Reviewerly/exHarmony huggingface dataset:
Index Point Detection for Text Summarization using Cosine Similarity in Educational Videos

Abstract Explosive growth in digital content creates a trend of video-based learning and knowledge sharing. Several educational videos are shared by various trainers/bloggers/ instructors daily. It is now evident that even during emergencies like the recent ongoing CoVid-19 outbreak, video lectures are the saviors when the whole world comes to rest. This rapid and expansive growth has encouraged the researchers to efficiently and automatically index, browse, and retrieve video data. This research aims to process videos and precisely identify the index points which are discussed within the video. Experimental results show a good performance of the topic detection model in finding keywords and reducing dimensionality.
On the Anonymity Guarantees of Anonymous Proof-of-Stake Protocols

In proof-of-stake (PoS) blockchains, stakeholders that extend the chain are selected according to the amount of stake they own. In S&P 2019 the "Ouroboros Crypsinous" system of Kerber et al. (and concurrently Ganesh et al. in EUROCRYPT 2019) presented a mechanism that hides the identity of the stakeholder when adding blocks, hence preserving anonymity of stakeholders both during payment and mining in the Ouroboros blockchain. They focus on anonymizing the messages of the blockchain protocol, but suggest that potential identity leaks from the network-layer can be removed as well by employing anonymous broadcast channels.In this work we show that this intuition is flawed. Even ideal anonymous broadcast channels do not suffice to protect the identity of the stakeholder who proposes a block.We make the following contributions. First, we show a formal network-attack against Ouroboros Crypsinous, where the adversary can leverage network delays to distinguish who is the stakeholder that added a block on the blockchain. Second, we abstract the above attack and show that whenever the adversary has control over the network delay – within the synchrony bound – loss of anonymity is inherent for any protocol that provides liveness guarantees. We do so, by first proving that it is impossible to devise a (deterministic) state-machine replication protocol that achieves basic liveness guarantees and better than (1−2f) anonymity at the same time (where f is the fraction of corrupted parties). We then connect this result to the PoS setting by presenting the tagging and reverse tagging attack that allows an adversary, across several executions of the PoS protocol, to learn the stake of a target node, by simply delaying messages for the target. We demonstrate that our assumption on the delaying power of the adversary is realistic by describing how our attack could be mounted over the Zcash blockchain network (even when Tor is used). We conclude by suggesting approaches that can mitigate such attacks.
Towards a Group Encryption Scheme Benchmark: A View on Centralized Schemes with Focus on IoT

The number of devices connected to the Internet of Things (IoT) is continuously increasing to several billion nowadays. As those devices often share sensitive data, encryption of those data is an important issue. The pure volume of data and the complexity of communication patterns increases, and, accordingly, the importanceof group encryption is recently gaining more importance. Still, the choice of the best-suited group encryption scheme for a specific application is complicated. Benchmarks can support this choice. However, while literature distinguishes three categories for theschemes (central, decentral, and hybrid), a one-fits-all benchmark seems challenging to achieve. In this paper, we go the first step towards a structured benchmark for group encryption schemes by presenting a benchmark for centralized group encryption schemes in an IoT scenario. To this end, our benchmark includes a descriptionof workloads, a baseline scheme, a measurement setup, and metrics while also considering the requirements and features of centralized group encryption schemes.
Convolution Neural Network Efficiency Research in Gender and Age Classification From Speech

In this article, deep learning is investigated in the formulated problem of age and gender classification based on a person's speech. We conducted theoretical research of the effectiveness of the methods currently used to identify and verify a person by voice and explored methods of audio preprocessing. The system uses a software package that consists of several interconnected convolutional neural networks. Also, we used open data to train our classification algorithm. In conclusion, we conducted an experiment to analyze the effectiveness of the algorithm in the problem of age and gender classification.
Improving Solar and PV Power Prediction with Ensemble Methods

Estimation of the generated power of renewable energy resources is in general important for planning operations as well as demand balance and power quality. This paper addresses the problem of the estimation of the short-term (3-hour ahead) and medium-term (1-day ahead) generated power of a photovoltaic plant. Firstly, the design of day-ahead solar radiation predictors is investigated with different setups of time series models, and with their combinations with the weather forecast services using ensemble methods. Support Vector Machine methods are also adopted in this stage, to cluster data. Secondly, under a similar ensemble framework, the generated power prediction is investigated. The whole generated power and solar radiation prediction tasks are then implemented on a low-cost, embedded mini PC module Raspberry Pi 3. As an application, the prediction is employed in the control system of a typical microgrid settings focusing on energy management problem. The impact of the quality of generated power prediction on the performance of the controller is also evaluated in this paper.
Learning deep cross-scale feature propagation for indoor semantic segmentation

