Reviewerly/exHarmony huggingface dataset:
On Restricting Real-Valued Genotypes in Evolutionary Algorithms

Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks.
Gesture-based Smart Wheelchair for Assisting Physically Challenged People

A lot of people in Bangladesh suffer from mobility impairments and being neglected in many steps of their life. Despite having the ability to do something outstanding, it is not possible to express that for this huge portion of people due to the physical challenges. Nowadays, technology is considered a blessing not only for normal peoples but also for physically challenged people. The rapid advancement of technology has opened new opportunities for people with physical challenges. Here, we have sketched a state-of-the-art solution to help physically challenged people. To do this, we have proposed an algorithm namely the Gesture-based Smart Wheelchair (GBSWC) control algorithm. This research-based project focuses on building a gesture-based smart wheelchair that is controllable by the hand movement of the user using IoT technology. The customized smart wheelchair has the ability to move forward, right, left, and stop of the wheelchair based on the command given to the system by the hand motion. As well as if at any time the user faces any kind of problem, difficulty, critical situation, or needs someone emergency then a simple single tap on the sensor will help to send an emergency text to given emergency contacts. On the other hand, a simple double tap on the sensor will stop the movement of the smart wheelchair. The prototype device is thoroughly tested here and the obtained result is significant enough which is presented in the result analysis section.
Salient instance segmentation with region and box-level annotations

In the field of saliency detection, salient instance segmentation is a novel challenging task that has received widespread attention. Due to the limited scale of the available dataset and the high cost of mask annotations, a substantial quantity of supervision sources is urgently required to train a high-performing salient instance model. To this end, we aim to train a novel salient instance segmentation model by weak supervisions that make full use of the existing salient object detection dataset. In this paper, we present a cyclic global context salient instance segmentation network (CGCNet) supervised by the combination of salient regions and bounding boxes from ready-made salient object detection datasets. To locate salient instances more accurately, a global feature refining layer is designed to expand the size of the features from the region of interest (ROI) to the global field in a scene. Moreover, a labeling updating scheme is embedded in the proposed framework to iteratively update the weak labels. Extensive experimental results demonstrate that our CGCNet trained by weak labels is competitive with the existing fully-supervised state-of-the-art methods.
Unconstrained Facial Expression Recogniton Based on Cascade Decision and Gabor Filters

Facial Expression Recognition (FER) research with Convolutional Neural Networks (CNN) has been active, especially under unconstrained natural conditions. From our observation, prior arts treat expressions equally in classification and the reconition accuracy of some expression are always higher than others. In this paper, we make the assumption that an expression with a higher accuracy is easier to be recognized, and those expressions easier to recognize will hinder the recognition of uneasy expressions. Then, we propose a novel algorithm for unconstrained FER based on cascade decision and Gabor filters. Easier expressions are recognized before the difficult expressions. This simple method trains up to five models to cascadedly recognize a given facial image expression. The first binary classifier model is for the classification of Happy with the highest accuracy. The second binary classifier model is for the classification of Surprise with the second high accuracy. The third binary classifier model is for the classification of Neutral with the third high accuracy. The forth model is for the classification of Sad with the forth high accuracy. And the final model is 3-class classifier for Angry, Disgust and Fear. Gabor filters are included in every model to enhance robustness on illumination variations and face poses. Extensive experiment results on several datasets validate the effectiveness of the proposed method. We obtain accuracy of 77.6% on FER2013 with the final models, outperforming the latest state-of-the-arts.
A blockchain-based marketplace platform for circular economy

Over the recent years, there has been an intense research on blockchain and its applications in real-life scenarios, both from academic and industrial research. This paper proposes an architecture for a blockchain-based marketplace platform. We have implemented and tested our design, which allows user to do simple marketplace transactions as well as more complicated features, such as auctions à la eBay. We explore the functionalities and the implementation details of our solution, while showing how we ensure respect with GDPR.
RLPS: A Reinforcement Learning–Based Framework for Personalized Search

