Reviewerly/exHarmony huggingface dataset:
A New Descriptor for Smile Classification Based on Cascade Classifier in Unconstrained Scenarios

In the development of human–machine interfaces, facial expression analysis has attracted considerable attention, as it provides a natural and efficient way of communication. Congruence between facial and behavioral inference in face processing is considered a serious challenge that needs to be solved in the near future. Automatic facial expression is a difficult classification issue because of the high interclass variability caused by the significant interdependence of the environmental conditions on the face appearance caused by head pose, scale, and illumination occlusions from their variances. In this paper, an adaptive model for smile classification is suggested that integrates a row-transform-based feature extraction algorithm and a cascade classifier to increase the precision of facial recognition. We suggest a histogram-based cascade smile classification method utilizing different facial features. The candidate feature set was designed based on the first-order histogram probability, and a cascade classifier with a variety of parameters was used at the classification stage. Row transformation is used to exclude any unnecessary coefficients in a vector, thereby enhancing the discriminatory capacity of the extracted features and reducing the sophistication of the calculations. Cascading gives the opportunity to train an extremely precise classification by taking a weighted average of poor learners’ decisions. Through accumulating positive and negative images of a single object, this algorithm can build a complete classifier capable of classifying different smiles in a limited amount of time (near real time) and with a high level of precision (92.2–98.8%) as opposed to other algorithms by large margins (5% compared with traditional neural network and 2% compared with Deep Neural Network based methods).
SemanticHash: Hash Coding Via Semantics-Guided Label Prototype Learning

In this article, we propose SemanticHash, a simple and effective deep neural network model, to leverage semantic word embeddings (e.g., BERT) in hash codes learning. Both images and class labels are compressed into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -bit binary vectors by using the visual (or the semantic) hash functions, which are jointly learned and aligned to optimize the semantic consistency. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -dimensional class label prototypes—projected from semantic word embeddings—guide the hash mapping on the image side and vice versa, creating the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -bit image hash codes being aligned with their semantic prototypes and therefore more discriminative. Extensive experimental results on four benchmarks, CIFAR10, NUS-WIDE, ImageNet, and MS-COCO datasets, demonstrate the effectiveness of our approach. We also perform studies to analyze the effects of quantization and word semantic spaces and to explain the relations among the learned class prototypes. Finally, the generalization capability of the proposed approach is further demonstrated. It achieves competitive performance in comparison with state-of-the-art unsupervised and zero-shot hashing methods. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—Hash code learning is an important technology that enables efficient image retrieval on large-scale data. While existing hashing algorithms can effectively generate compact binary codes in a supervised learning setting trained with a moderate-size dataset, they are demanding to be scalable to large datasets and do not generalize to unseen datasets. The proposed approach overcomes these limitations. Compared with state-of-the-art ones, our solution achieves 2.1% of average performance improvement on four moderate-size benchmarks and 4.7% of improvement on ImageNet, a large-scale dataset with over 1.2 M training images. With superior performance on popular benchmarks for binary hash code learning, the technology introduced performs well on cross-dataset and zero-shot (i.e., the testing concepts are unseen during training) scenarios too. Our approach attains over 17.7% of zero-shot retrieval performance improvement when compared to the state-of-the-art in the area. This article thus provides a powerful solution to utilize massive data for fast and accurate image retrieval in the big data era.
KDF: Kinodynamic Motion Planning via Geometric Sampling-Based Algorithms and Funnel Control

We integrate sampling-based planning techniques with funnel-based feedback control to develop KDF, a new framework for solving the kinodynamic motion-planning problem via funnel control. The considered systems evolve subject to complex, nonlinear, and uncertain dynamics (also known as differential constraints). First, we use a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">geometric</i> planner to obtain a high-level safe path in a user-defined extended free space. Second, we develop a low-level funnel control algorithm that guarantees safe tracking of the path by the system. Neither the planner nor the control algorithm uses information on the underlying dynamics of the system, which makes the proposed scheme easily distributable to a large variety of different systems and scenarios. Intuitively, the funnel control module is able to implicitly accommodate the dynamics of the system, allowing hence the deployment of purely geometrical motion planners. Extensive computer simulations and hardware experiments with a 6-DOF robotic arm validate the proposed approach.
Automatic Recording of the Target Location During Smooth Pursuit Eye Movement Testing Using Video-Oculography and Deep Learning-Based Object Detection

