Reviewerly/exHarmony huggingface dataset:
Bias-Variance Decomposition for Ranking

In machine learning and statistics, bias and variance of supervised learning models are well studied concepts. In this work, we try to better understand how these concepts apply in the context of ranking of items (e.g., for web-search or recommender systems). We define notions of bias and variance directly on pairwise ordering of items. We show that ranking disagreements between true orderings and a ranking function can be decomposed into bias and variance components akin to the similar decomposition for the squared loss and other losses that have been previously studied. The popular ranking metric, the area under the curve (AUC), is shown to admit a similar decomposition. We demonstrate the utility of the framework in understanding differences between models. Further, directly looking at the decomposition of the ranking loss rather than the loss used for model fitting can reveal surprising insights. Specifically, we show examples where model training achieves low variance in the traditional sense, yet results in high variance (and high error) on the ranking task.
SUKE: Embedding Model for Prediction in Uncertain Knowledge Graph

Graph embedding models are widely used in knowledge graph completion (KGC) task. However, most models are based on the assumption that knowledge is completely certain, and this is inconsistent with real-world situations. Although there are multiple studies on uncertain knowledge embedding tasks, they often use knowledge confidence to learn embedding and cannot make full use the structural and uncertain information of knowledge. This paper presents a new embedding model named Structural and Uncertain Knowledge Embedding (SUKE), which comprises two components: an evaluator and a confidence generator. For unknown triples, the evaluator learns the structural and uncertain information to evaluate its rationality and obtain a candidate set. The confidence generator then determines the confidence of the candidate set to achieve KGC. To verify the effectiveness of the proposed model, confidence prediction, triple evaluation, and fact classification tasks are performed on three data sets. Experimental results show that SUKE performs better than mainstream embedding methods. The model proposed in this paper can help advance the research on the embedding of uncertain knowledge graphs.
Batch Verification for Statistical Zero Knowledge Proofs

A statistical zero-knowledge proof ( $$\mathsf {SZK}$$ ) for a problem $$\varPi $$ enables a computationally unbounded prover to convince a polynomial-time verifier that $$x \in \varPi $$ without revealing any additional information about x to the verifier, in a strong information-theoretic sense. Suppose, however, that the prover wishes to convince the verifier that k separate inputs $$x_1,\dots ,x_k$$ all belong to $$\varPi $$ (without revealing anything else). A naive way of doing so is to simply run the $$\mathsf {SZK}$$ protocol separately for each input. In this work we ask whether one can do better – that is, is efficient batch verification possible for $$\mathsf {SZK}$$ ? We give a partial positive answer to this question by constructing a batch verification protocol for a natural and important subclass of $$\mathsf {SZK}$$ – all problems $$\varPi $$ that have a non-interactive $$\mathsf {SZK}$$ protocol (in the common random string model). More specifically, we show that, for every such problem $$\varPi $$ , there exists an honest-verifier $$\mathsf {SZK}$$ protocol for batch verification of k instances, with communication complexity $$\mathsf {poly}(n) + k \cdot \mathsf {poly}(\log {n},\log {k})$$ , where $$\mathsf {poly}$$ refers to a fixed polynomial that depends only on $$\varPi $$ (and not on k). This result should be contrasted with the naive solution, which has communication complexity $$k\cdot \mathsf {poly}(n)$$ . Our proof leverages a new $$\mathsf {NISZK}$$ -complete problem, called Approximate Injectivity, that we find to be of independent interest. The goal in this problem is to distinguish circuits that are nearly injective, from those that are non-injective on almost all inputs.
Applying ASP for Knowledge-Based Link Prediction With Explanation Generation in Feature-Rich Networks

Link prediction is challenging, especially based on (scarce) historic data or in cold start scenarios. In this paper, we show how to apply answer set programming (ASP) for formalizing link prediction in feature-rich networks, that is - in particular - using domain knowledge for network (and graph) analysis. We show, that applying ASP for link prediction provides a powerful declarative approach, as exemplified using simple predictors, and demonstrate according explanation generation using ASP. We present the application of the proposed methodological approach for explicative link prediction and analysis with explanation generation using different datasets. Keywords. Explainable AI, social network analysis, link prediction, answer set programming, Prolog, domain knowledge
Not All Samples are Trustworthy: Towards Deep Robust SVP Prediction

