Miracle-dz/UniFAR-FTUs huggingface dataset:
GAIM: Graph Attention Interaction Model for Collective Activity Recognition

Unbalanced interaction relationships at personal and group levels play a pivotal role in collective activity recognition, which has not been adaptively and jointly explored by previous approaches. In this paper, we propose a graph attention interaction model (GAIM) embedded with the graph attention block (GAB) to explicitly and adaptively infer unbalanced interaction relations at personal and group levels in a unified architecture, and further to learn the spatial and temporal evolutions of the collective activity from these interactions to predict the activity labels. We first design the spatiotemporal graphs tailored to the collective activity where the concurrent person and group nodes, respectively, represent individuals’ actions and the collective activity. The graphs provide both spatial structures and semantic appearance features for the collective activity. Then, GAB performs convolution-like filters on the graphs to infer unequal and two-level interaction relations in the collective activity by implementing graph convolutional networks with a shared attention mechanism. At the personal level, the GAB learns different levels of interactions for each person node from its neighbor person nodes under the guidance from the group node. At the group level, the GAB assesses various degrees of interactions to the group node contributed by person nodes. Equipped with the GRUs network, the GAIM learns the spatial and temporal evolutions of individuals’ actions as well as the collective activity from the captured interactions, and finally predicts the label of the collective activity. Experiments on four publicly available datasets and ablation studies are conducted to evaluate the performance of our GAIM, and the improved performance demonstrates the effectiveness of our model.
Improving the performance of dataflow systems for deep neural network training

Deep neural networks (DNNs) have led to significant advancements in machine learning. With deep structure and flexible model parameterisation, they exhibit state-of-the-art accuracies for many complex tasks e.g. image recognition. To achieve this, models are trained iteratively over large datasets. This process involves expensive matrix operations, making it time-consuming to obtain converged models. To accelerate training, dataflow systems parallelise computation. A scalable approach is to use parameter server framework: it has workers that train model replicas in parallel and parameter servers that synchronise the replicas to ensure the convergence. With distributed DNN systems, there are three challenges that determine the training completion time. In this thesis, we propose practical and effective techniques to address each of these challenges. Since frequent model synchronisation results in high network utilisation, the parameter server approach can suffer from network bottlenecks, thus requiring decisions on resource allocation. Our idea is to use all available network bandwidth and synchronise subject to the available bandwidth. We present Ako, a DNN system that uses partial gradient exchange for synchronising replicas in a peer-to-peer fashion. We show that our technique exhibits a 25% lower convergence time than a hand-tuned parameter-server deployments. For a long training, the compute efficiency of worker nodes is important. We argue that processing hardware should be fully utilised for the best speed-up. The key observation is it is possible to overlap the execution of several matrix operations with other workloads. We describe Crossbow, a GPU-based system that maximises hardware utilisation. By using a multi-streaming scheduler, multiple models are trained in parallel on GPU and achieve a 2.3x speed-up compared to a state-of-the-art system. The choice of model configuration for replicas also directly determines convergence quality. Dataflow systems are used for exploring the promising configurations but provide little support for efficient exploratory workflows. We present Meta-dataflow (MDF), a dataflow model that expresses complex workflows. By taking into account all configurations as a unified workflow, MDFs efficiently reduce time spent on configuration exploration. This thesis is dedicated to my mum.
Put-call ratio predictability of the 50ETF option

Abstract This article investigates the predictive power of the put-call ratios (PCRs) implied by China’s 50ETF option on the 50ETF return and its variance. By using simple partitional regressions, the relationship between the PCR and the 50ETF return is tested. This study conducts tests on their robustness based on different horizons, market conditions, moneyness status and time to maturity. Empirical results indicate that the PCR is a strong forward-looking indicator of the variance of 50ETF returns. A robust and negative correlation is detected. A significant linear correlation between the PCR and the 50ETF return only exists during the market crash. This study shows evidence that the PCR as seen in common trading practices may be misused and indicates a potential way of using it.
Maximization of Transfer Entropy leads to Evolution of Functional Differentiation of Swarms

