Reviewerly/exHarmony huggingface dataset:
Traceable Ciphertet-Policy Attribute-Based Encryption with Constant Decryption

We provide a traceable ciphertext-policy attribute based encryption (CP-ABE) construction for monotone access structures (MAS) based on composite order bilinear groups, which is secure adaptively under the standard model.We construct this scheme by making use of an "encoding technique" which represents the MAS by their minimal sets to encrypt the messages.To date, for all traceable CP-ABE schemes, their encryption costs grow linearly with the MAS size, the decryption costs grow linearly with the qualified rows in the span programs.However, in our traceable CP-ABE, the ciphertext is linear with the minimal sets, and decryption needs merely three bilinear pairing computations and two exponent computations, which improves the efficiency extremely and has constant decryption.At last, the detailed security and traceability proof is given.
Studi Literatur Keamanan dan Privasi Data Sistem Cloud Computing Pada Platform Google Drive

In today's era, we can do mobilization activities anytime and anywhere. Especially with the cloud computing system that makes it easy to interact from one computer to another without consuming a lot of money. This allows us to access data and information from computers or accounts that are connected. Google Drive is a data storage and synchronization service provided by Google. Where every user can use cloud computing in the form of file sharing and editing collaboration. The purpose of this paper is to find out how the challenges and solutions for data security and user privacy are on the Google Drive platform.
Multi-Label Local-to-Global Feature Selection

Recent years have witnessed the proliferation of multi-label feature selection, which is an effective data pre-processing step for multi-label learning. And label correlations provide critical information for multi-label feature selection. Existing methods consider global label correlations roughly by assuming that the same label correlations are shared by all samples. Nevertheless, there may exist different local inner label correlations in different local sample sets. Although some methods try to explore them respectively in these local sample sets separated by clustering, they ignore the exterior correlation structure of different local inner correlations, leading to performance degradation. To address this problem, we novelly extract local sample sets for each label through mining Markov blankets of these labels, based on which the proposed multi-label local-to-global feature selection algorithm (ML2G) is employed to select predictive features on these sample subsets. ML2G simultaneously learns different local inner label correlations in each local sample set and the exterior structure of these local inner correlations. Moreover, different from existing methods, ML2G extra considers the asymmetric label correlations, which could describe label correlations more accurately and thus improve the performance of ML2G. Empirical studies validate the superiority of ML2G against state-of-the-art methods on realworld datasets.
Enhancing the Performance of Traditional Hadoop Using AWS with EMR Approach

In this on-demand, on command digital universe we have data growing at exponential rate. This data is growing in its volume, variety and velocity as a result it is called “Big Data”. Analysis of this growing data is a challenging job due to the involvement of large distributed file system. Map-Reduce in traditional Hadoop architecture is well known for analyzing big data. Amazon Map-Reduce programming model is introduced by Amazon to work with Amazon computing model and its data storage services. This approach is elastic in its nature hence, called Elastic Map-Reduce(EMR). In Amazon Web Services (AWS) made use of Amazon EC2 model and Amazon S3 is used for storing large complex data. In this paper, the proposed work is to understand the analysis and implementationof Hadoop on top of Amazon  EMR approach. This make use of Amazon Services , nodes, compute services, security and  management tools.
Keyword Extraction for Improved Document Retrieval in Conversational Search.

Recent research has shown that mixed-initiative conversational search, based on the interaction between users and computers to clarify and improve a query, provides enormous advantages. Nonetheless, incorporating additional information provided by the user from the conversation poses some challenges. In fact, further interactions could confuse the system as a user might use words irrelevant to the information need but crucial for correct sentence construction in the context of multi-turn conversations. To this aim, in this paper, we have collected two conversational keyword extraction datasets and propose an end-to-end document retrieval pipeline incorporating them. Furthermore, we study the performance of two neural keyword extraction models, namely, BERT and sequence to sequence, in terms of extraction accuracy and human annotation. Finally, we study the effect of keyword extraction on the end-to-end neural IR performance and show that our approach beats state-of-the-art IR models. We make the two datasets publicly available to foster research in this area.
SOMA: Solving Optical Marker-Based MoCap Automatically