In this paper, we study the problem of estimating subjective visual properties (SVP) for images, which is an emerging task in Computer Vision. Generally speaking, collecting SVP datasets involves a crowdsourcing process where annotations are obtained from a wide range of online users. Since the process is done without quality control, SVP datasets are known to suffer from noise. This leads to the issue that not all samples are trustworthy. Facing this problem, we need to develop robust models for learning SVP from noisy crowdsourced annotations. In this paper, we construct two general robust learning frameworks for this application. Specifically, in the first framework, we propose a probabilistic framework to explicitly model the sparse unreliable patterns that exist in the dataset. It is noteworthy that we then provide an alternative framework that could reformulate the sparse unreliable patterns as a "contraction" operation over the original loss function. The latter framework leverages not only efficient end-to-end training but also rigorous theoretical analyses. To apply these frameworks, we further provide two models as implementations of the frameworks, where the sparse noise parameters could be interpreted with the HodgeRank theory. Finally, extensive theoretical and empirical studies show the effectiveness of our proposed framework.
A Deep Learning Based Spatial Super-Resolution Approach for Light Field Content

One of the main issues encountered when working with Light Field technology is the trade-off between spatial and angular resolution. Various approaches have been presented to super-resolve light field in the spatial dimension. The challenge with current techniques is that they underuse the full potential of the information in the light field, as they process each sub-aperture image separately and do not consider disparity consistency. In this paper, we present a novel method for Light Field spatial super-resolution using the full four-dimensional light field spatial and angular information. We propose a learning-based model for spatial super-resolution which considers simultaneously all the sub-aperture images and also takes advantage of the epipolar image plane (EPI) information to ensure smooth disparity between the generated views and in turn construct high spatial resolution light field sub-aperture images. Experimental results show that our proposed method outperforms state-of-the-art light field super-resolution techniques.
A Simulation-based Comparison between Industrial Autoscaling Solutions and COCOS for Cloud Applications

Dynamic resource allocation is the mechanism that allows one to change the resources associated with applications at runtime and match their actual needs. The autoscaling solutions offered by cloud infrastructures are probably the most widely-used incarnation of this concepts. Originally conceived to manage virtual machines according to user-defined rules, they are now much more sophisticated and can also allocate containers (lighter than virtual machines). This paper surveys the autoscaling solutions provided by the major cloud vendors and analyzes the services they provide. It also compares them against the solution we developed, called COCOS autoscaling. We simulated the different proposals and fed them with diverse workloads. Obtained results show that COCOS autoscaling outperforms its competitors in most of the cases: it optimizes resource allocation and keeps applications' response times under set thresholds.
Planning and Evaluation of Robotic Solutions in a Logistic Line Through Augmented Reality

The planning and evaluation of robotics solutions in logistic environments is a time consuming process. It usually requires to plan on paper or with 2D CAD software the setup and flow of the parts in a line, with the need to buy and set up expensive equipment in order to test and validate the planned solution. Augmented Reality (AR) is a promising technology for these applications. The required equipment and parts can be simulated in order to evaluate the system beforehand, computing the resulting performance information automatically. The simulated hardware can be superimposed at the desired position in the real scenario, giving the user a more precise information on how the components in the setup will behave. In this work we propose an AR system to plan and configure a line consisting of a robot and a conveyor belt. The proposed system architecture allows to adapt the behavior of the existing hardware components accordingly to the changes introduced in the simulation. The performances of the picking task are computed by the system and visualized to the user, as well as the simulated objects. A GUI allows the worker to change and adjust the speed of the robotics components and the position and flow of the parts. An additional robot can be placed and visualized in order to evaluate the improvements in the line performance deploying additional equipment.
Kawi Character Recognition on Copper Inscription Using YOLO Object Detection

Inscriptions are an important source for learning historical events that occurred in the past. Since the middle of the 8th century AD, in the era of the Indonesian kingdom, inscriptions were written using Kawi character. The problem of reading Kawi character on inscriptions occurs when the media used is experiencing interference. This research focuses on copper inscriptions and uses YOLO object detection to recognize Kawi character on copper inscriptions, especially those with patina. Dataset used for the training in this research were 1518 images (1311 train, 207 validation) that comes from 69 original images of Warungahan Inscription using data augmentation. This research using 657 classes in total. In the training, tested on the validation images, the model obtained a mAP of 97.17% (threshold =0.4). In the test, tested on test images, the model recognized Kawi character on the Warungahan Inscriptions properly, with an accuracy of97.93% (threshold =0.4).
A Feature Selection Method Based on Improved Genetic Algorithm

Fault diagnosis is an important research topic in the field of reliability, but with the process of industrialization, the amount of data becomes huge, its essential to feature selection before data analysis. This paper proposes a feature selection method based on improved genetic algorithm (GA). First sorts the features by feature evaluation criteria to exclude irrelevant features. In this way, the dimension of the features is reduced, and then based on the simulated annealing algorithm (SA) to solve the problem that the GA jumps out of the local optimal solution. then, using the SVM for fault classification verified the effectiveness of the method based on the Tennessee Eastman (TE) data set. The results show that this method has better applicability than the traditional genetic algorithm for feature selection.
Ten quick tips for making things findable

