Reviewerly/exHarmony huggingface dataset:
Effective Methods for Lightweight Image-Based and Video-Based Face Recognition

Face recognition has achieved significant advances with the rise of deep convolutional neural networks (CNNs) and the development of large annotated datasets. However, how to design deep models in lightweight face recognition is still a challenge when aiming at mobile and embedded devices. In this paper, we focus on recent efficient CNN architectures, speedup skills and reduction methods to design models for lightweight face recognition. We combine octave convolution with MobileNet and ResNet for those models sensitive to computation complexity, replace feature output layer for those models sensitive to memory and explore network scaling for more powerful representation. Further, we extract a subset from the whole training dataset to speed up the performance evaluation of different models. We provide a scaling method on MobileFaceNet to boost the performance with the limit of computational cost, and propose a simple supplementary method for average pooling which throws up those noise frames based on the cluster information in video face recognition. With the upper bound of 1G FLOPs computation complexity and 20MB model size, our best model achieves 99.80% accuracy on LFW, 98.48% on AgeDB, 98% on CFP-FP and 97.67% TAR@FAR 10^6 on MegaFace.
Optimization of the Morpher Morphology Engine Using Knowledge Base Reduction Techniques

Morpher is a novel morphological rule induction engine designed and developed for agglutinative languages. The Morpher engine models inflection using general string-based transformation rules and it can learn multiple arbitrary affix types, too. In order to scale the engine to training sets containing millions of examples, we need an efficient management of the generated rule base. In this paper we investigate and present several optimization techniques using rule elimination based on context length, support and cardinality parameters. The performed evaluation tests show that using the proposed optimization techniques, we can reduce the average inflection time to 0.52 %, the average lemmatization time to 2.59 % and the number of rules to 2.25 % of the original values, while retaining a high correctness ratio of 98 %. The optimized model can execute inflection and lemmatization in acceptable time after training millions of items, unlike other existing methods like Morfessor, MORSEL or MorphoChain.
Improving File Hierarchy Attribute-Based Encryption Scheme with Multi-authority in Cloud

With the rapid development of cloud computing technology, users tend to store their data remotely in the cloud to save storage space and enjoy scalable services. However, the cloud servers are not entirely trusted. Ciphertext-policy attribute-based encryption (CP-ABE) is considered as an effective cryptographic approach to prevent the untrusted cloud severs from leaking private data. Since in some areas such as medical and business, the shared data has the feature of multi-level hierarchy, so it makes sense to construct a hierarchy ABE scheme. Recently, Guo et al. proposed a PHR hierarchy multi-authority CP-ABE scheme, which implements global identifier (GID) hiding and hierarchical access control. Unfortunately, we find that the recursive operation (DecryptNode(CT, SK, (x, y))) defined in their scheme during the decryption phase is doubtable. Based on the analysis, we propose an improving file hierarchy MA-ABE scheme. The scheme preserves the security and privacy of the original scheme but reduces the user’s decryption overhead. In addition, we solve the shortcoming which exists in Guo’s scheme and the other corresponding schemes.
DANGEROUS INTERSECTION? HAZARDS, GEOSCIENCE, EMERGENCY MANAGEMENT, AND “THE PUBLIC”

Geoscientists can inform individual to policy-level decision-making for how society identifies, prepares for, responds to, and recovers from disasters and other effects of natural hazards. Making technical information accessible to non-technical end-users is a continual but not insurmountable challenge. Competing demands among finite resources require best available science presented in the most effective way(s) for the audience(s) at hand. Pre-incident contributions include characterizing hazards and identifying associated vulnerabilities, whether for discrete events (e.g., earthquakes, storms, flooding) or long-term changes to hazard profiles (e.g., climate change). These can drive land-use decisions and code adoption as well as more realistic hazard vulnerability analyses. Meaningfully communicating uncertainty, forecast confidence (as applicable), and potential range of impacts are significant challenges but essential in developing valid planning assumptions for response and recovery as well as hazard mitigation investments. For incidents allowing warning, critical geoscientific input includes: potential duration of the warning period and actual event; possible event paths; delineating potentially affected areas; guidance for initial and long-term response considerations. Regardless of warning, post-impact decisions require updated hazard information as well as potential obstacles to response and recovery (e.g., aftershocks, landslides, additional eruptions). Post-incident decision-making offers opportunities to capitalize on short-lived public awareness and interest, ideally driving sustainable recovery, more resilient communities, and more realistic planning assumptions for the future. Specific examples include: Oregon Resilience Plan and related science-based legislation Coordination plans for Mt. Hood and other volcanoes Standardized science-based classes to help communities address local hazards Land-use restrictions in delineated hazard zones Short-term debris-flow warnings
The Qualcomm Wireless Edge Services (QWES) Attestation Token