Personalized search is a promising way to improve search qualities by taking user interests into consideration. Recently, machine learning and deep learning techniques have been successfully applied to search result personalization. Most existing models simply regard the personal search history as a static set of user behaviors and learn fixed ranking strategies based on all the recorded data. Though improvements have been achieved, the essence that the search process is a sequence of interactions between the search engine and user is ignored. The user’s interests may dynamically change during the search process, therefore, it would be more helpful if a personalized search model could track the whole interaction process and adjust its ranking strategy continuously. In this article, we adapt reinforcement learning to personalized search and propose a framework, referred to as RLPS. It utilizes a Markov Decision Process ( MDP ) to track sequential interactions between the user and search engine, and continuously update the underlying personalized ranking model with the user’s real-time feedback to learn the user’s dynamic interests. Within this framework, we implement two models: the listwise RLPS-L and the hierarchical RLPS-H. RLPS-L interacts with users and trains the ranking model with document lists, while RLPS-H improves model training by designing a layered structure and introducing document pairs. In addition, we also design a feedback-aware personalized ranking component to capture the user’s feedback, which impacts the user interest profile for the next query. Significant improvements over existing personalized search models are observed in the experiments on the public AOL search log and a commercial log.
Improving Low-Resolution Image Classification by Super-Resolution with Enhancing High-Frequency Content

With the prosperous development of Convolutional Neural Networks, currently they can perform excellently on visual understanding tasks when the input images are high quality and common quality images.However, large degradation in performance always occur when the input images are low quality images.In this paper, we propose a new super-resolution method in order to improve the classification performance for low-resolution images.In an image, the regions in which pixel values vary dramatically contain more abundant high frequency contents compared to other parts.Based on this fact, we design a weight map and integrate it with a super-resolution CNN training framework.During the process of training, this weight map can find out positions of the high frequency pixels in ground truth high-resolution images.After that, the pixel-level loss function takes effect only at these found positions to minimize the difference between reconstructed high-resolution images and ground truth high-resolution images.Compared with other stateof-the-art super-resolution methods, the experiment results show that our method can recover more high frequency contents in high-resolution image reconstructing, and better improve the classification accuracy after low-resolution image preprocessing.
Mobile money adoption in rural Rwanda: A domestication perspective

With this explorative research, we investigate if and how farmers in Rwanda adopt mobile money, or m-money, and integrate it into their everyday life to foster their economic development and social well-being. To this end, we adapt a domestication perspective and base our research on qualitative evidence from 72 semi-structured interviews with farmers in rural Rwanda. Our findings reveal that – where available – Rwandan farmers continuously domesticate m-money. While they acknowledge the convenience of using m-money, they experience three major inhibitors that particularly affect its use for business: (1) limited opportunities for learning about m-money, (2) high and non-transparent costs, and (3) barely accessible network agents with insufficient liquidity. Based on our findings, we discuss how policymakers and service providers can increase the adoption of m-money among farmers, thereby reducing social exclusion of the unbanked and fostering economic growth in Rwanda and other emerging economies.
Learning Probabilistic Languages by k-Testable Machines

A k-testable machine is a finite automaton which recognizes a language L by only seeing a window of size k of each string in L. In this paper we use k-testable machines to recognize probabilistic languages and propose a novel algorithm to learn them. We work in the context of passive learning as our algorithm is based on a finite sample of strings belonging to the target language equipped with frequencies. Because our algorithm learns a probabilistic automaton, the resulting language is less sensitive to noise threshold than García's algorithm. When compared with the ALERGIA learning algorithm, our method provides a better result in the case of the target language being a k-testable language. In fact, in this case, for the given window k we can learn at the limit the target language exactly.
Compressed Network in Network Models for Traffic Classification

Accurate traffic classification is critical for network QoS provisioning and cyberspace security. Recently, classifying different traffic using convolutional neural networks (CNN) has achieved high accuracy. However, these large CNN models have millions of parameters, which are not suitable for edge computing hardware deployment. In this work, we propose a compressed network in network (NIN) model for traffic identification. A stepwise pruning and knowledge distillation (KD) is designed for training the compressed model, which aims at reducing storage and computing resources. Our method is validated with the public ISCX VPN-nonVPN traffic dataset. Experimental results show that without degrading classification accuracy, our minimum model can save more than 50% of the number of parameters and 30% of the computation time comparing with the uncompressed NIN model. The test set average F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 0.9805 of the minimum model is higher than that of the state-of-the-art model, which is a CNN model.
Hierarchical registration of brain images based on B-splines and Laplacian commutators

