Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Framework for Designing Representative Usage-Based Benchmarks for Smartphones

Smartphones dominate the consumer electronics market. Heterogeneous multicore processors are nowadays often integrated into smartphones. The power and performance evaluation of mobile multicore processors is a key issue in their design. This paper is the first to address the issue of improving the power and performance in modern, multicore smartphones by revisiting existing benchmarks used for evaluating these devices. Unlike earlier computers, modern consumer devices execute workloads that are characterized by heterogeneous concurrently running processes, which are launched at different times. Yet, modern smartphones architecture designs are still being evaluated using representative benchmark suites comprised of individual programs. This method of evaluation can be very misleading, as it does not represent the way these devices are actually being used. This paper first proposes a novel learning framework that identifies representative usage patterns of various real workloads, where each representative pattern represents a single class in a heterogeneous collection of actual usage patterns. We then use these representative patterns to optimize the power and performance of common multicore computer architectures integrated into smartphones. Our experiments show that using our representative patterns for optimization results in, on average, 11.5% and 16.7% power and performance improvements, respectively, compared to the state-of-art optimization methods.
Classification of Human Cancer Diseases Gene Expression Profiles using Genetic Algorithm by Integrating Protein Protein Interactions Along with Gene Expression Profiles

The classifications of different cancer types are most important in cancer diagnosis. The several researches are employed to diagnosis the cancer disease. In the existing research work, the classification of cancer disease is performed based on gene expression data. And it is initially presented Information Gain (IG) for Feature selection and Genetic Algorithm (GA) is used for feature reduction to remove the irrelevant features and noise data present in the given data set. Iteratively the Genetic Algorithm (GA) is classified the types of human cancer. The existing approach has some limitation to classify the cancer disease such as high computation time and required large amount of computational resource. In order to overcome this limitation, the integrated gene expression data with Protein- Protein interaction (IGEPPIN) is proposed for predicting cancer disease by prioritizing disease candidate proteins to rank each protein. In this approach initially find the beginning proteins of human and constructed the PPIN of seed proteins from human PPI data. Second, combine the function similarity of the gene expression profiles and protein to protein interactions confidence for calculating weight of each interacting protein for classification of human cancer disease. Subsequently, measure the disease relevance score for each protein by adding the interaction confidence, functional similarity of its neighbours. Finally calculated the weight of proteins in the specific disease and rank them based on descending order of their scores. The proposed system is estimated based on classification Accuracy, Precision, Recall, F- Measure and Specificity.
IQ Imbalance Robust OFDM Radar Waveform

We address the problem of IQ imbalance in OFDM radars. IQ imbalance is known to cause severe distortion in OFDM receivers, manifesting itself as interference from one side of the received spectrum to the other side and vice versa. Various methods have been designed in the wireless communications context to estimate and compensate the receiver IQ imbalance. We adopt here an approach where the radar waveform is designed to be robust against IQ imbalance. We derive an analytical criterion to achieve this robustness. We validate the effectiveness of our method with simulation results.
Optimizing Communication Costs based on the Evolved Variational Message Passing Algorithm using an Enhanced Version of Junction Tree in Wireless Networks

The Evolved Variational Message Passing algorithm (E-VMP) is a new technique for cooperative localization in Wireless Networks. It is able to determine accurate position estimates with less computational and time complexity compared to other cooperative localization algorithms. On the other hand, communication costs, efficiency power and battery life are decisive constraints for the evaluation of this technique. As a result, we propose in this paper applying this algorithm based on an enhanced version of the Junction Tree (JT) technique in order to create thin graphs. Accordingly, we can reduce communication costs engendered by the application of E-VMP algorithm based on the old JT version. This proposed approach is highly adequate in loopy networks. According to our simulation results, the proposed technique gives accurate location estimates compared to other cooperative localization techniques on the one hand and optimizes the power and battery consumption on the other hand for all values of the considered transmission radius.
Action Recognition Method Based on Sets of Time Warped ARMA Models

In this paper, we propose a novel method for recognizing human actions from sequential body skeleton data. Our method is based on ARMA (Autoregressive Mean Average) model, which is constructed from the matrix of 3D joint positions time-series. The intrinsic structure of an action can be compactly summarized by the observability matrix of the ARMA model. Since the column vectors of an observability matrix span a subspace, given two ARMA models, we can measure the similarity between them by the canonical angles between the corresponding subspaces. This framework based on subspace representation is useful for action recognition. However, it does not work well when handling various actions with different action speeds, since optimal row size of each observability matrix depends on the action speed. To address this limitation, we perform a random sampling operation to the row elements in each observability matrix, while preserving the order of the elements. By repeating this operation, we generate a set of various time-warped ARMA models with various local motion speeds. The essence of this idea is that a whole set of such time-warped ARMA models is invariant to the changes in action speed. Furthermore, to construct an effective classification framework, we applied Grassmann discriminant analysis to the time-warped ARMA models. The effectiveness of the proposed method is demonstrated through comparison experiments with state-of-the-art methods on two public datasets: MSR 3D action dataset and UT-Kinect dataset.
Multi-Channel Synthetic Aperture Radar Imaging of Ground Moving Targets Using Compressive Sensing

