Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-objective Optimization for Network Resource Management in Heterogeneous Vehicular Networks

Heterogeneous network integration is a promising technique to support efficient data services in vehicular networks. However, due to highly dynamics of vehicular mobility and heterogeneous performance of wireless interfaces, it is still challenging to design an efficient scheduling policy for information services in vehicular networks. In this paper, we propose a centralized service architecture for managing heterogeneous network resources. Particularly, we comprehensively investigate the heterogeneity of networks, as well as the diversity of service requests. On this basis, we formulate the heterogeneous network resource management (HNRM) problem as a multiple-objective problem, which aims at minimizing both the service delay and the network access cost simultaneously. Then, we propose a packet-encoding based multi-objective algorithm (PEMA), which consists of two components: packet encoding for data broadcast and multiobjective algorithm for network interface selection. Specifically, for improving bandwidth efficiency, we develop a multiple-packet encoding (MPE) technique to serve more requests simultaneously. For network selection, we propose a multi-objective evolutionary mechanism to further minimize both the service delay and the network access cost via population evolution. Finally, we give a comprehensive performance evaluation to demonstrate the superiority of PEMA under a wide range of scenarios.
EYRE 2019: 2nd International Workshop on EntitY REtrieval

Entity retrieval has received increasing research attention from both the Information Retrieval (IR) and Semantic Web communities. This workshop series provides a platform where interdisciplinary studies of entity retrieval can be presented, and focused discussions can take place. We also organize two shared tasks related to entity retrieval. The 2nd International Workshop on EntitY REtrieval (EYRE 2019) was a half-day workshop co-located with the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019) in Beijing, China.
Image-Guided Depth Upsampling via Hessian and TV Priors

We propose a method that combines sparse depth (LiDAR) measurements with an intensity image and to produce a dense high-resolution depth image. As there are few, but accurate, depth measurements from the scene, our method infers the remaining depth values by incorporating information from the intensity image, namely the magnitudes and directions of the identified edges, and by assuming that the scene is composed mostly of flat surfaces. Such inference is achieved by solving a convex optimisation problem with properly weighted regularisers that are based on the `1-norm (specifically, on total variation). We solve the resulting problem with a computationally efficient ADMM-based algorithm. Using the SYNTHIA and KITTI datasets, our experiments show that the proposed method achieves a depth reconstruction performance comparable to or better than other model-based methods.
Type-2 kernelized fuzzy c-means algorithm based on the uncertain width of Gaussian kernel with applications in MR image segmentation

Keywords: KFCM; Type-2 fuzzy sets; Gaussian kernel; MR image. Abstract. While fuzzy c-means is a popular soft clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel width is crucial for effective kernel clustering. Unfortunately, for most applications, it is not easy to find the right width. To design and manage the uncertainty for kernel width, we propose a type-2 kernelized fuzzy c-means algorithm (T2KFCM). We extend the type-1 fuzzy sets of membership to interval type-2 fuzzy sets using two widths and which creates a footprint of uncertainty for the membership. Experiments on MR (Magnetic Resonance) image are given that compare kernelized FCM (KFCM) with T2KFCM. The results show that T2KFCM compares favorably to both of the previous models.
A Fast-Transient Switched-Capacitor DC-DC Converter with a Current Sensing Control Technique

A low-power switched-capacitor step-down DC-DC converter using the proposed current sensing control technique to enhance the transient response is presented in this paper. By sensing the current outflow from the DC-DC converter and the load current simultaneously, the proposed technique scales the switching frequency (Fsw) accordingly from the very beginning of the load or the line transient. It leads to better transient response without depending on the output capacitance. It is suitable for sensor applications which usually use an LDO with a large output capacitor, resulting in lower efficiency. The converter is implemented in a 0.25-μm CMOS process with integrated flying capacitors and generates a fixed regulated output voltage of 1.8V from an input voltage ranging from 3.8V to 5.4V. From the measurement results, the transient response time is about 0. 3μs after a step load current from 20μA to 2.5mA which is sufficient for low-power sensor applications. Its power efficiency (η) is higher than 70% over a wide range of the load current with a peak value of 72.8%.
Studies in partial fulfilment of the requirements for the degree of MASTER OF SCIENCE IN BIOTECHNOLOGY