An attestation format based on the Entity Attestation Token (EAT) isdescribed. The Qualcomm Wireless Edge Services (QWES) token is used inthe context of device onboarding and authentication. It is verified inthe same manner as any CBOR Web Token (CWT).
Exploring Preference of Chronological and Relevancy Filtering in Effort Estimation

BACKGROUND: Effort estimation models are often built based on history data from past projects in an organization. Filtering techniques have been proposed for improving the estimation accuracy. Chronological filtering relies on the time proximity among project data and ignores much old data. Relevancy filtering utilizes the proximity of characteristics among project data and ignores dissimilar data. Their interaction is interesting because one would be able to make more accurate estimates if a positive synergistic effect exists. AIMS: To examine whether the chronological filtering and the relevancy filtering can contribute to improving the estimation accuracy together. METHOD: moving windows approaches as chronological filtering and a nearest neighbor approach as relevancy filtering are applied to a single-company ISBSG data. RESULTS: we observed a negative synergistic effect. Each of the filtering approaches brought better effort estimates than using the whole history data. However, their combination may cause worse effort estimates than using the whole history data. CONCLUSIONS: Practitioners should care about a negative synergistic effect when combining the chronological filtering and the relevancy filtering.
A New Variable Step-size and Updating Search Domains Glowworm Swarm Optimization Algorithm

To address slow convergence of Glowworm Swarm Optimization (GSO) algorithm in the late stage of optimizing multimodal functions, a new variable step-size glowworm swarm optimization (VSGSO) algorithm is proposed, which improves convergence performance by adaptively adjusting step-size based on luciferin carried by each glowworm. Based on VSGSO, VSGSO-D algorithm, which is capable of updating search domains, is proposed. It dramatically improves optimization precision of GSO, especially in the case of optimization of complex multimodal functions. Optimization experiments are carried out by ten benchmark functions. The experimental results show that the improved algorithms proposed in this paper improve the global optimization speed, precision, and stability of GSO. At the same time, the enhanced algorithms protect the diversity of glowworm population and improve the multi-local optimization ability of GSO.
Pengaruh Task-Technology Fit dan Utilization Siakad dan Ub-Feeder Terhadap Dampak Kinerja Individu di Universitas Brawijaya

This paper examinesÂ the effect Î¿f the task-technÎ¿1Î¿gy fit and UtilizatiÎ¿n Î¿n Siakad and UB-Feeder tÎ¿ the individual perfÎ¿rmance impact in UB.Â The research sample is academic staff whÎ¿ use Siakad as well as UB Feeder in Universitas Brawijaya with 99 respÎ¿ndents using the stratified randÎ¿m sampling methÎ¿d. Data was cÎ¿llected using a questiÎ¿nnaire that had been tested fÎ¿r reliability and validity, a measuring instrument with a 5-pÎ¿int Likert scale. The research mÎ¿del uses TechnÎ¿lÎ¿gy PerfÎ¿rmance Chain (TPC) with cÎ¿nstructs: Task Characteristic, TechnÎ¿lÎ¿gy Characteristics, Task- TechnÎ¿lÎ¿gy Fit, UtilizatiÎ¿n and PerfÎ¿rmance Impact. Analysis using descriptive statistical analysis (SPSS) and inferential statistical analysis (SEM-PLS).Â The results Î¿f the hypÎ¿thesis in this study are 1) Task Characteristics have a pÎ¿sitive and significant effect Î¿n Task-TechnÎ¿lÎ¿gy Fit; 2) TechnÎ¿lÎ¿gy Characteristics have a pÎ¿sitive and significant effect Î¿n Task-TechnÎ¿lÎ¿gy Fit; 3) Task-TechnÎ¿lÎ¿gy Fit has a pÎ¿sitive and significant effect Î¿n UtilizatiÎ¿n; 4) Task-TechnÎ¿lÎ¿gy Fit has a pÎ¿sitive and significant effect Î¿n PerfÎ¿rmance Impact; and 5) UtilizatiÎ¿n has a pÎ¿sitive and significant effect Î¿n PerfÎ¿rmance Impact.
Reevaluating Argument Component Extraction in Low Resource Settings

