Reviewerly/exHarmony huggingface dataset:
Spiral Salp Swarm Optimization Algorithm

Salps are the barrel-shaped sea tunicates generally remain in groups or chains and floated freely. In salp swarm algorithm, location all the follower salps are modified with respect to each other such that all the followers move towards the leading salp. Since, in SSA all the individual salps are moving towards the leading salp and leading salp often stick to some local solution due to accelerated exploitation. Thus, in this paper a modified variant of SSA, spiral movement based salp swarm optimization algorithm (SSSA) has been introduced. The performance of the SSSA is authenticated on 18 standard benchmark functions and outcomes are compared with 5 other recent algorithms in terms of accuracy and standard deviation. Moreover, to test the uniqueness of the algorithm, Wilcoxon rank some test is performed on each benchmark function. The experimental outcomes vindicate that the SSSA outruns the reviewed methods on the majority of test functions.
Design of Learning Modules Writing Narrative Text Based on Project Based Learning (PjBL) by Using Mobile Devices

Abstract Every aspect of education continues to experience innovation after the internet, digital tools and technological gadgets have been easily accessed everywhere marked by online reading, electronic books and audio books along with printed material. Based on that, this study aims to develop learning modules for learning to write narrative texts based on project based learning using mobile devices. This type of research is R&amp;D (research &amp; development). The development model used is a 4-D model (define, design, develop, and assess). The research instruments used were questionnaires, observation sheets, interview guides, and tests. In this paper, the results of the study are more focused on discussing the design of the developed learning modules. By understanding the design, readers can develop learning modules based on project based learning for types of narrative texts at various levels of education. The module design that was designed was validated by experts and concluded that the module was categorized as very valid and could be tested.
Extracurricular Activities in Engineering College and Its Impact on Students’ Tolerance Formation

The article deals with extracurricular activities that are considered to strengthen the interaction between the students and provide the essential skills for teamwork, conflict management and leadership. The paper aims to examine the role of extracurricular activities in the formation of students’ tolerance and to develop a pedagogical model of students’ tolerance beyond the classroom. These activities result in tolerance education and ability to perceive different cultures and views.
Development of scene knowledge: Evidence from explicit and implicit scene knowledge measures

In our daily lives, we rely on expectations of where to find objects in a scene. Every morning without conscious reflection, we find the milk in the refrigerator. How do these schemata develop during childhood? In the current study, we investigated the behavioral responses of 72 2- to 4-year-olds in two tasks that measured scene knowledge either directly by asking them to furnish a dollhouse or indirectly by observing their eye movements in a violation paradigm using scene photographs. In addition, we collected language acquisition measures for each child to investigate possible relations between the development of scene knowledge and language abilities. Results for both explicit and implicit measures indicated an increase of performance with age in terms of correct object placement relative to corresponding rooms/locations and a difference in first-pass dwell times between consistent and inconsistent objects. The consistency effect in eye movements was associated with shorter processing times for consistent objects, reflecting stronger predictions for objects in their familiar context/location. A reduction of first-pass dwell times to consistent objects was also predicted by the dollhouse performance measure of scene knowledge. Although strong links to language development could not be found, first indications are discussed together with possible improvements of future studies investigating such a link. In sum, our results imply that scene-related predictions effectively can influence implicit and explicit behavior by 4 years of age at the latest, allowing optimized attention allocation in scenes.
Light and Fast Hand Pose Estimation From Spatial-Decomposed Latent Heatmap

We present a light and efficient approach named Latent Fusion network for fast and accurate hand pose estimation from a single depth image. Our method innovatively decomposes 3D joint regression into 2D plane localization and 1D axis estimation from different spatial perspectives. We design multiple latent heatmap regression branches to predict hand pose separately and a fusion network to output the final result. Experiments on three public hand pose datasets (ICVL, NYU, MSRA) demonstrate that our system achieves state-of-the-art accuracy. Moreover, our method outperforms all top-ranked approaches by a large margin both in terms of inference speed (nearly a thousand frames per second) and model size (less than 10 MB).
Semantic-Gap-Oriented Feature Selection and Classifier Construction in Multilabel Learning

