Miracle-dz/UniFAR-FTUs huggingface dataset:
Blackmarket-Driven Collusion Among Retweeters–Analysis, Detection, and Characterization

The growth of online social media has led to a huge increase in the number of users who want to share and publicize various kinds of information. Twitter, the most popular micro-blogging platform, has become a hotbed for users who are involved in different activities such as news publishing, job hunting, recruiting, advertising and publicity. Retweeting a tweet is a major action to broadcast a user’s message out to millions of users. Retweet action has two major advantages: (i) gaining quick exposure to the content, and (ii) increasing likelihood of gaining new Twitter followers in return. The organic way of gaining a larger number of retweets is a time consuming process, which leads to the creation of unfair methods to gain retweets. Thus, Twitter users often approach various blackmarket services to gain retweets inorganically in a short duration. Blackmarkets spread their collusive ecosystem in such a way that Twitter is unable to detect them even after devoting significant effort to purge the platform off bots, trolls, and fake accounts. One major reason behind the evasion is that the collusive users involved in blackmarket services exhibit a mix of organic and inorganic behavior – they organically reweet some genuine tweets; at the same time, they inorganically retweet tweets submitted to blackmarket services. This paper is the first attempt to provide a thorough study of the collusive users involved in two types of blackmarket services – Premium and Freemium. We collect a novel dataset of collusive users comprising of users from both types of blackmarket services. We provide network-centric, profile-centric, timeline-centric and retweet-centric characteristics of these users and show how users involved in premium blackmarket services exhibit diverse behavior as compared to those involved in freemium services. We further employ human annotators to label collusive users into three types: bots, promotional customers, and normal customers. We then curate 63 novel features to run state-of-the-art classifiers in two settings – binary classification (collusive vs. genuine) and multi-class classification (bot, promotional customers, normal customers, and genuine users). Bagging achieves the best accuracy (macro F1-score of 0.892) in the former setting, whereas Random Forest outperforms others (macro F1-score of 0.791) in the latter setting. We also develop a chrome extension, SCoRe++ which can detect collusive retweeters in real time.
A CNN-based Approach for three-class classification of motor imagery EEG data including ‘rest state’ in hybrid multi-user BCI

Multi-user BCI (brain computer interface) refers to a kind of BCI in which there are several users participating in one task. Differently from P300 and SSVEP (steady-state visual evoked potential), MI (motor imagery)-BCI does not rely on external stimulus, which is more widely used in the assistance of disabled people. The main problem of MI-BCI is to achieve asynchronous control, which needs to improve the rest state recognition accuracy. We proposed a CNN-based approach for three-class classification of motor imagery EEG data including rest state in hybrid multi-user BCI. Firstly, we designed a two-user hybrid MI-BCI experimental paradigm and moreover proposed a CNN-based processing framework method which contains several strategies for inter-brain phase locked value (PLV) features fusion. Results show that the alpha band shows the significantly better performance than other bands and the classification performance of multi-user MI is better than single-user MI. Multi-user BCI is a potential way to enhance the performance of asynchronous MI-BCI.
Usability Analysis using Principal Component Analysis (PCA) Method for Online Fish Auction Application

Information systems or applications that are implemented and used by users need to go through top-level analysis, according to the needs of information systems or applications that can be used. Testing and usability analysis and user engagement scale needs to be done to measure the level of usage of the online fish auction application. This analysis uses the Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlett's Test of Sphericity methods for testing validity. And use the Principal Component Analysis method for dimension factors that represent all dimensions. Indicator test results show some of the most important variables, namely PUS5 and FI3 with the largest values of 0.926 and 0.792, which are indicators that represent the variables of Engagement (Perceived Usability and Felt Involvement).
Sampling Rate Enhancement for SAR-ADCs Using Adaptive Reset Approach for FOG Systems

In this investigation, an adaptive reset strategy of a 10-bit SAR-ADC particularly designed for the applications in FOG (fiber optic gyroscope) systems is demonstrated with TSMC 40 nm CMOS technology. The proposed adaptive reset approach takes advantage of high correlation of the phase shift generated by continuous FOG signals such that MSB bits of two consecutive samples likely remain the same, which implies there is no need to re-converted. By replacing conventional all-reset method with the adaptive-reset strategy, the throughput of the SAR ADC is theoretically increased up to around 100%. The sampling rate is increased by 2 times compared with the conventional counterpart.
Quantum-Dot Intermediate-band Solar Cell used as Bottom Cell

