Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Profiling of Modern Die-Stacked Memory

Die-stacked memories that integrate multiple DRAM dies into the processor package have reduced the interface bottleneck and improved efficiency, but demands for memory capacity and bandwidth remain unfulfilled. Additionally, the introduction of memory into the package further complicates heat removal. Memory power is therefore becoming a key architectural concern. To provide insight into these challenges, an architectural power model for High Bandwidth Memory is developed, validated, and used to provide detailed power profiles. Based on the resulting power trends, power is projected for potential future memory configurations with increased bandwidth and capacity. The results suggest that, without significant improvements in memory technology or architecture, the power utilization of in-package memories will continue to grow and limit the system power budget.
CLASSIFICATION BASED ON MISSING FEATURES IN DEEP CONVOLUTIONAL NEURAL NETWORKS

Artificial Neural Networks, notably Convolutional Neural Networks (CNN) are widely used for classification purposes in different fields such as image classification, text classification and others. It is not uncommon therefore that these models are used in critical systems (e.g. self-driving cars), where robustness is a very important attribute. All Convolutional Neural Networks used for classification, classify based on the extracted features found in the input sample. In this paper, we present a novel approach of doing the opposite – classification based on features not present in the input sample. Obtained results show not only that this way of learning is indeed possible but also that the trained models become more robust in certain scenarios. The presented approach can be applied to any existing Convolutional Neural Network model and does not require any additional training data.
IS Success Model for EDMODO E-learning User Satisfaction through TAM on Students

This study aimed to analyze the effect of IS Success Model variable on Edmodo's e-learning user satisfaction partially, both directly and indirectly through the TAM variable. This research was conducted through an ex-post-facto approach to undergraduate students. Data collection instruments used questionnaire and observation. The questionnaire was developed from the technology acceptance model (TAM) and information system (IS) success model. The collected data were analyzed through descriptive statistics and path analysis using IBM SPSS 20 program. The results showed that the three core variables of the IS Success model directly had a significant effect on the two core variables of TAM, and the two core variables of TAM directly affected user satisfaction. Indirectly, the three core variables of IS Success model had a significant effect on user satisfaction through the two core variables of TAM. Service Quality, also, to provide a more significant direct effect on perceived usefulness, also indirectly influenced user satisfaction through perceived usefulness. Perceived ease of use had more significant direct and indirect effects through perceived usefulness on Edmodo e-learning user satisfaction. Â Â Â
Towards Semantic Document Modelling of Business Processes

In this paper, we introduce a document–based approach to business process modelling. We argue that declarative semantic modelling should be preferred against the procedural one, which is typically used in software implementations of business processes. Semantic modelling allows for a transparent description of business processes, which is accessible both to manual and automated analysis, verification, and reuse. We present the idea of semantic document modelling and report on its implementation in a web platform, which has been successfully applied to automate business processes of real-world
Indoor Mobility Interaction Model: Insights into the Customer Flow in Shopping Malls

Interaction in large-scale outdoor space has been extensively studied due to its importance in understanding the human-land relationship. Models such as the gravity have been proven to be able to quantify the interaction between two places. However, interaction in indoor space still remains unclear even though humans spend over 70% of their time indoors. Few studies attempt to construct an indoor interaction model. In this study, we analyze the interaction between stores in shopping malls via customer flow to determine whether indoor mobility interaction follows the gravity law and what are its influencing factors. Based on indoor positioning data, two customer flow measures (connectivity flow, indicating the direct connection between stores, and association flow, indicating the association relationship between stores) and two distance measures (path distance, indicating the minimum travel cost, and store distance, indicating the mean travel cost) are defined to fit the traditional and extended gravity models (considering store floor and type). We find that 1) interaction between stores follows a power law distribution, indicating that only a small fraction of store pairs is closely related; 2) customer mobility is governed by the gravity law, where the distance decay exponent is 1–2 for the connectivity flow and 0–1 for the association flow; and 3) store floor and type are two important factors that affect the interaction between stores. These findings provide insights for modeling indoor interactions and can support indoor settings to optimize their layout, estimate the customer flow and promote sales.
Characterizing neural mechanisms of attention-driven speech processing