Multilabel learning focuses on assigning instances with different labels. In essence, the multilabel learning aims at learning a predictive function from feature space to a label space. The predictive function learning procedure can be regarded as a feature selection procedure and as a classifier construction procedure. For feature selection, we extract features for each label based on the learned positive and negative feature-label correlations. The positive and negative relationships can illustrate which labels can and cannot be well presented by the corresponding features, respectively, due to the semantic gap. For classifier construction, we perform sample-specific and label-specific classifications. The interlabel and interinstance correlations are combined in these two kinds of classifications. These two correlations are learned from both input features and output labels when the output labels are too sparse to reveal the informative correlation. However, there exists the semantic gap when combining input and output spaces to mine the labelwise relationship. The semantic gap can be bridged by the learned feature-label correlation. Finally, extensive experimental results on several benchmarks under four domains are presented to show the effectiveness of the proposed framework.
The role of globalization in modulating the effect of environmental degradation on inclusive human development

This study assesses how globalization modulates the effect of environmental degradation on inclusive human development in 44 countries in Sub-Saharan Africa. First, a trade openness (imports + exports) threshold of between 80 and 120% of GDP is the maximum level required for trade openness to effectively modulate CO2 emissions (metric tonnes per capita) and induce a positive effect on inclusive human development. Second, a minimum threshold required for trade openness to modulate CO2 intensity (kg per kg of oil-equivalent energy use) and induce a positive effect on inclusive human development is 200% of GDP. Third, there is a net positive effect on inclusive human development from the relevance of trade openness in modulating the effect of CO2 emissions per capita on inclusive human development and a negative net effect on inclusive human development from the importance of trade openness in moderating the effect of CO2 intensity on inclusive human development.
Matching Reads to Many Genomes with the <i>r</i>-Index

The r-index is a tool for compressed indexing of genomic databases for exact pattern matching, which can be used to completely align reads that perfectly match some part of a genome in the database or to find seeds for reads that do not. This article shows how to download and install the programs ri-buildfasta and ri-align; how to call ri-buildfasta on an FASTA file to build an r-index for that file; and how to query that index with ri-align.
Single shot phase-shifting interferometry with q = ±1 optical vortices and modulation of polarization

This research describes a proposal of a single-shot interferometer with four interferograms incorporating the usage of phase vortices of unitary topological charge. The system features vortices generated with a LCoS as the system’s transfer function. By employing polarization phase shifting techniques and field replication methods several copies of the interferogram of interest are achieved, each copy with its prescribed phase shift. Experimental results of wrapped optical phases calculated by using a four-step algorithm are shown, as well as a discussion of the used deconvolution method to recover the unwrapped phase. In order to show the feasibility of the proposal, experimental results corresponding to the discrimination of peaks or valleys are presented and a comparison with the zero topological case.
WordNet Based Sign Language Machine Translation: from English Voice to ISL Gloss

Communication is an essential part of human life. For the person with hearing and speaking disability, it is inconvenient to communicate with other people. In this paper, an end-to-end system to convert English voice to Indian Sign Language (ISL) gloss (written form of sign language) is proposed which will help deaf to communicate with others and vice versa. This system accepts English voice as an input and, converts it into the text using the speech recognition. From the recognized English text, ISL gloss is generated using the lexical database called WordNet. The focus of our work is to build a robust sign language machine translation system to convert the English text to ISL gloss using the linguistic database WordNet.
Blockchain-based Integrity Protection System for Cloud Storage

Data are now an invaluable asset; therefore, in most machine-aided human activities, it guides all organizational decisions. Attacks to data integrity are therefore of paramount importance, as essential organization decisions can be maliciously affected by manipulation of information. This problem is particularly true in cloud computing settings where information owners are unable to monitor basic information elements such as access control and physical data storage. Recently, Blockchain has appeared as an intriguing technology that offers convincing data integrity characteristics among others. In this paper, we design a blockchain-based cloud architecture, focusing the threats of data integrity. We propose a methodology in peer to peer cloud storage to verify integrity based on blockchain, making checking more transparent, open, and publicly available. In this framework, we confirm the data integrity by using merkle trees and design a smart contract for the authentication process. Furthermore, we present an elementary model of an e□icient blockchain structure for cloud computing settings. Thus, this methodology helps in enhancing the security and integrity of the cloud storage system.
Modeling and Matching Digital Data Marketplace Policies

Recently, Digital Data Marketplaces (DDMs) are gaining wide attention as a sharing platform among different organizations. That is due to the fact that sharing the information and participating in research collaborations play an important role in addressing multiple scientific challenges. To increase trust among participating organizations multiple contracts and agreements should be established in order to determine regulations and policies about who has access to what. Describing these agreements in a general model to be applicable in different DDMs is of utmost importance. In this paper, we present a semantic model for describing the access policies by means of semantic web technologies. In particular, we use and extend the Open Digital Rights Language (ODRL) to describe the pre-established agreements in a DDM.
Multilingual Grapheme-To-Phoneme Conversion with Byte Representation