The distribution of scholarly content today happens in the context of an immense deluge of information found on the internet. As a result, researchers face serious challenges when archiving and finding information that relates to their work. Library science principles provide a framework for navigating information ecosystems in order to help researchers improve findability of their professional output. Here, we describe the information ecosystem which consists of users, context, and content, all 3 of which must be addressed to make information findable and usable. We provide a set of tips that can help researchers evaluate who their users are, how to archive their research outputs to encourage findability, and how to leverage structural elements of software to make it easier to find information within and beyond their publications. As scholars evaluate their research communication strategies, they can use these steps to improve how their research is discovered and reused.
Human head pose estimation based on HF method

This Paper addresses the problem of head pose estimation. Driving assistance technology utilizes head pose estimation as an indicator for visual focus and mental attention of the driver. Head pose estimation detects head orientation with respect to the camera. Model based and appearance-based methods are the two approaches in head pose estimation. The first approach uses the facial features to create a face geometrical models whereas the second method only takes into consideration the entire face image. The proposed appearance-based method work is performed using Hough transform and random forest to classify ninety-three classes of Hough values in order to find the exact head pose. The performance of the proposed work is evaluated based on accuracy and the time taken to detect the head pose. The paper outperforms many other previous works.
Combatting hate and trolling with love and reason? A qualitative analysis of the discursive antagonisms between organized hate speech and counterspeech online

An increasingly organized culture of hate is flourishing in today’s online spaces, posing a serious challenge for democratic societies. Our study seeks to unravel the workings of online hate on popular social media and assess the practices, potentialities, and limitations of organized counterspeech to stymie the spread of hate online. This article is based on a case study of an organized “troll army” of online hate speech in Germany, Reconquista Germanica, and the counterspeech initiative Reconquista Internet. Conducting a qualitative content analysis, we first unpack the strategies and stated intentions behind organized hate speech and counterspeech groups as articulated in their internal strategic documents. We then explore how and to what extent such strategies take shape in online media practices, focusing on the interplay between users spreading hate and users counterspeaking in the comment sections of German news articles on Facebook. The analysis draws on a multi-dimensional framework for studying social media engagement (Uldam &amp; Kaun, 2019) with a focus on practices and discourses and turns to Mouffe’s (2005) concepts of political antagonism and agonism to operationalize and deepen the discursive dimension. The study shows that the interactions between the two opposing camps are highly moralized, reflecting a post-political antagonistic battle between “good” and “evil” and showing limited signs of the potentials of counterspeech to foster productive agonism. The empirical data indicates that despite the promising intentions of rule-guided counterspeech, the counter efforts identified and scrutinized in this study predominantly fail to adhere to civic and moral standards and thus only spur on the destructive dynamics of digital hate culture.
Performance Evaluation of Analytics Models for Trends Analysis of News

Abstract Microblogging services, especially Twitter, allow the user to share their most recent thoughts, feelings or news freely and almost immediately. Hence, the number of news tweets generated by the news media is increasing exponentially. Mining the valuable data from the large volume of tweets can help increase the revenue of organisations by allowing them to engage with the public faster and better by responding to the latest topics of interest. In this work, mining the hot keywords and being able in classifying the news tweets, trending topic and keywords in the news tweets. Both supervised and unsupervised machine learning models are used. Several machine learning algorithms are being used to compare the accuracy in classifying the tweets.
Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study

Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning. Its prevalence on the web, however, is still not well understood - if we run GPT-2 detectors across the web, what will we find? Our work is twofold: firstly we demonstrate via human evaluation that classifiers trained to discriminate between human and machine-generated text emerge as unsupervised predictors of "page quality", able to detect low quality content without any training. This enables fast bootstrapping of quality indicators in a low-resource setting. Secondly, curious to understand the prevalence and nature of low quality pages in the wild, we conduct extensive qualitative and quantitative analysis over 500 million web articles, making this the largest-scale study ever conducted on the topic.
A Blockchain Technology Approach for the Security and Trust in Trade Finance

Majority of current businesses are interested in building communities by collaborating with each other for solving common business problems forming decentralized peer to peer network. International trade is one of such industries which is striving to work in collaboration. Multiple entities involved like buyer, seller, service providers and regulators want to work together but having major trust and security concerns. Such applications are distributed in nature therefore require distributed control and security mechanisms. The current practical security solutions have centralized approach, so they might be inefficient for these applications. The blockchain technology is distributed in nature. Prominent features of blockchain like distributed ledger Technology and smart contracts makes it a promising solution for decentralized and distributed cross border trade business. The aim of this paper is to provide pain points of global trade system with respect to security and trust, and to provide solution by blockchain technology considering Letter of credit as a method of trade finance. We propose a blockchain technology enabled smart contract approach for ensuring the security of and trust in the trade ecosystem.
Synchronous Phasor Data Compression Based on Swing Door Trending in WAMS

