Miracle-dz/UniFAR-FTUs huggingface dataset:
Subsurface Heat stress detection in plants using machine learning regression models

The heat stress due to the progressive increase in the earth’s atmospheric temperature impacts plant growth endangering ecosystem quality and world food security. Such less favorable climatic conditions create greater risks that affect crop production as well as threatens food security. Tracking plant health can help mitigate the heat stress due to global warming. Technology plays a central role in mitigating the pressure the farming industry is facing. Smart sensor systems take advantage of machine learning algorithms and modern computer hardware to create a smart node that is tailored specifically for sensing applications. These sensor systems combine ML and IoT technologies to assist the agriculture industry with innovative ideas and technological advancements to increase yields and improve farm performance. Also, having a good machine learning model with good performance could help farmers and governments to make early decisions to mitigate the impact of environmental changes that reduce the quantity or quality of crops. In our research, we attempt to detect heat stress by using an in-situ ML-enabled subsurface sensor system in an agricultural field so that the device can independently make inferences. The sensor node is buried in the soil and we need a machine-learning model to predict the ambient temperature based on the soil characteristics data and the heat stress would be detected using the ambient temperature. In order to find a machine learning model, a publicly available dataset with the soil variables was evaluated for performance using different machine learning algorithms to predict the air temperature at the subsurface. This trained ML model would be deployed in an FPGA in the field that is equipped with soil sensors. The heat stress metric is based on the ambient temperature and duration of exposure to heat that would be wirelessly transmitted from the node. This paper discusses the development of the software framework for the subsurface wireless sensor node.
Automated pallet handling via occlusion-robust recognition learned from synthetic data*

Vision-based perception is a key enabling technology when attempting to convert human work processes into automated robotic workflows in diverse production and transport scenarios. Automation of such workflows, however, faces several challenges due to the diversity governing these scenarios: various objects to be handled, differing viewing conditions, partial visibility and occlusions. In this paper we describe the concept of an occlusion-robust pallet recognition methodology trained fully in the synthetic domain and well coping with varying object appearance. A key factor in our representation learning scheme is to entirely focus on geometric traits, captured by the surface normals of dense stereo depth data. Furthermore, we adopt a local key-point detection scheme with regressed attributes allowing for a bottom-up voting step for object candidates. The proposed geometric focus combined with local key-point based reasoning yields an appearance-independent (color, texture, material, illumination) and occlusion-robust detection scheme. A quantitative evaluation of recognition accuracy for two network architectures is performed using a manually fine-annotated multi-warehouse dataset. Given the standardized pallet dimensions, spatially accurate pose estimation and tracking, and robotic path planning are carried out and demonstrated in two automated forklift demonstrators. These demonstrators exhibit the ability to consistently perform automated pick-up and drop-off of pallets carrying arbitrary items, under a wide variation of settings.
Design and Performance Comparison of X-Masking Models in DFT Applications

VLSI technology advancements are rapidly evolving with the growing demands of human lifestyles. With all these advancements the circuit complexity and speeds are also increasing making the circuits even more difficult to test. From time to time various test schemes are being implemented to increase the quality of tests to bring more reliability to systems. Introduction of BIST was a major change in the testing method used till then. With the increase in complexity the occurrence of unknown values also increase. For the same different techniques are used as the presence of ’X’ values significantly degrade the tests and test results. In this design a two stage X-masking model is implemented that is capable of masking the ’X’ states that occur during the test cycle. Mostly in medical and space related systems require high level of accuracy where tolerance methods can reduce the efficiency of these systems. The design proposed here is an X-Masking model that monitors and masks the unknown values that occur during the test phase. The enhanced design is then compared for its utilisation and power consumption with a basic concept model for the masking. In the design scan chains are monitored for the occurrence of unknown values and they are masked using a two layered masking scheme preventing the propagation of unknown values into the MISR. The design is is simulated using Xilinx Vivado 2020.1 and implemented in Basys-3 FPGA board.
Time series prediction and anomaly detection with recurrent spiking neural networks

In the recent years, Spiking Neural Networks have gain much attention from the research community. They can now be trained using the powerful gradient descent and have drifted from the neuroscience to the Machine Learning community. An abundant literature shows that they can perform well on classical Artificial Intelligence tasks such as image or signal classification while consuming less energy than state-of-the-art models like Convolutional Neural Networks. Yet, there is very little work about their performance on unsupervised anomaly detection and time-series prediction. Indeed, the processing of such temporal data requires different encoding and decoding mechanisms and rises questions about their capacity to model a dynamical signal with long term temporal dependencies. In this paper, we propose for the first time a Sparse Recurrent Spiking Neural Network with specific encoding and decoding mechanisms to successfully predict time-series and do Unsupervised Anomaly Detection. We also provide a framework to describe in detail our model computational costs and fairly compare them with state-of-the-art models. Despite improvable performances, we show that our model perform well on these tasks and open a door for further studies of such applications for Spiking Neural Networks.
Feature Selection using Complex Networks to Support Price Trend Forecast in Energy Markets

