Reviewerly/exHarmony huggingface dataset:
Efficient and Secure Storage for Renewable Energy Resource Data Using Parquet for Data Analytics

The era of time series data as its implementations and interpretation have recently become increasingly relevant in different domains and fields. Numerous fields of industry and science rely on collecting and examining vast quantities of time series data—finance and economics, the Internet of things, medicine, environmental protection, hardware surveillance, among several others. The main structural problems in time series data analysis are the collection and retrieval in vast quantities of data. In addition, time series data analysis seems to be of tremendous value, since previous patterns are helpful in predicting future results. Owing to the considerable latency in data volume changes, the frequency of persistence and the absence of inborn structures in time series, the traditional hierarchical data retrieval method would not seem to be able to analyze time series data efficiently. Often, many traditional data storage solutions do not support time series-based operators, which result in unreliable time series service. The description of effective and stable local data storage for time series data has been provided in this paper. The study of wind/solar time series data is represented by changing the number of rows, the number of days and the number of columns with a time resolution of 10, 15, 30 and 60 min. The results of the experiment display time series data access time, write period and storage size of the parquet data storage format that has proved to be an effective and reliable data storage device for the local centralized server. The data storage model of Apache Parquet appears to be effective as compared to other file storage formats such as CSV, HDF5 and FEATHER.
Adaptive Inverse Control of Nonlinear Systems Using Dynamic Neural Networks

It has been demonstrated by many researchers that an unknown dynamic plant can be made to track an input command signal if the plant is preceded by a controller which approximates the inverse of the plant’s transfer function. Precascading a plant with its inverse model provides an unity mapping between the input and output signal space. This concept of inverse modeling has been referred to as adaptive inverse control. However, the concept of transfer function is limited to linear systems, and the control algorithms developed under this framework can not be extended to nonlinear systems. Due to the functional approximation and learning capabilities, the artificial neural networks can be employed to extend the concept of adaptive inverse control to nonlinear systems. In this paper, two dynamic neural structures, called recurrent neural network and dynamic neural processor, are used to coerce the nonlinear systems to follow the desired trajectories based on the principle of adaptive inverse control. In the process, we compare the performance of these two dynamic neural structures as applied to the inverse control problems.
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo

In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of keyframes. To increase the robustness, we propose a novel tracking front-end that performs dense direct image alignment using depth maps rendered from a global model that is built incrementally from dense depth predictions. To predict the dense depth maps, we propose Cascade View-Aggregation MVSNet (CVA-MVSNet) that utilizes the entire active keyframe window by hierarchically constructing 3D cost volumes with adaptive view aggregation to balance the different stereo baselines between the keyframes. Finally, the predicted depth maps are fused into a consistent global map represented as a truncated signed distance function (TSDF) voxel grid. Our experimental results show that TANDEM outperforms other state-of-the-art traditional and learning-based monocular visual odometry (VO) methods in terms of camera tracking. Moreover, TANDEM shows state-of-the-art real-time 3D reconstruction performance.
An Image Strategy Based on Saliency Detection Using Luminance Contrast for Artificial Vision with Retinal Prosthesis

Due to the limited number of implantable microelectrodes, subjects worn by retinal prosthesis received only limited discrete light spot (called artificial vision). Thus, the external abundant information is largely lost. Whereas there is little room for significant improvement in the electrodes number, it would be a feasible way to optimize the low-resolution information in the prosthetic vision by effective image processing. Based on luminance contrast, a real-time saliency detection strategy was presented to enhance the artificial vision, which included color space conversion and a visual attention simulation processing method. The strategy was evaluated by two benchmark databases, and its superiority was validated in extracting image foreground compared with the basic luminance contrast and other algorithms. Meanwhile, simulated experiments were conducted to verify the proposed strategy in the retinal prosthesis.
Robustness Analysis of Deep Learning Frameworks on Mobile Platforms

With the recent increase in the computational power of modern mobile devices, machine learning-based heavy tasks such as face detection and speech recognition are now integral parts of such devices. This requires frameworks to execute machine learning models (e.g., Deep Neural Networks) on mobile devices. Although there exist studies on the accuracy and performance of these frameworks, the quality of on-device deep learning frameworks, in terms of their robustness, has not been systematically studied yet. In this paper, we empirically compare two on-device deep learning frameworks with three adversarial attacks on three different model architectures. We also use both the quantized and unquantized variants for each architecture. The results show that, in general, neither of the deep learning frameworks is better than the other in terms of robustness, and there is not a significant difference between the PC and mobile frameworks either. However, in cases like Boundary attack, mobile version is more robust than PC. In addition, quantization improves robustness in all cases when moving from PC to mobile.
The FMEA Traditional Modifications (FMEA Improvement) in IT Risk Assessment: The FMEA Traditional Modifications

