Miracle-dz/UniFAR-FTUs huggingface dataset:
G3SR: Global Graph Guided Session-Based Recommendation

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.
Sign Language Fingerspelling Recognition using Synthetic Data

Sign Language Recognition (SLR) is a Computer Vision (CV) and Machine Learning (ML) task, with potential applications that would be beneficial to the Deaf community, which includes not only deaf persons but also hearing people who use Sign Languages. SLR is particularly challenging due to the lack of training datasets for CV and ML models, which impacts their overall accuracy and robustness. In this paper, we explore the use of synthetic images to augment a dataset of fingerspelling signs and we evaluate whether this could be used to reliably increase the performance of an SLR system. Our model is based on a pretrained convolutional network, fine-tuned using synthetic images, and tested using a corpus dataset of real recordings of native signers. An accuracy of 71% recognition was achieved using skeletal wireframe image training datasets and using the MediaPipe pose estimation model in the test pipeline. This compares favourably with state-of-the-art CV models which achieve up to 62% accuracy with “in-the-wild” fingerspelling test datasets.
Panta Rhei: Curiosity-Driven Exploration to Learn the Image-Schematic Affordances of Pouring Liquids

Despite rapid progress, cognitive robots have yet to match the facility with which humans acquire and ﬁnd ways to reuse manipulation skills. An important component of human cognition seems to be our curiosity-driven exploration of our environments, which results in gener-alizable theories for action outcome prediction via analogical reasoning. In this paper, we implement a method to emulate this curiosity drive in simulations of the situation of pouring liquids between containers, and to use these simulations to construct a symbolic theory of pouring. The theory links qualitative descriptions of an initial state and manner of pouring with observed behaviors, and can be used to predict qualitative outcomes or select manners of pouring towards achieving a goal.
pytwanalysis: Twitter Data Management And Analysis at Scale

Trends and communities in social media networks shape news cycles, politics, public governing, and economy these days. There is a wealth of information in the way users interact in the large social media networks, and state-of-the-art of mining network data from e.g. Twitter platform is limited by the narrow field of research or computing power. In this paper, we describe the new end-to-end Twitter network data management pipeline. We propose a scalable way to gather, store, and model rich relationships from Twitter networks. We also propose to analyze Twitter data using a combination of graph-clustering and topic modeling techniques at scale using multiple data science methods for graph construction and tweet data processing. We evaluate the proposed system on over 9 million tweets over five different Twitter datasets. We invite the community to add more features, as this end to end pipeline is released as an open source gitHub repository pytwanalysis [1], and as a python pip package pytwanalysis [2].
Real-Time Anomaly Detection and Classification from Surveillance Cameras Using Deep Neural Network

With increasing security threats, anomaly detection and classification are highly recommended work nowadays. Anomaly detection and classification from surveillance videos are more complex tasks due to the prevalence of anomalous activity. There are still some exceptional problems that require advanced approaches. Deep learning has recently made it possible to detect and classify anomalies in a critical way. In this paper, we have proposed a fine-tuned ResNet-50 model to learn anomalous patterns by exploiting 14 types of anomalous images. In our approach, we have first augmented the image data before passing it to the model. Instead of a fully connected layer, we have added an average pooling layer, dropout layer, flatten, dense layer, and dense layer followed by an activation function (softmax). We also introduce a new dataset that consists of 10483 real-world anomalous images, with 14 realistic anomalies, including abuse, fire, road accident, robbery, suicide attempt, etc. Increasing the classification performance, such baselines demonstrate that it is incredibly tough for our dataset and opens up more opportunities for future work1. In terms of accuracy, our proposed model can acquire 100% accuracy for anomaly detection, and for anomaly classification, on average, it acquires 79.69% accuracy with a computational cost of 61.45 milliseconds per frame.
Solid-State Marx Generator vs. Linear Transformer Driver: Comparison of Parasitics and Pulse Waveforms for Nanosecond Pulsers

Wide bandgap devices are increasingly used in novel pulse modulator designs, especially for applications requiring nanosecond pulse widths and a high flat-top stability. For fast pulse generator designs, mainly two topologies have been investigated in recent years: Solid-state Marx generators and linear transformer drivers. In both concepts, the mounting space of the mechanical assembly – mainly determined by the required isolation distances – results in stray inductances and capacitances, which significantly influence the pulse shape of the generator. While various designs and measurements of the two topologies have been presented in literature, a detailed discussion and comparison of the parasitics is missing. This paper presents the calculation and comparison of the parasitics based on analytic equations and 3D FEM simulations. The obtained values are then related to equivalent circuits, which are used for transient simulations of the pulse waveforms.
Continuum and atomistic models of surface elasticity and applications