Title of Thesis/Dissertation : Molecular characterization of wheat varieties suitable for plains of Jammu Name of Student : Riya Sharma Admission No : J-14-MB-22 Major subject : Biotechnology Name and Designation of : Dr. R.K. Salgotra Major Advisor Professor and Coordinator Degree to be awarded : M.Sc. Biotechnology Year of award of Degree : 2016 Name of the University : Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu(J&K) Bread wheat (Triticum aestivum L.) constitutes an important food crop and serves as food material for a large population globally. With increasing population and change in climatic conditions, there is need for further improvement of the crop for sustainable production. Genetic diversity analysis is the initial and foremost step for improvement of any crop. The systematic evaluation of the molecular diversity encompassed in wheat genetic resources is a prerequisite for its efficient utilization in breeding. The present study was undertaken in bread wheat for its molecular as well as phenotypic characterization. The phenotypic data for various morphological traits like plant height, effective tillers per plant, spike length, days to flowering, days to maturity, grains per spike, 1000grain weight and yield per plant were recorded. The genotype JAUW-584 showed maximum values for plant height, effective tillers per plant and yield per plant while RAJ-1114 showed minimum value for effective tillers per plant, yield per plant and days to maturity. Significantly positive correlation was observed in case of grain yield per plant and effective tillers per plant and in case of grain yield per plant and 1000-grain weight. Molecular characterization was done using twenty genic SSRs and the capabilities of these SSRs to discriminate among genotypes were assessed based on various parameters. Highest PIC value of 0.70 was observed in Xgwm257 while lowest value of 0.11 was observed in case Xgwm247 with an overall average of 0.44. The dendrogram generated using molecular data from polymorphic markers grouped 16 genotypes into 3 distinct clusters and 10 sub clusters. The study inferred the diversity analysis of wheat genotypes and the understanding of these diverse genotypes can be helpful for selection of parental lines to breed for important agronomic traits.
The Emotion Analysis on the Chinese Comments from News portal and Forums

The people’s emotion reflect their attitude towards life and society. This paper proposes an approach to measure the positive emotion of Chinese comments from news portal and forums. A large emotion lexicon is first built by the Point mutual information (PMI) and expression symbols, and the method to calculate the emotion values of each comment is also provided based on this lexicon. Experiments on manual annotation dataset show that our method achieves important improvement compared with the baseline. Finally, we investigate the emotional differences among users’ comments from different web sources, and find that the more strict the rules of the network platform, the more positive the user comments.
Effects of radio-frequency driving power, gas pressure, and nitrogen seeding on the transition dynamics in argon inductively coupled plasmas

The influences of rf driving power, neutral gas pressure, and nitrogen seeding on the electrostatic-to-electromagnetic (E-H) mode transition dynamics in radio-frequency argon inductively coupled plasmas (ICPs) in a pressure range of 50–100kPa are investigated, both experimentally and theoretically. The E-H mode transition dynamics and its characteristic transition time scale are investigated by observing the high-speed imaging (13500fps) as well as the temporal change of plasma loading impedance. The experimental results reveal that the E-H mode transition time is not fixed at any operating conditions rather it depends on some important parameters such as the rf driving power, neutral gas pressure, gas type. It is found that the E-H mode transition time depends on the unique parameter Eθ∕p; the so-called effective induced electric field, rather than the independent parameter: the rf power or neutral gas pressure. It is also found that longer E-H mode transition time is required to ignite the high-pressure.
Fast video facial expression recognition by deeply tensor-compressed LSTM neural network on mobile device

Poster: Mobile devices usually suffer from limited computation and storage resource which seriously hinders them from deep neural network applications. In this paper, we introduce a deeply tensor-compressed LSTM neural network for fast facial expression recognition (FER) in videos on mobile devices. Firstly, a spatio-temporal FER LSTM model is built by extracting time-series feature maps from facial clips. The LSTM model is further deeply compressed with tensorization. Based on dataset of Acted Facial Expression in Wild (AFEW) 7.0, experimental results show that the proposed method achieves 55.60% classification accuracy; and significantly compresses the size of network model by 219X. Our work is further implemented on RK3399Pro IoT device with Neural Process Engine, and the runtime of feature extraction part can be reduced by 12.83X with only 7.73W power consumption.
Convexity of Network Restricted Games Induced by Minimum Partitions

