Reviewerly/exHarmony huggingface dataset:
The Best Parameter Tuning on RNN Layers for Indonesian Text Classification

Recurrent Neural Network (RNN) is a deep learning architecture commonly used to process time series and sequence data. Various architectures have been developed to improve the performance of the algorithm in terms of both accuracy and computation time. Besides, the use of appropriate parameter values when building a neural network model also plays an important role in the quality and the outcome of the learning model. In this study, the model trained using RNN-Vanilla, LSTM, and GRU each with 4 different combinations of parameter settings, namely bidirectional mode (True, False), the number of neuron units on each layer (64, 128, 256), the number of RNN layers on the neural network (1, 2, 3), and the batch size when training the model (32, 64, 128). By combining all the parameter values, 162 trials were carried out to perform the task of classifying Indonesian language customer support tickets with four category classes. This study gives the result that the same network architecture but with different parameter combinations results in significant differences in the level of accuracy. The lowest accuracy of all experiments was 32.874% and the highest accuracy resulted was 84.369%. Overall, by calculating the average accuracy of each parameter value, the results obtained are: GRU has the best performance, accuracy tends to increase by activating bidirectional mode, increasing the number of neuron units in the hidden layer, and reducing the batch size. Meanwhile, the addition of the number of RNN layers on the neural network has no impact on increasing the level of accuracy.
NING MODEL ASSISTED BY BLOG ON THE LEARNING OUTCOMES OF CLASS IV IPS

Based on the result of preresearch in the form of observation, interviews, and data on learning outcomes conducted in the fourth grade students at Nyi Ageng Serang Cluster Elementary school in Semarang, it was known that the social studies learning outcomes were not optimal because the learning model and media used were also not optimal. Although the teacher had used problem-based learning model, the students still had difficulties to identify problems and the media in the form of A4 print out pictures were not enough to help them. This study aimed to test the effectiveness of problem-based learning model assisted by blog media on the social studies learning outcomes in fourth grade students at Nyi Ageng Serang Cluster Elementary School in Semarang. This research used quantitative approach with the experimental type and nonequivalent control group design. The sample technique used cluster random sampling, with population of 172 students. The data collection used tests, observations and documentation. The hypothesis test result showed that the value of tcount = 4.007, and ttable = 2.009, because tcount ˃ ttable (4.007 ˃ 2.009) which means that the problem based learning model assisted by blog media was effective to increase the social studies learning outcomes of the fourth grade students. The n-gain test result of the experimental group was 0.40 categorized in medium criteria, whereas the n-gain test result of control group was 0.15, categorized in low criteria. The observation of students' activities using observation sheets showed the average score of the activity of experimental class students was 82%, which is better than the control class that only got 64%. The conclusion of this research is that the problem-based learning model assisted by blog media was effectively used in social studies learning in fourth grade students at Nyi Ageng Serang Cluster Elementary School in Semarang.
Library Home: Off campus access to Booth Library : Intro

If you are researching from off campus and need access to electronic resources - research databases or eBooks, here are the details!
EFL Students’ Speaking Activities: The Significance of Games in Classroom Context

ABSTRACT&#x0D; The learning process of language teaching seems to be very demanding and complicated, especially for speaking skill. Speaking, alongside with writing, is considered to be productive skill in which it requires students to produce the language in spoken form. The students need to be able to speak the language by considering some aspects of speaking such as fluency and accuracy. In other words, not only the student has to speak correctly, but also it has to be as smooth as possible. Such particular case becomes one of the reasons why it is difficult to master the skill itself. in another case, it has become much more difficult for the students to grasp the speaking material. Moreover, designing a speaking environment for language teaching in classroom context is no difference. Due to the fact that the scope of speaking skill is complex, the students are reluctant to say something or use the language. That is why the English teacher needs to come up with specific plans to teach English as second or foreign language. One of ways to overcome the problem is by implementing game. The nature of game will help the English teacher to develop such amusing and enjoyable atmosphere in the teaching and learning process.
An Information Systems Design Theory for Service Network Effects

