Reviewerly/exHarmony huggingface dataset:
Application of Amazon Web Services within teaching &amp; learning at Coventry University Group

Since September 2020, CU Group offers a Cloud Computing BSc(Hons) degree program developed with support from Amazon Web Services (AWS), leveraging their platform for teaching and learning and building upon their Cloud Competency Framework (CCF). This paper seeks to share how industry cloud technology stacks can be employed within authentic student learning. This has ensured a scenario led assessment for learning approach as an alternative to a simulated learning environment. CU Group is also an AWS Academy and Educate member, facilitating certification and platform access. This incorporates the experience of an industry leader in direct collaboration with HE to embed cloud skills within existing programs or develop specific curricula to target-fill skill gaps within the cloud landscape. Current students will graduate in August 2022, the first degree level graduates globally for HEIs working within the CCF.
Multi-level reversible data hiding for crypto-imagery via a block-wise substitution-transposition cipher

The research of reversible data hiding in crypto-imagery has attracted considerable attention due to privacy concerns in cloud computing applications. The schemes that vacate spare room after block-wise image encryption can avoid errors and provide more embedding capacities. However, the visual qualities of the directly decrypted images in the related schemes are unsatisfactory, and the spare room is not sufficiently utilized. Therefore, in this paper, we proposed an improved scheme to enhance conventional schemes to achieve a better performance. The block-wise substitution-transposition technique is used for image encryption to avoid carrying bit errors of the directly decrypted image. Then, prediction histogram shifting technique is applied for data embedding in the more centralized prediction error histogram generated by an efficient block predictor. Additionally, our proposed scheme supports multi-level embedding and therefore offers a high embedding rate. The receiver who holds the data hiding key can extract the embedded data, whereas the receiver who possesses the image encryption key is able to obtain a decrypted image of high visual quality. The original image can be losslessly recovered when both keys are presented. Experimental results demonstrate that the PSNRs of the decrypted images and the embedding rates of our proposed scheme are significantly improved compared to related schemes .
Self-supervised learning of monocular depth using quantized networks

Learning monocular depth in a self-supervised manner is desirable for numerous applications ranging from autonomous driving, robotics to augmented reality. However, the current challenges lie in the problems of scale ambiguity, dynamic scene and hardware limitations. To this end, a self-supervised approach is proposed in this work for monocular depth learning and estimation. Specifically, we first introduce a self-supervised depth learning framework that learns a part of the camera intrinsics and the stereo extrinsics, which ensures the absoluteness of the predicted depth while achieving enhanced performance (i.e., lower error between the predicted depth and the ground-truth) on depth estimation. Besides, we further improve the accuracy and the efficiency (i.e., shorter inference time and lower weight/activation footprints) via a specially-designed network that exploits multi-scale context across multi-level feature maps. In addition, we propose a quantization scheme for our depth estimation networks. The scheme allows the network inference to be carried out using INT4-INT8 arithmetic while keeping a high performance on depth estimation. Extensive experiments on KITTI and Make3D datasets demonstrate that our approach substantially boosts the performance compared to the existing state-of-the-art methods on monocular depth estimation.
Design and usability assessment of DocManS: A document management system with security and social media features

A document management system (DMS) is required to handle any documents within an institution efficiently. However, several important features are lacking in the current DMS, such as security and social media features. This paper proposes a new solution to tackle the issues by developing a new document management system with security and social media features called DocManS. The development process and usability evaluation by users are presented in this paper. The usability assessment is performed with the use of the System Usability Scale (SUS) framework. The results show that the current version falls within the B range of the SUS framework and several enhancements for better usability in terms of social media sharing and privacy are recommended.
Proposal and Evaluation of a Gesture Authentication Method with Peep Resistance for Smartwatches

The use of mobile terminals with small touchscreens such as smartwatches has increased dramatically in recent years. A PIN or pattern lock is commonly used for personal authentication on such devices, but because the information is displayed directly, authentication information can be leaked to someone peeking at the screen. In this paper, we propose a gesture-based smartwatch authentication method that reduces the likelihood of leaks by allowing the owner to include a dummy (or "fake") number at the time of authentication registration. The dummy number enables the user to perform an authenticating gesture different from that indicated by the icon displayed on the screen. In a series of experiments, we confirmed the usability of the proposed method, evaluated the effect of long-term familiarity and assessed the method’s resistance to shoulder-surfing. In the usability experiment, the authentication time and success rate were 10.9 seconds and 73.3%, respectively. The authentication time and success rate in the long-term familiarity experiment improved to 8.8 seconds and 93%, and the leakage rate in the peep experiments was 0%.
Comparison of Texture and Shape Features Performance for Leukemia Cell Images using Support Vector Machine

Leukemia or often called blood cancer is one type of cancer caused by excessive white blood cells. Excessive white blood cells will cause disruption of normal function of other blood cells. To find out leukemia, we can do a physical examination in the form of a blood sample or can also use a spinal cord biopsy. In general, doctors take blood samples to see and look for abnormalities of the white blood cell count. To reduce human error in diagnosing leukemia, the study created two systems that can classify leukemia using the Hu moment invariant (HMI) and Support Vector Machine (SVM) methods and the Grey Level Co-occurance Matrix (GLCM) and SVM methods. Classification systems are used to classify acute and normal leukemia image classes using 10-fold cross validation in the sharing of its image data. The best classification results are the GLCM -SVM system with an accuracy value of 99&#x0025; and the HMI-SVM system produces an accuracy value of 90&#x0025;.
Recurrent Neural Network-Based Temperature Control System Weight Pruning Based on Nonlinear Reconstruction Error

