Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM And NB Methods

Abstract The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword “Brimob” with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%.
Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification

In this work, we focus on the misalignment problem in person re-identification. Human body parts commonly contain discriminative local representations relevant with identity recognition. However, the representations are easily affected by misalignment that is due to varying poses or poorly detected bounding boxes. We thus present a two-branch Deep Joint Learning (DJL) network, where the local branch generates misalignment robust representations by pooling the features around the body parts, while the global branch generates representations from a holistic view. A Hierarchical Feature Aggregation mechanism is proposed to aggregate different levels of visual patterns within body part regions. Instead of aggregating each pooled body part features from multi-layers with equal weight, we assign each with the learned optimal weight. This strategy also mitigates the scale differences among multi-layers. By optimizing the global and local features jointly, the DJL network further enhances the discriminative capability of the learned hybrid feature. Experimental results on Market-1501 and CUHK03 datasets show that our method could effectively handle the misalignment induced intra-class variations and yield competitive accuracy particularly on poorly aligned pedestrian images.
Figure-Ground Organization in Natural Scenes: Performance of a Recurrent Neural Model Compared with Neurons of Area V2

A crucial step in understanding visual input is its organization into meaningful components, in particular object contours and partially occluded background structures. This requires that all contours are assigned to either the foreground or the background (border ownership assignment). While earlier studies showed that neurons in primate extrastriate cortex signal border ownership for simple geometric shapes, recent studies show consistent border ownership coding also for complex natural scenes. In order to understand how the brain performs this task, we developed a biologically plausible recurrent neural network that is fully image computable. Our model uses local edge detector ( B ) cells and grouping ( G ) cells whose activity represents proto-objects based on the integration of local feature information. G cells send modulatory feedback connections to those B cells that caused their activation, making the B cells border ownership selective. We found close agreement between our model and neurophysiological results in terms of the timing of border ownership signals (BOSs) as well as the consistency of BOSs across scenes. We also benchmarked our model on the Berkeley Segmentation Dataset and achieved performance comparable to recent state-of-the-art computer vision approaches. Our proposed model provides insight into the cortical mechanisms of figure-ground organization.
Deeply Supervised Z-Style Residual Network Devotes to Real-Time Environment Perception for Autonomous Driving

The visual environment perception plays an irreplaceable role for autonomous driving. Wider and deeper convolutional neural network (CNN) architectures are explored in most approaches which focus on improving performance. However, for real-world applications, the environment perception need to be accurate enough to ensure safety and fast enough to guarantee prompt control. Toward this goal, we propose a deeply supervised Z-style residual network (DSZRNet) to join object and lane detection via a unified architecture, named DSZRNet, which consists of three main parts: 1) the Z-style structure commits to fuse the feature information of shallow and deep layers for effective and robust representations; 2) the residual net is to gradually improve the location accuracy; and 3) the deeply supervised lane detection branch is grafted on the shared convolutional layers by adding only a little computational burden. Our DSZRNet is simple and can be trained end-to-end. To effectively evaluate the proposed approach, a large-scale China urban traffic (LSCUT) dataset is collected which contains objects that have a large intra-class distance because of the large-scale variance, occlusions, etc, and a variable number of lanes. Besides, the real-world computer vision KITTI dataset is also used for performance evaluation. The experimental results we obtained demonstrate that the DSZRNet gains the best tradeoff between the detection speed and detection accuracy than the other published works, and it is well suited for environment perception for autonomous driving. The download links of the LSCUT dataset will be at: https://github.com/DaiCV/LSCUT.
Towards generating scalable personalized recommendations: Integrating social trust, social bias, and geo-spatial clustering