Service platforms make available software applications as a service to end-users. Platforms enable noticeable economic benefits for scaling and transforming a business. Their long-term competitiveness is ensured in controlled cooperation with channel intermediaries and network partners. Hence, service platforms must be designed to harness self-enforcing effects of value generation, so-called network effects. In an exaptation of existing knowledge, we present an information systems design theory to inform the design of methods that analyze, describe, and guide the design of service platforms through the means of causal loops and control methods. We describe the theory’s purpose and scope as well as the underlying justificatory knowledge behind the constructs and principles of form and function. The design theory covers the design of all service platform participants and activities as well as their transactions and influences in areas of staged platform authority, using enforcing and incentivizing control methods. We demonstrate the principles of implementation with an expository instantiation and apply it to the M-Engineering service platform, which offers surveillance, control, and data acquisition solutions. Furthermore, we present and discuss testable propositions and a study design to evaluate our design principles.
PERBEDAAN HASIL BELAJAR MENGGUNAKAN MEDIA PERALATAN KANTOR DAN MEDIA GAMBAR

The study examines the relationship between the application of internet media to the increase in social studies learning outcomes of class XI students of SMK Al-Washliyah 12 SeiRempah. The population in this study is less than 100, so that the entire population is used as a sample, that is, with a total of 50 students. The data analysis technique in this study uses the product moment correlation formula and the t test is used to test the hypothesis in the study. The results of this study found that the contribution of the application of internet media (X) to the increase in social studies learning outcomes (Y) was 0.515 or 51.5%. So from these calculations it can be seen that the contribution of the application of internet media (X) to the increase in IPS learning outcomes (Y) is 51.5%. while 48.5% are influenced by other variables. Hypothesis testing shows that the magnitude of tcount is 4.163, while the magnitude of ttable with a confidence level of 95% (level a = 0.05) and dk 50 - 2 = 39 is 2.012. Because t count> t table or 4,163> 2,012. From the results of these studies, the formulation of the hypotheses that have been determined in this study is acceptable, which means: There is a significant influence between the use of the internet on social studies learning outcomes of XI graders of Al Washliyah Vocational School 12 SeiRempah Academic Year 2018-2019.
STUDENTS INCREASING STUDENT VOCABULARY BY USING CROSSWORD PUZZLE EIGHT GRADE OF SMP NEGERI 1 GALANG

The objective of the research is to prove whether the use of crossword puzzle is effective to increase students’ vocabulary. This pre-experimental study uses 23 students as a sample. They are grade VIII c of SMP Negeri 1 Galang. The procedure of this research consists of pretest, treatment, and posttest. The result of the analysis shows that crossword puzzle contributes greatly to increse the students’ vocabulary. It can be seen by the difference of the mean score between pre-test and post-test. The mean score of pre-test is 67.30 while in the post-test increase up to 90.87. The data of this research are analyzed by using SPSS Program version 16.0. Then the data of both tests are analyzed by using T-test, and the result shows that the T-test value 8.502 is high than T-table value 2.074. It can be inferred that crossword puzzle is very effective to increase students’ vocabulary. Keywords: Increasing, Crossword Puzzle, Vocabulary.
Evaluation Method of User Experience of public service system Using Mobile Eye Tracking

This study proposes a usability evaluation method using mobile eye-tracking technology to evaluate the user satisfaction of a public service system, more efficiently and objectively. Based on the theoretical viewpoint of usability testing, the method analyzes the recorded visual image data, gaze points and voice data of the service contact flow process, and extracts the key task nodes of the business process to count the task completion time, task success rate, the proportion of blocked points and other quantitative data, and heuristically evaluates the image data recorded in the test. The method has a novel perspective, which is more targeted compared with the interview, questionnaire survey and observation methods.
Peranan Penataan Kearsipan Sebagai Sumber Informasi Bagi Pimpinan Dalam Pengambilan Keputusan

In every organization, has several functions which are connected each others in term of support for the whole organization goals. The most important organizational beside resources human resource is the information, with those information it s able to support the decision making addressed to the top manager. In this case, the information resources can be found among the archives. In the condition that the archives should be manage as well as possible and with the archive’s managerial manner including: collecting, processing, technically maintaining, classification, recording, filling and retrieval sistem
Lightweight Deep Neural Network-based Real-Time Pose Estimation on Embedded Systems.