Failure mode effect and analysis (FMEA) was an essential technique in risk management. Various studies have revealed the shortcomings of FMEA, one of them is about subjectivity and the consistency of the results issues. This research contributed to the FMEA improvement as the improvement Model to minimize traditional FMEA consistency issues. Based on previous research, the present study will discuss in more detail the FMEA improvement method. The research questions formulated for this research are: (1) How was the use of the FMEA improvement model in the case study; and (2) What were the implications of the FMEA improvement model. The FMEA Improvement Model consisted of four main stages, namely: (1) the determination of risk assessment requirements; (2) risk identification; (3) risk analysis; and (4) evaluation and the recommendation control documentation. From the results of risk level categorization, one risk was at the highest level. This risk was classified as severe because it often happens almost every day. Two risks were at the medium to the high level, three risks were at the medium level, 13 risks were at the low to medium level, and 18 risks were low. Therefore, this paper's contribution was to guide researchers or IT experts to apply the FMEA improvement.
PushdownDB: Accelerating a DBMS using S3 Computation

This paper studies the effectiveness of pushing parts of DBMS analytics queries into the Simple Storage Service (S3) engine of Amazon Web Services (AWS), using a recently released capability called S3 Select. We show that some DBMS primitives (filter, projection, aggregation) can always be cost-effectively moved into S3. Other more complex operations (join, top-K, group-by) require reimplementation to take advantage of S3 Select and are often candidates for pushdown. We demonstrate these capabilities through experimentation using a new DBMS that we developed, PushdownDB. Experimentation with a collection of queries including TPC-H queries shows that PushdownDB is on average 30% cheaper and 6.7X faster than a baseline that does not use S3 Select.
ISO Disk Images Data Curation Primer

This work was created as part of the “Specialized Data Curation” Workshop #3 held at Washington University in St. Louis, MO on November 5-6, 2019.
Pelatihan Dasar Microsoft Office Terhadap Santriwan Santriwati Yayasan Ar-Rahman Krukut

Information and Communication Technology (ICT) is currently very much needed by students and teaching staff at the Ar-Rahman Foundation. Especially for female students, training in the use of information and communication technology devices is needed so that later students can be used properly. The students and teaching staff cannot use Microsoft Excel and PowerPoint because they are not taught in schools, and the cost of computer courses is high among students in the Krukut area. One of them is to train the ability to use a word processing application, namely Microsoft Word. Community service activities are emphasized in the form of training on the application of Microsoft Office for students and teaching staff of the Ar-Rahman Krukut Foundation. It is hoped that with this training, students and teaching staff can better know the techniques of using Microsoft Office by utilizing Microsoft Word, Microsoft Excel, Microsoft PowerPoint. The results of the dedication carried out provide experience and skills for students and teaching staff in using Microsoft Office. Thus, the implementation of community service activities at the Ar-Rahman Foundation Using Microsoft Word provides significant benefits for improving the skills of students and teaching staff in utilizing information technology and computers, they are very enthusiastic to take part in the next activity. They already understand and can operate Microsoft Office.
Visual Cognition in Early Infancy

Visual activity is an obviously useful object of study for research on visual sensitivity and visual processing. The work described in this chapter concerns both the newborn period and the period of infancy from 3 weeks to 4 months of age. It is important to appreciate that measurement of the video record of visual activity can be carried out on several levels that require different degrees of technical sophistication. Kearsley used an observational measure in a study of newborns’ orienting and defensive reactions to various parameters of sound. Of the recording, measurement, and analysis triad, analysis is by far in the most primitive state. A major frustration in faithfully preserving the original visual behavior is the difficulty of translating that behavior into numbers that do it justice. One of the most difficult aspects of doing eye-movement research is the tedium involved in measuring pupil center and reflected infrared light markers after the data are recorded.
Transformed puzzle for preventing selfish mining: A non-viable way to defend zero block algorithm

