Reviewerly/exHarmony huggingface dataset:
Genie in the Model

Advances in deep neural networks (DNNs) have fostered a wide spectrum of intelligent mobile applications ranging from voice assistants on smartphones to augmented reality with smart-glasses. To deliver high-quality services, these DNNs should operate on resource-constrained mobile platforms and yield consistent performance in open environments. However, DNNs are notoriously resource-intensive, and often suffer from performance degradation in real-world deployments. Existing research strives to optimize the resource-performance trade-off of DNNs by compressing the model without notably compromising its inference accuracy. Accordingly, the accuracy of these compressed DNNs is bounded by the original ones, leading to more severe accuracy drop in challenging yet common scenarios such as low-resolution, small-size, and motion-blur. In this paper, we propose to push forward the frontiers of the DNN performance-resource trade-off by introducing human intelligence as a new design dimension. To this end, we explore human-in-the-loop DNNs (H-DNNs) and their automatic performance-resource optimization. We present H-Gen, an automatic H-DNN compression framework that incorporates human participation as a new hyperparameter for accurate and efficient DNN generation. It involves novel hyperparameter formulation, metric calculation, and search strategy in the context of automatic H-DNN generation. We also propose human participation mechanisms for three common DNN architectures to showcase the feasibility of H-Gen. Extensive experiments on twelve categories of challenging samples with three common DNN structures demonstrate the superiority of H-Gen in terms of the overall trade-off between performance (accuracy, latency), and resource (storage, energy, human labour).
Research on Hybrid Recommendation Algorithm Based on Incremental Clustering

In this paper, a hybrid recommendation algorithm based on incremental clustering is proposed to solve the problem of low efficiency of traditional recommendation algorithms due to sparse scoring data. First, we use incremental clustering algorithm to cluster the initial data set, and then find the clustering center of each user. Instead of the target user, the clustering center is put into KNN algorithm to find the nearest neighbor of the user, and then singular value decomposition algorithm is used to decompose the matrix to find the best effect parameters. Finally, experiments are carried out on movielens data set. Experimental results show that the effect of this algorithm is significantly improved.
Construction of a Training Device in Pedagogical Engineering from the “My Scenari” Model

This work intends to present a design work of a research design which aims to experiment with a training device in pedagogical engineering. Thanks to the “Scenari” suite, the training on the implementation of a pedagogical project made it possible to test the validity of the hypothesis that the instrumental design of a training in pedagogical engineering develops the professional skills of teachers. During the duration of the workshops, participatory observation raised several factors of performance and failure.
Towards Interactive Facial Image Inpainting by Text or Exemplar Image

Facial image inpainting aims to fill visually realistic and semantically new pixels for masked or missing pixels in a face image. Although current methods have made progress in achieving high visual quality, the controllable diversity of face inpainting remains an open issue. This paper proposes a new facial image inpainting interaction mode, which enables filling semantic contents based on the texts or exemplar images provided by users. We use the powerful image-text representation abilities from the recently introduced Contrastive Language-Image Pre-training (CLIP) models to achieve this interactive face inpainting. We present two thoughts on our method. Specifically, we first explore a simple and effective optimization-based text-guided facial inpainting method in which a CLIP model is used as a loss network to modify the latent code iteratively in response to the text prompt. Next, we describe a multi-modal inpainting mapper network to map the input conditions (e.g., text or image) into corresponding latent code changes, supporting the guidance of different text prompts and exemplars within one model. We also introduce an exemplar-semantic similarity loss, which maps the inpainted facial image and the exemplar image into the CLIP’s space to measure their similarity. This loss enables the generated image to include high-level semantic attributes from the exemplar image. Through extensive experiments, we demonstrate the effectiveness of our method in interactive facial inpainting based on the texts or exemplars.
Practical and Secure Federated Recommendation with Personalized Mask

Federated recommendation addresses the data silo and privacy problems altogether for recommender systems. Current federated recommender systems mainly utilize cryptographic or obfuscation methods to protect the original ratings from leakage. However, the former comes with extra communication and computation costs, and the latter damages model accuracy. Neither of them could simultaneously satisfy the real-time feedback and accurate personalization requirements of recommender systems. In this work, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness. In more details, we introduce the new idea of personalized mask generated only from local data and apply it in FedMMF. On the one hand, personalized mask offers protection for participants’ private data without effectiveness loss. On the other hand, combined with the adaptive secure aggregation protocol, personalized mask could further improve efficiency. Theoretically, we provide security analysis for personalized mask. Empirically, we also show the superiority of the designed model on different real-world data sets.
ViSig