Integrated with the array technique, multi-channel processing can be applied to synthetic aperture radar of ground moving target imaging (SAR GMTIm), which is very powerful in remote sensing of smart city. To reduce the data sampling amounts, compressive sensing can be used by exploiting a sparse prior of moving targets. In this paper, the SAR GMTIm from data of compressive sampling is addressed by proposing a novel reweighted sparse algorithm. Here, we mainly focus on sparse imaging and clutter suppression for heterogeneous scene of urban areas. In the scheme, the phase of interferogram and the magnitude after displaced phase center antenna are incorporated to derive the weights on sparsity-constraint. Due to the joint usage of magnitude and phase, the proposed reweighted sparse algorithm can improve the performance of clutter suppression. Finally, experiments using the simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.
DTFA: A Dynamic Threshold-Based Fuzzy Approach for Power-Efficient VM Consolidation

Dynamic virtual machine (VM) consolidation is considered an effective approach for improving power consumption and computing resource utilization in cloud-based data centers. However, the ever-changing workload in a data center makes it difficult for VM consolidation to prevent service level agreement (SLA) violations and optimize power consumption. Detection of overutilized and underutilized physical machines (PMs) plays a significant role in effective VM consolidation, immediately improving resource utilization, SLA violations, and power consumption. This paper presents a new proposal for the dynamic adjustment of threshold values that aims to minimize the number of migrations in varying workload environments. The proposed approach, named the ‘dynamic threshold-based fuzzy approach’ (DTFA), is a fuzzy threshold-based approach used for adjusting the threshold values of PMs in a cloud environment. The proposed approach allows the number of migrations caused by overloading to be reduced and SLAs to be met. Three sets of experiments with different workloads were conducted to validate the proposed approach. The results demonstrate that DTFA outperforms existing solutions by an average of 22.52%, 45.63% and 56.68% in power consumption, VM migration count, and SLA violations, respectively.
Adaptive Reinforcement Learning-Based Routing Protocol for Wireless Multihop Networks

This paper presents a research on a topic of development of an adaptive packet routing scheme for wireless multihop networks, based on reinforcement learning optimization algorithm. A brief overview of classical approaches for data routing in multihop networks is provided, emphasizing main drawbacks of such algorithms, caused by ineffective hop count routing metric used in traditional multihop routing algorithms. Then, an approach based on reinforcement learning theory is presented, that has a potential to select more effective routes, relying on feedback information from neighboring nodes. An algorithm based on reinforcement learning optimization function is proposed, as well as additional functions are introduced for initial route weights distribution and dynamic route probability selection, depending from the current packet loss ratio (PLR) and receive signal strength indicator (RSSI) factors. The elaborated adaptive routing scheme then has been tested in real wireless multihop topology, where a programming implementation of the proposed algorithm - RLRP protocol, showed better routing performance characteristics in terms of PLR and RRT (Route Recovery Time), compared to a traditional improved proactive scheme of wireless multihop routing, implemented in widely used B.A.T.M.A.N. (Better Approach to Mobile Ad hoc Networking) protocol.
Minimizing Social Cost of Vaccinating Network SIS Epidemics

Reducing the economic costs (losses) as much as possible is one of the main goals of controlling virus spreading and worm propagation on complex networks. Taking into account the interactions and conflicts of interests among egoistic individuals (nodes) in a network, we introduce the zero-determinant (ZD) strategy into our proposed non-cooperative networking vaccination game with the economic incentive mechanism to optimize the social cost against a susceptible-infected-susceptible (SIS) epidemic process. It is critical to select an appropriate node as the administrator to implement the social-cost-based ZD strategy. We study the relationship between the coercive capability of the administrator and its degree in some representative networks, and find that the amount of control that the administrator can implement is negative correlated with its degree. After exploring the topological influence on the performance of the ZD strategy in vaccinating the SIS network epidemics, we find that the minimal social cost of the network increases with its average degree. We propose a coarse graining ZD strategy for a multi-community network with numerical simulations to verify the effectiveness of vaccinating the network epidemics.
Delegating Quantum Computation Using Only Hash Functions

