Reviewerly/exHarmony huggingface dataset:
Non-dominated sorting based multi-page photo collage

Abstract The development of social networking services (SNSs) revealed a surge in image sharing. The sharing mode of multi-page photo collage (MPC), which posts several image collages at a time, can often be observed on many social network platforms, which enables uploading images and arrangement in a logical order. This study focuses on the construction of MPC for an image collection and its formulation as an issue of joint optimization, which involves not only the arrangement in a single collage but also the arrangement among different collages. Novel balance-aware measurements, which merge graphic features and psychological achievements, are introduced. Non-dominated sorting genetic algorithm is adopted to optimize the MPC guided by the measurements. Experiments demonstrate that the proposed method can lead to diverse, visually pleasant, and logically clear MPC results, which are comparable to manually designed MPC results.
Leveraging vCPU-utilization rates to select cost-efficient VMs for parallel workloads

The increasing use of cloud computing for parallel workloads involves, among many problems, resources wastage. When the application does not fully utilize the provisioned resource, the end-of-the-month bill is unnecessarily increased. This is mainly caused by the user's inexperience and naïve behavior. Many studies have attempted to solve this problem by searching for the optimal VM flavor for specific applications with specific inputs. However, most of these solutions require knowledge about the application or require the application's execution on multiple VM flavors. In this work, we propose four new heuristics that recommend cost-effective VMs for parallel workloads based solely on the vCPU-utilization rate of the currently executing VM flavor. We also evaluate them on two scenarios and show that the core-heuristic is capable of recommending VM flavors that have minimal impact on performance and reduce the applications cost, on average, by 1.5x (3.0x) on high (low) vCPU-utilization rate scenarios.
Mapping built-up land and settlements: a comparison of machine learning algorithms in Google Earth engine

Monitoring the growth of built-up land finds its challenge as a never-ending mapping process. Since the built-up maps are taken into account in development planning and measuring the achievement of SDGs objectives (Goal 11 - indicator 11.3.1), the best mapping method should be attempted. Therefore, all efforts were worked on to speed up the mapping process without compromising accuracy. Various methods have been proposed, but numerous difficulties remain in accurate and efficient built-up and settlement extraction. With the combination of passive and active sensors images, we try several machine learning methods using Google Earth Engine (GEE) platforms to map built-up land and settlements in Purwokerto, Banyumas, Central Java. Around 369 samples were occupied to distinguish four classes of land covers: settlements, built-up land, waters, and others. The decision tree-based algorithms give the best performances, scilicet Random Forest (RF) and Gradient Tree Boost (GTB). Random forest is a collection of many decision trees, while Gradient Boosting is a machine learning algorithm that uses an ensemble of decision trees to predict values. Thus, the algorithms can handle complex patterns and data when linear models cannot. On the whole, RF and GTB classifiers can distinguish between settlements and non-settlement with an overall accuracy of 80%. The Support Vector Machine (SVM) classifier produces 71.43% accuracy with Kappa = 0.61, and the Minimum Distance (Mahalanobis) classifier gain overall accuracy of 74.29% (Kappa = 0.64).
KLPPS: A k-Anonymous Location Privacy Protection Scheme via Dummies and Stackelberg Game

Protecting location privacy has become an irreversible trend; some problems also come such as system structures adopted by location privacy protection schemes suffer from single point of failure or the mobile device performance bottlenecks, and these schemes cannot resist single-point attacks and inference attacks and achieve a tradeoff between privacy level and service quality. To solve these problems, we propose a k-anonymous location privacy protection scheme via dummies and Stackelberg game. First, we analyze the merits and drawbacks of the existing location privacy preservation system architecture and propose a semitrusted third party-based location privacy preservation architecture. Next, taking into account both location semantic diversity, physical dispersion, and query probability, etc., we design a dummy location selection algorithm based on location semantics and physical distance, which can protect users’ privacy against single-point attack. And then, we propose a location anonymous optimization method based on Stackelberg game to improve the algorithm. Specifically, we formalize the mutual optimization of user-adversary objectives by using the framework of Stackelberg game to find an optimal dummy location set. The optimal dummy location set can resist single-point attacks and inference attacks while effectively balancing service quality and location privacy. Finally, we provide exhaustive simulation evaluation for the proposed scheme compared with existing schemes in multiple aspects, and the results show that the proposed scheme can effectively resist the single-point attack and inference attack while balancing the service quality and location privacy.
Are You Aware of Your Data Privacy? The Case of Digital Contact Tracing Applications (MySejahtera)

