Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Thermal Management for Heterogeneous Multicores

Heterogeneous multicores have attracted a major focus in recent years, as they provide many possibilities for performance improvements. However, due to the discontinuation of Dennard scaling, on-chip power densities are continuously increasing along with technology scaling, and hence on-chip temperatures are elevated. Therefore, several thermal management techniques have emerged to keep the temperature of the chip within safe limits. These techniques, however, lead to performance losses which ultimately erase a big portion of the expected performance gains from the heterogeneous multicores. Thus, it is indispensable to deploy thermal management techniques that are able to make efficient decisions which satisfy temperature constraints while at the same time maximizing the performance. This paper presents smart thermal management techniques for heterogeneous multicores that exploit relevant information about several heterogeneity parameters at the chip level and at the application level to increase thermal efficiency1. Compared to the state of the art, the presented techniques are able to obtain significant performance improvements under the same thermal constraint.This paper is part of the DATE 2019 special session on "Smart Resource Management and Design Space Exploration for Heterogeneous Processors". The other two papers of this special session are [1] and [2].
Where You Really Are: User Trip Based City Functional Zone Ascertainment

As one convenient and green transportation mode, Public Bike System (PBS) offers increasing service. It has generated a great mass of transit records as a famous application around the world. To mine the trip information in the records is an effective way to understand the city from some aspects such as the city functional zone distribution and the crowd mobility. According to the observation from daily life, it's quite often found that some users are willing to shift their bikes at some intermedia stations so as to decrease charge. The phenomenon indicates that the drop-off stations in the system records may not be their real destinations. Focusing on this phenomenon, this paper proposes a new method named User trip based city functional Zone Ascertainment (UZA) to redefine the city functional zone. This paper divides the city into adjacent and non-overlapping regions by the Voronoi Diagram method, and discovers the topic feature distribution of each region based on the user trips. By clustering, we then integrate regions into zones and finally ascertain their functions. Extensive experiments are conducted on the real dataset collected from Hangzhou PBS, consisting of more than 23 million transit records. Compared with the Hangzhou city planning graph, the result indicates our method is reliable and effective.
Carry-on State Service Handover between Edge Hosts for Latency Strict Applications in Mobile Networks

Edge computing brings computing and storage resources to the edge of the network, enabling cloud computing capabilities and an IT service environment with low latency communication. It is also expected to extend the capabilities of low-capability user equipment (UE) and also reduce power consumption on the UE by offloading computing tasks to an edge computing host server. This form of edge computing is called Multi-access Edge Computing (MEC) and is considered as one of the key technologies for fifth generation mobile communication [1]. When applying MEC to a mobile communication network, the UE’s mobility when providing a requested service from an appropriate MEC host (MEH) needs to be considered. There are several studies on service migration to a more appropriate MEH as the UE moves through the mobile network. However, it requires some time to migrate the service between two MEHs. And whether those ideas can be applied to applications with strict latency requirements has not been investigated. Therefore, this paper analyzes the characteristics of service migration and identifies the problems associated with it. Moreover, we propose a potentially alternative way to resume the ongoing service at a new appropriate MEH during UE movement by having the UE carry the service state, and present the advantages of this approach.
Distinguishing Drone Types Based on Acoustic Wave by IoT Device

A drone detection system is essential to protect people from misuses, infringement on privacy, and other harms by drones. In this paper, we investigate to distinguish drone types based on acoustic wave. Our proposed drone detection technique employs Dejavu that is an acoustic fingerprint identification tool and random forest to classify types of drones. In the study, we first collected a dataset for three types of drones: DJI Spark, Quadcopter AR Drone 2.0, and Parrot Mambo drone. Then, we compared the detection performance based on the dataset. The experimental results showed that each drone sound can be classified by random forest.
Paraphrase Identification for Twitter by Co-Training Based on Words and Characters

Paraphrase Identification is a critical problem for many NLP tasks such as question answering, information extraction, machine translation, and others. Recently, neural network models have achieved state-of-the-art results on several textual similarity measurement datasets. While training neural network models requires a large amount of labeled data which is costly and time consuming, unlabeled data is relatively easy to collect from a social media such as Twitter. Co-training is a semi-supervised learning method to exploit the unlabeled data by partitioning a feature set into two conditionally independent views. In this paper, we assume that a combination of word-level representation and character-level representation is effective in identifying paraphrases by capturing lexical and superficial features respectively, and investigate the application of co-training on neural network paraphrase identification models by using both word-level and character-level information. We evaluate the co-training method on Twitter URL Paraphrase Corpus. As a result, the experiments demonstrated that our approach achieved a robust performance in comparison to simple self-training algorithm.
Optimal Fast Charging Station Location for Public Electric Transportation in Smart Power Distribution Network

