Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Thermal Model For System-Level Electric Drivetrain Simulation With Respect To Heat Exchange Between Components

Monitoring critical temperatures in the electric drivetrain components is becoming more and more crucial for operational safety and achieving better efficiency. Instead of a distributed thermal model for each component, in this contribution a centralized compact lumped-parameter thermal network model for the electric drivetrain is set up, so that the thermal coupling between inverter, electric motor and gearbox can be considered. The measured and calibrated loss maps as well as empirical functions for losses distribution in the permanent magnet synchronous machine are used to calculate the losses of components. In the thermal modeling, a-priori system knowledge is taken into account in order to reduce parameter identification effort. A global linear parameter-varying identification approach is applied to find the parameters of the lumped-parameter thermal network model. The parametrized thermal model is cross-validated by independent experimental data on the chassis dynamometer. The maximum estimation error of circa 7 °C is achieved at the ambient temperature around 20 °C with the realistic coolant profiles for automotive scenario. The simulation results demonstrate how good the temperatures can be estimated by a centralized lumped-parameter thermal network regarding the thermal coupling between the components.
End-to-End Speech Endpoint Detection Utilizing Acoustic and Language Modeling Knowledge for Online Low-Latency Speech Recognition

Speech endpoint detection (EPD) benefits from the decoder state features (DSFs) of online automatic speech recognition (ASR) system. However, the DSFs are obtained via the ASR decoding process, which can become prohibitively expensive especially in limited-resource scenarios such as the embedded devices. To address this problem, this paper proposes a language model (LM)-based end-of-utterance (EOU) predictor, which is trained to determine the framewise probabilities of the EOU token conditioned on the previous word history obtained from the 1-best decoding hypothesis of the ASR system in an end-to-end manner without an actual decoding process in the test step. Further, a novel end-to-end EPD strategy is presented to incorporate a phonetic embedding (PE)-based acoustic modeling knowledge and the proposed EOU predictor-based language modeling knowledge into an acoustic feature embedding (AFE)-based EPD approach within the recurrent neural networks (RNN)-based EPD framework. The proposed EPD algorithm is built upon the ensemble RNNs, which are independently trained for the three parts, which are the proposed LM-based EOU predictor, AFE-based EPD, and PE-based acoustic model (AM) in accordance with each target. The ensemble RNNs are concatenated at the level of the last hidden layers and then attached into the fully-connected deep neural networks (DNN)-based EPD classifier, which is trained in accordance with the ultimate EPD target. Thereafter, they are jointly retrained at the second step of the DNN training to yield the lower endpoint error. The proposed EPD framework was evaluated in terms of the endpoint accuracy and word error rate for the CHiME-3 and large-scale ASR tasks. The experimental results turn out that the proposed EPD algorithm efficiently outperforms the conventional EPD approaches.
File Entropy Signal Analysis Combined With Wavelet Decomposition for Malware Classification

With the rapid development of the Internet, malware variants have increased exponentially, which poses a key threat to cyber security. Persistent efforts have been made to classify malware variants, but there are still many challenges, including the incapacity to deal with various malware variants belonging to similar families, the problem of time and resource consuming, etc. This paper proposes a novel method, called Malware Entropy Sequences Reflect the Family (MESRF), to improve the classification of malware based on the entropy sequences features. In prior research, entropy demonstrated good performance in many areas. First, the global features of the signals were extracted from the entropy sequences by some statistical methods. Next, some local features (i.e. structural entropy features) are extracted based on the discrete wavelet decomposition algorithm and vectorized by the Bag-of-words model, endowing it the high accuracy of malware classification. To evaluate our method, we conducted numerous experiments on the malware datasets with more than 20,000 samples. Through experiments, MESRF showed superiority comparing with other malware classification models, and the accuracy and ROC of the method even could reach 99.83% and 99.98% respectively on the malimg dataset.
A New Framework for DDoS Attack Detection and Defense in SDN Environment

While software defined network (SDN) brings more innovation to the development of future networks, it also faces a more severe threat from DDoS attacks. In order to deal with the single point of failure on SDN controller caused by DDoS attacks, we propose a framework for detection and defense of DDoS attacks in the SDN environment. Firstly, we deploy a trigger mechanism of DDoS attack detection on data plane to screen for abnormal flows in the network. Then, we use a combined machine learning algorithm based on K-Means and KNN to exploit the rate characteristics and asymmetry characteristics of the flows and to detect the suspicious flows determined by the detection trigger mechanism. Finally, the controller will take corresponding actions to defense against the attacks. In this paper, we propose a new framework of cooperative detection methods of control plane and data plane, which effectively improve the detection accuracy and efficiency, and prevent DDoS attacks on SDN.
Shallow Neural Models for Top-N Recommendation