It is highly desirable that the complementary information of computed tomography (CT) for dose planning and magnetic resonance imaging (MRI) for accurate organ delineation should be merged in oncology. In this paper, we propose a hierarchical method based on B-splines and Laplacian commutators for registration between MR and CT brain images. Firstly, we apply histogram equalization on the CT image for intensifying its local details. Then, the principal component analysis (PCA) is adopted to align MR and CT images roughly. We use PCA to find the centroid and orientation of these two images to initialize the translation and rotation. Next, registration is refined via a free-form deformation which is based on B-splines for describing local deformations. Here we adopt Laplacian commutators as a similarity metric in both stages of registration, and the limited memory Broyden-Fletcher-Goldfarb-Shannon (L-BFGS) optimization method has been applied for optimization process. Experimental results have shown that the performances of the obtained images are improved by comparing with the state-of-the-art methods, demonstrating potentials of the proposed method to be applied for brain tumor radiation therapy.
Classifying Medical Histology Images Using Computationally Efficient CNNs Through Distilling Knowledge

Breast cancer is one of the most prevalent cancer types among women, and early detection of carcinogenic tissue from histology images can go a long way in effective treatment as well. Recent approaches to solving this problem of classification of tissues utilize heavy deep learning-based architectures, which take higher inference time and storage space for model parameters. In our work, we utilize a CNN architecture with a lesser number of parameters that can be effectively deployed on a resource-constrained device. For this, we use two different approaches, namely the knowledge distillation and a hybrid technique combining attention transfer and knowledge distillation techniques. We evaluate our approach on high-resolution Breast Cancer Histology slides of the BACH 2018 dataset.
Predictable Model for Detecting Sybil Attacks in Mobile Social Networks

Mobile Social Networks have become one of the most convenient services for users to share information everywhere. This crowdsourced information is often meaningful and recommended to users, e.g., reviews on Yelp or high marks on Dianping, which poses the threat of Sybil attacks. To address the problem of Sybil attacks, previous solutions mostly use indirect/direct graph model or clickstream model to detect fake accounts. However, they are either dependent on strong connections or solely preserved by servers of social networks. In this paper, we propose a novel predictable approach by exploiting users' custom patterns to distinguish Sybil attackers from normal users for the application of recommendation in mobile social networks. First, we introduce the entropy of spatial-temporal features to profile the mobility traces of normal users, which is quite different from Sybil attackers. Second, we develop discriminative entropy-based features, i.e., users' preference features, to measure the uncertainty of users' behaviors. Third, we design a smart Sybil detection model based on a binary classification approach by combining our entropy-based features with traditional behavior-based features. Finally, we examine our model and carry out extensive experiments on a real-world dataset from Dianping. Our results have demonstrated that the model can significantly improve the detection accuracy of Sybil attacks.
Incorporating global multiplicative decomposition and local statistical information for brain tissue segmentation and bias field estimation

Abstract Medical image is a cornerstone of modern healthcare providing unique diagnostic. However, intensity nonuniformity is quite often found in medical images and unavoidably poses many challenges for precise image segmentation. In this paper, we present an incorporating global multiplicative decomposition and local statistical information level set technique for tissue segmentation and bias estimation that handles both high noise and intensity nonuniformity. An image can be factored into two multiplicative parts (namely, bias-free image and bias field), which are used to capture global intensity statistics. At the same time, our technique considers statistical information with not only the mean but also the variance. Pixel intensity generally fluctuates around its mean in a local region. The variance can adjust the disagreement of each pixel to its mean, and is explored to overcome the effects of intensity nonuniformity. Finally, the level set energy formulation is in the form of the global multiplicative decomposition and local statistical information. To verify the availability of our technique, we have thoroughly experimented on composite and real images. Our techniques are also applied to the lesion regions of brains. Experimental results display that our technique can segment different kinds of images with high noise and intensity nonuniformity more accurately, and outperforms other solutions in comparison.
How the ideology of monolingualism drives us to monolingual interaction

