Reviewerly/exHarmony huggingface dataset:
Reshaping Deep Neural Networks for Efficient Hardware Inference

Author(s): Khodamoradi, Alireza | Advisor(s): Kastner, Ryan RK | Abstract: The latest Deep Learning (DL) methods for designing Deep Neural Networks (DNN) have significantly expanded our ability to train data processing systems. Coupled with exponential growth in available digital data, we have seen dramatic accuracy improvements in DNNs and widespread adoption of these models in different applications.This increased demand has motivated innovations in DNN architecture design to deliver high-quality output. For example, advanced DL models can include irregular connections between their layers, have more parameters, and employ computationally complex neurons. Unfortunately, these new architectural additions often increase the implementation complexity of the DNNs on hardware, particularly when deploying DL models for inference in scale-out and power-limited systems.Currently, to deploy a DNN on a custom platform, an abstract of the DL model is used to create a functionally identical realization. However, because altering this abstract changes the functionality of the DL model, hardware designers keep the model unchanged for a lossless implementation.This thesis shows that a co-design approach can improve the hardware implementation of DL models. In a co-design approach, the designer reshapes the DNN architecture to better fit a target processing platform and preserves its accuracy by retraining the model.We describe a custom accelerator for Spiking Neural Networks (SNN) with improved computational cost and memory utilization because of reshaping the layers and neurons of the model. We then apply these changes to the existing SNN models and show that they can maintain their accuracy after the reshaping and retraining. In addition, we introduce novel applications for SNNs based on the new architecture. We also present a stochastic noise filter for pre-processing SSN's input with improved accuracy and memory utilization. Furthermore, we explain a reshaping method for Residual Networks (ResNet) to reduce their memory footprint while preserving their accuracy. This thesis also introduces a method for accelerating the co-design process. Reshaping DL models can increase the complexity of their training stage. We present an auto tuner for the learning rate (an essential parameter for training DNNs) that simplifies the manual tuning for this parameter and can accelerate the retraining of DL models.
AN EFFICIENCT CLUSTERING ON HYBRID ITEM DEPENDENCY USING SCFCM AND SVM TECHNIQUES

Clustering is an AI system that incorporates the social event of focused data which is a technique for unaided learning and is a normal method for verifiable data examination used in various fields. Clustering of information is a technique by which expansive arrangements of information are gathered into groups of little arrangements of comparative information. Fuzzy C-means clustering (FCM) is a way for clustering which enables one little bit of records to have a place with  or extra clustering to discover the item dependency in hybrid datasets(explicit, implicit and hidden items). The proposed Semi Conquer Fuzzy C-Means (SCFCM) algorithmthe measurement method is also used to look into the numerous forms of facts with common dataset. It is a kind of partition clustering where more incredible data can be clustered simultaneously on basis of their size and its functionality as it grouping the data in a relational manner with both implicit and explicit datasets. Obviously, the Support vector machine (svm) category device is a classification version that absolutely classifies the records, but the length is extensively used. In this paper, a set of datasets is implanted and common parameters together with overlapping, information partitioning, excessive dimensional statistics and beside the point statistics clustering are checked within the experimental clustering examine.
Exploration and preference satisfaction trade-off in reward-free learning

Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of behaviour that lead to predictable states -- necessary for survival. In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction. For this, we introduce a model-based Bayesian agent equipped with a preference learning mechanism (pepper) using conjugate priors. These conjugate priors are used to augment the expected free energy planner for learning preferences over states (or outcomes) across time. Importantly, our approach enables the agent to learn preferences that encourage adaptive behaviour at test time. We illustrate this in the OpenAI Gym FrozenLake and the 3D mini-world environments -- with and without volatility. Given a constant environment, these agents learn confident (i.e., precise) preferences and act to satisfy them. Conversely, in a volatile setting, perpetual preference uncertainty maintains exploratory behaviour. Our experiments suggest that learnable (reward-free) preferences entail a trade-off between exploration and preference satisfaction. Pepper offers a straightforward framework suitable for designing adaptive agents when reward functions cannot be predefined as in real environments.
The Effect of Hyper-Parameters on the Performance of Third Order Neural Network Algorithms on Medical Classification Data