Machine learning algorithms have been increasingly used to solve problems related to financial markets. Predicting the price or trends of assets in different markets is the subject of several studies. As other emerging markets, the Brazilian energy market has grown in number of traded volume and players, providing a business environment that can be explored through price trend forecasting techniques. The formation of prices of energy products depends on several factors. As the source of energy production in Brazil is predominantly through hydroelectric plants, information on water storage and natural flow between hydroelectric plants can directly impact price fluctuations. We propose a new strategy based on machine learning and complex networks for predicting the price trend in energy markets. The new strategy uses storage and flow data from the most important water stations in a hydroelectric power network. For this, we developed a feature selection method based on complex networks to identify and filter the main storage stations of the Brazilian water network. Data from the selected stations are then used as input for machine learning algorithms. We use three methods to extract centrality metrics from storage stations. Experimental results indicate that better performance is obtained by the proposed strategy, when compared to two baseline methods using different machine learning algorithms. The better performance is confirmed by a financial analysis of the results when simulating the investment strategies using algotrading.
Light-Weight Visualvoice: Neural Network Quantization On Audio Visual Speech Separation

As multi-modal systems show superior performance on more tasks, the huge amount of computational resources they need becomes one of the critical problems to be solved. In this work, we explore neural network quantization methods to compress the resource requirement of VisualVoice, a state-of-the-art audio-visual speech separation system. The model is firstly fine-tuned by an ADMM-based quantization-aware training approach to produce the fixed-precision quantized version. Then three strategies, including manual selection, Hessian trace-based selection and KL divergence-based greedy search are explored to find the optimal mixed-precision setting of the model. The result shows that by applying the optimal strategy, we obtain a satisfying trade-off between space, speed and performance for the final system. The KL divergence-based strategy reaches 7.2 dB in SDR at 3-bit equivalent setup, which outperforms the fixed-precision setup and the other two mixed-precision strategies. More-over, we also discuss the influence caused by quantizing different parts of the multi-modal system.
An Opto-mechanical MEMS Accelerometer with Noise Floor of 40 ng/Hz1/2

Miniaturized accelerometers are in high demand for consumer electronics, acoustic monitoring, and unmanned aerial vehicles. However, there is a tradeoff between accelerometer resolution and size, which poses a major challenge. In this study, we introduce an optomechanical microelectro mechanical system (MEMS) accelerometer based on a Fabry-Perot (F-P) microcavity. The F-P microcavity consists of a fixed hemispherical mirror and a harmonic oscillator made of monocrystalline silicon. Despite its small size (only 10 mm), the MEMS accelerometer exhibits an acceleration noise floor of 40 ng/Hz$^{1/2}$ at 180 Hz, a bandwidth of 500 Hz (-3 dB), and a dynamic range of 80 dB. To suppress dominant laser intensity noise and the disturbance of temperature and pressure on the optical fiber, a balanced detection method is employed, reducing the overall noise floor from 250 ng/Hz$^{1/2}$ to 40 ng/Hz$^{1/2}$, which approaches the mechanical thermal noise limit (37 ng/Hz$^{1/2}$). These results demonstrate that the opto-mechanical coupling between the oscillator and microcavity is an excellent platform for precision measurement and opto-mechanics, benefiting from its extremely small mode volume and ability to restrain photons.
CMTN: A Convolutional Multi-Level Transformer to Identify Suicidal Behaviors Using Clinical Notes

Suicide has become a significant cause of concern worldwide over recent years. The early identification and providing treatment of individuals having suicidal tendencies are necessary for preventing suicides. Past suicidal behavior information of an individual is recorded in the electronic health records (EHR) reports which can help to understand a patient’s current mental health condition. In this paper, to identify the people who are ideating and are anticipating attempting suicide, we propose a novel model named CMTN, which utilizes the textual EHR data for the prediction of suicidal behaviors. The proposed framework employs convolutional and transformer layers to capture local and global relationships in the text and the attention mechanism to assess the significance of various input text components. Overall, the suggested model has achieved the highest precision for the SA class with a score of 0.97 and the highest recall and f1-score of 0.56 and 0.52, respectively, for the SI class, compared with all other state-of-the-art and baseline models. We have also employed different embeddings such as BERT, BioBERT, and PubMedBERT to our state-of-the-art model and illustrated the model’s improved performance. In addition, we have also shared the data alignment and annotation extraction algorithms in this paper, allowing other researchers to generate the dataset, thereby expediting development in the prevention of suicides.
TIDE: Affective Time-aware Representations for Fine-grained Depression Identification on Social Media