With the advent of Web 2.0, recommender systems have become a viable means to harness relevant information online. In the past decades, extensive research have been conducted in the field of recommendations — model-based collaborative techniques being the most favored ones. Recently, a new paradigm of trust-based recommendation approach has emerged wherein structural features from social network resulted in an improved efficacy of the algorithms. However, majority of these approaches assume that users' ratings are impacted by all his social connections in friendship network and completely ignore their preferential similarity, which is essential for personalized recommendations. Herein, we address this pivotal issue and propose a two-stage clustering based matrix-factorization algorithm, 'Cluster REfinement on Preference Embedded MF (CREPE MF)' using a subgraph of social network that integrates preferential similarity score. Also, an immense surge in mobile device usage has been observed in recent times, thereby paving the way for tracking users' locations en-route to physical entity recommendations. As users' locations are geo-spatially co-located, we extend CREPE MF to Geographical CREPE MF (gCREPE MF) by incorporating geo-spatial influence. These two proposed algorithms have been systematically evaluated with state-of-the-art algorithms in terms of prediction accuracy and runtime complexity using two real-world data sets, namely Yelp and Gowalla. Gratifyingly, our approach CREPE MF outperforms the state-of-the-art algorithms; depending on the underlying data sets it achieves an improvement of 6.50% to 17.93% in accuracy and 11.67% to 74.23% in runtime. Extended model gCREPE MF further achieves 18.06% to 83.44% reduction in runtime without compromising on accuracy.
Multitask bilateral learning for real‐time image enhancement

Abstract Nowadays, deep neural networks (DNNs) for image processing are becoming more complex; thus, reducing computational cost is increasingly important. This study highlights the construction of a DNN for real‐time image processing, training various image processing operators efficiently through multitask learning. For real‐time image processing, the proposed algorithm takes a joint upsampling approach through bilateral guided upsampling. For multitask learning, the overall network is based on an encoder‐decoder architecture, which consists of encoding, processing, and decoding components, in which the encoding and decoding components are shared by all the image processing operators. In the processing component, a semantic guidance map, which contains processing information for each image processing operator, is estimated using simple linear shifts of the shared deep features. Through these components, the proposed algorithm requires an increase of only 5% in the number of parameters to add another image processing operator and achieves faster and higher performance than that of deep‐learning‐based joint upsampling methods in local image processing as well as global image processing.
Creative Flow+ Dataset

We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth. Our dataset includes 3000 animated sequences rendered using styles randomly selected from 40 textured line styles and 38 shading styles, spanning the range between flat cartoon fill and wildly sketchy shading. Our dataset includes 124K+ train set frames and 10K test set frames rendered at 1500x1500 resolution, far surpassing the largest available optical flow datasets in size. While modern techniques for tasks such as optical flow estimation achieve impressive performance on realistic images and video, today there is no way to gauge their performance on non-photorealistic images. Creative Flow+ poses a new challenge to generalize real-world Computer Vision to messy stylized content. We show that learning-based optical flow methods fail to generalize to this data and struggle to compete with classical approaches, and invite new research in this area. Our dataset and a new optical flow benchmark will be publicly available at: www.cs.toronto.edu/creativeflow/. We further release the complete dataset creation pipeline, allowing the community to generate and stylize their own data on demand.
IGE-Net: Inverse Graphics Energy Networks for Human Pose Estimation and Single-View Reconstruction

Inferring 3D scene information from 2D observations is an open problem in computer vision. We propose using a deep-learning based energy minimization framework to learn a consistency measure between 2D observations and a proposed world model, and demonstrate that this framework can be trained end-to-end to produce consistent and realistic inferences. We evaluate the framework on human pose estimation and voxel-based object reconstruction benchmarks and show competitive results can be achieved with relatively shallow networks with drastically fewer learned parameters and floating point operations than conventional deep-learning approaches.
Pattern recognition neural classifier for fall detection in rock climbing

From an athlete’s perspective, the identification of falls during rock climbing is of major importance. It constitutes a solid performance indicator, but more importantly, it could be used to trigger an instantaneous alarm to rescue teams, thus reducing the negative health consequences for the climber. In this context, an artificial neural network–based technique for fall detection during rock climbing is presented in this study. The output of this tool could be used for safety and performance monitoring purposes. The proposed method exploits a neural network for binary pattern recognition. This network is fed with a set of features extracted in real time from the acceleration and altitude signals acquired by means of a wearable device. The classifier is trained and validated with experimental datasets recorded during real climbing sessions of eight athletes through different route grades and conditions. This article illustrates the architecture of the proposed algorithm, feature extraction process, and evaluation of its accuracy. In addition, an analysis of the severity level of the detected falls is conducted. The method is able to identify real fall events with a high success rate, while yielding very few false positive indications of a fall.
Dual Security Method for Digital Image using HBV Encryption and Least Significant Bit Steganography

