Reviewerly/exHarmony huggingface dataset:
Serving the Need: Engaging in Virtual Video Reference with Students

In March 2020, many academic librarians were abruptly forced into an online service environment due to the global COVID-19 pandemic crisis. Although some librarians had been providing synchronous video reference consultations prior to the pandemic, evidence of the practice in Library Science literature is scant. This paper presents scenarios sourced from anonymized student responses to a digital badge on the library’s chat reference service to illustrate when a video consultation may be the appropriate modality to address an information need.
Efficient Utilization of Resources of Virtual Machines Through Monitoring the Cloud Data Center

In cloud computing paradigm, the management and utilization of infrastructural resources are a challenging process to service providers as well as the cloud users. A cloud user accesses services based on the service provider’s pay and use strategy. In this case, the users fail to utilize the computational resources effectively; it does not only lead to paying more, but also degrade the performance of the cloud data center. To overcome these inopportune situations, we propose a conceptual framework with a heterogeneous environment in a cloud data center. This model is proposed to enhance the management of infrastructural resources by provisioning virtual machines effectively and to improve the efficiency of resource utilization of applications running inside virtual machines (VMs). This paper presents the initial framework for data center setup with the results.
Deep-learning based single object tracker for night surveillance

Tracking an object in night surveillance video is a challenging task as the quality of the captured image is normally poor with low brightness and contrast. The task becomes harder for a small object as fewer features are apparent. Traditional approach is based on improving the image quality before tracking is performed. In this paper, a single object tracking algorithm based on deep-learning approach is proposed to exploit its outstanding capability of modelling object’s appearance even during night. The algorithm uses pre-trained convolutional neural networks coupled with fully connected layers, which are trained online during the tracking so that it is able to cater for appearance changes as the object moves around. Various learning hyperparameters for the optimization function, learning rate and ratio of training samples are tested to find optimal setup for tracking in night scenarios. Fourteen night surveillance videos are collected for validation purpose, which are captured from three viewing angles. The results show that the best accuracy is obtained by using Adam optimizer with learning rate of 0.00075 and sampling ratio of 2:1 for positive and negative training data. This algorithm is suitable to be implemented in higher level surveillance applications such as abnormal behavioral recognition.
A Novel Hybrid data security algorithm for Electronic Health Records security

Data security is a critical aspect in this digital world. Data security algorithms are designed to suit specific applications. Electronic Health Records (EHR) are the digital documents that contain the digital data of the patients. The data includes personal information of the patient along with the medical and treatment histories. The radiology images and medical information is also contained in the digital form in the EHRs. This data is usually stored in the cloud servers because of the data storage issues. The common practice is that the data is divided into multiple segments and stored in different cloud servers. The issue of reconstruction using searchable metrics is the problem that is addressed in this paper using a novel hybrid algorithm. The personal data of the patient is preserved using the k-Nearest Neighbor classification algorithm to secure the data. The proposed method is novel and works in different stages. The results are analyzed and the future scope discussed.
Making sense of human threats and errors

Many reports and surveys point to the fact that humans within your organisation are responsible from anywhere between 30-90% of all your cyber security woes. That's a big range, and even at the lower end a large enough percentage to worry about. But, it's also a very broad statement, and perhaps the reason why no one seems to be able to agree on how big the issue of humans within the organisation actually is. People within your organisation are responsible for anything up to 90% of all your cyber security woes. While many new techniques are being developed to detect and respond to insider threats, dealing with humans requires a different strategy and approach than dealing with malware. Ultimately, it's a delicate balancing act. Technology is not sufficiently advanced at the moment to fully understand humans and make rational decisions. And this is why everyone has a role to play in ensuring the security of the organisation, explains Javvad Malik of KnowBe4.
Hybrid methods for Segmenting and Identifying the Marathi Text

Nowadays tremendous research is carried on document analysis and classification digitally. Old manuscripts like magazines, novels, study material, business cards, and post cards can be straightforwardly construed to computer via these OCRs. Segmentation and Identification of the handwritten or typed lettering is thorny due to a variety of aspect. Words need to be fragmented perfectly and with more precision, else the recognition rate decreases. At this point the projected methodology is presented which performs the job of line, word and character fragmentation. For line segmentation customized horizontal histogram approach has been designed. Words are split up using the regionprops and bounding box method which provide the accuracy in the segmentation. Characters are fragmented by using the vertical projection amid the upper and lower modifier fragmentation using the cut line concept. Lines and Words are also split with good precision and accuracy rate in typed documents, but in handwritten document it also splits the separated or broken characters as extraneous words. Further study need to be done to cover up many more issues, which are not identified and solved during this work.
A Cloud-Based Privacy-Preserving e-Healthcare System Using Particle Swarm Optimization