In this paper, we construct a new scheme for delegating a large circuit family, which we call "C+P circuits". "C+P" circuits are the circuits composed of Toffoli gates and diagonal gates. Our scheme is non-interactive, only requires small quantum resources on the client side, and can be proved secure in the quantum random oracle model, without relying on additional assumptions, for example, the existence of fully homomorphic encryption. In practice the random oracle can be replaced by appropriate hash functions or symmetric key encryption schemes, for example, SHA-3, AES. This protocol allows a client to delegate the most expensive part of some quantum algorithms, for example, Shor's algorithm. The previous protocols that are powerful enough to delegate Shor's algorithm require either many rounds of interactions or the existence of FHE. The quantum resources required by the client are fewer than when it runs Shor's algorithm locally. Different from many previous protocols, our scheme is not based on quantum one time pad, but on a new encoding called "entanglement encoding". We then generalize the garbled circuit to reversible garbled circuit to allow the computation on this encoding. To prove the security of this protocol, we study key dependent message(KDM) security in the quantum random oracle model. Then as a natural generalization, we define and study quantum KDM security. KDM security was not previously studied in quantum settings.
Recurrent Neural Network Based Small-footprint Wake-up-word Speech Recognition System with a Score Calibration Method

In this paper, we propose a small-footprint wake-up-word speech recognition (WUWSR) system based on long short-term memory (LSTM) recurrent neural network, and we design a novel back-end calibration scoring method named modified zero normalization (MZN). First, LSTM is trained to predict posterior probability of context-dependent state. Next, MZN is adopted to transfer posterior probability to normalized score, which is then converted to confidence score by dynamic programming. Finally, a certain wake-up-word is recognized according to the confidence score. This WUWSR system can recognize multiple wake-up words and change wake-up words flexibly. This system can guarantee low latency by omitting decoding network. Equal error rate (EER) is adopted as the evaluation metric. Experimental results show that the proposed LSTM-based system achieves 33.33% relative improvement compared with a baseline system based on deep feed-forward neural network. Combining the front-end LSTM acoustic model with back-end MZN method, our WUWSR system can achieve 51.92% relative improvement.
Symbolic Formulas for Settling Time and Phase Margin of Op-Amp with Arbitrary Number of Poles and Zeros

Settling time (ST) and phase margin (PM) of an op-amp with an arbitrary number of poles/zeros are estimated using the approximate pole/zero symbolic expressions. The method is illustrated by finding the symbolic expressions of ST and PM for a fully differential two-stage op-amp in terms of load, feedback, and Miller capacities. In this case the closed loop voltage gain has 8 poles and 7 zeros.
Estimating Power Spectral Density of Unmanned Aerial Vehicle Rotor Noise Using Multisensory Information

A method to accurately estimate the power spectral density (PSD) of an unmanned aerial vehicle (UAV) is proposed, in anticipation of being used for a UAV-mounted audio recording system that clearly captures target sound while suppressing rotor noise. The method utilises UAV rotor characteristics as well as microphone recorded signals to combat practical limitations seen in a previous study. The proposed method was evaluated on a simulation platform modelled after the UAV used in the previous study. Results showed that the proposed method was able to estimate the rotor noise PSD to within 1.3-3.3 dB log spectral distortion (LSD) regardless of the presence of surrounding sound sources.
Child Maltreatment Forecast Using Bigdata Intelligent Approaches

Child Welfare associations collect large datasets that they are required to process to assess the risk posed to children within their living environment. Current methods for dealing with these large datasets reduce the time caseworkers are able to spend with the children assigned to their care. Within the following research work, methods for obtaining trends in child abuse and neglect datasets are outlined using self-populated datasets. The potential of Machine Learning algorithms in supporting child welfare associations is illustrated through the use of two unsupervised machine learning algorithms. The simple unsupervised learning cycle of the C4.5 algorithm together with the Apriori algorithm assist in ensuring changes in trends can be identified. The results from using Big Data intelligent algorithms indicate that child maltreatment cases can be more efficiently prioritized and handled through Big Data methods. The datasets and machine learning algorithms were run and stored on the Hippo Cluster.
Active and reactive power regulation in single-phase PV inverters

This work presents the design of a control to regulate the active and the reactive power in single-phase PV inverters. The control is composed by an inner loop with a passivity-based control in charge to track the current reference generated by the outer loop and PI controllers in the outer loop that regulate the power injection of the PV inverter. The passivity-based control ensures global asymptotic convergence of the tracking error to zero and the PV inverter shows robust current tracking with fast dynamics. Furthermore, a model of the overall control system is derived and the PI controllers parameters design guidelines which guarantee local asymptotic stability and robustness with respect to the unknown grid impedance are also provided. Numerical simulations when the power references, the PV irradiance or the grid impedance are changed validate the control performance.
Nonownership Business Models in the Manufacturing Industry: The Role of Uncertainty and the Industrial Internet of Things

