A key challenge for maximizing the world’s food supply is crop yield prediction. In this study, three machine models are used to predict the fresh weight (yield) of lettuce plants that are grown inside indoor farms hydroponically using the vertical farming infrastructure, namely, support vector regressor (SVR), random forest regressor (RFR), and deep neural network (DNN).The climate data, nutrient data, and plant growth data are passed as input to train the models to understand the growth pattern based on the available features. The study of evaluation metrics majorly covers Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared, and Adjusted R-squared values.The results of the project have shown that the Random Forest with all the features is the best model having the best results with the least cross-validated MAE score and good cross-validated Adjusted R-squared value considering that the error of the prediction is minimal. This is followed by the DNN model with minor differences in the resulting values. The Support Vector Regressor (SVR) model gave a very poor performance with a huge error value that cannot be afforded in this scenario. In this study, we have also compared various evaluating metrics mentioned above and considered the cross-validated MAE and cross-validated Adjusted R-squared metrics. According to our study, MAE had the lowest error value, which is less sensitive to the outliers and adjusted R-squared value helps to understand the variance of the target variable with the predictor variable and adjust the metric to prevent the issues of overfitting.

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Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. As a result, DNN implementation on embedded system devices requires customized accelerators with tailored hardware due to their numerous computations, latency, power consumption, etc. Moreover, the computational capacity, provided by potent microprocessors or graphics processing units (GPUs), is necessary to unleash the full potential of DNN, but these computational resources are often not easily available in embedded system devices. In this thesis, we propose an innovative method to evaluate and improve the efficiency of DNN implementation within the constraints of resourcelimited embedded system devices. The Evolutionary Multi-Objective Neuron Architecture Search-Binary One Optimization (EMONAS-BOO) optimizes both the image classification accuracy and the innovative Binary One Optimization (BOO) objectives, with Multiple Objective Optimization (MOO) methods. The EMONAS-BOO automates neural network searching and training, and the neural network architectures’ diversity is also guaranteed with the help of an evolutionary algorithm that consists of tournament selection, polynomial mutation, and point crossover mechanisms. Binary One Optimization (BOO) is used to evaluate the difficulty in implementing DNNs on resource-limited embedded system peripherals, employing a binary format for DNN weights. A deeper implementation of the innovative Binary One Optimization will significantly boost not only computation efficiency but also memory storage, power dissipation, etc. It is based on the reduction of weights binary 1’s that need to be computed and stored, where the reduction of binary 1 brings reduced arithmetic operations and thus simplified neural network structures. In addition, analyzed from a digital circuit waveform perspective, the embedded system, in interpreting the neural network, will register an increase in zero weights leading to a reduction in voltage transition frequency, which, in turn, benefits power efficiency improvement. The proposed EMONAS employs the MOO method which optimizes two objectives. The first objective is image classification accuracy, and the second objective is Binary One Optimization (BOO). This approach enables EMONAS to outperform manually constructed and randomly searched DNNs. Notably, 12 out of 100 distinct DNNs maintained their image classification accuracy. At the same time, they also exhibit superior BOO performance. Additionally, the proposed EMONAS ensures automated searching and training of DNNs. It achieved significant reductions in key performance metrics: Compared with random search, evolutionary-searched BOO was lowered by up to 85.1%, parameter size by 85.3%, and FLOPs by 83.3%. These improvements were accomplished without sacrificing the image classification accuracy, which saw an increase of 8.0%. These results demonstrate that the EMONAS is an excellent choice for optimizing innovative objects that did not exist before, and greater multi-objective optimization performance can be guaranteed simultaneously if computational resources are adequate. / Customized Deep Neural Network (DNN)-acceleratorer har blivit alltmer populära i olika applikationer, från autonom körning och naturlig språkbehandling till sjukvård och ekonomi, etc. Att distribuera dem direkt på kringutrustning för inbyggda system inom realtidsoperativsystem (RTOS) är dock inte lätt på grund av paradoxen med komplexiteten hos DNN och enkelheten hos inbyggda systemenheter. Som ett resultat kräver DNNimplementering på inbäddade systemenheter skräddarsydda acceleratorer med skräddarsydd hårdvara på grund av deras många beräkningar, latens, strömförbrukning, etc. Dessutom är beräkningskapaciteten, som tillhandahålls av potenta mikroprocessorer eller grafikprocessorer (GPU), nödvändig för att frigöra den fulla potentialen hos DNN, men dessa beräkningsresurser är ofta inte lätt tillgängliga i inbyggda systemenheter. I den här avhandlingen föreslår vi en innovativ metod för att utvärdera och förbättra effektiviteten av DNN-implementering inom begränsningarna av resursbegränsade inbäddade systemenheter. Den evolutionära Multi-Objective Neuron Architecture Search-Binary One Optimization (EMONAS-BOO) optimerar både bildklassificeringsnoggrannheten och de innovativa Binary One Optimization (BOO) målen, med Multiple Objective Optimization (MOO) metoder. EMONAS-BOO automatiserar sökning och träning av neurala nätverk, och de neurala nätverksarkitekturernas mångfald garanteras också med hjälp av en evolutionär algoritm som består av turneringsval, polynommutation och punktövergångsmekanismer. Binary One Optimization (BOO) används för att utvärdera svårigheten att implementera DNN på resursbegränsade kringutrustning för inbäddade system, med ett binärt format för DNN-vikter. En djupare implementering av den innovativa Binary One Optimization kommer att avsevärt öka inte bara beräkningseffektiviteten utan också minneslagring, effektförlust, etc. Den är baserad på minskningen av vikter binära 1:or som behöver beräknas och lagras, där minskningen av binär 1 ger minskade aritmetiska operationer och därmed förenklade neurala nätverksstrukturer. Dessutom, analyserat ur ett digitalt kretsvågformsperspektiv, kommer det inbäddade systemet, vid tolkning av det neurala nätverket, att registrera en ökning av nollvikter, vilket leder till en minskning av spänningsövergångsfrekvensen, vilket i sin tur gynnar en förbättring av effekteffektiviteten. Den föreslagna EMONAS använder MOO-metoden som optimerar två mål. Det första målet är bildklassificeringsnoggrannhet och det andra målet är Binary One Optimization (BOO). Detta tillvägagångssätt gör det möjligt för EMONAS att överträffa manuellt konstruerade och slumpmässigt genomsökta DNN. Noterbart behöll 12 av 100 distinkta DNN:er sin bildklassificeringsnoggrannhet. Samtidigt uppvisar de också överlägsen BOOprestanda. Dessutom säkerställer den föreslagna EMONAS automatisk sökning och utbildning av DNN. Den uppnådde betydande minskningar av nyckelprestandamått: BOO sänktes med upp till 85,1%, parameterstorleken med 85,3% och FLOP:s med 83,3%. Dessa förbättringar åstadkoms utan att offra bildklassificeringsnoggrannheten, som såg en ökning med 8,0%. Dessa resultat visar att EMONAS är ett utmärkt val för att optimera innovativa objekt som inte existerade tidigare, och större multi-objektiv optimeringsprestanda kan garanteras samtidigt om beräkningsresurserna är tillräckliga.