We consider restricted games on weighted communication graphs associated with minimum partitions. We replace in the classical definition of Myerson's graph-restricted game the connected components of any subgraph by the sub-components corresponding to a minimum partition. This minimum partition P min is induced by the deletion of the minimum weight edges. We provide necessary conditions on the graph edge-weights to have inheritance of convexity from the underlying game to the restricted game associated with P min. Then we establish that these conditions are also sufficient for a weaker condition, called F-convexity, obtained by restriction of convexity to connected subsets. Moreover we show that Myerson's game associated to a given graph G can be obtained as a particular case of the P min-restricted game for a specific weighted graph G ′. Then we prove that G is cycle-complete if and only if a specific condition on adjacent cycles is satisfied on G ′ .
Use of R-trees to improve reconstruction time in pixel trackers

Computing time is becoming a key issue for tracking algorithms both online and off-line. Programming using adequate data structures can largely improve the efficiency of the reconstruction in terms of time response. We propose using one such data structure, called R-tree, that performs a fast, flexible and custom spatial indexing of the hits based on a neighbourhood organisation. The overhead required to prepare the data structure shows to be largely compensated by the efficiency in the search of hits that are candidate to belong to the same track when events present a large number of hits. The study, including different indexing approaches, is performed for a generic pixel tracker largely inspired in the upgrade of the LHCb vertex locator with a backwards reconstruction algorithm of the cellular automaton type.
An Industrial IoT Solution for Evaluating Workers' Performance Via Activity Recognition

The Industrial Internet of Things (IIoT) is a key pillar of the Fourth Industrial Evolution or Industry 4.0. It aims to achieve direct information exchange between industrial machines, people, and processes. By tapping and analysing such data, IIoT can more importantly provide for significant improvements in productivity, product quality, and safety via proactive detection of problems in the performance and reliability of production machines, workers, and industrial processes. While the majority of existing IIoT research is currently focusing on the predictive maintenance of industrial machines (unplanned production stoppages lead to significant increases in costs and lost plant productivity), this paper focuses on monitoring and assessing worker productivity. This IIoT research is particularly important for large manufacturing plants where most production activities are performed by workers using tools and operating machines. With this aim, this paper introduces a novel industrial IoT solution for monitoring, evaluating, and improving worker and related plant productivity based on workers activity recognition using a distributed platform and wearable sensors. More specifically, this IIoT solution captures acceleration and gyroscopic data from wearable sensors in edge computers and analyses them in powerful processing servers in the cloud to provide a timely evaluation of the performance and productivity of each individual worker in the production line. These are achieved by classifying worker production activities and computing Key Performance Indicators (KPIs) from the captured sensor data. We present a real-world case study that utilises our IIoT solution in a large meat processing plant (MPP). We illustrate the design of the IIoT solution, describe the in-plant data collection during normal operation, and present the sensor data analysis and related KPI computation, as well as the outcomes and lessons learnt.
Evaluation of the Spatial Consistency Feature in the 3GPP Geometry-Based Stochastic Channel Model

Spatial consistency, meaning the spatial correlation of small scale fading, is a recently added feature in the geometry-based stochastic channel model used by 3rd Generation Partnership Project (3GPP) standardization. This feature is relevant for schemes that utilize channel correlation among users, e.g. clustering according to second order channel statistics. This paper provides a simulation based evaluation of the spatial consistency feature implemented in the open source quasi-deterministic radio channel generator (QuaDRiGa) channel model in terms of angle difference of multi-path components over distance as well as similarity of transmit covariance matrices. This evaluation helps future work that uses the spatial consistency feature in setting their parameters.
New attacks on prime power N = prq using good approximation of φ(N)

This paper proposes three new attacks. Our first attack is based on the RSA key equation ed − kφ(N) = 1 where φ(N) = pr-1(p-1)(q-1). Let q <p <2q and 2p 3r+2/r+1 |p r-1/r+1 – q r-1/r+1| < 1/6Ny with d = Nδ. If δ < 1-y/2 we shows that k/d can be recovered among the convergents of the continued fractions expansions of e/N-2N r/r+1 + N r-1/r+1. We furthered our analysis on j prime power moduli Ni = priqi satisfying a variant of the above mentioned condition. We utilized the LLL algorithm on j prime power public keys (Ni, ei) with Ni = priqi and we were able to factorize the j prime power moduli Ni = priqi simultaneously in polynomial time.
Classification and Recognition of Medical Images Based on the SGTM Neuroparadigm