We aimed to investigate the principle of emerging interactions between swarms using the functional differentiation theory of the brain. We propose a heterogeneous swarms model, where two swarms having different parameters evolve to maximize transfer entropy between them. In our simulation, we found the emergence of heterogeneous behavior among the swarms, and the appearance of several interaction patterns depending on the degree of the transfer entropy. Our results imply that the same principle of functional differentiation may underlie both the brain and swarms, leading to a novel design of brain-inspired swarm intelligence.
Sequence generation for learning: a transformation from past to future

Purpose The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static. Design/methodology/approach The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review. Findings The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation. Originality/value The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.
ANAPHORIC CONNECTIVE DATABASE

The paper introduces a lexical database covering anaphoric discourse connectives. The database contains 80 most frequently used connective units consisting of (in their current use or historically) a preposition and an anaphoric element, cf. the connective thereafter containing a preposition after and an anaphoric part there. The database has two parts. The first part includes German anaphoric connectives and their most common equivalents in Czech and English. The second one consists of Czech anaphoric connectives and their counterparts in German and English. The involvement of German, Czech and English enables the use of the database in foreign language teaching with a focus on deepening the productive language skills of students. In addition, the database can be used by translators as well as in the machine translation area.
Workflow Activity Task Controller: an Approach to Distribute Knowledge and Information in Collaborative Product Development

This paper presents a theoretical approach to workflow process management using well established PDM functions for controlling knowledge and information to support collaborative product development processes within the WWW. The new methodology is called Workflow Activity Task Controller (WATC) that can securely distribute design and manufacturing knowledge to support process planning with information exchange during the early stages of the product development phases. Most specifically, WATC uses the methods described herein to link the workflow management of a PDM system with a knowledge-based system and a process planning system. In addition, this framework is flexible and adaptive and can be extended to adapt to a specific company’s needs. The objective and the intention of the development are to improve information flows, promote sharing of design and manufacturing knowledge and subsequently to maximise effectiveness of product development activities and task efficiency.
Hybrid Synthetic Approach to Animal Interaction

Not only studying and analyzing real lives on earth but also creating life as it could be on computer simulations can be our strong method to understand what life is (Langton, 1989). It becomes the main philosophy in artificial life community and various types of simulations have been developed in different levels of life such as artificial chemistry, evolutionary robotics, multi-agent social system, etc. The synthetic approaches have been applied not only to computer simulations but also to chemistry and robots in our real world. Regardless of the virtual or real world as experimental grounds, when we can reconstruct life there, we could understand the essential mechanisms that make an entity capable of being life. Those experiments have been performed in either real or virtual world separately. As rather new direction, in this paper, we investigate a hybrid synthetic approach that combines the real and virtual experiments. In biology, there is a close idea for our hybrid synthetic approach called playback experiments where real animals interact with the virtual individuals, which can be robots or computer graphics displayed on a monitor. For example, Nakayasu et al. examined how much real medaka (small fish, Oryzias latipes) can be attracted by the computer graphics animation or the biological motions, which has been made from the actual behaviors of medaka (Nakayasu and Watanabe, 2014). They showed that the color, shape, and movements of the virtual individuals affect the real medaka’s movements and change attractiveness. However, the playback experiments cannot be our hybrid approach in a sense that real individuals just react to virtual one and there is no mutual interaction. In the real interactions, the behaviors that individuals have performed in the past affect the future behaviors of the others mutually. It is known that the mutuality is important for the interaction in the human and animal interactions. In this paper, we establish a hybrid experiment where the real and virtual individuals interact with each other and investigate if there is a difference between mutual and playback interactions. We used medaka for test animals for the sake of the simplicity Figure 1: Experimental environment.
Automated Bahasa Indonesia essay evaluation with latent semantic analysis

Essay Examination is one of many ways to evaluate the learning process of students. The Essay test can measure a student’s capability of memory and to develop an idea. With the vast development of Information Technology, an essay test is done in a more sophisticated way with a platform called e-learning. This research implemented an e-learning interface to conduct an essay test, starting with giving out questions, answering it and marking it. The system will generate an automatic mark, using the Latent Semantic Analysis (LSA) method by measuring the relevance of the key answer and the student’s answer. Before entering the LSA phase, the answer will go through pre-processing step which is cleansing, case folding, tokenization, stop word, convert negation and stemming. The result of the accuracy of this system compared to teacher’s manual assessment is 83.3%. Expectedly, this research will help teachers in making their assessment process more efficient.
The Effect of Scientific Creativity in Inquiry Learning to Promote Critical Thinking Ability of Prospective Teachers

