Reviewerly/exHarmony huggingface dataset:
SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) 2022

This is the second edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) workshop. Also this year, our goal is to bring together researchers from all over the world interested in studying and developing new interactive techniques for fostering the social and cultural inclusion of individuals from different realities, with particular attention to the vulnerable and at-risk categories. In particular, this year large space is devoted to socially assistive robots and their use in specific contexts. The invited talk will also deal with the problem of developing intelligent robotic applications in the interaction between the robot and the human, including the assistance of children in hospitals and people with dementia.
An Initial Usability Testing for Improving Acceptance Criteria in A Scrum Project: An Angkasa LMS case study

A sprint backlog is a placeholder for user requirements written as user stories and acceptance criteria for user stories. However, there is no standard technique used to involve users in validating the requirements at the beginning of the sprint. In this research, we conducted usability testing, involving seven participants, for three given tasks before a sprint planning in the development of Angkasa Learning Management System (LMS), a pay-as-you-go LMS on top of a cloud service. In the sprint review, five participants were asked for feedback and selected the best sprint increments between the sprint A dan sprint B. For this research, both sprints were identical which the sprint B had the same user stories, but the sprint B was tested using usability testing. The results show that the user acceptances in the sprint review were increased and user requirements in the user stories were validated by the users. This research contributes to the studies of user experience practices in the Agile-based projects to increase acceptance of software products.
A system of the granite weathering degree assessment using hyperspectral image and CNN

Due to weathering processes that occur all around the world, rocks that are exposed eventually undergo physical and chemical changes. These weathering processes have the potential to turn fresh rocks into soft and weakened derivatives, leading to the collapse and subsidence of structures, such as tunnels, dams, and others, as well as natural structures, such as cliffs and slopes. This study attempts to employ hyperspectral imaging and a deep learning network called Convolutional Neural Network (CNN) in the effort to achieve a reliable process for rock weathering degree assessment. This study employs a hyperspectral image camera capable of detecting electromagnetic radiation from 400 nm-1000 nm with a spectral resolution of 204 bands. The CNN algorithm used in this study is based on a modified VGG-16 architecture. The total amount of data from granite samples employed amounts to 19,456 files. 80% of the total data were used as learning data and the remaining 20% as testing and validation data. The results show that the system has an average precision of over 94%. The study, therefore, concludes that the conjunction of hyperspectral imaging with CNN is a practicable process for the evaluation of the degree of weathering granite without the need for specialised human expertise or prejudice.
Video face swap with DeepFaceLab

DeepFaceLab, which is also known as AI face swap, is a new task with practical value in computer vision field that has become popular in recent years. As it can replace any face in an existing video with another face whichever you want without revealing flaws, swapping face technology is now becoming more and more welcomed by the entertainment, film and art industries, producing high commercial value. A number of works based on convolutional neural network or generative adversary network have been proposed to extract facial features and accomplish face swap. However, their network architecture can't complete the small face swapping very well and the effect of the generated video is also not ideal. In this paper, we train on the SAEHD model based on a single shot scale-invariant face detector. We use the scale-equitable-face detection framework to ensure that we can extract enough features on different scales for face swap. In addition, according to the effective receptive field, the anchors of different scales are defined on different feature maps, and we adopt the proportional sampling method to ensure that the sampling density of the anchors on different feature maps is consistent. By replacing the face by frame, we have achieved very good results on the DeepFaceLab task, achieving relatively small source and destination loss, respectively, with the real-time speed.
Performance Model and Profile Guided Design of a High-Performance Session Based Recommendation Engine

Session-based recommendation (SBR) systems are widely used in transactional systems to make personalized recommendations to the end-user. In online retail systems, recommendations-based decisions need to be made at a very high rate especially during peak hours. The required computational workload is very high especially when there is a larger number of products involved. Session Based Recommendation (SBR) models incorporate the learning-based product buying pattern from various user interaction sessions and try to recommend the top-K products, the user is likely to purchase. These models comprise several functional layers that widely vary in their compute and data access patterns. To support high recommendation rates, all these layers need a performance optimal implementation, which can be a challenge given the diverse nature of the computations involved. For this reason, one compute platform - whether it is CPU, GPU, or a Field Programmable Gate Array (FPGA) may not be able to provide an optimal implementation for all the layers. In this paper, we describe performance modeling and profile-based design approach to arrive at an optimal implementation, comprising of the hybrid CPU, GPU, and FPGA platforms for NISER - a session-based recommendation model that avoids popularity bias in recommendations. In addition, the design for the CPU-FPGA hybrid platform is implemented for NISER and we observed that experimental results closely follow the results predicted by the performance model for the implemented deployment option.
Awareness logic: Kripke lattices as a middle ground between syntactic and semantic models

