Miracle-dz/UniFAR-FTUs huggingface dataset:
The Serverless Scheduling Problem and NOAH

The serverless scheduling problem poses a new challenge to Cloud service platform providers because it is rather a job scheduling problem than a traditional resource allocation or request load balancing problem. Traditionally, elastic cloud applications use managed virtual resource allocation and employ request load balancers to orchestrate the deployment. With serverless, the provider needs to solve both the load balancing and the allocation. This work reviews the current Apache OpenWhisk serverless event load balancing and a noncooperative game-theoretic load balancing approach for response time minimization in distributed systems. It is shown by simulation that neither performs well under high system utilization which inspired a noncooperative online allocation heuristic that allows tuning the trade-off between for response time and resource cost of each serverless function.
Recovery and the Data Processing Inequality for Quasi-Entropies

We prove a number of quantitative stability bounds for the cases of equality in Petz’s monotonicity theorem for quasi-relative entropies <inline-formula> <tex-math notation="LaTeX">$S_{f}(\rho ||\sigma)$ </tex-math></inline-formula> defined in terms of an operator monotone decreasing functions <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> and, in particular, the Rényi relative entropies. Included in our results are bounds in terms of the Petz recovery map, but we obtain more general results. The present treatment is entirely elementary and developed in the context of finite dimensional von Neumann algebras, where the results are already non-trivial and of interest in quantum information theory.
Cooperative Motion Generation in a Distributed Network of Redundant Robot Manipulators With Noises

In this paper, a distributed scheme is proposed for the cooperative motion generation in a distributed network of multiple redundant manipulators. The proposed scheme can simultaneously achieve the specified primary task to reach global cooperation under limited communications among manipulators and optimality in terms of a specified optimization index of redundant robot manipulators. The proposed distributed scheme is reformulated as a quadratic program (QP). To inherently suppress noises originating from communication interferences or computational errors, a noise-tolerant zeroing neural network (NTZNN) is constructed to solve the QP problem online. Then, theoretical analyses show that, without noise, the proposed distributed scheme is able to execute a given task with exponentially convergent position errors. Moreover, in the presence of noise, the proposed distributed scheme with the aid of NTZNN model has a satisfactory performance. Furthermore, simulations and comparisons based on PUMA560 redundant robot manipulators substantiate the effectiveness and accuracy of the proposed distributed scheme with the aid of NTZNN model.
Sign Language Production using Neural Machine Translation and Generative Adversarial Networks

We present a novel approach to automatic Sign Language Production using stateof- the-art Neural Machine Translation (NMT) and Image Generation techniques. Our system is capable of producing sign videos from spoken language sentences. Contrary to current approaches that are dependent on heavily annotated data, our approach requires minimal gloss and skeletal level annotations for training. We achieve this by breaking down the task into dedicated sub-processes. We first translate spoken language sentences into sign gloss sequences using an encoder-decoder network. We then find a data driven mapping between glosses and skeletal sequences. We use the resulting pose information to condition a generative model that produces sign language video sequences. We evaluate our approach on the recently released PHOENIX14T Sign Language Translation dataset. We set a baseline for text-to-gloss translation, reporting a BLEU-4 score of 16.34/15.26 on dev/test sets. We further demonstrate the video generation capabilities of our approach by sharing qualitative results of generated sign sequences given their skeletal correspondence.
Adaptive Permutation Invariant Training with Auxiliary Information for Monaural Multi-Talker Speech Recognition

In this paper, we extend our previous work on direct recognition of single-channel multi-talker mixed speech using permutation invariant training (PIT). We propose to adapt the PIT models with auxiliary features such as pitch and i-vector, and to exploit the gender information with multi-task learning which jointly optimizes for the speech recognition and speaker-pair prediction. We also compare CNN-BLSTMs against BLSTM-RNNs used in our previous PIT-ASR model. The experimental results on the artificially mixed two-talker AMI data indicate that our proposed model improvements can reduce word error rate (WER) by ~ 10.0% relative to our previous work for both speakers in the mixed speech. Our results also confirm that PIT can be easily combined with advanced techniques to improve the performance on multi-talker speech recognition.
Multichannel Kalman Filtering for Speech Ehnancement

The use of spatial information in multichannel speech enhancement methods is well established but information associated with the temporal evolution of speech is less commonly exploited. Speech signals can be modelled using an autoregressive process in the time-frequency modulation domain, and Kalman filtering based speech enhancement algorithms have been developed for single-channel processing. In this paper, a multichannel Kalman filter (MKF) for speech enhancement is derived that jointly considers the multichannel spatial information and the temporal correlations of speech. We model the temporal evolution of speech in the modulation domain and, by incorporating the spatial information, an optimal MKF gain is derived in the short-time Fourier transform domain. We also show that the proposed MKF becomes a conventional multichannel Wiener filter if the temporal information is discarded. Experiments using the signals generated from a public head-related impulse response database demonstrate the effectiveness of the proposed method in comparison to other techniques.
Deficiency of the Endocytic Protein Hip1 Leads to Decreased Gdpd3 Expression, Low Phosphocholine, and Kypholordosis