Internet has become an integral element of our daily lives owing to its increasing usage. During this model, users will share their information and collaborate with others simply through social communities. The e-healthcare community service significantly resolves the issues of individual patients who are remotely situated, have embarrassing medical conditions, or have caretaker responsibilities which will prohibit them from getting satisfactory face-to-face medical and emotional support. Participation in online social collaborations may not be easy due to cultural and language barriers. This paper proposes a privacy-preserving collaborative e-healthcare system that connects and integrates patients or caretakers into different groups. This system enables patients or caretakers to chat with other patients with similar problems, understand their feelings, and share many issues of their own. But during this process, private and sensitive information cannot be disclosed to anyone at any point of time. The recommended model uses a special technique, particle swarm optimization to cluster e-profiles based on their similarities. Finally, clustered profiles are encrypted using distributed hashing technique to persevere patients’ personal information. The results of proposed framework are compared with well-known privacy-preserving clustering algorithms by using popular similarity measures.
Structured Domain Adaptation for Unsupervised Person Re-identification.

Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled source-domain dataset to an unlabeled target-domain dataset. The task of UDA on open-set person re-identification (re-ID) is even more challenging as the identities (classes) do not overlap between the two domains. One major research direction was based on domain translation, which, however, has fallen out of favor in recent years due to inferior performance compared to pseudo-label-based methods. We argue that translation-based methods have great potential on exploiting the valuable source-domain data but they did not provide proper regularization on the translation process. Specifically, these methods only focus on maintaining the identities of the translated images while ignoring the inter-sample relation during translation. To tackle the challenge, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term. During training, the person feature encoder is optimized to model inter-sample relations on-the-fly for supervising relation-consistency domain translation, which in turn, improves the encoder with informative translated images. An improved pseudo-label-based encoder can therefore be obtained by jointly training the source-to-target translated images with ground-truth identities and target-domain images with pseudo identities. In the experiments, our proposed framework is shown to outperform state-of-the-art methods on multiple UDA tasks of person re-ID. Code is available at this https URL.
Enabling Efficient Question Answering over Hundreds of Linked Datasets

In this paper we introduce an approach, called LODQA, for open domain Question Answering over Linked Open Data. We confine ourselves to three kinds of questions: factoid, confirmation, and definition questions. By using LODQA it is feasible to answer questions over 400 millions of entities of any domain without using any training data, since we exploit simultaneously 400 Linked datasets. In particular, we exploit the services of LODsyndesis, a suite of services (based on semantics-aware indexes) which supports cross-dataset reasoning over hundreds of Linked datasets and 2 billion triples. The proposed Question Answering process follows an information extraction approach and comprises several steps including question cleaning, heuristic based question type identification, entity recognition, linking and disambiguation using Linked Data-based methods and pure NLP methods (specifically DBpedia Spotlight and Stanford CoreNLP), WordNet-based question expansion for tackling the lexical gap (between the input question and the underlying sources), and triple scoring for producing the final answer. We discuss the benefits of this approach in terms of answerable questions and answer verification, and we investigate, through experimental results, how the aforementioned steps of the process affect the effectiveness and the efficiency of question answering.
Low SNR Sonar Image Restoration Based on Mixed Probability Statistical Model in Wavelet Domain

Since it is difficult to get texture details for sonar image denoising with strong noise and weak feature information. A sonar image enhancement algorithm in wavelet domain based on Gaussian mixture model and Gaussian mixture model is proposed to preserve the weak feature information of the sonar image. Under strong noise interference, the traditional enhancement method has certain difficulties in measuring the similarity of the details of the sonar image. For this reason, wavelet multi-scale analysis is performed on the sonar image to extract the weak feature information of each resolution. Secondly, a directed probability map model between adjacent scales in the wavelet domain is constructed to realize the similar weak feature information association. According to the parent node state probability and the transition probability matrix, the state values of the corresponding child nodes are determined, and the relationship between the state information of parent and child node is constructed. Thirdly, the Gaussian mixture model is used to fit the wavelet coefficient state distribution, and the neighborhood coefficient correlation is used to describe the relationship between the weak feature information in the scale. Finally, the wavelet coefficient estimation of the restored image signal is calculated by the state probability obtained in the expectation maximization (EM) algorithm, and the sonar image is reconstructed. The results of the contrast experiment are verified by visual effects and objective evaluation. The proposed algorithm can preserve the weak edge and contour information while suppressing the noise of the sonar image. It has better peak signal-to-noise ratio, signal-to-noise ratio and Structural similarity.
Analysis of a cloud migration framework for offline risk assessment of cloud service providers

