Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Validation of Process-Induced Variability Aware SPICE Simulation Platform for Sub-20 nm FinFET Technologies

We propose an experimentally validated physics-based process-induced variability (PIV) aware SPICE simulation framework–enabling the estimation of performance variation due to line-edge-roughness (LER), metal-gate-granularity (MGG), random-dopant-fluctuation (RDF), and oxide-thickness-variation (OTV) at sub-20 nm technology node devices. The framework utilizes LER, RDF, OTV, and MGG defining parameters such as fin-edge correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>), autocorrelation length (<inline-formula> <tex-math notation="LaTeX">$\Lambda $ </tex-math></inline-formula>), grain-size (GS), <inline-formula> <tex-math notation="LaTeX">$\sigma [\text {EOT}]$ </tex-math></inline-formula>, etc. as the inputs, and produces <inline-formula> <tex-math notation="LaTeX">${I}_{d} $ </tex-math></inline-formula>,–<inline-formula> <tex-math notation="LaTeX">${V}_{g}$ </tex-math></inline-formula> distribution of ensemble size 250 as an output. We have validated the framework against 14 nm FinFET experimental data for <inline-formula> <tex-math notation="LaTeX">${I}_{d}$ </tex-math></inline-formula>,–<inline-formula> <tex-math notation="LaTeX">${V}_{g}$ </tex-math></inline-formula> trends as well as for the threshold-voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula>), ON-current (<inline-formula> <tex-math notation="LaTeX">${I}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>), and subthreshold slope (SS) distributions for a range of device dimensions with a reasonably good match. The worst and the best case <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> square errors are 0.64 and 0.98, respectively, for the validation. The very nature of the proposed framework allows the designers to use it for a vast range of process technologies. Such models are of dual importance, as it enables a PIV aware prediction of circuit-level performance, and provides a platform to estimate PIV parameters efficiently, on-par with sophisticated structural characterization tools.
A SiC IGBT Behavioral Model with High Accuracy and Fast Convergence

In order to study the mechanisms of SiC IGBT, optimize the SiC IGBT based power conversion system and predict the electro-thermal performance of the circuits, a simple, high-speed and accurate behavioral model of SiC IGBT is proposed. In this model, three controlled current sources are used to simulate the voltage and displacement current of the three parasitic capacitors of SiC IGBT. The other two controlled current sources are used to simulate the I-V characteristics and tail current characteristics of SiC IGBT respectively. In the model, the interpolation method instead of the conventional polynomial fitting method is adopted, which could simulate the static I-V characteristics and C-V characteristics more accurately. The method to extract the C-V curves by using dv/dt and displacement current is proposed, which could accurately simulate the punch-through effect of SiC IGBT under high voltage. The proposed model is more concise, more accurate and faster than the existing complex physical based mathematical model, which is suitable for system level circuit simulation based on SiC IGBT.
TideFC: Learning Temporal Interaction for Dynamic Embedding via Feature Crossing

Recommendation system is becoming more and more important. Specially in social network, such as Twitter, Facebook, and YouTube. In the beginning, a number of studies learn the relationship of users and items from a bipartite graph. They embed each user and item in an embedding space. However, they ignore temporal properties. They regard users and items as static embeddings. As time passed, user preferences and item concepts can change, and node embeddings should be dynamically adjusted in the embedding space. Nevertheless, most of existing models update embeddings only when user takes an interaction with an item. In this paper, we propose TideFC, a novel model based on the state-of-the-art dynamic embedding model JODIE. TideFC can predict the future trajectories of users’ and items’ embeddings. Besides, we take advantage of t-batch that creates time-consistent batches to make the training stage more efficient. More importantly, we incorporate feature crossing to generate high-order feature interactions in our TideFC. Experiments conducted on multiple real datasets demonstrate the promising performance of TideFC, compared with the state-of-the-art JODIE.
Spectrum Sharing in CBRS Using Blockchain

We make a case for using the emerging blockchain technology for spectrum sharing in Citizen’s Broadband Radio Service (CBRS). We show that blockchain can dynamically allocate channels in a transparent and fair manner by achieving distributed channel de-confliction. Furthermore, blockchain helps automate communication between the Spectrum Access Systems (SASs) belonging to competing, non-trusting organizations by providing consensus, while at the same time preserving confidentiality of sensitive information. We implement our CBRS blockchain architecture using the Hyperledger Fabric platform. In order to support high channel request rates, we also develop a novel feature that adaptively pre-processes channel allocation requests at the SAS before sending those to the blockchain network. We evaluate our blockchain solution under different settings and show that it meets the FCC latency requirements. We also analyze the scalability of our system and show that the latency and throughput guarantees can be maintained in scenarios involving multiple organizations.
Deep Learning Based Prediction Towards Designing A Smart Building Assistant System

