Reviewerly/exHarmony huggingface dataset:
Ensemble learning approach for delinquency risk assessment in adolescents

Adolescent delinquency is defined as an adolescent or teenager’s tendency of committing criminal acts. Children develop more advanced reasoning patterns during the onset of adolescence (10-12 years age). Hence, there is a higher risk of development of deviance behavior in them during this period which may lead to involvement in delinquent activities in future. So, early detection of such behavior in adolescents is crucial. Due to the necessity to correlate a significant number of variables before making a final decision, human screening of these individuals by a psychologist is tough. As a result, some effective computer- assisted mechanism, such as machine learning, must be devised for the early detection of adolescents with suspicious deviant conduct utilizing quantitative analysis. Taking this problem into consideration an effort has been made to implement multiple machine learning based ensemble technique using bagged decision tree, stacked ensemble model and adaptive boosting for classification of delinquent behavior in adolescents into three groups viz. mild, moderate and severe based on the level of severity. The Indian version of the International Self-Report Delinquency Study (ISRD-3) questionnaire, along with the required regional rationalization has been used to analyze behavioral data of 126 adolescents from ten schools of Ranchi, Jharkhand, India. The simulation results on our dataset shows that the bagged ensemble model outperforms individual classifiers as well as stacked ensemble and AdaBoost meta classifiers for classification of juvenile delinquency in terms of accuracy, AUC (Area Under the Curve), Kappa Value and FI Score.
Unsupervised Domain Adaptation via Shared Content Representation for Semantic Segmentation

The training of large neural networks working with image data, as is the case for pixel-wise semantic segmentation of images, usually requires large amounts of precisely annotated training data. In addition, the training of models intended to work with examples from the real domain typically also requires training data from the same environment. The field of domain adaptation tries to tackle this approach by trying to use data from a different domain, as for example extracted from a simulation environment, to solve or at least increase the performance of certain models in the target domain. However, most of the time, no paired data from two different domains is available. This is especially true for the case of synthetic and real driving scenes. We propose a two-step training approach, extending the unsupervised MUNIT framework by a U-Net-style network to perform a semantic segmentation task trained purely using synthetic data. The domain-invariant output of the content encoder of a MUNIT image-to-image translation model created in a first training step acts, in a second step, as input encoding for the segmentation model. This second training step is a supervised one, using only synthetic labeled data which at inference is applied to real examples. Our approach is evaluated using synthetic data from GTA 5 adapted to real images from the Cityscapes dataset and is able to achieve results besting the current state of the art.
WINVC: One-Shot Voice Conversion with Weight Adaptive Instance Normalization

This paper proposes a one-shot voice conversion (VC) solution. In many one-shot voice conversion solutions (e.g., Auto-encoder-based VC methods), performances have dramatically been improved due to instance normalization and adaptive instance normalization. However, one-shot voice conversion fluency is still lacking, and the similarity is not good enough. This paper introduces the weight adaptive instance normalization strategy to improve the naturalness and similarity of one-shot voice conversion. Experimental results prove that under the VCTK data set, the MOS score of our proposed model, weight adaptive instance normalization voice conversion (WINVC), reaches 3.97 with five scales, and the SMOS reaches 3.31 with four scales. Besides, WINVC can achieve a MOS score of 3.44 and a SMOS score of 3.11 respectively for one-shot voice conversion under a small data set of 80 speakers with 5 pieces of utterances per person.
A Controller Without Control. Embedded Artificial Intelligence – A Catalyst to Reconsider the Controller/Processor Relationship of the GDPR

The current understanding of ‘controllership’ in the GDPR typically excludes device manufacturers from this role where processing of personal data occurs locally only, ie on the device without any access to the data by the manufacturer. The ‘user’ of such a device is deemed to be the only possible controller, regardless of whether the processing falls under the household exemption or not. This is because the concept of controllership is understood more narrowly than its actual definition permits. However, in particular the development of ‘smart’ devices with ‘local’ or ‘edge’ computing upends the assumption that device manufacturers automatically fall outside the scope of the GDPR. Device manufacturers may often have sufficient influence on the processing to be deemed ‘controllers’, even where personal data is processed on-device only without any direct processing by the manufacturer. This is because device manufacturers may in certain cases ‘determine the means and purposes of the processing’. This expanded understanding of the concept of controllership is supported by CJEU and Supervisory Authority case law and an interpretation of the GDPR which should be adopted by practitioners, authorities, and courts. Doing so would address the mischief that comes from categorically excluding device manufacturers from the scope of the GDPR.The CLSR published version of this paper can be found: https://doi.org/10.1016/j.clsr.2022.105762
SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection

3D object detection is a fundamental technique in autonomous driving. However, current LiDAR-based single-stage 3D object detection algorithms do not pay sufficient attention to the encoding of the inhomogeneity of LiDAR point clouds and the shape encoding of each object. This paper introduces a novel 3D object detection network called the spatial and part-aware aggregation network (SPANet), which utilizes a spatial aggregation network to remedy the inhomogeneity of LiDAR point clouds, and embodies a part-aware aggregation network that learns the statistic shape priors of objects. SPANet deeply integrates both 3D voxel-based features and point-based spatial features to learn more discriminative point cloud features. Specifically, the spatial aggregation network takes advantage of the efficient learning and high-quality proposals by providing flexible receptive fields from PointNet-based networks. The part-aware aggregation network includes a part-aware attention mechanism that learns the statistic shape priors of objects to enhance the semantic embeddings. Experimental results reveal that the proposed single-stage method outperforms state-of-the-art single-stage methods on the KITTI 3D object detection benchmark. It achieved a bird’s eye view (BEV) average precision (AP) of 91.59%, 3D AP of 80.34%, and heading AP of 95.03% in the detection of cars.
CaCL: Class-Aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns

Weakly supervised learning has been rapidly advanced in biomedical image analysis to achieve pixel-wise labels (segmentation) from image-wise annotations (classification), as biomedical images naturally contain image-wise labels in many scenarios. The current weakly supervised learning algorithms from the computer vision community are largely designed for focal objects (e.g., dogs and cats). However, such algorithms are not optimized for diffuse patterns in biomedical imaging (e.g., stains and fluorescence in microscopy imaging). In this paper, we propose a novel class-aware codebook learning (CaCL) algorithm to perform weakly supervised learning for diffuse image patterns. Specifically, the CaCL algorithm is deployed to segment protein expressed brush border regions from histological images of human duodenum. Our contribution is three-fold: (1) we approach the weakly supervised segmentation from a novel codebook learning perspective; (2) the CaCL algorithm segments diffuse image patterns rather than focal objects; and (3) the proposed algorithm is implemented in a multi-task framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) via joint image reconstruction, classification, feature embedding, and segmentation. The experimental results show that our method achieved superior performance compared with baseline weakly supervised algorithms. The code is available at https://github.com/ddrrnn123/CaCL.
Predictive Intelligence Approaches for Security Technologies

AI techniques are increasingly being used in security systems. Among them, Predictive Intelligence based approaches can also be used to define new types of security features and cryptographic protocols using perceptual or user-oriented features of authorized persons. This work will describe such the possibilities of using Predictive Intelligence technology in the development of modern data security protocols.
Handwriting Trajectory Reconstruction Using Spatial-Temporal Encoder-Decoder Network

Chinese handwriting characters have complex strokes and various writing styles, which makes it difficult to reconstruct handwriting. Aiming at this problem, we propose a handwriting reconstruction method based on a spatial-temporal encoder-decoder network with constrains. Different from other models that generate trajectory coordinates through a fully connected network, the method proposed in this paper outputs heat map sequence. The model is consists of three modules: key point detection module, spatial encoder-decoder module and reconstruction constraint module. The key point detector module and the spatial encoder part of encoder-decoder module are composed of a full convolutional network. The former generates heat maps of all key points which is a branch of the spatial encoder, and the mainly encoding the spatial information of each position on the offline image. The temporal decoder module is composed of a GRU network and an MLP network. Finally, we combine temporal information and reconstruction constraints to generate the final sequence. At each time, the features encoding by the spatial encoder module are combined with the features at the previous time that generate a corresponding heat map. The main contribution of the work of this paper is to propose a method that more suitable for handwriting reconstruction of Chinese handwritten characters. Experimental results show that the CT [] accuracy of our method has already reached 87.6% on OLHWDB1.1 dataset.
Vibrotactile feedback models to explore virtual reality without going round in circles

We propose two navigation metaphors based on a vibrotactile glove allowing semi-guided navigation in virtual environments. The objective is to allow a user who is immersed in a large virtual environment in the first person to be able to discover and explore it while orienting him towards a goal. This is a mix between two navigation modes: exploration and naïve search. This haptic modality allows the user to be oriented while leaving the visual and audio channels free to interact with the environment. The first metaphor proposes to orient the user into4 directions in relation to his/her current orientation and position. The second metaphor implements the idea of the “hot and cold” game as if the goal to be reached hides an object to be found. Both metaphors were implemented and tested with 6 users each. A post questionnaire was then used to collect their feedback and analyse the potential of the two semi-free navigation metaphors.
Detection and Classification of Prostate Cancer Using Dual-Channel Parallel Convolution Neural Network

