Reviewerly/exHarmony huggingface dataset:
Quick Execution Time Predictions for Spark Applications

The Apache Spark cluster computing platform is being increasingly used to develop big data analytics applications. There are many scenarios that require quick estimates of the execution time of any given Spark application. For example, users and operators of a Spark cluster often require quick insights on how the execution time of an application is likely to be impacted by the resources allocated to the application, e.g., the number of Spark executor cores assigned, and the size of the data to be processed. Job schedulers can benefit from fast estimates at runtime that would allow them to quickly conFigure a Spark application for a desired execution time using the least amount of resources. While others have developed models to predict the execution time of Spark applications, such models typically require extensive prior executions of applications under various resource allocation settings and data sizes. Consequently, these techniques are not suited for situations where quick predictions are required and very little cluster resources are available for the experimentation needed to build a model. This paper proposes an alternative approach called PERIDOT that addresses this limitation. The approach involves executing a given application under a fixed resource allocation setting with two different-sized, small subsets of its input data. It analyzes logs from these two executions to estimate the dependencies between internal stages in the application. Information on these dependencies combined with knowledge of Spark's data partitioning mechanisms is used to derive an analytic model that can predict execution times for other resource allocation settings and input data sizes. We show that deriving a model using just these two reference executions allows PERIDOT to accurately predict the performance of a variety of Spark applications spanning text analytics, linear algebra, machine learning and Spark SQL. In contrast, we show that a state-of-the-art machine learning based execution time prediction algorithm performs poorly when presented with such limited training data.
Video captioning with text-based dynamic attention and step-by-step learning

Automatically describing video content with natural language has been attracting much attention in computer vision and natural language processing communities. Most existing methods predict one word at a time, and by feeding the last generated word back as input at the next time, while the other generated words are not fully exploited. Furthermore, traditional methods optimize the model using all the training samples in each epoch without considering their learning situations, which leads to a lot of unnecessary training and can not target the difficult samples. To address these issues, we propose a text-based dynamic attention model named TDAM, which imposes a dynamic attention mechanism on all the generated words with the motivation to improve the context semantic information and enhance the overall control of the whole sentence. Moreover, the text-based dynamic attention mechanism and the visual attention mechanism are linked together to focus on the important words. They can benefit from each other during training. In addition, the model is trained through two steps: “starting from scratch” and “checking for gaps”. The former uses all the samples to optimize the model, while the latter only trains for samples with poor control. Experimental results on the popular datasets MSVD and MSR-VTT demonstrate that our non-ensemble model outperforms the state-of-the-art video captioning benchmarks.
Change of exposure time mid-test in high temperature DIC measurement

Abstract Performing digital image correlation (DIC) at extreme temperatures has been greatly challenging due to the radiation which saturates the camera sensor. At such high temperatures, the light intensity emitted from an object is occasionally so powerful that the acquired images are overwhelmingly saturated. This induces data loss, potentially ruining the test, thus requiring the user to restart the test. For this reason, selection of an appropriate camera sensitivity plays a crucial role prior to beginning the test. Exposure time is a factor contributing to camera sensitivity and it is the easiest setting to manipulate during the test since it introduces minimal errors when comparing to other factors, especially in quasi-static tests. This paper examines the influence of changing exposure time mid-test on DIC measurement uncertainty. The investigation was conducted by rigid body motion experiments at room temperature and 1600 °C, respectively. Thereby, some recommendations are given to help DIC users assess their images at room temperature to extrapolate the exposure at extreme temperatures along with accompanying solutions to salvage data at high temperature.
Privacy-preserving high-dimensional data publishing for classification

