Reviewerly/exHarmony huggingface dataset:
Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline. The pre-classification pipeline first performs classification on each video snippet, and then, aggregates the snippet-level classification scores to obtain the video-level classification score. In contrast, the post-classification pipeline aggregates the snippet-level features first and then predicts the video-level classification score based on the aggregated feature. Although the classifiers in these two pipelines are used in different ways, the role they play is exactly the same-to classify the given features to identify the corresponding action categories. To this end, an ideal classifier can make both pipelines work. This inspires us to simultaneously learn these two pipelines in a unified framework to obtain an effective classifier. Specifically, in the proposed learning framework, we implement two parallel network streams to model the two localization-by-classification pipelines simultaneously and make the two network streams share the same classifier. This achieves the novel Equivalent Classification Mapping (ECM) mechanism. Moreover, we discover that an ideal classifier may possess two characteristics: 1) the frame-level classification scores obtained from the pre-classification stream and the feature aggregation weights in the post-classification stream should be consistent; and 2) the classification results of these two streams should be identical. Based on these two characteristics, we further introduce a weight-transition module and an equivalent training strategy into the proposed learning framework, which assists to thoroughly mine the equivalence mechanism. Comprehensive experiments are conducted on three benchmarks and ECM achieves accurate action localization results.
Enhancing Pre-trained Language Models by Self-supervised Learning for Story Cloze Test

Story Cloze Test (SCT) gains increasing attention in evaluating the ability of story comprehension, which requires a story comprehension model to select the correct ending to a story context from two candidate endings. Recent advances, such as GPT and BERT, have shown success in incorporating a pre-trained transformer language model and fine-tuning operation to improve SCT. However, this framework still has some fundamental problems in effectively incorporating story-level knowledge from related corpus. In this paper, we introduce three self-supervised learning tasks (Drop, Replace and TOV) to transfer the story-level knowledge of ROCStories into the backbone model including vanilla BERT and Multi-Choice Head. We evaluate our approach on both SCT-v1.0 and SCT-v1.5 benchmarks. The experimental results demonstrate that our approach achieves state-of-the-art results compared with baseline models.
Distributed Camera Architecture for Seamless Detection and Tracking of Dynamic Obstacles

Tracking of dynamic objects is a key element especially for in-door logistic solutions but also for e.g. manufacturing plants and warehouses. This paper is dealing with a tracking approach which is based on multiple cameras that are permanently mounted on the ceiling. In comparison to currently applied methods of detecting and tracking dynamic objects in the field of AGVs this approach is innovative. This is due to the shift in the capture of tracking data to ceiling cameras exclusively, for the reason of gaining a holistic overview of the complete tracking area. The application case discussed in this paper is for AGVs in the frame of a research project. However, this approach can be applied for different use cases which require a pursuit and tracking of dynamic objects that is based on multiple cameras.
BFPF-Cloud: Applying SVM for Byzantine Failure Prediction to Increase Availability and Failure Tolerance in Cloud Computing

One of the most important aspects of distributed systems is automatic failure recovery. In general, systems must be able to confront any type of failure. One issue is commonly overlooked in the subject of confronting the failures in services. Byzantine failures are the worst kind of arbitrary failures, either. The client should be ready for the worst possible conditions, especially if the server gives an answer that should never give. Sometimes, several servers, hand in hand together, make false answers deliberately. On the other hand, systems have no plan to protect themselves against byzantine failures that happen when the whole processes are not committed to a subject. It is possible that a server responds, but should not, and there is no way to detect that this is incorrect. The complexity of such failures is the main reason in cloud computing systems. However, several algorithms have been presented to detect failures by inspecting responses and address the problem. To detect Byzantine failures, the requests should be executed; first, responses should be created and gathered, and then, the responses are compared altogether. The failures occur when all the processes are unable to reach a consensus on an issue. In other words, Byzantine failures must occur first, and then, a solution should be considered to solve the problem. Accordingly, in cloud computing as a distributed infrastructure, the system should not be involved in severe failures. BFT-Cloud (Byzantine fault-tolerance cloud) as the previous research guarantees the robustness of systems when up to f of totally 3f + 1 resource providers are faulty. It uses replication techniques for overcoming failures, since a broad pool of nodes are available in the cloud. The challenge of the model is that a request should be executed several times to create a correct response that increases the number and duration of executions, either. While it is expected that all responses to requests to be correct without repeating the requests and needing to re-execute them. In this study, a framework is presented called BFPF-Cloud that introduces several features to be applied in algorithms that are based on support vector machine (SVM) to predict Byzantine failures. The reactive policy, along with the proactive one, is applied together to handle Byzantine failures. The main goal is to maintain reliability besides system availability. The experiments show that selecting the characteristics such as latency, PesNumber, MIPS, and failure probability of the replicas are the best features that SVM uses to predict Byzantine failures. In comparison to BFT-Cloud, the number of request re-execution and the execution time are decreased 69.91% on average; the number of repeated requests are decreased 69.78% on average; and throughput is improved 69.90% on average.
A Framework for a Large-Scale B2B Recommender System

