Miracle-dz/UniFAR-FTUs huggingface dataset:
An Inverse-Free and Scalable Sparse Bayesian Extreme Learning Machine for Classification Problems

Sparse Bayesian Extreme Learning Machine (SBELM) constructs an extremely sparse and probabilistic model with low computational cost and high generalization. However, the update rule of hyperparameters (ARD prior) in SBELM involves using the diagonal elements from the inversion of the covariance matrix with the full training dataset, which raises the following two issues. Firstly, inverting the Hessian matrix may suffer ill-conditioning issues in some cases, which hinders SBELM from converging. Secondly, it may result in the memory-overflow issue with computational memory <inline-formula> <tex-math notation="LaTeX">$O(L^{3})$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>: number of hidden nodes) to invert the big covariance matrix for updating the ARD priors. To address these issues, an inverse-free SBELM called QN-SBELM is proposed in this paper, which integrates the gradient-based Quasi-Newton (QN) method into SBELM to approximate the inverse covariance matrix. It takes <inline-formula> <tex-math notation="LaTeX">$O(L^{2})$ </tex-math></inline-formula> computational complexity and is simultaneously scalable to large problems. QN-SBELM was evaluated on benchmark datasets of different sizes. Experimental results verify that QN-SBELM achieves more accurate results than SBELM with a sparser model, and also provides more stable solutions and a great extension to large-scale problems.
Modeling Citywide Crowd Flows using Attentive Convolutional LSTM

Understanding the movement patterns of humans and vehicles traveling in a city is important for many applications like emergency evacuation and rescue, as well as city planning and management. In this paper, we aim to predict citywide crowd flows within a period in the future to give aid to urban management, through modeling spatiotemporal patterns of recent crowd flows. We present a novel deep model for this task, called "AttConvLSTM", which leverages a convolutional LSTM (ConvLSTM), Convolutional Neural Networks (CNNs) along with an attention mechanism, where ConvLSTM keeps spatial information as intact as possible during sequential analysis, and the attention mechanism can focus important crowd flow variations which cannot be identified by the recurrent module. We conducted extensive experiments for performance evaluation using three large datasets, including Beijing Taxi dataset, Rome Taxi dataset, and Chengdu Didi chauffeuring trace. The experimental results show that AttConvLSTM significantly outperforms several widely-used baselines in terms of Root Mean Squared Error (RMSE), and Mean Average Percentage Error (MAPE), indicating that our approach can deal with crowd flows with different dynamics in both spatial and temporal domains, and make valid predictions several steps ahead.
A Novel Intrusion Detection Method Based on WOA Optimized Hybrid Kernel RVM

In recent years, various machine learning algorithms and intelligent optimization algorithms have emerged one after another, and are widely used in intrusion detection. As a highly sparse model, the relevance vector machine (RVM) is very suitable for intrusion detection scenarios with large scale data. The selection of the parameters of the intrusion detection model directly affects the performance of intrusion detection. Therefore, the selection and determination of parameters is a very critical point to obtain better detection performance. At the same time, the classification performance of RVM obviously depends on the kernel function. To ensure the diversity of kernel function, we adopt a hybrid kernel function formed by linear combination. In addition, RVM is easy to fall into the local optimum, and it has large initial value randomness and poor convergence. Aiming at the limitations of the RVM algorithm, we propose a novel WOA-HRVM model, which optimizes the parameters of the hybrid kernel RVM by WOA algorithm to obtain better performance. The proposed WOA-HRVM is evaluated on NSL-KDD and CICIDS2017 dataset. Compared with other algorithms tested, the proposed WOA-HRVM algorithm significantly improves the accuracy and speed of intrusion detection.
An Ultra-Low Latency Multicast Router for Large-Scale Multi-Chip Neuromorphic Processing

Neuromorphic simulation is fundamental to the study of information processing mechanism of the human brain and can further inspire application development of event-driven spiking neural networks. However large-scale neuromorphic simulation requires massive parallelism on multi-chip processing and imposes great challenges on dealing with data transmission latency and congestion problems between chips, especially when the number of simulated neurons reaches to billions or even trillions level. In this paper, we propose an ultra-low-latency on-chip router together with a multicast routing algorithm that focuses on reducing global loads and balancing loads between links. Additionally, we build a large-scale neuromorphic simulation platform consisting of 64 FPGA chips and evaluate the proposed design on it. The experiment results suggest that this design benefits from the proposed multicast routing algorithm in global communication loads and simulation capacity. This work has 4.1% $\sim$ 5.2% reduction of global loads comparing to previous works and can achieve a latency as low as 25ns and a maximum data throughput of 6.25Gbps/chip.
Survey of Content Naming Schemes in Vehicular Named Data Network