Visual body signals are designated body poses that deliver an application-specific message. Such signals are widely used for fast message communication in sports (signaling by umpires and referees), transportation (naval officers and aircraft marshallers), and construction (signaling by riggers and crane operators), to list a few examples. Automatic interpretation of such signals can help maintaining safer operations in these industries, help in record-keeping for auditing or accident investigation purposes, and function as a score-keeper in sports. When automation of these signals is desired, it is traditionally performed from a viewer's perspective by running computer vision algorithms on camera feeds. However, computer vision based approaches suffer from performance deterioration in scenarios such as lighting variations, occlusions, etc., might face resolution limitations, and can be challenging to install. Our work, ViSig, breaks with tradition by instead deploying on-body sensors for signal interpretation. Our key innovation is the fusion of ultra-wideband (UWB) sensors for capturing on-body distance measurements, inertial sensors (IMU) for capturing orientation of a few body segments, and photodiodes for finger signal recognition, enabling a robust interpretation of signals. By deploying only a small number of sensors, we show that body signals can be interpreted unambiguously in many different settings, including in games of Cricket, Baseball, and Football, and in operational safety use-cases such as crane operations and flag semaphores for maritime navigation, with &gt; 90% accuracy. Overall, we have seen substantial promise in this approach and expect a large body of future follow-on work to start using UWB and IMU fused modalities for the more general human pose estimation problems.
Business - IT Alignment in Cloud Environment Comparative Study

In recent years, much research has focused on Business Process reuse to reduce the cost and time of process development. However, in a competitive market where business services are provided in a cloud environment, there is a need to develop non-installable applications or so-called “multi-tenant” applications. BPM practitioners and researchers are involved in improving the BPaaS (Business Process as Service) model, which today defines business process management in the context of the cloud. However, the problem of aligning IT operations in the cloud, bridging the gap between business requirements and technical cloud solutions, is appearing as an emerging issue in digital transformation. This is because cloud solutions are described technically, making it difficult for business stakeholders, who typically understand business language, to properly evaluate the most appropriate cloud solution. Identifying the most appropriate cloud solutions in this context, therefore, requires specifying the requirements and capabilities of service, both at the business and IT levels. In this paper, we present a comparative study to evaluate approaches that explore the problem of IT-Business alignment in the cloud to analyze how the alignment has been modeled and then extract the limitations and weaknesses of each approach, to propose an approach that will be able to address most of these critical points.
3D Deformation Capture via a Configurable Self-Sensing IMU Sensor Network

Motion capture technologies reconstruct human movements and have wide-ranging applications. Mainstream research on motion capture can be divided into vision-based methods and inertial measurement unit (IMU)-based methods. The vision-based methods capture complex 3D geometrical deformations with high accuracy, but they rely on expensive optical equipment and suffer from the line-of-sight occlusion problem. IMU-based methods are lightweight but hard to capture fine-grained 3D deformations. In this work, we present a configurable self-sensing IMU sensor network to bridge the gap between the vision-based and IMU-based methods. To achieve this, we propose a novel kinematic chain model based on the four-bar linkage to describe the minimum deformation process of 3D deformations. We also introduce three geometric priors, obtained from the initial shape, material properties and motion features, to assist the kinematic chain model in reconstructing deformations and overcome the data sparsity problem. Additionally, to further enhance the accuracy of deformation capture, we propose a fabrication method to customize 3D sensor networks for different objects. We introduce origami-inspired thinking to achieve the customization process, which constructs 3D sensor networks through a 3D-2D-3D digital-physical transition. The experimental results demonstrate that our method achieves comparable performance with state-of-the-art methods.
MSTRM: An Efficient Multi-scale Text Recognition Model

Text images in natural scenes pose a huge challenge for text recognition due to their complex background, perspective distortion, and excessive curvature. We therefore propose an efficient multi-scale text recognition model (MSTRM), which consists of a multi-target correction network, a two-branch multi-scale feature fusion module, and an attention sequence-based recognition network. Firstly, for irregular text images, a text correction network is used to correct distortions such as deformation and misalignment in the original image to reduce the recognition difficulty. Secondly, the features extracted from the feature extraction backbone are stitched and reused using different multi-scale branches with different sizes of convolution kernels. Finally, the attention-based sequence recognition network performs the recognition and outputs the prediction results sequentially. The experimental results of MSTRM on five commonly used datasets show that the model can effectively decode character sequences with location information and global semantic information.
Development of Android-Based Colloidal Learning Media to Improve Students’ Science Literacy in Chemistry Learning