The ability to selectively attend to speech in adverse listening environments plays a key role in human communication. However, listening to speech in everyday multi-talker environments can be challenging for hearing-impaired listeners, even when wearing modern hearing aids. Studies in normal-hearing listeners have shown that it is possible to decode which of two speakers a listener is attending to from scalp electroencephalography (EEG). This has led to the idea that EEG-based brain-computer interfaces (BCIs) could be integrated into hearing assistive devices to help hearing-impaired listeners by selectively amplifying attended sound sources. To accomplish this, however, single-trial EEG measures of attention to speech must first be investigated in hearing-impaired listeners, in everyday listening scenarios, and under different task demands. This thesis explored single-trial cortical EEG correlates of selective attention to speech. In the first study, the influence of sensorineural hearing loss on cortical EEG responses to tones and to naturalistic speech was investigated. It was shown that hearing impairment enhances the fidelity of the low-frequency cortical EEG entrainment to envelopes of simple tones and to envelopes of attended speech streams. For loudness-matched competing speech streams, the attended target could be classified from single-trial EEG responses with equally high classification accuracies in normaland hearing-impaired listeners. The second study explored single-trial EEG correlates of selective auditory attention to speech in reverberant, multi-talker environments. It was shown that the attentional selection of normal-hearing listeners could be decoded from single-trial EEG data with equally high classification accuracies in both anechoicand reverberant listening environments. The third study analyzed how different constraints on EEG-based stimulus-response models influence the predictive power of the models. The results from this study suggested that stimulus-response model regularization is important for maintaining high classification accuracies with backward decoding models. The fourth study investigated if working memory demands affect single-trial EEG correlates of speech
Optimum Location of Pss and Its Parameters by Using Particle Swarm Optimization

This paper deals with stability of interconnected power system using optimum power system stabilizer (PSS) present at generators. Tuning of PSS parameters has been done using particle swarm optimization (PSO) algorithm. This can be achieved by the modal analysis of IEEE 10 machine 39 bus New England system in Matlab-Simulink. A comparative analysis of IEEE 10 machine 39 bus system has been carried out for investigating the effectiveness of PSS at all generators and with PSS only at generators which were having modal analysis based higher participation factor. Tuning of PSS is an important issue in wide area control system, hence we can tune only the PSS at optimal locations of generators and tuning time got decreased however system was still stable. Investigation shows that the optimum PSS increases the damping of the electromechanical modes and stabilizes the entire system disturbance.
Real Time Detection and Analysis of Facial Features to Measure Student Engagement with Learning Objects

This paper describes a software application that records student engagement in an on-screen task. The application records in real time the on-screen activity and simultaneously estimates the emotional state and head pose of the learner. The head pose is used to detect when the screen is being viewed and the emotional state provides feedback on the form of engagement. The application works without recording images of the learner. On completing the task, the percentage of time spent viewing the screen and statistics on emotional state (neutral, happy, sad) are produced. A graph depicting the learner’s engagement and emotional state synchronised with the screen captured video is also produced. It is envisaged that the tool will find application in learning activity and learning object design.
Intelligent analysis and data visualisation for teacher assistance tools: The case of exploratory learning

While it is commonly accepted that Learning Analytics (LA) tools can support teachers' awareness and classroom orchestration, not all forms of pedagogy are congruent to the types of data generated by digital technologies or the algorithms used to analyse them. One such pedagogy that has been so far underserved by LA is exploratory learning, exemplified by tools such as simulators, virtual labs, microworlds and some interactive educational games. This paper argues that the combination of intelligent analysis of interaction data from such an Exploratory Learning Environment (ELE) and the targeted design of visualisations has the benefit of supporting classroom orchestration and consequently enabling the adoption of this pedagogy to the classroom. We present a case study of LA in the context of an ELE supporting the learning of algebra. We focus on the formative qualitative evaluation of a suite of Teacher Assistance tools. We draw conclusions relating to the value of the tools to teachers and reflect with transferable lessons for future related work. Practitioner NotesWhat is already known about this topic Learning Analytics (LA) tools can support teachers' orchestration but most of the work so far has focus mostly on instructionist pedagogies or Computer‐Supported Collaborative Learning.Exploratory Learning Environments (ELEs) encourage students to freely explore a domain and follow a variety of solutions and have the potential to lead to more engagement and deeper learning.The freedom in ELEs introduces orchestration challenges that hinder the adoption of such pedagogies in classroom.What this paper adds A set of usage scenarios for LA tools for exploratory environments derived from teacher input and design and methodological guidelines based on a reflection from the study.Showcases a suite of Teacher Assistance tools purposefully designed for teachers' orchestration requirements during Exploratory Learning.Empirical evidence of the perceived usefulness of these tools and feedback from a large number of teachers thanks to a methodology of re‐using previously collected classroom data.Implications for practice and/or policy A methodology for involving teachers in the design and evaluation of LA.Highlights teacher training needs for LA.Teachers should be provided with tools that help them orchestrate different activities including ones with digital tools underpinned by a constructionist pedagogy. [ABSTRACT FROM AUTHOR]
Compressed Beam Alignment with Out-of-Band Assistance in Millimeter Wave Cellular Networks

