Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review and Classification of Multi-Criteria Recommender Systems

Recommender systems (RSs) are personalization tools that gives recommendations for items to users by exploiting various methods. Conventional collaborative filtering (CF) based RSs provide suggestions to users based on overall rating of items which is not an efficient procedure as users in system may have different choices on different criteria. So, multicriteria recommender systems (MCRS) came into existence as an extension of traditional CF based RSs. MCRS recommends items to users based on number of criteria. Recommending products to users from the vast catalog is still a challenge for researchers. This paper presents a review of some significant work in the area of multi-criteria recommender system. After a brief introduction, we present review of existing methods categories according to heuristic and model based approach, and some of the popular approaches are classified into different sets such as recommendation fields, research problem, data mining and machine learning techniques. Insights and possible future work in the area of MCRSs are also discussed.
The transcription factor Zfhx1a is expressed in the embryonic chick spinal cord glial cells

In Drosophila, the zing-finger homeodomain transcription factors zfh are expressed in many tissues including nervous system. Two vertebrate homologues, Zfhx1a and Zfhx1b have been characterized. These genes have been implicated in epithelial-mesenchymal transitions (EMT) in normal development as well as in tumorigenesis. However, the expression and the role of these genes in developing vertebrates nervous system is unknown. Here we describe the expression patterns of Zfhx1a in the embryonic chick spinal cord as determined by immunohistoch emistry from days 1.5 (E1.5) to 14 (E14) of incubation. We have shown that Zfhx1a is feebly expressed in the neural progenitors in the ventricular zone (VZ) at around day 4. This expression becomes strong at E6. Starting from E7, we have observed that ZFHx1a + cells stream away from the VZ and migrate to the mantle layer of the spinal cord. Double immunostaining shows no overlap between Zfhx1a and pan-neuronal markers (NeuN and HuC/D). However, there was extensive overlap between Zfhx1a and either oligodendrocyte markers (Olig2 and Nkx2.2) or astrocyte marker (GFAP). We conclude that Zfhx1a is expressed in glial cells in the chick embryo spinal cord.
Data Encryption & Decryption Using Steganography

Video steganography is a method that processes secure communication. When we see the history of steganography, it was hidden in many ways such as tablets covered with wax, & written on the stomachs of rabbits. Here in this paper, considering the video steganography methods to perform secure steganography communication. Many methods have been proposed for video steganography but they're no more different types of formats, secured, quality, of the results. So here propose secure steganography methods i.e. Secure base LSB method, Neural Networks & Fuzzy logic, and check their using PSNR and MSE data of the methods. That data-set has collected is from video streams. And the result was seen with the more formats, more security, quality of outputs, & accuracy values of PSNR & MSE which is better than other proposed methods.
Rogue Access Point Localization Leveraging Compressive Sensing via Kernel Optimization

With the pervasive infrastructures of WLAN, user’s privacy has emerged as an important security and privacy problem. Rogue Access Points (AP), as one of the threat, is expected to be detected and located accurately. Therefore, in this paper, we propose a novel rogue AP localization method leveraging compressive sensing (CS) via kernel optimization. Although the CS based technique has been widely used in mobile user localization system, this is the first time to apply it to reversely localize AP. In addition, designing an appropriate kernel is the key to successful application of CS technique, however, traditional Gaussian or Bernoulli random kernels could not be utilized in rogue AP localization system, due that the kernel is related to the number and distribution of monitors, which could not randomly change every time. Hence, for CS kernel optimization, we firstly deduce the minimum number of monitors required in this system through a theoretical analysis which aims at justifying the validity of problem formulation. Then an Equiangular Tight Frame (ETF) based monitors distribution scheme is presented to achieve higher location accuracy. Finally, we perform both simulations and experiments to demonstrate the superiority of our approach as compare to other methods theoretically and practically.
Electric Field-Induced Transmembrane Potential Depends on Cell Density and Organizatio