Digital Contact Tracing (DCT) apps have been widely used as one of the control measures in mitigating the COVID-19 pandemic all over the countries. Despite the various benefits, the use of the DCT apps has raised several issues, particularly in the area of data privacy. Thus, it is important for users to be aware of their privacy while using such apps. This study was conducted to identify the level of user awareness on data privacy and investigate whether the data privacy concern can influence the adoption of the MySejahtera application. A total of 505 responses were obtained from an online survey distributed through social media. The data were then analyzed quantitatively using descriptive statistics and Structural Equation Modelling (SEM) techniques. Empirical results found that the user awareness level on data privacy in using MySejahtera App is moderately high. The respondents are highly aware that their personal data were collected while using the app although they demonstrate less awareness on the privacy risk towards misusing of their personal data. The study also found out that data privacy concern does influence the adoption of MySejahtera App. Even though the awareness level towards their data privacy is considered high, it is believed that this finding is largely influenced by the enforcement made by the Malaysian Government to mitigate the CO VID-19 outbreak. This study provides insights into users' understanding of data privacy pertaining to DCT applications focusing on MySejahtera. The findings offer the government some recommendation to formulate effective strategies to reduce personal concern towards the apps. The government can make use of the findings in handling the stored data for strengthening public trust.
Anomaly Detection in Industrial IoT Applications Using Deep Learning Approach

Internet of Things (IoT) becomes popular in last two decade as it provides many advantages such as flexibility, autonomous, cost effective and productivity. Many industries adopted IoT to improve the efficiency, security and predictive maintenance. To improve the quality of service (QoS), it is essential to identify various types of anomalies in productive maintenance. An anomaly is a value, a status of resources or outcome that deviates from expected or normal values and it affects QoS of production. In this paper, a multi-agent based anomaly detection scheme is introduced to improve the QoS. The fog computing infrastructure is used to reduce the latency of communication. Multiple agents are deployed in fog node to perform the various operations of detecting anomalies. The proposed anomaly detection scheme uses a multi-step prediction technique and applies an anomaly detection algorithm to detect anomalies. The Gated Recurrent Unit (GRU) model is used for Multi-step time series prediction and a bio-inspired Artificial Bee Colony algorithm is used for tuning the GRU model hyperparameters to improve the accuracy. The proposed model detects various types of anomalies in the fog computing environment. The Google Colab using TensorFlow library Keras is used for experimental evaluation. The proposed model increases accuracy over existing approaches, according to the experiment evaluation.
Cross-layer progressive attention bilinear fusion method for fine-grained visual classification

Fine-grained visual classification (FGVC) is a critical task in the field of computer vision. However, FGVC is full of challenges due to the large intra-class variation and small inter-class variation of the classes to be classified on an image. The key in dealing with the problem is to capture subtle visual differences from the image and effectively represent the discriminative features. Existing methods are often limited by insufficient localization accuracy and insufficient feature representation capabilities. In this paper, we propose a cross-layer progressive attention bilinear fusion (CPABF in short) method, which can efficiently express the characteristics of discriminative regions. The CPABF method involves three components: 1) Cross-Layer Attention (CLA) locates and reinforces the discriminative region with low computational costs; 2) The Cross-Layer Bilinear Fusion Module (CBFM) effectively integrates the semantic information from the low-level to the high-level 3) Progressive Training optimizes the parameters in the network to the best state in a delicate way. The CPABF shows excellent performance on the four FGVC datasets and outperforms some state-of-the-art methods.
Improved Face Detection System