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Sheena Erete, Editor
Graph-embedded subspace support vector data description

In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-class techniques as its special cases and provides further insight on what these techniques actually optimize. The framework allows to incorporate other meaningful optimization goals via the graph preserving criterion and reveals a spectral solution and a spectral regression-based solution as alternatives to the previously used gradient-based technique. We combine the subspace learning framework iteratively with Support Vector Data Description applied in the subspace to formulate Graph-Embedded Subspace Support Vector Data Description. We experimentally analyzed the performance of newly proposed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification.
Voice Usability Scale: Measuring the User Experience with Voice Assistants

Voice Assistants (VAs) have grown in popularity over recent years and forecasted for exceptional growth over the coming years. Yet many issues still exist which prevent their adoption. Therefore, the usability of VAs is the crucial area that recent research should focus on, keeping in mind their commercialization aspect. This study aims to construct a valid and reliable scale for subjective usability evaluation of voice assistants. The Voice Usability Scale (VUS) is formulated based on three themes: information quality and relevance, semantic intelligence, and user satisfaction, extracted from extant literatures. In this study, a subjective experiment is performed with 61 subjects across some popular commercially available (VAs) to check the scale's validity and reliability. The reliability and face validity of the scale is high (Cronbach's alpha: 0.807, Face validity index (FVI): 0.96). Usability practitioners can use this scale in subjective evaluations of VAs and research purposes.
Angular Sparsemax for Face Recognition

The Softmax prediction function is widely used to train Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition and other applications. The limitation of the softmax activation is that the resulting probability distribution always has a full support. This full support leads to larger intraclass variations. In this paper, we formulate a novel loss function, called Angular Sparsemax for face recognition. The proposed loss function promotes sparseness of the hypotheses prediction function similar to Sparsemax [1] with Fenchel-Young regularisation. By introducing an additive angular margin on the score vector, the discriminatory power of the face embedding is further improved. The proposed loss function is experimentally validated on several databases in terms of recognition accuracy. Its performance compares well with the state of the art Arcface loss.
Vibration-Based Indoor Human Sensing Quality Reinforcement via Thompson Sampling

This paper presents an online learning algorithm that recommends sensor locations for structural vibration-based indoor human sensing systems to achieve optimal sensing quality for high learning accuracy. The intuition is that we model the deployment environment features (physics models) to reflect the environmental impact on the signals, which would further impact the sensing applications' performance. We acquire these features by re-purposing the ambient sensing data used for sensing applications. Then we combine these environment features and the application performance acquired as user feedback (data-driven knowledge) to recommend the sensor locations with optimal sensing quality. We formulate the task as a multi-armed bandit problem and develop an online learning algorithm based on Thompson sampling. Real-world datasets are collected to validate the proposed algorithm for both online and offline learning scenarios. The system achieves 56% for [email protected] and 90% for [email protected], which demonstrate an up to 2.5× improvement compared to baseline approaches.
An Optimization of IMU Sensors-Based Approach for Malaysian Sign Language Recognition

Malaysian Sign Language (MSL) has been the main communication medium for Malaysian deaf people but only a small portion of ordinary people can comprehend this MSL. A common approach to convey the meaning of sign language is by using sign language recognition (SLR) method. A video-based SLR approach has been researched extensively but still heavily vulnerable to environment error and suffer from portability issue. Conversely, a sensor-based SLR solution can mitigate these problems at the cost of having bulky design due to the number and placement of the sensors required to achieve sufficient accuracy. Therefore, in this paper, we investigated the optimal number and placement of the inertial measurement unit (IMU) sensors required to perform an accurate conversion. Experimental results demonstrated our proposed optimization process is capable of achieving more than 98% of recognition accuracy. This could be a benchmark for a less bulky and leaner design of sensor-based hand glove as well as overcoming the usability and portability issues.