The aim of a recommender system is to suggest relevant items in order to improve purchasing experience and minimise information overload. Despite extensive research in the area of B2C recommender systems, business-to-business (B2B) distributors can not directly benefit from the results. Mainly because the data from these large scale retailers is not publicly available to researchers and also their problems are not widely known to the outside world. These companies have complex structures for their items and customers, e.g. the huge number of items and customers leading to data sparsity or the high level of accuracy required in recommending safety items. Furthermore, one of the key requirements for such businesses is bulk recommendations to be able to meet their market demands. A unique hybrid approach to recommendation with an emphasis on knowledge components is needed for such businesses. It is critical to have a careful analysis of item-category specific features for any recommendation as well as the customer context. In this paper, we propose a large scale B2B recommender framework to address the above requirements.
Building ED-XRF datasets for sourcing rhyolite and quartzite artifacts: A case study on the Homa Peninsula, Kenya

Determining the provenance of archeological material is essential for documenting the movement of ancient resources and placing fossils and artifacts in the correct stratigraphic context. Non-destructive geochemical compositional analysis with Energy Dispersive X-ray Fluorescence (ED-XRF) spectrometry has the potential to provide a versatile approach to studying the provenance of archeological material. It is possible to build large regional XRF datasets that are useful for understanding the movements of toolmakers because this technique can be applied to a large number of samples non-destructively. However, incorporating new provenance data into existing datasets poses several potential challenges. Expanded provenance datasets often require the comparison of geochemical data across XRF instruments and settings. In particular the addition of different lithologies requires an understanding of diverse degrees of surface weathering and matrix heterogeneity. On the Homa Peninsula in Kenya, Oldowan tools from Kanjera South were previously linked to primary and secondary sources based on trace element geochemistry determined through ED-XRF. In this study, we established comparability across two ED-XRF instruments and incorporated samples from additional sources of quartzite and rhyolite into the existing raw material sourcing dataset. The new sources revealed regional differences in the geochemistry of rhyolite and quartzite that derive from drainages on and around the Homa Peninsula. This demonstrates the compatibility of quartzite and rhyolite materials with non-destructive ED-XRF sourcing and provides a methodological establishment of cross-study comparison in elemental values derived from non-uniform whole rock samples. This study highlights the importance of calibration using overlapping reference standards and linear regressions to determine offsets necessary for direct comparison.
Pitting optic flow, object motion, and biological motion against each other

Heading estimation from optic flow is crucial for safe locomotion but becomes inaccurate if independent object motion is present. In ecological settings, such motion typically involves other animals or humans walking across the scene. An independently walking person presents a local disturbance of the flow field, which moves across the flow field as the walker traverses the scene. Is the bias in heading estimation produced by the local disturbance of the flow field or by the movement of the walker through the scene? We present a novel flow field stimulus in which the local flow disturbance and the movement of the walker can be pitted against each other. Each frame of this stimulus consists of a structureless random dot distribution. Across frames, the body shape of a walker is molded by presenting different flow field dynamics within and outside the body shape. In different experimental conditions, the flow within the body shape can be congruent with the walker’s movement, incongruent with it, or congruent with the background flow. We show that heading inaccuracy results from the local flow disturbance rather than the movement through the scene. Moreover, we show that the local disturbances of the optic flow can be used to segment the walker and support biological motion perception to some degree. The dichotomous result that the walker can be segmented from the scene but that heading perception is nonetheless influenced by the flow produced by the walker confirms separate visual pathways for heading estimation, object segmentation, and biological motion perception.
On The Feature Extraction For Sentiment Analysis of Movie Reviews Based on SVM