Marker-based optical motion capture (mocap) is the gold standard method for acquiring accurate 3D human motion in computer vision, medicine, and graphics. The raw output of these systems are noisy and incomplete 3D points or short tracklets of points. To be useful, one must associate these points with corresponding markers on the captured subject; i.e. labelling. Given these labels, one can then solve for the 3D skeleton or body surface mesh. Commercial auto-labeling tools require a specific calibration procedure at capture time, which is not possible for archival data. Here we train a novel neural network called SOMA, which takes raw mocap point clouds with varying numbers of points, labels them at scale without any calibration data, independent of the capture technology, and requiring only minimal human intervention. Our key insight is that, while labeling point clouds is highly ambiguous, the 3D body provides strong constraints on the solution that can be exploited by a learning-based method. To enable learning, we generate massive training sets of simulated noisy and ground truth mocap markers animated by 3D bodies from AMASS. SOMA exploits an architecture with stacked self-attention elements to learn the spatial structure of the 3D body and an optimal transport layer to constrain the assignment (labeling) problem while rejecting outliers. We extensively evaluate SOMA both quantitatively and qualitatively. SOMA is more accurate and robust than existing state of the art research methods and can be applied where commercial systems cannot. We automatically label over 8 hours of archival mocap data across 4 different datasets captured using various technologies and output SMPL-X body models. The model and data is released for research purposes at https://soma.is.tue.mpg.de/.
La educación superior en tiempos de pandemia y su realidad en el Ecuador

Universidades y centros de estudios se han visto forzados a cambiar su metodologia de ensenanza a partir de la amenaza que representa el COVID-19 en Ecuador. La afectacion de la economia familiar ha repercutido directamente en los estudiantes universitarios, al dificultarle la adquisicion de los recursos elementales para el desarrollo universitario y la adquisicion del conocimiento para la formacion de los futuros profesionales. La paralizacion y un extenso confinamiento ha conducido a la Institucion de Educacion Superior (IES) a promover acciones para resistir los impactos economicos del cierre temporal o parcial de las universidades. El e-learning ha constituido una herramienta primordial para la continuidad de los estudios universitarios, con la ayuda de las Tecnologias de la Informacion y las Comunicaciones (TICs). Es de destacar que la actualidad, Ecuador presenta una serie de factores que afectan el desarrollo de la educacion en la ensenanza superior. Es por ello que el presente trabajo se enfoca en analizar y determinar los factores o causas potenciales que frenan el desarrollo de la educacion universitaria en tiempos de COVID-19 mediante el uso y el analisis del metodo Delphi.Palabras clave: COVID-19, ensenanza superior, Delphi. ABSTRACTUniversities and study centers have been forced to change their teaching methodology due to the threat posed by COVID-19 in Ecuador. The impact on the family economy has had a direct impact on university students, making it difficult for them to acquire basic resources for university development and the acquisition of knowledge for the training of future professionals. The stoppage and extensive confinement have led the Higher Education Institution (IES) to promote actions to resist the economic impacts of the temporary or partial closure of the universities. E-learning has been an essential tool for the continuity of university studies, with the help of Information and Communication Technologies (ICTs). It is noteworthy that currently, Ecuador presents a series of factors that affect the development of education in higher education. That is why this work focuses on analyzing and determining the potential factors or causes that slow down the development of university education in times of COVID-19 through the use and analysis of the Delphi method.Keywords: COVID-19, Higher Education, Delphi.
MENINGKATKAN KEMAMPUAN MEMBACA PERMULAAN ANAK USIA 5-6 TAHUN MELALUI MEDIA LEGO HURUF

The ability to read is a characteristic that allows children to recognize symbols relating to letters into pools that allow children. The purpose of this research is to know how to apply media and discussion about ability in various media form at Kemala Bhayangkari 01 Serang-Banten kindergarten. Benefits of the application of the media lego letters can help children in the process of reading activities, make children understand well, and help teachers in using reading skills in a way that is fun and interesting for students, The method used in this research is action research methods (PT ), in this study consist of two cycles. In the first cycle of 8 actions and cycle II as much as 4 times the action, reaching 60%. The subjects of this study were children aged 5-6 years with a total of 18 children consisting of 10 girls and 8 boys. Based on the results of the research, the reading ability of children 5-6 years old through the lego media with the results of the research At this time is 30%, then in the first cycle increased to 59%, while in cycle II experienced significant improvement reached 91% and apply to already targeted indicators. Based on the results of data that have been obtained can be concluded that with the application of media lego letter can improve the ability to read the beginning of children 5-6 years in kindergarten Kemala Bhayangkari 01 Serang-Banten.
SFTrack++: A Fast Learnable Spectral Segmentation Approach for Space-Time Consistent Tracking.