Grapheme-to-phoneme (G2P) models convert a written word into its corresponding pronunciation and are essential components in automatic-speech-recognition and text-to-speech systems. Recently, the use of neural encoder-decoder architectures has substantially improved G2P accuracy for mono- and multi-lingual cases. However, most multilingual G2P studies focus on sets of languages that share similar graphemes, such as European languages. Multilingual G2P for languages from different writing systems, e.g. European and East Asian, remains an understudied area. In this work, we propose a multilingual G2P model with byte-level input representation to accommodate different grapheme systems, along with an attention-based Transformer architecture. We evaluate the performance of both character-level and byte-level G2P using data from multiple European and East Asian locales. Models using byte representation yield 16.2%– 50.2% relative word error rate improvement over character-based counterparts for mono- and multi-lingual use cases. In addition, byte-level models are 15.0%–20.1% smaller in size. Our results show that byte is an efficient representation for multilingual G2P with languages having large grapheme vocabularies.
CNN-Based Cost Volume Analysis as Confidence Measure for Dense Matching

Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e.g. autonomous driving, which needs a high degree of confidence as mandatory prerequisite. Especially, the introduction of deep learning based methods resulted in an increasing popularity of this field in recent years, caused by a significantly improved accuracy. Despite this remarkable development, most of these methods rely on features learned from disparity maps only, not taking into account the corresponding 3-dimensional cost volumes. However, it was already demonstrated that with conventional methods based on hand-crafted features this additional information can be used to further increase the accuracy. In order to combine the advantages of deep learning and cost volume based features, in this paper, we propose a novel Convolutional Neural Network (CNN) architecture to directly learn features for confidence estimation from volumetric 3D data. An extensive evaluation on three datasets using three common dense stereo matching techniques demonstrates the generality and state-of-the-art accuracy of the proposed method.
Recognizing Unintentional Touch on Interactive Tabletop

A multi-touch interactive tabletop is designed to embody the benefits of a digital computer within the familiar surface of a physical tabletop. However, the nature of current multi-touch tabletops to detect and react to all forms of touch, including unintentional touches, impedes users from acting naturally on them. In our research, we leverage gaze direction, head orientation and screen contact data to identify and filter out unintentional touches, so that users can take full advantage of the physical properties of an interactive tabletop, e.g., resting hands or leaning on the tabletop during the interaction. To achieve this, we first conducted a user study to identify behavioral pattern differences (gaze, head and touch) between completing usual tasks on digital versus physical tabletops. We then compiled our findings into five types of spatiotemporal features, and train a machine learning model to recognize unintentional touches with an F1 score of 91.3%, outperforming the state-of-the-art model by 4.3%. Finally we evaluated our algorithm in a real-time filtering system. A user study shows that our algorithm is stable and the improved tabletop effectively screens out unintentional touches, and provide more relaxing and natural user experience. By linking their gaze and head behavior to their touch behavior, our work sheds light on the possibility of future tabletop technology to improve the understanding of users' input intention.
Human-Robot Collaboration by Intention Recognition using Deep LSTM Neural Network

When a robot is required to perform specific tasks in human-robot-collaboration scenario, it is necessary for the robot to recognize human intention to more effectively and efficiently assist and interact with human. The relationship of the skeleton-based sequence of human motion provides a possible solution for the robot to recognize human intention. In this paper, we present a deep long short term memory (LSTM) neural network to recognize human intention. Considering the characteristics of deep LSTM neural network, it is a typically multiple stacked LSTM model, which combine the advantages of single LSTM layer and overcome the weakness of learning long-range time dependencies for RNN. The experimental results showed that the deep LSTM network with 2-layers have better prediction performance even only 40% of motion sequences is utilized.
Comparative Study of Lightweight Hashing Functions for Resource Constrained Devices of IoT