Abstract The literature on awareness modelling includes both syntax-free and syntax-based frameworks. Heifetz, Meier and Schipper (HMS) propose a lattice model of awareness that is syntax-free. While their lattice approach is elegant and intuitive, it does not explicitly distinguish uncertainty from unawareness. Contra this, the most prominent syntax-based solution, the Fagin–Halpern (FH) model, accounts for this distinction and offers a simple representation of awareness but lacks the intuitiveness of the lattice structure. Here, we combine these two approaches by providing a lattice of Kripke models, induced by atom subset inclusion, in which uncertainty and unawareness are separate. We show that our model is equivalent to both HMS and FH models by defining transformations between them which preserve satisfaction of formulas of a language for explicit knowledge and obtain completeness through our and HMS’ results. Lastly, we prove that the Kripke lattice model can be shown equivalent to the FH model (when awareness is propositionally determined) also with respect to the language of the Logic of General Awareness, for which the FH model was originally proposed.
SWC-based Smart Contract Development Guide Research

Smart contracts are consensus protocols in which contracts are automatically concluded by code. Despite the growing popularity of smart contracts recently, no guidelines have been presented for developers to refer to when developing smart contracts. Therefore, using SWC, which classifies the weaknesses of smart contracts, guidelines that developers can use in the development process from the perspective of security, maintenance, and reuse are presented based on the weaknesses. The presented indicators can be used to reduce costs incurred later during development.
Robust pattern for face recognition using combined Weber and pentagonal-triangle graph structure pattern

Face recognition is a challenging task in computer vision and information retrieval due to the large variation in facial images in terms of pose, expression and illumination. These challenges motivate us to propose a robust pattern for face recognition using combined Weber pattern (CWP) and pentagonal-triangle graph structure pattern (PTGSP). Firstly, abrupt intensity variations in facial images are removed by multiblock operation. Further, by exploring the relationship among center, neighboring and adjacent pixels, CWP consisting of three unique Weber patterns is computed. In order to obtain the pattern that is more robust to facial variations, PTGSP has been computed on the facial images. PTGSP covers the widely used features of face images. A feature set of high dimension is produced by combining the features of CWP and PTGSP. Dimensionality of the proposed feature and also variance within class is reduced through principal component analysis (PCA) plus linear discriminant analysis (LDA) algorithm. The classification performance of the proposed method is compared with various state-of-the-art methods on variety of benchmark face datasets with variation in pose, expression, illumination and occlusion. Experiments on FEI, Georgia, ORL, Yale and Faces94 databases clearly prove the robustness of the proposed method in contrast to existing handcrafted feature extraction techniques.
Classification of imbalanced medical data: An empirical study of machine learning approaches

Thousands of patients around the world affecting their health with various factor as age, body mass index, cholesterol levels, albumin levels and several other factor. Prediction of health outcome due to these factors at a proper time can be served as an early warning. Recent growth in machine learning algorithm inspired us to build a predictive model for better healthcare facilities. In our work we have focused on problem of noisy and imbalanced dataset in which majority class is favored over minority one that leads to false prediction. We have experimented with two publicly available medical imbalanced dataset which varies in its size as MIT’s GOSSIS death and PIMA Indians Diabetes Dataset based on binary class. In this model we have investigated 3 oversampling techniques (Synthetic Minority Oversampler, Random Oversampler and Adaptive Synthetic Sampler) along with two undersampling techniques (Random Undersampler and Near Miss) which were paired with 3 data reduction and cleaning methods namely Tomek Links, One Sided Selection and Edited Nearest Neighbors. At last, we found that combination of Adaptive Synthetic Sampler along with One Sided Selection perform better in case of large size dataset while combination of random oversampler along with Tomek Link showed better performance in case of low size data dataset. We have also analyzed that oversampling technique gives quite promising results in comparison to undersampling methods specifically when applied with machine learning classifiers as these classifiers are data hungry algorithms.
Visual Analysis of English Teaching Model Based on Scientific Programming

