Reviewerly/exHarmony huggingface dataset:
Improving Slice-Based Model for Person Re-ID with Multi-Level Representation and Triplet-Center Loss

Person Re-Identification has received extensive study in the past few years and achieves impressive progress. Recent outstanding methods extract discriminative features by slicing feature maps of deep neural network into several stripes. Still there have improvement on feature fusion and metric learning strategy which can help promote slice-based methods. In this paper, we propose a novel framework that is end-to-end trainable, called Multi-level Slice-based Network (MSN), to capture features both in different levels and body parts. Our model consists of a dual-branch network architecture, one branch for global feature extraction and the other branch for local ones. Both branches process multi-level features using pyramid feature alike module. By concatenating the global and local features, distinctive features are exploited and properly compared. Also, our proposed method creatively introduces a triplet-center loss to elaborate combined loss function, which helps train the joint-learning network. By demonstrating the comprehensive experiments on the mainstream evaluation datasets including Market-1501, DukeMTMC, CUHK03, it indicates that our proposed model robustly achieves excellent performance and outperforms many of existing approaches. For example, on DukeMTMC dataset in single-query mode, we obtain a great result of Rank-1/mAP =85.9%(+1.0%)/74.2%(+4.7%).
Freedom of Expression in the Age of Online Platforms: Operationalising a Human Rights-Based Approach to Content Moderation

In today’s digital public sphere, individuals have little choice but to participate on online platforms, whose design choices shape what is possible, content policies influence what is permissible, and personalization algorithms determine what is visible. Ensuring that online content moderation is aligned with the public interest has emerged as one of the most pressing challenges for freedom of expression in the twenty-first century. Taking this challenge as its focus, this Article examines the promise and pitfalls of a human rights-based approach to content moderation—with a specific focus on the choices and challenges that online platforms are likely to confront in adhering to their corporate responsibility to respect human rights in this context. The Article examines three dimensions of a human rights-based approach to platform moderation in particular: a substantive dimension, encompassing the alignment of content moderation rules with international human rights law; a process dimension, encompassing the standards of transparency and oversight that platforms should implement as part of their human rights due diligence processes; and a procedural-remedial dimension, encompassing the procedural guarantees and remediation mechanisms that platforms should integrate within their systems of content moderation. The Article concludes by reflecting on some of the limits of the human rights-based approach and cautioning against viewing human rights as a panacea.
A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results

Segmentation convolutional neural networks (SCNNs) are now popular for the semantic segmentation (i.e., dense pixel-wise labeling) of remote sensing imagery, such as color or hyperspectral satellite imagery. One desirable property of SCNNs when applied to remote sensing problems is rotational equivariance. This property implies that the class label assigned to a particular pixel (building, road, etc.) does not change if the input imagery is rotated by an arbitrary angle. We argue that recently proposed methods to make rotational equivariant SCNNs fall into two broad categories: easily employed methods that are somewhat ineffective, and highly effective methods that are complicated and potentially incompatible with state-of-the-art SCNN techniques. We propose a simple addition to the standard SCNN loss function that encourages the SCNN to be rotationally equivariant, and is easily added to modern SCNNs. We test the method on the Inria building labeling dataset and compare it to the popular simple approach of adding random rotational augmentations of the input imagery during training. We show that the proposed approach (i) achieves improved equivariance and (ii) yields performance improvements on average.
Playing Well on the Data FAIRground: Initiatives and Infrastructure in Research Data Management

Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices. In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix. Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.
A Hybrid Model for Fraud Detection on Purchase Orders

Frauds on the purchasing area impacts companies all around the globe. One of the possibilities to tackle this issue is through the usage of audits, however, due to the massive volume of the data available today, it is becoming impossible to manually check all the transactions of a company, hence only a small sample of the data is verified. This work presents a new approach through the usage of signature detection with clustering techniques to increase the probability of inclusion of fraud-related documents in sample sets of transactions to be audited. Due to a non-existence of a public database related to the purchase area of companies for fraud detection, this work uses real procurement data to compare the probability of selecting a fraudulent document into a data sample via random sampling versus the proposed model as well as exploring what would be the best clustering algorithm for this specific problem. The proposed model improves the current state-of-the-art since it does not require pre-classified datasets to work, is capable of operating with a very high number of data records and does not need manual intervention. Preliminary results show that the probability of including a fraudulent document on the sample via the proposed model is approximately seven times higher than random sampling.
Experts or Dummies? Quality of E-Book Pool and User Selections in a Consortial Demand Driven Acquisition Program