Blockchain has become a one-stop solution to achieve data integrity. Blockchain is the series of blocks that contains the transactions or data of any kind. All of these blocks (data) is guarded by using cryptographic principles. The block will be added once the cryptographic puzzle is solved. The main aim of miners (people who solve the cryptographic puzzle) behind adding a new block is to achieve reward. But to achieve more rewards, miners keep the generated block private and these miners will generate more blocks based on the block which was generated previously by the corresponding miners. These miners will publish more blocks at a time to get a big reward. It is called as selfish mining. Moreover, a solution is being proposed known as the zero block algorithm. This paper presents a modified zero block algorithm in such a way that it can resist selfish mining. The original zero block algorithm generates dummy zero blocks whereas the modified zero block algorithm will not generate any dummy zero blocks and is more efficient than the zero block algorithm.
Aplikasi Layanan Pemesanan Kamar Sewa Berbasis Android

Many students who come to Pekanbaru to study make them have to find a place to live. Due to the lack of information, students choose a place to live that is not in accordance with their expectations, which are modest facilities with too expensive rents. To solve this problem, an android application was developed to make it easier for students to get rental room information and booking rental rooms as expected. This application uses the Unified Modeling Language (UML) for system design and using java for the programming language, as well as using firebase for its database. This application utilizes realtime database technology from Google, namely Firebase with fast access, equipped with an online booking feature. The results of the application testing are that students can find out the facilities and positions of the rental rooms and make it easier to booking rental rooms.Keywords : Android, Booking, Firebase, Rental Room
Quantitative and Stream Extensions of Answer Set Programming

Answer Set Programming has separately been extended with constraints, to the streaming domain, and with capabilities to reason over the quantities associated with answer sets.We propose the introduction and analysis of a general framework that incorporates all three directions of extension by exploiting the strengths of Here-and-There Logic and Weighted Logic.
Hubungan Bimbingan Belajar dalam Keluarga dengan Prestasi Belajar Matematika Kelas Tinggi SDN 26 Watang Palakka

The problem in this study is the lack of tutoring in the family has an urgent task in education. This study is a quantitative study with a correlation model that aims to determine the relationship betweem tutoring in the family and high grade  mathematics learning achievement at SDN 26 Watang Palakka, The data analysis technique using statistical analysis obtained tutoring in the family has an average of 84,5 and a percentage of 84,5% and the average student learning achievement in mathematics is 82,17 and an achievement of 82,17% with a very good category. Then based on the results of inferential statistical analysis, it shows that  (5,5406) is greater (>) than the value of  (1,66277) at a significant level of 5% so that it can be concluded that there is a significant relationship between tutoring in the family and learning achievement in mathematics students from these conclusions. The impact generated in this study with the existence of tutoring in the famiy can improve student achievement.
S.A.M.I.R.: Supporting Tele-Maintenance with Integrated Interaction Using Natural Language and Augmented Reality

Remote maintenance is becoming a crucial aspect in the industrial life cycle, especially in context where different countries, languages, and time zones are involved. Supplying on-site operators with smart tools to rapidly and easily request support can boost maintenance procedure execution times and solving of unexpected problems, can reduce the number of interventions and can speed up novice training, resulting in cost reduction and equipment use maximization. Authoring tools are needed to properly create new multimedia content and to process, organize and index legacy company knowledge, regardless of type. In this paper the SAMIR solution is presented, it defines a SaaS platform integrating Augmented Reality and Natural Language Processing to supply an effective support during maintenance intervention together with authoring tools to create multimedia content and procedures.
REMOTE PATIENT MONITORING SYSTEM (RPMS) MODEL TO IMPROVE EMERGENCY SERVICES IN PAKISTAN DURING COVID 19 - A CONCEPTUAL FRAMEWORK USING ICT TOWARDS OVERCOMING HEALTHCARE PROBLEMS IN PANDEMIC HISTORY

The history of the world during the pandemic era shows a worse kind of treatment to the effected people. The year 2020 is known as a terrifying year due to pandemics in the world. It badly exposes the healthcare system's weakness especially in the developing countries like Pakistan. Ambulance services played a major role in transporting affected people during pandemics. However, an independent survey shows that around 130 million people have very limited access to these facilities in Pakistan. The majority of the population of Pakistan lives in remote and rural areas and is deprived of these services. In this regard, remote monitoring of patients while they are in transit is very crucial. Considering the importance of providing better healthcare services to patients, we propose a model for remote patient monitoring systems (RPMS) integrated with emergency services in Pakistan. This healthcare model continuously keeps track of people including patients and nearest ambulances (the in-context emergency service and the key resource) for helping patients by transporting them to the appropriate healthcare center, as per needed assistance on the go. It helps reducing response time and yet increasing golden time by directly engaging the ambulance from the nearest location (using spatial and temporal features) and bypassing the call center to save the inevitable consumption of time shattered by conventional method. The represented model may enhance the availability of emergency healthcare facilities by reducing service time and allow efficient use of resources. © 2021 Asian Research Publishing Network (ARPN). All Rights Reserved.
Classification of Facial Expressions using Machine Learning