Deficiency of huntingtin-interacting protein 1 (Hip1) results in degenerative phenotypes. Here we generated a Hip1 deficiency allele where a floxed transcriptional stop cassette and a human HIP1 cDNA were knocked into intron 1 of the mouse Hip1 locus. ABSTRACT Deficiency of huntingtin-interacting protein 1 (Hip1) results in degenerative phenotypes. Here we generated a Hip1 deficiency allele where a floxed transcriptional stop cassette and a human HIP1 cDNA were knocked into intron 1 of the mouse Hip1 locus. CMV-Cre-mediated germ line excision of the stop cassette resulted in expression of HIP1 and rescue of the Hip1 knockout phenotype. Mx1-Cre-mediated excision led to HIP1 expression in spleen, kidney and liver, and also rescued the phenotype. In contrast, hGFAP-Cre-mediated, brain-specific HIP1 expression did not rescue the phenotype. Metabolomics and microarrays of several Hip1 knockout tissues identified low phosphocholine (PC) levels and low glycerophosphodiester phosphodiesterase domain containing 3 (Gdpd3) gene expression. Since Gdpd3 has lysophospholipase D activity that results in the formation of choline, a precursor of PC, Gdpd3 downregulation could lead to the low PC levels. To test whether Gdpd3 contributes to the Hip1 deficiency phenotype, we generated Gdpd3 knockout mice. Double knockout of Gdpd3 and Hip1 worsened the Hip1 phenotype. This suggests that Gdpd3 compensates for Hip1 loss. More-detailed knowledge of how Hip1 deficiency leads to low PC will improve our understanding of HIP1 in choline metabolism in normal and disease states.
Software-Defined and Fog-Computing-Based Next Generation Vehicular Networks

Vehicular networks have attracted much attention from both industry and academia. Due to the high-speed and complex network topology of vehicles, vehicular networks have become extremely challenging. The development of mobile communication technologies, such as SDN and FC, provides a great platform for vehicular networks. SDN separates the software plane and data plane, which enables efficient management and centralized control of vehicular networks. FC is an extension of cloud computing. By pushing significant storage, control, management, and communication mechanisms onto the network edge or user equipment, FC alleviates the pressure of the core network. Accordingly, we consider a novel SDFC-VeNET architecture in this article. Based on the SDFC-VeNET architecture, mobility management and resource allocation are discussed. Simulation results show the superiority of the presented SDFC-VeNET architecture, as well as the associated handover scheme and resource allocation mechanism. Furthermore, the existing challenges and open issues are discussed.
On the Construction of Generator Matrices for Relaxed Polar Codes

Relaxed polar codes can reduce the complexities of the encoding and decoding of original polar codes by relaxing the polarization of some sub-channels which are polarized to be sufficiently good or sufficiently bad in the process of polarization. In this paper, we first consider the construction of relaxed polar codes to compute the error probabilities of sub-channels by using Gaussian approximation, and then propose a construction algorithm of generator matrices for relaxed polar codes. Simulations show that the indices of the information sub-channels chosen using the proposed method for relaxed polar codes are almost in accordance with that of the information sub-channels using the method of Gaussian approximation (GA) for full polar codes. And also, relaxed polar codes constructed by using the proposed algorithm can obtain almost the same performance as original polar codes under the reduced encoding and decoding complexities.
Goal-Capability-Commitment based Mediation for Multi-Agent Collaboration

Multi-agent collaboration in the agent oriented Collective Adaptive Systems could offset the failure of goal achievement by an independent agent. Commitments among agents to meld collaborative patterns typically defined in design time, which may cannot service the request of ACSs in response to changes in the system itself or in its environment. To address this problem, we propose a Goal-Capability-Commitment (GCC) based multi-agent collaboration mediation for ACSs at run-time. There are two issues with this state of the art approach: (1) the ability to represent the capability of agent under different contexts and diagnose the invalids of goals and agents leaded by the changing environments, and (2) the ability of mediation to select an appropriate capability or generate a new commitment in order to satisfy adaptive goal. We illustrate our approach through two experiments over the simulated scenario of automated hospital cart transportation system and discuss the results of experiments.
Fast Variational Level Set Based Image Segmentation via Two-Scale Filtering Model

