Reviewerly/exHarmony huggingface dataset:
Self-adaptively commensal learning-based Jaya algorithm with multi-populations and its application

Jaya algorithm is an advanced optimization algorithm, which has been applied to many real-world optimization problems. Jaya algorithm has better performance in some optimization field. However, Jaya algorithm exploration capability is not better. In order to enhance exploration capability of the Jaya algorithm, a self-adaptively commensal learning-based Jaya algorithm with multi-populations (Jaya-SCLMP) is presented in this paper. In Jaya-SCLMP, a commensal learning strategy is used to increase the probability of finding the global optimum, in which the person history best and worst information is used to explore new solution area. Moreover, a multi-populations strategy based on Gaussian distribution scheme and learning dictionary is utilized to enhance the exploration capability, meanwhile every subpopulation employed three Gaussian distributions at each generation, roulette wheel selection is employed to choose a scheme based on learning dictionary. The performance of Jaya-SCLMP is evaluated based on 28 CEC 2013 unconstrained benchmark problems. In addition, three reliability problems, i.e., complex (bridge) system, series system and series–parallel system, are selected. Compared with several Jaya variants and several state-of-the-art other algorithms, the experimental results reveal that Jaya-SCLMP is effective.
Frisson Waves: Sharing Frisson to Create Collective Empathetic Experiences for Music Performances

We propose Frisson Waves, a real-time system to detect, trigger and share frisson during music performances. The system consists of a physiological sensing wristband for detecting frisson and a thermo-haptic neckband for inducing frisson. This project aims to improve the connectedness of audience members and performers during music performances by sharing frisson. We present the results of an initial concert workshop and a feasibility study of our prototype.
Deep-Neural Genetic Algorithm Optimization Analysis for Forgery Detection of Banknotes

Metaheuristic algorithms aim to find high performing, near-optimal solutions with reasonable computing costs. Using the process of natural selection that belongs to a set of evolutionary algorithms, are Genetic Algorithms. These algorithms intelligently exploit random search over the data in search of a solution space for better performance. Detection of forgery in legal documents by an automated system remains a valid problem of today. In this paper, the identification and detection of forgery in the monetary notes is performed by using deep neural-based Genetic Algorithms. An Artificial Neural Network was used for training on the banknote dataset and the learned weights were vectorized for Genetic Algorithm processing. Genetic Algorithm is explained in detail along with the working of the Fitness model that determines the efficiency of the trained model. The model achieved a high-performance accuracy of 94% with the fitness score of95.2. The improvement of the model with the infusion of trainable weights from a deep neural model is represented and analyzed.
Improving QoS of Cloudlet Scheduling via Effective Particle Swarm Model

Cloud computing (CC) is an emerging area that includes the provisioning of dynamic and virtualized resources in a pay-as-you-go manner. Much exploration is required to enhance the scalability, effectiveness, and equilibrium in load balancing for better scheduling. Various scheduling algorithms are proposed to meet the user’s requirements, but most of them failed to balance the load in critical resource demand hours. In this study, an effective particle swarm algorithm (EPSO) is addressed to solve the scheduling problem. For a faster discovery of resources, a reverse variation technique is employed in the proposed approach. The EPSO model is applied after readjustment and fine-tuning of its hyper-parameters to get precise and optimized results. This study provides a better quality of service (QoS) by optimizing resource utilization, service availability, and service-level agreement (SLA). Standard deviation (SD) is one of the critical statistical load distribution parameters computed to confirm the correct results. Results demonstrated in the form of graphs, tables, FCFS, RR, and SJF scheduling models confirm that EPSO provides a better outcome than the other state-of-the-art methods.
Design Beku: Toward Decolonizing Design and Technology through Collaborative and Situated Care-in-Practices