This paper proposes a novel real-time pose estimation system on embedded devices for a driver and a front passenger. The main goal of the proposed system is to operate in real time with limited hardware resources while preserving the high accuracy. The proposed system is divided into an object detection and a pose estimation. In the object detection, we eliminate the redundant and inaccurate bounding boxes by considering the characteristics of the target image domain. In the pose estimation, a single-person pose estimation with a lightweight deep learning model has been proposed and knowledge distillation has been adopted to maximize the performance while maintaining the high speed. In the experimental results, the proposed pose estimation has up to 9 % of the accuracy and the 9 times less computation compared to the previous methods. The operation speed is 195 frame per second on NVIDIA Jetson TX2.
NPTCLibGuides: Access to HE: Humanities: Home

A guide for students studying Access to Higher Education: Humanities at NPTC Group of Colleges
The Fox, the Hedgehog, and the Quest for Artificial General Intelligence

Artificial general intelligence continues to be elusive in part because of a focus on a narrow range of problems that can all be described as path problems. A researcher sets up the goals, the optimization methods, and the parameters of the problem, which determine the potential solutions that a machine can learn. Machine learning is the process of finding the right parameter values that solve the problem. Formal path problems, like the games of chess or go, are fully described by their rules and the current state. They can be treated as purely mathematical processes, independent of any physical instantiation. Other, less formal problems, such as how to drive a vehicle on a busy road, do depend on the physical properties of their instantiation and on feedback from the physical world. Path problems are well defined with relatively easy to evaluate metrics, but they are not the only kind of problem that a generally intelligent agent needs to address. Other problems, called insight problems, require the solver to not just evaluate well-defined functions, but to create those functions. Failure to recognize the need to solve multiple types of problems leads people to believe that computers will at some point be able to make themselves arbitrarily intelligent (the technological singularity), potentially to the detriment of human existence. Path problem solving has provided a good model for special purpose problem solvers, but not for general intelligence. As the ancient Greek poet, Archilochus observed, “a fox knows many things, but a hedgehog one important thing.” Artificial intelligence researchers have been able to build very sophisticated hedgehogs, but foxes remain elusive. And foxes know how to solve insight problems.
Green Cloud Computing: A New Approach to Reduce Energy Consumption in Cloud Data Center

This is the era of modern science and technology. Cloud data center are one of the great inventionsof modern science and technology for storing data and keep them in a secure place and also accessthem anytime from anywhere. In today’s world use of data centers are increasing day by day andin future it will increase more. The main problem regarding data centers is the energy consumption.Because data center needs lots of energy as it has to be turned on all day long. For the source ofenergy most of the data center around the world are using coal, fossil fuel or oil power plant formaintaining their needs of energy. But the problem is, this methods of producing energy alsoproduce harmful elements to our environment such as CO2 which is causing serious problem toour environment. Because in today’s world climate change is a common problem and for that,every country have to face serious natural disaster every year and one of the biggest reasons of thisproblem is, increasing huge amount of CO2 in our environment. we need to take necessary stepsso that we could reduce the amount of CO2 production in our environment as soon as possible. Ifwe do not do that, then it will pause a big question mark on the existence of the human being. In adata center energy needed for various reason such as powering up the computers, cooling themachineries, operating the systems etc. It is not possible to measure exact amount of energy thatis needed for a data center because it depends on different factors such as load capacity, size ofdata center, processing of data etc. Now a days, it has become very important to reduce the amountof energy consumed by the data centers using various methods and also how to make more andmore green energy so that our environment does not get affected by the harmful elements.
Shopping Using Augmented Reality

The advent of opportunities to shop online has made inroads into virtually all modes of businesses worldwide. The success and future of online businesses depend on how well they adopt newer technologies. The online platforms are presented with a much faster and suitable option to buy, but have not been able to deliver the experience of touch and feel shopping to its customers. Technologies such as Augmented Reality (AR) steps in this direction—bringing an entirely new aspect to the online selling platforms. Our project presents a smartphone-based augmented reality application which makes it possible to enhance the whole-in store shopping experience with low cost, easy to use smartphone-based augmented reality platform.
The Use of Technical Experts in Software Copyright Cases: Rectifying the Ninth Circuit’s 'Nutty' Rule