Biometric applications have been using face detection approaches for security purposes such as human–crowd surveillance, many security-related areas, and computer interaction. It is a crucial arena of recent research because there is no fixed system to find the faces in a test image. Face detection is challenging due to varying illumination conditions, pose variations, the complexity of noises, and image backgrounds. In this research, we present a system that can detect and recognize in the face by different pre-processing techniques; Viola-Jones process adds together Haar Cascade, GLCM & Gabor Filter and Support Vector Machine (SVM), is proposed for gaining better accuracy level in the detection of facial portions and recognition of faces. The proposed system has achieved better than other face detection and recognition systems. The experiment has done in a MATLAB environment on different images of the FEI Face, Georgia Tech faces, Faces95, Faces96, and MITCBCL databases. The experimental result achieves detection of faces that represent reasonable accuracy rates of an average of 98.32%.
Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.
Ultrasound Image Texture Feature Learning-Based Breast Cancer Benign and Malignant Classification

The use of ultrasound images to acquire breast cancer diagnosis information without invasion can reduce the physical and psychological pain of breast cancer patients and is of great significance for the diagnosis and treatment of breast cancer. There are some differences in the texture of breast cancer between benign and malignant cases. Therefore, this paper proposes an adaptive learning method based on ultrasonic image texture features to identify breast cancer. Specifically, firstly, we used dictionary learning and sparse representation to learn the ultrasonic image texture dictionary of benign and malignant cases, respectively, and then used the combination of the two dictionaries to represent the test image to obtain the texture distribution characteristics of the test image under the two dictionary representations, which called the sparse representation coefficient. Finally, these above features were filtered by sparse representation and sent to sparse representation classifier to establish benign and malignant classification model. 128 cases were randomly divided into training and testing sets according to 2: 1 for training and testing. The proposed method has achieved state-of-the-art results, with an accuracy of 0.9070 and the area under the receiver operating characteristic curve of 0.9459. The results demonstrate that the proposed method has the potential to be used in the clinical diagnosis of benign and malignant breast cancer.
A dynamic adjustment model of saccade lengths in reading for word-spaced orthographies: evidence from simulations and invisible boundary experiments

Contemporary models of eye movement control in reading assume a discrete target word selection process preceding saccade length computation, while the selection itself is assumed to be driven by word identification processes. However, a potentially more parsimonious, dynamic adjustment view allows both next word length and its content (e.g. orthographic) to modulate saccade length in a continuous manner. Based on a recently proposed center-based saccade length account a new regression model of forward saccade length is introduced and validated in a simulation study. Further, additional simulations and gaze-contingent invisible boundary experiments were used to study the cognitive mechanisms underlying skipping. Overall, the results support the plausibility of dynamic adjustment of saccade length in word-spaced orthographies. In the future, the present regression formula-based computational model will allow a straightforward implementation of influences of current and next word content (visual, orthographic, or contextual) on saccade length computation.
CryptoNight Mining Algorithm with YAC Consensus for Social Media Marketing Using Blockchain

Social media is a platform in which user can create, share and exchange the knowledge/information. Social media marketing is to identify the different consumer's demands and engages them to create marketing resources. The popular social media platforms are Microsoft, Snapchat, Amazon, Flipkart, Google, eBay, Instagram, Facebook, Pin interest, and Twitter. The main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their needs. Disruptive technology is replacing old approaches in the social media marketing to new technology-based marketing. However, this disruptive technology creates some issues like fake news, insecure, inconsistency, inaccuracy and so on. These issues contribute economic instability in the society, diminishing the level of trustworthy. To overcome these issues, this paper we present blockchain as disruptive technology for social media marketing. Blockchain plays a vital role on social media marketing by providing secure to the company page in the website. The properties of disruptive potential of blockchain on social media marketing is transparency, security, reliability and immutability. This paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm [BCDSMM-CNYAC]. This mining algorithm provides high CPU efficiency, high dimensionality of secure and detecting falsifying data attack in the social media marketing. For the data analysis we proposed ANOVA analysis method regarding to the factors of age, time, frequency visiting times of social media platform. For reliability analysis of data Cronbach's alpha tests are implemented.
Histogram Equalization for Grayscale Images and Comparison with OpenCV Library

The noisy images collected during historical research make it difficult to detail the studies and draw more comprehensive findings. Detailing and updating these images makes it much easier to find information and increases the density of data. Therefore, in this study a histogram equalization method is proposed to reduce the noise in historical images. The method processes the image’s pixel values one by one while also applying the normalization process to keep the density graph steady. In this way, the harmony between density transitions ensures that the quality of the image is higher. The proposed method is compared to the OpenCV algorithm. As a result of this comparison, it is shown that the proposed algorithm is more successful in linearization.
Sparse Deep Neural Network Acceleration on HBM-Enabled FPGA Platform