In order to continuously enrich the connotation of teachers’ teaching ability in the context of the deep integration of information technology and English teaching, thus changing the relationship between the important elements of college English teaching ecology, a visual analysis of English pronunciation teaching mode based on speech recognition technology is proposed, with the support of Internet technology, designed and implemented a visual analysis platform for English phonetic transcription-assisted learning, introduced two types of random graph models G(n, p) and G(n, M), and then introduced the concept of phase transition. A research model is established, using the algorithm of generating random graphs and the Hamiltonian circuit logic program to point out that the critical function value of G(n,p) existence loop is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mi>n</mi> </math> . The test results show that the platform can basically meet the requirements.
Convolutional neural networks ensembles through single-iteration optimization

Convolutional Neural Networks have been widely employed in a diverse range of computer vision-based applications, including image classification, object recognition, and object segmentation. Nevertheless, one weakness of such models concerns their hyperparameters’ setting, being highly specific for each particular problem. One common approach is to employ meta-heuristic optimization algorithms to find suitable sets of hyperparameters at the expense of increasing the computational burden, being unfeasible under real-time scenarios. In this paper, we address this problem by creating Convolutional Neural Networks ensembles through Single-Iteration Optimization, a fast optimization composed of only one iteration that is no more effective than a random search. Essentially, the idea is to provide the same capability offered by long-term optimizations, however, without their computational loads. The results among four well-known datasets revealed that creating one-iteration optimized ensembles provides promising results while diminishing the time to achieve them.
Multi‐graph convolutional clustering network

The relationship between objects can be described from different angles. Although multiple kinds of relationships make the connections between objects complex, they bring in more discriminative information for the clustering tasks. Therefore, how to effectively fuse multiple kinds of relationships becomes a critical problem. In this paper, we propose a novel Multi-graph Convolutional Clustering Network which deeply explores the feature information of nodes and fuses the multiple kinds of relationships between nodes. Unlike most graph convolutional clustering methods that only exploit the single graph or directly fuse multiple graphs into a unified graph before the graph convolution operation, we firstly build multiple parallelled graph convolution layers for each graph to learn diverse data representations, which fully exploits different statistics information between graphs. Then, a designed multi-graph attention module fuses above data representations and considers the importance of each graph. Besides, the proposed model completes the transition from single graph to multiple graphs, which reduces the dependence of the quality of the single graph and enhances the robustness to graphs. Experimental results verify that the proposed multi-graph convolution clustering performs better than the traditional single-graph convolution clustering.
Face-to-face with the new normal: libraries' readiness and perspectives toward the changing service environment

Purpose This paper seeks to explore how library management in academic and school institutions has been adapting to the changes brought about by the global COVID-19 pandemic. It discusses how their priorities have shifted in reinventing their strategic plans and innovative library responses; and their readiness to meet new expectations to support their communities, as necessitated by the changes. Design/methodology/approach Employing the change readiness concept as a framework, it encompasses inquiry into the library management's response to the changes in the service and work environments at the onset of change, while in the process of adapting to the current changes, and readiness to the perceived changes in the next normal future service environment. Quantitative design was employed for this study, and self-selection strategy as a non-probability sampling technique was used in collecting data from respondents. Target respondents were head librarians. An online questionnaire was used as instrument for data gathering, which was tested valid and reliable. Findings Findings reveal that respondents have achieved quite a lot in preparing to adapt to change and generally agree that they are ready to deal with the perceived, future-wise changes in all aspects. Research limitations/implications The study was open to all types of libraries as subjects; however, one-person libraries were excluded. Being self-selected, the respondent group for this study is not a true representative of the target population. Data analysis was strictly based on the gathered results. Practical implications The study offers insights on the value of integrating measures pertinent to the emergence of new roles and needs in the changing service environment. Originality/value The results of the study are seen to have a potential contribution to the library organization's growth as they oversee their operations, prospectively.
Application of Impulse Process Models with Multirate Sampling in Cognitive Maps of Cryptocurrency for Dynamic Decision Making

