Reviewerly/exHarmony huggingface dataset:
A Bayesian nonparametric model for bounded directional data on the positive orthant of the unit sphere

Directional data appears in several branches of research. In some cases, those directional variables are only defined in subsets of the K-dimensional unit sphere. For example, in some applications, angles as measured responses are limited on the positive orthant. Analysis on subsets of the K-dimensional unit sphere is challenging and nowadays there are not many proposals that discuss this topic. Thus, from a methodological point of view, it is important to have probability distributions defined on bounded subsets of the K-dimensional unit sphere. Specifically, in this paper, we introduce a nonparametric Bayesian model to describe directional variables restricted to the first orthant. This model is based on a Dirichlet process mixture model with multivariate projected Gamma densities as kernel distributions. We show how to carry out inference for the proposed model based on a slice sampling scheme. The proposed methodology is illustrated using simulated data sets as well as a real data set.
SGCN: spatially gradient convolution network for certificate document image manipulation localization

Current tampering detection methods pay more attention to natural content images. The research on tampering algorithms for certificate document images is relatively limited, but certificate document images are the most commonly tampered with images, and they cause great harm to society. Our work presents a method for detecting certificate-like image manipulation using the ASGC-Net network. To achieve a network that can better localize text tampering cues. In addition, we propose a spatially constrained convolution that can effectively suppress image content and learn manipulation detection features by capturing different features between the neighborhood and the center of the convolution space. To increase the network's ability to capture tampering cues at multiple scales of images, we add multilayer cross-scale connections inspired by FPN networks. Experiments show that the algorithm is more accurate than general-purpose manipulation detection algorithms in locating tampered regions of certificate document images.
Quantum signature based on multi-arbitrators and product states

In this paper, based on multi-arbitrators and the product states, a new quantum signature scheme (QSS) is proposed. In our scheme, two arbitrators are used to share private keys with the signer. The signer encodes the bit message into orthogonal product states, which are locally indistinguishable. Then, to produce the signature, the signer signs the product states with the key-controlled Hadamard operator and Y operator. Our scheme has the good virtues as follows. First, our signature not only has the security property of unforgeability, but also has the property of strong non-repudiation. Second, when signing a message and verifying a signature, the partners do not need to prepare or transmit any entangled particle. Third, the partners do not need to perform the state comparison algorithm. Fourth, the signer does not need to share any private key with the signature verifier. For our scheme, the signer only shares two private keys with the arbitrators. At last, in our scheme, the repudiation can be flexibly solved. Compared with the other schemes with semi-trusted arbitrator, ours has better performances in security, practicability and efficiency.
What makes a scene? Investigating generated scene information at different visual processing stages.

In recent years, deep-learning research has produced a range of neural networks that are able to classify and generate images from a variety of stimulus domains. At the same time, advances in cross-domain analysis methods (e.g., representational similarity analysis, RSA) have allowed us to make inferences about the human visual system based on the representational spaces of these networks. State-of-the-art generative networks provide a useful testbed for probing and manipulating representations at different stages of scene generation which could help us better understand “what makes a scene?”. To investigate how well a generative network (PROGGAN) captures real-world scene gist and whether the information content of generated scenes is rich enough to trick human observers we conducted an online experiment. Participants had to detect real from generated images of scenes for varying presentation-times (50 vs. 500ms) and rate the degree of realism for each generated scene. We found that for short presentation times participants performed only slightly above chance, while for longer presentation times sensitivity (d’) increased significantly and response bias became more conservative. Interestingly, realism ratings correlated with the false alarm rate of generated images in both conditions. Combining RSA and generalized linear modelling we found that late layer DNN representational geometries served as significant predictors for the representational structure emerging from false alarm rates in the 50 ms condition. Our results imply that during a first glimpse especially high level information contained in generated scenes triggers processes in the visual system that are similar to those triggered by real-world scenes, but over time generated scene information is perceived as less realistic. Based on these explicit and implicit measures of realism, we can use network dissection on the generator at different representational stages to better understand the content and structure of real-world scene representations in the human visual system.
Hierarchical relations of objects in real-world scenes