The artificial neural network (ANN) particularly back propagation (BP) algorithm has recently been applied in many areas. It is known that BP is an excellent classifier for nonlinear input and output numerical data. However, the popularity of BP comes with some drawbacks such as slow in learning and easily getting stuck in local minima. Improving training efficiency of BP algorithm is an active area of research and numerous papers have been reviewed in the literature. Furthermore, the performance of BP algorithm also highly influenced by the size of the datasets and the data preprocessing techniques that been chosen. This paper presents an improvement of BP by adjusting the two term parameters on the performance of third order neural network methods. This work also demonstrates the advantages of using preprocessing dataset in order to improve the BP convergence. The efficiency of the proposed method is verified by means of simulation on medical classification problems. The results show that the proposed implementation significantly improves the learning speed of the general back-propagation algorithm.
Developing COVID-19 Web and Mobile Web Situation Reporting Applications for the State Health Authority

If Malaysia ends up entering the subsequent COVID-19 outbreak, many hospitals will no longer support the victims conveniently. There will be numerous victims around, and these hospitals will not have enough human resources. Exceptional ICT-based communication backbone should also be in place to fully recommend these hospitals day-to-day operations. For this reason, COVID-19 situational reporting web and mobile web applications were developed to assist the health professionals in the State of Sabah in doing their job with a high level of assurance. Several real-time technologies were put together as the foundation in developing these applications. This work-in-progress paper presents the design and development techniques employed in rapidly developing the COVID-19 situational reporting web and mobile web applications for State Health Authority of Sabah.
A Study of Pronunciation Error in English Consonant

The aim of the study to find out what are the five of most English consonants which error to pronounce at second grade of MTs in Pasuruan and to find the reason what makes them the error to pronounce. The subject of the study was in the second grade of MTs. The research design was descriptive qualitative and focused on the pronounce of analysis. The findings conclude that there is eight consonant as the most mispronounce to pronounce. From the research done by the researchers, the researchers finally found eight of the 24 consonants that are difficult to pronounce, eight of which are consonants ch [ʧ], y [j], th [ð], v [v], th [θ], zhe [ʒ], gy [ʤ], and ght. Then from some of the analyzes, the researchers found some errors consonants that conical researchers into the eight most misunderstood consonants in pronunciation alone. Eight of them are consonants ch [ʧ], zh [ʒ], th [ð], v [v], and th [θ].
Subject guides: Library Information for Business Students: APA 7th

A landing page and central hub for library resources, events and information for Business students.
O ESTADO DO CONHECIMENTO SOBRE AS SALAS DE RECURSOS MULTIFUNCIONAIS: AS PRODUÇÕES ACADÊMICAS DOS PROGRAMAS DE PÓS-GRADUAÇÃO

This article aims to present the State of Knowledge of academic productions, stricto sensu graduate programs in Brazil, which has as its object the Multifunctional Resource Rooms. For this survey, we sought the academic production in the Digital Library of Theses and Dissertations (BDTD) and the casting was performed through categories related to the use and current situation of the resource rooms. It was evidenced that few works bring the resource rooms as a theme, and out of the analyzed works, most of them present the subject of teaching training.
Analisis Genangan Banjir Akibat Debit Puncak di DAS Baubau Menggunakan HEC-RAS dan GIS

The history of floods that have occurred in Baubau City from 2009 to 2018 occurred as many as 20 cases, submerging population buildings with a total of 1,206 units. To reduce the impacts that have previously occurred, this study aims to determine the potential flood areas in the Baubau watershed with a return period of 5 to 100 years. The first thing to do is to know the design flood discharge using 10 years of rainfall data analyzed by the Nakayasu Synthetic Unit Hydrograph method. From the results of the field survey data on river hydrological characteristics and watershed morphometric parameters were obtained then analyzed with a hydraulic model using HEC-RAS 5.0.7 software then exported to HEC-GeoRAS is a tool of ArcGis version 10.5. Based on the results of the hydrological model integrated into the hydraulic model in the findings of this study, an estimate of physical, social and economic losses is obtained as well as maps of spatial-based flood potential areas that occur in 3 sub-districts namely Murhum sub-district, Batupoaro sub-district and Wolio sub-district scattered in 7 urban villages through which the Baubau river flows with a high risk class status level.
The Effectiveness of Graphic Providers In Teaching Text Verbs of Accounts (Quasi Experimental Study In Public Junior High Schools)