In this chapter a cognitive map (CM) of cryptocurrency application in the financial market is developed, based on which the dynamic model of impulse processes of CM in the form of a system of difference equations (Roberts equations) with multirate sampling is described. The original CM model is decomposed into subsystems with fast- and slow-measured node coordinates. The subsystems are interconnected with each other and are represented with multirate sampling of coordinates. The realization of external control vectors for the subsystem with fast-measured and slow-measured node coordinates by varying some coordinates that can be measured by a decision maker is performed. Closed-loop subsystems of CM impulse process control are implemented, which include multidimensional discrete controllers designed based on the method of invariant ellipsoids. The controllers in the subsystems generate external controls with multirate sampling and affect directly the CM nodes by varying their coordinates. The problem of designing the above discrete-time controllers for suppression of constrained internal and external disturbances is solved. External disturbances include a variety of information disturbances acting on the system. Internal disturbances include changes in the influence of CM nodes on each other, for instance, fluctuations of weight coefficients of CM with respect to their basic values. The basic values are determined by an expert based on cause-effect relations or by the preliminarily identification of the CM model parameters. The mutual influence of interconnected CM subsystems on each other is also taken into account as internal disturbances. The designed control is implemented by a decision maker in the corresponding sections of the CM. By numerical simulation, the efficiency of the designed discrete controllers was investigated and the system performance was compared in the presence and absence of controls.
Research on the Teaching Method of Environmental Landscape Design Practice Based on IoT Network and Digital VR Technology

The traditional practical teaching methods are based on teachers’ narration and demonstration, with few practical opportunities for students, lack of interactivity in teaching, low interest of students in teaching, and poor teaching effect. In order to improve the teaching effect of environmental landscape design and enhance students’ learning enthusiasm, a practical teaching method of environmental landscape design based on IoT network and digital VR technology will be studied. After guiding students to carry out the cognitive construction of environmental landscape design, VR technology is used to establish virtual teaching scenes for students. To create effective virtual reality engagement in the teaching process and enable students to better grasp important design information, gesture recognition technology is employed to recognize students’ landscape design operations. After the application of the proposed teaching method in teaching was verified through examples, the students’ learning interest was significantly increased, and the excellent rate of students’ design works was increased by about 16.6%, with good teaching effect.
A modified projection neural network with fixed-time convergence

In this study, we propose a modified projection neural network (PNN) with fixed-time convergence to solve the nonlinear projection equations. Under the assumptions of Lipschitz continuity and strict monotonicity, the existence of the solution and the stability in the Lyapunov sense of the proposed modified PNN are proved, which guarantee the convergence in fixed time. The convergence time of the proposed PNN has an upper bound which is independent ofarbitrary initial conditions. Compared with other existing PNN,the presented modified PNN can also solve the non-smooth, nonlinear, and constrained convex optimization problems. In the final step, the numerical simulations demonstrate that the presented modified PNN has a faster convergence rate and gets a more precise solution than the existing methods.
Erlang planning network: An iterative model-based reinforcement learning with multi-perspective

For model-based reinforcement learning (MBRL), one of the key challenges is modeling error, which cripples the effectiveness of model planning and causes poor robustness during training. In this paper, we propose a bi-level Erlang Planning Network (EPN) architecture, which is composed of an upper-level agent and several multi-scale parallel sub-agents, trained in an iterative way. The proposed method focuses upon the expansion of representation by environment: a multi-perspective over the world model, which presents a varied way to represent an agent’s knowledge about the world that alleviates the problem of falling into local optimal points and enhances robustness during the progress of model planning. Moreover, our experiments evaluate EPN on a range of continuous-control tasks in MuJoCo, the evaluation results show that the proposed framework finds exemplar solutions faster and consistently reaches the state-of-the-art performance.
A feature selection strategy for improving software maintainability prediction

Software maintainability is a significant contributor while choosing particular software. It is helpful in estimation of the efforts required after delivering the software to the customer. However, issues like imbalanced distribution of datasets, and redundant and irrelevant occurrence of various features degrade the performance of maintainability prediction models. Therefore, current study applies ImpS algorithm to handle imbalanced data and extensively investigates several Feature Selection (FS) techniques including Symmetrical Uncertainty (SU), RandomForest filter, and Correlation-based FS using one open-source, three proprietaries and two commercial datasets. Eight different machine learning algorithms are utilized for developing prediction models. The performance of models is evaluated using Accuracy, G-Mean, Balance, &amp; Area under the ROC Curve. Two statistical tests, Friedman Test and Wilcoxon Signed Ranks Test are conducted for assessing different FS techniques. The results substantiate that FS techniques significantly improve the performance of various prediction models with an overall improvement of 18.58%, 129.73%, 80.00%, and 45.76% in the median values of Accuracy, G-Mean, Balance, &amp; AUC, respectively for all the datasets taken together. Friedman test advocates the supremacy of SU FS technique. Wilcoxon Signed Ranks test showcases that SU FS technique is significantly superior to the CFS technique for three out of six datasets.