The growing availability of the Internet provides opportunities for depression screening. In recent years, depression analysis based on social media texts has shown great promise, yet most works have focused on treating it as a binary problem. Meanwhile, existing methods need the perception of fine-grained emotions in historical contexts. Moreover, they do not consider the time interval between posts and thus ignore the decay of emotions over time. In this paper, we propose a Time-aware Depression identification model based on Emotion capturing (TIDE), a novel method to identify the severity of depression in social media users. We define the assessment as an ordinal regression problem to distinguish differences in depression levels. Specifically, TIDE uses Plutchik's wheel of emotions to characterize the emotional historical spectrum, then models the emotion decay process of historical context using a Time-aware LSTM (T-LSTM). We experiment on two Reddit public datasets to demonstrate that our approach outperforms state-of-the-art models. Then, ablation studies and qualitative analysis further demonstrate the validity of the proposed modules. Overall, our work can facilitate social media-based analysis of depression and shows potential for application to mental health-related issues.
Analysis of Free Fatty Acids (FFA) in Palm Oils Based on the Raman Spectra

Palm oil is a stable vegetable oil with various uses in both the food and non-food industries. The quality of edible palm oil is measured by its content of free fatty acids (FFA), which should be below 5% to be safely consumed and used, according to The Palm Oil Refiners Association of Malaysia (PORAM). Current titration methods require that the oil sample be dissolved in neutralized ethanol/diethyl, which is a long process that can damage the sample and cause delays and losses in the process. The focus of this study is to analyze the FFA in palm oils based on the Raman spectra of palm oil samples and oil palm fruitlets. Four samples of commercially available palm oils of different qualities, as well as 47 samples of oil palm fruitlets, were used in this study. The Raman spectra of all samples were collected via point scanning using a confocal microRaman imaging spectrometer with a 532 nm laser source. Manual spectrum pre-processing was performed with baseline correction and data filtering using Savitzky-Golay filters. Possible FFA peaks characteristics on the Raman spectrum were then identified through signal interpolation, signal deconvolution, and curve fitting. Statistical analysis was conducted on the extracted features to remove those with low statistical values, thus helping the machine learning algorithms to train the classifier models more efficiently. From this study, it was found that peaks at 1155 cm−1, 1525 cm−1, 1440 cm−1, and 1655 cm−1 from the Raman spectrum were highly correlated with the FFA levels of palm oils obtained through titration. The peak ratios of 1525 cm−1/1655 cm−1 and 1155 cm−1/1655cm−1 also showed a good and fast relationship for analyzing the FFA content of palm oil.
Enhancing IoT Security with Federated Deep Learning Techniques

In addition to the most recent advances in machine learning-based IoT security techniques, Machine learning algorithms can learn from data and adapt to new security risks, allowing IoT systems to defend against unanticipated cyber-attacks. ML-based security solutions are flexible and dynamically updateable. This is critical in dealing with emerging IoT based security threats. In IoT security, there are two types of ML: supervised learning and unsupervised learning. To detect anomalies and categorize security risks, the supervised learning technique employs various algorithms such as decision trees, support vector machines, and neural networks. On the other hand, the unsupervised learning approach includes clustering techniques, such as k-means clustering and hierarchical clustering to detect anomalies and identify unknown security threats. In addition, reinforcement learning (RL) has been employed to enhance the security of IoT systems by training the system to identify and respond to cyber-attacks in real time. Additionally, these solutions can provide real-time security monitoring and incident response, allowing for quicker and more effective mitigation of cyber-physical security risks in IoT systems. Henceforth ML-based security solutions have shown promise in addressing the cyber-physical security challenges that arise in IoT systems. These solutions can learn from data, adapt to new and evolving threats, and provide real-time security monitoring and incident response. As the number of connected devices in IoT systems continues to grow, it is essential to adopt modern and innovative security solutions to ensure the safety and privacy of IoT users.
Optimal Sparse MIMO Transceiver Design for Joint Automotive Sensing and Communications