We propose an object tracking method, SFTrack++, that smoothly learns to preserve the tracked object consistency over space and time dimensions by taking a spectral clustering approach over the graph of pixels from the video, using a fast 3D filtering formulation for finding the principal eigenvector of this graph's adjacency matrix. To better capture complex aspects of the tracked object, we enrich our formulation to multi-channel inputs, which permit different points of view for the same input. The channel inputs could be, like in our experiments, the output of multiple tracking methods or other feature maps. After extracting and combining those feature maps, instead of relying only on hidden layers representations to predict a good tracking bounding box, we explicitly learn an intermediate, more refined one, namely the segmentation map of the tracked object. This prevents the rough common bounding box approach to introduce noise and distractors in the learning process. We test our method, SFTrack++, on seven tracking benchmarks: VOT2018, LaSOT, TrackingNet, GOT10k, NFS, OTB-100, and UAV123.
Socio-Technical HCI Design in a Wider Context

This chapter reflects on the use of the HWID platform in a wider context of ethical value exchange, post-humanism, and the anthropocene. It asks the question if we should design socio-technical HCI design that basically helps humans to come to terms with ‘living with monsters’, that is, powerful algorithms. It attempts to explain the difference between socio-technical and sociomaterial and how they are both useful for design. At the same time, the chapter argues that human psychology should be given a prominent place in the use of the HWID platform and suggests to study socio-technical HCI design phenomena related to the human user tendency to anthropomorphize technology such as AI, robots, etc. The chapter ends with pointing to the need for designing on a global scale, while reflecting on the psychology of the planetary interaction designs. In this way, the chapter becomes a powerful argument for keeping a strong focus on the technical in the social sciences, without falling into a trap of technological determinism.
36. REVEALING LATENT STRUCTURE OF CORTICAL THICKNESS USING GENETIC AND DISEASE ASSOCIATIONS

This paper discusses the issue of periodicity and finite-time periodic synchronization of discontinuous complex-valued neural networks (CVNNs). Based on a modified version of Kakutani’s fixed point theorem, general conditions are obtained to guarantee the periodicity of discontinuous CVNNs. Next, several criteria for finite-time periodic synchronization (FTPS) are given by using a new proposed finite-time convergence theorem. Different from the traditional convergence lemma, the estimated upper bound of the derivative of the Lyapunov function (LF) is allowed to be indefinite or even positive. In order to achieve FTPS, novel discontinuous control algorithms, including state-feedback control algorithm and generalized pinning control algorithm, are designed. In the generalized pinning control algorithm, a guideline is proposed to select neurons to pin the designed controller. Finally, two simulations are given to substantiate the main results.
MOBILE WALLET SYSTEM FOR STUDENTS IN BOARDING SCHOOL

This paper will introduce a new implementation of existing technology that aims to help the parents to monitor their children expenditure from home. This research needs to study the existing mobile wallet system, to develop mobile wallet system for boarding school students and to evaluate the acceptance of the parents towards the usage of mobile wallet. E-wallet is an upcoming technology that has been a tremendous growth in the past year. Nowadays, most of boarding school has allowed the students to bring their smartphone with certain regulations that need to obey. The parents able to track the history transaction and observe the pattern amount of money spent by their kids by using smartphone. To conduct the preliminary studies, literature review of the project and surveying to the potential users will be done. For the mobile application development, Rapid Application Development (RAD) model will be apply for the System Development Life Cycle (SDLC) technique. E-wallet has a great potential to help the students in their spending without bringing cash money while their parents can observe their children expenditures.
Parameter Determination of Probability Distribution by Bayesian Inference

Neural networks or deep learning have become synonymous with artificial intelligence (AI), and the idea that human intellectual work will soon be possible with AI has come to be born. But that doesn't mean that deep learning is perfect. For example, it is known that there are many problems such as "inference is a black box", "unexpected answer by overfitting", and "large-scale network and long learning". Bayesian inference can provide learning and inference that is completely different from neural networks. Therefore, it might be possible to overcome the problems of neural networks. In this paper, we discuss the application of Bayesian inference to the estimation of parameters of the probability distribution. It also discusses the effects of prior probabilities and the relationship between Bayesian inference and maximum likelihood.
Efficient Automatic Change Detection in Software Maintenance and Evolutionary Processes

Software maintenance is such an integral part of its evolutionary process that it consumes much of the total resources available. Some estimate the costs of maintenance to be up to 100 times the am ...
LibGuides: Library Intranet: Research support

To share teaching, learning and research support information The page shares research support activities, reports and documents
An Implementation Approach Towards The Automation Of Document Formatting Using Python

Documentation is an essential component for reporting / documenting the workdone. Manual document formatting might lead to some minor human errors which wouldremain unnoticed for most of the time. Documents like project report, research papers,thesis etc. often require a certain set of rules and standards which needs to be followedstrictly. If the formatting is not done according to those rules, it might even result inrejection of the document. Automation of document formatting will help in solving theseproblems.Automation helps in speeding up the documentation process by automatically makingcorrections in the user’s document by comparing it against a template document. Thetemplate document will have the formatting rules to be followed and using which the user’sdocument can be validated and verified. Thus this process would not just eliminate minorhuman errors but also reduce a lot of human effort and time.The users can access the automation tool through a web application interface which wouldallow them to upload and format the document based on a particular template and alsoallows the user to create new custom templates.
Pathologic results of stereotactic core needle biopsy in patient with malignancy suspicious to microcalcification on mammography

