Reviewerly/exHarmony huggingface dataset:
On Incremental Structure from Motion Using Lines

Humans tend to build environments with structure, which consists of mainly planar surfaces. From the intersection of planar surfaces arise straight lines. Lines have more degrees-of-freedom than points. Thus, line-based Structure-from-Motion (SfM) provides more information about the environment. In this paper, we present solutions for SfM using lines, namely, incremental SfM. These approaches consist of designing state observers for a camera's dynamical visual system looking at a 3D line. We start by presenting a model that uses spherical coordinates for representing the line's moment vector. We show that this parameterization has singularities, and therefore we introduce a more suitable model that considers the line's moment and shortest viewing ray. Concerning the observers, we present two different methodologies. The first uses a memory-less state-of-the-art framework for dynamic visual systems. Since the previous states of the robotic agent are accessible -- while performing the 3D mapping of the environment -- the second approach aims at exploiting the use of memory to improve the estimation accuracy and convergence speed. The two models and the two observers are evaluated in simulation and real data, where mobile and manipulator robots are used.
Disrupted object-scene semantics boost scene recall but diminish object recall in drawings from memory

Humans are highly sensitive to the statistical relationships between features and objects within visual scenes. Inconsistent objects within scenes (e.g., a mailbox in a bedroom) instantly jump out to us and are known to catch our attention. However, it is debated whether such semantic inconsistencies result in boosted memory for the scene, impaired memory, or have no influence on memory. Here, we examined the relationship of scene–object consistencies on memory representations measured through drawings made during recall. Participants (N = 30) were eye-tracked while studying 12 real-world scene images with an added object that was either semantically consistent or inconsistent. After a 6-minute distractor task, they drew the scenes from memory while pen movements were tracked electronically. Online scorers (N = 1,725) rated each drawing for diagnosticity, object detail, spatial detail, and memory errors. Inconsistent scenes were recalled more frequently, but contained less object detail. Further, inconsistent objects elicited more errors reflecting looser memory binding (e.g., migration across images). These results point to a dual effect in memory of boosted global (scene) but diminished local (object) information. Finally, we observed that participants fixate longest on inconsistent objects, but these fixations during study were not correlated with recall performance, time, or drawing order. In sum, these results show a nuanced effect of scene inconsistencies on memory detail during recall.
Synthetic Radar Dataset Generator for Macro-Gesture Recognition

Recent developments in mmWave technology allow the detection and classification of dynamic arm gestures. However, achieving a high accuracy and generalization requires a lot of samples for the training of a machine learning model. Furthermore, in order to capture variability in the gesture class, the participation of many subjects and the conduct of many gestures with different arm speed are required. In case of macro-gestures, the position of the subject must also vary inside the field of view of the device. This would require a significant amount of time and effort, which needs to be repeated in case that the sensor hardware or the modulation parameters are modified. In order to reduce the required manual effort, here we developed a synthetic data generator that is capable of simulating seven arm gestures by utilizing Blender, an open-source 3D creation suite. We used it to generate 600 artificial samples with varying speed of execution and relative position of the simulated subject, and used them to train a machine learning model. We tested the model using a real dataset recorded from ten subjects, using an experimental sensor. The test set yielded 84.2% accuracy, indicating that synthetic data generation can significantly contribute in the pre-training of a model.
Isolating cloud induced noise to improve generation of synthetic surface solar irradiances

Variable cloud cover causes fluctuations in photovoltaic output that are hard to predict. As solar penetration increases optimizing the mix of back up generation and energy storage will become increasingly important. In these situations where sufficient historical data is not available, synthetic data with the correct statistical properties can be of great value. Fourier detrending, autoregressive moving average, and machine learning techniques have been used for this purpose. Here we investigate several methods for implementing generating synthetic solar signals. Additionally, this work introduces a method which isolates the cloud induced noise using the clear sky irradiance. By applying seasonal and daily detrending methods to clear sky irradiance noise signal to account for seasonal and daily oscillations the performance of autoregressive movement average methods can be improved in representative statistical measures
Research on the framework of traditional culture innovation system based on artificial intelligence