Purpose: To accurately record the movements of a hand-held target together with the smooth pursuit eye movements (SPEMs) elicited with video-oculography (VOG) combined with deep learning-based object detection using a single-shot multibox detector (SSD). Methods: The SPEMs of 11 healthy volunteers (21.3 ± 0.9 years) were recorded using VOG. The subjects fixated on a moving target that was manually moved at a distance of 1 m by the examiner. An automatic recording system was developed using SSD to predict the type and location of objects in a single image. The 400 images that were taken of one subject using a VOG scene camera were distributed into 2 groups (300 and 100) for training and validation. The testing data included 1100 images of all subjects (100 images/subject). The method achieved 75% average precision (AP75) for the relationship between the location of the fixated target (as calculated by SSD) and the position of each eye (as recorded by VOG). Results: The AP75 for all subjects was 99.7% ± 0.6%. The horizontal and vertical target locations were significantly and positively correlated with each eye position in the horizontal and vertical directions (adjusted R2 ≥ 0.955, P < 0.001). Conclusions: The addition of SSD-driven recording of hand-held target positions with VOG allows for quantitative assessment of SPEMs following a target during an SPEM test. Translational Relevance: The combined methods of VOG and SSD can be used to detect SPEMs with greater accuracy, which can improve the outcome of clinical evaluations.
Overview and Insights from Scope Detection of the Peer Review Articles Shared Tasks 2021

In the current paper, we will present the results of our shared task at The First Workshop & Shared Task on Scope Detection of the Peer Review Articles (SDPRA) collocated with PAKDD 2021. It aims to develop system(s) which can help in the peer-review process in the initial screening usually performed by the editor(s). We received four submissions in total: three from academic institutions and one from the industry. The quality of submission shows a greater interest in the task by the research community.
Self-adapting data migration in the context of schema evolution in NoSQL databases

Abstract When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.
From CNNs to Adaptive Filter Design for Digital Image Denoising Using Reinforcement Q-Learning

Multi-modal image acquisition techniques have allowed digital images to penetrate domains from micro-scale medical imaging to mega-scale satellite imaging. For postprocessing, deep learning techniques have widely been used for image denoising and artifact suppression. However, far little work has been done to summarize their effectiveness concerning adaptive filter design, e.g., salt and pepper noise, stochastic Poisson, or additive white noise. Because different images, natural or urban, structured or unstructured scenes, and objects produce different types of noise, from the modality as well as from the imaging medium, devising a single method for all noise types is impractical. This paper proposes to use reinforcement learning (Q-learning) to adaptively design filters of a convolutional neural network (CNN). In contrast to the popular state of the art methods that use filter designs based on the noise model, CNN filters lack the power to do so. We have attempted to address this limitation of CNN by introducing a new modality of reinforcement learning for adaptive filter design. The qualitative and quantitative analysis of the proposed method is done and its efficacy is demonstrated using the following evaluation metrics: Peak Signal to Noise Ratio (PSNR), Contrast to Noise Ratio (CNR), and Structure Similarity Index (SSIM).
Taking a step back for a leap forward: policy formation for the digitalisation of schools from the views of Swedish national policymakers

The modest impact of national policy efforts on school digitalisation relates to a gap between views among policy-makers and practitioners, giving rise to complexity in translating policy into action. Acknowledging changes in governing through alternative policy formation-processes, and Ward and Parr’s (2011) arguing for the importance of strategic- and operational policy coherence, the focus of this paper is the forming of a national plan of action for the digitalisation of schools in Sweden (#skolDigiplan). Within this interview study, the views on policy work and challenges of digitalisation of schools are explored among an exclusive management group of non-traditional Swedish policy-makers appointed to produce the #skolDigiplan. Based on the findings, I conclude that national policy making regarding the digitalisation of schools may be conducted through a collective process, with several educational stakeholders contributing. Furthermore, I suggest that non-traditional national policy-makers, arguing a lack of digital competence knowledge concerning schools at the governing or authority level, may consider taking a step back in the policy-formation process as a supportive action. Teacher training programmes, despite being portrayed as important for the policy outcome, were declared distant in this policy process.
Text Segmentation of Historical Arabic Handwritten Manuscripts Using Projection Profile