With increasing amounts of personal information being collected by various organizations, many privacy models have been proposed for masking the collected data so that the data can be published without releasing individual privacy. However, most existing privacy models are not applicable to high-dimensional data, because of the sparseness of high-dimensional search space. In this paper, we present our solution to release high-dimensional data for privacy preservation and classification analysis. The challenge facing us is how to reduce high dimensions from the perspective of privacy models while preserving as much information as possible for classification. Our proposed approach tackles it by using an idea of vertical partition, which is to vertically divide the raw data into different disjointed subsets of smaller dimensionality. Specifically, our partition metric considers both the correlation between attributes and the proportion of attributes in each subset. Then a generalization method based on local recoding is employed to each subset separately for achieving k-anonymity. Considering the hardness of the optimal implementation of k-anonymity, the local recoding method finds a near-optimal solution with the goal of improving efficiency. The proposed approach was evaluated using two datasets, and the experimental results showed that it outperformed two related approaches in data utility at the same privacy level.
An Algebraic Attack on Ciphers with Low-Degree Round Functions: Application to Full MiMC

Algebraically simple PRFs, ciphers, or cryptographic hash functions are becoming increasingly popular, for example due to their attractive properties for MPC and new proof systems (SNARKs, STARKs, among many others). In this paper, we focus on the algebraically simple construction MiMC, which became an attractive cryptanalytic target due to its simplicity, but also due to its use as a baseline in a competition for more recent algorithms exploring this design space. For the first time, we are able to describe key-recovery attacks on all full-round versions of MiMC over $${\mathbb {F}}_{2^n}$$ , requiring half the code book. In the chosen-ciphertext scenario, recovering the key from this data for the n-bit full version of MiMC takes the equivalent of less than $$2^{n-\log _2(n) +1}$$ calls to MiMC and negligible amounts of memory. The attack procedure is a generalization of higher-order differential cryptanalysis, and it is based on two main ingredients. First, we present a higher-order distinguisher which exploits the fact that the algebraic degree of MiMC grows significantly slower than originally believed. Secondly, we describe an approach to turn this distinguisher into a key-recovery attack without guessing the full subkey. Finally, we show that approximately $$\lceil \log _3(2\cdot R)\rceil $$ more rounds (where $$R = \lceil n \cdot \log _3(2)\rceil $$ is the current number of rounds of MiMC-n/n) can be necessary and sufficient to restore the security against the key-recovery attack presented here. The attack has been practically verified on toy versions of MiMC. Note that our attack does not affect the security of MiMC over prime fields.
Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection

In scene understanding, robots benefit from not only detecting individual scene instances but also from learning their possible interactions. Human-Object Interaction (HOI) Detection infers the action predicate on a <human, predicate, object> triplet. Contextual information has been found critical in inferring interactions. However, most works only use local features from single human-object pairs for inference. Few works have studied the disambiguating contribution of subsidiary relations made available via graph networks. Similarly, few have leveraged visual cues with the intrinsic semantic regularities embedded in HOIs. We contribute Visual-Semantic Graph Attention Networks (VS-GATs): a dual-graph attention network that effectively aggregates visual, spatial, and semantic contextual information dynamically from primary human-object relations as well as subsidiary relations through attention mechanisms for strong disambiguating power. We achieve competitive results on two benchmarks: V-COCO and HICO-DET. The code is available at https://github.com/birlrobotics/vs-gats.
The Asymptotic Capacity of Private Search

The private search problem is introduced, where a dataset comprised of L i.i.d. records is replicated across N non-colluding servers, and a user wishes to search for all records that match a privately chosen value, without revealing any information about the chosen value to any individual server. Each record contains P symbols, and each symbol takes values uniformly and independently from an alphabet of size K. Considering the large number of records in modern datasets, it is assumed that L is much larger than the alphabet size K. The capacity of private search is the maximum number of bits of desired information that can be retrieved per bit of download. The asymptotic (large K) capacity of private search is shown to be 1 - 1/N, even when the scope of private search is further generalized to allow OR search, AND search, NOT search and sequence search. The results are based on the asymptotic behavior of a new converse bound for private information retrieval with arbitrarily dependent messages. The asymptotic behavior is also applicable to T-colluding servers or (N, T)-MDS coded servers.
Interweaving Multimodal Interaction With Flexible Unit Visualizations for Data Exploration