Electrochemotherapy is a novel technique to enhance the delivery of chemotherapeutic drugs into tumor cells. In this procedure, electric pulses are delivered to cancerous cells, which induce membrane permeabilization, to facilitate the passage of cytotoxic drugs through the cell membrane. This study examines how electric fields interact with and polarize a system of cells. Specifically, we consider how cell density and organization impact on induced cell transmembrane potential due to an external electric field. First, in an infinite volume of spherical cells, we examined how cell packing density impacts on induced transmembrane potential. With high cell density, we found that maximum induced transmembrane potential is suppressed and that the transmembrane potential distribution is altered. Second, we considered how orientation of cell sheets and strands, relative to the applied field, affects induced transmembrane potential. Cells that are parallel to the field direction suppress induced transmembrane po.
CFO Estimation Based on Auto-Correlation With Flexible Intervals for OFDM Systems With 1-bit ADCs

For orthogonal frequency division multiplexing (OFDM) systems, the carrier frequency offset (CFO) estimation is required for the frequency synchronization before the data reception. When 1-bit ADCs are employed in OFDM systems, the available information is limited to the sign of the received signal, and the acquisition of the CFO is not easy problem. To overcome this limitation, we utilize the extended preamble and construct the bank of the CFO estimators based on auto-correlation (AC) taking the samples from the preamble with flexible intervals, which produces the candidates for CFO estimate. We propose the CFO estimation algorithms using the normalized squared errors (NSEs) as the concept to check the quality of the candidates of CFO estimate. The simulation results show that the proposed algorithm works well in the multipath fading channel, and is superior to the conventional CFO estimation method based on AC with fixed interval.
Transceiver Design for Ful1-Duplex Ultra-Reliable Low-Latency Communications with Finite Blocklength

In this paper, we jointly optimize the transceiver design and decoding error probability of a full-duplex (FD) ultrareliable low-latency communication (URLLC) system, where the base-station (BS) operates in an FD mode while the uplink (UL) and downlink (DL) users work in a half-duplex (HD) mode. Accordingly, an optimization problem is formulated for an FD URLLC system under the finite blocklength (FBL) to maximize the achievable total (UL plus DL) rate subject to the reliability (i.e., the decoding error probability) of each link and total transmission power constraints at the UL user and the BS. We convexify the formulated non-convex problem by analyzing the problem structure. Next, an efficient iterative algorithm is proposed to find the near-optimal power allocation for the UL user and the transceiver weights for the BS. Simulation examples show the impact of the blocklength and decoding error probability on the system performance.
Distributed Deep Reinforcement Learning with Wideband Sensing for Dynamic Spectrum Access

Dynamic Spectrum Access (DSA) improves spectrum utilization by allowing secondary users (SUs) to opportunistically access temporary idle periods in the primary user (PU) channels. Previous studies on utility maximizing spectrum access strategies mostly require complete network state information, therefore, may not be practical. Model-free reinforcement learning (RL) based methods, such as Q-learning, on the other hand, are promising adaptive solutions that do not require complete network information. In this paper, we tackle this research dilemma and propose deep Q-learning originated spectrum access (DQLS) based decentralized and centralized channel selection methods for network utility maximization, namely DEcentralized Spectrum Allocation (DESA) and Centralized Spectrum Allocation (CSA), respectively. Actions that are generated through centralized deep Q-network (DQN) are utilized in CSA whereas the DESA adopts a non-cooperative approach in spectrum decisions. We use extensive simulations to investigate spectrum utilization of our proposed methods for varying primary and secondary network sizes. Our findings demonstrate that proposed schemes outperform model-based RL and traditional approaches, including slotted-Aloha and Whittle index policy, while % 87 of optimal channel access is achieved.
Image Forgery Detection and Deep Learning Techniques: A Review