Summary Generic notions of security on cloud platforms make clients apprehensive about fully migrating their applications on these platforms. The challenge lies in the capability of personalizing the security assessments of different cloud service providers from the perspective of the security requirements of the client applications to be hosted on them. This challenge was addressed by the previously proposed offline risk assessment framework for cloud service providers. This article presents a comprehensive analysis of a cloud migration framework that has been designed by adapting the novel security assessment principles of the offline risk assessment framework. The migration strategy has been modeled as a multiobjective optimization problem to further study the performance of numerous evolutionary algorithms in designing various cloud migration scenarios. The overall effectiveness of the proposed framework has been examined using a use‐case application scenario and semisynthetic cloud service providers.
Compressed Holistic Convolutional Neural Network-based Descriptors for Scene Recognition

Deep convolutional neural networks (CNN) have recently been widely used in many computer vision and pattern recognition applications. With the help of high-level image description features provided by CNN, the deep architecture models perform significantly better than state-of-the-art solutions that use traditional hand-crafted features. In this paper, we concentrate on the scene recognition problem especially for changing environments, such as view angle changes, illumination variations, occlusion, different weather conditions and seasons. We propose a new scene recognition system using the deep residual convolutional neural network (ResNet) as the image feature extractor. The initial feature vectors are chosen from specific layers of the network and after a series of post-processes, we can obtain the final image descriptor vectors for scene similarity measurement. The performance of our proposed methods is evaluated on four popular open datasets by comparing it with the classic FabMap method and some other deep learning-based methods.
A Benchmark Dataset of Online Handwritten Gurmukhi Script Words and Numerals

This paper presents an online handwritten benchmark dataset (OHWR-Gurmukhi) for Gurmukhi script. TIET, Patiala released the unconstrained online handwriting databases, OHWR-GNumerals and OHWR-GScript, which contain isolated strokes samples produced by 190 writers. The OHWR-GNumerals covers 10 stroke classes and OHWR-GScript covers 95 stroke classes to represent the Gurmukhi character set. For data collection, two data sets of Gurmukhi words have been finalized after having a consultation with language experts in order to collect the balanced stroke samples. The preprocessing methods used to prepare these datasets include: size normalization, removing duplicate points, interpolating missing points and re-sampling. The purpose of this benchmark is to create a common platform and make the benchmark dataset publically available for research endeavors in the area of online handwriting recognition. The dataset is available as supplement at https://sites.google.com/view/ohwr-gurmukhi-script/.
Double-Constraint Inpainting Model of a Single-Depth Image

In real applications, obtained depth images are incomplete; therefore, depth image inpainting is studied here. A novel model that is characterised by both a low-rank structure and nonlocal self-similarity is proposed. As a double constraint, the low-rank structure and nonlocal self-similarity can fully exploit the features of single-depth images to complete the inpainting task. First, according to the characteristics of pixel values, we divide the image into blocks, and similar block groups and three-dimensional arrangements are then formed. Then, the variable splitting technique is applied to effectively divide the inpainting problem into the sub-problems of the low-rank constraint and nonlocal self-similarity constraint. Finally, different strategies are used to solve different sub-problems, resulting in greater reliability. Experiments show that the proposed algorithm attains state-of-the-art performance.
Development of a Pedagogical Graphical Interface for the Reinforcement Learning

In this paper the development of a pedagogical graphical interface for the Reinforcement Learning is presented. The proposed framework aims to help students from simulations of traditional domains in literature: Travelling Salesman Problem, Multidimensional Knapsack Problem and Autonomous Navigation. The modules developed in MATLAB are described and case studies are exemplified in the text.
Service Association Factor (SAF) for Cloud Service Selection and Recommendation

Cloud computing is one of the leading technology in IT and computer science domain. Business IT infrastructures are equipping themselves with modern regime of clouds. In the presence of several opportunities, selection criteria decision becomes vital when there is no supporting information available. Global clouds also need evaluation and assessment from its users that what they think about and how new ones could make their selection as per their needs. Recommended systems were built to propose best services using customer's feedback, applying quality of service parameters, assigning scores, trust worthiness and clustering in different forms and models. These techniques did not record and use interrelationships between the services that is true impact of service utilization. In the proposed approach, service association factor calculates value of interrelations among services used by the end user. An intelligent leaning based recommendation system is developed for assisting users to select services on their respective preferences. This technique is evaluated on leading service providers and results show that learning base system performs well on all types of cloud models.
The Effect of Single Harmonic Tuning on Vocal Loudness