Argument component extraction is a challenging and complex high-level semantic extraction task.As such, it is both expensive to annotate (meaning training data is limited and low-resource by nature), and hard for currentgeneration deep learning methods to model.In this paper, we reevaluate the performance of state-of-the-art approaches in both singleand multi-task learning settings using combinations of character-level, GloVe, ELMo, and BERT encodings using standard BiLSTM-CRF encoders.We use evaluation metrics that are more consistent with evaluation practice in named entity recognition to understand how well current baselines address this challenge and compare their performance to lower-level semantic tasks such as CoNLL named entity recognition.We find that performance utilizing various pre-trained representations and training methodologies often leaves a lot to be desired as it currently stands, and suggest future pathways for improvement.
The Students Value Attitude to Healthy Lifestyle

The relevance of the investigated problem is caused by the increased attention to the issues of a healthy lifestyle among students in Russia.Promotion of a healthy lifestyle, taking into account the individual interests and preferences of students can be embodied not only in the class but also in the framework of elective programs and courses, that are targeted on a healthy lifestyle.The authors reveal approaches, principles and pedagogical conditions for having a healthy lifestyle in the framework of training in educational institutions.The issues of bad habits influence the development and formation of a value attitude to a healthy lifestyle are considered.
MODEL BASED SOFTWARE PROCESS

This paper aims to provide an understanding of what the Model Based SoftwareProcess is and why it is critical to embed it early in the software development process.The Model Based Software Process is best used in highly complex safety-critical applications with short development cycles.It lays the framework to allow for dynamic growth and traceability from the development to validation phase.The motivation behind this process is to reduce the risk of software failures to as low as reasonably practicable
Using Trust and “Utility” for Group Formation in the Cloud of Things

In this paper we consider a CoT (Cloud of Things) scenario where agents cooperate to perform complex tasks. Agents have to select reliable partners and, in some cases, they don’t have enough information about their peers. In order to support agents in their choice and to maximize the benefits during their cooperation, we combined several contributions. First of all, we designed a trust model which exploits the recommendations coming from the ego networks of the agents. Secondly, we propose to partition the agents in groups by exploiting trust relationships to allow agents to interact with the most reliable partners. To this aim, we designed an algorithm named DAGA (Distributed Agent Grouping Algorithm) to form agent groups by exploiting available reliability and reputation and the results obtained in a simulated scenario confirmed its potential advantages.
Segmentation Performance Analysis of Superpixel Methods for Brain MR Images

Magnetic resonance imaging produces large amounts of data, and the manual processing of these data results in high computational complexity. In order to solve the high computational complexity, the grouping process which is commonly used in computer vision systems is recommended. This grouping process is called superpixel. Superpixels, used espically in image and video segmentation applications, are the visiual structures composed of pixels having same color, intensity and texture behavior. Brain MR images from BrainWeb data set were studied. The performances of 3 superpixel algorithms which are widely used in the literature and have high success are compared. In order to test the success of superpixel algorithms, precise reference data of the images were used. Success metrics were calculated between the boundaries created by the superpixel algorithms and the exact reference.
Transitivity of Sensory Verb in Indonesian