The large amount of data collected by the accessed phasor measurement units(PMUs) requires large storage space and may cause the communication system delay. This paper proposes a real-time synchronous phasor data compression based on swing door trending(PSDT) in the wide area measurement system(WAMS). The raw data comes from PMUs of a hydropower plant in Guizhou Power Grid in Southwest China. The wavelet-based data compression and the SDT are compared with the proposed compression technique. TVE and NMSE are used as indicators of the accuracy of data reconstruction. The compression ratio of PSDT is higher than that of compared compression. The result demonstrates that PSDT can obtain a high compression ratio and reduce the number of original data under the condition of guaranteeing the reconstruction accuracy.
Bounding the Complexity of Formally Verifying Neural Networks: A Geometric Approach

In this paper, we consider the computational complexity of formally verifying the behavior of Rectified Linear Unit (ReLU) Neural Networks (NNs), where verification entails determining whether the NN satisfies convex polytopic specifications. Specifically, we show that for two different NN architectures – shallow NNs and Two-Level Lattice (TLL) NNs – the verification problem with (convex) polytopic constraints is polynomial in the number of neurons in the NN to be verified, when all other aspects of the verification problem held fixed. We achieve these complexity results by exhibiting explicit (but similar) verification algorithms for each type of architecture. Both algorithms efficiently translate the NN parameters into a partitioning of the NN’s input space by means of hyperplanes; this has the effect of partitioning the original verification problem into polynomially many sub-verification problems derived from the geometry of the neurons. We show that these sub-problems may be chosen so that the NN is purely affine within each, and hence each sub-problem is solvable in polynomial time by means of a Linear Program (LP). Thus, a polynomial-time algorithm for the original verification problem can be obtained using known algorithms for enumerating the regions in a hyperplane arrangement. Finally, we adapt our proposed algorithms to the verification of dynamical systems, specifically when these NN architectures are used as state-feedback controllers for LTI systems. We further evaluate the viability of this approach numerically.
Metallogenesis and depositional environment of the Archean-Proterozoic carbonaceous phyllites from the Dharwar Craton, India

Organic carbon rich meta-sedimentary rocks are important repositories of gold, PGE and vestiges of the biogeochemical cycles that prevailed during the Precambrian time. In this study, we report gold, PGE and δ34S concentrations of carbonaceous phyllites from Neoarchean Chitradurga, Gadag greenstone belts of western Dharwar Craton and Mangampeta of Proterozoic Cuddapah basin, eastern Dharwar Craton to understand their metallogenesis and depositional environments. Chitradurga phyllites (avg. Au = 180 ppb) show elevated concentrations of refractory gold compared to Gadag (avg. Au = 38 ppb) and Mangampeta shales (avg. Au = 23 ppb). The Archean carbonaceous phyllites display three generations of pyrites and their mineral chemistry suggests hydrothermal imprints while Proterozoic shales have two generations of diagenetic pyrites. Gadag has high PGE concentration (ΣPGE = 55 ppb) compared to Chitradurga (ΣPGE = 37 ppb) and Mangampeta (ΣPGE = 27 ppb). The PGE geochemistry is characterized by enrichment of PPGE over IPGE with Au > Pt > Pd and the Au/Pd, Pd/Pt, Ru/Ir, Ir/Pd ratios coupled with negative Pt (Pt/Pt*) anomalies indicate hydrothermal source. The Chitradurga and Gadag samples yielded low sulphur isotopic signatures (δ34S = 1.8–2 and 1.3–2‰ respectively) whereas the Proterozoic Mangampeta samples have comparatively higher values (δ34S = 8.4–34.3‰) suggesting hydrothermal and sea water sulphate sources respectively. Sulphate present in Archean and Proterozoic sea water was converted to sulphur by bacterial sulphate reduction which led to the formation of pyrites. In the Archean phyllites, the metal bearing bisulphide complexes were adsorbed into the pyrites while the Proterozoic shales are characterized by the partitioning of organo-metallic complexes into the pyrite during the diagenesis. Strong positive correlations of transition metals, redox sensitive elements with PGE indicate hydrothermal source and V/(V + Ni), U/Th, Th/U, authigenic U attest euxinic paleo-redox conditions. Thus, biogenically mediated redox depositional environments which prevailed during Archean and Proterozoic times have enhanced the metal sequestration processes that subsequently resulted in the ore formation in these carbonaceous phyllites.