Reviewerly/exHarmony huggingface dataset:
Analog Computing and a Hybrid Approach to the Element Base of Artificial Intelligence Applications

Strong demand for artificial intelligence technology is driving the development of complex high-performance applications with less power consumption. Analog computing is of high-performance and has simplified system, which simulates the physical processes occurring in nature. The universality of the digital coding allows getting a fairly accurate calculation result and provides saving without loss and additional restoration. The benefits of digital and analog computing systems can be enhanced by their hybridization. The type and level of hybrid computing depend on the complexity of the task set. Hardware implementation of a neural network offers promising solutions for computing tasks that require compact and low-power computing technologies. Artificial neural networks or ANNs, like biological neurons, are characterized by their capacity of learning and memorizing information, depending on architecture and weight of the latter. The literature review shows that stable weight storage can be achieved using digital weights and analog multipliers to reduce footprint. The proposed methodology for the network architecture provides optimal conditions for maintaining synaptic weights, increasing processing speed by the parallel weight perturbation.
PENGEMBANGAN SISTEM INFORMASI AKUNTANSI DI YAYASAN GELORA MADANI BATAM

The Gelora Madani Batam Foundation (YGMB) is a non-governmental organization engaged in social, religious and educational fields. In its implementation, YGMB experienced problems in managing financial reports. Therefore, this research will develop an Accounting Information System (SIA) to solve the problems faced by YGMB. In this study using qualitative methods and systems development systems using the waterfall method. SIA is developed based on web using codeigniter framework
LibGuides: Electrical Trades: Example Articles & Search Tips

Research resources, tips, and support from SCC librarians For APLED 121 students and others
More than coding: Positioning STEM education in practice

In order to prosper as a society, science, technology, engineering and mathematics (STEM) education needs to be a focus for all stakeholders and at all levels. The acronym of STEM was itself coined by the National Science Foundation in the United States in the mid 1990s. The big ideas of science are both interdisciplinary and transdisciplinary and thus provide a conceptual basis for STEM initiatives in education and beyond. By choosing real-world scenarios and challenges as teaching contexts, STEM education is an exciting way of enhancing children’s natural curiosity in science and showing them the relevance of science to their future. There is a global policy push for space to be found to accommodate and integrate STEM learning and teaching into classroom activities. STEM education is certainly much more than the integration of digital technologies into practice. The chapter also presents an overview of the key concepts discussed in this book.
A Note on Skew Cyclic Codes over a Class of Rings

We study skew cyclic codes over a class of rings [Formula: see text], where each [Formula: see text] [Formula: see text] is a finite field. We prove that a skew cyclic code of arbitrary length over R is equivalent to either a usual cyclic code or a quasi-cyclic code over R. Moreover, we discuss possible extension of our results in the more general setting of [Formula: see text]-dual skew constacyclic codes over R, where δ R is an automorphism of R.
Inside the Coconut: Reviewing Socioemotional Aspects of an Antagonist’s Adolescence (With Reference to Character of Duryodhana in Selected Retellings of the Epic Mahabharata )

The Mahabharata, a legendary tale is considered among the most prominent epics that providesdeep insight in the philosophical ideas, morality and exposition of Dharma and Society. The reinterpretedworks of Mahabharata rather focus on the individual or self than the society or dharma. It has been apractice to glorify the Heroes and vilified the anti Heroes and their friends in these old mythologies, judgingthem on the basis of set normative values, dharma and social dogmatic religious theories. This paper aims atlooking the character of Duryodhana in the light of developmental psychology, peer victimization and hisconflict between society and self through the interpretations from Retelling of The Mahabharata in themythological fiction Ajaya: Roll of the Dice by Anand Neelakanthan. The unconventional queer side of himas a very sincere and valiant prince, willing to stand for what he believed in, is made alive in the personalityof Suyodhana. This paper reviews the Socioemotional aspects of an individual in his adolescence yearsleading to subtle marginalization and how the insecurities of the society improbably disconnect a personfrom his essential core and create a fallacious, insincere personality around him and force him to berecognized with it in the name of Dharma or Education, ignoring his inner self swinging between theconflict of self and society, making Duryodhana out of Suyodhana
LibGuides: How to Request Interlibrary Loans via Summon: Home

A step by step guide on using the request feature in summon to acquire articles via Interlibrary Loan.
The development of the financialsystem and economic growth in Sweden : A Granger causality analysis

The development of the financialsystem and economic growth in Sweden : A Granger causality analysis
Home: Collection Development Policy: Electronic and Internet Resources

This guide outlines the Ike Skelton Combined Arms Research Library's Collection Development Policy.
Interval Type-2 Fuzzy Logic Expert System for Investment Analysis