Academic libraries are increasingly purchasing electronic books (e-books) via demand driven acquisitions (DDA) programs. However, there is no guarantee about the quality of DDA titles. This is especially true for consortially managed DDA pools or when pools include all titles from selected publishers. This study analyzes data from EBSCO’s GOBI acquisitions platform to assess the quality of the pool and purchased titles from the Colorado Alliance of Research Libraries (CARL) publisher-based DDA program. Results showed that most available and selected titles were appropriate for academic libraries. Popular and lower level academic titles made up a relatively small portion of the DDA pool but were selected at a proportionally higher rate than other titles. The DDA pool was weighted towards titles that had been previously purchased by few GOBI libraries, but users tended to select titles that had been purchased by more GOBI libraries. Implications of these results are discussed from the point of view of a consortium member library using the DDA program as a supplement to its broader collections of print and electronic books.
IMPLEMENTASI METODE BATU PIJAR DALAM PEMBELAJARAN MATEMATIKA UNTUK MENINGKATKAN AKTIFITAS DAN HASIL BELAJAR SISWA SD NEGERI 47 KOTA JAMBI

This research was conducted with the aim to find empirical evidence about the impact of the application of the Batu Pijar method in learning mathematics to learning activities and student achievement in learning mathematics. This research was conducted following a class action research procedure with the Kemmis and Taggart design. The subjects of this study were students of class VI C of Elementary School 47 Jambi City in the even semester of the 2018/2019 school year consisted of 30 people. The learning outcomes using the Batu Pijar method show that there was an increase in student learning activities. Positive results on student learning activities in mathematics can be seen from the tendency of increasing student learning activities in each cycle. in the first cycle (with an average of 9.33), at the time before the application of the Batu Pijar method the result was 12.4 in the first cycle and continued to increase to 13.17 in the second cycle, a good classification with an average of 14.67 in the cycle III and also with good classification with an average of 14.6 in the IV cycle.. Likewise student achievement has increased in terms of average and mastery learning. Average learning outcomes increased by 2.51% (from 79.7 to 81.7) and mastery learning increased by 11.11% (from 90% to 100%). Based on these results it can be said that the implementation of the Batu Pijar method in learning mathematics, contributes positively in increasing learning activities and student achievement in mathematics
Hate Speech by Supporters of Indonesian Presidential Candidates on Social Media

The phenomenon of hate utterances for the support of Indonesian presidential candidates and vice-presidential candidates in the 2019-2024 period on social media is very interesting. The people seem tosplit into two camps or hostile groups supporting each of their chosen candidates. Social media is an effective means of communicating the expressions of support. There were various forms of utterances of support to the pairs of presidential candidate well as utterances ofhatred and incitement for the unwanted candidates. The phenomenon of the emergence of hated speech by supporters of each pair can trigger an attitude of hostility and division of the nation. The purpose of this study is to describe and analyze the form of hate speechexpressed by the people supporting each of the presidential candidates. The research method used is descriptive with a qualitative approach. The data is in the form of speech or public statements containing hatred, incitement, provocation, and insults. The datasources are from Facebook and Instagram, and also from news portals that upload images and videos. Then the data is analyzed using contextual analysis. The results of the study show that each of the supporters of the presidential candidate has used forms of speechthat are insulting, blasphemous, defamation, defamation to unwanted potential candidates and fellow supporters. Meanwhile, each supporter gave flattery, praise, and support to the presidential candidates he chose.
A Framework for Parallelizing Hierarchical Clustering Methods

Hierarchical clustering is a fundamental tool in data mining, machine learning and statistics. Popular hierarchical clustering algorithms include top-down divisive approaches such as bisecting k-means, k-median, and k-center and bottom-up agglomerative approaches such as single-linkage, average-linkage, and centroid-linkage. Unfortunately, only a few scalable hierarchical clustering algorithms are known, mostly based on the single-linkage algorithm. So, as datasets increase in size every day, there is a pressing need to scale other popular methods. We introduce efficient distributed algorithms for bisecting k-means, k-median, and k-center as well as centroid-linkage. In particular, we first formalize a notion of closeness for a hierarchical clustering algorithm, and then we use this notion to design new scalable distributed methods with strong worst case bounds on the running time and the quality of the solutions. Finally, we show experimentally that the introduced algorithms are efficient and close to their sequential variants in practice.
Improving Transformer Models by Reordering their Sublayers