Nowadays, smart building infrastructures are equipped with hundreds of sensors to monitor building environments and provide smart solutions for occupant comfortability and energy efficiency. Ideally, an automated system can predict and adjust the physical features (e.g., lighting, air quality, temperature, and so on) in a person’s office based on his/her personalized preferences and activities. However, since the data is from one person, there may not be sufficient data for machine learning model training, and the data’s quality may be low (e.g., with noises). Then, it is a challenge to conduct accurate predictions to provide personalized environment adjustment. To handle this problem, in this paper, we propose a smart building assistance system consisting of different sensor data analysis approaches and a deep neural network (DNN)-based prediction model to make a more accurate prediction despite low-quality sensor data. First, we collected a year-long smart building dataset from four different data sources (i.e., sensors, calendar, weather, and survey). Second, we perform different feature engineering approaches (i.e., concretization, one-hot encoding, and multiple feature combination) on the data as inputs for the prediction models. Third, we identify a support vector regression-based prediction model and propose a hybrid DNN model consisting of several recurrent neural network blocks and a feed-forward DNN block to predict different preferred physical features considering different activities of a person (e.g., meeting, lunch, research activities). Finally, we conduct experimental studies to evaluate the performance of the proposed prediction models compared to other existing machine learning models in terms of accuracy. Our predicted preferred physical features match the occupant’s preferred ranges of different physical features during a specific activity. We also open-sourced our code on GitHub.
A Survey of Biometric and Machine Learning Methods for Tracking Students’ Attention and Engagement

The skills of focusing and paying attention are critical to student learning. According to Piontkowski et al. [40], "Educators often talk about attention as a general mental state in which the mind focuses on some special feature of the environment. As such, attention is considered essential for learning. It is hard to believe that the student who disregards instruction will benefit from it. Thus, the teacher needs reliable signs of the student’s state of attention. "It is challenging, however, for instructors to ascertain signs of student attention in large classrooms with many students. Additional challenges arise in online classrooms, which often limit instructors to watching students’ body language in video feeds, where they cannot see, for example, distractions in the students’ environment. Biometrics and machine learning approaches can help instructors evaluate their students’ level of attentiveness in both physical and online classrooms and introduce appropriate interventions to improve learning outcomes.Although the field of automated attention tracking research is steadily amassing new publications, no survey works have charted the progress of research or encouraged new research. We have filled this gap with this survey of salient works that use biometrics and machine learning to track attention. Specifically, we focus on discussions and analyses of methods that use eye gazing, facial movements and expressions, behavioral biometrics such as body movements, analyses of brainwave signals and psychological states, and multimodal biometrics. We conclude with a discussion of promising future research directions with focus on multimodal biometric techniques.
Artificial Intelligence Applied to Stock Market Trading: A Review

The application of Artificial Intelligence (AI) to financial investment is a research area that has attracted extensive research attention since the 1990s, when there was an accelerated technological development and popularization of the personal computer. Since then, countless approaches have been proposed to deal with the problem of price prediction in the stock market. This paper presents a systematic review of the literature on Artificial Intelligence applied to investments in the stock market based on a sample of 2326 papers from the Scopus website between 1995 and 2019. These papers were divided into four categories: portfolio optimization, stock market prediction using AI, financial sentiment analysis, and combinations involving two or more approaches. For each category, the initial introductory research to its state-of-the-art applications are described. In addition, an overview of the review leads to the conclusion that this research area is gaining continuous attention and the literature is becoming increasingly specific and thorough.
In Situ Generated 1:1 Zn(II)-Containing Polymer Complex Sensor for Highly Enantioselective Recognition of N-Boc-Protected Alanine

A novel chiral (S)-BINAM-based fluorescence polymer sensor was designed and synthesized by the polymerization of 5,5′-((2,5-dioctyloxy-1,4-phenylene)bis(ethyne-2,1-diyl)bis(2-hydroxy-3-(piperidin-1-ylmethyl)benzaldehyde (M-1) with (S)-2,2′-binaphthyldiamine (S-BINAM, M-2) via Schiff’s base formation. The resulting chiral polymer sensor shows very weak fluorescence but exhibits the obvious fluorescence enhancement response toward Zn2+. The in situ generated 1:1 Zn(II)-containing complex of chiral polymer can serve as a fluorescence sensor for highly enantioselective recognition of N-Boc-protected alanine, and the value of enantiomeric fluorescence difference ratio (ef) can reach as high as 6.90. This is the first report on the in situ generated chiral polymer complex used as a fluorescence sensor for highly enantioselective recognition of N-Boc-protected alanine.
Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO2