An attempt to apply quantum-dot (QD) intermediate band solar cell (IBSC) as bottom cell in 4-terminal tandem solar cell instead of a conventional bulk bottom cell is presented. Considering the inferior lattice qualities in QD cells, we introduce a parameter ηIC = 1 ~ 0.0001 to represent slow or fast conduction-band-to-dots non-radiative relaxation rate respectively corresponding to strong or weak phonon-bottleneck effects, aiming to develop a more general IBSC model. Our results exhibit a well-designed QD solar cell can more sufficiently utilize the below-1.41eV photons passing through the GaAs top cell, which would be a better candidate as bottom cell. Compared with the highest efficiency (11.15% absolute) achieved by the best bulk bottom cell (Eg = 0.93eV), QD bottom cell with Eg = 1.74eV and E1 = 1.04eV will raise an over 3% absolute of efficiency boost, when phonon bottleneck effects are running well (ηIC ≥0.1). If use a GaAs-host QD bottom cell with E1 = 0.98eV, 1.5% absolute of efficiency add-on can still be achieved, which attributes to a significant voltage boost by the separation of quasi-Fermi levels between IB and CB. Even for those QD bottom cells with inferior lattice qualities (ηIC <<1), where the phonon-bottleneck effect is broken, their efficiencies degenerate into the radiative limit of bulk bottom cell whose bandgap is equal to E1.
Energy Aware Parking Lot Availability Detection Using YOLO on TX2

Finding a parking space is a tedious and time-consuming task in a metropolitan city. Due to this problem, many researchers proposed an automatic parking lot occupancy detection system using a camera with a deep learning method to provide useful information in the smart city system. Since object detection for the parking lot is performed in real-time by utilizing CPU and GPUs while parking detection is working 24 hours a day and 365 days a year, therefore power saving is important to reduce the electricity cost. However, the energy-aware is not considered in most related works. In this paper, we proposed an energy-saving algorithm for parking lot availability detection using YOLO running on the TX2 machine. We experiment using small parking lot prototype and remote control cars. In the experiment, we compare our algorithm with the direct application of original YOLO for parking lot detection, the results show that it reduces power by 97 percent when there is no moving object in the parking lot area and 71 percent when there are moving objects in the parking lot area.
Unravelling Optical and Electrical Degradation of Perovskite Solar Cells and Impact on Perovskite/Silicon Monolithic Tandem Modules

Perovskite materials have emerged as promising candidates for high-efficiency silicon based tandem solar cells. Critically, the different degradation rates of perovskite and silicon cells can affect the lifetime performance of tandem modules. In this paper we design and conduct experiments to investigate the impact of electrical and optical degradation of perovskite cells. Experiment results indicate that degradation dominated by fill factor and current reduction can change the optical transmittance of the perovskite cells. The long-term module performance is then simulated accordingly. A maximum permissible perovskite cell degradation rate of 0.9%/year is calculated to meet the current industry warranty requirements, while a minimum required tandem cell efficiency of 28.7% is estimated for a two-terminal tandem module to be economically superior to a silicon module.
Dtree2vec: A High-Accuracy and Dynamic Scheme for Real-Time Book Recommendation by Serialized Chapters and Local Fine-Grained Partitioning

With the rapid development of social computing technologies and online reading platforms, the proportion of e-books, especially online serialized novels, has been increasing. Identifying ways to add updated serialized books to readers’ real-time recommendation lists has become an urgent problem to be solved. While serialized books are still underwritten and in the process of production, their features and categories can constantly evolve and change, lacking complete text content information and complete global scoring data. Therefore, this paper proposes a dynamic DTree2Vec scheme for serialized books that models text of varying degrees of completion to achieve unified semantic feature representation to measure the semantic relevance of books. At the same time, it ensures recommendation quality by tracking the update status of serialized books and by adding the subsequent chapter content in real time. This scheme establishes a dynamic hierarchical tree structure for serialized books and applies a cosine-type local reconstruction model to reconstruct new semantic features of books. In addition, the fine-grained factor of chapter partitioning is introduced to the reconstruction process to adjust the proportion of global and local features to better represent the semantic features of books. We analyzed effects of the content of serialized chapters on the recommendation results and used semantic features of the reconstruction to represent the content of serial novels in real time. The experiment results prove that the proposed DTree2Vec scheme can achieve higher degrees of recommendation accuracy when dealing with unfinished serialized books, effectively alleviating dynamic capture problems and real-time recommendations of book recommendations.
Classification of Geophysical Basins Derived From SRTM and Cartosat DEMs via Directional Granulometries