Abstract This study proposes a combination of two security techniques, namely steganography and cryptography. The proposed steganography technique is LSB because it has advantages in data embedding capacity and message imperceptibility. The LSB technique is very simple and predictable, so to improve message security HBV encryption techniques are applied. HBV encryption is applied to the message before pinning it. HBV encryption is a combination of the Beaufort algorithm and Vigenere cipher so that the message security will be stronger. By combining HBV and LSB encryption, the message can be hidden properly, and its awake. Based on the experimental results, the encryption and embedding process can run well and produce good quality encryption and stego images. Similarly, the extraction and decryption process can run perfectly.
Is Regularization Uniform across Linguistic Levels? Comparing Learning and Production of Unconditioned Probabilistic Variation in Morphology and Word Order

(2021). Is Regularization Uniform across Linguistic Levels? Comparing Learning and Production of Unconditioned Probabilistic Variation in Morphology and Word Order. Language Learning and Development: Vol. 17, Introduction to the Theory of Mind, pp. 158-188.
Control of quantum entanglement and entropic uncertainty in open quantum system: Via adjusting Ohmic parameter

We investigate the dynamical behaviors of quantum entanglement and quantum-memory-assisted entropic uncertainty in the dephasing noise. The influence of Ohmic parameters and placement positions of the quantum register on the dynamics of quantum entanglement and entropic uncertainty are analyzed in detail. The numerical simulations show that the influence of Ohmic parameter at low temperature can be divided into two different regions in terms of the critical value s=2.5. If s<2.5, the quantum entanglement decays monotonically with time, revealing the feature of Markovian dynamics. If s>2.5, the quantum entanglement decreases firstly and then raises and tends to a steady-state value. Thus, it is feasible to protect the quantum entanglement by choosing the super-Ohmic thermal reservoirs with s>2.5. This scheme is also beneficial to reduce entropic uncertainty. We find that, changing the positions of the quantum register do not affect the dynamics behavior and uncertainty of the system in the dephasing reservoir.
Logistic Map and Contourlet-Based Robust Zero Watermark for Medical Images

Aiming at malicious tampering and privacy protection of medical data during transmission and storage, we proposed a logistic map and contourlet transform robust zero watermark for encrypted medical images. We selected meaningful binary images as embedded watermarking. First, the original image was encrypted by using chaotic logistic sequence, and then that image was encrypted by contourlet two-level transform, the obtained low-frequency subband was constructed feature sequence after DCT transform. We used zero-watermark technology to associate the watermarking with the feature sequence, and the watermarking was embedded and extracted in the encrypted image. Our experimental results show that the proposed algorithm has good robustness against common and geometric attacks. The original image was not changed during the embedding and extraction of the watermarking. Because encrypted images and watermarking were both in encrypted domain, the embedding and extraction of watermarking can be done by a third party to improve image information security.
Sentiment Analysis on Tweets with Punctuations, Emoticons, and Negations

Social media allows people to instantly communicate and share information with each other in a public forum. Twitter is a social media website where people compose short text messages (commonly known as tweets) which contains emotions and feelings. Furthermore, Twitter is considered to be a large repository of emotions, sentiments, and moods. Prevailing studies show that analysis of sentiments can be done through machine learning. Algorithms, features, and tools may be of use to train a model to classify sentiments of a tweet. In this study, tweets which includes different parameters (emoticons, negations, and punctuations) were gathered and annotated by experts to label their sentiments. Machine learning was applied in this study in order to formulate an optimal model. The results of the experiment shows that the features included provided a significant performance in order to identify the sentiment of a given microblog statement. The algorithms used to build the model are KNN and Naive Bayes with on both English and Filipino datasets.
Personalized recommendation algorithm based on consumer psychology of local group purchase e-commerce users

The recommendation system is an important means to solve the “information overload” of e-commerce today. Consumer psychology believes that consumer psychology dominates consumer behavior, and consumer behavior is the external manifestation of consumer psychology. Therefore, the personalized recomme ndation algorithm of user consumption psychology is studied based on the specific perspective of local group-buying e-commerce. By constructing a user social relationship network, the personalized recommendation algorithm is evaluated and the final recommendation result is obtained. A personalized recommendation model is proposed based on multi-dimensional space, which is compared with the existing personalized recommendation model. The simulation results show that the improved collaborative filtering recommendation method has a large recall rate and accuracy during the daytime. And F value; when the number of recommended results is small at night, the traditional recommendation method has a slightly larger recall rate, accuracy rate and F value, but as the number of recommended results increases, the recommended effects decrease. In general, the proposed method of the recommended algorithm has a good effect. The method proposed in this paper can improve the accuracy of recommendation and partially eliminate the cold start problem of users, which has certain enlightenment for the expansion of personalized recommendation algorithm and the improvement of e-commerce user management.
Faces are Protected as Privacy: An Automatic Tagging Framework Against Unpermitted Photo Sharing in Social Media