Due to the easily available software for tampering images, image manipulation has become quite common. Since the tampered images are non-distinguishable by the naked eye, they are being circulated on various platforms giving rise to rumors and misleading many. This has led researchers to work on various techniques for the detection of manipulated images with improved accuracy. Traditional works on image forgery detection are mostly based on extracting simple features that are specific for detecting some particular type of forgery. Recently, works on forgery detection based on neural networks have proved to be very efficient in detecting image forgery. Neural networks are capable of extracting complex hidden features of an image, thus giving better accuracy. Contrary to the traditional methods of forgery detection, a deep learning model automatically builds the required features, hence it has become the new area of research in image forgery. The paper initially discusses various types of image forgery techniques and later on compares different approaches involving neural networks to identify forged images.
Survey Paper:Introduction to Generative Adversarial Networks in Speech Imitation

Speech imitation, that is, the method of converting a source speaker’s utterance in a manner such that it sounds as though it were uttered by the target has been an active and volatile area of research in current times. The paper focuses on the techniques of modifying sounds emitted by the original speaker (also called the source). Speech Imitation involves metamorphosing the source speaker’s speech to present it in a way as though it were produced by the selected speaker (also called the target). Most systems specializing in speech imitation compulsorily necessitate the usage of parallel data to construct natural or unprocessed sounding speech. Examples of such systems would include RBM [1]. However, there are various obstacles associated with parallel data such as it being an arduous task to gather in real-time and the requirement for the vocabulary to be an exact match which perhaps might not be available to train the model. To remove such dependency on parallel data, a recently innovated model, GANs [3] are being explored.
ProSCH: Proxy aided Secondary Cell Handover in Ultra-Dense mmWave Network

The ultra-dense network (UDN) is a promising technology that overcomes the instability of high-frequency millimeter wave (mmWave) communication in a cellular network. With a short distance between cells and variability of mmWave, a handover occurs frequently in UDN with mmWave. Hence fast handover is essential to provide reliable service. One of the important things for fast handover is prompt signaling between base stations (BSs) through backhaul. However, conventional handovers assumed backhaul has very short latency with ideal deployment. In practice, with non-ideal latency and deployment of backhaul, handover is delayed for backhaul. We find that this delayed handover led to the performance reduction of TCP, the dominant traffic of nowadays. So we propose a ProSCH, the novel handover scheme that operates on a practical backhaul network while guaranteeing TCP performance. ProSCH reduces a handover interruption time by considering the backhaul latency in the handover signaling process and uses a TCP proxy on BS to forward data without loss after handover. Through extensive ns-3 simulation, we show that ProSCH outperforms conventional schemes in terms of handover interruption time, backhaul load, throughput, and delay.
Optimizing Transmission and Propulsion Powers for Flying Base Stations

Unmanned aerial vehicles acting as flying base stations (FlyBSs) have been considered as an efficient tool to enhance capacity of mobile networks and to facilitate communication in emergency cases. The enhancement provided by such network necessitates a dynamic positioning of the FlyBSs with respect to the users. Despite that, the power consumption of the FlyBS remains an important issue to be addressed due to limitations on the capacity of FlyBS’s batteries. In this paper, we propose a novel solution combining a transmission power control and the positioning of the FlyBS in order to ensure quality of service to the users while minimizing total consumed power of the FlyBS. We derive a closed-form solution for joint transmission and propulsion power optimization in a single future step. Moreover, we also provide a numerical method to solve the joint propulsion and transmission power optimization problem when a realistic (i.e. inaccurate) prediction of the users’ movement is available. According to the simulations, the proposed scheme brings up to 26% of total FlyBS’s power saving compared to existing solutions.
Popularity Prediction with Federated Learning for Proactive Caching at Wireless Edge