Indoor semantic segmentation is a long-standing vision task that has been recently advanced by convolutional neural networks (CNNs), but this task remains challenging by high occlusion and large scale variation of indoor scenes. Existing CNN-based methods mainly focus on using auxiliary depth data to enrich features extracted from RGB images, hence, they pay less attention to exploiting multi-scale information in exracted features, which is essential for distinguishing objects in highly cluttered indoor scenes. This paper proposes a deep cross-scale feature propagation network (CSNet), to effectively learn and fuse multi-scale features for robust semantic segmentation of indoor scene images. The proposed CSNet is deployed as an encoder-decoder engine. During encoding, the CSNet propagates contextual information across scales and learn discriminative multi-scale features, which are robust to large object scale variation and indoor occlusion. The decoder of CSNet then adaptively integrates the multi-scale encoded features with fusion supervision at all scales to generate target semantic segmentation prediction. Extensive experiments conducted on two challenging benchmarks demonstrate that the CSNet can effectively learn multi-scale representations for robust indoor semantic segmentation, achieving outstanding performance with mIoU scores of 51.5 and 50.8 on NYUDv2 and SUN-RGBD datasets, respectively.
The Named Entity Recognition of Chinese Cybersecurity Using an Active Learning Strategy

In data-driven big data security analysis, knowledge graph-based multisource heterogeneous threat data organization, association mining, and inference analysis attach increasinginterest in the field of cybersecurity. Although the construction of knowledge graph based on deep learning has achieved great success, the construction of a largescale, high-quality, and domain-specific knowledge graph needs a manual annotation of large corpora, which means it is very difficult. To tackle this problem, we present a straightforward active learning strategy for cybersecurity entity recognition utilizing deep learning technology. BERT pre-trained model and residual dilation convolutional neural networks (RDCNN) are introduced to learn entity context features, and the conditional random field (CRF) layer is employed as a tag decoder. Then, taking advantages of the output results and distribution of cybersecurity entities, we propose an active learning strategy named TPCL that considers the uncertainty, confidence, and diversity. We evaluated TPCL on the general domain datasets and cybersecurity datasets, respectively. The experimental results show that TPCL performs better than the traditional strategies in terms of accuracy and F1. Moreover, compared with the general field, it has better performance in the cybersecurity field and is more suitable for the Chinese entity recognition task in this field.
A Legendre pair of length 77 using complementary binary matrices with fixed marginals

We provide a search method for Legendre pairs of composite length based on generating binary matrices with fixed row and column sums from compressed, complementary integer vectors. This approach yielded the first construction of a Legendre pair of length 77, as well as the first exhaustive generation of Legendre pairs of length 55.
MDLC model for developing multimedia e-learning on energy concept for primary school students

Abstract This study aims at developing multimedia instructional material at the elementary school level by using the MDLC (Multimedia Development Life Cycle) model. This research developed multimedia platform which consisted of learning modules and videos. MDLC constitutes methods consisting of concept, design, collection of materials, assembly, testing, and distribution. In developing multimedia by using MDLC, it was based on a flowchart system and a learning video story board. The results indicated that the multimedia developed was in accordance with the MDLC steps. Based on the results of alpha and beta tests, it indicated that the e-learning multimedia developed is feasible and practical to be used by elementary school students. Multimedia that was developed employs a variety of learning sources that are able to enrich student’s knowledge in a more interactive presentation.
HHO Algorithm for Cloud Service Provider Selection

To handle data storage and rapid growth of social media, IoT, wireless communication and digitization, cloud computing is an evolving paradigm. It is also a profitable business for the large players hence many providers are coming forward to facilitate the cloud provider solutions for their customers. Since many providers are enabling such services to their customers, selecting the best one is a multi-criteria selection problem and needs to be carried out. This paper proposes a mathematical model for constrained multi-criteria federated cloud provider selection. The HHO meta-heuristics algorithm is discussed and compared with TLBO and Jaya. For the comparison, robust design method of Taguchi's was used for selecting the algorithm parameters, feasible solution is rendered by Analytic Hierarchy Process (AHP), a widely used technique for solving the problem of cloud provider selection in a federated cloud. Results show that HHO, TLBO Algorithm, Jaya improved the AHP solution by 68.8%, 61.43%, and 62.02%, respectively.
Multi-level Connection Enhanced Representation Learning for Script Event Prediction