One major difficulty in medical image segmentation is intensity inhomogeneity, which manifests itself with a slow intensity variation over the whole image domain. Recently, a local binary fitting (LBF) model has been proposed to solve this problem within level set segmentation framework. However, the LBF model has two main problems, i.e., high computational cost and sensitivity to initialization. By analyzing the LBF model, we find that the most computational part is the calculation of two cluster images, which need to be updated in each iteration during the evolution of level set function. With this observation in mind, we propose a novel two-scale filtering (TSF) model, in which the two cluster images can be pre-calculated before evolution. Additionally, we implicitly utilize order constraint to restrict the order of two cluster images. As a result, the proposed TSF model is less sensitive to initialization. Extensive experiments on real medical images illustrate the desirable performances, as compared with the state-of-the-art models.
CBLDNN-Based Speaker-Independent Speech Separation Via Generative Adversarial Training

In this paper, we propose a speaker-independent multi-speaker monaural speech separation system (CBLDNN-GAT) based on convolutional, bidirectional long short-term memory, deep feedforward neural network (CBLDNN) with generative adversarial training (GAT). Our system aims at obtaining better speech quality instead of only minimizing a mean square error (MSE). In the initial phase, we utilize log-mel filterbank and pitch features to warm up our CBLDNN in a multi-task manner. Thus, the information that contributes to separating speech and improving speech quality is integrated into the model. We execute GAT throughout the training, which makes the separated speech indistinguishable from the real one. We evaluate CBLDNN-GAT on WSJ0-2mix dataset. The experimental results show that the proposed model achieves 11.0d-B signal-to-distortion ratio (SDR) improvement, which is the new state-of-the-art result.
Multi-Scale Object Detection with Feature Fusion and Region Objectness Network

Though tremendous progresses have been made in object detection due to the deep convolutional networks, one of the remaining challenges is the multi-scale object detection(MOD). To improve the performance of MOD task, we take Faster region-based CNN (Faster R-CNN) framework and work on two specific problems: get more accurate localization for small objects and eliminate background region proposals, when there are many small objects exist. Specifically, a feature fusion module is introduced which jointly utilize the high-abstracted semantic knowledge captured in higher layer and details information captured in the lower layer to generate a fine resolution feature maps. As a result, the small objects can be localized more accurately. Besides, a novel Region Objectness Network is developed for generating effective proposals which are more likely to cover the target objects. Extensive experiments have been conducted over UA-DETRAC car datasets, as well as a self-built bird dataset (BSBDV 2017) collected from Shenzhen Bay coastal wetland, which demonstrate the competitive performance and the comparable detection speed of our proposed method.
X-Sources Analysis for Improving the Test Quality

Achieving very high test coverage (e.g. 99.9% stuck-at fault model) is becoming a standard for ICs used in the high reliable systems like automotive vehicle. X-sources (unknown value sources) are one of the common root causes preventing designs from achieving the test coverage goal. The paper first summarizes common X-sources in industry designs. A novel approach is then proposed to systematically identify and analyze all of the X-sources that impacts the test coverage most with accurate estimation by utilizing the Automatic Test Pattern Generator (ATPG). Consequently, users are able to take the analysis result and make necessary and minimum changes to eliminate Xs to achieve the test quality goal effectively.
Induction of apoptosis and G1 phase cell cycle arrest by Aster incisus in AGS gastric adenocarcinoma cells.

In recent decades, various bioactive compounds from plants have been investigated for their potential use in the treatment of diseases in humans. Aster incisus extract (AIE) is the extract of a common plant that is mostly found in Asia. It has traditionally been used for medicinal purposes in South Korea. In this study, we evaluated the potential anticancer effects of a methanolic extract of Aster incisus in a normal human cell line (HaCaT keratinocytes) and in 4 different types of human cancer cell lines (A549, lung cancer; Hep3B, liver cancer; MDA‑MB‑231, breast cancer; and AGS, gastric cancer). The HaCaT, A549, Hep3B, MDA‑MB‑231 and AGS cells were treated with various concentrations of AIE and following treatment, cell survival was evaluated. Additional analyses, such as WST-1 assay, western blot analysis, DAPI staining, flow cytometry, immunofluorescence staining and wound healing assay were performed to elucidate the mechanisms and pathways involved in the cell death induced by AIE. Treatment with AIE induced morphological changes and considerably reduced the viability of the both normal and cancer cell lines. Further analysis of the AGS gastric cancer cells revealed that AIE led to the induction of apoptosis and a high accumulation of cells in the G1 cell phase following treatment with AIE in a dose-dependent manner. The results also revealed that AIE successfully suppressed the migration of the AIE-treated AGS cells. The results of western blot analysis indicated that AIE increased the expression of pro-apoptotic proteins, particularly Bid, Bad, Bak, cytochrome c, apoptosis inducing factor (AIF), cleaved caspase‑3, -8 and -9 and cleaved poly(ADP-ribose) polymerase (PARP). Additionally, AIE decreased the expression of the anti-apoptotic proteins, Bcl-2 and Bcl-xL. On the whole, the findings of this study demonstrate that AIE induces apoptosis through the activation of the caspase‑dependent pathway mediated by the mitochondrial pathway and by arresting the cell cycle in AGS cells.
Understanding How the Ad Hoc use of Big Data Analytics Impacts Agility: A Sensemaking-Based Model