Teaching critical thinking (CT) to the prospective teacher has garnered attention for a while, and the teaching conduction to trained it is important to develop. This study aims to find out the effect of teaching implementation of scientific creativity in inquiry learning to promote the CT ability of prospective teachers. Scientific creativity in inquiry learning is apellation as the inquiry creative process (ICP) learning model. This study is a experimental research conducting with the randomized pretest-posttest control group design. Samples were chosen to be treated as experimental and control group. Two sample groups were prospective teacher of physic (PTP) in the faculty of teacher training and education (FKIP), Mataram University, Indonesia. The data of CT ability collected by instrument refer to Ennis-Weir Critical Thinking Essay Test. The data analysis descriptively and statistically were done to process the data of research result. Generally, the results shown that ICP learning model had a significant effect on the improvement of CT ability of prospective teacher of physic. The description of the research findings are described in this article.
Machine learning approaches for determining effective seeds for k -means algorithm

In this study, I investigate and conduct an experiment on two-stage clustering procedures, hybrid models in simulated environments where conditions such as collinearity problems and cluster structures are controlled, and in real-life problems where conditions are not controlled. The first hybrid model (NK) is an integration between a neural network (NN) and the k-means algorithm (KM) where NN screens seeds and passes them to KM. The second hybrid (GK) uses a genetic algorithm (GA) instead o f the neural network. Both NN and GA used in this study are in their simplestpossible forms. In the simulated data sets, I investigate two properties: clustering performance comparisons and effects o f five factors (scale, sample size, density, number o f clusters, and number o f variables) on the five clustering approaches (KM, NN, NK, GA, GK). Density, number o f clusters, and dimension influence the clustering performance o f all five approaches. KM, NK, and GK classify well when all clusters contain a similar number o f observations, while NK and GK perform better than the KM. NN performs well when one cluster contains more observations than any other cluster. The two hybrid models perform at least as well as KM, although the environments are in favor of the KM. The most crucial information, the true number o f clusters, is provided to the KM only. In addition, the cluster structures are simple: the clusters are well separated; the variances and cluster sizes are uniform; the correlation between any pair o f variables
Contribution to multi-objective optimization under constraints : applications to structural mechanics

The objective of this thesis is the development of multi-objective optimization methods for solving mechanical design problems. Indeed, most of the real problems in the field of mechanical structures have several objectives that are often antagonistic. For example, it is about designing structures by optimizing their weight, their size, and their production costs. The goal of multi-objective optimization methods is the search for compromise solutions between objectives given the impossibility to satisfy all simultaneously. Metaheuristics are optimization methods capable of solving multi-objective optimization problems in a reasonable calculation time without guaranteeing the optimality of the solution (s). In recent years, these algorithms have been successfully applied to solve the problem of structural mechanics. In this thesis, two metaheuristics have been developed for the resolution of multi-objective optimization problems in general and of mechanical structures design in particular. The first algorithm called MOBSA used the crossover and mutation operators of the BSA algorithm. The second one named NNIA+X is a hybridization of an immune algorithm and three crossover inspired by the original crossover operator of the BSA algorithm. To evaluate the effectiveness and efficiency of these two algorithms, tests on some problems in literature have been made with a comparison with algorithms well known in the field of multi-objective optimization. The comparison results using metrics widely used in the literature have shown that our two algorithms can compete with their predecessors.
Application Of Neural Network On PLC-based Automation Systems For Better Fault Tolerance And Error Detection