This paper discusses the transitivity of sensory verb in Indonesian.This verb is sufficiently productive and analytical in its morphological process.The structural approach is used as to explain the transitivity of sensory verb in Indonesian.This research is conducted under three methodological procedures.They are a.collecting data, b. analyzing data, and c. displaying the result of the data analysis.The distributional method is used to analyze the data.On the data collecting, the method used is observational technique whereas in analyzing the data, the method used is distributional one using grammatical test, and the result of the analysis is presented using both informal and formal method.Owing to the research already done, thus, it was found that the sensory verb in Indonesian may undergo various morphological processes.Such morphological processes bring about implication to both categorical change and grammatical functions.Focusing upon the verbal features of sensory verb in Indonesian, it seems that Indonesian belongs to agglutinative language.This might be proved that 1. Indonesian is an affixed language, 2. The affix could be segmented with an obvious boundary of free morpheme and bound one, and 3.The morphological process on the verb brings about the change of grammatical relation or the verb transitivity that is prefix Vtr.+ ma-in which ma-'nominativeaccusative', suffix verb Vtr.-(k)an 'causative', and prefix Vtr.di-'passive'
STRATEGIES IN COPING WITH PSYCHOLOGICAL STRESS IN FOREIGN LANGUAGE ACQUISITION OF STUDENTS MAJORING IN PEDAGOGY, UNIVERSITY OF LANGUAGES AND INTERNATIONAL STUDIES – VIETNAM NATIONAL UNIVERSITY, HANOI

Changes in training methods from fixed - curriculum system to credit system with teaching methods towards creative and innovation thinking and entrepreneur using advanced technology… has put students at the University of Languages and International Studies - Vietnam National University Hanoi in difficulty, and at risk of psycholgical stress in learning activities. When students encounter psycholgical stress without strategies to overcome, their academic performances will be severely influenced. The research aims to clarify the effectiveness of strategies to cope with psychological stress in foreign language acquisition of students at the University of Languages and International Studies. By using a system of research methodologies, the researcher clarified the theoretical and practical foundation of the strategies in coping with psychological stress in foreign languages acquisition of the students. Research results showed that students encoutered heavily psychological stress in terms of cognitive and behaviour rather than physiology and emotion; the level of psychological stress in foreign language acquisition of experimental group students was minimized due to the influence of impact measures. Its minimization was more than that of control group students. This finding allows to affirm that Instructing students to adjust their cognitive in foreign language acquisition is an appropriate and effective strategy to minimize psychological stress in foreign language acquisition of students majoring in pedagogy, the University of Langugaes and International Studies - Vietnam National University Hanoi.
Research on the gray distribution model of stripe in structured light 3D measurement

Because of the advantages of high measurement accuracy and efficiency, the structured light method has been become a research hotpot in 3D measurement field. The stripe pattern has been widely used in structured light system because of the advantages of anti-noise and high accuracy. Modulated by color and shape of the measurement object, the distribution of the stripe is degenerated. The accuracy of stripe center location, which is a key factor of measurement accuracy, is decreased by the degeneration. In this study, a method for modeling the distribution of stripe in shot image is proposed. Based on the method, several factors which influence the accuracy of locating the stripe center are simulated and analyzed. Furthermore, the performance of typical methods for locating stripe center are simulated. The experiments verified that the method does work. This method not only provide evidence for calculating the measurement accuracy of specific object, but also provide a tool to assess the performance of method for locating stripe center.
Development of Classification Model of Power System Fault by Using PMU Big-Data

Recently, innovative techniques in artificial intelligence such as machine learning have emerged to efficiently process huge amounts of big data delivered from PMUs to WAMS. Through processing raw data and analyzing big data, It delivers highly useful and valuable system status information to system operators. The types of machine learning vary depending on the usage, but the CNN (Convolution Neural Network) model is mainly used for the post analysis and fault detection(classification) in the power system. In this paper, based on PMU big data, we study the power system fault classification model by using CNN Model. Using Convolution neural network model based on KERAS, the database for each fault type was built and supervised learning was conducted for the model. The constructed model was verified with test data and the validity of the model was verified by inputting the actual power system fault data for the trained model. As a result, developed model classified correctly for the actual fault.
Book Review: Ethical Questions in Name Authority Control