The sources that guide attention in real-world scenes are manifold and interact in complex ways. We have been arguing for a while now that attention during scene viewing is mainly controlled by generic scene knowledge regarding the meaningful composition of objects that make up a scene (a.k.a. scene grammar). Contrary to arbitrary target objects placed in random arrays of distractors, objects in naturalistic scenes are placed in a very rule-governed manner. In this talk, I will highlight some recent studies from my lab in which we have tried to shed more light on the hierarchical nature of scene grammar. In particular, we have found that scenes can be decomposed into smaller, meaningful clusters of objects, which we have started to call "phrases". At the core of these phrases you will find so-called "anchor objects", which are often larger, stationary objects that anchor strong relational predictions about where other objects within the phrase are expected to be. Thus, within a "phrase" the spatial relations of objects are strongly defined. Manipulating the presence of anchor objects, we were able to show that both eye movements and body locomotion are strongly guided by these anchor objects when carrying out actions with naturalistic 3D settings. Overall, the data I will present will provide further evidence for the crucial role that anchor objects play in structuring the composition of scenes and thereby critically affecting visual search, object perception and the forming of memory representations in naturalistic environments.
Keynote Speaker 3: Scaling Blockchains

Blockchain is a promising and revolutionary technology that has the potential to impact almost all industry segments. However, scalability is emerging as one the key challenging issues to its wide adoption. This talk will overview existing solutions to Blockchain scalability, which can be classified into two categories: First layer and second layer solutions. First layer solutions propose modifications to the Blockchain (e.g., Blockchain structure and consensus protocol) while second layer solutions propose mechanisms that are implemented outside of the Blockchain (e.g., state channels and rollups). It will also cover the advantages and tradeoffs of the different scaling solutions.
Reflections on my remote internship with Biology Open

Sophie Johnson recounts her experience completing a remote internship at Biology Open, hosted by Editor-in-Chief Steve Kelly. Sophie is a third year BBSRC Doctoral Training Partnership (DTP) PhD student studying plant vein development at the University of Oxford. She was keen to get some experience in academic publishing and so carried out a PIPS (Professional Internships for PhD Students) placement working with Biology Open.
Trademark law, AI-driven behavioral advertising, and the Digital Services Act: toward source and parameter transparency for consumers, brand owners, and competitors

In its Proposal for a Digital Services Act, the European Commission highlighted the need for new transparency obligations to arrive at accountable digital services, ensure a fair environment for economic operators and empower consumers. However, the proposed new rules seem to focus exclusively on transparency measures for consumers. Statements addressing the interests of trade mark owners and underlying trade mark policy are sought in vain. Against this background, the analysis sheds light on AI-driven behavioural advertising practices and draws a parallel with keyword advertising cases to demonstrate that not only consumers but also trade mark owners have a legitimate interest in receiving information on the parameters that are used to target consumers. Moreover, lessons from the keyword advertising debate are important because they broaden the spectrum of policy rationales and guidelines for new transparency rules. Information on alternative offers in the marketplace, for instance, can strengthen trust in AI-driven, personalized advertising.
Creating a Low-Code Business Process Execution Platform With Python, BPMN, and DMN

How our software development team used an open source Python library and the popular modeling notation, BPMN, to shed light on a set of complex approval processes at a university. In the course of heavily extending the existing library, and developing 100’s of diagrams, we learned many lessons. Here we will provide a brief high-level introduction to BPMN and DMN and cover what we learned about BPMN training for the emerging Citizen Developer role. We will also discuss the benefits we found in the untraditional combination of BPMN and Python, and close with our current efforts and plans for continued open source development.
5 Cluster Analysis