In order to improve the effect of traditional cultural innovation, this paper proposes a cultural algorithm with dual knowledge, and improves the effect of the algorithm to obtain a cultural algorithm with dual knowledge. Each individual corresponds to its unique dual knowledge, so that the individual’s evolution can move towards the current optimal solution. This paper constructs a traditional cultural innovation system architecture based on artificial intelligence, analyzes its functional modules, and constructs the system structure from the perspectives of cultural classification and cultural innovation. After constructing the system, this paper designs experiments to verify the system performance. The research results show that the system constructed in this paper performs well in traditional cultural analysis and traditional cultural innovation, and can provide references for related research.
Breaking the Circuit Size Barrier for Secure Computation Under Quasi-Polynomial LPN

In this work we introduce a new (circuit-dependent) homomorphic secret sharing (HSS) scheme for all $$\log /\log \log $$ -local circuits, with communication proportional only to the width of the circuit, and polynomial computation, assuming the super-polynomial hardness of learning parity with noise ( $$\mathsf {LPN}$$ ). At the heart of our new construction is a pseudorandom correlation generator (PCG), which allows two partie to locally stretch, from short seeds, pseudorandom instances of an arbitrary $$\log /\log \log $$ -local additive correlation. Our main application, and the main motivation behind this work, is a generic two-party secure computation protocol for every layered (boolean or arithmetic) circuit of size s with total communication $$O(s/\log \log s)$$ and polynomial computation, assuming the super-polynomial hardness of the standard learning parity with noise assumption (a circuit is layered if its nodes can be partitioned in layers, such that any wire connects adjacent layers). This expands the set of assumptions under which the ‘circuit size barrier’ can be broken, for a large class of circuits. The strength of the underlying assumption is tied to the sublinearity factor: we achieve communication O(s/k(s)) under the $$s^{2^{k(s)}}$$ -hardness of LPN, for any $$k(s) \le \log \log s / 4$$ . Previously, the set of assumptions known to imply a PCG for correlations of degree $$\omega (1)$$ or generic secure computation protocols with sublinear communication was restricted to LWE, DDH, and a circularly secure variant of DCR.
The Effect of Using Light Stemming for Arabic Text Classification

Arabic is one of the Semitic languages in antiquity and one of the six official languages of the UN.Also, Arabic classification plays a significant and essential role in modern applications.There is a big difference between handling English text and Arabic text classification; preprocessing is also challenging for Arabic text.This paper presents the implementation of a Naïve Bayes classifier for Arabic text with and without stemmer.A set of four categories and 800 documents were used from the Text Retrieval Conference (TREC) 2001 dataset.The results showed that Naïve Bayes with light stemmer achieves better results than Naïve Bayes without stemmer.The findings of the classifier accuracy by employing stemmer and without stemmer are as preprocessing.It reveals that the accuracy resulted from the light stemmer was better than the classifier without stemmer detection, which Naïve Bayes Classification with light stemmer got 35.0745 higher than the Naïve Bayes Classification 33.831% without stemmer.After contrasting them, the stemmer got better accuracy than the classifier.
Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems

The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.
DeepNetQoE: Self-Adaptive QoE Optimization Framework of Deep Networks

Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depend heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the network model is recognized by many researchers. This leads to huge computing consumption, while satisfactory results are not always expected when computing resources are limited. Therefore, it is necessary to find a balance between resources and model performance to achieve satisfactory results. This article proposes a self-adaptive quality of experience (QoE) framework, DeepNetQoE, to guide the training of deep networks. A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve. To maximize the experience value, a resource allocation model and solutions need to be established. Finally, we carry out experiments based on four network models to analyze the experience values with respect to the crowd counting example. Experimental results show that the proposed DeepNetQoE is capable of adaptively obtaining a high experience value according to user requirements and therefore guiding users to determine the computational resources allocated to the network models.
Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation

With the recent advances of open-domain story generation, the lack of reliable automatic evaluation metrics becomes an increasingly imperative issue that hinders the fast development of story generation.According to conducted researches in this regard, learnable evaluation metrics have promised more accurate assessments by having higher correlations with human judgments.A critical bottleneck of obtaining a reliable learnable evaluation metric is the lack of high-quality training data for classifiers to efficiently distinguish plausible and implausible machine-generated stories.Previous works relied on heuristically manipulated plausible examples to mimic possible system drawbacks such as repetition, contradiction, or irrelevant content in the text level, which can be unnatural and oversimplify the characteristics of implausible machine-generated stories.We propose to tackle these issues by generating a more comprehensive set of implausible stories using plots, which are structured representations of controllable factors used to generate stories.Since these plots are compact and structured, it is easier to manipulate them to generate text with targeted undesirable properties, while at the same time maintain the grammatical correctness and naturalness of the generated sentences.To improve the quality of generated implausible stories, we further apply the adversarial filtering procedure presented by Zellers et al. (2018) to select a more nuanced set of implausible texts.Experiments show that the evaluation metrics trained on our generated data result in more reliable automatic assessments that correlate remarkably better with human judgments compared to the baselines.Human Written Story: jenny liked fresh fish.she decided to go fishing to catch her own.she brought her worms and pole and a chair.she sat there all day but didn't catch anything.she packed it up and went home disappointed.Sentence Manipulation: jenny liked fresh fish.she decided to go fishing to catch her own.she wrote songs every single day.she sat there all day but didn't catch
Understanding Information Security Policy Violation from a Situational Action Perspective

Insiders’ negligence or abuse is regarded as a leading cause of information security breaches in organizations. As most of the extant studies have largely examined insider threats at a high level of abstraction, the role of situational moral reasoning for information security policy (ISP) violations in specific situations has received little attention. To advance this line of research, this paper opens up a potentially fruitful path for IS researchers by applying situational action theory (SAT) to contextually examine why employees violate ISPs in particular situations. We consider the violations of password security policy, internet use policy, and confidential data security policy, and examine specific violation intents ranging from altruistic to malicious. The results support most of the assertions derived from SAT. We found situational moral beliefs to be the predominant driver for ISP violations across three situations in an organizational setting. However, the moderation effect of moral beliefs was only significant in situations involving sharing passwords and selling confidential data. Sanction certainty and sanction severity were also found to have different effects across situations. We conclude by presenting implications for IS security practitioners and suggestions for future research.
Is Mandatory Vaccination for COVID-19 Constitutional under Brazilian Law?

Mandatory vaccination for COVID-19 has been the object of heated debate in Brazil. This article discusses the legality and constitutionality of such a policy. First, it analyzes the laws, regulations, and Supreme Court decisions that provide for the possibility of mandatory COVID-19 vaccination. Subsequently, it analyzes the constitutionality of a mandatory vaccination policy through the proportionality method to address the conflict between, on one side, the right to individual autonomy, which includes the right to refuse a medical intervention, and, on the other, health policies that interfere with individual autonomy to protect the rights to life and health. The application of this method allows for the identification of key questions that need to be answered to determine the constitutionality of a mandatory vaccination program. These questions cannot be answered a priori and in the abstract because they depend on the concrete circumstances of the pandemic, on the characteristics of the vaccine(s) against COVID-19, and on how a mandatory vaccination policy might be designed and implemented by authorities.
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction

Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces. Two classes of word representations have been explored for BLI: static word embeddings and contextual representations, but there is no studies to combine both. In this paper, we propose a simple yet effective mechanism to combine the static word embeddings and the contextual representations to utilize the advantages of both paradigms. We test the combination mechanism on various language pairs under the supervised and unsupervised BLI benchmark settings. Experiments show that our mechanism consistently improves performances over robust BLI baselines on all language pairs by averagely improving 3.2 points in the supervised setting, and 3.1 points in the unsupervised setting.
Unsupervised Knowledge Selection for Dialogue Generation