II TABLE OF CONTENTS . III LIST OF FIGURES . V LIST OF TABLES . VII 1. CHAPTER-1: PROBLEM DESCRIPTION . 1 2. CHAPTER-2: PREVIOUS WORK . 3 2.1 PROTOCOLS: . 3 2.2 SIGNAL FEEDBACKS FROM EDGE DEVICES: . 4 2.3 HUMAN MACHINE INTERFACE (HMI): . 4 2.4 MACHINE LEARNING: . 5 2.4. 1 ARTIFICIAL NEURAL NETWORKS(ANN): . 6 2.5 MERGING NEURAL NETWORKS TO PLC OPERATIONS: . 8 3. CHAPTER-3: SOLUTION . 9 3.1 THE CONCEPTUAL DESIGN: . 9 3.1. 1 NNPLC IN TRAINING CONFIGURATION: . 10 3.1. 2 NNPLC IN DEPLOYED CONFIGURATION: . 11 3.2 TACKLING KEY CHALLENGES TO IMPLEMENT THE CONCEPT DESIGN: . 12 3.3 CHALLENGE#1: . 12 3.3.1 CONTENTS OF TRAINING DATA SET: . 13 3.3.2 HARVESTING DIGITAL VALUES: . 15 3.3.2 HARVESTING ANALOG VALUES: . 20 3.3.3 HARVESTING TIMER VALUES: . 22 3.4 CHALLENGE#2: . 22 3.4. 1 ERROR BACKPROPAGATION ALGORITHM (EBP): . 28 3.4. 2 LEVENBERG-MARQUARDT ALGORITHM (LM): . 31 3.4. 3 NEURON-BY-NEURON ALGORITHM (NBN): . 33 3.4. 4 SCALED CONJUGATE GRADIENT: . 35 3.4. 5 CHOOSING TRAINING ALGORITHMS FOR NNPLC: . 37 3.5 CHALLENGE#3: . 38 3.5. 1 DYNAMICALLY DEPLOYED: . 39 3.5. 2 STATICALLY DEPLOYED: . 39 3.5. 3 COMPARISON: . 40 3.6 TESTING PLATFORMS FOR NEURAL NETWORKS: . 41
MH Cache

Mobile devices have become the most important devices in our life. However, they are limited in battery capacity. Therefore, low-power computing is crucial for their long lifetime. A spin-transfer torque RAM (STT-RAM) has become emerging memory technology because of its low leakage power consumption. We herein propose MH cache, a multi-retention STT-RAM-based cache management scheme for last-level caches (LLC) to reduce their power consumption for mobile hardware rendering systems. We analyzed the memory access patterns of processes and observed how rendering methods affect process behaviors. We propose a cache management scheme that measures write-intensity of each process dynamically and exploits it to manage a power-efficient multi-retention STT-RAM-based cache. Our proposed scheme uses variable threshold for a process’ write-intensity to determine cache line placement. We explain how to deal with the following issue to implement our proposed scheme. Our experimental results show that our techniques significantly reduce the LLC power consumption by 32% and 32.2% in single- and quad-core systems, respectively, compared to a full STT-RAM LLC.
An Evaluation Of A Linguistically Motivated Conversational Software Agent Framework

This paper presents a critical evaluation framework for a linguistically motivated conversational software agent (CSA). The CSA prototype investigates the integration, intersection and interface of the language, knowledge, and speech act constructions (SAC) based on a grammatical object, and the sub-model of belief, desires and intention (BDI) and dialogue management (DM) for natural language processing (NLP). A long-standing issue within NLP CSA systems is refining the accuracy of interpretation to provide realistic dialogue to support human-to-computer communication. This prototype constitutes three phase models: (1) a linguistic model based on a functional linguistic theory – Role and Reference Grammar (RRG), (2) an Agent Cognitive Model with two inner models: (a) a knowledge representation model, (b) a planning model underpinned by BDI concepts, intentionality and rational interaction, and (3) a dialogue model. The evaluation strategy for this Java-based prototype is multi-approach driven by grammatical testing (English language utterances), software engineering and agent practice. A set of evaluation criteria are grouped per phase model, and the testing framework aims to test the interface, intersection and integration of all phase models. The empirical evaluations demonstrate that the CSA is a proof-of-concept, demonstrating RRG’s fitness for purpose for describing, and explaining phenomena, language processing and knowledge, and computational adequacy. Contrastingly, evaluations identify the complexity of lower level computational mappings of NL – agent to ontology with semantic gaps, and further addressed by a lexical bridging solution.
Accelerating Quantitative Magnetic Resonance Imaging

This work focuses on developing novel methods to accelerate Magnetic Resonance Imaging (MRI), to overcome its persistent fundamental challenge: the acquisition time. To this end this thesis presents scientific contributions in three areas of quantitative MRI: hyperpolarized 13C metabolic imaging in preclinical rodent models of cancer, motion encoding methods to map diffusion and flow in vivo, and multiparametric mapping techniques for fast, quantitative neuroimaging in the human brain.
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.