Multimodal interfaces that combine direct manipulation and natural language have shown great promise for data visualization. Such multimodal interfaces allow people to stay in the flow of their visual exploration by leveraging the strengths of one modality to complement the weaknesses of others. In this work, we introduce an approach that interweaves multimodal interaction combining direct manipulation and natural language with flexible unit visualizations. We employ the proposed approach in a proof-of-concept system, DataBreeze. Coupling pen, touch, and speech-based multimodal interaction with flexible unit visualizations, DataBreeze allows people to create and interact with both systematically bound (e.g., scatterplots, unit column charts) and manually customized views, enabling a novel visual data exploration experience. We describe our design process along with DataBreeze's interface and interactions, delineating specific aspects of the design that empower the synergistic use of multiple modalities. We also present a preliminary user study with DataBreeze, highlighting the data exploration patterns that participants employed. Finally, reflecting on our design process and preliminary user study, we discuss future research directions.
Quality of experience (QoE) assessment of games on workstations and mobile

Gaming is a growing hobby in the young generation around the world and games are played from different platforms such as personal computer (PC)/mobile applications, online server and cloud services. Quality of experience (QoE) domain used for the assessment of user experience during the playing of the game. In this paper, we assess the QoE of the game players while playing different popular games such as Need for Speed Underground II and Subway Surfers, when they play the game from PC/mobile applications and online by using PC and mobile devices. The purpose of the research is to measure the satisfaction level for game playing from a different device and source by avoiding gaming CDs and kiosks. We provide descriptive and inferential analysis of user’s data and results show users are satisfied with application-based gaming visual quality, and big-screen display and online gaming have worst ratings for visual quality due to low graphics quality.
GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera

Over the years, gaze input modality has been an easy and demanding human–computer interaction (HCI) method for various applications. The research of gaze-based interactive applications has advanced considerably, as HCIs are no longer constrained to traditional input devices. In this paper, we propose a novel immersive eye-gaze-guided camera (called GazeGuide) that can seamlessly control the movements of a camera mounted on an unmanned aerial vehicle (UAV) from the eye-gaze of a remote user. The video stream captured by the camera is fed into a head-mounted display (HMD) with a binocular eye tracker. The user’s eye-gaze is the sole input modality to maneuver the camera. A user study was conducted considering the static and moving targets of interest in a three-dimensional (3D) space to evaluate the proposed framework. GazeGuide was compared with a state-of-the-art input modality remote controller. The qualitative and quantitative results showed that the proposed GazeGuide performed significantly better than the remote controller.
Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System

In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree-support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection.
Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers

These days, videos can be easily recorded, altered and shared on social and electronic media for deception and false propaganda.However, due to sophisticated nature of the content alteration tools, alterations remain inconspicuous to the naked eye and it is a challenging task to differentiate between authentic and tampered videos.During the process of video tampering the traces of objects, which are removed or modified, remain in the frames of a video.Based on this observation, in this study, a new method is introduced for discriminating authentic and tampered video clips.This method is based on deep model, which consists of three types of layers: motion residual (MR), convolutional neural network (CNN), and parasitic layers.The MR layer highlights the tampering traces by aggregation of frames.The CNN layers encode these tampering traces and are learned using transfer learning.Finally, parasitic layers classify the video clip (VC) as authentic or tampered.The parasitic layers are learned using an efficient learning method based on extreme learning theory; they enhance the performance in terms of efficiency and accuracy.Intensive experiments were performed on various benchmark datasets to validate the performance and the robustness of the method; it achieved 98.89% accuracy.Comparative analysis shows that the proposed method outperforms the state-of-the-art methods.
Gun Detection in Surveillance Videos using Deep Neural Networks