Script event prediction (SEP) aims to choose a correct subsequent event from a candidate list, given a chain of ordered context events. Event representation learning has been proposed and successfully applied to this task. Most previous methods learning representations mainly focus on coarse-grained connections at event or chain level, while ignoring more fine-grained connections between events. Here we propose a novel framework which can enhance the representation learning of events by mining their connections at multiple granularity levels, including argument level, event level and chain level. In our method, we first employ a masked self-attention mechanism to model the relations between the components of events (i.e. arguments). Then, a directed graph convolutional network is further utilized to model the temporal or causal relations between events in the chain. Finally, we introduce an attention module to the context event chain, so as to dynamically aggregate context events with respect to the current candidate event. By fusing threefold connections in a unified framework, our approach can learn more accurate argument/event/chain representations, and thus leads to better prediction performance. Comprehensive experiment results on public New York Times corpus demonstrate that our model outperforms other state-of-the-art baselines. Our code is available in https://github.com/YueAWu/MCer.
Cognitive plasticity induced by gaze-control technology: Gaze-typing improves performance in the antisaccade task

The last twenty years have seen the development of gaze-controlled computer interfaces for augmentative communication and other assistive technology applications. In many applications, the user needs to look at symbols on a virtual on-screen keyboard and maintain gaze to make a selection. Executive control is essential to learning to use gaze-control, affecting the uptake of the technology. Specifically, the user of a gaze-controlled interface must suppress looking for its own sake, the so-called "Midas touch" problem. In a pre-registered study (https://osf.io/2mak4), we tested whether gaze-typing performance depends on executive control and whether learning-dependent plasticity leads to improved executive control as measured using the antisaccade task. Forty-two university students were recruited as participants. After five 30-min training sessions, we found shorter antisaccade latencies in a gaze-control compared to a mouse-control group, and similar error-rates. Subjective workload ratings were also similar across groups, indicating the task in both groups was matched for difficulty. These findings suggest that executive control contributes to gaze-typing performance and leads to learning-induced plasticity.
Barriers and Enablers to the Implementation of Intelligent Guidance Systems for Patients in Chinese Tertiary Transfer Hospitals: Usability Evaluation

Since the early 2000s, information systems have been widely employed across hospitals in China, changing the way in which the processes are managed, improving customer satisfaction and strengthening business competence. Intelligent guidance systems for patients (IGSP), which resemble humanoid characteristics using artificial intelligence, assist patients in wayfinding, obtaining medical guidance, consultations, and other medical services, and can improve user experiences before, during, and after hospital visits. However, despite their widespread adoption, usability studies on such systems are scarce. To date, there is no practical or standardized measurement for system usability, leading to difficult inspection, maintenance, and servicing processes. In this article, we aim to determine the usability deficiency of IGSP and understand how various factors influence user satisfaction during their use. We employ the requirements set out in the ISO9241-11:2018 standard using two inspection methods with three experts and 346 valid end-users. First, a heuristic evaluation method is employed to detect usability problems and to demonstrate the violations of Nielsen's ten heuristic principles. Second, a system usability scale is applied to evaluate participants’ satisfaction toward IGSP. Finally, the analysis of variance tests and multiple linear regression analyses is performed to establish the correlations between the user satisfaction and characteristics. The results show that a total of 78 problems violated the heuristic principles 169 times. These are divided into five categories: voice interaction, in-hospital navigation, medical consultation, interactive interface design, and miscellaneous. This article contributes to the existing literature on new technologies in healthcare organizations, demonstrating that IGSP can improve customer satisfaction during hospital visits.
Rational functions of degree four that permute the projective line over a finite field

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Automated Image and Video Quality Assessment for Computational Video Editing

We study non-reference image and video quality assessment methods, which are of great importance for computational video editing. The object of our work is image quality assessment (IQA) applicable for fast and robust frame-by-frame multipurpose video quality assessment (VQA) for short videos.We present a complex framework for assessing the quality of images and videos. The scoring process consists of several parallel steps of metric collection with final score aggregation step. Most of the individual scoring models are based on deep convolutional neural networks (CNN). The framework can be flexibly extended or reduced by adding or removing these steps. Using Deep CNN-Based Blind Image Quality Predictor (DIQA) as a baseline for IQA, we proposed improvements based on two patching strategies, such as uniform patching and object-based patching, and add intelligent pre-training step with distortion classification.We evaluated our model on three IQA benchmark image datasets (LIVE, TID2008, and TID2013) and manually collected short YouTube videos. We also consider interesting for automated video editing metrics used for video scoring based on the scale of a scene, face presence in frame and compliance of the shot transitions with the shooting rules. The results of this work are applicable to the development of intelligent video and image processing systems.
Data Privacy in Trigger-Action Systems

Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users create trigger-compute-action rules that pass data between disparate Internet services. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to sensitive data for millions of users. To avoid this risk, we propose eTAP, a privacy-enhancing trigger-action platform that executes trigger-compute-action rules without accessing users’ private data in plaintext or learning anything about the results of the computation. We use garbled circuits as a primitive, and leverage the unique structure of trigger-compute-action rules to make them practical. We formally state and prove the security guarantees of our protocols. We prototyped eTAP, which supports the most commonly used operations on popular commercial TAPs like IFTTT and Zapier. Specifically, it supports Boolean, arithmetic, and string operations on private trigger data and can run 100% of the top-500 rules of IFTTT users and 93.4% of all publicly-available rules on Zapier. Based on ten existing rules that exercise a wide variety of operations, we show that eTAP has a modest performance impact: on average rule execution latency increases by 70 ms (55%) and throughput reduces by 59%.
WITHDRAWN: Relative performance of Roulette wheel GA and Rank GA is dependent on chromosome parity

This paper aims to compare two recommender systems and each using different selection techniques in Genetic Algorithm (GA) for making user clusters. The recommender systems under consideration are: Information Gain Clustering through Rank based Genetic Algorithm (IGCRGA) and Information Gain Clustering through Roulette Wheel Genetic Algorithm (IGCRWGA) both of which taking influence from IGCN (Information Gain through Clustered neighbors) Algorithm. Unlike k-means, which is used by IGCN, GAsare not dependent on initial configuration for giving global solution. These two recommender systems were separately experimented and then evaluated using Mean Absolute Error (MAE) as evaluation metric by Abdul Hameed et al. Both the techniques showed good results when compared with Popularity, Entropy, IGCN, IGCGA (Information Gain Clustering through Genetic Algorithms), and IGCEGA (Information Gain Clustering through Elitizt Genetic Algorithm). As these two techniques are not compared with each other, we, being the first to do so, experimented these two techniques to find out which works best in giving quality recommendations. As MAE shows false positives the experimentation used Expected Utility (EU) as its second Evaluation metric in addition with MAE. Based on previous work it was expected that IGCRGA would perform better than IGCRWGA but the current work witnessed surprising results which favored IGCRWGA over IGCRGA. Results of EU showed that the performance of RWGA and RGA is purely context based.
Is Query Performance Prediction With Multiple Query Variations Harder Than Topic Performance Prediction?

Accurately estimating the retrieval effectiveness of different queries representing distinct information needs is a problem in Information Retrieval (IR) that has been studied for over 20 years. Recent work showed that the problem can be significantly harder when multiple queries representing the same information need are used in prediction. By generalizing the existing evaluation framework of Query Performance Prediction (QPP) we explore the causes of these differences in prediction quality in the two scenarios. Our empirical analysis demonstrates that for most predictors, this difference is solely an artifact of the underlying differences in the query effectiveness distributions. Our detailed analysis also demonstrates key performance distribution properties under which (QPP) is most and least reliable.
Research on C4.5 algorithm improvement strategy based on MapReduce

By analyzing the shortcomings of C4.5 decision tree algorithm, this paper combines MapReduce parallel model in Hadoop platform with C4.5 decision tree algorithm, so that C4.5 algorithm can be executed in parallel, so as to improve the efficiency of the algorithm. The improved C4.5 algorithm implementation process is given and the process is analyzed. Finally, the C4.5 algorithm improved experimental environment configuration is explained.
Hybrid Fusion with Intra- and Cross-Modality Attention for Image-Recipe Retrieval

Image-recipe retrieval, which aims at retrieving the relevant recipe from a food image and vice versa, is now attracting widespread attention, since sharing food-related images and recipes on the Internet has become a popular trend. Existing methods have formulated this problem as a typical cross-modal retrieval task by learning the image-recipe similarity. Though these methods have made inspiring achievements for image-recipe retrieval, they may still be less effective to jointly incorporate the three crucial points: (1) the association between ingredients and instructions, (2) fine-grained image information, and (3) the latent alignment between recipes and images. To this end, we propose a novel framework namedHybrid Fusion with Intra- and Cross-Modality Attention (HF-ICMA) to learn accurate image-recipe similarity. Our HF-ICMA model adopts an intra-recipe fusion module to focus on the interaction between ingredients and instructions within a recipe, and further enriches the expressions of the two separate embeddings. Meanwhile, an image-recipe fusion module is devised to explore the potential relationship between fine-grained image regions and ingredients from the recipe, which jointly forms the final image-recipe similarity from both the local and global aspects. Extensive experiments on the large-scale benchmark dataset Recipe1M show that our model significantly outperforms the state-of-the-art approaches on various image-recipe retrieval scenarios.