Reviewerly/exHarmony huggingface dataset:
Data-Intensive Workload Consolidation in Serverless (Lambda/FaaS) Platforms

A significant amount of research studies in the past years has been devoted on developing efficient mechanisms to control the level of degradation among consolidate workloads in a shared platform. Workload consolidation is a promising feature that is employed by most service providers to reduce the total operating costs in traditional computing systems [1]–[3]. Serverless paradigm - also known as Function as a Service, FaaS, and Lambda - recently emerged as a new virtualization run-time model that disentangles the traditional state of applications' users from the burden of provisioning physical computing resources, leaving the difficulty of providing the adequate resource capacity on the service provider's side. This paper focuses on a number of challenges associated with workload consolidation when a serverless platform is expected to execute several data-intensive functional units. Each functional unit is considered to be the atomic component that reacts to a stream of input data. A serverless application in the proposed model is composed of a series of functional units. Through a systematic approach, we highlight the main challenges for devising an efficient workload consolidation process in a data-intensive serverless platform. To this end, we first study the performance interference among multiple workloads to obtain the capacity of last level cache (LLC). We show how such contention among workloads can lead to a significant throughput degradation on a single physical server. We expand our investigation into a general case with the aim to prevent the total throughput never falling below a predefined utilization level. Based on the empirical results, we develop a consolidation model and then design a computationally efficient controller to optimize the throughput degradation among a platform consists fs multiple machines. The performance evaluation is conducted using modern workloads inspired by data management services, and data analytic benchmark tools in our in-house four node platform showing the efficiency of the proposed solution to mitigate the QoS violation rate for high priority applications by 90% while can enhance the normalized throughput usage of disk devices by 39 %.
Spatial-Temporal Asynchronous Normalization for Unsupervised 3D Action Representation Learning

Unsupervised 3D action representation learning from skeleton sequences has attracted increasing attention in recent years. Existing methods have successfully applied autoencoder network to learn 3D action representation by reconstructing original skeleton sequence. However, these methods ignore motion cues thus suffer from distinguishing actions especially with similar shape information and slightly different motion information. Instead of reconstructing original skeleton sequence, we learn distinctive 3D action representation with autoencoder network by reconstructing normalized motion sequence extracted from original input. To obtain the normalized motion sequence, we specifically design a novel spatial-temporal asynchronous normalization (STAN) method, which normalizes original skeleton sequence in two steps. First, STAN reduces redundant temporal information and extracts motion sequence by subtracting mean value along the temporal dimension. Second, STAN further normalizes the motion sequence along the spatial dimension and generates normalized motion sequence that suffers less from the effect of different human body shapes. Extensive experiments on large scale NTU RGB+D 60 and NTU RGB+D 120 datasets verify the effectiveness of our proposed STAN method, which achieves comparative results with state-of-the-art methods, and also outperforms alternative normalization methods.
A systematic framework to explore the determinants of information security policy development and outcomes

Purpose This paper aims to develop an effective information security policy (ISP), which is an important mechanism to combat insider threats. Design/methodology/approach A general framework based on the Nine-Five-circle was proposed for developing, implementing and evaluating an organisation's ISP. Findings The proposed framework outlines the steps involved in developing, implementing and evaluating a successful ISP. Research limitations/implications The study took place in Germany, and most of the data was collected virtually due to the different locations of the organisation. Practical implications In practice, this study can be a guide for managers to design a robust ISP that employees will read and follow. Social implications Employee compliance with the ISP is a critical aspect in any organisation and therefore a rigorous strategy based on a systematic approach is required. Originality/value The main contribution of the paper is the application of a comprehensive and coherent model that can be the first step in defining a “checklist” for creating and managing ISPs.
ASR Rescoring and Confidence Estimation with Electra

In automatic speech recognition (ASR) rescoring, the hypothesis with the fewest errors should be selected from the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -best list using a language model (LM). However, LMs are usually trained to maximize the likelihood of correct word sequences, not to detect ASR errors. We propose an ASR rescoring method for directly detecting errors with ELECTRA, which is originally a pre-training method for NLP tasks. ELECTRA is pre-trained to predict whether each word is replaced by BERT or not, which can simulate ASR error detection on large text corpora. To make this pre-training closer to ASR error detection, we further propose an extended version of ELECTRA called phone-attentive ELECTRA (P-ELECTRA). In the pre-training of P-ELECTRA, each word is replaced by a phone-to-word conversion model, which leverages phone information to generate acoustically similar words. Since our rescoring method is optimized for detecting errors, it can also be used for word-level confidence estimation. Experimental evaluations on the Librispeech and TED-LIUM2 corpora show that our rescoring method with ELECTRA is competitive with conventional rescoring methods with faster inference. ELECTRA also performs better in confidence estimation than BERT because it can learn to detect inappropriate words not only in fine-tuning but also in pre-training.
Gender recognition from facial images using multichannel deep learning framework