Increasingly there is a design impulse to “solve” problems for communities in India, especially through technological imaginations and interventions imported from elsewhere that are often irrelevant and unsuited to their context. As public goods, such as education, heritage, health care, and the like, in India shift from ideals of social equity to profit-making, designers engaged in “development” face an important question about their role in the change-making process, with ethical and political implications. In response to this question, we describe our efforts as a newly formed collective, Design Beku, which emerged from our desire to explore if and how technology and design can be decolonial, local, and ethical. Informed by cross-disciplinary theoretical and methodological underpinnings, we present a series of autoethnographic accounts by the collective’s founding members, woven together to describe our experiences, influences, learnings, and reflections on the life of the collective so far. Through articulating the commonalities and differences across our projects, we highlight how co-design becomes for us a care-in-practice, an onto-epistemology enabling us to align with local matters of concern to collaboratively evolve systemic solutions, enabled by but not led by technology. We believe the most powerful critique and challenge to the fascism of corporate and politically motivated technological regimes is by responding through community-centric design practice. We hope that our experiences and reflections will find resonance with other practitioners working within the larger context of the themes articulated by the call for this special issue.
Semantic Nighttime Image Segmentation Via Illumination and Position Aware Domain Adaptation

Due to the lack of the annotated nighttime images, general image segmentation models trained on the daytime image dataset do not perform well in nighttime scenes. The difference of the illumination condition and the difficulty to obtain the position information between daytime and nighttime makes the nighttime image segmentation tough. As a consequence, this paper proposes an end-to-end nighttime segmentation network based on the following two points: 1) Utilizing illumination adaptation with the different illumination condition on the daytime or nighttime to close the distribution gap at the feature map level; 2) With the prior information about the position of each object in the outdoor scene, some classification errors could be corrected by incorporating the self-attention mechanism. The scheme is tested on the open-source nighttime dataset Dark Zurich and night driving, with a 2.5% improvement compared to the base segmentation network.
A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs

This paper proposes a new approach to produce classification rules based on evolutionary computation with novel crossover and mutation operators customized for execution on graphics processing unit (GPU). Also, a novel method is presented to define the fitness function, i.e. the function which measures quantitatively the accuracy of the rule. The proposed fitness function is benefited from parallelism due to the parallel execution of data instances. To this end, two novel concepts; coverage matrix and reduction vectors are used and an altered form of the reduction vector is compared with previous works. Our CUDA program performs operations on coverage matrix and reduction vector in parallel. Also these data structures are used for evaluation of fitness function and calculation of genetic operators in parallel. We proposed a vector called average coverage to handle crossover and mutation properly. Our proposed method obtained a maximum accuracy of 99.74% for Hepatitis C Virus (HCV) dataset, 95.73% for Poker dataset, and 100% for COVID-19 dataset. Our speedup is higher than 20% for HCV and COVID-19, and 50% for Poker, compared to using single core processors.
Gat-Assisted Deep Hashing For Multi-Label Image Retrieval

Multi-Label hash methods have achieved excellent performance in multi-label image retrieval, but how to leverage the semantic information of label to improve retrieval quality is still a challenge in this field. This paper proposes GAT-Assisted Deep Hashing (DHGAT). Our model uses Convolutional Neural Network (CNN) to extract image-level features, along with graph attention network (GAT) to extract label-level features. Assisted by GAT, DHGAT is able to pay more attention on the co-occurrence of label. In order to solve the problem of feature fusion, we propose Multi-modal Max-Pooling Bilinear (MMB) mechanism, which MMB fuses image-level feature and label-level feature to generate abundant semantic features, so that the model can output discriminative hash code. Extensive experiments demonstrate that the proposed method can generate hash codes which achieve better retrieval performance on two benchmark datasets, NUS-WIDE and MS-COCO.
Cross-Modal Graph With Meta Concepts for Video Captioning

Video captioning targets interpreting the complex visual contents as text descriptions, which requires the model to fully understand video scenes including objects and their interactions. Prevailing methods adopt off-the-shelf object detection networks to give object proposals and use the attention mechanism to model the relations between objects. They often miss some undefined semantic concepts of the pretrained model and fail to identify exact predicate relationships between objects. In this paper, we investigate an open research task of generating text descriptions for the given videos, and propose Cross-Modal Graph (CMG) with meta concepts for video captioning. Specifically, to cover the useful semantic concepts in video captions, we weakly learn the corresponding visual regions for text descriptions, where the associated visual regions and textual words are named cross-modal meta concepts. We further build meta concept graphs dynamically with the learned cross-modal meta concepts. We also construct holistic video-level and local frame-level video graphs with the predicted predicates to model video sequence structures. We validate the efficacy of our proposed techniques with extensive experiments and achieve state-of-the-art results on two public datasets.
On self-dual skew cyclic codes of length p over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub><mml:mo linebreak="goodbreak" linebreakstyle="after">+</mml:mo><mml:mi>u</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><…