The study addresses the benefit of tuning single harmonics with vocal tract resonances to increase vocal loudness. The loudness of theoretically constructed vocal sounds with variable levels of sound energy in the first, second, and third harmonics is computed on the basis of ISO standard 226:2003. In comparison to increased loudness with changes in overall spectral slope, it is shown that single harmonic tuning requires a greater range of SPL to produce a similar range of loudness. For example, a 10–40 dB increase in the level of a single harmonic produces less than two doublings of loudness, whereas a spectral slope change from −12 dB/octave to −3 dB/octave can produce a similar doubling of loudness with only a 5 dB SPL increase.
INTRUSION DETECTION SYSTEM USING MODIFIED J48 DECISION TREE ALGORITHM

We have been used most popular J48 algorithm to evaluate the data. This algorithm is mainly used to evaluate different applications and carry out the exact results over the evaluation process. The main goal of developing this modified J48 choice tree algorithm is to reduce the quest manner in compare with the modern-day lively listing. We have implemented the MAC cope with of the device into CADL. To become aware of an outsider they have been used changed J48 choice tree set of rules. This algorithm produces the similar result as GA.
Kebijakan Standar Nasional Pendidikan

Abstract: National Education Standards are minimum criteria regarding the education system in the entire jurisdiction of the Unitary Republic of Indonesia. The policy regarding National Education Standards is contained in Government regulation No. 19 of 2005 This regulation is a translation of Law No. 20 of 2003 concerning the National Education System. And along with the enactment of the 2013 curriculum, the policy changed to Government Regulation No. 32 of 2013 concerning Amendments to Government Regulation No. 19 of 2005 concerning National Education Standards. National Education Standards contain 8 minimum criteria which include: graduate competency standards, content standards, process standards, teacher and education staff standards, facilities and infrastructure standards, management standards, financing standards, education assessment standards.&#x0D;
Spider Monkey Optimization Algorithm in Data Science: A Quantifiable Objective Study

This chapter addresses an algorithm, namely Spider Monkey Optimization (SMO) for the researchers interested in optimization, swarm intelligence is an engaging realm. Several nature-inspired computing techniques and algorithms have been evolved by simulating the swarming traits of creatures such as ants, fishes, honey-bees, fireflies, etc. The results put forth are indeed extremely promising when applied to find the best-fit solution to a real-world optimization problem. Spider Monkey Optimization Algorithm (SMO) is detailed which is modeled on the basis of the social behavior of spider monkeys. This chapter focuses on detailing the nuances of SMO specifically the phases involved, namely the Leader Phase, Learning Phase, and Decision Phase. It also aims to give an introduction to the basic mathematical jargon and fundamentals that are required to model an SMO algorithm for finding the best-fit solution to an in-hand problem. Various variants of SMO are also covered in this chapter with a detailed overview of the pros and cons of each of the variants focusing on the research gaps. A wealth of information in the form of practical use-cases and implementation nuances is provided in this section, which will be a ready reckoner for a researcher reader aiming to flatten the learning curve associated with experimenting on basic SMO as well as trying out its variants for optimization problems in their specific realm of research. Further, the chapter gives an overview of a transformed version of SMO, Binary SMO with a real-world application and its comparison against other optimization techniques. It also comprises a case study of SMO where it is applied to Traveling Salesman Problem (TSP). SMO is used for this optimization problem to find the least travel cost covering all the cities. The chapter concludes by giving the reader an insight into the latest research trends in SMO.
Long-term Motion Generation for Interactive Humanoid Robots using GAN with Convolutional Network

In this report, we propose a framework for generating long-term human-like motion based on a deep generative model. Thanks to the network structure, the proposed method allows us generating seem- less long-term motions while the model is trained by 4 seconds long short motion samples. The computer graphics of generated motions seem to be reproduced scenes where a pair of persons talking to each other.
The Conflict of Rigidity and Precision in Designation

My paper provides reasons in support of the view that vague identity claims originate from a conflict between rigidity and precision in designation. To put this stricly, let x be the referent of the referential terms P and Q. Then, that the proposition “that any x being both a P and a Q” is vague involves that the semantic intuitions at work in P and Q reveal a conflict between P and Q being simultaneously rigid and precise designators. After having shortly commented on an example of vague identity claim, I make the case for my proposal, by discussing how reference by baptism conflicts with descriptive attitudes towards understanding conceptual contents.