Vehicular Ad-hoc Networks (VANETs) has been one of the hot topics for the researchers, industries and academia for the past few decades. However, current IP based architecture is not able to cope up to the demands and researchers have looked out for ways to extend VANET architecture. Two possible collaborations which are strongly focused include Software Defined Networks (SDN) and a paradigm which is considered as part of future internet architecture Named Data Networks (NDN). The NDN architecture provides an edge because of the fundamental working principle, wherein the content is addressed rather than the host. Although, Vehicular Named Data Network (VNDN) is a relatively new field, the advantages it brings about has urged the researchers to explore it more. There are a few issues like content/data forwarding, routing strategies, naming schemes, channel constraints and adding security to the content without increasing overhead, creating gateways an standardization. The paper focuses on various content naming schemes employed in named data based vehicle networks.
iGCACS-IoD: An Improved Certificate-Enabled Generic Access Control Scheme for Internet of Drones Deployment

Due to wide-spread use of the Information and Communications Technology (ICT) and Internet of Things (IoT) enabled smart devices, called unmanned aerial vehicles (UAVs) (popularly known as drones), a lot of potential applications of Internet of Drones (IoD) are available ranging from the military to civilian applications. Access control mechanism is an important potential security service that is needed to secure communication among the drones in their respective flying zones, and also among the drones and the Ground Service Station (GSS). In 2021, Chaudhry <italic>et al.</italic> proposed a certificate based generic access control scheme for IoD environment, called GCACS-IoD. Their claims about the possible security attacks resistant of GCACS-IoD is not justified. In fact, we first prove that GCACS-IoD is unable to protect the disclosure of the private key <inline-formula> <tex-math notation="LaTeX">$r_{CR}$ </tex-math></inline-formula> of the trusted control room <inline-formula> <tex-math notation="LaTeX">$(CR)$ </tex-math></inline-formula>, which is extremely unfortunate careless design flaw and it leads to compromise the entire network. Using the disclosed private key <inline-formula> <tex-math notation="LaTeX">$r_{CR}$ </tex-math></inline-formula>, we further show that GCACS-IoD is completely insecure against other serious attacks, such as malicious drones deployment attack, drone/GSS impersonation attacks and Ephemeral Secret Leakage (ESL) attack, which lead to compromise the session key between any two drones communicating in a particular flying zone. We thus feel that there is a strong need to remedy such serious weaknesses found in Chaudhry <italic>et al.</italic>’s GCACS-IoD. An improved certificate-enabled generic access control scheme for IoD deployment, called as <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>GCACS-IoD, has been suggested, which overcomes the weaknesses found in the previous GCACS-IoD. The practical demonstration of <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>GCACS-IoD has been done through formal security verification and also through NS2 simulation study.
Overview of Present Methods to Predict Social Media Metrics of a Post

Social Media is an ever-growing tool which will only continue to grow as time goes by. As the commercial applications of social media increase exponentially, interest in using AI techniques to predict the performance of posts has also increased. In this paper we have discussed four major approaches and methodologies to analyse the information present in social media posts and on the basis of the historical data obtained, predict the popularity of any future post in terms of number of likes, comments and shares. These approaches include Deep Neural Network (DNN), Multimodal approach by using the multimodal information embedded in a social media post on a given social media platform, Social Network Services (SNS) for the collection and inference of user-related data and Social Forecast method for the accurate forecast of popularity of content put up on a social media platform. All these methodologies are performed on a dataset in the form of algorithms to obtain results of predictions. These methods take into consideration textual and multimedia content such as images and videos and on the basis of data acquired from these, eventually draw results. Our hope is that this paper can provide an insight into the current state of ML approaches towards social media metrics prediction.
Substring Similarity Search with Synonyms

To allow deeper semantic understanding of strings, string matching with synonyms has recently received increasing attention. However, all the works focus on string semantics, which requires matching of entire strings, and cannot handle partial matching with substring semantics, resulting in its limited applications. To remedy this issue, we first propose a novel similarity measure between two strings allowing substring matching with synonyms, and develop an efficient algorithm to find the strings that have a substring semantically similar to the query string. Since considering synonyms on substrings enlarges the search space significantly, we devise efficient filtering methods to reduce the number of expensive similarity computations. Experiments with real-life datasets show the efficiency of our algorithm.
Estimating the extent of the effects of Data Quality through Observations