Network transmission over millimeter-wave (mmW) bands has a big potential to provide orders of higher bandwidth. However, beamforming is generally needed to compensate for the high path loss. As mmW antennas have a potentially large number of candidate beamforming directions, to achieve high network throughput, the finding of a high gain direction between a base station and each mobile in the mmW network may involve a large overhead if training signals are directly sent along all possible directions or according to a large volume of codebook. Taking advantage of the block sparse characteristics of the mmW channel and coexistence of legacy antennas, we propose a comprehensive design for more efficient beam direction finding. Different from existing compressive-sensing-based schemes which just take a random subset of directions to measure, taking advantage of the path clustering feature of the mmW channel, we develop a self-adaptive block sparse algorithm which can benefit from preliminary channel estimation during each iteration of the problem solving to significantly improve the overall channel estimation accuracy thus the beam alignment gain. We also explore two methods to exploit co-located legacy antennas to provide further guidance for transmission direction finding. Simulation results indicate that our proposed beam alignment scheme outperforms the baseline and peer schemes in terms of the beamforming gain and training cost. By taking advantage of the block sparse properties of mmW channel, our proposed design is able to achieve the transmission throughput comparable with the exhaustive direction search at much lower overhead.
Detecting and classifying small objects in thermal imagery using a deep neural network

In recent years the rise of deep learning neural networks has shown great results in image classification. Most of the previous work focuses on classification of fairly large objects in visual imagery. This paper presents a method of detecting and classifying small objects in thermal imagery using a deep learning method based on a RetinaNet network. The result shows that a deep neural network with a relative small set of labelled images can be trained to classify objects in thermal imagery. Objects from classes with the most training examples (cars, trucks and persons) can with relative high confidence be classified given an object size of 32×32 pixels or smaller.
Automated Theme Search in ICO Whitepapers

The authors explore how topic modeling can be used to automate the categorization of initial coin offerings (ICOs) into different topics (e.g., finance, media, information, professional services, health and social, natural resources) based solely on the content within the whitepapers. This tool has been developed by fitting a latent Dirichlet allocation (LDA) model to the text extracted from the ICO whitepapers. After evaluating the automated categorization of whitepapers using statistical and human judgment methods, it is determined that there is enough evidence to conclude that the LDA model appropriately categorizes the ICO whitepapers. The results from a two-population proportion test show a statistically significant difference between topics in the success of an ICO being funded, indicating that the topics are usefully differentiated and suggesting that the topic model could be used to help predict whether an ICO will be successful. TOPICS: Statistical methods, simulations, big data/machine learning Key Findings • Categorization of ICO whitepapers can be done via topic modeling with the latent Dirichlet allocation (LDA) model. • Statistical and human judgment methods confirms that there is enough evidence to conclude that the LDA model appropriately categorizes ICO whitepapers. • Statistical tests suggests that the categorization results from the LDA model provides useful information on predicting whether an ICO will be successful funded.
A Reconfigurable Three-Port DC–DC Converter for Integrated PV-Battery System

In this article, a new nonisolated three-port dc–dc converter to integrate a battery storage with a photovoltaic (PV) module is proposed for off-grid solar-power applications. The proposed converter can be used to integrate the PV module with a backup battery to minimize the impacts of renewable-energy intermittency and unpredictable load demand. The proposed converter is reconfigurable and able to operate as a conventional boost converter, a buck-boost converter, or a forward converter in different modes to support several power flow combinations and achieve power conditioning and regulation among the PV module, battery, and output port, simultaneously. Nevertheless, the power stage only consists of two switches, one coupled inductor, one diode, and two capacitors. A high-voltage conversion ratio is achieved by using a coupled inductor and by combining the PV module and the battery in series. Experimental results of the proposed converter operating in the steady state and during transitions between different modes are reported.
Classification of common discharges in outdoor insulation using acoustic signals and artificial neural network