In the ﬁeld of Information Retrieval, word embedding models have shown to be effective in several tasks. In this paper, we show how one of these neural embedding techniques can be adapted to the recommendation task. This adaptation only makes use of collaborative ﬁltering information, and the results show that it is able to produce effective recommendations efﬁciently.
Detection and Tracking on Automotive Radar Data with Deep Learning

Reliable tracking of road users plays a critical part on the way to safe automated driving. In this paper, a machine learning based tracking approach on radar data is presented utilizing the radar target point clouds from multiple time steps as input to detect road users and to predict their tracking information. The detection and tracking of objects is achieved by applying a combination of known feature extractors from lidar and camera detection tasks. The generated feature maps are used as input to two branches - one branch for detection and one for tracking. In experiments on an extensive real-world radar data set, the proposed model achieves promising results in tracking performance compared to a basic clustering and a classification assisted tracking approach.
A Stable 24 A, 100 kHz Transconductance Amplifier

A transconductance amplifier was constructed as part of a trilateral South American project to develop a reference system for measuring electric power. The amplifier can convert a 4 V signal voltage applied to its input into a ground-referenced stable current up to 24 A rms (with ±7 V rms compliance voltage) over a frequency range from dc to 100 kHz. It is based on a design conceived in the 1990s where a number of precision bipolar voltage-to-current converters are paralleled. The present design version includes Vishay Z-Foil resistors (VTA) with low TCR in all critical circuit nodes to increase the output current stability. The amplifier short-term magnitude and phase stability are respectively a few parts in 106 from de to 100 kHz and less than one microradian at power frequencies.
A novel method on discrete particle swarm optimization for fixed-outline floorplanning

The VLSI fixed-outline fioorplanning which is an important research problem of VLSI design is an NP-hard problem. Particle swarm optimization (PSO) as one of the AI methods, has been proved to be an effective global optimization method. In this paper, an improved discrete PSO combining discussion mechanism and chaos strategy is proposed for the fixed-outline fioorplanning (DCP-FF). A natural sequence representation is adopted for the particle coding, the cost function and related operations are redefined according to the characteristics of the problem. The GSRC benchmarks are employed to test the performance of DCP-FF. The results show that the proposed method is feasible and effective for the fixed-outline floorplanning.
Building the Body Image Conceptual Framework Based on the Theory of Planned Behavior (TPB)

The aim of this study is to identify structural relationships between aspects of body image and behavioral intention. The study uses the theory of planned behavior to identify the latent antecedents for individual intention to improve their body image by adopting a cross-sectional design and using questionnaires to collect data. Responses from 460 participants were analyzed using structural equation modeling (SEM) to examine the research hypotheses. The results demonstrate that internal and external factors were positively related to the belief formation mechanism, and that self-identity was positively related to body image dissatisfaction. Additionally, the belief formation mechanism influenced behavioral intention through the theory of planned behavior. These results provide some body image strategy priorities for the health and beauty industries. Moreover, it is important for the health and beauty industries to manage intentions through internal and external factors of body image, such as self-identity and the healthcare climate. Strong support was found for the proposed theoretical model. Implications for research and practice are also discussed.
An Agile Holistic Framework to Support Teachers in Pedagogical Gamification Design

The paper aims to pragmatically develop a method to support teachers to transfer their pedagogical knowledge into game requirements for educational purposes. The paper utilizes a qualitative approach, ‘think aloud’ interview protocol. The case study includes six teachers from a varied background and teaching experience in the Kingdom of Saudi Arabia (KSA). The analogy between teachers’ in-theory categorization and practical task design was used to validate the practicality of the framework components in helping teachers to identify game requirements from a pedagogical point of view. The outcome of this research identifies the factors that facilitate the collaboration between both teachers and game developers. An Agile Holistic Gamified Pedagogical Design framework(AHGPD) has been developed to provide a structured guidance for teachers regardless of their technical knowledge level. Future work will be to integrate the AH-GPD framework to produce a practical Pedagogical Game Design Document that can be used to build a repository of games requirements for educational purposes and enable faster roll out to support teachers in school environments.
A Memory RAS System Design and Engineering Practice in High Temperature Ambient Data Center