In this work, we improved the performance of germanium (Ge) channel Charge Trapping MemTransistors (CTMTs) as synaptic device by using Ti-doped HfO2 as charge trapping layer (CTL). We manipulated the amount of Ti dopant within the HfO2 CTL to perform the band engineering by varying the Hf/Ti cycle ratio in atomic layer deposition (ALD). The content of Ti was quantified and the energy band structures of the gate stack was constructed with the aid of transmission electron microscope (TEM) images and X-ray photoelectron spectroscopy (XPS) analysis. We then fabricated the charge trapping capacitors and characterized their memory characteristics such as memory windows. By the implementation of amphoteric trap model, thermal activated electron retention model and advanced charge decay model, the trap distribution of the CTL was extracted. Finally, we fabricated the CTMTs with Ti-doped HfO2 as the CTL and characterized their performance as synaptic device such as nonlinearity of depression and potentiation and also conductance on/off ratio. We used NeuroSim simulator with multilayer perceptron and convolutional neural network models to evaluate the pattern recognition accuracy of neural network hardware accelerator using CTMTs as synaptic devices and benchmarked the performance of our CTMT with those of other types of synaptic devices.
Blackhole Attack Cooperative Prevention Method in MANETs

Blackhole (BH) attacks are one of the most serious threats in mobile ad-hoc networks. A BH is a security attack in which a malicious node absorbs data packets and sends fake routing information to neighboring nodes. BH attacks are widely studied. However, existing defense methods wrongfully assume that BH attacks cannot overcome the most common defense approaches. A new wave of BH attacks is known as smart BH attacks. In this study, we used a highly aggressive type of BH attack that can predict sequence numbers to overcome traditional detection methods that set a threshold to sequence numbers. To protect the network from this type of BH attack, we propose a detection-and-prevention method that uses local information shared with neighboring nodes. Our experiments show that the proposed method successfully detects and contains even smart BH threats. Consequently, the attack success rate decreases.
Comparison of LSTM Architecture for Malware Classification

An experimental study of malware and benign classification from Windows API call sequences dataset using a deep learning framework is presented. We conduct a series of Long Short-Term Memory (LSTM) modifications, Bidirectional Long Short-Term Memory (BiLSTM). The proposed one architecture, such a half per half input sequence processed on the Siamese BiLSTM network looks. All three base models are treated fairly with scenario series of modification such a callback, batch normalization, dropout, and attention mechanism. As the results of this experiment, adding dropout and attention mechanisms show improvement from baseline models. In addition, we find that our proposed architecture with dropout and attention mechanism slightly outperform from other models.
Machine learning-based IP Camera identification system

With the development of technology, application of the Internet in daily life is increasing, making our connection with the Internet closer. However, with the improvement of convenience, information security has become more and more important. How to ensure information security in a convenient living environment is a question worth discussing. For instance, the widespread deployment of IP-cameras has made great progress in terms of convenience. On the contrary, it increases the risk of privacy exposure. Poorly designed surveillance devices may be implanted with suspicious software, which might be a thorny issue to human life. To effectively identify vulnerable devices, we design an SDN-based identification system that uses machine learning technology to identify brands and probable model types by identifying packet features. The identifying results make it possible for further vulnerability analysis.
AgriSen - A Dataset for Crop Classification

The large amount of collected data in the field of Earth Observation has created the need for automatization in processing and extraction information from it. Thus, deep learning (DL) techniques have gained popularity among the remote sensing community. Agriculture is one of the domains where DL can improve the current state-of-the-art. In this paper, we focus on the task of crop type classification, a key task in the process of assessing the agricultural market and yield. To this purpose, we introduce a new dataset, based on publicly available data (images from satellite Sentinel-2 and annotations from Land Parcel Identification System), to be used for further research in this field.
A Distributed Data Sampling and Relay Scheme for Obtaining Fresh Updates in Multihop Networks