Electric vehicle (EV) become increasingly active in public transportation system. The battery and the charging system to keep it running are essential. For tech-economic benefits on the installation of the charging station, the location design is an important part. Include management to make it appropriate for the radial distribution system. This article presents an analysis to determine the optimal charging station for the radial distribution system. By simulating the charging station according to IEEE 33 bus standards. The Artificial Bee Colony (ABC) algorithm is used to find the location of the best charging station for use with the radial distribution system. The results show that the proposed method finds the optimal charging station for EV. It can reduce the cost of creating additional locations including a suitable supply of the electrical system and it can also be applied to transportation systems within the city or between cities.
The glass ceiling in Irish healthcare: a nursing perspective.

PURPOSE The purpose of this study is to describe and explore from the perspectives of top-level nurses holding the formal position of director of nursing their perceptions and interpretations of their experiences in the Irish healthcare system. DESIGN/METHODOLOGY/APPROACH This paper presents some findings from a nation-wide study, which is qualitative in approach using grounded theory methodology. Semi-structured interviews were conducted with 50 directors of nursing representing general and psychiatric nursing. FINDINGS The study in this paper was conducted in the context of a changing healthcare system, which emphasises the flattening of organisational pyramids. However, this study's findings indicate that, while structures might be changing, behaviours remain unaltered. In particular, the medicine nursing power base remains unchanged, while the power shift between nursing and general management continues to widen in favour of general management. RESEARCH LIMITATIONS/IMPLICATIONS Research in this paper shows that directors of nursing who chose to participate in this study may have different perceptions of experiences from those who chose not to participate. ORIGINALITY/VALUE In the paper the reality is neither medicine nor general management, individually or collectively, are going to share or devolve power and influence to nursing. This study's findings indicate that nursing needs to confront this power imbalance. Nursing needs to take the first steps towards shattering the glass ceiling by really examining its own behaviours, in maintaining the status quo, in the traditional balance of power.
Neuronal IGF‐1 resistance reduces Aβ accumulation and protects against premature death in a model of Alzheimer's disease

Alzheimer's disease (AD) is characterized by progressive neurodegeneration leading to loss of cognitive abilities and ultimately to death. Postmortem investigations revealed decreased expression of cerebral insulin‐like growth factor (IGF)‐1 receptor (IGF‐1R) and insulin receptor substrate (IRS) proteins in patients with AD. To elucidate the role of insulin/IGF‐1 signaling in AD, we crossed mice expressing the Swedish mutation of amyloid precursor protein (APPSW, Tg2576 mice) as a model for AD with mice deficient for either IRS‐2, neuronal IGF‐1R (nIGF‐1R−/−), or neuronal insulin receptor (nIR−/−), and analyzed survival, glucose, and APP metabolism. In the present study, we show that IRS‐2 deficiency in Tg2576 mice completely reverses premature mortality in Tg2576 females and delays β‐amyloid (Aβ) accumulation. Analysis of APP metabolism suggested that delayed Aβ accumulation resulted from decreased APP processing. To delineate the upstream signal responsible for IRS‐2‐mediated disease protection, we analyzed mice with nIGF‐1R or nIR deficiency predominantly in the hippocampus. Interestingly, both male and female nIGF‐1R−/−Tg2576 mice were protected from premature death in the presence of decreased Aβ accumulation specifically in the hippocampus formation. However, neuronal IR deletion had no influence on lethality of Tg2576 mice. Thus, impaired IGF‐1/IRS‐2 signaling prevents premature death and delays amyloid accumulation in a model of AD. —Freude, S. , Hettich, M. M. , Schumann, C. , Stohr, O. , Koch, L. , Kohler, C. , Udelhoven, M. , Leeser, U. , Müller, M. , Kubota, N. , Kadowaki, T. , Krone, W. , Schroder, H. , Bruning, J. C. , Schubert, M. Neuronal IGF‐1 resistance reduces Aβ accumulation and protects against premature death in a model of Alzheimer's disease. FASEB J. 23, 3315–3324 (2009). www.fasebj. org
Enhanced IIP2 Chopper Stabilized Direct Conversion Mixer Architecture

This paper presents an improved Second Order Input Intercept Point (IIP2) Direct Conversion Mixer architecture by introducing chopping in the standard active double balanced mixer. The mixing frequency required is half of the RF frequency. This technique improves the second order linearity of the mixer. There is only one active mixer in the signal path enabling low power design. Simulation results for the enhanced mixer are compared with a standard active double balanced mixer. An IIP2 improvement of around 8 dB is demonstrated.
Design and Application of Fog Computing Model Based on Big Data