Condition monitoring of outdoor insulation systems is crucial to the integrity of distribution and transmission overhead lines and substations. The objective of this study is to use a commercial acoustic sensor along with artificial neural network (ANN), to classify different typical types of discharges in outdoor insulation systems. First, ANN was used to distinguish between five common electrical discharges that were generated under controlled conditions. Next, this approach was extended to include outdoor ceramic insulators. Three types of defects were tested under laboratory conditions, i.e. a crack in the ceramic disc, surface pollution discharge, and corona near the insulator surface. Both a single disc, and three discs connected in an insulator string were tested with respect to these defects. For both controlled samples and full insulators, a recognition rate of more than 85% was achieved.
Antecedents of Technology Selection for Project Use

ABSTRACT The adoption and use of technology have been an important topic of research in Information Systems. However, projects create a different environment for technology adoption in that they are temporary organizations which are formed specifically for the project and then disbanded as opposed to the continuing organizations which have been traditionally studied. We term this adoption and use in temporary organizations as project use. The purpose of this study is to examine how previous experience with a technology in a project affects the selection of that technology within subsequent projects. The findings support Technology Continuation Theory as a partial explanation for project use but the key factors for project use are different from those in continuing organizations. Project use seems to be a much more pragmatic than hedonic decision. The question “does it work satisfactorily? ” seems to be more important than “is it easy or pleasant to use? ”
Inferring Precise Polymorphic Specifications for the Hoare State Monad

The Hoare state monad provides a powerful means of structuring the verification of higher-order, stateful programs. However, most prior works involving the Hoare monad have been in the context of interactive proof assistants, imposing a significant cost both for computing verification conditions and for discharging their proofs. This paper aims to reduce this cost by automatically inferring verification conditions for programs using the Hoare monad. The inferred conditions can then be fed to a standard, first-order, automated theorem prover, e.g., an SMT solver. Our approach has several novelties and benefits. Most prominently, the specifications inferred for stateful functions involve the use of predicate transformers applied to arbitrary, polymorphic post-conditions. This allows us to easily implement a syntactic, unification-based type inference algorithm, even in the presence of calls to higher-order functions. We show how to structure specifications so that despite the use of higher-order logic in the types of higher-order functions, we can generate first-order verification conditions for many programs. The present work also has some limitations: we focus primarily on specification inference for (recursion-free) clients of higher-order libraries; the libraries themselves (as well as all loop invariants) require manual specifications. We have implemented our inference algorithm as a front-end to the F compiler and report on a preliminary evaluation of our tool on a collection of benchmarks.
Multi-Vehicle Map Fusion using GNU Radio

In this paper, we present a representative open-source application for fully/semiautonomous vehicles operating as a collaborative swarm using GNU Radio. This application is the driver of the DARPA-sponsored EPOCHS project led by IBM, which focuses on agile heterogeneous system-on-achip (SoC) development. The EPOCHS Reference Application (ERA) incorporates local sensing, creation of occupancy grid maps, vehicle-to-vehicle (V2V) communication of grid maps between neighboring vehicles using GNU Radio-based dedicated short-range communication (DSRC), and map fusion to create a joint higher-accuracy grid map. This paper analyzes the GNU Radio-based DSRC transceiver workload on a Xilinx Zynq UltraScale+ MPSoC, identifying computation kernels for software and/or hardware acceleration. In particular, we present optimizations of Viterbi decoding and complex exponential that result in a measured 5:4 performance improvement over a CPU-only baseline, with projections of a fully-optimized version of this hardware accelerator showing signicantly higher benets.
Connecting Learning Analytics and Problem-Based Learning – Potentials and Challenges

Learning analytics (LA) are a young but fast-growing field, which, according to some authors, holds big promises for education. Some claim that LA solutions can help measure and support constructivist classrooms and 21 century skills, thus creating a potential of making an alignment between LA and PBL principles and practices. Despite this argument, LA have not yet gained much interest among the Problem-Based Learning (PBL) practitioners and researchers and the possible connections between PBL and LA have not yet been properly explored. The purpose of this paper is, therefore, to investigate how LA can potentially be used to support and inform PBL practice. We do this by identifying central themes that remain constant across various orchestrations of PBL (collaboration, self-directed learning, and reflection) and present examples of LA tools and concepts that have been developed within LA and neighbouring fields (e.g. CSCL) in connection to those themes. This selection of LA solutions is later used as a basis for discussing wider potentials, challenges and recommendations for making connections between PBL and LA.