Joint automotive sensing and communication assisted by optimal sparse MIMO transceiver design is a promising technology for autonomous driving as it reduces hardware cost while preserving high angular resolution. In this paper, we propose to co-design a shared sparse MIMO transceiver within the paradigm of joint sensing and communication (JSAC). Antenna selection is performed to minimize the Cramer–Rao bound (CRB) for accurate tracking with enhanced direction of arrival (DOA) estimation. Meanwhile, the spatial precoding matrix for communications, which exhibits the same sparsity structure with the shared transmitter for automotive sensing, is optimized to deliver a desired quality of service. A solution of this problem requires the application of a series of convex relaxation strategies to transform the resultant non-convex co-design problem into a convex form. The fractional inequality is transformed into the denominator inequality with a constrained numerator and reweighted l1-norm minimization is utilized to promote binary sparsity. Simulations are provided to demonstrate the effectiveness of the optimal sparse MIMO transceiver obtained by the proposed method.
Re-Envisioning Industrial Control Systems Security by Considering Human Factors as a Core Element of Defense-in-Depth

The security of Industrial Control Systems is relevant both for reliable production system operations and for high-quality throughput in terms of manufactured products. Security measures are designed, operated and maintained by different roles along product and production system lifecycles. Defense-in-Depth as a paradigm builds upon the assumption that breaches are unavoidable. The paper at hand provides an analysis of roles, corresponding Human Factors and their relevance for data theft and sabotage attacks. The resulting taxonomy is reflected by an example related to Additive Manufacturing. The results assist in both designing and redesigning Industrial Control System as part of an entire production system so that Defense-in-Depth with regard to Human Factors is built in by design.
Knowledge Base Learning Portal For Organization

The knowledge base learning site's objective is to arrange the training materials and provide the trainee with a "Role-Based learning" path. Students can access resources to meet various learning needs by starting at the knowledge base learning portal. Giving access to a cutting-edge learning environment and courses is the main objective of this initiative. Any course taken only requires an internet connection; there is no need to download any additional software. The course can be accessed at any time and go at the user’s own pace. In addition to maintaining digital records free of duplicate entries, the company can monitor the trainee's actions and timely performance.
Intelligent fault diagnosis of rolling bearing based on a deep transfer learning network

Rolling bearing of rotating machinery's key component will inevitably fail due to the complex and changeable operating environment such as variable speed, large disturbance, high and low temperature. It is quite challenging to obtain abundant labeled bearing fault samples because the rotating machinery is typically in a healthy and operational state. For addressing the issue, an intelligent fault diagnosis method based on a deep transfer learning network is proposed. First, a bidirectional gated recurrent unit (Bi-GRU) network is utilized to mine the latent relationship between labeled source domain samples and few labeled target domain samples, the parameters of Bi-GRU are trained to obtain the instance transfer bidirectional gated recurrent unit model (ITBi-GRU), and auxiliary samples are generated based on the ITBi-GRU. Second, as a feature transfer learning method, joint distribution adaptation is used to simultaneously decrease the distribution discrepancies between the generated auxiliary samples and the unlabeled target domain samples. Finally, extensive experiments are employed to evaluate the effectiveness of the proposed method in the case of scarce labeled samples.
HOCO formation in astrochemical environments by radical-induced H-abstraction from formic acid

Rate constants for the radical-induced hydrogen abstraction from formic acid, HCOOH, are presented here. Only those reactions leading to the formation of HOCO were investigated. The astrochemically relevant radicals OH, NH2, and H were considered to induce the H-abstraction. Tunnelling was taken into account by using the instanton method for rate constant calculations. For reactions relevant on grain surfaces, the unimolecular rate constant is of particular importance. For the reactions with OH and NH2, a corresponding deep pre-reactive minimum can be found that contributes to the barrier height and thus slows down the reaction. In general though, abstraction induced by OH radicals is found to be the fastest. The reaction with the H atoms becomes increasingly important at low temperatures, because of the narrow barrier through which tunnelling is efficient. The reaction with NH2 radicals has both a high and broad barrier and consequently shows significantly smaller low-temperature rate constants.
Neural Network Module Decomposition and Recomposition with Superimposed Masks

We propose a modularization method that decomposes a deep neural network (DNN) into small modules as binary classifiers and recomposes a new model of some of these modules for a different task. Our goal is to develop appropriate models by quickly reusing existing well-designed models. To decompose a DNN into modules, we use weight masks based on the lottery ticket hypothesis to extract small single-function winning tickets as modules. By ensuring that each module shares as many parameters as possible, the size of the recomposed model is not much larger than the modules and significantly smaller than the original DNN. Furthermore, since the recomposed model is specialized for fewer classes, its accuracy is superior to models of comparable size produced by model compression. This method can be applied to DNNs of any architecture. We demonstrate that the proposed method can decompose and recompose DNNs with a high compression ratio and high accuracy, and is superior to existing methods.