OF THE DISSERTATION Continuum and atomistic models of surface elasticity and applications by Lixin Hu Dissertation Director: Professor Liping Liu We present an analysis of surface elasticity from the Born-Oppenheimer approximation for monatomic crystals. The analysis shows that the relaxations of crystal planes parallel to a free surface can be sufficiently determined by a low-rank algebraic Riccati equation instead of a full-scale molecular dynamic (MD) simulation, and gives new restrictions on physically reasonable atomistic models and simple criteria for surface reconstructions. In the case of surface relaxations, we calculate surface elasticity properties from atomistic models, which are compared with experimental data and prior simulation results. This fundamental research is useful in a variety of applications. First, with the help of the proposed algorithm we quickly calculate the surface tension and determine the equilibrium shape of crystals. Secondly, in previous studies of wave propagation the impact of surface elasticity was not noticed. We find that when the surface/interface gains its own elasticity, the inhomogeneities between the bulk and the surface/interface result in nonlinearity for both interfacial and bulk wave propagation aspects. We study the interfacial wave between two half-spaces with surface elasticity taken into account.
Development of a Portable Electrospinning Device with Tunable Voltage for Nanofiberm Production

Electrospinning is a unique approach in nanofabrication that has been gaining much interest recently. This paper aims to develop a portable electrospinning device with varying voltage to produce nanofibers using Polyethylene Oxide (PEO). A sample of 103 strands per input voltage was measured through the magnified image from the Scanning Electron Microscope and uploaded to ImageJ. The data gathered were used in a two-tailed unpaired T-Test to compare the diameters of the electrospun nanofibers. Using a 95% confidence interval, the T-score falls into the critical region bounded by the T-critical value; the input voltage shows a significant difference in the diameter of the electrospun nanofibers. It concludes that the relationship between the voltage and the nanofiber diameter is inversely proportional. The researchers recommend expanding the input voltages range and creating a constant flow rate for future studies. Also, to test the device’s capability for medical applications, a trial on pigskin is recommended.
Python Based Defect Classification of Theobroma Cacao Bean using Fine-Tuned Visual Geometry Group16

The study aims to classify cacao bean defects based on the captured image using vgg16. Seven classes of cacao beans were gathered including broken, cluster, flat, germinated, good, insect and moldy. One hundred images per class were captured using an enclosed capturing box with c920 Logitech camera inside and LED as light source. Image augmentation was done to increase dataset. Transfer learning technique was implemented by utilizing the pre-trained vgg16 model architecture adding 10% Dropout after FC2 layer and using default weights of several layers through fine-tuning. Three methods of fine-tuning were conducted by freezing the convolutional blocks. Performance of the trained model using several optimizers (such as Adam, RMSprop and SGD) and loss functions (such as categorical crossentropy and mean squared error) were analysed. The effect of the no. of epochs as well as different learning rates during training was considered and checked. The metrics used in choosing the model were based on the confusion matrix. The chosen model is using vgg16 architecture with 10% dropout + adam optimizer + 0.0001 learning rate + categorical crossentropy loss function run in 20 epochs. It has 95.33% average accuracy. The model was embedded in a processor for actual testing. It has an accuracy of 97.29% based on the actual testing on prototype with 37 testing samples.
DQN Based Exit Selection in Multi-Exit Deep Neural Networks for Applications Targeting Situation Awareness

Smart infrastructure targeting situation awareness for first responders enables intelligent and faster response time. Such an application requires an edge device to process information. The edge hardware is limited by computation capability for running Deep Neural Networks (DNNs). Multi-exit DNNs are used to provide static exit selection for faster inference. We propose using Deep Q Network (DQN) based technique for dynamic exit selection in multi-exit DNNs. The DQN learns an optimal policy using hardware and multi-exit DNN based state information. Our system achieves performance improvement with 63.5 % decrease in inference time, 33 % reduction in energy and classifies 2.2 x more inputs.
Data Completeness-aware Transmission Control for Large Spatio-Temporal Data Retention

With the development of IoT technology, various kinds of data are generated by IoT devices. Some of this data contains information on geographical location and time. We refer to such data as spatio-temporal data (STD). Since the “local production and consumption” of STD is effective for location-dependent applications, we have proposed an STD retention system that utilizes vehicles as data sources. STD sources ranges from simple sensory data to large video data. However, since previous methods assumed to retain an STD consisting of only one packet, retention of large STD sources may suffer from frequent packet collisions due to the increase in the number of packet transmissions. In this paper, we propose a data completeness-aware transmission control mechanism for large STD retention. Our simulation results showed that the proposed scheme reduces channel collision by suppressing the forwarding of incomplete data, and achieved a nearly 100% coverage rate.
Feasibility Study of a W-Band Multibeam Heterodyne Receiver for the Gregorian Focus of the Sardinia Radio Telescope