Watching a movie is one of the activities that reduce bored, so it is necessary to look for information about the movie, which is packaged in the form of a movie review to determine whether the movie considered for viewing or no. However, in searching for information through movie reviews, there are obstacles because there are many reviews conducted by reviewers. Therefore, sentiment analysis is needed aims to classify the movie review into positive and negative sentiments. Machine learning methods can use as a sentiment analysis classification because that can produce the best performance, the method called Support Vector Machine (SVM). That was a reason SVM classification used in sentiment analysis on movie review data. Use feature extraction of Term Frequency-Inverse Document Frequency (TF-IDF) was also carried out in the research this as a method of weighting words which then combined with the extraction of Latent features Dirichlet Allocation (LDA) as a method of modeling topics that can overcome the shortcomings of SVM. This research produced the best performance on a combination of TF-IDF and LDA, with 240 topics has 29792 features, which is 82.16%.
An Effective Approach for Noise Robust and Rotation Invariant Handwritten Character Recognition Using Zernike Moments Features and Optimal Similarity Measure

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Combining Multi-Threshold Saliency with Transfer Learning for Ship Detection and Information Extraction From Optical Satellite Images

In terms of both the military and civilian industries, ship detection and information extraction in satellite optical images are critical. Nevertheless, with dynamic surroundings, such as tides, small islands, clouds and the variety of ships of different locations, forms and sizes, the problem became extremely difficult. To solve these issues, this paper explored a combination of multi-threshold saliency and transfer learning to detect and extract ship information. Unlike traditional methods, the proposed method is general, simple and designed for various types of images with different weather conditions and regions. Visual saliency that concentrates on the emphasis on the outstanding signal from scenes combined with the multi-threshold to extract candidate regions. Then these candidates were discriminated against and classified using the CNN model that was built by the transfer learning technique. For those ships that have been detected, we extracted ship rectangle bounding boxes to measure ships by converting the rectangle size to the actual size. Finally, we determined the ship heading by using the ship head discrimination CNN model. Experimental results on numerous satellite images showed our method's good performance compared with state of the art approaches.
Algorithmic Discrimination and Input Accountability under the Civil Rights Acts

The disproportionate burden of COVID-19 among communities of color and a necessary renewed attention to racial inequalities have lent new urgency to concerns that algorithmic decision-making can lead to unintentional discrimination against members of historically marginalized groups. These concerns are being expressed through Congressional subpoenas, regulatory investigations, and an increasing number of algorithmic accountability bills pending in both state legislatures and Congress. To date, however, prominent efforts to define algorithmic accountability have tended to focus on output-oriented policies that may facilitate illegitimate discrimination or involve fairness corrections unlikely to be legally valid. Worse still, other approaches focus merely on a model’s predictive accuracy—an approach at odds with long-standing U.S. anti-discrimination law.We provide a workable definition of algorithmic accountability that is rooted in case law addressing statistical discrimination in the context of Title VII of the Civil Rights Act of 1964. Using instruction from the burden-shifting framework codified to implement Title VII, we formulate a simple statistical test to apply to the design and review of the inputs used in any algorithmic decision-making process. Application of the test, which we label the Input Accountability Test, constitutes a legally viable, deployable tool that can prevent an algorithmic model from systematically penalizing members of protected groups who are otherwise qualified in a legitimate target characteristic of interest.
Emotions and Digital Well-Being: The Rationalistic Bias of Social Media Design in Online Deliberations

In this chapter we argue that emotions are mediated in an incomplete way in online social media because of the heavy reliance on textual messages which fosters a rationalistic bias and an inclination towards less nuanced emotional expressions. This incompleteness can happen either by obscuring emotions, showing less than the original intensity, misinterpreting emotions, or eliciting emotions without feedback and context. Online interactions and deliberations tend to contribute rather than overcome stalemates and informational bubbles, partially due to prevalence of anti-social emotions. It is tempting to see emotions as being the cause of the problem of online verbal aggression and bullying. However, we argue that social media are actually designed in a predominantly rationalistic way, because of the reliance on text-based communication, thereby filtering out social emotions and leaving space for easily expressed antisocial emotions. Based on research on emotions that sees these as key ingredients to moral interaction and deliberation, as well as on research on text-based versus non-verbal communication, we propose a richer understanding of emotions, requiring different designs of online deliberation platforms. We propose that such designs should move from text-centred designs and should find ways to incorporate the complete expression of the full range of human emotions so that these can play a constructive role in online deliberations.
Put Your Money Where Your Mouth Is – Towards Blockchain-based Consent Violation Detection