Dinh et al. (2018) [10] obtained all self-dual constacyclic codes of length ps over R2=Fpm+uFpm, where p is a prime number and u2=0. In this paper, we determine the structure of (Euclidean) dual of some special skew cyclic codes of length ps over R2, and establish all of them which are self-dual. As a special case, we conclude all of results appeared in the above paper for cyclic codes.
PS-LSTM:Popularity Analysis And Social Network For Point-Of-Interest Recommendation In Previously Unvisited Locations

With the development of technology and the gradual convenience of transportation, due to various reasons such as travel or business trip, people often leave their familiar areas and visit relatively unfamiliar areas in real life.It will be presents a new problem to the recommendation algorithm, namely the POI recommendation in previously unvisited locations. In order to make the recommendation system still provide users with high quality POI recommendation in this situation, this paper proposes a POI recommendation algorithm based on deep learning and popularity analysis.In the model, we integrate the popularity of POI and social network to alleviate the cold start problem .When integrating social information, we use attention mechanism to measure the influence of different friends on users, and then weigh it to get the social impact. When fusing the information of POI popularity, we consider that the influence of POI popularity varies with geographical distance and time, so we construct a dynamic popularity influence vector in the model.For making better utilize of the characteristics of the check-in sequence, we use the recurrent neural network to model the spatiotemporal sequence of the users’ POI check-in. Finally, MLP is used to integrate social impact, popularity characteristics and sequence information to recommend for users.The proposed algorithm is verified on yelp which is a real city dataset and compared with several classic POI recommendation algorithms.Experimental results show that the proposed algorithm could achieve a better accuracy.
Robust Cross-lingual Task-oriented Dialogue

Cross-lingual dialogue systems are increasingly important in e-commerce and customer service due to the rapid progress of globalization. In real-world system deployment, machine translation (MT) services are often used before and after the dialogue system to bridge different languages. However, noises and errors introduced in the MT process will result in the dialogue system's low robustness, making the system's performance far from satisfactory. In this article, we propose a novel MT-oriented noise enhanced framework that exploits multi-granularity MT noises and injects such noises into the dialogue system to improve the dialogue system's robustness. Specifically, we first design a method to automatically construct multi-granularity MT-oriented noises and multi-granularity adversarial examples, which contain abundant noise knowledge oriented to MT. Then, we propose two strategies to incorporate the noise knowledge: (i) Utterance-level adversarial learning and (ii) Knowledge-level guided method. The former adopts adversarial learning to learn a perturbation-invariant encoder, guiding the dialogue system to learn noise-independent hidden representations. The latter explicitly incorporates the multi-granularity noises, which contain the noise tokens and their possible correct forms, into the training and inference process, thus improving the dialogue system's robustness. Experimental results on three dialogue models, two dialogue datasets, and two language pairs have shown that the proposed framework significantly improves the performance of the cross-lingual dialogue system.
Collaborative and Multilevel Feature Selection Network for Action Recognition

The feature pyramid has been widely used in many visual tasks, such as fine-grained image classification, instance segmentation, and object detection, and had been achieving promising performance. Although many algorithms exploit different-level features to construct the feature pyramid, they usually treat them equally and do not make an in-depth investigation on the inherent complementary advantages of different-level features. In this article, to learn a pyramid feature with the robust representational ability for action recognition, we propose a novel collaborative and multilevel feature selection network (FSNet) that applies feature selection and aggregation on multilevel features according to action context. Unlike previous works that learn the pattern of frame appearance by enhancing spatial encoding, the proposed network consists of the position selection module and channel selection module that can adaptively aggregate multilevel features into a new informative feature from both position and channel dimensions. The position selection module integrates the vectors at the same spatial location across multilevel features with positionwise attention. Similarly, the channel selection module selectively aggregates the channel maps at the same channel location across multilevel features with channelwise attention. Positionwise features with different receptive fields and channelwise features with different pattern-specific responses are emphasized respectively depending on their correlations to actions, which are fused as a new informative feature for action recognition. The proposed FSNet can be inserted into different backbone networks flexibly, and extensive experiments are conducted on three benchmark action datasets, Kinetics, UCF101, and HMDB51. Experimental results show that FSNet is practical and can be collaboratively trained to boost the representational ability of existing networks. FSNet achieves superior performance against most top-tier models on Kinetics and all models on UCF101 and HMDB51.
Monetary policy and economic growth in Ghana: Does financial development matter?