Fog computing based on big data is a hot topic in the research of computing technology at home and abroad. With the wide application and popularity of IoT (Internet of Things), the big data generated by edge devices is exploding, and cloud computing models are becoming increasingly inadequate to meet the needs of big data processing and communication, which is mainly manifested as follows. Slow data processing, insufficient storage space, prolonged communication and many other issues. Fog computing, of which the advantage is distributed computing, namely the „de-centralized„ mode calculation, is the suitable solution to solve these problems. In the IoT system, fog computing model based on big data is constructed to distribute the big data computing, storage and communication in the system to the edge device. The purpose is to make the system structure simpler, more modular and intelligent, duce network congestion, exploit advantages of edge devices and improve high quality intelligence of IoT applications, and moreover, to reduce the deployment of IoT hardware and operating costs. Taking the cloud robotics as an example, it is proposed to embed the fog computing technology in the cloud robotics system, which greatly improves the computing function of the cloud robotics system. In short, it provides theoretical support and scientific experimental basis for the informationization and intelligence of all walks of life, and its research has certain value and significance.
Review of Signature-based Techniques in Antivirus Products

Existing antivirus products employ diverse types of techniques to detect malware or any suspicious activities. The majority of such techniques rely on signature-based detection algorithms. However, the speed of such detection algorithms may adversely impact the performance of the antivirus products (e.g., if used for online virus scanning). In this paper, we review existing research that proposed fast and efficient signature-based algorithms to dynamically improve the time and accuracy of virus detection. We classify and discuss the different algorithms according to the type of analysis they perform (i.e., static, dynamic or hybrid). In addition, we evaluate existing virus detection algorithms using different design issues and performance criteria, namely (a) memory cost, (b) time complexity, and (c) detection rates. In addition, we discuss how certain design choices of signature-based approaches can only apply to specific virus detection circumstances. Finally, we present the current research challenges of using signature-based algorithms for investigating cybercrime activities.
LR-LRU: A PACS-Oriented Intelligent Cache Replacement Policy

An intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the logistic regression (LR) algorithm with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called LR-LRU. The LR-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented LR-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The LR algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. In addition, <inline-formula> <tex-math notation="LaTeX">$\ell 1$ </tex-math></inline-formula>-regularization was used to reduce the absolute values of the variables’ coefficients. This screens some variables that have little influence on the model by causing their coefficients to approach zero, which achieves the effect of screening the variables. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the <inline-formula> <tex-math notation="LaTeX">$\ell 1$ </tex-math></inline-formula>-regularized LR model is superior to the LR and <inline-formula> <tex-math notation="LaTeX">$\ell 2$ </tex-math></inline-formula>-regularized LR models. The LR-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies, such as LRU, LFU, SIZE, GDF, and GDSF.
A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index

The ‘integrated discrimination improvement’ (IDI) and the ‘net reclassification index’ (NRI) are statistics proposed as measures of the incremental prognostic impact that a new biomarker will have when added to an existing prediction model for a binary outcome. By design, both measures were meant to be intuitively appropriate, and the IDI and NRI formulae do look intuitively plausible. Both have become increasingly popular. We shall argue, however, that their use is not always safe. If IDI and NRI are used to measure gain in prediction performance, then poorly calibrated models may appear advantageous, and in a simulation study, even the model that actually generates the data (and hence is the best possible model) can be improved on without adding measured information. We illustrate these shortcomings in actual cancer data as well as by Monte Carlo simulations. In these examples, we contrast IDI and NRI with the area under ROC and the Brier score. Unlike IDI and NRI, these traditional measures have the characteristic that prognostic performance cannot be accidentally or deliberately inflated. Copyright © 2013 John Wiley & Sons, Ltd.
Improving Performance of a Path-Based Equivalence Checker Using Counter-Examples

Path-based equivalence checkers (PBECs) have been successfully applied for verification of programs from diverse domains and at various stages of high-level synthesis. These verifiers can be sound but not complete. Therefore, non-equivalence cases require further investigation of the two programs being compared by some human expert. In this work, we show how a counter-trace (cTrace) can be generated in the case of non-equivalence reported by the PBEC. We show how a Bounded Model Checker (CBMC) can be used to find suitable initialization values for input variables (i.e., a counter-example) for a given cTrace. With our counter-example generation framework, we show how a strong non-equivalence decision can be taken in a PBEC. We also show that some false negative cases of the PBEC can also be revealed using this framework. Experimental results demonstrate the usefulness of our method.
Unsupervised Music Segmentation with the Genetic Algorithm