Recognition of facial expressions is one of the most powerful and challenging tasks in Non-verbal communication.Normally major part of communication involves verbal Channels.But Non-verbal gestures are majorly expressed through facial expressions.Our project is based on classification of various human expressions using various types of Face Expression Recognition (FER) techniques which include the three major stages such as preprocessing, feature extraction and classification.We have carried out all these techniques using Convolutional Neural Networks (CNN).Our project is inspired by VGG and Xception model.Datasets used are FER 2013 (for emotion classification), IMDB (for gender classification), FEC (Google facial expression comparison).Using CNN, we classify 7 different expressions like Happy, Sad, Anger, Disgust, Fear, Surprise and Neutral.
Research Guides: Tips for Using Library Search: Access Changes due to COVID-19

Provides some tips and strategies for effectively using the University of Michigan Library search engine.
Media Image of the Screen as Representation of Sociocultural Reality

The article touches upon issues of the common and distinctive characteristics of documentary films and popular science TV series. It concerns special aspects of representation in these two screen forms in their sociocultural context and gives the author's vision of their sociocultural and anthropological foundations. The role of media images of the TV screen and the movie screen in the modern media space is substantiated. Approaching the audio-visualization of visuals from the angle of cultural studies and philosophy, mental activities of TV and movie viewers are opposed. Epistemologically, the article traces special accents typical for the popular science genre on the TV screen and in the cinema, boosted by documentaries. The relation between form and content in the screen visualization of ideas is substantiated. The present work touches upon issues related to the goals of creating documentary films, on the one hand, and popular science TV series, on the other. It covers the pictorial scheme of directing documentary films. Furthermore, the bases of visual representation in documentary film are presented. The complex process of creating a popular science series is considered as opposed to the versatility of this genre. The author proposes certain ideas about the productive mental activity of the viewers in terms of epistemological approach. He resorts to polemic arguments in the discussion about the development of an objective worldview. What offers the true image of the world around us, a popular science series or a documentary? The article puts up the elements of evidence as preconditions for the conformation of media semantic images through the screen. The special aspects of visual anthropology are touched upon. The author speculates about the possible correlation between documentary filmmaking and audiovisual information in ethnographic terms. The article ends with a number of conclusions.
Formal Event-B Modeling of the MICONIC Application

Automatic planning has a de facto standard language called PDDL for describing planning problems. The dynamic analysis tools associated with this language do not allow sufficient verification and validation of PDDL descriptions. Indeed, these tools, namely planners and validators, allow a posteriori error detection. In this paper, we recommend a formal approach coupling the two languages Event-B and PDDL. Event-B supports a formal development process based on the refinement technique with mathematical proofs. Thus, we propose a refinement strategy for obtaining reliable PDDL descriptions from an ultimate Event-B model that is correct by construction. The correctness is guaranteed via the verification and validation tools supported by Event-B. We have chosen the MICONIC application managing modern elevators to illustrate our approach while recognizing that the MICONIC application is already modeled in PDDL without formal proof of its correctness.
Library System: Library Partnerships: Home

The Gordon-Conwell Library System partners with many other libraries to offer services to our students both on and off campus.
A Hitchhiker's Guide to Ontology

A knowledge base (KB) is a computer-processable collection of knowledge about the world. In its simplest variant, a KB takes the form of a labeled graph, where the nodes are entities (such as people, organizations, and geographical locations), and the edges represent the links between these entities in the real world (such as who was born where, which organization is headed by whom, which city is the capital of which country etc.). Knowledge bases provide the background knowledge for different artificial intelligence applications, ranging from personal assistants to Web search, question answering, and text analysis. In particular, KBs are useful in information retrieval (IR), where they serve for structured search and entity disambiguation. Research has made extraordinary progress in the automated construction of KBs, and today's KBs contain billions of entities [1]. Nevertheless, KBs are still far from perfect. In this keynote talk, I outline several challenges in the construction and maintenance of KBs, and show how our research group approached them.
Research Guides: AR/VR/MR Community of Practice: Home

This guide is a tool for locating educational resources and other general information to support the Community of Practice in AR/VR/MR at the University at Buffalo, SUNY.