This study was conducted under the considerations of the observation in SMPN 20 Kendari that many students found difficulties in understanding vocabulary. The objectives of this study were to find out the significance of the use of the graphic organizers on vocabulary achievement and to find out whether the use of graphic organizers  for  teaching  vocabulary  of  recount  text  is effective  to  the  eighth graders of SMPN 20 Kendari. To  gain  the  objectives,  the  writer  did  a  quasi  experimental  research  by using non- randomized control group pretest-posttest design. The subjects of this study were the students in class 8A and 8H of SMPN 20 Kendari. 8A was the control group and 8H was the experimental group. In this study, the writer gave the  students  pre-test,  treatment,  and  post-test.  The  pre-test  was  given  to  both groups using the same instrument. They were asked to answer some questions of vocabulary test. The treatment was given in three meetings. The treatment in the experimental  group  was  using  graphic  organizers,  while  the  treatment  in  the control group was lecturing. The post-test was conducted after the treatment given to  both  groups  using  the same  instrument as  the  pre-test  but  the  position of question numbers were reshuffled. The result of the analysis using t-test showed that the experimental group got better score than the control group. In the pre-test, the average score of the experimental group was 68.87 and the control group was 68.39. In the post-test, the  average  score  of  experimental  group was  83.55  and  the  control  group  was 74.52. tTe result of the t-test was 2.14 and t-table was 2.00. It means that the t- value was higher than t-table (2.14>2.00). In conclusion, graphic organizers were effective to improve students‟ mastery of vocabulary in recount text. Moreover, the writer hopes that the other English teachers would use graphic organizers as one of strategies in teaching vocabulary.
An Account of Constructions in ASD-STE100

Constructional meaning is essential to understand any piece of natural language; its formalization, however, poses challenges that have to be faced for an adequate processing of natural language. This paper addresses this issue by providing a formalized description of the role that constructional structures play in the controlled natural language ASD-STE100. Based on a corpus of instructional texts written in this language, we will assess the existence in such texts of the four levels of constructional meaning described by the Lexical Constructional Model (Ruiz de Mendoza & Mairal-Uson, 2008; Mairal-Uson & Ruiz de Mendoza, 2009) which, in turn, shape the Constructicons in the FunGramKB knowledge base (Perinan-Pascual, 2013; Perinan-Pascual & Arcas-Tunez, 2014). Our findings show that the technical nature of the controlled language has direct influence on the type of constructions employed, making some of the constructional levels irrelevant. A formalized account of the main types of constructions encountered is also provided.
The Bifurcation of Nigerian Cybercriminals: Narratives of the Economic and Financial Crimes Commission (EFCC) Agents

While this article sets out to advance our knowledge about the characteristics of Nigerian cybercriminals (Yahoo-Boys), it is also the first study to explore the narratives of the Economic and Financial Crimes Commission (EFCC) officers concerning them. It appraises symbolic interactionist insights to consider the ways in which contextual factors and worldview may help to illuminate officers’ narratives of cybercriminals and the interpretations and implications of such accounts. Semi-structured interviews of forty frontline EFCC officers formed the empirical basis of this study and were subjected to a directed approach of qualitative content analysis. While prior studies, for example, indicated that only a group of cybercriminals deploy spiritual and magical powers to defraud victims (i.e. modus operandi), our data analysis extended this classification into more refined levels involving multiple features. In particular, analysis bifurcates cybercriminals and their operations based on three factors: educational-attainment, modus-operandi, and networks-collaborators. Results also suggest that these cybercriminals and their operations are embedded in “masculinity-and-material-wealth”. These contributions thus have implications for a range of generally accepted viewpoints about these cybercriminals previously taken-for-granted. Since these criminals have victims all over the world, insights from our study may help various local and international agencies [a] to understand the actions/features of these two groups of cybercriminals better and develop more effective response strategies. [b] to understand the vulnerabilities of their victims better and develop more adequate support schemes. We also consider the limitations of social control agents’ narratives on criminals.
Forensic steganalysis for identification of steganography software tools using multiple format image