The paper discusses methods and algorithms for medical images preprocessing, their classification and recognition, which are oriented to use in machine vision systems. The structure and description of a number of software subsystems of image processing have been developed. The paper considers and analyzes the effectiveness of using methods for improving the visual quality of images as a stage of images pre-processing before classification. It is shown that image pre-processing is an effective and has significant impact on the accuracy of the images classification. The simulation of methods for improving the images’ quality showed the correspondence of the practical results with the theoretical results, confirming to the reliability of the proposed approaches and full working capacity of the developed software product. For implementation of the subsystem of medical images classification, a neuroparadigm of successive geometric transformations model is adapted.
On the Maximization of Secrecy Energy Efficiency in Full-Duplex Bidirectional System With SWIPT

This paper studies the secrecy energy efficiency (SEE) maximization problem in a full-duplex (FD) bidirectional system, where an FD base station (BS) communicates with an FD user equipment (UE) with the existence of an external user who harvests energy from the ambient radio frequency (RF) signal with SWIPT and acts as a potential eavesdropper. To balance the secrecy rate and the energy efficiency, an SEE maximization problem is formulated subject to the minimum secrecy rate, the maximum transmitted power and the minimum harvested energy. The formulated non-convex problem is solved with a two-layer optimization algorithm, where Dinkelbach method is employed to deal with the fractional programming of the outer problem and a convex approximation method based on Taylor expansion is applied to transfer the inner problem into a convex iterative program. Numerical results demonstrate the effectiveness of the proposed algorithm and the advantages of FD operation in improving SEE.
Energy-Efficient Collaborative Task Offloading in D2D-assisted Mobile Edge Computing Networks

With emerging requirement of local low-latency services, Mobile Edge Computing (MEC) is a promising solution to tackle the challenge between urgent demands for computation capability and limited battery energy of mobile devices. Moreover, the sharing property of applications costs waste as for the processing of redundant data, which derives an imperative need for the collaboration among users. In this paper, by leveraging these features, we design a D2D-assisted MEC system for energy efficiency of devices with the consideration of task delay. For sake of energy minimization, a strategy that jointly optimizes resource allocation and tasks offloading assignment is proposed. Further, a low-complexity algorithm is developed to decompose the original problem into two subproblems and get the sub-optimal solution efficiently. Simulation results present the efficient and effective performance of the proposed algorithm with different application parameters. Particularly, it is shown that our proposed algorithm gets 48.41%~90.58% and 37.33%~96.63% improvement of energy consumption than those of non-collaboratively offloading scheme and randomly offloading scheme, respectively.
Robust Beamforming Design for OTFS-NOMA

This paper considers the design of beamforming for orthogonal time frequency space modulation assisted non-orthogonal multiple access (OTFS-NOMA) networks, in which a high-mobility user is sharing the spectrum with multiple low-mobility NOMA users. In particular, the beamforming design is formulated as an optimization problem whose objective is to maximize the low-mobility NOMA users’ data rates while guaranteeing that the high-mobility user’s targeted data rate can be met. Both the cases with and without channel state information errors are considered, where low-complexity solutions are developed by applying successive convex approximation and semidefinite relaxation. Simulation results are also provided to show that the use of the proposed beamforming schemes can yield a significant performance gain over random beamforming.
Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder

The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accuracy for the star-galaxy classification task, based on manually labeled data set. In this work, we propose a novel unsupervised approach for the star-galaxy recognition task, namely Cascade Variational Auto-Encoder (CasVAE). Our empirical results show our method outperforms the baseline model in both accuracy and stability.
Multi-defect microscopy image restoration under limited data conditions

Deep learning methods are becoming widely used for restoration of defects associated with fluorescence microscopy imaging. One of the major challenges in application of such methods is the availability of training data. In this work, we pro-pose a unified method for reconstruction of multi-defect fluorescence microscopy images when training data is limited. Our approach consists of two steps: first, we perform data augmentation using Generative Adversarial Network (GAN) with conditional instance normalization (CIN); second, we train a conditional GAN(cGAN) on paired ground-truth and defected images to perform restoration. The experiments on three common types of imaging defects with different amounts of training data, show that the proposed method gives comparable results or outperforms CARE, deblurGAN and CycleGAN in restored image quality when limited data is available.
Low Complexity Sparse Channel Estimation for Wideband mmWave Systems: Multi-Stage Approach

We consider the problem of channel estimation in hybrid transceiver architectures operating in millimeter wave (mmWave) band. Due to the dynamic features of the environment and the sensitivity of mmWave bands to blockage and deafness, it is important to estimate mmWave channels with a low complexity and high performance algorithm. In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. In order to solve the estimation problem, we propose a multi-stage based low complexity algorithm. Simulation results show that the proposed algorithm significantly reduces the computational complexity while preserving the quality of the estimation.