Spatial patterns from terrestrial surfaces have great potential to better understand the surficial phenomena and processes. This article provides an approach for classification of the hierarchically partitioned basins, subbasins, and watersheds with respect to high-directional granulometric index, low-directional granulometric index, and global granulometric index. Grayscale global and directional granulometric indexes of basin and corresponding watersheds derived from Shuttle Radar Topographic Mission (SRTM) and Cartosat digital elevation models (DEMs) of the Lower Indus subbasin of peninsular India are computed. The end results shown are in the forms of watersheds categorized and classified according to global and high- and low-directional grayscale granulometric indexes. It is interesting to observe that the classification of watersheds derived from SRTM and Cartosat DEMs, respectively, yielded similar results, suggesting it would be worthwhile to explore the potential relationships between the directional granulometric indexes of the watersheds and their resistance to perturbations caused by endogenic and exogenic forces. It is also observed that the orientation of watersheds and the general flow direction within those watersheds are in the direction in which the watersheds yield high-directional granulometric index.
Machine Learning Techniques for Automated Melanoma Detection

The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. Computer-Aided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, pre-processing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.
Continuous Deep Maximum Entropy Inverse Reinforcement Learning using online POMDP

A vehicle navigating in an urban environment must obey traffic rules by properly setting its speed, such as staying below the road speed limit and avoiding collision with other vehicles. This is presumably the scenario that autonomous vehicles will face: they will share the traffic roads with other vehicles (autonomous or not), cooperatively interacting with them. In other words, autonomous vehicles should not only follow traffic rules, but should also behave in such a way that resembles other vehicles behavior. However, manually specification of such behavior is a time-consuming and error-prone task, since driving in urban roads is a complex task, which involves many factors. This paper presents a multitask decision making framework that learns an expert driver's behavior driving in an urban scenario containing traffic lights and other vehicles. For this purpose, Inverse Reinforcement Learning (IRL) is used to learn a reward function that explains the expert driver's behavior. Most IRL approaches require solving a Markov Decision Process (MDP) in each iteration of the algorithm to compute the optimal policy given the current rewards. Nevertheless, the computational cost of solving an MDP is high when considering large state spaces. To overcome this issue, the optimal policy is estimated by sampling trajectories in regions of the space with higher rewards. To do so, the problem is modeled as a continuous Partially Observed Markov Decision Process (POMDP), in which the intentions of other vehicles are only partially observed. An online solver is employed in order to sample trajectories given the current rewards. The efficiency of the proposed framework is demonstrated through simulations, showing that the controlled vehicle is be able to mimic an expert driver's behavior.
Comparison Between Back Projection Algorithm and Range Migration Algorithm in Terahertz Imaging

Back projection algorithm (BPA) and range migration algorithm (RMA) are two common approaches for image reconstruction in synthetic aperture radar (SAR) imaging. Imaging is implemented in spatial domain and spatial frequency domain, respectively and these two methods are often considered to generate the same results. This is true in normal microwave/millimeter wave SAR imaging systems, since the object of interests is located far away and the applied wavelength is long. In this case, the location of the object is the most important issue and there is no impetus to image the object with very fine details. In case of terahertz (THz) imaging, we have found some differences by using the two algorithms above, including the position of the object and resolution in 2D and 3D imaging. Some insights are given and we believe there should be some guidelines regarding the pros and cons before choosing the exact algorithm, especially when high-performance imaging is pursued.
Circuit Design Challenges in Computing-in-Memory for AI Edge Devices

Computing-in-memory (CIM) structures are meant to overcome the memory bottleneck and improve energy efficiency for artificial intelligence (AI) edge devices. In this article, we review recent trends in the development of CIM macros for the Internet of Things and AI applications. We also look at recent advances in the development of CIMs based on SRAM and nonvolatile memory for AI edge devices as well as the challenges involved in circuit design.
RFID and GPS antennas design of Heterogeneous tracking system for school children safety