In this paper, a higher degree of fuzzy logic type-2 fuzzy logic is presented as decision making process of investment. There is a key difference between type-2 and type-1 fuzzy logic. The application is made on the Czech stock market and is used to decide on investing in PX index stocks. The proposed type 2 fuzzy model uses the return and risk of investment instruments as input variables. The system created is able to generate aggregate models from a certain number of language rules, allowing the investor to understand the generated financial model. The use of T2FLS can lead to more realistic and accurate results than T1FLS.
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces

Direct optimization is an appealing framework that replaces integration with optimization of a random objective for approximating gradients in models with discrete random variables. A$^\star$ sampling is a framework for optimizing such random objectives over large spaces. We show how to combine these techniques to yield a reinforcement learning algorithm that approximates a policy by finding trajectories that optimize a random objective. We call the resulting algorithms direct policy gradient (DirPG) algorithms. A main benefit of DirPG algorithms is that they allow the insertion of domain knowledge in the form of upper bounds on return-to-go at training time, like is used in heuristic search, while still directly computing a policy gradient. We further analyze their properties, showing there are cases where DirPG has an exponentially larger probability of sampling informative gradients compared to REINFORCE. We also show that there is a built-in variance reduction technique and that a parameter that was previously viewed as a numerical approximation can be interpreted as controlling risk sensitivity. Empirically, we evaluate the effect of key degrees of freedom and show that the algorithm performs well in illustrative domains compared to baselines.
LibGuides: eBooks: Find eBooks

Finding and reading eBooks at University of South Wales Mae'r canllaw hon hefyd ar gael yn Gymraeg An introduction to eBooks and how you can find them.
Dance with Shadows - Capturing tacit knowledge with smart device augmented reality (SDAR)

Tacit knowledge has been notified with its involvement in the creative and innovative process of design. However, it has been an elusive subject due to its difficulty to be articulated, recorded, and communicated. Augmented Reality (AR) is introduced as an affordable, accessible, and collaborative way to revisit tacit knowledge in the design process. In this paper, a computational design approach with Smart Device Augmented Reality (SDAR) is proposed for a real-time fenestration design in a targeted room. In comparison to standard methods of showcasing daylighting metrics, the use of Smart Device Augmented Reality (SDAR) is an alternative method as it delivers a dynamic experience by combining both the real and digital environments, enabling the visualization of the design in its intended site context with real-time feedback. The implementation of the proposed approach is explained and the design process with SDAR is also demonstrated in this paper.
Path Planning and Tracking in Scenario of Emergency Collision Avoidance

To consider the problem of collision avoidance and stability when planning the emergency collision avoidance path of lateral lane under high speed scenarios，a path planning algorithm based on non-uniform B-spline curve is proposed，which meets the requirements of multi-constraint. The proposed collision-free path model constructed by two non-uniform B-spline curves has continuous curvature with trapezoidal change and satisfies the slope and curvature constraint at the starting point and end point; and then，the environmental information is combined to generate a collision-avoidance path cluster whose path shape is controlled by two parameters. On this basis，a passenger comfort objective function is constructed，aiming to select a local path with the best comfort from the path cluster when the collision avoidance requirement is met. Furthermore，the objective function and multi-constrains of the vehicle are used to obtain the optimized longitudinal acceleration so as to complete the collision avoidance motion planning. Finally，the model predictive control (MPC) algorithm specialized in multi-constrained problem is selected to develop a linear time-varying MPC controller，and the feasibility of the proposed path planning method is verified. Keyw ords :  emergency collision avoidance,  non-uniform B-spline curve,  path planning,  multi-constraint,  path tracking
BEAUTY EXPERT SYSTEM TO DETERMINE THE SKIN PROBLEM AND THE TREATMENT FOR PATIENT AT JASMINE CLINIC

As people get busy, they want fast and easy service. This also applies to skin services in beauty clinics. The patient wants ease in terms of determining the type of facial skin problem and what treatments are appropriate for them. However, due to the limited number of dermatologists, patients often have to wait a long time to be examined and sometimes even cannot be served due to limited schedules. This expert system is based on how doctors determine the type of facial problems in patients and what treatments are appropriate for the problem. By using a mobile application, this system can be accessed by patients from home, making it easier for patients to get a quick and accurate examination. This application was built using the forward chaining method where later patients will answer questions that are used as a decision branch to determine the skin problem. Eventually the patient can make the required treatment and skin care needs and take them to the clinic.
Text mining Twitter social media for Covid-19 : Comparing latent semantic analysis and latent Dirichlet allocation

In this thesis, the Twitter social media is data mined for information about the covid-19 outbreak during the month of March, starting from the 3’rd and ending on the 31’st. 100,000 tweets were col ...
'The Invention, The Patent, and The Claim' in Patent Law: An Open-Access Casebook