The ongoing epidemic of gun violence worldwide has compelled various agencies, businesses and consumers to deploy closed-circuit television (CCTV) surveillance cameras in attempt to combat this epidemic. An active-based CCTV system extends this platform to autonomously detect potential firearms within a video surveillance perspective. However, autonomously detecting a firearm across varying CCTV camera angles, depth and illumination represents an arduous task which has seen limited success using existing deep neural networks models. This challenge is in part due to the lack of available contextual hand gun information from CCTV images, which remains unresolved. As such, this paper introduces a novel large scale dataset of hand guns which were captured using a CCTV camera. This dataset serves to substantially improve the state-of-the-art in representation learning of hand guns within a surveillance perspective. The proposed dataset consist of 250 recorded CCTV videos with a total of 5500 images. Each annotated CCTV image realistically captures the presence of a hand gun under 1) varying outdoor and indoor conditions, and 2) different resolutions representing variable scales and depth of a gun relative to a cameras sensor. The proposed dataset is used to train a single-stage object detector using a multi-level feature pyramid network (i.e. M2Det). The trained network is then validated using images from the UCF crime video dataset which contains real-world gun violence. Experimental results indicate that the proposed dataset increases the average precision of gun detection at different scales by as much as 18% when compared to existing approaches in firearms detection.
Monitoring and Assessment of Rehabilitation Progress on Range of Motion After Total Knee Replacement by Sensor-Based System

For total knee replacement (TKR) patients, rehabilitation after the surgery is key to regaining mobility. This study proposes a sensor-based system for effectively monitoring rehabilitation progress after TKR. The system comprises a hardware module consisting of the triaxial accelerometer and gyroscope, a microcontroller, and a Bluetooth module, and a software app for monitoring the motion of the knee joint. Three indices, namely the number of swings, the maximum knee flexion angle, and the duration of practice each time, were used as metrics to measure the knee rehabilitation progress. The proposed sensor device has advantages such as usability without spatiotemporal constraints and accuracy in monitoring the rehabilitation progress. The performance of the proposed system was compared with the measured range of motion of the Cybex isokinetic dynamometer (or Cybex) professional rehabilitation equipment, and the results revealed that the average absolute errors of the measured angles were between 1.65° and 3.27° for the TKR subjects, depending on the swing speed. Experimental results verified that the proposed system is effective and comparable with the professional equipment.
Detection of Arabic Cyberbullying on Social Networks using Machine Learning

Recently, cyberbullying has grown significantly on social platforms, impacting users especially teenagers and young adults. The effects of this threat are so severe and damaging that could lead to suicide. Lately, this threat has become a significant issue in Arab countries, especially with the wide adoption of social media by the young generation. Most of existing research proposed solutions for detecting cyberbullying, mainly in English language. However, only few papers studied cyberbullying detection in Arabic Social Media Communications. This paper used machine learning for automatic detection of cyberbullying in Arabic. The proposed scheme detects cyberbullying using Naive Bayes(NB) classifier algorithm by training and testing the classifier with real data set which was collected from Youtube and Twitter.
The Role of Domain Knowledge in Search as Learning

The purpose of this paper is to explore the differences in learning outcomes between domain experts and non-experts interacting with a digital library. This paper provides a preliminary analysis of a subset of data from a larger study and compares the learning outcomes of 10 domain experts and 10 non-experts. Participants completed three search tasks designed to elicit different cognitive processes and behaviors; learning outcomes were explored with pre- and post-session written summaries and pre- and post-task questions. We used existing metrics to examine the learning breadth and depth of the written summaries. General searchers wrote longer summaries than domain experts, but there were no significant differences in the learning outcomes between the two groups.
A Facial Expression Recognition Method Using Deep Convolutional Neural Networks Based on Edge Computing

The imbalanced number and the high similarity of samples in expression database can lead to overfitting in facial recognition neural networks. To address this problem, based on edge computing, a facial expression recognition method using deep convolutional neural networks is proposed. In order to overcome the shortcoming that circular consensus adversarial network model can only be mapped one-to-one, we construct a constrained circular consensus generative adversarial network by adding class constraint information. Discriminators and classifiers in this network can share network parameters. In addition, for the problems of unstable training and easy to encounter model collapse in original GAN networks, this paper introduces gradient penalty rule into discriminator's loss function to achieve the normative constraint on gradient changes. Using this network not only generates sample data for a few classes in the training set of expression database, but also performs effective expression classification. Compared with other methods, the improved discriminative classifier network structure can enhance the diversity of samples and get a higher expression recognition rate. Even if other expression feature extraction methods are used, the higher recognition rate can still be obtained after using proposed data augmentation framework.