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Indiana University-Purdue University Indianapolis (IUPUI) / Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.

<p>Advances in Artificial Intelligence (AI), or more precisely on Neural Networks (NNs), and fast processing technologies (e.g. Graphic Processing Units or GPUs) in recent years have positioned NNs as one of the main machine learning algorithms used to solved a diversity of problems in both academia and the industry. While they have been proved to be effective in solving many tasks, the lack of security guarantees and understanding of their internal processing disrupts their wide adoption in general and cybersecurity-related applications. In this dissertation, we present the findings of a comprehensive study aimed to enable the absorption of state-of-the-art NN algorithms in the development of enterprise solutions. Specifically, this dissertation focuses on (1) the development of defensive mechanisms to protect NNs against adversarial attacks and (2) application of NN models for anomaly detection in enterprise networks.</p><p>In this state of affairs, this work makes the following contributions. First, we performed a thorough study of the different adversarial attacks against NNs. We concentrate on the attacks referred to as trojan attacks and introduce a novel model hardening method that removes any trojan (i.e. misbehavior) inserted to the NN models at training time. We carefully evaluate our method and establish the correct metrics to test the efficiency of defensive methods against these types of attacks: (1) accuracy with benign data, (2) attack success rate, and (3) accuracy with adversarial data. Prior work evaluates their solutions using the first two metrics only, which do not suffice to guarantee robustness against untargeted attacks. Our method is compared with the state-of-the-art. The obtained results show our method outperforms it. Second, we proposed a novel approach to detect anomalies using LSTM-based models. Our method analyzes at runtime the event sequences generated by the Endpoint Detection and Response (EDR) system of a renowned security company running and efficiently detects uncommon patterns. The new detecting method is compared with the EDR system. The results show that our method achieves a higher detection rate. Finally, we present a Moving Target Defense technique that smartly reacts upon the detection of anomalies so as to also mitigate the detected attacks. The technique efficiently replaces the entire stack of virtual nodes, making ongoing attacks in the system ineffective.</p><p> </p>