Automated gender recognition is an attractive research area in computer vision. It has many application areas, such as biometric, human-computer interaction, demographic statistics, retail shops, surveillance, etc. It aims to identify gender through the appearance, behavior, and other aspects of a person. Our proposed convolutional neural network (CNN)-based approach does not use raw facial images for learning the features. First, featured images are constructed from raw images to represent a set of salient features. Then, more salient features are extracted using CNN-based networks in which the featured images are given as input. Then, the extracted salient features are fused together and given as input to the classifier, support vector machine, for recognizing the gender. Our proposed novel framework is tested using Adience benchmark dataset and the stated functionality requirements are met. From this work, it is inferred that using a CNN-based approach with featured images as input, effective gender recognition can be done and the learned features are more amenable for effective classification. The framework is able to handle the faces that are captured in totally diverse environments. It can be applied to several visual pattern recognition problems.
Machine optimized quantum metrology of concurrent entanglement generation and sensing

Abstract Entanglement is one of the key ingredients for enhancing the measurement precision of quantum sensors. Generally, there is a trade-off between state preparation and sensing within a limited coherence time. To fully exploit temporal resources, concurrent entanglement generation and sensing with designed sequence of rotations are proposed. Based on twist-and-turn dynamics, modulated rotations along only one axis may be sufficient to drive the state to the optimal one for tiny estimated parameter. However, when the estimated parameter is not tiny, it may impact the evolved state and hence degrade the final measurement precision. Here, we introduce another modulated rotations along different axis and find out the optimal control sequences by means of machine optimization. The optimal measurement precision bounds become independent on the estimated parameter, which improves the dynamic range of the machine designed sensors. Particularly, by optimizing the interaction strength for different particle number and the time-modulated rotations along two different axes via machine optimization, the Heisenberg-limited precision scaling can be attained. Our work points out a way for designing optimized quantum-enhanced metrology protocols, which is promising for developing practical quantum sensors.
Model Behavior Preserving for Class-Incremental Learning

Deep models have shown to be vulnerable to catastrophic forgetting, a phenomenon that the recognition performance on old data degrades when a pre-trained model is fine-tuned on new data. Knowledge distillation (KD) is a popular incremental approach to alleviate catastrophic forgetting. However, it usually fixes the absolute values of neural responses for isolated historical instances, without considering the intrinsic structure of the responses by a convolutional neural network (CNN) model. To overcome this limitation, we recognize the importance of the global property of the whole instance set and treat it as a behavior characteristic of a CNN model relevant to model incremental learning. On this basis: 1) we design an instance neighborhood-preserving (INP) loss to maintain the order of pair-wise instance similarities of the old model in the feature space; 2) we devise a label priority-preserving (LPP) loss to preserve the label ranking lists within instance-wise label probability vectors in the output space; and 3) we introduce an efficient derivable ranking algorithm for calculating the two loss functions. Extensive experiments conducted on CIFAR100 and ImageNet show that our approach achieves the state-of-the-art performance.
A Cloud Resource Prediction and Migration Method for Container Scheduling

With the continuous evolution of cloud-native, more and more applications are deployed on the container. As the business platform runs many container instances and has complex dependency relationships, the load of cloud resources fluctuates due to service status, resulting in task scheduling difficulties and affecting service stability. In this paper, we propose a container resource migration scheduling algorithm called UVPOC, and establish a two-level scheduler mechanism to monitor global real-time resources, choosing LTSM to predict the trend of the dominant resource utilization and determine whether to perform container migration or Virtual Machines (VMs) pre-boot to implement resource allocation. In addition, we choose CloudSim open source tool for simulation experiments. The results show that our UVPOC algorithm can improve the global resource utilization of containers and virtual machines, and reduce the energy consumption of data center resources.
Coarse-to-Fine Image Registration for Multi-Temporal High Resolution Remote Sensing Based on a Low-Rank Constraint