In a nonownership business model (NOBM), a company maintains product ownership and sells only product performance to its customers. While NOBMs are not an entirely new phenomenon, the digital technologies—such as the Industrial Internet of Things (IIoT)—that facilitate the use of such BMs are relatively new. An increasing number of manufacturing companies are looking into offering nonownership services. However, prior research highlights that, so far, manufacturers have had mixed experiences with servitization strategies including IIoT-based services. Therefore, there is an important theoretical and practical impetus for research on how manufacturers can benefit from NOBMs, and how new technologies help manage the uncertainties associated with such BMs. Drawing on a multiple-case study with three globally operating manufacturers, our study examines the uncertainty upsides and downsides resulting from the adoption of NOBMs, as well as the role of IIoT capabilities in unlocking and mitigating these upsides and downsides, respectively. Preliminary study findings are presented, followed by a discussion of contributions and opportunities for future research.
Merging Neurons for Structure Compression of Deep Networks

Deep neural networks are increasingly used in many fields, such as pattern recognition, computer vision, and natural language processing. However, how to apply deep neural networks in mobile settings has become an urgent issue, as mobile devices are getting more and more popularity. This is mainly due to the fact that mobile devices usually have very limited computation and storage resources, which prevents from running a large-scale deep network. This paper proposes a novel method for structure compression of deep neural networks. The main idea is to merge the neurons and connections of the original network using clustering methods. To the end, the new network after compression possesses much less parameters, which leads to reduced requirements for computation and storage resources. Experiments on benchmark data sets demonstrate that the proposed method can greatly improve the efficiency of deep neural networks, while retain their learning capability.
Plant Organ Shapes Are Regulated by Protein Interactions and Associations With Microtubules

Plant organ shape is determined by the spatial-temporal expression of genes that control the direction and rate of cell division and expansion, as well as the mechanical constraints provided by the rigid cell walls and surrounding cells. Despite the importance of organ morphology during the plant life cycle, the interplay of patterning genes with these mechanical constraints and the cytoskeleton is poorly understood. Shapes of harvestable plant organs such as fruits, leaves, seeds and tubers vary dramatically among, and within crop plants. Years of selection have led to the accumulation of mutations in genes regulating organ shapes, allowing us to identify new genetic and molecular components controlling morphology as well as the interactions among the proteins. Using tomato as a model, we discuss the interaction of Ovate Family Proteins (OFPs) with a subset of TONNEAU1-recruiting motif family of proteins (TRMs) as a part of the protein network that appears to be required for interactions with the microtubules leading to coordinated multicellular growth in plants. In addition, SUN and other members of the IQD family also exert their effects on organ shape by interacting with microtubules. In this review, we aim to illuminate the probable mechanistic aspects of organ growth mediated by OFP-TRM and SUN/IQD via their interactions with the cytoskeleton.
Design and Analysis of Tower Structure for Solar Thermal Power Plant

World is witnessing the shift of global dependencies from fossil fuels to renewable resources. Solar thermal power plants are now replacing conventional power plants for the power production. There is a dire need to design new technologies for clean power generation. In this paper solar tower structure is designed for a 50MW solar thermal power plant. A review of different types of towers used in solar thermal power plant is included at the start. Design process of tower structure is started by designing a tower structure based on the height requirement obtained from ray trace analysis. Then three variants of this structure are designed using angles of different cross section. All variants are analyzed using CAE software and deformation is calculated. Plots of deformation, bending moments and shear force are included. A comparison of all these structures is carried out and the structure which shows minimum deformation is finalized.
Primary User Authentication in Cognitive Radio Network Using pre-generated hash digest

The primary objective of Cognitive Radio Networks (CRN) is to opportunistically utilize the available spectrum for efficient and seamless communication. Like all other radio networks, Cognitive Radio Network also suffers from a number of security attacks and Primary User Emulation Attack (PUEA) is vital among them. Primary user Emulation Attack not only degrades the performance of the Cognitive Radio Networks but also dissolve the objective of Cognitive Radio Network. Efficient and secure authentication of Primary Users (PU) is an only solution to mitigate Primary User Emulation Attack but most of the mechanisms designed for this are either complex or make changes to the spectrum. Here, we proposed a mechanism to authenticate Primary Users in Cognitive Radio Network which is neither complex nor make any changes to spectrum. The proposed mechanism is secure and also has improved the performance of the Cognitive Radio Network substantially.