Prostate cancer (PCa) treatment strategies and optimal diagnostic are constantly changing. The goal of the pathologists is to analyze the whole slide images (WSIs) and distinguish a malignant from a benign tumor. The pathologists must have long-term experience in the detection of biomarkers of a particular disease. Medical imaging is primarily considered as one of the most significant sources to derive information regarding the biomarkers. Therefore, to assist the pathologists and clinicians, we developed Artificial intelligence (AI) based computer-aided detection (CAD) system. This paper presents an AI technique, the dual-channel parallel convolution neural network (DCPCNN) for fine-grained detection and classification. The histological grades of PCa considered for this study are benign, grade 3, grade 4, and grade 5. Therefore, the proposed AI model aims to perform multiclass classification using multiple patterns of images extracted from WSIs of a prostate biopsy. Moreover, stain normalization was performed to standardize the intensity and contrast level of the image. DCPCNN consists of two CNNs whose convolution layer outputs are concatenated for the final classification. The overall accuracy of multiclass classification achieved by DCPCNN is 98.4% indicates that the proposed model predicted the images almost perfectly accurately. Also, the research findings, limitations of the existing method, and future scope have been described distinctly in this paper.
MDP-based Motion Planning for Grasping in Dynamic Scenarios

Path planning for robotic manipulation is a well understood topic as long as the execution of the plan takes place in a static scene. Unfortunately, for applications involving human interaction partners a dynamic obstacle configuration has to be considered. Furthermore, if it comes to grasping objects from a human hand, there is not a single goal position and the optimal grasping configuration may change during the execution of the grasp movement. This makes a continuous re-planning in a loop necessary. Besides efficiency and security concerns, such periodic planning raises the additional requirement of consistency, which is hard to achieve with traditional sampling based planners. We present an online capable planner for continuous control of a robotic grasp task. The planner additionally is able to resolve multiple possible grasp poses and additional goal functions by applying an MDP-like optimization of future rewards. Furthermore, we present a heuristic for setting edges in a probabilistic roadmap graph that improves the connectivity and keeps edge count low.
LF-MAGNet: Learning Mutual Attention Guidance of Sub-Aperture Images for Light Field Image Super-Resolution

Many light field image super-resolution networks are proposed to directly aggregate the features of different low-resolution sub-aperture images (SAIs) to reconstruct high-resolution sub-aperture images. However, most of them ignore aligning different SAI’s features before aggregation, which will generate sub-optimal light field image super-resolution results. To handle this limitation, we design a mutual attention mechanism to align the SAI’s features and propose a Light Field Mutual Attention Guidance Network (LF-MAGNet) constructed by multiple Mutual Attention Guidance blocks (MAGs) in a cascade manner. MAG achieves the mutual attention mechanism between center SAI and any surrounding SAI with two modules: the center attention guidance module (CAG) and the surrounding attention guidance module (SAG). Specifically, CAG first aligns the center-SAI features and any surrounding SAI features with the attention mechanism and then guides the surrounding SAI feature to learn from the center-SAI features, generating refined-surrounding SAI features. SAG aligns the refined-surrounding SAI feature and the original surrounding SAI feature and guides the refined surrounding SAI feature to learn from the original surrounding SAI features, generating the final outputs of MAG. With the help of MAG, LF-MAGNet can efficiently utilize different SAI features and generate high-quality light field image super-resolution results. Experiments are performed on commonly-used light field image super-resolution benchmarks. Qualitative and quantitative results prove the effectiveness of our LF-MAGNet.
A Unified Modular Framework with Deep Graph Convolutional Networks forMulti-label Image Recognition

With the rapid development of handheld photographic devices, a large number of unlabeled images have been uploaded to the Internet. In order to retrieve these images, image recognition techniques have become particularly important. As there is often more than one object in a picture, multi-label image annotation techniques are of practical interest. To enhance its performance by fully exploiting the interrelationships between labels, we propose a unified modular framework with deep graph convolutional networks (MDGCN). It consists of two modules for extracting image features and label semantic respectively, after which the features are fused to obtain the final recognition results. With classical multi-label soft-margin loss, our model can be trained in an end-to-end schema. It is important to note that a deep graph convolutional network is used in our framework to learn semantic associations. Moreover, a special normalization method is employed to strengthen its own connection and avoid features from disappearing in the deep graph network propagation. The results of experiments on two multi-label image classification benchmark datasets show that our framework has advanced performance compared to the state-of-the-art methods.
Use of Multimethods in Assessing Records Management Practices in the Judiciary of Namibia