Faulty access control in API-based multi-service setups can lead to violations of consent declarations through unauthorized Third Parties. This threatens Service Providers to lose the trust of their Service Consumers and to be exposed to sensitive fines as defined by the GDPR.Addressing this problem, in this paper, we propose a novel, blockchain-based approach for enabling economically motivated and technically mediated detection of violations of consent declarations in multi-service setups and derive its legal viability from a thorough analysis of the GDPR. The herein introduced Violation Detection mechanism allows for a censorship-resistant and publicly verifiable detection of violations to registered Consent Policies based on off-chain computed violation claims utilizing non-interactive zero-knowledge proofs. The corresponding System Design specifies all required roles and artifacts to integrate the Violation Detection mechanism with standard procedures for consent-based access control. The integration of our system supports Service Providers to fulfill legal requirements and, therefore, paves the way towards automated policy violation detection within GDPR-compliant consent-based access control solutions.
Network Scheduling and Compute Resource Aware Task Placement in Datacenters

To improve the performance of data-intensive applications, existing datacenter schedulers optimize either the placement of tasks or the scheduling of network flows. The task scheduler strives to place tasks close to their input data (i.e., maximize data locality) to minimize network traffic, while assuming fair sharing of the network. The network scheduler strives to finish flows as quickly as possible based on their sources and destinations determined by the task scheduler, while the scheduling is based on flow properties (e.g., size, deadline, and correlation) and not bound to fair sharing. Inconsistent assumptions of the two schedulers can compromise the overall application performance. In this paper, we propose NEAT+, a task scheduling framework that leverages information from the underlying network scheduler and available compute resources to make task placement decisions. The core of NEAT+ is a task completion time predictor that estimates the completion time of a task under given network condition and a given network scheduling policy. NEAT+ leverages the predicted task completion times to minimize the average completion time of active tasks. Evaluation using ns2 simulations and real-testbed shows that NEAT+ improves application performance by up to 3.7x for the suboptimal network scheduling policies and up to 33% for the optimal network scheduling policy.
Symbolic regression for better specification

This note introduces the concept of symbolic regression (SR) to tourism and hospitality research. SR uses genetic programming to find the model that best fits the data without a need to pre-specify a functional form or to impose a certain model as a starting point. In other words, SR helps to uncover the intrinsic characteristics of the data at hand. Our view is that SR can serve as an improved method of testing for misspecification. In this note, we propose to derive the true functional form of the residual using SR. We then use this information to improve the forecasts of the linear regression model and, to perform hypothesis tests if needed.
Learning Content Recommendations on Personalized Learning Environment Using Collaborative Filtering Method

Personal Learning Environment (PLE) is an e-learning concept that allows users to manage their learning environment both in terms of content and process. However, significant problems with PLE implementation in distance learning are excessive information and difficulties in finding the suitable learning content for learners. To overcome these problems, an experimental study was conducted to explore a learning content recommendation system for learners. The learning content recommendation system uses the Collaborative Filtering (CF) algorithm for the basis. CF is a method for filtering information by collecting ratings and combining it with similar information needs or interests of other users. This study intends to build the concept of PLE distance learning by applying the CF recommendation system to find learning content that is appropriate to the needs of learners. The test results reveal that the proposed PLE application is compliant with the PLE attributes. This study has also succeeded in applying a recommendation system using the CF algorithm with the concept of PLE in distance learning. Moreover, the Mean Absolute Error (MAE) calculation reveals that the best-obtained recommendation results reached by K=10. Based on the experimental data obtained, the greater the value of K used in the CF algorithm, the greater the average error.
Facial expression recognition based on CNN

Abstract Facial expression recognition usually can be defined one of the important research in the field of AI and pattern recognition. Facial expression contains rich invisible information, which can help to understand human emotions and intentions, which has great research value. Aiming at solve problems that usually happened such as low recognition accuracy and weak generalization ability of traditional facial expression recognition methods, this paper introduces the theory of the convolution neural network and establishes the convolution neural network model to realize facial expression recognition. Using the crawler to crawl the network image data and using the Viola-Jones algorithm to detect the original data set, after screening, the face expression database is finally established. The convolution neural network model is applied to face expression recognition of the database. We can find that this method can effectively recognize facial expression, and provides a new way to solve this problem.