Introduction: Due to the fact that breast cancer has a high prevalence in the society and in a significant number of patients, its initial manifestation is only in the form of microcalcification on mammography, this study was performed with aim to evaluate the pathologic results of stereotactic core needle biopsy to determine the prevalence of malignant and benign lesion and subtype in each group in patients with suspicious microcalcifications on mammography based on BIRADS classification. Methods: In this descriptive study, 59 patients referred to mammographic clinic in Mashhad city between April2017-September2019, and underwent sterotactic core biopsy because of the presence of suspicious microcalcifications (including morphology and distribution) without mass or focal asymmetry on mammography. In all patients, calcification was identified in at least one core needle specimen after performing specimen mammography. The specimens were submitted and referred to pathologic laboratory and analyzed by two pathologists. The total prevalence of benign and malignant lesions and also the prevalence of subtype lesions in each group were evaluated. The results of different mammographic appearance of suspicious microcalcifications were also compared with pathologic diagnosis. Data were analyzed using SPSS statistical software (version 16) and descriptive statistics methods and frequency distribution of variables in the form of tables. P<0.05 was considered statistically significant. Results: Benign lesions were seen in 42 patients (71) and malignant lesions in 17 patients (29) in histopathologic examination. Total prevalence of benign lesions was more than malignant lesions and the prevalence of non-proliferative lesions more than proliferative lesions. Fibrocystic change was the most common lesion in non-proliferative group (54). In malignant group, prevalence of Dcis Carcinoma and invasive ductal carcinoma was 74 and 26, respectively. Prevalence of malignancy was more in pleomorphic type of microcalcifications than amorphous and coarse heterogeneous types. Conclusion: According to the significant prevalence of malignancy especially the prevalence of DCIS, sterotactic biopsy can be used as a noninvasive biopsy method for Microcalcifications which have a suspicious morphology and distribution to detect early breast cancer.
TAM-132 Machine Shop Print Reading II

Students will be able to solve complex blueprint problems related to tool and shop applications. Section views, surface textures, screw threads, geometric tolerancing, steel identification, fasteners, castings, and coatings will be examined.
THE CORRELATION BETWEEN THE ADVANTAGES OF LISTENING TO ENGLISH SONG AND STUDENTS’ SPEAKING COMPETENCY

Many advantages has received by students by using English song. Through qualitative method the data gathered showed that listening to English song can improve students’speaking competency. The sample was taken 19 students from all of pupulation. Based on the findings of this study, it can be concluded that using English song greatly increases vocabulary and selfconfidence the interest of students in improving students' speaking competency. Moreover, by using English song in learning English, which ultimately can enrich their self-confidence to speak english better.
Legal Causal Reasoning. Cause-effect Analysis using a Neutrosophic Environment

Causal reasoning is implicit in all areas of human life, which is why it is strongly applied within the legal framework. This is valid in both criminal and civil proceedings. For the construction of legal causal models, moral reasoning and understanding of the subject are required. It is a process that implies subjectivity and neutrality. Therefore, it is intended to illustrate the usefulness of causal analysis techniques for decision-making in the dynamics of legal reasoning in a neutrosophic environment given the subjectivity of the process. For that purpose, the methods applicable to this research will be enunciated and will be illustrated in a case study. With the use of the Ishikawa Diagram, Pareto analysis, and Neutrosophic Cognitive Maps, we were able to establish the cause-effect tree as well as the main conclusions concerning legal causal reasoning applied to the increment of traffic accidents in Ecuador.
Out-of-Core Surface Reconstruction via Global TGV Minimization

We present an out-of-core variational approach for surface reconstruction from a set of aligned depth maps. Input depth maps are supposed to be reconstructed from regular photos or/and can be a representation of terrestrial LIDAR point clouds. Our approach is based on surface reconstruction via total generalized variation minimization ($TGV$) because of its strong visibility-based noise-filtering properties and GPU-friendliness. Our main contribution is an out-of-core OpenCL-accelerated adaptation of this numerical algorithm which can handle arbitrarily large real-world scenes with scale diversity.
TWITTER MINING FOR NATURAL DISASTER RESPONSE

Recent years, billions of people are using social media and billions of contents are generated by users daily. The common social media platforms used worldwide are Twitter, Facebook and Instagram and the contents are of vast topics such as fashion, politics, business, education, etc. In a way, social media is a useful source of information for conducting sentiment analysis to understand a user’s attitude or emotions towards the topic by classifying it into positive and negative category. The main purpose of this project is to develop sentiment analysis focusing on a single domain which is to detect natural disaster in Tweets by using the widely known lexicon-based approach. Python programming language will be utilized for the development of the system. This project may assist first responders to improve situational awareness and crisis management. In order to understand sentiment analysis and lexicon-based approach, research and literature review was done, and each topic is explained separately.