Multilayer transformer networks consist of interleaved self-attention and feedforward sublayers. Could ordering the sublayers in a different pattern lead to better performance? We generate randomly ordered transformers and train them with the language modeling objective. We observe that some of these models are able to achieve better performance than the interleaved baseline, and that those successful variants tend to have more self-attention at the bottom and more feedforward sublayers at the top. We propose a new transformer pattern that adheres to this property, the sandwich transformer, and show that it improves perplexity on multiple word-level and character-level language modeling benchmarks, at no cost in parameters, memory, or training time. However, the sandwich reordering pattern does not guarantee performance gains across every task, as we demonstrate on machine translation models. Instead, we suggest that further exploration of task-specific sublayer reorderings is needed in order to unlock additional gains.
Future-Proof Human Capabilities: Raising the Future Employability of Graduates

While many frameworks have emerged purporting to describe human skills, soft skills, employability skills, or future capabilities, there exists sparse research comparing these frameworks or isolating the most important skills within an Australasian future workforce context.Given the importance of using a skills framework to better analyse and predict future skills demand in the workforce, educators, employers and recruits need to better appreciate the value certain skills will hold over others in terms of future employability.Using a data-driven approach based on text analysis we will analyse the aggregate information contained in existing frameworks to discover the future capabilities.These future capabilities provide a framework and logic that allow us to more flexibly examine the relationship between people and jobs, and to create a taxonomy that allows us to create capability stacks that confirm the requirements for a future learning and work.
Smart Panel System using Internet of Things

Technological developments that require humans to constantly innovate, utilize electricity in the community, assist the public about the dangers that exist.Electrical energy becomes very dangerous if the user process is not equipped with a good protection system.Conventional overload protection devices, such as MCB, can cut off electricity only from load points and cannot cut selective loads.Today's internet technology can be used not only for communication between individuals but also for communication between objects.By using the internet, devices can send data to each other and send requests to other devices.The Internet-based Smart Panel Is designed to protect components, and monitor the remote use of electrical energy and control equipments via a smartphone.This tool uses NODEMCU and ESP12 for wireless communication.The PZEM004T sensor module is used as a current load reader.
Subjective speckle suppression for 3D measurement using one-dimensional numerical filtering

Laser speckle projection is a reliable method to generate statistical illumination patterns for 3D reconstruction purposes as in stereo photogrammetry. This type of pattern has several advantages compared to incoherent methods. However, the biggest disadvantage is given by the coherent noise, the so-called "subjective speckles," developing when a coherently illuminated surface is imaged by a lens system. Some experimental techniques have been published already, being costly in measurement time or leading to loss in light intensity and/or depth of field. In this work we want to present numerical one-dimensional filtering techniques that reduce this kind of noise and increase the performance of 3D reconstruction, while no experimental changes to the classical speckle projection technique have to be made. Therefore, a model describing the expectable contrast reduction is derived, the dependency between filter orientation and setup geometry is investigated, and results from simulations and real experiments are shown. It is found that for small filter sizes the results can be improved independent of the filter, but that in the general case a vertical orientation of the filter towards the setup geometry is most useful.
Learning Perceptual Inference by Contrasting

"Thinking in pictures," [1] i.e., spatial-temporal reasoning, effortless and instantaneous for humans, is believed to be a significant ability to perform logical induction and a crucial factor in the intellectual history of technology development. Modern Artificial Intelligence (AI), fueled by massive datasets, deeper models, and mighty computation, has come to a stage where (super-)human-level performances are observed in certain specific tasks. However, current AI's ability in "thinking in pictures" is still far lacking behind. In this work, we study how to improve machines' reasoning ability on one challenging task of this kind: Raven's Progressive Matrices (RPM). Specifically, we borrow the very idea of "contrast effects" from the field of psychology, cognition, and education to design and train a permutation-invariant model. Inspired by cognitive studies, we equip our model with a simple inference module that is jointly trained with the perception backbone. Combining all the elements, we propose the Contrastive Perceptual Inference network (CoPINet) and empirically demonstrate that CoPINet sets the new state-of-the-art for permutation-invariant models on two major datasets. We conclude that spatial-temporal reasoning depends on envisaging the possibilities consistent with the relations between objects and can be solved from pixel-level inputs.
Person re-identification with effectively designed parts