The colloid system, which is taught in class XI, is a subject that is extremely closely related to phenomena that occur in everyday life.For that we need a media that is able to convey information accurately, clearly and pleasantly so that students are taught the content's notion so that they may comprehend the material on the colloid system.Mobile learning developed is an Androidbased colloidal learning media that uses smart apps creator software.In this colloidal learning media there are several features, namely competencies, materials, videos, evaluations, libraries and developers that can be accessed with Android.This study aims to determine the characteristics of Android-based mobile learning that was developed, to determine the effect of Android-based mobile learning to student activity, and to determine the effectiveness of Android-based colloidal learning media to improve students' scientific literacy in chemistry learning.This is research and development that follows the development paradigm of analysis, design, development, implementation, and assessment (ADDIE).Data were collected by means of response questionnaires, validation questionnaires, and scientific literacy tests.The data were analyzed with the percentage that met the requirements, qualified categorization, and the N-gain test.Practitioner and student responses were highly qualified with 80.95% and 89.02%, respectively.The result of effectiveness experiment got 0.62 N-gain with medium category.It can be concluded that android-based mobile learning is effective for improving students' scientific literacy on colloidal system material.
FedAUXfdp: Differentially Private One-Shot Federated Distillation

Federated learning suffers in the case of non-iid local datasets, i.e., when the distributions of the clients’ data are heterogeneous. One promising approach to this challenge is the recently proposed method FedAUX, an augmentation of federated distillation with robust results on even highly heterogeneous client data. FedAUX is a partially $$(\epsilon , \delta )$$ -differentially private method, insofar as the clients’ private data is protected in only part of the training it takes part in. This work presents a fully differentially private modification, termed FedAUXfdp. To specify the amount of noise in the applied Gaussian mechanism, we further contribute an upper bound on the $$l_2$$ -sensitivity of regularized multinomial logistic regression. In experiments with deep networks on large-scale image datasets, FedAUXfdp with strong differential privacy guarantees performs significantly better than other equally privatized SOTA baselines on non-iid client data in just a single communication round. Fully privatizing the FedAUX modification results in a negligible reduction in accuracy at all levels of data heterogeneity.
Human Parsing with Joint Learning for Dynamic mmWave Radar Point Cloud

Human sensing and understanding is a key requirement for many intelligent systems, such as smart monitoring, human-computer interaction, and activity analysis, etc. In this paper, we present mmParse, the first human parsing design for dynamic point cloud from commercial millimeter-wave radar devices. mmParse proposes an end-to-end neural network design that addresses the inherent challenges in parsing mmWave point cloud (e.g., sparsity and specular reflection). First, we design a novel multi-task learning approach, in which an auxiliary task can guide the network to understand human structural features. Secondly, we introduce a multi-task feature fusion method that incorporates both intra-task and inter-task attention to aggregate spatio-temporal features of the subject from a global view. Through extensive experiments in both indoor and outdoor environments, we demonstrate that our proposed system is able to achieve ~ 92% accuracy and ~ 84% IoU accuracy. We also show that the predicted semantic labels can increase the performance of two downstream tasks (pose estimation and action recognition) by ~ 18% and ~ 6% respectively.
Channel-Stationary Entropy Model for Multispectral Image Compression

Multispectral image have a large number of complex features. The existing network models have good compression performance for multispectral image, but the encoding and decoding time is long. In order to extract latent statistical features to guide arithmetic coding and save compression time as much as possible, a multispectral image compression framework based on channel-stationary is proposed. The multi-spectral image first go through the feature extraction network to get the feature maps, they can be down-sampling and compressed and then input to the arithmetic encoder and entropy priori module respectively. The weight features obtained by the entropy prior module feed the aid into the image features of the autoregressive module for entropy coding. The auto-regressive module can realize decoding of images, thus saving the time required for encoding and decoding. In addition, the balance between image reconstruction quality and bit rate is achieved by using distortion optimization technology. Then the image is reconstructed by inverse quantization, up-sampling and deconvolution module. The experimental results show that the method is better than JPEG2000 under the condition of similar bit rate, the compression time is shorter, and the network time complexity of the method is higher.
An Improved Chaotic Sine Cosine Firefly Algorithm for Arabic Feature Selection

Arabic documents are massively rising due to numerous contents utilized in electronic media and the web. The classification of such documents in labelled categories is a significant and vital task that deserves more attention. The main problem in Arabic text classification is how to reduce the size of the documents by selecting the most relevant features, improving the accuracy of classification. In this paper, a new variation such as chaotic sine cosine based Firefly Algorithm is proposed with hybridation of chaos theory and FA. The model is validated using kalimat Dataset, and SVM classifiers. Results reveal that the chaos-FA improve the performance of FA with an increase of 2%.
Safe Contrastive Clustering