Hierarchical Agglomerative Clustering (HAC) is likely the earliest and most flexible clustering method, because it can be used with many distances, similarities, and various linkage strategies. It is often used when the number of clusters the data set forms is unknown and some sort of hierarchy in the data is plausible. Most algorithms for HAC operate on a full distance matrix, and therefore require quadratic memory. The standard algorithm also has cubic runtime to produce a full hierarchy. Both memory and runtime are especially problematic in the context of embedded or otherwise very resource-constrained systems. In this section, we present how data aggregation with BETULA, a numerically stable version of the well known BIRCH data aggregation algorithm, can be used to make HAC viable on systems with constrained resources with only small losses on clustering quality, and hence allow exploratory data analysis of very large data sets.
Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. One experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.
REPRESENTASI DATA HASIL ANALISA SPASIAL DAERAH RAWAN PENYAKIT CAMPAK MENGGUNAKAN METODE WEIGHT PRODUCT MODEL

Measles is a part of many diseases that occur in the tropics as happened in East Java. Measles disease data recorded in the Health Profile Book contains information on tabular data on the number of measles cases, the fatality rate of measles cases, and data that contain infant measles immunization. The purpose of the discussion of this paper is to represent spatial and attribute data resulting from spatial data processing in the spatial analysis process by Weight Product Model (WPM) methods and in the Multiple Attribute Decision Making (MADM). Data representation to determine areas prone to tropical diseases based on infant immunization status, nutritional status, epidemics, and PD3I. The results of the spatial data modeling will be represented into spatial data and attribute data obtained from the preferential value of Vi with the category of classification of tropical disease-prone areas with good, average, fair, and poor immunization status.
Time and Space Measures for a Complete Graph Computation Model

We present a computation model based on a subclass of GP 2 graph programs which can simulate any off-line Turing machine of space complexity O(s(n) log s(n)) in space O(s(n)). The simulation only requires a quadratic time overhead. Our model shares this property with Sch\"onhage's storage modification machines and Kolmogorov-Uspenskii machines. These machines use low-level pointer instructions whereas our GP 2-based model uses pattern-based transformation rules and high-level control constructs.
Application of deep learning approach for recognition of voiced Odia digits

Automatic speech recognition in a regional language like Odia is a challenging field of research. Voiced Odia digit recognition helps in designing automatic voice dialler systems. In this study, a deep learning approach is used for the recognition of voiced Odia digits. The spectrogram representation of voiced samples is given as the input to the deep learning models after considering the feature extraction using MFCC. Various performance metrics are obtained by considering several experiments with different epoch sizes and variation in the dataset using the train-validate-test ratio. Experimental outcomes reveal that the CNN model provides improved accuracy of 91.72% in epoch size of 500 with a split ratio of 80-10-10 as compared to the other two models that use VSL and DNN. From the reported outcome it unravels that, the proposed CNN model has better average recognition accuracy as compared with contemporary models like HMM and SVM.
AlignVE: Visual Entailment Recognition Based on Alignment Relations

Visual entailment (VE) is to recognize whether the semantics of a hypothesis text can be inferred from the given premise image, which is one special task among recent emerged vision and language understanding tasks. Currently, most of the existing VE approaches are derived from the methods of visual question answering. They recognize visual entailment by quantifying the similarity between the hypothesis and premise in the content semantic features from multi modalities. Such approaches, however, ignore the VE's unique nature of relation inference between the premise and hypothesis. Therefore, in this paper, a new architecture called AlignVE is proposed to solve the visual entailment problem with a relation interaction method. It models the relation between the premise and hypothesis as an alignment matrix. Then it introduces a pooling operation to get feature vectors with a fixed size. Finally, it goes through the fully-connected layer and normalization layer to complete the classification. Experiments show that our alignment-based architecture reaches 72.45\% accuracy on SNLI-VE dataset, outperforming previous content-based models under the same settings.
Compulsão alimentar e fatores associados em universitários da área da saúde durante a pandemia de covid-19