Real time location system for tracking and monitoring of the human beings or any systems in indoor and outdoor environment proposed in this paper with the aid of the antennas. Two antennas are designed one for RFID (indoor) and another antenna for GPS (outdoor) applications. RFID antenna is a ring slotted antenna for indoor applications and the second antenna is GPS microstrip patch antenna for outdoor applications. The proposed RFID antenna consists of lightening shaped ring and rectangular slot at the ground plane. First antenna resonates at 2.5 GHz achieving circular polarization while second antenna resonates at 1.5 GHz. This proposed work concentrates on the design of reader antennas to ensure the safety of school children with the help of the wearable technology.
Analytical comparision of Dual Active Bridge converter with Push Pull Converter for power transfer efficiency

Dual Active Bridge (DAB) Converter and PushPull Converters are mainly used for Conversion of power with large cycles per second. Dual active bridge converter consists of couple of full bridge converters linked through a transformer. Two way power flow capacity, high power density, high proficiency, soft switching, less number of passive elements and galvanic separation are the important characteristics of DAB pulling in industry. The push-pull converter is utilized as DC-DC converters are reasonable for low and medium power application. High efficiency, low switching loss and high power density are the important characteristics of this converter. By increasing the switching frequency, it results in the reduction of complexity of the circuit. However the switching loss increases as the switching frequency is increased. In this paper, the study is focused on a comparative analysis of DAB converter and Push-Pull converter.
3D Point Cloud Attribute Compression via Graph Prediction

3D point clouds associated with attributes are considered as a promising data representation for immersive communication. The large amount of data, however, poses great challenges to the subsequent transmission and storage processes. In this letter, we propose a new compression scheme for the color attribute of static voxelized 3D point clouds. Specifically, we first partition the colors of a 3D point cloud into clusters by applying k-d tree to the geometry information, which are then successively encoded. To eliminate the redundancy, we propose a novel prediction module, namely graph prediction, in which a small number of representative points selected from previously encoded clusters are used to predict the points to be encoded by exploring the underlying graph structure constructed from the geometry information. Furthermore, the prediction residuals are transformed with the graph transform, and the resulting transform coefficients are finally uniformly quantified and entropy encoded. Experimental results show that the proposed compression scheme is able to achieve better rate-distortion performance at a lower computational cost when compared with state-of-the-art methods.
Application of speech recognition technology in IoT smart home

This paper presents an approach to incorporate speech recognition technology in deploying Internet of Things (IoT) for smart home system. In the process of implementing speech recognition, it is especially important to deal with the problem of speech misidentification. Misidentification problems can be improved by improving algorithms and setting speech recognition strategies. Under the same equipment conditions, voice recognition mechanisms such as voice trigger and error absorption can effectively improve the accuracy of speech recognition. At the same time, the smart home systems are also inseparable from functions such as intelligent lighting, temperature and humidity control, security alarms, fire warnings, and intelligent terminal controls. On this basis, combined with speech recognition technology, a complete smart home human-computer interaction system can be realized.
Adaptive Clock and Data Recovery for Asymmetric Triangular Frequency Modulation Profile

Clock and Data Recovery (CDR) is an important block in Serializer/Deserializer (SerDes) systems used to recover the clock from the bitstream. With high data rates, the effect of Electromagnetic Interference (EMI) is increased. Several spreading spectrum profiles are able to reduce the effect of EMI. Asymmetric spreading spectrum profile is one of the types that can be implemented easily and can make a significant reduction on EMI. The challenge in CDR design is increased with asymmetric spreading profiles as the slew-rate is high with respect to the other profiles. Many researches provided several architectures for adaptive CDR with symmetric spreading profiles. In this research, a new architecture is proposed to detect the large phase error to reach suitable CDR controller parameters needed to recover the clock with high slew-rate profiles. The proposed architecture is able to get locking with the incoming data in short-time even with an initial large difference between transmitter and receiver clock frequency and at the same time achieve minimum jitter. A white Gaussian noise with 30 dB Signal to Noise Ratio (SNR) is used on a channel model to simulate the actual behavior on SIMULINK. Also, a search algorithm is proposed to find the best CDR settings in the proposed architecture. Results obtained from MATLAB shows the efficiency of the proposed architecture to track the data with largest slew-rate using sudden step change on transmitter clock frequency.
Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor’s performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe0.5Se0. 5 and CdSe0.5S0. 5. The ML framework used in this work can be extended to any class of semiconductors.