This is a draft chapter, Invention, The Patent, and The Claim, from the forthcoming book, Patent Law: An Open-Access Casebook. In this chapter, we introduce students to different ways in which the contribution that an inventor has made to the art relates to the claims that grant her some legal rights, as mediated by the patent document. We begin with The Incandescent Lamp Patent Case, which we use to illustrate the nature of the inventive process. We then show how the role of the claim has changed over time, from Winans v. Denmead to Merrill v. Yeomans to Liebel-Flarsheim v. Medrad. That history demonstrates the range of possible roles that claims might play, and serves as a basis for class discussions arising from the still unsettled relationship between the invention, the patent, and the claim.
Smooth migration of a feature-rich vulnerability analysis engine within a security portal for users with divergent skill levels

We have been operating a vulnerability management portal site for the DMZ network, namely the network which allows connections from the Internet. In the DMZ network, security management is crucially important, and vulnerability management is useful for maintaining security. The portal site is named DMZ User's Portal and has been successfully operating for 13 years. For DMZ User's Portal, we have adopted the same series of a vulnerability analysis engine, which has many advantages, but a more powerful inspection performance of the engine has gradually become required in preparation for today's hard security circumstances. Now, we decided to replace the engine with a more powerful and complex one. With the replacement, it is desirable to continue the successful experiences and contributions of the portal site. However, it is quite a difficult task without the careful design and development of the modules in advance.This paper presents the design and methods for the smooth migration of the feature-rich vulnerability analysis engine within the security portal site. The key point is the careful consideration of the module dependency. To achieve a lower degree of module dependency, the techniques of Object-Relational (O/R) mapping, code generation, wrapper architecture, template engine consolidation, and test case were leveraged. We can continue to operate the portal site while inheriting the successful experiences as well as gaining the benefits of a new and powerful vulnerability analysis engine.
DEVELOPMENT OF LEARNING MEDIA PICTURE BOOKS BASED ON LOCAL WISDOM KUDUS INDONESIAN LANGUAGE SUBJECTS POETRY WRITING MATERIAL FOR GRADE V STUDENTS OF PRIMARY SCHOOLS

This research was conducted to describe the analysis of the needs of students and teachers towards the development of picture book learning media based on sacred local wisdom in grade V elementary school students. Making prototypes of learning media picture books on Indonesian subjects poetry writing material for fifth grade elementary school students. the use of learning media Indonesian picture books subject matter poetry writing for elementary school students in grade V.Using research and development methods, or also called Research and Development. Research and Development method (RnD) is a research method used to produce certain products, and test the effectiveness of these products. The research was conducted at SD 4 Peganjaran, SD 4 Gribig, SD 1 Gribig and SD NU Kartika in Kudus City. The research subjects were all teachers, fifth grade students from four different schools namely 3 Peganjaran Public Elementary School in Bae sub-district, 4 Gribig Public Elementary School in Gebog sub-district, 1 Gribig Public Elementary School in Gebog sub-district and Nawa Kartika elementary school in Kota sub-district.The test results of the effectiveness of picture book learning media are based on holy local wisdom in Indonesian language. Testing effectiveness is done by comparing the results of each school's pretest and posttest. Based on the value of t test at SD 3 Peganjaran obtained So the acquisition of t count = 4, 542 while T table = 1.79, t test can be concluded t count t table So Ho is rejected and Ha is accepted, so Ha is accepted and media of effective picture book applied. Based on the test value at SD 4 Gribig obtained t count = 4.73 while T table = 1.73 t test can be concluded t count t table Then Ho is rejected and Ha is accepted so that Ha is accepted and media picture book is effectively applied. Based on the t test on SD 1 Gribig obtained t value = 6.98 while T table = 1.68 t test can be concluded t count t table Then Ho is rejected and Ha is accepted, so that Ha is accepted and media picture book is effectively applied, Based on the value Nawa Kartika's NU SD test obtained t count = 8.07 while T table = 1.68 t test can be concluded t count t table So Ho is rejected and Ha is accepted, so Ha is accepted and media picture book is effectively applied.
Predicting pedestrian path using optical flow as context cue

A human driver can gauge the intention and signals given by other road users indicative of their future behaviour. The intentions and signals are identified by looking at the cues originating from vulnerable road users or their surroundings (hand signals, head orientation, posture, traffic signals, distance to curb, etc.). Taking all these cues into account by creating a separate detector for each is an extremely difficult task. Instead, this MSc Thesis will explore the possibility of using a generic contextual cue in optical flow originating from a pedestrian with deep learning methods to improve the path prediction in a naturalistic driving scenario. The contribution of this work is to examine multiple ways to extract relevant information from the optical flow and also explore the possibility of using the entire the high-dimensional optical flow using convolutions and soft-attention to help identify relevant pixels for the prediction task. This work elaborates on the extraction and processing of optical flow features. It proposes 2 Recurrent neural networks (RNN) based model: one to work with the histogram of optical flow features and the other one to take in the dense optical flow directly. Also, visualization of the soft-attention weights is done to add a step that helps in the interpretability of the RNN model incorporating dense optical flow. From the experimental results, optical flow features have shown significant improvements in terms of predicting probabilistic confidence for tracks with some changes in their motion mode. It was seen that the convolution-attention RNN model was able to work with dense optical flow features and position of pedestrians as input to obtain better results among all the combinations of features and models compared in this work.
PRAVDA How-To Guide