File popularity prediction plays an important role in proactive edge caching. The widely-used methods for popularity prediction are based on centralized learning, which needs to collect the request information and even more personal information from users, incurring the privacy-disclosure risk. In this paper, we propose a method of predicting file popularity with federated learning to address the privacy issue, where the request data of each user for each file is only employed for local training at each user. To facilitate the popularity prediction at the base station (BS) without information disclosure as well as the supervised training of a neural network at each user, we let each user upload a weighted sum of its own preference and file popularity to the BS. The neural network is employed to predict the weighted sum at each user by training with the user’s historical request records for each file. The local models and uploaded results of the users are aggregated at the BS. We show the convergence of the proposed popularity prediction method with a synthetic dataset. We use simulation results with a real dataset to show that the proposed method performs closely to the centralized learning based method in terms of caching performance.
Kinematic Coregistration of Sentinel-1 TOPSAR Images Based on Sequential Least Squares Adjustment

The Sentinel-1 provides an unprecedented opportunity for InSAR research and applications, especially in the field of fast and accurate damage assessment, thanks to its extra wide swath, short revisit interval, and free policy. Challenges also exist in Sentinel-1 terrain observation by progressive scans mode synthetic aperture radar (TOPSAR) interferometric processing, for example, the coregistration of TOPSAR images requires an accuracy of 0.001 pixels to reduce the phase jumps at the burst overlap region to 3°. To obtain the accuracy of 0.001 pixels for the coregistration of a stack of multitemporal TOPSAR images, joint estimation method and network-based method were proposed and implemented statically. However, when new images are added, the existing methods cannot coregister them kinematically. In order to resolve this issue, we first give a brief review for the existing static methods, including the single master-, temporally transferred-, and network-based methods, for coregistering multitemporal TOPSAR images. Then, we propose a kinematic coregistration method to coregister newly added TOPSAR images by introducing the sequential weighted least square adjustment. Experimental results demonstrate that the proposed method can achieve an accuracy of 0.001 pixels for kinematic coregistrations of multitemporal TOPSAR images. Compared with the static network-based coregistration method, the proposed method is superior in terms of both coregistration accuracy and computational efficiency. It will contribute a great deal to the globally acquired big SAR data (e.g., Sentinel-1 TOPSAR) and their near real-time processing.
Sales Prediction using Online Sentiment with Regression Model

The main aim of this paper is to predict the sales of a vehicle using sentiment analysis from various places on the internet. The online presence of a vehicle, as well as its brand, plays a key role in the sales of the vehicle. However, many other parameters are required and will be discussed in this paper. Sales prediction in today’s market is not only beneficial for the manufacturer but also for the various other companies that manufacture parts or accessories for vehicles. It can also be considered as a boon for retailers, showroom owners, and service mechanics. In this application, we have made use of linear regression for sentiment analysis and polynomial regression for sales prediction.
Avoiding Unmeasured Premise Variables in Designing Unknown Input Observers for Takagi–Sugeno Fuzzy Systems

This letter investigates the design of unknown input (UI) observers for a large class of nonlinear systems using Takagi-Sugeno (TS) fuzzy modeling. To avoid the well-known issue on the unmeasured premise variables in fuzzy observer design, we reformulate the nonlinear systems in a TS fuzzy form with local nonlinear models. A particular feature of these so-called N-TS fuzzy models is that all the unmeasured nonlinearities are isolated in a nonlinear consequent. Together with a judicious use of the differential mean value theorem, the N-TS fuzzy reformulation enables an effective framework to design fuzzy UI observers. Based on an UI decoupling technique, no specific information on the UI is required for fuzzy observer design. The asymptotic estimations of both the state and the UI are guaranteed with fuzzy Lyapunov arguments. The observer gains can be effectively computed following an LMI-based design procedure. Numerical illustrations are given to demonstrate the interests of the proposed method over related existing results.
On the head morphology of Tetraphalerus, the phylogeny of Archostemata and the basal branching events in Coleoptera