For multi-temporal high resolution remote sensing images, the image registration is important but difficult due to the high resolution and low-stability land-cover. Especially, the changing of land-cover, solar altitude angle, radiation intensity, and terrain fluctuation distortion in the overlapping areas can represent different image characteristics. These time-varying properties cause traditional registration methods with known reference information to fault. Therefore, in this paper we propose a comprehensive coarse-to-fine registration (CCFR) algorithm. First, we design a low-rank constraint-based batch reference extraction (LRC-BRE) method. Under the low-rank constraint, the stable features with highly spatial co-occurrence can be reconstructed via matrix decomposition, and are set as reference images to batch registration. Second, we improve the general feature registration with block feature matching and local linear transformation (BFM-LLT) operators including match outlier filtering (MOF) on regional mutual information and dual-weighted block fitting (DWBF). Finally, based on combining LRC-BRE and BFM-LLT, CCFR is integrated. Experimental results show that the proposed method has a good batch alignment effect, especially in the registration of large difference image pairs. The proposed CCFR achieves a significant performance improvement over many state-of-the-art registration algorithms.
Understanding People’s Expectations When Designing a Chatbot for Cancer Patients

With the overall increase of cancer patients, there is a growing demand for better healthcare services, more patient-centred care, and more user-centred eHealth tools. Chatbots are great tools to bridge communications between health providers and patients and have already been used with success in healthcare. In the present study, we set out to explore how people perceive a cancer chatbot and to understand preferences and expectations concerning the communication between a chatbot and newly diagnosed cancer patients. The insights from the remote co-creation sessions will enable us to design better chatbot dialogues, with human-like characteristics, that communicate with appropriate content and tone of voice.
Text-Enhanced Knowledge Graph Representation Model in Hyperbolic Space

The representation learning of knowledge graph refers to embedding entities and relations in knowledge graph into a low-dimensional dense vector space. Existing knowledge graph embedding models mostly chose Euclidean Space as their vector space and consider each fact triple in knowledge graph independently. However, Euclidean Space is unable to represent knowledge effectively due to its strict constraints and mathematical expression. Besides, entities in knowledge graph are not isolated, while these models ignore the association between entities. To solve the problems above, we proposed a text-enhanced knowledge graph representation learning model in Hyperbolic Space. We utilize rich semantic information of entity description by using Transformer Encoder to enhance the ability of knowledge representation. Besides, we embed entities and relations into Hyperbolic Space, which can better capture hierarchical information of the knowledge graph. Experiments on benchmark dataset show that our method achieves better performance compared with other state-of-art methods.
Fuzzy Association Rules Mining based on Type-2 Fuzzy Sets over Data Stream

Fuzzy set theory aims to solve the problem of mining more valuable human reasoning knowledge from quantitative databases, and to narrow the gap between computer and human understanding. Fuzzy Frequent Itemset (FFI) mining, which is very important in fuzzy association rule mining, converts quantitative values into language, and finds fuzzy frequency itemset according to the defined membership function. In the traditional fuzzy frequent pattern mining method, only the language item with the largest scalar base is considered, and the uncertainty of the membership function is not considered. At the same time, it is mainly considered for the fixed data set. This paper proposes a Fuzzy Frequent Pattern mining algorithm based on the Type-2 Fuzzy Set (T2FS) theory of the data stream. Under the T2FS theory, the data stream is dynamically divided based on the sliding window method, and the ambiguity is quickly found from the numerical data stream. In order to reduce the search space and improve over-mining, this paper designs a dynamic compressed lists structure and trees method. Under different minimum support degrees and sliding window sizes, many experiments have been carried out, and the experiments have verified the efficiency and effectiveness of the designed method in terms of running time and memory usage. The results can also contribute to exchange the information and preferences among the people who cooperate in decision-making.
Preschool teacher’s beliefs about creativity and children creativeness

The aim of this study was to determine what beliefs preschool teachers hold about creativity in general and about children’s creativeness. A total of 366 preschool teachers and preschool teaching assistants participated in the survey, by completing a questionnaire designed for the purposes of this study in Slovenia. The results show that participants predominantly believe that creativity is innate, that it is a characteristic of all people, and it can be developed, in some specific areas, such as the arts. Participants also believe that all children are creative, but not at the same level and not in all areas. They agree that children need motivation to express and develop their creativity, which indicates an awareness of the importance of encouraging creativity. An analysis of the different views on creativity among teachers and teaching assistants shows differences in perception of children’s creativeness It follows that the longer training of preschool teachers can provide not only more knowledge and a sense of competence in working with children, but also the possibility of an over-structured, curriculum-constrained and pre-determined framework of seeing the child. This is why the results of the survey - an insight into the perspectives of preschool teachers and preschool teaching assistants - represent an important contribution in the area of developing the creativity of preschool children. The survey results reflect on how to guide the work of preschool teachers in the direction of developing and supporting the creativity of preschool children, pointing to the fact that (i) preschool teachers must be trained how to identify, foster and facilitate children’s creativity and (ii) they should acquire these competences during initial teacher training programmes and nurture them through continuous professional development programmes.
The Gaussian Mutational Barebone Dragonfly Algorithm: From Design to Analysis