Person re-IDentification (re-ID) is an important research topic in the computer vision community, with significance for a range of applications. Pedestrians are well-structured objects that can be partitioned, although detection errors cause slightly misaligned bounding boxes, which lead to mismatches. In this paper, we study the person re-identification performance of using variously designed pedestrian parts instead of the horizontal partitioning routine typically applied in previous hand-crafted part works, and thereby obtain more effective feature descriptors. Specifically, we benchmark the accuracy of individual part matching with discriminatively trained Convolutional Neural Network (CNN) descriptors on the Market-1501 dataset. We also investigate the complementarity among different parts using combination and ablation studies, and provide novel insights into this issue. Compared with the state-of-the-art, our method yields a competitive accuracy rate when the best part combination is used on two large-scale datasets (Market-1501 and CUHK03) and one small-scale dataset (VIPeR).
You Only Move Once: An Efficient Convolutional Neural Network for Face Detection

Our ``You Only Move Once”(YOMO) detector based on depthwise separable convolutions is a single stage face detector that balances accuracy and latency. YOMO performs scale-invariantly by utilizing top-down architecture with feature agglomeration, and multiple detection modules instead of in an image pyramid approach. At the same time, we propose a semi-soft random cropping algorithm that enables different detection module adequately trained by different scales of samples. Several experiments are conducted on the FDDB dataset with discrete and continuous measures, to demonstrate that the methods have strongly competitiveness results. After using an ellipses regressor, the recall rates reached a satisfactory 97.59% and 83.66%, respectively. Surprisingly, YOMO has only 21 million parameters and achieves superior performance with 51 frames per second (FPS) for a 544 × 544 input image on a GPU.
Trackify: A Robust System For Preserving Money Transactions

Money exchange is one of the most ordinary days to day activities done by humans in the daily market. This paper presents an approach for money tracking over a Blockchain. This approach consists of three core components: serial localization, serial recognition, and Blockchain to store all transactions and ownership transfers. The system was examined with a total of 110 banknotes with different currency types and achieved an average accuracy of 91.17%.
Direct Optical-Flow-Aware Computational Framework for 3D Reconstruction

In this paper, a direct computational method is presented which combines optical flow and structure from motion (SfM) by putting the SfM problem in the framework of optical flow estimation. In other word, the optical flow is reparametrized in term of the camera's motion and scene's depth, resulting in a similar variation optimization as in optical flow estimation. Meanwhile, three techniques are proposed to improve the accuracy and robustness of the direct approach, including the fast guided interpolation (FGI), the left-right consistency constraint and the soft segment constraint. Experimental results on the Middlebury dataset and KITTI2012 dataset show that the proposed approach can achieve highly-accurate 3D reconstruction with the dense and smooth surface which results in a state-of-the-art performance in optical flow.
"Let's date!" A 360-degree video application to support foreign language learning

New technologies are changing the way of learning foreign languages. However, one of the main challenges for software developers and mobile assisted language learning designers remains the creation of learning environments for students’ language immersion in and outside the classroom. This paper describes the design and evaluation of a VR-based mobile app called Let’s date!. The app enables Common European Framework of Reference for languages (CEFR) A1 level German language learners to interact with an immersive environment and to practise several language skills. The results have proven that the use of 360° videos based on realistic situations and combined with a conversational agent is suitable to reinforce students’ foreign language competencies.
Narrative Planning Model Acquisition from Text Summaries and Descriptions

AI Planning has been shown to be a useful approach for the generation of narrative in interactive entertainment systems and games. However, the creation of the underlying narrative domain models is challenging: the well documented AI planning modelling bottleneck is further compounded by the need for authors, who tend to be non-technical, to create content. We seek to support authors in this task by allowing natural language (NL) plot synopses to be used as a starting point from which planning domain models can be automatically acquired. We present a solution which analyses input NL text summaries, and builds structured representations from which a pddl model is output (fully automated or author in-the-loop). We introduce a novel sieve-based approach to pronoun resolution that demonstrates consistently high performance across domains. In the paper we focus on authoring of narrative planning models for use in interactive entertainment systems and games. We show that our approach exhibits comprehensive detection of both actions and objects in the system-extracted domain models, in combination with significant improvement in the accuracy of pronoun resolution due to the use of contextual object information. Our results and an expert user assessment show that our approach enables a reduction in authoring effort required to generate baseline narrative domain models from which variants can be built.