The research in the field of recognizing Arabic handwritten texts is flourishing with the passage of time. Despite that, progress in this field is considered insignificant and has many challenges, one of which is that the available Arabic datasets are limited. Also, due to the complexity of Arabic language, it is difficult to perform necessary preprocessing steps, such as lines and characters segmentation, especially characters segmentation since Arabic letters are usually overlapped into each other. In this study, we perform lines and characters segmentation based on Projection Profile methods (PP) which will be applied on a historical Arabic manuscript that was collected from Umm Al-Qura University's King Abdullah Bin Abdulaziz Library, Manuscripts department. The manuscript is a Moroccan Quranic manuscript that was written before more than 250 years ago. The results on lines segmentation are considered promising. On the other hand, poor results were achieved on characters segmentation due to the problem of complexity that was mentioned earlier.
Locating people in Real-World for Assisting Crowd Behaviour Analysis Using SSD and Deep SORT Algorithm

In security-sensitive areas, tracking individuals can be of high importance or may even be used to understand crowd activity, how people change their movement within the crowd or most common shops. Crowd activity trends can help to determine the most common locations of public places, which can help attract a wider audience in advertising placement. A growing concern has been public safety; it can be a harrowing experience to track these mischievous elements inside the crowd. This paper provides an approach that can map people with a geographical coordinate, which denotes an individual's realtime position in the real world through a camera feed. This data helps us to track the road, the direction of motion and where a person has turned on a map. These insights include descriptions of in-depth crowd movement that can assist with better surveillance and evaluating crowd activity.
Graph-Homomorphic Perturbations for Private Decentralized Learning

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to share raw data due to privacy concerns. Nevertheless, in the absence of additional privacy-preserving mechanisms, the exchange of local estimates, which are generated based on private data can allow for the inference of the data itself. The most common mechanism for guaranteeing privacy is the addition of perturbations to local estimates before broadcasting. These perturbations are generally chosen independently at every agent, resulting in a significant performance loss. We propose an alternative scheme, which constructs perturbations according to a particular nullspace condition, allowing them to be invisible (to first order in the step-size) to the network centroid, while preserving privacy guarantees. The analysis allows for general nonconvex loss functions, and is hence applicable to a large number of machine learning and signal processing problems, including deep learning.
Knowledge Graph construction of Thangka icon characters based on Neo4j

Thangka is a kind of Tibetan painting art, Thangka knowledge is too fragmentary, lacks integrated information generalization, and users have too few ways to understand it. In order to popularize users' knowledge of Thangka and better manage the retrieval of thangka knowledge service mode, this paper proposes the construction of the knowledge graph of Thangka graph icon characters based on Neo4j. Through constructing the Schema layer of Thangka knowledge in a top-down way, designing the data layer of Thangka knowledge graph in a bottom-up way, and using Neo4j graph database to store the data of knowledge graph. The construction of the knowledge graph of thangka icon characters can serve as the foundation for the future information retrieval, visual display and the application of knowledge questions.
A rough set fuzzy logic algorithm for visual tracking of blockchain logistics transportation labels

The development of blockchain technology has introduced new elements to the logistics service supply chain, which provides solutions to the related problems in the development at present. In this paper, mainly the relevant contents of blockchain technology are analyzed. At the same time, the composition and application of the logistics service supply chain algorithm based on the blockchain technology are clarified. A fuzzy logic algorithm logistics based on the rough set visual image label tracking method is put forward for container transportation to address the problems in the application process. The overall visual features of the container are taken as the label feature, and the image segmentation transformation method is used to extract the features from the images collected from container logistics transportation, which has provided a basis for the logistics transportation visual tracking. The background is separated based on the key feature frames of the container images collected. The separated features are recorded, and the similarity of the features is used to complete the visual tracking of logistics transportation. The experimental results suggest that the application of the proposed method to the visual tracking of container logistics transportation can improve the accuracy of container tracking.
DeepFakes Detection in Videos using Feature Engineering Techniques in Deep Learning Convolution Neural Network Frameworks