Knowledge selection is an important and challenging task which could provide the appropriate knowledge for informative dialogue generation.However, the needed gold knowledge label is difficult to collect in reality.In this paper, we study knowledge selection for dialogue generation in the unsupervised scenario and propose a novel Distilled Distant Supervision Loss (DDSL) to supervise knowledge selection when the gold knowledge label is unknown.Specifically, we first obtain an oracle knowledge label via distant supervision and then leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision.Furthermore, we propose a pretraining-finetuning strategy to deal with the mismatch knowledge selection problem that models tend to select the mismatched knowledge for dialogue generation in the unsupervised setting and will cause the degeneration of knowledge-aware decoder.Experiments on two knowledge-grounded dialogue datasets show that our approach manages to select knowledge more accurately in the unsupervised setting and generates more informative responses, even outperforming many strong supervised baselines. 1
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore

Neural machine translation models are often biased toward the limited translation references seen during training.To amend this form of overfitting, in this paper we propose fine-tuning the models with a novel training objective based on the recently-proposed BERTScore evaluation metric.BERTScore is a scoring function based on contextual embeddings that overcomes the typical limitations of n-gram-based metrics (e.g.synonyms, paraphrases), allowing translations that are different from the references, yet close in the contextual embedding space, to be treated as substantially correct.To be able to use BERTScore as a training objective, we propose three approaches for generating soft predictions, allowing the network to remain completely differentiable end-to-end.Experiments carried out over four, diverse language pairs have achieved improvements of up to 0.58 pp (3.28%) in BLEU score and up to 0.76 pp (0.98%) in BERTScore (F BERT ) when finetuning a strong baseline.
Online And Offline Learning ComparisonIn The New Normal Era

There are many learning media during the Covid 19 pandemic, some use online media and offline media, online media are also of many kinds, some are in the form of videos, pictures or writing, but there are also many learning media that combine all learning media, examples are online media youtube, with youtube media students will be able to view learning material repeatedly because videos can be downloaded. The method used in research is to use the literature review method, a method that can help in this study because it can find new problems in future research. The problem raised in this research is wanting to know the right media and making comparisons of online and offline media during the new normal period after the COVID-19 pandemic. This research produces data that can prove that the right media is in the new normal period, this is done to common interests and the nation.
Flow guided mutual attention for person re-identification

Person Re-Identification (ReID) is a challenging problem in many video analytics and surveillance applications, where a person's identity must be associated across a distributed non-overlapping network of cameras. Video-based person ReID has recently gained much interest given the potential for capturing discriminant spatio-temporal information from video clips that is unavailable for image-based ReID. Despite recent advances, deep learning (DL) models for video ReID often fail to leverage this information to improve the robustness of feature representations. In this paper, the motion pattern of a person is explored as an additional cue for ReID. In particular, a flow-guided Mutual Attention network is proposed for fusion of bounding box and optical flow sequences over tracklets using any 2D-CNN backbone, allowing to encode temporal information along with spatial appearance information. Our Mutual Attention network relies on the joint spatial attention between image and optical flow feature maps to activate a common set of salient features. In addition to flow-guided attention, we introduce a method to aggregate features from longer input streams for better video sequence-level representation. Our extensive experiments on three challenging video ReID datasets indicate that using the proposed approach allows to improve recognition accuracy considerably with respect to conventional gated-attention networks, and state-of-the-art methods for video-based person ReID.
The Blockchain Evolution and Revolution of Accounting

Blockchain, the technology behind digital currency, is a decentralized, distributed ledger that records transactions in digital assets. By authenticating and recording immutable transactions, decentralized blockchains perform the same function as many intermediaries in our society that establish trust and maintain integrity between transacting parties. Due to its natural relation to accounting and possible uses in accounting functions, business operations, and financial services, it is important that accountants learn about blockchain technology and its opportunities and limitations. This chapter explores applications of blockchain technology in finance, auditing, financial reporting, and supply chain. We first discuss the classification, characteristics, and issuance of cryptoassets and the evolving regulatory environment. Then, we address potential innovative uses of blockchain in auditing and financial reporting, keeping in mind the limitations of its application. Finally, we explore how blockchain technology can enhance communication and trust between organizations in a supply chain or in contracting relationships.
Web Scraping of COVID-19 News Stories to Create Datasets for Sentiment and Emotion Analysis

ABSTRACT