Existing data quality works have so far focused on the computation of many data characteristics as a mean of quantifying different quality dimensions, like freshness, consistency, accuracy, or completeness, that are all defined about some ideal (clean) dataset. We claim that this approach falls short in providing a full specification of the quality of the data since it does not take into consideration the task for which the data is to be used, neither any future instances of the dataset. We argue that apart from the difference from the clean dataset, it is equally important to know the degree to which such difference affects the results of the task at hand. Thus, we extend the existing data quality definition to include that degree. Our approach, not only allows data quality to be considered in the context of the intended task, but can also provide useful information even in the absence of the clean dataset, and proffer an understanding of the effect of data quality in future dataset instances. We describe a system and its implementation that computes this extended form of data quality through a principled approach of systematic noise generation and task result evaluation. We perform numerous experiments illustrating the effectiveness of the approach and how this allows contextualizing traditional data quality measures.
Authenticated Keyword Search in Scalable Hybrid-Storage Blockchains

Blockchain has emerged as a promising solution for secure data storage and retrieval for decentralized applications. To scale blockchain systems, a prevailing approach is to employ a hybrid storage model, where only small meta-data are stored on-chain while the raw data are outsourced to an off-chain storage service provider. The key issue for query processing in such a system is the design of gas-efficient authenticated data structure (ADS) to authenticate the query results. In this paper, we study novel ADS schemes for authenticated keyword search in hybrid-storage blockchains. We first propose the Suppressed Merkle inverted (Merkleinv) index, which maintains only a partial ADS structure on-chain that can be securely updated with a logarithm-sized cryptographic proof. Moreover, we propose a Chameleon inverted (Chameleoninv) index that leverages the chameleon vector commitment to achieve a constant maintenance cost. It is further optimized with Bloom filters to enhance the query and verification performance. We prove the security of the proposed ADS schemes and evaluate their performance using real datasets on the Ethereum platform. Experimental results show that, compared to a baseline solution, the proposed Merkleinv and Chameleoninv indexes reduce the average on-chain maintenance cost from US$10.39 down to US$2.50 and US$0.24, respectively, without sacrificing much the query performance.
New PV Performance Loss Methodology Applying a Self-Regulated Multistep Algorithm

Analyses of performance loss rates in photovoltaic (PV) systems are not yet standardized, and are typically carried out by a linear regression of the evolution of a certain performance metric (e.g., performance ratio, yield, etc.) over time. In this article, we propose a novel methodology of advanced PV system performance loss rate modeling applying a self-regulated multistep algorithm. The developed algorithm automatically detects the number and positions of breakpoints in nonlinear performance time series and divides the performance trend into an adequate number of linear segments. Instead of calculating one linear performance loss rate, given in percentage per year, as is common practice, multiple linear performance loss values are determined, depending on the trend of the time series and subsequently the number of breakpoints. The algorithm is fully automated. We have applied our methodology on data of an experimental PV installation in Bolzano/Italy, which consists of 26 different PV systems. The overall linear performance loss rate of the facility's crystalline silicon systems is between <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>0.5 and <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>1.3%/year, whereas the thin-film PV systems experience values between <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>0.6 and <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>2.4%/year. Based on our results, the algorithm appears to be stable and accurate. The methodology is to be used as a fast and automated check of PV systems in operation to detect anomalies affecting performance (in an early stage). By building up a large database of detected issues in the field this algorithm will enable us to better understand the performance evolution of different PV system types in varying climates.
Accelerating Monte Carlo Transport in the Trade-off of Performance and Power

Random simulation for particle transport theory is the main method for solving particle transport questions, which is widely used in medicine and computational physics. In this work, we present a multi-core reconfigurable architecture that aims to meet the performance per watt requirements of future Domain Specific Architectures (DSAs). The architecture proposed in this paper consists of heterogeneous lightweight cores, a reconfigurable cache structure, and High Bandwidth Memory. By targeting the different feature requirements of the Monte Carlo transport code at different stages, we design more necessary and efficient features for the lightweight calculating core, and continue to provide a trade-off of performance and energy consumption through reconfiguration. We designed and validated the accelerator architecture using gem5. Experiments show that compared with the traditional architecture composed of multiple out-of-order core, this architecture can obtain more than 3x in performance per watt. Some conclusions explored are not limited to the architecture proposed in this paper, but lay the foundation for further studies of large-scale transport accelerators.
Data-Selective Least Squares Methods for Elliptic Localization With NLOS Mitigation