Courts have long been skeptical about the use of expert witnesses in copyright cases. More than four decades ago, and before Congress extended copyright law to protect computer software, the Ninth Circuit in Krofft Television Prods., Inc. v. McDonald’s Corp., ruled that expert testimony was inadmissible to determine whether Mayor McCheese and the merry band of McDonaldland characters infringed copyright protection for Wilhelmina W. Witchiepoo and the other imaginative H.R. Pufnstuf costumed characters. Since the emergence of software copyright infringement cases in the 1980s, substantially all software copyright cases have permitted expert witnesses to aid juries in understanding software code. As the Second Circuit recognized in Computer Associates Int’l, Inc. v. Altai, Inc., the ordinary observer standard may well have served its purpose when the material under scrutiny was limited to art forms readily comprehensible and generally familiar to the average lay person,” but as to computer programs, district courts must have “discretion . . . to decide to what extent, if any, expert opinion, regarding the highly technical nature of computer programs, is warranted in a given case.”In a shocking departure from the decisions of every other circuit that has confronted software copyright infringement litigation, the Ninth Circuit reaffirmed and applied the bar on expert testimony originating in Krofft Television Prods. to all copyright disputes, including those involving highly technical computer software code. The court in Antonick v. Electronic Arts held that lay juries must decipher and analyze software code—distinct hexadecimal assembly code languages for different processors—without the assistance of expert witnesses, a rule that the authoring judge characterized at the oral argument as “nutty.”The Ninth Circuit’s rule overlooks the key distinction between the use of technical experts to analyze substantial similarity as opposed to enabling lay judges and jurors to perceive the underlying works. Just as it would be absurd to ask a lay jury with no familiarity with Kanji characters to assess whether a translation of HARRY POTTER AND THE PHILOSOPHER’S STONE into Japanese infringed the English original without the aid of a bilingual translator, it makes no sense to ask a non-technical jury to compare computer source codes written in different assembly languages to determine substantial similarity without expert assistance. We contend, consistent with the views of every court outside of the Ninth Circuit that has addressed the issue, that courts should permit the use of technical experts to enable lay judges and juries to perceive the meaning of computer languages and computer code.
Statistics meets Machine Learning

The goal of this workshop is twofold: (a) exchange knowledge on successful algorithmic approaches and discuss some of the existing challenges, and (b) to bring together researchers in statistics and machine learning with the aim of sharing expertise and exploiting possible differences in points of views to obtain a better understanding of some of the common important problems.
Use Of The Works Of Eastern Thinkers In Preparation Of Future Primary School Teachers For Professional Activity

The current global changes in the socio-economic and scientific-technical spheres require the improvement of the system of training, in particular, the system of future specialists on the basis of innovative and integrative education. This means paying special attention to the definition of pedagogical and psychological bases of teaching on the basis of innovative-integrative education, the development of creative and professional approaches of students to secondary education in the educational process.
Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications

Author(s): Akhlaghi, Vahideh | Advisor(s): Gupta, Rajesh K. | Abstract: Deep Neural Network (DNN) models are now commonly used to automate and optimize complicated tasks in various fields. For improved performance, models increasingly use more processing layers and are frequently over-parameterized. Together these lead to tremendous increases in their compute and memory demands. While these demands can be met in large-scale and accelerated computing environments, they are simply out of reach for the embedded devices seen at the edge of a network and near edge devices such as smart phones and etc. Yet, the demand for moving these (recognition, decision) tasks to edge devices continues to grow for increased localized processing to meet privacy, real-time data processing and decision making needs. Thus, DNNs continue to move towards the edges of the networks at `edge' or `near-edge' devices, even though a limited off-chip storage and on-chip memory and logic on the edge devices prohibit the deployment and efficient computation of large yet highly-accurate models. Existing solutions to alleviate such issues improve either the underlying algorithm of these models to reduce their size and computational complexity or the underlying computing architectures to provide efficient computing platforms for these algorithms. While these attempts improve computational efficiency of these models, significant reductions are only possible through optimization of both the algorithms and the hardware for DNNs.In this dissertation, we focus on improving the computation cost of DNN models by taking into account the algorithmic optimization opportunities in the models along with hardware level optimization opportunities and limitations. The techniques proposed in this dissertation lie in two categories: optimal reduction of computation precision and optimal elimination of inessential computation and memory demands. Low precision but low-cost implementation of highly frequent computation through low-cost probabilistic data structures is one of the proposed techniques to reduce the computation cost of DNNs. To eliminate excessive computation that has no more than minimal impact on the accuracy of these models, we propose a software-hardware approach that detects and predicts the outputs of the costly layers with fewer operations. Further, through the design of a machine learning based optimization framework, it has been shown that optimal platform-aware precision reduction at both algorithmic and hardware levels minimizes the computation cost while achieving acceptable accuracy. Finally, inspired by parameter redundancy in over-parameterized models and the limitations of the hardware, reducing the number of parameters of the models through a linear approximation of the parameters from a lower dimensional space is the last approach proposed in this dissertation. We show how a collection of these measures improve deployment of sophisticated DNN models on edge devices.