attacks in the Era of IoT - A Survey

Hearing is one of human's senses that helps in the interaction with the surrounding world.Earlier researches proved that excessive pitched exposure or even regular exposure to acoustic noise with a high sound pressure levels leads to side effects on the hearing sense; severe impacts leads to Noise Induced Hearing Loss (NIHL) which in turn affects humans quality of life.In the era of Internet of Things (IOT), the interaction with machines or devices became very common, and with Voice Controlled Systems (VCS), the barriers almost dissolved between humans and machines.By all counts, and with proven results there are side effects of acoustics on devices as well; either those behave actively through acoustics "dependent on acoustics", or those passively affected by acoustics "independent on acoustics".In this survey, we put an answer for this.
An Improved Heuristic Function for A∗-Based Path Search in Detailed Routing

One approach to solve detailed routing is net-by-net using a fast A∗-based path search algorithm to handle long connections. Such path search algorithms usually rely on the manhattan distance to implement their heuristic function, providing a poor lower bound since the search is constrained by the global routing guide. This work proposes a new technique to provide a more realistic lower bound in this scenario. We precompute some lower bounds before the path search by applying a modified version of Dijkstra's algorithm on tunnels (sections of the global routing guide). This information is used during the search, reducing the runtime by 58% in average, in comparison to the classic manhattan distance, on ISPD 2018 benchmarks. We also show an improvement of our technique over existing work [10]. Our preprocessing method runtime is negligible, maximizing the benefit of using the improved heuristic function during the path search.
Dynamic heuristic acceleration of linearly approximated SARSA($$\lambda $$): using ant colony optimization to learn heuristics dynamically

Heuristically accelerated reinforcement learning (HARL) is a new family of algorithms that combines the advantages of reinforcement learning (RL) with the advantages of heuristic algorithms. To achieve this, the action selection strategy of the standard RL algorithm is modified to take into account a heuristic running in parallel with the RL process. This paper presents two approximated HARL algorithms that make use of pheromone trails to improve the behaviour of linearly approximated SARSA( $$\lambda $$ ) by dynamically learning a heuristic function through the pheromone trails. The proposed dynamic algorithms are evaluated in comparison to linearly approximated SARSA( $$\lambda $$ ), and heuristically accelerated SARSA( $$\lambda $$ ) using a static heuristic in three benchmark scenarios: the mountain car, the mountain car 3D and the maze scenarios.
Online Density Estimation Over High-Dimensional Data Streams

This paper presents an algorithm for density estimation over non-stationary high-dimensional data streams. It is based on a blockized implementation of the Bayesian sequential partitioning (BSP) algorithm. We discuss how to decide the optimum block size, based on features of the stream, the application requirements, and the available resources. Simulation results are presented to show the applicability of the proposed method to non-stationary streams. It is also shown that the proposed framework satisfies the general design criteria for systems with the mission of online machine learning and data mining over data streams.
Typology and contexts of article errors: Investigation into the use of English articles by Hong Kong Cantonese ESL learners

Abstract This paper provides a taxonomy of errors in the use of English articles by Hong Kong Cantonese ESL learners. A study was conducted with 387 Hong Kong Cantonese ESL learners, including 65 students from three local universities and 322 students from five local secondary schools. They did two free writing tasks of about 200–300 words. Over 600 pieces of free writing were obtained. The use of English articles was analyzed and errors were identified. Irrespective of learners’ proficiency levels, similar types of article errors have been observed. While over-extension errors and under-extension errors are both quite common, co-occurrence errors are nearly non-existent. Intended generic reference triggers most problems, although this reference has been least attempted. The function of the noun phrase in which an article error occurs may play a part in error severity, with Object creating most problems for learners, yet the precise effects of noun functions are yet to be determined. It is suggested that more emphasis be put on the teaching of English articles to learners at different proficiency levels. Advanced learners, such as university students, should be engaged in metalinguistic analyses uncovering the subtle differences between the use of different articles in the same linguistic context.
Towards a Typology of Self-Tracking Gaps

This paper introduces an emerging typology of the 'absences' that confound the study of self-tracking. A review of the literature, and the ongoing work of the authors on the long-term value of self-tracking data, is used as a resource to develop descriptions of levels and types of ?gaps' that emerge as part of the activities, behaviors, technologies, and data practices of self-tracking. Such gaps are shown to be both common and insightful, highlighting the economic, social, behavioral, and psychological layers that undergird self-tracking.
A Content-Adaptive Median Filtering Detection Using Markov Transition Probability Matrix of Pixel Intensity Residuals

Excessive dependence on digital images has raised the need for its forensic analysis. Median filtering, due to its nonlinear nature, is extensively used in image antiforensics to hide the evidence of image forgery. There exist several methods based on the Markov transition probability matrix to detect median filtering. In this article, to detect median filtering, a content-adaptive thresholding approach is applied on pixel intensity residuals and features are extracted using a Markov transition probability matrix. This modified approach provides better results even on low-resolution and highly compressed images in comparison to existing methods.