Data center infrastructure uptime increasing, unplanned downtime reduction and data integrity main-taining are increasingly critical in today’s real-time, service-level agreement (SLA)-driven cloud service business environment. Server, as backbone of cloud computing, has been developing and evolving with diverse challenges of preserving data integrity, increasing availability, minimizing planned downtime, especially in high temperature ambient (HTA) data center environment So, rock robust server system design for reliability, availability, and serviceability (RAS) are crucial for cloud service providers.Memory errors in server are among the most common hardware causes of machine crashes in production sites with large-scale systems. The higher temperature environment, the more errors. The typical response to memory failures is to replace any affected memory modules, which makes memory modules among the most commonly replaced server components. So, memory failures and their correction are very costly. Based on data collection and failure analysis from Baidu infrastructure maintenance group, server system memory (uncorrectable error) failure rate is Top 1 in data center. For reducing memory failure rate and related server downtime, Baidu developed an advanced server handling memory correctable and uncorrectable errors throughout a "6 pillars" complete application stack, from the underlying hardware to the scheduling system. Such solutions involve three components: (1) reliability, how the solution preserves data integrity; (2) availability, how it guarantees uninterrupted operation with minimal degradation; and (3) serviceability, how it simplifies proactively and reactively dealing with failed or potentially failed components. Availability is not an independent vector.This paper addresses Baidu rack server memory RAS architecture and design, scoping from Intel Xeon processor hardware errors avoidance, detection and correction RAS features for system reliability and improves fault tolerance; failure identification and reconfiguration such as leakage bucket based software-enhanced error recovery and error containment; extending to kernel level page retirement as well as high availability scheduler. Also, Memory RAS system design specific for HTA data center environment is detail introduced. Then, the related lab test procedure, data, and observations are summarized at the end of each section. Overall conclusion and future work plan are summarized in the end.
Frequency division using a soliton-injected semiconductor gain-switched frequency comb

Semiconductor gain-switched lasers and microresonator solitons work together to synthesize optical and microwave frequencies. With optical spectral marks equally spaced by a frequency in the microwave or the radio frequency domain, optical frequency combs have been used not only to synthesize optical frequencies from microwave references but also to generate ultralow-noise microwaves via optical frequency division. Here, we combine two compact frequency combs, namely, a soliton microcomb and a semiconductor gain-switched comb, to demonstrate low-noise microwave generation based on a novel frequency division technique. Using a semiconductor laser that is driven by a sinusoidal current and injection-locked to microresonator solitons, our scheme transfers the spectral purity of a dissipative soliton oscillator into the subharmonic frequencies of the microcomb repetition rate. In addition, the gain-switched comb provides dense optical spectral emissions that divide the line spacing of the soliton microcomb. With the potential to be fully integrated, the merger of the two chipscale devices may profoundly facilitate the wide application of frequency comb technology.
Genome-wide association mapping identifies QTLs and candidate genes for salt tolerance in soybean

Salinity is a common abiotic stress causing soybean yield loss worldwide. Use of tolerant cultivars is an effective and economic approach to coping with this stress. Toward this, research is needed to identify salt-tolerant germplasm and b etter understand the genetic and molecular basis of salt tolerance in soybean. The objectives of this study were to identify salt-tolerant genotypes, to search for SNPs and QTLs associated w i h salt tolerance, and to identify candidate salt tolerance genes. A total of 192 diverse soybea n lines and cultivars were screened for salt tolerance in the greenhouse based on visual leaf sc orch scores after 15 18 days of 120 ml NaCl stress, among which 94 were tolerant while 87 were sensitive. These genotypes were further genotyped using the SoySNP50K iSelect BeadChip with 52,041 single nucleotide polymorphism (SNP) markers, among which 37,281 SNPs were polymor phic with minor allele frequency (MAF) > 5% and present in 75% of all genotypes. Gen ome-wide association mapping showed that 62 SNP markers representing 6 genomic regions on Chromosomes 2, 3, 5, 6, 8, and 18, respectively were significantly associated with sal t to erance (P < 0.001). 52 SNP markers on Chromosome 3 are mapped at or near the major salt t olerance QTL previously identified in S100 (Lee et al., 2004). Three SNPs on Chromosome 18 map near the s alt tolerance QTL previously identified in Nannong1138-2 (Chen et al., 2008). The other significant SNPs represent four putative minor QTLs for salt toleran ce newly identified in this study. Ten genes, which are mapped at or near (< 35 kb) the significa nt SNPs, appear to be potential candidates involved in ion metabolisms and salt stress respons es in soybean. Gene expression analysis indicated that GmUBC2, an ubiquitin-conjugating enz yme, and GmNHX1, a vacuolar Na /H antiporter, are both up-regulated in salt-tolerant (Lee 68 and S-100) and salt-sensitive genotypes (Dare and Glenn). However, GmUBC2 expression is hig her in salt-tolerant genotypes than in salt-sensitive genotypes. As for GmNHX1, Dare exhib ited a higher level of expression than the other three genotypes. These results imply potentia l roles of GmUBC2 and GmNHX1 in conferring salt tolerance in soybean.
Characterization of the Thermal Boundary Resistance of a Ga2O3/4H-SiC Composite Wafer