The dragonfly algorithm is a swarm intelligence optimization algorithm based on simulating the swarming behavior of dragonfly individuals. An efficient algorithm must have a symmetry of information between the participating entities. An improved dragonfly algorithm is proposed in this paper to further improve the global searching ability and the convergence speed of DA. The improved DA is named GGBDA, which adds Gaussian mutation and Gaussian barebone on the basis of DA. Gaussian mutation can randomly update the individual positions to avoid the algorithm falling into a local optimal solution. Gaussian barebone can quicken the convergent speed and strengthen local exploitation capacities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of engineering design. To verify the superiorities of GGBDA, this paper sets 30 benchmark functions, which are taken from CEC2014 and 4 engineering design problems to compare GGBDA with other algorithms. The experimental result show that the Gaussian mutation and Gaussian barebone can effectively improve the performance of DA. The proposed GGBDA, similar to the DA, presents improvements in global optimization competence, search accuracy, and convergence performance.
Learning With Dual Demonstration Domains: Random Domain-Adaptive Meta-Learning

Although robots have been widely applied in various fields, allowing a robot to perform a wide range of tasks like humans is a significant challenge. One promising method is meta-learning, which enables robots to learn from demonstrations with the concept of &#x201C;learning to learn.&#x201D; However, most meta-learning methods only focus on teaching robots to learn from a single demonstration domain, i.e., demonstrations recording videos of a human or robot performing tasks (human or robot demonstrations). Given that humans can acquire and merge knowledge from various related domains, this letter proposes a novel yet efficient Random Domain-Adaptive Meta-Learning (RDAML) framework to teach the robot to learn from multiple demonstration domains (e.g., human demonstrations + robot demonstrations) with different random sampling parameters. Once training is complete, the trained model can adapt to new environments given a corresponding visual demonstration. Extensive experimental results show that the model trained by our proposed RDAML algorithm obtains better generalization capability. We have demonstrated the effectiveness of our RDAML on real-world placing experiments using a UR5 robot arm, which significantly outperforms current state-of-the-art methods using either human demonstrations or robot demonstrations to teach the robot during testing.
FPGA based Adaptive Hardware Acceleration for Multiple Deep Learning Tasks

Machine learning, and in particular deep learning (DL), has seen strong success in a wide variety of applications, e.g. object detection, image classification and self-driving. However, due to the limitations on hardware resources and power consumption, there are many challenges to deploy deep learning algorithms on resource-constrained mobile and embedded systems, especially for systems running multiple DL algorithms for a variety of tasks. In this paper, an adaptive hardware resource management system, implemented on field-programmable gate arrays (FPGAs), is proposed to dynamically manage the on-chip hardware resources (e.g. LUTs, BRAMs and DSPs) to adapt to a variety of tasks. Using dynamic function exchange (DFX) technology, the system can dynamically allocate hardware resources to deploy deep learning units (DPUs) so as to balance the requirements, performance and power consumption of the deep learning applications. The prototype is implemented on the Xilinx Zynq UltraScale+ series chips. The experiment results indicate that the proposed scheme significantly improves the computing efficiency of the resource-constrained systems under various experimental scenarios. Compared to the baseline, the proposed strategy consumes 38% and 82% of power in low working load cases and high working load cases, respectively. Typically, the proposed system can save approximately 75.8% of energy.
Characteristic profile

The rapid uptake of rooftop solar photovoltaic systems is introducing many challenges in the management of distribution networks, energy markets, and energy storage systems. Many of these problems can be alleviated with accurate short term solar power forecasts. However, forecasting the power output of distributed rooftop solar PV systems can be challenging, since many complex local factors can affect solar output. A common approach when forecasting such systems is to extract the daily seasonality from the time series using some form of seasonality model, and then forecast only the residuals that remain after seasonality extraction. In this work, we explore in detail the effectiveness of three commonly used seasonality models, and we propose a new one, called the "characteristic profile". We find that when seasonality models are integrated into the forecasting process, significant gains in forecast accuracy may be obtained - particularly for machine learning based approaches, which have a reduction in forecast error of 5-25%. Among the seasonality models, the characteristic profile demonstrates the highest forecast accuracy, resulting in reductions in forecast error of 8% or more compared to forecasting models that do not take seasonality into account. The benefits of this approach are particularly pronounced when forecasting solar PV systems that are curtailed, suffer from local shading, or consist of multiple sets of panels having different orientations and tilts. Our results are demonstrated on a high resolution dataset obtained from 258 sites in Western Australia over the course of a full year.