In this letter, we consider the problem of 2-D elliptic localization, where multiple spatially separated sensors, including the transmitters and receivers, are exploited to locate the signal reflecting/relaying target in the mixed line-of-sight/nonline-of-sight (NLOS) environments. We begin by revisiting a plain closed-form linear least squares (LS) solution. As it is vulnerable to the existence of erroneous time-sum-of-arrival (TSOA) measurements under the NLOS conditions, we then devise two new data-selective LS methods, by which the outliers can be identified and mitigated and a higher level of resistance to the NLOS bias errors can be provided. To conduct data selection, the first algorithm combines the use of the traditional linear LS estimator and an additional cost function, whereas the second relies on the parameterization of the TSOA-defined ellipses and follows a nonlinear LS estimation criterion. Based on the simulations, we demonstrate the effectiveness of the proposed methods in NLOS error mitigation at acceptable computational costs.
Face Emotion Based Intelligent Chaterbot

Recognition of emotion is the detection of human feelings from facial expressions. Emotion recognition based on facial images has over the years been regarded as a challenging subject in the field of affective computing. Although there are several methods to solve this problem, due to their computational sophistication, they cannot be implemented on the embedded devices involved. We use three light and quantized deep learning models that can be deployed on embedded devices to address this problem. To obtain real-time output of emotion recognition, lightweight and quantized deep learning models are required. We use the model ensemble technique to improve accuracy, where we take the weighted fusion of two lightweight and quantized deep learning models so that they can be deployed with reduced computational complexity and improved accuracy on android devices. We were thus able to obtain real-time recognition of emotions on Android devices.
Randomized Low- Rank Matrix Approximation-Based Channel Estimation for Massive MU-MIMO systems

It is a critical issue to estimate the large-dimensional channel matrix for massive multi-user multiple-input multiple-output (MU-MIMO) systems within the limited coherence time interval. In this paper, the low-rank property of the channel matrix under the finite scattering channel model is exploited to enhance the performance of least squares (LS) estimation. First, we propose a channel estimation method based on low-rank matrix approximation (LRMA) to substantially improve the performance of LS estimation. Then, in order to effectively eliminate the computational bottleneck brought by performing singular value decomposition (SVD) for the large-dimensional channel matrix and enhance the performance of LS estimation, a novel randomized LRMA-based channel estimation method is proposed. Finally, numerical results are provided to corroborate our theoretical results and to show the superiority of the proposed methods.
Machine Understandable Contracts with Deep Learning

This research investigates the automatic translation of contracts to computer understandable rules trough Natural Language Processing. The most challenging aspect, which is studied throughout this paper, is to understand the meaning of the contract and express it into a structured format. This problem can be reduced to the Named Entity Recognition and Rule Extraction tasks, the latter handles the extraction of terms and conditions. These two problems are difficult, but deep learning models can tackle them. We think that this paper is the first work to approach Rule Extraction with deep learning. This method is data-hungry, so the research also introduces data sets for these two tasks. Additionally, it contributes to the literature by introducing Law-Bert, a model based on BERT which is pre-trained on unlabelled contracts. The results obtained on Named Entity Recognition and Rule Extraction show that pre-training on contracts has a positive effect on performance for the downstream tasks.
Design and Analysis of III-V Tunnel FET based Energy Efficient Digital Circuits

The objective of this work is to incorporate a Look-up table based analytical model of TFET using Verilog-A in Cadence Design framework and thus using this integrated model we are able to design and simulate some of the digital circuits. The Look-up-tables are obtained from TCAD simulations. III-V materials are taken into consideration, as these devices are having higher driving capability as per ITRS Norms. A library is created in Cadence and based on N-type TFET P-type TFET, Inverter, NAND, NOR, half adder are realized. A comparative analysis is made with the state of arts of FinFET using 22nm technology. We proposed an Inverter circuit using hybrid TFET-FinFET. As the TFET technology can attain a low subthreshold slope of nearly 40mV/decade, we opted for this technology for designing. Therefore, this promotes the TFET-based circuits to work with a 0.5V supply voltage which leads to a reduction in dynamic power consumption and leakage current when compared to the current CMOS technology. As the results suggest that many more advantages and we can choose TFET for designing. The schematics and circuit simulation is done using the Cadence Virtuoso tool.