Today many steganographic software tools are freely available on the Internet, which helps even callow users to have covert communication through digital images. Targeted structural image steganalysers identify only a particular steganographic software tool by tracing the unique fingerprint left in the stego images by the steganographic process. Image steganalysis proves to be a tough challenging task if the process is blind and universal, the secret payload is very less and the cover image is in lossless compression format. A payload independent universal steganalyser which identifies the steganographic software tools by exploiting the traces of artefacts left in the image and in its metadata for five different image formats is proposed. First, the artefacts in image metadata are identified and clustered to form distinct groups by extended K-means clustering. The group that is identical to the cover is further processed by extracting the artefacts in the image data. This is done by developing a signature of the steganographic software tool from its stego images. They are then matched for steganographic software tool identification. Thus, the steganalyser successfully identifies the stego images in five different image formats, out of which four are lossless, even for a payload of 1 byte. Its performance is also compared with the existing steganalyser software tool.
Implementation Of A Hybrid Color Image Compression Technique Using Principal Component Analysis And Discrete Tchebichef Transform

The proliferation of scientific and technological demands of high-resolution multimedia contenthas extremely increased the data volume in enterprise data centers and servers on the internet.The characterization of high-resolution picturescreates a great challenge to transfer files with the immensely colossal volume of image data over communication networks. The uploading/downloading time of large images has always been a keyproblemon the Internet. In addition to data communicationissues, high-resolution photo consumes larger storage capacity. Therefore, compression is an almost inevitable process towards the reduction of the transmission time and/or storage capacity requirements of images.Principle component analysis (PCA) and Discrete Tchebichef Transform (DTT) algorithmsare often employed for image compression in several references. In this paper, we propose a hybrid color image compression approachbased on PCA and DTT algorithms (PCADTT), which integrates the benefits of both PCA and DTT algorithms.Our hybrid approach exploits (i) PCA to reduce the dimensionality of the image; and (ii) DTT algorithm to enhance the image quality.The proposed technique has been assessed and related to a compression method that integrates DTT with a singular value decomposition (SVD) scheme (DTTSVD)using different performancemeasures including compression time (CT),compression ratio (CR), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). The experimental results reveal that our proposed method outperformsthe existing method for all kinds of image content at a high compression ratio with lower computational complexity and retaining thequality of imagedata.
Book culture – the socio-communication phenomenon in theoretical, historical and prognostic aspects: the results of “The Twelfth Makushin readings”

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The Use of PowerPoint Slide Media to Improve Student Learning Outcome In Mathematics Learning

Generally, this study aims to describe the improvement of learning outcome using PowerPoint slide media on arithmetic learning in fourth-grade elementary school students. The method used was descriptive in Classroom Action Research (CAR). This study was done in three cycles and found the description about a learning plan, learning implementation, characteristic of PowerPoint slide media, implementation technique of learning evaluation, and improvement of student learning outcomes. There is an improvement of learning in every cycle. The student reaching the target of learning (KKM) was increased from 37% in cycle 1 up to 68% in cycle 3.
Design Analysis of Web-Based Employee Information System in Office of Technical Implementation Unit of Education Department

KUPT Education Agency of Gunung Alip Sub-District is institution that handles education of elementary/kindergarten level in Gunung Alip Sub-District and all employees in KUPT Education Agency. KUPT Education Agency of Gunung Alip Sub-District in maintaining administration manually. Therefore Information System is needed to can help manage administration, especially personnel, quickly and easily. In making this information system the author used the waterfall method, Website programming. The resulting information system is expected to be used to manage employee data at the KUPT Education Office in Gunung Alip Sub-District.
Penerapan Metode Clustering dengan Fuzzy C-Means untuk Memetakan Daerah Rawan Kecelakaan Lalu Lintas di Surakarta

Along with the development of Surakarta's infrastructure, the need for transportation has also increased. Indirectly, it will also cause several problems that must be considered, such as traffic accidents. Data regarding traffic accidents can be used to classify road sections based on the characteristic similarity factor inherent in the data. The sample data used were 1429 accident data from 89 road data in the city of Surakarta. The clustering method used to get the expected results is the Fuzzy C-Means method. The results of accident data grouping are displayed using tables and maps that describe the mapping of road sections in the jurisdiction of the Surakarta City Police. The variables used to cluster data are the number of events, the number of victims who died, and the number of injured victims. The result of this research is a system application that can classify accident-prone areas using the Fuzzy C-Means method into 3 clusters, where the first cluster consists of 5 data, the second cluster consists of 20 data, and the third cluster consists of 64 data.