Internal and external features of Tetraphalerus bruchi were studied using X‐ray microtomography (µ‐CT) and other techniques, and head structures were described in detail. µ‐Ct is highly efficient for the assessment of anatomical data. A data matrix with 90 morphological characters of recent and fossil beetles was analyzed with different approaches (parsimony, Bayesian analysis). The results of the parsimony analysis resulted in the following branching pattern: (†Tshekardocoleidae + (†Permocupedidae, †Rhombocoleidae + (†Triadocupedidae + ((Adephaga + (Myxophaga + Polyphaga))) + Archostemata s.str. [including Jurodidae]))). Sikhotealinia is placed as sister group of †Jurodes (Jurodidae), and Jurodidae as sister group of the remaining Archostemata (Bayesian analysis) or of a clade comprising Micromalthidae, Crowsoniellidae, †Ademosynidae, †Schizophoridae and †Catiniidae. The monophyly of Ommatidae and Cupedidae is well supported and Priacma is placed as the sister group of all other Cupedidae. Important events in the early evolution of Coleoptera are the shortening of the elytra and the transformation of the elytral venation (Coleoptera excluding †Tshekardocoleidae), the formation of a closed subelytral space (Coleoptera excluding †Tshekardocoleidae and †Permocupedidae), the reduction of two apical antennomeres, and the loss of the broad prothoracic postcoxal bridge (Coleoptera excluding †Tshekardocoleidae, †Permocupedidae and †Rhombocoleidae). Plesiomorphic features preserved in extant Archostemata are the tuberculate cuticle, the elytral pattern with parallel longitudinal ribs and window punctures, a mesoventrite with a transverse ridge, triangular mesocoxae with a distinct meron, and the exposed metatrochantin. The fossils included in the analyses do not only contribute to the reconstruction of character evolution but also influence the branching pattern. An understanding of the major evolutionary events in Coleoptera would not be possible without considering the rich fossil record of Permian and Mesozoic beetles.
Electricity Market Customer Segmentation Based on DBSCAN and k-Means : —A Case on Yunnan Electricity Market

Customer segmentation is a proven and efficient method of differential management that has been widely used in various fields. By providing different types of customers with specific services to meet their heterogeneous needs, companies achieve profit growth and other objectives. Determined by the unique market background, especially the settlement mechanism, the trading behavior evaluation in use and the customer segmentation in plan together constitute the differential management system in Yunnan Electricity Market. They help Kunming Power Exchange to mine the information covered in participants’ data, so that the exchange can be aware of their situation and better maintain the stable development of the market. A segmentation algorithm based on density-based spatial clustering of applications with noise (DBSCAN) and k-means method is designed to satisfy the requirement in Yunnan Electricity Market. A case analysis using the proposed algorithm is carried out, and the result shows that the segmentation of retailers using their actual operational data is effective and the mechanism can be considered reliable.
Arduino-based Automated Washroom Sanitizing System

The proposed Arduino-based automated washroom sanitizing system, mainly deals with solving the problem of the unhygienic condition of public toilets. The automation of systems for everyone is turning very frequently in the present. Yet, common people are facing many issues in their daily routine. Sanitation is one of the largest problems faced by people in our country. Even though 6 percent of the urban people are relying on public toilets for their daily use, they are still not hygienic. This has become one of the most basic issues faced by people everywhere. Providing the best solution to this issue is the aim of our project. The existing method involves manual cleaning done by a human which is not at all an easy task and may not even exist in all areas. Implementing a facility to clean the major units of a washroom after several cycles will maintain a sufficient hygienic environment. The cleaning process is aimed to be automated and simple. Such a provision will ease the job of regular janitors as well as the users. Placing a sensor-controlled water flusher attached to the toilet will perform the cleaning task and meanwhile, the number of cycles used is recorded to activate the automated cleaning process. On selecting Arduino Uno as a suitable interface, we aim to provide an easily compatible facility at an economically feasible rate. We aim to ease the brushing technology using a simple belt and DC motor-driven mechanism. Pressure pump along with proper designing of pipes are also incorporated. Hence, on adopting this methodology, we will be able to increase the standard of public and community toilets and facilitate people to use these effectively.