Documentation for Portably Reformat Audio and Video to Digital from Analob (PRAVDA), a kit for rescuing audio and video from obsolete physical carriers.
Gravhögarna på Långön : ett metodtest av statistisk prediktionsmodellering i Norrlands inland

In an attempt to improve archaeological predictive modeling, two predictive models were developed through QGIS, Excel, GeoDa and R. These models were then tested with statistical quality tests. The ...
Boldly Going Where No Prover Has Gone Before

I argue that the most interesting goal facing researchers in automated reasoning is being able to solve problems that cannot currently be solved by existing tools and methods. This may appear obvious, and is clearly not an original thought, but focusing on this as a primary goal allows us to examine other goals in a new light. Many successful theorem provers employ a portfolio of different methods for solving problems. This changes the landscape on which we perform our research: solving problems that can already be solved may not improve the state of the art and a method that can solve a handful of problems unsolvable by current methods, but generally performs poorly on most problems, can be very useful. We acknowledge that forcing new methods to compete against portfolio solvers can stifle innovation. However, this is only the case when comparisons are made at the level of total problems solved. We propose a movement towards focussing on unique solutions in evaluation and competitions i.e. measuring the potential contribution to a portfolio solver. This state of affairs is particularly prominent in first-order logic, which is undecidable. When reasoning in a decidable logic there can be a focus on optimising a decision procedure and measuring average solving times. But in a setting where solutions are difficult to find, average solving times lose meaning, and whilst improving the efficiency of a technique can move potential solutions within acceptable time limits, in general, complementary strategies may be more successful.
Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria

Eligibility criteria in the clinical trials specify the characteristics that a patient must or must not possess in order to be treated according to a standard clinical care guideline.As the process of manual eligibility determination is time-consuming, automatic structuring of the eligibility criteria into various semantic categories or aspects is the need of the hour.Existing methods use hand-crafted rules and feature-based statistical machine learning methods to dynamically induce semantic aspects.However, in order to deal with paucity of aspect-annotated clinical trials data, we propose a novel weakly-supervised co-training based method which can exploit a large pool of unlabeled criteria sentences to augment the limited supervised training data, and consequently enhance the performance.Experiments with 0.2M criteria sentences show that the proposed approach outperforms the competitive supervised baselines by 12% in terms of micro-averaged F1 score for all the aspects.Probing deeper into analysis, we observe domain-specific information boosts up the performance by a significant margin.
Modeling Visual Minutiae: Gestures, Styles, and Temporal Patterns

Author(s): Ginosar, Shiry Sara | Advisor(s): Efros, Alexei A | Abstract: The human visual system is highly adept at making use of the rich subtleties of the visual world such as non-verbal communication signals, style, emotion, and the fine-grained details of individuals. Computer vision systems, by contrast, excel in categorical tasks, such as classification and detection, where training often relies on single-word or simple bounding-box annotations. These simple annotations do not capture the richness of the visual world which is often hard to describe in words or localize in an image. Our current systems are thus left to only make use of the obvious, easily describable parts of the visual input. This dissertation investigates several initial directions toward modeling visual minutiae and endowing computer vision systems with rich perception.Part I describes methods for learning directly from video data without the need for human-provided annotations. The section begins by discussing the use of multi-modal correlations between audio and motion for modeling conversational gestures---an essential part of human communication that is currently ignored by machine perception. The section then proposes a simple method for capturing the appearance details of individual people in motion, which can be used to implement a do-as-I-do'' motion-transfer application.Part II explores ways to discover temporal visual patterns in historical data. The section begins by discussing data-mining methods in a dataset of historical high school yearbook portraits where fashion and behavioral styles change over time. The rest of the section proposes an unsupervised method to learn to disentangle the time-varying visual factors from the permanent ones in a large dataset of urban scenes.Part III discusses one possible avenue for testing whether our man-made systems have achieved human-like rich perception by comparing their performance to that of humans on a unique dataset of abstract art.
An inquiry into the efficacy ofconvolutional neural networks in low-resolution video feeds for object detection

In this thesis, various famous models have been investigated and compared to a custom model for people detection in low resolution video feeds. YOLOv3 and SSD in particular are famous models which ...