Les différences entre conditions d'apprentissage et conditions de test peuvent considérablement dégrader la qualité des transcriptions produites par un système de reconnaissance automatique de la parole (RAP). L'adaptation est un moyen efficace pour réduire l'inadéquation entre les modèles du système et les données liées à un locuteur ou un canal acoustique particulier. Il existe deux types dominants de modèles acoustiques utilisés en RAP : les modèles de mélanges gaussiens (GMM) et les réseaux de neurones profonds (DNN). L'approche par modèles de Markov cachés (HMM) combinés à des GMM (GMM-HMM) a été l'une des techniques les plus utilisées dans les systèmes de RAP pendant de nombreuses décennies. Plusieurs techniques d'adaptation ont été développées pour ce type de modèles. Les modèles acoustiques combinant HMM et DNN (DNN-HMM) ont récemment permis de grandes avancées et surpassé les modèles GMM-HMM pour diverses tâches de RAP, mais l'adaptation au locuteur reste très difficile pour les modèles DNN-HMM. L'objectif principal de cette thèse est de développer une méthode de transfert efficace des algorithmes d'adaptation des modèles GMM aux modèles DNN. Une nouvelle approche pour l'adaptation au locuteur des modèles acoustiques de type DNN est proposée et étudiée : elle s'appuie sur l'utilisation de fonctions dérivées de GMM comme entrée d'un DNN. La technique proposée fournit un cadre général pour le transfert des algorithmes d'adaptation développés pour les GMM à l'adaptation des DNN. Elle est étudiée pour différents systèmes de RAP à l'état de l'art et s'avère efficace par rapport à d'autres techniques d'adaptation au locuteur, ainsi que complémentaire. / Differences between training and testing conditions may significantly degrade recognition accuracy in automatic speech recognition (ASR) systems. Adaptation is an efficient way to reduce the mismatch between models and data from a particular speaker or channel. There are two dominant types of acoustic models (AMs) used in ASR: Gaussian mixture models (GMMs) and deep neural networks (DNNs). The GMM hidden Markov model (GMM-HMM) approach has been one of the most common technique in ASR systems for many decades. Speaker adaptation is very effective for these AMs and various adaptation techniques have been developed for them. On the other hand, DNN-HMM AMs have recently achieved big advances and outperformed GMM-HMM models for various ASR tasks. However, speaker adaptation is still very challenging for these AMs. Many adaptation algorithms that work well for GMMs systems cannot be easily applied to DNNs because of the different nature of these models. The main purpose of this thesis is to develop a method for efficient transfer of adaptation algorithms from the GMM framework to DNN models. A novel approach for speaker adaptation of DNN AMs is proposed and investigated. The idea of this approach is based on using so-called GMM-derived features as input to a DNN. The proposed technique provides a general framework for transferring adaptation algorithms, developed for GMMs, to DNN adaptation. It is explored for various state-of-the-art ASR systems and is shown to be effective in comparison with other speaker adaptation techniques and complementary to them.

Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.<br>

Recently, Deep Learning has revolutionized many fields, where one such area is Voice Activity Detection (VAD). This is of great interest to sectors of society concerned with detecting speech in sound signals. One such sector is the police, where criminal investigations regularly involve analysis of audio material. Convolutional Neural Networks (CNN) have recently become the state-of-the-art method of detecting speech in audio. But so far, understanding the impact of noise and sampling rates on such methods remains incomplete. Additionally, there are evaluation metrics from neighboring fields that remain unintegrated into VAD. We trained on four different sampling rates and found that changing the sampling rate could have dramatic effects on the results. As such, we recommend explicitly evaluating CNN-based VAD systems on pertinent sampling rates. Further, with increasing amounts of white Gaussian noise, we observed better performance by increasing the capacity of our Gated Recurrent Unit (GRU). Finally, we discuss how careful consideration is necessary when choosing a main evaluation metric, leading us to recommend Polyphonic Sound Detection Score (PSDS).

This essay critically examines the limitations of artificial intelligence (AI) in achieving human-like understanding and intelligence. Despite significant advancements in AI, such as the development of sophisticated machine learning algorithms and neural networks, current systems fall short in comprehending the cognitive depth and flexibility inherent in human intelligence. Through an exploration of historical and contemporary arguments, including Searle's Chinese Room thought experiment and Dennett's Frame Problem, this essay highlights the inherent differences between human cognition and AI. Central to this analysis is the role of metaphorical thinking and embodied cognition, as articulated by Lakoff and Johnson, which are fundamental to human understanding but absent in AI. Proponents of AGI, like Kurzweil and Bostrom, argue for the potential of AI to surpass human intelligence through recursive self-improvement and technological integration. However, this essay contends that these approaches do not address the core issues of experiential knowledge and contextual awareness. By integrating insights from contemporary scholars like Bender, Koller, Buckner, Thorstad, and Hoffmann, the essay ultimately concludes that AI, while a powerful computational framework, is fundamentally incapaple of replicating the true intelligence and understanding unique to humans.