Contrastive clustering is an effective deep clustering approach, which learns both instance-level consistency and cluster-level consistency in a contrastive learning fashion. However, the strategies of data augmentation used by contrastive clustering is an important prior knowledge such that inappropriate strategies may severely cause performance degradation. By converting the different strategies of data augmentations into a multi-view problem, we propose a safe contrastive clustering method which is guaranteed to alleviate the reliance on prior knowledge of data augmentations. The proposed method can maximize the complementary information between these different views and minimize the noise caused by the inferior views. Such a method addresses the safeness that contrastive clustering with multiple data augmentation strategies is no worse than that with one of those strategies. Moreover, we provide the theoretical guarantee that the proposed method can achieve empirical safeness. Extensive experiments demonstrate that our method can reach safe contrastive clustering on popular benchmark datasets.
Spatial Genetic Programming

An essential characteristic of brains in intelligent organisms is their spatial organization, in which different parts of the brain are responsible for solving different classes of problems. Inspired by this concept, we introduce Spatial Genetic Programming (SGP) - a new GP paradigm in which Linear Genetic Programming (LGP) programs, represented as graph nodes, are spread in a 2D space. Each individual model is represented as a graph and the execution order of these programs is determined by the network of interactions between them. SGP considers space as a first-order effect to optimize which aids with determining the suitable order of execution of LGP programs to solve given problems and causes spatial dynamics to appear in the system. RetCons are internal SGP operators which enhance the evolution of conditional pathways in SGP model structures. To demonstrate the effectiveness of SGP, we have compared its performance and internal dynamics with LGP and TreeGP for a diverse range of problems, most of which require decision making. Our results indicate that SGP, due to its unique spatial organization, outperforms the other methods and solves a wide range of problems. We also carry out an analysis of the spatial properties of SGP individuals.
The NCKU-VTF Dataset and a Multi-scale Thermal-to-Visible Face Synthesis System

We propose a multi-scale thermal-to-visible face synthesis system to achieve thermal face recognition. A generative adversarial network is constructed by one generator that transforms a given thermal face into a face in the visible spectrum, and three discriminators that consider multi-scale feature matching and high-frequency components, respectively. In addition, we provide a new paired thermal-visible face dataset called VTF that mainly contains Asian subjects captured in various visual conditions. This new dataset not only poses technical challenges to thermal face recognition, but also enables us to point out the race bias issue in current thermal face recognition methods. Overall, the proposed system achieves the state-of-the-art performance in both the EURECOM and NCKU-VTF datasets.
Real-World Application of Early Detection of Sudden Changes in Driver’s Physical Condition Using Machine Learning and Interpretation Method

In order to decrease fatal traffic accidents, early detection of sudden changes in driver’s physical condition is desired. In this paper, we reported an example of modeling process of "driver’s normal behavior model," which is a part of driver state sensing technology we are developing. By combining machine learning and domain knowledge, we have achieved the model for normal steering operation that had both high prediction accuracy and high interpretability/explainability under real-world conditions.
画像解析の最前線 ～物体検出技術の進化～

This paper presents history and evolution of object detection technologies in recent years. Since the introduction of face detection technology by P. Viola and M. Jones in the early 2000s, various object detection methods have been proposed. In particular, deep learning technology has been attracted much attention at IMAGENET Large Scale Visual Recognition Challenge 2012, many excellent object detection methods using deep learning have been proposed. Therefore, this paper focuses on object detection technologies and explains how they have evolved from the past to the present.
Low Research-Data Availability in Educational-Psychology Journals: No Indication of Effective Research-Data Policies

Research-data availability contributes to the transparency of the research process and the credibility of educational-psychology research and science in general. Recently, there have been many initiatives to increase the availability and quality of research data. Many research institutions have adopted research-data policies. This increased awareness might have raised the sharing of research data in empirical articles. To test this idea, we coded 1,242 publications from six educational-psychology journals and the psychological journal Cognition (as a baseline) published in 2018 and 2020. Research-data availability was low (3.85% compared with 62.74% in Cognition) but has increased from 0.32% (2018) to 7.16% (2020). However, neither the data-transparency level of the journal nor the existence of an official research-data policy on the level of the corresponding author’s institution was related to research-data availability. We discuss the consequences of these findings for institutional research-data-management processes.