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
An Indicators-of-Risk Library for Industrial Network Security

This paper introduces an “Indicator of Risk (IoR) Library” that leverages the MITRE ATT&CK for Industrial Control Systems (ICS) knowledge base to support continuous risk monitoring. This allows also making use of variables that are already being monitored to analyse risks in a continuous basis. IoRs broaden the concept of Indicators of Compromise by combining detection strategies with probabilistic inference as a tool for quantifying cyber-security risks. The latest version of the Library has 95 IoRs and has been reviewed by professionals from three major companies and cross-referenced against detection use-cases implemented by other researchers to validate its potential to identify variables for monitoring cyber-risks in ICS.
The adoption of Software Engineering practices in a Scrum environment

The competition in the software market demands that the time required for any software product to reach the market be reduced if the product is to survive competition from other developers. The pursuit of this goal has led to the adoption of agile software development methodologies. While other agile methodologies provide guidelines as to the software engineering (SE) practices to be used during the development lifecycle, Scrum does not. The purpose of this study is twofold: first, to identify the usage and level of importance of software engineering practices in the Scrum development environment; and second, to investigate how Scrum teams adopt an appropriate set of SE techniques and whether a hybrid Scrum/Extreme Programming (XP) methodology is an appropriate approach to take. This research was conducted by examining sample data from five organizations using the Scrum methodology. The sample included a range of industries including communications and embedded systems, financial asset management, software development houses and consulting firms in South Africa. The study employed a mixed method approach. A key finding was that, regardless of the fact that Scrum does not explicitly recommend engineering practices, there was extensive use of these practices by all of the participating organizations. The study also found that the lack of software engineering practices in Scrum does not constitute a barrier to a successful adoption of Scrum, provided the 'inspect and adapt' principle inherent in Scrum is properly followed. The study discusses the findings, explains the implications and suggests future research.
Stereo Feature Learning Based on Attention and Geometry for Absolute Hand Pose Estimation in Egocentric Stereo Views

Egocentric hand pose estimation is significant for wearable cameras since the hand interactions are captured from an egocentric viewpoint. Several studies on hand pose estimation have recently been presented based on RGBD or RGB sensors. Although these methods provide accurate hand pose estimation, they have several limitations. For example, RGB-based techniques have intrinsic difficulty in converting relative 3D poses into absolute 3D poses, and RGBD-based techniques only work in indoor environments. Recently, stereo-sensor-based techniques have gained increasing attention owing to their potential to overcome these limitations. However, to the best of our knowledge, there are few techniques and no real datasets available for egocentric stereo vision. In this paper, we propose a top-down pipeline for estimating absolute 3D hand poses using stereo sensors, as well as a novel dataset for training. Our top-down pipeline consists of two steps: hand detection and hand pose estimation. Hand detection detects hand areas and then is followed by hand pose estimation, which estimates the positions of the hand joints. In particular, for hand pose estimation with a stereo camera, we propose an attention-based architecture called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StereoNet</i> , a geometry-based loss function called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StereoLoss</i> , and a novel 2D disparity map called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StereoDMap</i> for effective stereo feature learning. To collect the dataset, we proposed a novel annotation method that helps reduce human annotation efforts. Our dataset is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/seo0914/SEH</uri> . We conducted comprehensive experiments to demonstrate the effectiveness of our approach compared with the state-of-the-art methods.
Con2Vec: Learning embedding representations for contrast sets

Contrast sets are used in many knowledge-based systems to capture data patterns relevant to a target variable. While they have many advantages such as being highly interpretable, they do not come with a similarity measure or feature vectors for downstream tasks such as regression or classification. To address these disadvantages, we propose Con2Vec (Contrast set to Vector), a method to embed contrast sets into a low-dimensional continuous vector space. Con2Vec defines two novel similarity and co-occurrence contexts for a contrast set, and then leverages a neural embedding model to learn low-dimensional continuous vectors (aka embeddings) for contrast sets. We further apply contrast set embeddings to construct the feature vectors for transactional data. We extensively evaluate our method Con2Vec on four real-world datasets, compared against state-of-the-art embedding and non-embedding methods where the results demonstrate the clear advantages of our method.