Using more than one method of data collection or analysis in research is a well-established tradition in social sciences. The study adopted qualitative research and employed a multimethod approach for data collection. Findings revealed that the existing records management infrastructure in courts was highly inadequate. The study concluded that the current state of records management practices was likely to impede on the discharge of justice. The study recommends that, for the judiciary to improve the discharge of justice, records management should be driven by establishing a records management policy, building records management staff capacity and hiring qualified personnel, and following a top-down approach in records management with buy-in from all stakeholders involved in the processes of the justice system. Furthermore, a best practice framework for managing records in support of the discharge of justice is provided. This study contributes to the understanding between multimethod and mixed methods research.
ROC and SAT Analysis of Different Grayscale Test Images (Distractors L and Target T) to Customize a Visual-Search Attention Task

Nowadays, there is a great need to investigate the effects of fatigue on physical as well as mental performance.The issues that are generally associated with extreme fatigue are that one can easily lose one's focus while performing any particular activity whether it is physical or mental and this decreases one's motivation to complete the task at hand efficiently and successfully.In the same line of thought, myriads of research studies posited the negative effects of fatigue on mental performance, and most techniques to induce fatigue to require normally long-time and repetitive visual search tasks.In this study, a visual search algorithm task was devised and customized using performance measures such as d' (d-prime) and Speed Accuracy Trade-Off (SATF) as well as ROC analysis for classifier performance.The visual search algorithm consisted of distractors (L) and a target (T) whereby human participants had to press the appropriate keyboard button as fast as possible if they notice a target or not upon presentation of a visual stimulus.It was administered to human participants under laboratory conditions, and the reaction times, as well as accuracy of the participants, were monitored.It was found that the test image Size35Int255 was the best image to be used in terms of sensitivity and AUC (Area under Curve).Therefore, ongoing researches can use these findings to create their visual stimuli in such a way that the target and distractor images follow the size and intensity characteristics as found in this research.
The Shortcomings of the Existing Economic Theory and Their Elimination

Th e paper analyzes the state and management of country’s economics. A tuple of event-driven optimal management as a method of artifi cial intelligence has been developed. Th e characteristics of event-driven quality management of associative and structurally complex systems and processes are given. Th e events and probabilities in the management of economics and the state are considered. A measure of invalidation has been introduced for parameters. Th e method of synthesis of the probability of an event based on expert information is presented. Th e necessity of orthogonalization of the logical function and the transition to the probabilistic function have been substantiated. Th e eff ect of repeated initiating events is considered. One-dimensional optimization of the system on a logical model instead of arithmetic multiparameter optimization is presented. Schemes for managing of development and exit of economics from stagnation are given. Th e tools for event-driven quality management of systems and processes are described. Th e analysis of the shortcomings of the existing economic theory and the possibility of their elimination is carried out.
An RDF Data Management System for Conflict Casualties

In a world embroiled in armed conflicts, documenting conflict casualties is an important goal for many NGOs. Most of such documented records of casualties are however managed through internal databases, spreadsheets or Web forms. As such, exploring and querying such data becomes extremely chaotic. In this paper, we demonstrate CasualtIS, an RDF data management system for conflict casualties. Our system models conflict casualties data as RDF graphs and allows users to query such data using a SPARQL endpoint. Our system also includes a template-based natural-language querying interface to support non-expert users. Our system can be used for various purposes by end users, such as fact-checking certain claims about conflict casualties, aggregating casualties over time and location, and finding contextual information about casualties, such as the cause of death, actors involved, and other similar critical information. We demonstrate our system using two case studies, one related to casualties in the Iraqi war and the other related to casualties in the Syrian war.
Fine-Grained Element Identification in Complaint Text of Internet Fraud

Existing system dealing with online complaint provides a final decision without explanations. We propose to analyse the complaint text of internet fraud in a fine-grained manner. Considering the complaint text includes multiple clauses with various functions, we propose to identify the role of each clause and classify them into different types of fraud element. We construct a large labeled dataset originated from a real finance service platform. We build an element identification model on top of BERT and propose additional two modules to utilize the context of complaint text for better element label classification, namely, global context encoder and label refiner. Experimental results show the effectiveness of our model.