The β-gallium oxide (Ga<inf>2</inf>O<inf>3</inf>) material system offers the potential to dramatically improve the electrical performance and cost-effectiveness of next-generation power electronics. This is because of its ultra-wide bandgap (~4.8 eV) and the availability of high-quality single-crystal bulk substrates. However, the low thermal conductivity of Ga<inf>2</inf>O<inf>3</inf> (11-27 W/m-K) implies that significant thermal challenges need to be overcome to commercialize Ga<inf>2</inf>O<inf>3</inf> devices. In the present work, a single crystal (010) Ga<inf>2</inf>O<inf>3</inf> wafer was integrated with a 4H-SiC substrate via fusion bonding to address this concern of poor thermal conductivity. A differential steady-state thermoreflectance method was established to measure the thermal boundary resistance at the Ga<inf>2</inf>O<inf>3</inf>/SiC interface (100 m<sup>2</sup>K/GW), which has yet to be reported due to the limited probing depth of conventional frequency- and time-domain thermoreflectance techniques.
SocialCCF: Graph-text Collaborative Cleaning Framework Based on Social Networks

Although user-generated social media data contains rich information, many methods clean a large amount of raw data and can not build high-quality datasets for analysis. Recently, some data cleaning approaches using machine learning have demonstrated that they can potentially improve the quality of datasets. However, they only consider the text data but ignoring the heterogeneous social media data, such as images. To address these issues, we propose the novel graph-text collaborative cleaning framework based on social networks named SocialCCF, which leverages an end-to-end model from heterogeneous social media to obtain the specific-topic datasets to enhance the performance of the analysis. We do the experiments on three real datasets, and the results demonstrate that our model improves the performance and outperforms the whole baselines.
Reproducibility of the Realization of the Kilogram Based on the Planck Constant by the XRCD method at NMIJ

The new definition of the kilogram based on the Planck constant was implemented on May 20 2019. Based on the new definition, the kilogram will be realized at the National Metrology Institute of Japan (NMIJ) by the X-ray crystal density method using a 28Si-enriched sphere. An international comparison of the realizations of the new definition is presently ongoing, where the volume measurement of a 28Si-enriched sphere and the sphere surface characterization required for the realization were performed by NMIJ. Details of the measurements and the uncertainty evaluation for the realization are described.
Towards an Optimal Management of the 5G Cloud-RAN through a Spatio-Temporal Prediction of Users’ Dynamics

In the emerging 5G architecture, the Cloud-Radio Access Network (Cloud-RAN) offers the possibility to dynamically configure virtual resources and network functionalities very close to end-users, while jointly considering bandwidth, computing, latency, and memory capabilities requested by heterogeneous applications, the channel quality experienced by end-users, mobility, and any kind of system constraints. By capitalizing on recent scientific results and standardization activities on 5G, this short paper presents a preliminary design of an ETSI-NFV compliant architecture willing to support the implementation of advanced protocols, algorithms, and methodologies for the optimal management of the 5G Cloud-RAN. Its components and functionalities have been sketched by harmoniously integrating Software-Defined Networking (SDN) facilities, Multi-access Edge Computing (MEC), and deep learning. Herein, spatio-temporal users’ dynamics are collected by SDN controllers and predicted by a high-level orchestrator through a Convolutional Long Short-Term Memory scheme. Then, the outcomes of the prediction process are adopted to dynamically configure the Cloud-RAN (i.e., by using any kind of customizable algorithm). Some of the capabilities of the proposed approach are preliminarily evaluated by considering the autonomous driving use case and real mobility traces. Moreover, the paper concludes by reporting an overview of future directions of this research activity.
Unconditionally secure quantum bit commitment: Revised

Bit commitment is a primitive task of many cryptographic tasks. It has been proved that the unconditionally secure quantum bit commitment is impossible from Mayers-Lo-Chau No-go theorem. A variant of quantum bit commitment requires cheat sensible for both parties. Another results shows that these no-go theorem can be evaded using the non-relativistic transmission or Minkowski causality. Our goal in this paper is to revise unconditionally secure quantum bit commitment. We (cid:12)rstly propose new quantum bit commitment using distributed settings and quantum entanglement which is used to overcome Mayers-Lo-Chau No-go Theorems. The present protocol is perfectly concealing , perfectly binding , and cheating sensible in asymptotic model against entanglement-based attack and splitting attack from quantum networks. It is then extended to commit secret bits against eavesdroppers. We further pro-pose two new applications. One is to commit qubit states. The other is to commit unitary circuits. These new schemes are useful for commit-ting several primitives including sampling models, random sources, and Boolean functions in cryptographic protocols.