We report on the feasibility study of a W-band multibeam heterodyne receiver for the Sardinia Radio Telescope (SRT), a general purpose fully steerable 64-m diameter antenna located on the Sardinia island, Italy, managed by INAF (“Istituto Nazionale di Astrofisica,” Italy). The W-band front-end is designed for the telescope Gregorian focal plane and will detect both continuum and molecular spectral lines from astronomical sources and radio emission from the Sun in the 3 mm atmospheric window. The goal specification of the receiver is a <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> focal plane array operating in dual-linear polarization with a front-end consisting of feed-horns placed in cascade with waveguide Orthomode Transducers (OMTs) and LNAs (Low Noise Amplifiers) cryogenically cooled at <inline-formula> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula>20 K. The instantaneous FoV (Field of View) of the telescope is limited by the shaping of the 64-m primary and 7.9-m secondary mirrors. The cryogenic modules are designed to fit in the usable area of the focal plane and provide high-quality beam patterns with high antenna efficiency across the 70 – 116 GHz Radio Frequency (RF) band. The FoV covered by the <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> array is <inline-formula> <tex-math notation="LaTeX">$2.15\times 2.15$ </tex-math></inline-formula> arcmin<sup>2</sup>, unfilled, with separation between contiguous elements of 43 arcsec. Dual-sideband separation (2SB) down-conversion mixers are designed to be placed at the cryostat output and arranged in four four-pixel down-conversion modules with 4 – 12 GHz Intermediate Frequency (IF) bands (both Upper Side Band and Lower Side Band selectable for any pixel and polarization). The receiver utilizes a mechanical derotator to track the parallactic angle.
Comparison and Analysis of Humorous Factors in Text

Humor, and Irony are two parts that have challenged machine learning for many years. In this paper, to improve the machine performance in the semantic field, we analyze the data in SemEval 2020 and consider SemEval 2021 experiments as a comparison. We aim to find out the factor that could affect the humor grade level. For example, some statistic methods, regression analysis, were used in the paper to test some of the hypotheses to improve many applications in AI fields, for instance, human-machine interaction. We show how some factors affect the humor levels, such as the relationship between original words and substitute words, the word number of the headlines, some elements in the headlines. Finally, some basic conclusion about some factors that could affect humor level was concluded. Indeed, the relevance of two words is a factor that could affect the humor level, while the word number of the headlines could not be a factor in this field. In this way, cosine distance may be a shortcut for improving machines' ability to analyze the meaning of sentences.
Using Unevenly Spaced Time Series Data Set in a Convolutional Reconstruction Autoencoder Algorithm

Time series has been utilized for years, especially in the financial and economic sectors. These data are usually measured over a specific period at evenly spaced intervals, and only a few have analyzed time series on unevenly spaced time series intervals. Such uneven time series are natural phenomena like storms, earthquakes, and tsunamis. The researchers used five randomly generated, unevenly spaced time series datasets with five different data averages as an input to a convolutional reconstruction autoencoder algorithm to determine its reconstruction performance using such type of time series. At the end of the study, the algorithm yielded an average mean absolute error of 0.216 with 0.16 as the lowest mean absolute error and 0.26 as the highest mean absolute error value. Therefore, the researchers can conclude that a convolutional reconstruction autoencoder algorithm can be used with unevenly spaced time series data. However, it is not without drawbacks, as the algorithm could not properly reconstruct the later parts of the dataset. Furthermore, the algorithm’s performance can be improved by extending its training period through more dataset entries.
Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network

Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into predefined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment. In this paper, we propose a method for DAC from audio recordings using a multi-scale dilated depthwise separable convolutional network (DSCN). The DSCN is a lightweight neural network with small size of parameters and thus suitable to be deployed in portable terminals with limited computing resources. To expand the receptive field with the same size of DSCN’s parameters, dilated convolution, instead of normal convolution, is used in the DSCN for further improving the DSCN’s performance. In addition, the embeddings of various scales learned by the dilated DSCN are concatenated as a multi-scale embedding for representing property differences among various classes of domestic activities. Evaluated on a public dataset of the Task 5 of the 2018 challenge on Detection and Classification of Acoustic Scenes and Events (DCASE-2018), the results show that: both dilated convolution and multi-scale embedding contribute to the performance improvement of the proposed method; and the proposed method outperforms the methods based on state-of-the-art lightweight network in terms of classification accuracy.
Chemical Reaction Optimization (CRO) of Deep Neural Network (DNN) Model for Characterization of Algae Drying Kinetics