Due to the covid-19 pandemic, many university students had their routine affected and their mental health weakened, consequently, many changed their habits and lifestyle, in addition to being more vulnerable to the development of eating disorders, such as binge eating. To verify the factors associated with binge eating among university students in the health area during the covid-19 pandemic. A cross-sectional, analytical and non-probabilistic sample study was carried out with university students in the health area. The research took place from October to December 2021, through the Google Forms platform. Poisson regression was performed using Stata® version 16.0. A total of 130 university students were interviewed, with a prevalence of binge eating in 31.30%, being alcohol consumption (PR: 2.01; CI: 1.04 - 3.90) and smoking (PR: 2.38; CI: 1.09 – 5.18) factors associated with binge eating. Binge eating was associated with smoking and alcohol consumption. There was also a reduction in the quality of food consumption of respondents during the covid-19 pandemic. Therefore, it is essential that public health measures are implemented in order to ensure the necessary multidisciplinary support and support to minimize the physical and psychological damage generated by the SARS-CoV-2 pandemic.
Hierarchical Long-Short Transformer for Group Activity Recognition

Group activity recognition is a challenging task in computer vision, which needs to comprehensively model the diverse spatio-temporal relations among individuals and generate group representation. In this paper, we propose a novel group activity recognition approach, named Hierarchical Long-Short Transformer (HLSTrans). Based on Transformer, it both considers long- and short-range relationship among individuals via Long-Short Transformer Blocks. Moreover, we build a hierarchical structure in HLSTrans by stacking such blocks to obtain abundant individual relations in multiple scales. By long- and short-range relation modeling in hierarchical mode, HLSTrans is able to enhance the representation of individuals and groups, leading to better recognition performance. We evaluate the proposed HLSTrans on Volleyball and VolleyTactic datasets, and the experimental results demonstrate state-of-the-art performance.
Data-Driven PMU Noise Emulation Framework using Gradient-Penalty-Based Wasserstein GAN

Availability of phasor measurement unit (PMUs) data has led to research on data-driven algorithms for event monitoring, control and ensuring stability of the grid. Unavail-ability of infrequent critical event field PMU data with component failures is driving the need to generate realistic synthetic PMU data for research. The synthetic data from power system simu-lation softwares often neglect noise profiles of received phasors, thus creating some discrepancies between real PMU data and synthetic ones. To address this issue, this work presents an initial study on the noise characteristics of PMUs, as well as presenting models for recreating their unique noise signatures. The proposed method, utilizing the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) architecture, provides an excellent benchmark for matching the noise distribution. One can use a well-learned GAN model to draw noise signatures from a distribution that seemingly mirrors the real PMU noise distribution, while also being able to be detached from the PMU data once the training is done. Based on the observed results and employed data-driven methodology, it is expected that the proposed methods can be adapted to replicate the behavior of other sensors, providing research and other applications with a tool for data synthesis and sensor characterization.
THE STUDY OF PRODUCTIVE THINKING THROUGH THE PRISM OF THE VALUE-SEMANTIC SPHERE OF PERSONALITY STUDENTS OF A PEDAGOGICAL UNIVERSITY

The article presents the results of the study of the concepts of “projection”, “value-semantic sphere”, “projective thinking”, the structure of projective thinking among students of a pedagogical university is established and recreated. In the empirical part of the study, the author notes that undergraduate students have extremely low internal support, which is expressed in a high degree of dependence, conformity, lack of independence of the subject (“externally directed” personality), a high external locus of control. In the studied group of students, part of the first and second hypotheses of our study were confirmed: the more holistic the images obtained from the subject in the projective assessment, the higher his indicators of goal-setting, locus-control of the Ego in assessing the value-semantic sphere of personality; the more positive the images obtained from the subject in the projective assessment, the higher the indicators of synergy when assessing the self-ctualization of the individual. Also, in the studied group of students, part of the first and second hypotheses of our study were confirmed: the more holistic the images obtained from the subject in the projective assessment, the higher his indicators of goal-setting, locus-control of the Ego in assessing the value-semantic sphere of personality; the more positive the images obtained from the subject in the projective assessment, the higher the indicators synergies in the assessment of self-actualization of personality.