New uses of wireless communication are envisioned to realize real-time control applications through networked monitoring. In such scenarios, the freshness of collected status updates is of critical importance for system performance, which can be quantified by the performance metric called Age of Information (AoI). In this paper, we apply this performance metric to study an age minimization problem in a wireless monitoring system where sensor nodes are employed to collect status information about their surroundings. The sampled data are forwarded to central controller via multiple hops. The problem formulation involves joint consideration of data sampling at source, flow routing, and link scheduling along the transmission path. The formulated problem falls in the form of an integer nonlinear program (INLP). To efficiently solve the problem, we construct AoI reward function to decouple the time coupling among sampling and relay decisions. Considering the system stability, we employ Lyapunov optimization technique to develop a distributed sampling and relay scheme. Simulation results show that the performance of our proposed scheme is competitive in terms of delivering status updates in a timely manner.
The Development of Mobile-based Application for Mental Health Counseling during the COVID-19 Pandemic

The impact of the Corona Virus Disease 19 (Covid-19) Pandemic is very large for the mental health of the population in Indonesia. Based on data from observations made by Kompas, 69% of people in Indonesia have mental health problems, due to stress, fear of being exposed to the corona virus. People need consultation with psychiatric experts during a pandemic by not visiting hospitals or health clinics for fear of contracting Covid-19. Based on this, the researchers conducted a study at a mental health counseling clinic in the South Tangerang area, Banten to build an Android-based online mental health consultation service information system, to make it easier for people to consult psychologists or psychiatry experts without creating new clusters. The system development method used is prototyping through literature review and observation to obtain updated data and information. The contribution of this research is a mental health consultation system application based on an Android application with a web service. The application developed is expected to be able to dynamically generate user interfaces in the consultation process from the initial state to obtain a conclusion. This application provides several features related to patient case records and patient journals to facilitate deeper understanding and ongoing services to maintain a patient's mental health condition.
Topic and Sentiment Analysis Matrix Factorization on Rating Prediction for Recommendation

In today’s online services, users’ feedback such as numerical rating, textual review, time of purchase and so on for each product is often encouraged to provide. Many recommender systems predict the products that the users may like and purchase in the future using users’ historical ratings. With the increase of user data in the systems, more detailed and interpretable information about product features and user sentiments can be extracted from textual reviews that are relative to ratings. In this paper, we propose a novel topic and sentiment matrix factorization model, which leverages both topic and sentiment drawn from the reviews simultaneously. First, we conduct topic analysis and sentiment analysis on reviews using Latent Dirichlet Allocation (LDA) and a lexicon construction technique, respectively. Second, we combine the user consistency, which is calculated from his/her reviews and ratings, and helpful votes from other users on reviews to obtain a reliability measure to weight the ratings. Third, we integrate the obtained reliability measure and the results of the topic and sentiment analysis of reviews into the matrix factorization framework for prediction. Our experimental comparison using eight Amazon datasets indicates that the proposed method significantly improves performance compared to traditional matrix factorization up to 20.82%.
Computer simulation of the structural effect of pressure on the hydrophobic hydration of methane

Monte Carlo simulations have been carried out on solutions containing one methane solute in 499 water molecules, at pressures of 1, 100, 200, 400, 800 and 1600 atm and 320 K. We observe that, at all pressures, hydration-shell water molecules have a smaller number of hydrogen bonds, a smaller number of near neighbours and larger tetrahedral bond-order parameters than bulk water molecules; however, these differences tend to decrease as pressure increases. In both the hydration shell and the bulk, the rise of pressure produces an increase in the number of hydrogen bonds and near neighbours per water molecule, accompanied by an increase in the tetrahedrality. Enhanced structuring of the hydration-shell water molecules is revealed by an increase of the first peak in the water oxygen—oxygen radial distribution function as pressure is increased. At low pressures, a weak second hydration shell around methane develops, and only at higher pressures does it become increasingly significant.
A Deep Learning Framework for Ensuring Responsible Play in Skill-based Cash Gaming

Games of skill are an excellent source of recreation, entertainment and relaxation. However when these games are played with real money, the important facet of responsible game play must be addressed rigorously and efficiently by game providers. Ensuring game prudence, whereby users play real-money skill games only for entertainment purposes, and do so well within their resourceful means, necessary for the wellness of players, and also for the sustained engagement and retention of players in the system. To this end, in this work we present a deep learning model that helps identify players who are on the verge of displaying irresponsible or addictive game play in a fully unsupervised manner. We use a combination of long short-term memory and adversarial auto-encoder networks to analyze game play along three tell-tale dimensions of immoderation, namely, money, time and despair. Our model provides a state of the art solution for identifying a precise set of problem gamers in skill-based cash games well ahead of time, effectively addressing the challenges of (i) extreme class imbalance, (ii) sparse and incomplete ground truth, (iii) overlapping behavioral patterns between risky and non-risky but highly engaged players.