Constructing reasonable structures of sequences is one of the most important problems. Especially, artificial sequences such as music and languages have some potential structures as human beings tend to build their constructions in creating. Although a recurrent neural network (RNN) has shown remarkable performance in processing sequences, it apparently cannot handle particularly long structures. This paper proposes a novel approach for building structures of sequences based on a genetic algorithm (GA) and a hierarchical RNN. The evaluation of the GA focuses on the negative log-likelihoods when the hierarchical RNN predicts the next elements. Thus, our proposal can be used for different types of sequences. In the experiments, musical notes are applied to this method, so the GA exhibits great structures minimizing the negative log-likelihoods and the hierarchical RNN shows better performance than stacked long short-term memory as a baseline.
Research on the Influence of Cultural Differences between China and Japan on Employee Behavior Based on Hofstede Theory

China has maintained a close association with Japan in some aspects since ancient times, including politics, economy and culture. Thus, there are some similarities between the two countries in the cultures of leadership, and some significant differences cannot be ignored. The essay will analyze different characteristics of leadership between China and Japan supporting by Hofstedes' dimensions. Additionally, how to communicate effectively and acceptably with colleagues in an emergency during a project is also considered.
Inverse Design of Three-Dimensional Nanoantennas for Metasurface Applications

Recent advances in manufacturing techniques have been made to match the demand for high performance optical devices. To this end, tremendous research activity has been focused on optical metasurfaces as they offer a unique potential to achieve disruptive designs when paired with innovative fabrication techniques and inverse design tools. However, most metasurface designs have revolved around canonical geometries. While these elements are relatively easy to fabricate, they represent only a small portion of the design space, and rarely offer peak performance in transmission, phase range or field of view. In this work, a Lazy Ant Colony Optimization (LACO) technique is applied in conjunction with a full-wave solver using the Periodic Finite Element Boundary Integral (PFEBI) method to reveal high performing three-dimensional nanoantenna designs with potential applications for a variety of optical devices.
Design and Implementation of Battery Management System for On-Grid System

Battery Management System is an electronic system that manages a rechargeable battery cell or battery pack. The main purpose of a BMS is protecting the battery from operating outside its safe operating area by monitoring its state, calculating secondary data, reporting the data, controlling its environment, authenticating or balancing it. In this research, the authors aim to create a battery management system prototype that will supervise batteries charging using solar panel. Parameters that are monitored by BMS are voltage, current, and temperature. The BMS also has an additional feature which is on-grid system. This feature allows the BMS to maximize the energy usage produced by solar panel. The BMS is created using microcontroller, sensors, and other components including Arduino Mega, temperature sensor, voltage sensor, current sensor, real time clock, relay, multiplexer, and dc fan. The BMS is applied to light street lamp everyday from 06.00 am to 06.00 pm. This BMS has three modes which are charging, discharging, and on-grid mode. Charging mode is when the BMS is used to monitor the battery charging process which last for 5 hours (10.00 am to 03.00 pm). Discharging mode is when the batteries is use to light up the street lamp. Ongrid mode is when the solar panel still able to produce energy beside charging and discharging time.
16-Bit Low Power Feedforward Architecture Sigma-Delta ADC with Integrator Time Multiplexing for Microprocessors

This study presents a design of low power 3rd order 16-bit Sigma Delta Analog to Digital Converter in Feedforward Architecture with Integrator Time Multiplexing Technique implemented in TSMC 65nm CMOS technology. The designed is composed of a 3rd-order modulator in feedforward architecture to improve loop stability and with integrator time multiplexing technique for reduction of power consumption. The Analog to Digital Converter (ADC) is designed to consume a total power of 0.342mW and with a modulator sampling rate of 25600Hz. This ADC uses a 2V supply for analog circuit blocks and a 1V supply for digital circuit block. The ADC has a dimension of 333.35um x 241.64um.
A Deep Forest Improvement by Using Weighted Schemes

A modification of the confidence screening mechanism based on adaptive weighing of every training instance at each cascade level of the Deep Forest is proposed. The modification aims to increase the classification accuracy. It is carried out by assigning weights to training instances at each forest cascade level in accordance with their classification accuracy. Larger values of accuracy produce smaller weights. Two strategies for using the weights are considered. The first one when the weights are regarded as probabilities of choosing the corresponding instances in building decision trees. According to the second strategy, the weights are used in splitting rules. The modification increases the classification accuracy and may reduce the training time for many real datasets. Numerical experiments illustrate good performance of the proposed modification in comparison with the original Deep Forest proposed by Zhou and Feng.