With the drastic growth in the addition of smart objects into the Internet, it has led to many challenges like standardizing the infrastructure, ensuring communication security and keeping user credential private. As these devices are having less resources it is necessity to have a lighter encryption and authentication schemes to ensure data integrity and user privacy. This work concentrates on evaluating the faster hashing function used for elliptic curve digital signature algorithm (ECDSA) that enables user authentication. Traditionally, SHA hash functions are used in various public key cryptography and applications. But SHA has 80 different iterations that consumes smart device's energy. Thus a lighter hash function called BLAKE2 based hashing is used to compare the time efficiency of ECDSA. BLAKE2 uses 8 rounds of permutations and combinations to produce the message digest of 256/512bits. This reduction in computation has attracted researchers to use BLAKE2 hashing functions for various applications. Thence this study concentrates on comparison of the time efficiency under ECDSA based digital signature for different message sizes and hashing functions. Through various experiments conducted on different operating systems, BLAKE2 has shown better computational efficiency than SHA family of hashing functions for ECDSA based scheme on different operating system.
Improving Money Laundering Detection Using Optimized Support Vector Machine

Identification of financial transactions as suspicious or fraudulent transactions that are indicated as money laundering is mostly done manually so that it is not optimal. Data mining techniques can be a solution to overcome the limitations of the manual method. The main challenge in applying data mining techniques for financial fraud detection is an imbalanced dataset, where the proportion of fraud class is much smaller than non-fraud. This causes the model to produce unbalanced precision and recall, resulting in a low f1score. It means that the model can predict one class well, but not with another class. In this paper, the approach to fraud detection in financial transactions is carried out with classifier optimization based on Support Vector Machine (SVM). Optimization is performed by tuning the kernels and hyperparameters combined with the Random Under Sampling (RUS) technique. Specifically, RUS is used to handle imbalanced datasets and cut model training time. With this combination technique, the classifier can detect fraud more effectively with an increase in precision of 40.82% and f1-score of 22.79% compared to the previous study. A combination technique can be an approach to cover weaknesses left behind by a single method.
Giving Faces to Data

Creating personas from large amounts of online data is useful but difficult with manual methods. To address this difficulty, we present Automatic Persona Generation (APG), which is an implementation of a methodology for quantitatively generating data-driven personas from online social media data. APG is functional, and it is deployed with several organizations in multiple industry verticals. APG employs a scalable web front-end user interface and robust back-end database framework processing tens of millions of user interactions with tens of thousands of online digital products across multiple online platforms, including Facebook, Google Analytics, and YouTube. APG identifies audience segments that are both distinct and impactful for an organization to create persona profiles. APG enhances numerical social media data with relevant human attributes, such as names, photos, topics, etc. Here, we discuss the architecture development and central system features. Overall, APG can benefit organizations distributing content via online platforms or with online content that relates to commercial products. APG is unique in its algorithmic approach to processing social media data for customer insights. APG can be found online at https://persona.qcri.org.
Anomaly Event Detection Using Generative Adversarial Network for Surveillance Videos

Anomalous event detection is advantageous for real-time video surveillance systems in terms of safety and security. Current works mostly run offline and struggle with abnormal event detection in crowded scenes. We propose unsupervised anomaly event detection using Generative Adversarial Network (GAN) with Optical Flow to obtain spatiotemporal features in appearance and motion representations. In training, GAN is used to train only the normal event images to generate their corresponding optical flow image. Hence, in testing, since the model knows only the normal patterns, any unknown events are considered as the anomaly event which can be detected by subtracting the pixels between the generated and the real optical flow images. We implement on the publicly available benchmark datasets and compare with state-of-the-art methods. Experiment results show that our model is effective for anomaly event detection in real-time surveillance videos.
Remote sensing image segmentation using geodesic-kernel functions and multi-feature spaces

Image representation is the key factor influencing the accuracy of remote sensing image segmentation. Traditional algorithms rely on the pixel-wise characteristics exhibited in the feature space. They introduce spatial information by establishing the connections between neighboring pixels in the neighborhood system. But the spectral-spatial features cannot be well expressed. In this paper, a Riemannian manifold space is introduced to express the contextual information by jointly mapping the spectral features of a pixel and its neighboring ones on to it. To benefit from the expression ability and geometric properties of the Riemannian manifold, a data submanifold and a parameter submanifold are established to depict the characteristics of the detected image and all possible segmentation results. On the parameter submanifold, only points representing objects of the current segmentation are active. Then distance between a point on the data submanifold and an active point on the parameter submanifold is measured by a geodesic-kernel function. Consequently, four geodesic-kernel function-based manifold projection criteria are proposed to explore the complementation between features expressed in different feature spaces. Experiments on synthetic and real remote sensing images demonstrated that the proposed geodesic-kernel function-based manifold projection algorithm outperforms traditional ones and features expressed in different feature spaces did contain some complementary information.