Cataloging ethics have received significant attention in recent years, notably via a series of events and discussions held under the umbrella of the Association for Library Collections &amp; Technical Services (ALCTS) Cataloging and Metadata Management Section (CaMMS) exploring the potential development of a code of cataloging ethics. At the same time, the “critlib” (critical librarianship) movement has grown, creating both virtual and physical spaces for exploring social justice principles in the context of library work. Catalogers have initiated conversations about social justice in metadata work under the “critcat” banner. The publication of Ethical Questions in Name Authority Control is timely in this environment, where both ethics and social justice are leading concerns for many catalogers and metadata professionals.
Analysis of influencing factors on the accuracy of close-range photogrammetry system in blade model detection

The blade model is a three-dimensional surface structure that uses a close-range industrial photogrammetry system to measure the blade model. This paper firstly verifies the reliability of the system detection marker point algorithm, and then analyzes the accuracy of the close-range photogrammetry system by three factors: shooting distance, number of coding points and location of the camera station. Experiments show that the shooting distance has a great influence on the calculation accuracy, while the number of coding points and camera position have a little influence. Besides, we get a better data acquisition scheme through experiments, which solve the problem that the system accuracy is easily affected by the shooting conditions, and the system accuracy is further improved.
POSTER: Augmented Situated Visualization for Spatially Aware Decision-Making

The present work proposes the use of data visu-alization techniques allied to an Augmented Reality (AR) user interface to provide information for users to define the most convenient location to sit down at an event. This accounts for different sets of arbitrary demands by projecting 3D information directly atop the seats. The users can also rearrange the data to narrow down the search and switch the attribute being displayed, e.g. temperature, stage visibility, exit access or Wi-Fi signal. The proposed approach was tested against a comparable 2D interactive visualization of the same data. Each user performed twelve location choosing tasks in an average sized classroom. Qualitative and quantitative data indicated that the augmented solution is promising in some senses, exposing that AR may help users to make better decisions.
THE CORRELATION BETWEEN STUDENTS’ LISTENING MOTIVATION, VOCABULARY MASTERY AND SPEAKING ABILITY

Purpose of this research is to know the correlation between the students’ Listening Motivation, Vocabulary Mastery, and Speaking ability. The methodology used is in correlation design by using a quantitative method. The data collecting technique is giving the questionnaire, test, and interview. The data analyzing technique used the correlation of product moment formula to measure the variables. The research findings indicate that there is a significant correlation between students listening motivation, vocabulary mastery, and speaking ability. It was proved by the value of ryx was greater than r table. The correlation between X1 - Y (ryx1) has a value is 0.98, the correlation between X2 - Y (ryx2) has a value is 0.99, the correlation between X1 - X2 (rx1x2) has a value is 0.97, and the correlation between X1 - X2 - Y (rx1x2y) has a value is 0.99. It means that have very high reliability.
Real-time image encryption and decryption methods based on the Karhunen-Loeve transform

There is always a need for a reliable, fast, and accurate encryption algorithm where marginal concessions are not acceptable. The choice of encrypting images in the frequency domain is more suitable as the image components are de-correlated, which provides a suitable platform to classify those values according to their significance. In this work and based on the Karhunen-Loeve transform and the advanced encryption standard algorithm a symmetric lossless encryption and decryption algorithms are proposed. The proposed encryption scheme is applicable for applications that require protected delivery of high-quality data. In terms of quality, the proposed decryption algorithm outperforms the rest of the algorithms in this domain by providing lossless decrypted images that are identical to the originals. Moreover, well-known statistical tests and security measures have been performed in order to validate the security of the proposed encryption algorithm, the results outperform the state-of-the-art algorithms in this domain.