Drying is an essential step needed to improve the extraction of lipids and other valuable compounds in the algae for biodiesel production. However, there is a limited amount of information available regarding its drying kinetics. Previous studies have used computational intelligence e.g., artificial neural networks (ANN) and deep neural networks (DNN) to characterize the drying kinetics of algae. Chemical Reaction optimization (CRO), a recently introduced metaheuristic optimization approach, is employed in this study to identify the ideal number of neurons to use in a Deep Neural Network (DNN) model that will produce the lowest root mean squared error (RMSE). CRO can reduce the computational time since the population does not need to be coordinated in each computing units. The molecular structure in the CRO contains the set of neurons, while the potential energy (II) corresponds to the RMSE of the DNN model. At a minimum RMSE value, the accuracy of the moisture removal rate prediction increases given maximum temperature, sample temperature, time of drying, heat rate, and percent weight of the remaining algae. The DNN model created obtained an RMSE value of 4.9430 x$10^{-4}$ which corresponds to R -value of 0.9996 and 0.99958 in the training and validation phases.
Marcellus Shale drilling and brominated THMs in Pittsburgh, Pa., drinking water

In 2010 the Pittsburgh (Pa.) Water and Sewer Authority (PWSA) observed a significant increase in the concentration of total trihalomethanes (TTHMs), especially brominated THM species, in its finished water. In an effort to explain these changes, PWSA and the University of Pittsburgh's Swanson School of Engineering investigated bromide concentrations in the Allegheny River (PWSA's source water) and THM formation in PWSA's drinking water. Results of the investigation indicated that elevated bromide concentrations in the source water were associated with increased concentrations of TTHMs, especially brominated THMs, in the drinking water. Additionally, a survey of the river system suggested that industrial wastewater treatment plants (brine plants) treating Marcellus Shale wastewater, as well as other wastewaters, were major contributors of bromide in the raw water. The study results also indicated that PWSA's conventional treatment process, which includes enhanced coagulation and secondary sedimentation, was ineffective at removing bromide from the source water. The increase in bromide concentrations in the Allegheny River system could affect the ability of conventional drinking water plants drawing water from this source to comply with the Stage 2 Disinfectants/Disinfection Byproducts Rule.
Towards an Optimised Vehicle Detection Algorithm for Multi-Object Tracking in Traffic Surveillance

Smart cities implementation has been increasing in recent years, using computer vision techniques which help to reduce the problems of traﬃc congestion and also monitor traﬃc. Computer vision-based detection and tracking methods using convolutional neural networks are being successful in implementing smart cities, using cameras on the streets and at a low cost for the internet of things. Convolutional neural networks are deep learning algorithms used to analyse visual imagery. However, the same vehicle is detected multiple times in a particular frame at a particular timestamp, which thereby increases the time complexity of the algorithm by detecting the same vehicle multiple times. This paper proposes a new optimised algorithm that solves the vehicle duplication problem. Our proposed approach is based on extending and optimising the state of the art Point-RCNN (Region-based Convolutional Neural Network) algorithm by combining it with the D-Hash (Diﬀerence hash) algorithm. The D-Hash is a robust image hashing algorithm used for the identiﬁcation of duplicate images. These images are then processed onto a 3D multiple object tracking system called Point-RCNN which is used for the bounding and identiﬁcation of vehicles. The proposed algorithm was tested on the KITTI 3D object detection benchmark data set. Our experiments show that the vehicle duplication issue is eliminated without sacriﬁcing the accuracy of data object detection results. In addition, our proposed approach decreases the time complexity by passing 70 more frames per second (FPS) when compared to the Point-RCNN baseline. The execution time (speed) of the proposed algorithm is also improved by almost 34% compared to the baseline.
Scheduling Real-Time IoT Workflows in a Fog Computing Environment Utilizing Cloud Resources with Data-Aware Elasticity

In a fog computing environment the supplementary public cloud resources should be managed as effectively as possible, utilizing a dynamic scaling mechanism, in order to provide monetary cost savings and resilience against workload fluctuations. Furthermore, the dynamic scaling strategy should take into account the data dependencies of the workload, in order to prevent data loss. Towards this direction, in this paper we propose a reactive data-aware dynamic scaling mechanism for the provision of cloud resources, along with a heuristic for the scheduling of real-time Internet of Things (IoT) workflows, in a three-tier IoT-fog-cloud architecture. The performance of the proposed scheme is evaluated and compared via simulation to a static provisioning case, under different patterns of incoming workload. The simulation results provide useful insights into how each workload pattern affects the performance of the framework under study, in each of the provisioning cases of the cloud resources.