On social platforms like Facebook, it is popular and pleasurable to share photos among friends, but it also puts other participants in the same picture in jeopardy when the photos are released online without the permission from them.To solve this problem, recently, the researchers have designed some fine-grained access control mechanisms for photos shared on the social platform.The uploader will tag each participant in the photo, then they will receive internal messages and configure their own privacy control strategies.These methods protect their privacy in photos by blurring out the faces of participants.However, there is still some defect in these strategies due to the unpredictable tagging behaviors of the uploader.Malicious users can easily manipulate unauthorized tagging processes and then publish the photos, which the participants want them to be confidential in social media.To address this critical problem, we propose a participant-free tagging system for photos on social platforms.This system excludes potential adversaries through automatic tagging processes over two cascading stages: 1) an initialization stage will be applied to every new user to collect his/her own portrait samples for future internal searching and tagging, and; 2) the remaining unidentified participants will be tagged in cooperative tagging stage by the users who have been identified in the first stage.For the system evaluation of efficiency and effectiveness, we conducted a series of experiments.The results demonstrated the tagging efficiency (96% tagging rate and 0.77s/photo tagging speed on average), photo masking and unmasking efficiency (0.13s/face on average), and the privacy preserving performance (over 90% identities in both group and individual photo are preserved).
Efficient empiricism: Streamlining teaching, research, and learning in empirical courses

The increasing importance of empirical analysis in economics highlights the need for efficient ways to bring these skills to the classroom. R Markdown is a new technology that provides a solution by integrating writing, statistical work and computation into a single document. R Markdown benefits students and instructors by streamlining teaching, research, and collaboration. The authors report on their use of R Markdown in undergraduate teaching, including core courses, electives, and senior theses. They discuss the costs and benefits of adoption, and explain the advantages of R Markdown in teaching reproducibility of empirical work, avoiding time-consuming and error-prone “cut and paste,” and facilitating a one-stop solution for importing, cleaning, manipulating, visualizing, and communicating with data.
Unpacking the “I” in GIS: Information, Ontology, and the Geographic World

As a tool dealing with information rather than matterMatters, GIS shares with other informationTechnologies technologiesInformation technologies the conceptual challenges of its medium. For a number of years now, ontology development has helped harness the complexityComplexity of the notion of information and has emerged as an effective means for improving the fitness for use of information products. More recently, the broadening range of usersUsers and user needs has led to increasing calls for “lightweight” ontologies very different in structure, expressivity, and scope from the traditional foundational or domain-oriented ones. This paper outlines a conceptual modelModels conceptual suitable for generating micro-ontologies of geographic information tailored to specific userUsers needs and purposes, while avoiding the traps of relativismRelativism that ad hoc efforts might engender. The model focuses on the notion of information decomposed into three interrelated “viewsViews”: that of measurements Measurements and formal operations Operations formal on these, that of semantics that provide the meaningMeanings, and that of the context Context within which the information is interpreted and used. Together, these three aspects enable the constructionConstruction of micro-ontologies, which correspond to userUsers-motivated selectionsSelections of measurements to fit particular, task-specific interpretationsInterpretations. The model supersedes the conceptualFrameworks conceptual framework previously proposed by the author (Couclelis, Int J Geogr Inf Sci 24(12):1785–1809, 2010), which now becomes the semantic viewViews. In its new role, the former framework allows informational threads to be traced through a nested sequence of layersLayers of decreasing semantic richness, guided by userUsers purpose. “Purpose” is here seen as both the interface between micro-ontologies and the social worldWorld social that motivates userUsers needs and perspectivesPerspectives, and as the primary principle in the selectionSelections and interpretationInterpretations of Information most appropriate for the representational task at hand. Thus, the “I” in GIS also stands for the Individual whose need the tool serves.