As business environments become increasingly complex and turbulent, organizations are required to be more agile. Use of big data analytics (BDA) can be a differentiator for organizations seeking to improve agility to quickly sense and respond to novel and complex events. Usage of BDA comprises two types: the routine use and the ad hoc use. The latter is more associated with the unplanned analysis of big data to understand unexpected events, and its effects have not been studied in distinction to the former in the analytics literature. We draw on sensemaking, the organizational theory of the process of understanding novel and complex events, to investigate how the ad hoc use of BDA improves agility of organizations. Analysis of a survey of 107 business executives and senior managers demonstrated the positive effects of the ad hoc use of BDA on agility, through mediation by sensemaking.
Attention-Based Bi-LSTM Model for Anomalous HTTP Traffic Detection

Recently, cyber-attacks with complex types have occurred more frequently than before, while communication traffic provides a clue to probe anomalous network behaviors. Therefore, how to detect malicious network attacks from large scale communication traffic in a timely manner and grasp their attack characteristics is a major challenge for website security. Since the content of the network traffic complies with strict writing specifications and structural standards, it is usually modeled and analyzed as natural language. In this paper, we propose a deep neural network model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. The application of attention mechanism can assist in detecting anomalous traffic and discovering critical parts of anomalous traffic. Extensive experiments over large traffic data have illustrated that the proposed model has outstanding performance in malicious HTTP traffic detection.
Says Who? Deep Learning Models for Joint Speech Recognition, Segmentation and Diarization

The field of speech recognition has seen tremendous advances in the recent past owing to the development of powerful deep learning architectures. However, the closely related fields of speech segmentation and di-arization are still primarily dominated by sophisticated variants of hierarchical clustering algorithms. We propose a powerful adaptation of the state-of-the-art Speech Recognition models for these tasks and demonstrate the effectiveness of our techniques on standard datasets. Our architectures are a combination of Bidirectional Long Short Term Memory (LSTM) Networks, Convolutional Networks, and Fully Connected Networks, trained by Gradient Descent to minimize the Cross Entropy and the Connectionist Temporal Classification (CTC) losses. We adapt the Libri Speech corpus for the task of segmentation and diarization. We obtained comparable results with respect to state-of-the-art in both tasks.
Bit Error Rate Performance Analysis of Different Diversity Techniques for various Channels and also modeling the channel effect

Signal fading is a large problem in the wireless communication system. In wireless communication the requirements of high data rate, high signal power, and link reliability call a new technique i.e. MIMO system which satisfies the above requirements and minimize signal fading. MIMO system uses multiple antennas at both transmitter and receiver. MIMO system achieves slight capacity gain and diversity gain than conventional single antenna system. In this paper we have constructed four different MIMO channels of different sample time, different frequency, different path delay and different path gain. we also compare the BER for different transmit diversity vs. receive diversity technique for this four MIMO channels. We also model the channel effect i.e. how the signal fading is randomly changed when an input signal passes through different constructed MIMO channels. The MIMO channel is the frequency selective Rayleigh channel. We also conclude for which channel and diversity technique BER is minimum. Here we have used BPSK digital modulation technique. For simulation we have used MAT LAB 2009b software.
Mapping and Analyzing Process of Cloud-based Education as a Service (CEaaS) Model for Cloud Computing Adoption in Higher Education Institutions

A newly evolved computing paradigm used to deliver everything-as-a-service (XaaS) for various business organizations is known as Cloud Computing (CC). It is used most especially by educational organizations for educational purposes anywhere and anytime via scalability and an approach that saves cost (pay-per-use). There are a number of problems faced by Higher Education Institutions (HEIs) such as the growing need of infrastructure and IT, providing good and affordable educational services, increasing of participants, increasing education quality provision. Cloud Computing (CC) provides an excellent substitute for HEIs by supporting a reduction in cost, improvement in quality, through this means sustainability in education is obtained by providing good educational services. Recent research patterns on CC are directly focused on the applications, benefits, costs, technology, and security of CC. However, it was noticed that less empirical evidence and research focus is been given to the adoption of CC at an organization level. A model is proposed with a mapping matrix of its constructs based on the four dominant theories in the field of this adopted technology, the theories are: Technology-organization-environment (TOE) theory, The Fit-Viability Model (FVM), Diffusion of Innovation (DOI) Theory, and Institutional (INT) theory. Implications, potential contributions to research, and suggestions for future study are discussed.