In this paper, we discuss the intermediate results of our on-going study of DeepFakes detection in videos. Our core focus is in exploitation of feature engineering as a precursor filtering technique, to the deep learning-based convolution neural network (CNN) classification frameworks. In previous research, we focused on the standard deviation of the points of interest from SIFT or Scale-Invariant Feature Transform, to detect whether visual media has been compromised with misinformation. Hence, in this new approach we rely on classical frequency analysis of images that reveals different behaviors at higher frequencies. We noticed in literature review that there lies a distinct contrast of range of frequency component that emphasize important information in images [11]. We plan to use Discrete Wavelet Transform, abbreviated as DWT, and anticipate improvement of detection accuracy when used on complex and poor-quality images of FaceForensics++. The DWT features will be input to the CNN binary classification to further analyze the performance essentially when detecting this fraudulent information and to compare the detection performance against previous research which used Scale-Invariant Feature Transform.
Landing route planning method for micro drones based on hybrid optimization algorithm

Aiming at the obstacle avoidance trajectory planning problem in the landing process of the micro drone, this paper proposes a swarm optimization algorithm that combines the dragonfly optimization method and the differential evolution method. The orthogonal learning mechanism is adopted to realize the adaptive switch between the two algorithms. In the process of landing route planning, the planning plane is first obtained by making the gliding plane tangent to the obstacle. In the planning plane, the projection of obstacle is transformed into multiple unreachable line segments. By designing an optimization model, the 3D landing route planning problem is transformed into a 2D obstacle avoidance route optimization problem. Taking the shortest route as the optimization objective, the penalty factor is introduced into the cost function to avoid the intersection of the landing route and obstacle. During the optimization process, through the orthogonal learning of the intermediate iterative results, the hybrid algorithm can adaptively select the next iterative algorithm, so it can give full play to the respective advantages of the two algorithms. The optimization results show that, compared with the single optimization algorithm, the hybrid optimization algorithm proposed in this paper can better solve the problem of landing route planning for micro-small UAVs.
The effectiveness of the digital books’ usage to improve the XII IPS 3 class students’ motivation at SMAN Pakusari Jember

Abstract This research aims to determine the effectiveness of the digital books’ usage in increasing student motivation in XII IPS 3 class at SMAN Pakusari Jember. The method used in this research is descriptive qualitative. The use of digital books in learning is useful to make it easier for students to learn and it also can reduce the weaknesses in printed books. The students feel easy and confortable to use them in learning process, so it increases the students’ motivation at XII IPS 3 of SMAN Pakusari. The results showed that the use of digital books was very effective in increasing student motivation. The score of student learning motivation before using digital books is at a value of 3.4 which is included in the medium category. After using digital books in the learning process, the student’s motivation score increased to 4.5 which is in the high category. For further research, it is hoped that researcher can use digital books with more complete features and materials than the digital books used in this study.
Enhanced Feature Pyramid Network for Semantic Segmentation

Multi-scale feature fusion has been an effective way for improving the performance of semantic segmentation. However, current methods generally fail to consider the semantic gaps between the shallow (low-level) and deep (high-level) features and thus the fusion methods may not be optimal. In this paper, to address the issues of the semantic gap between the feature from different layers, we propose a unified framework based on the U-shape encoder-decoder architecture, named Enhanced Feature Pyramid Network (EFPN). Specifically, the semantic enhancement module (SEM), edge extraction module (EEM), and context aggregation model (CAM) are incorporated into the decoder network to improve the robustness of the multilevel features aggregation. In addition, a global fusion model (GFM), which in the encoder branch is proposed to capture more semantic information in the deep layers and effectively transmit the high-level semantic features to each layer. Extensive experiments are conducted and the results show that the proposed framework achieves the state-of-the-art results on three public datasets, namely PASCAL VOC 2012, Cityscapes, and PASCAL Context. Furthermore, we also demonstrate that the proposed method is effective for other visual tasks that require frequent fusing features and upsampling.