Domain-specific architectures (DSAs) for deep neural networks (DNNs) are becoming mainstream because of high energy-efficiency and performance. Currently, most of these DSAs perform dense linear algebra efficiently by exploiting temporal and spatial locality, high data reuse and regular memory access patterns. However, state-of-the-art DNN models require DSAs with very high amount of compute, interconnect and memory resources. Since sparsity in DNN parameters can be exploited to reduce the resources and complexity of the DSAs, emerging DSAs are adding native support for sparsity [1], [2]. This paper presents a sparse DNN inference engine on HBM-enabled Alveo U280 platform, built around FPGA-optimized DSA blocks. The DSA blocks have native support for completely unstructured sparsity. We demonstrate the effectiveness of proposed inference engine by running inference models of the Sparse DNN Challenge [3]. Results for the challenge benchmarks show that the proposed inference engine and multi-DSA parallelization on HBM-enabled Alveo U280 achieve up to 3.7 billion edges per second inference throughput.
Perceptual improvements for Super-Resolution of Satellite Imagery

Super-resolution of satellite imagery poses unique challenges.We propose a hybrid method comprising two existing deep network super-resolution approaches, namely a feedforward network called DeepSUM, and ESRGAN, a GAN-based approach, to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image.We also introduce a novel loss function, called variation loss, to better define edges and textures to create a sharper, perceptually better output.Using our hybrid, we inherit some of the advantages of both deep learning approaches, resulting in super-resolved images that better show boundaries, textures, and details.
Scalable grid‐based approximation algorithms for partially observable Markov decision processes

Abstract Partially observable Markov decision processes (POMDPs) are a well‐established sequential decision making framework. Once a problem is modeled using this framework, a suitable POMDP solution algorithm is employed to obtain a policy that guides the user throughout the decision making process. However, POMDPs are notoriously difficult to solve to optimality. Therefore, there exist many approximate solution algorithms that are designed to generate policies for large‐scale POMDP models. On the other hand, many such approaches lack performance guarantees in terms of the solution quality. In this article, we focus on exact solution methods as well as approximate methods that provide bounds on the optimal value. Specifically, we investigate the performance improvements for the POMDP solution algorithms obtained through distributed implementations. We provide a detailed empirical analysis on various test problems, which highlights the benefits of the proposed approach.
Apart from In-field Sensor Defects, are there Additional Age Traces Hidden in a Digital Image?

Approximating the age of a digital image based on traces left during the acquisition pipeline is at the core of temporal image forensics. Well-known and investigated traces are those caused by in-field sensor defects. The presence of these defects is exploited in two available age approximation methods. A very recent approach in this context, however, trains a Convolutional Neural Network for age approximation. A Convolutional Neural Network independently learns the classification features used. In this context, the following questions arise: how relevant is the presence of strong in-field sensor defects, or does the Convolutional Neural Network learn other age-related features (apart from strong in-field sensor defects)? We investigate these questions systematically on the basis of several experiments in this paper. Furthermore, we analyse whether the learned features are position invariant. This is important since selecting the right input patches is crucial for training a Convolutional Neural Network.
Sentiment Analysis of Face-to-face Learning during Covid-19 Pandemic using Twitter Data

Covid-19 pandemic has massive impacts on the activity of human in the world, including in Indonesia. To reduce the transmission of the virus, Indonesian government issues a policy to restrict daily public activities, affecting key national sectors, such as education systems. All learning activities are switched from the conventional face-to-face mode to being remote via the use of the Internet. After the pandemic begins to subside, the government then plans to reopen all schools and to allow face-to-face learning. However, this decision has sparked controversy in the social media, including Twitter. This paper describes a methodology to perform sentiment analysis on a collection of tweets that are in connection with the restart of the face-to-face learning mode. In particular, our experiments using hand-crafted features based on the tweets demonstrate that data-driven models are useful for automatic sentiment orientation classification on Twitter data. The best model achieved in this study has 69,1% accuracy, 68.6% precision, 69.1% recall, and 67,8% F1-Score. This result is achieved by using unigram, Support Vector Machine, and tweet + number of words (count) feature combinations.