Recurrent Neural Networks (RNNs) have been widely applied in various fields. However, in real-world application, because most devices like mobile phones are limited to the storage capacity when processing real-time information, an over-parameterized model always slows down the system speed and is not suitable to be employed. In our proposed temperature control system, the RNN-based control model processes the real-time temperature signals. It is necessary to compress the trained model with acceptable loss of control performance for further implementation in the actual controller when the system resource is limited. Inspired by the layer-wise neuron pruning method, in this paper, we apply the nonlinear reconstruction error (NRE) guided layer-wise weight pruning method on the RNN-based temperature control system. The control system is established based on MATLAB/Simulink. In order to compress the model size to save the memory capacity of temperature controller devices, we first prove the validity of the proposed reference-model (ref-model) guided RNN model for real-time online data processing on an actual temperature object; relative experiments are implemented based on a digital signal processor. On this basis, we then verified the NRE guided layer-wise weight pruning method on the well-trained temperature control model. Compared with the classical pruning method, experiment results indicate that the pruned control model based on NRE guided layer-wise weight pruning can effectively achieve the high accuracy at targeted sparsity of the network.
pMACH: Power and Migration Aware Container scHeduling

Data center workload fluctuations need periodic, but careful scheduling to minimize power consumption while meeting the task completion time requirements. Existing data center scheduling systems tightly pack containers to save power. However, with the growth of multi-tiered applications, there is a significant need to account for the affinity between application components, to minimize communication overheads and latency. Centralized container scheduling systems using graph partitioning algorithms cause a significant number of task migrations, with associated downtime.We design pMACH, a novel distributed container scheduling scheme for optimizing both power and task completion time in data centers. It minimizes task migrations and packs frequently communicating containers together without overloading servers. pMACH operates at peak energy efficiency, thus reducing energy consumption while also providing greater headroom for unpredictable workload spikes. We also propose in-network monitoring using smartNICs (sNIC) to measure the communications and then perform scheduling in a hierarchical, parallelized framework to achieve high performance and scalability. pMACH is based on incremental partitioning and it leverages the previous scheduling decision to significantly reduce the number of containers moved between servers, avoiding application downtime.Both testbed measurements and large-scale trace-driven simulations show that pMACH saves at least 13.44% more power compared to previous scheduling systems. It speeds task completion, reducing the 95th percentile by a factor of 1.76-2.11 compared to existing container scheduling schemes. Compared to other static graph-based approaches, our incremental partitioning technique reduces migrations per epoch by 82%.
Developing a Social-Engineering Course

This Innovative Practice Full Paper describes an implementation of a Social Engineering course. Social Engineering (SE), which is the process of obtaining unauthorized access to systems by exploiting humans, has been flagged by the FBI and US Banking Industry as a threat to national security. According to a report by McAfee, the global cost of cybercrime has exceeded $1 trillion since 2018, and an additional $145 billion was spent on cybersecurity in 2020. Research has shown that 97% of cybersecurity attacks employ some element of social engineering. This Innovative Practice paper will present an effective and hands-on course designed to introduce students to social engineering penetration testing. The course is based on a social constructivism approach to learning, where students first develop an understanding of the individual and social context of social engineering. It uniquely combines the psychology of human behavior, legal precepts, and computer science to identify how psychological principles are applied in cyberspace to exploit individuals and how the same principles can be applied during SE penetration testing to detect vulnerabilities.
Discrete Optimization of Adaptive State Lattices for Iterative Motion Planning on Unmanned Ground Vehicles

Robust motion planners for unmanned ground vehicles must minimize risk while obeying vehicle mobility constraints. Algorithms such as the State Lattice (SL) utilize offline computation to generate expressive control sets which form recombinant search spaces, enabling the use of heuristic search to efficiently produce feasible motion plans online. The Adaptive State Lattice (ASL) demonstrated that local optimizations of the continuous states explored by heuristic search can produce lower-cost solutions in less time than more densely sampled unadapted lattices in sufficiently complex environments. However, the computational cost of this online adaptation limits the application of ASL for mobile robot navigation. We present the Efficiently Adaptive State Lattice (EASL), a novel formalism for online discrete ASL adaptation to overcome this limitation. By discretizing the space of states considered during adaptation, EASL limits the set of feasible motions which could arise during search. This permits the precomputation of an approximation of all motions that could be expressed by an ASL. This approximation removes the online trajectory generation component of the ASL while retaining the benefits of lattice adaptation and enables the use of precomputed swaths for evaluating edge costs. Experimental results demonstrate how an EASL-based planner can generate lower-cost paths than a SL-based planner in roughly equal to or less than the same amount of time.
Practical Frequency-Hiding Order-Preserving Encryption with Improved Update

Order-preserving encryption (OPE) that preserves the numerical ordering of plaintexts is one of the promising solutions of cloud security. In 2013, an ideally secure OPE, which reveals no additional information except for the order of underlying plaintexts, was proposed, along with the notion (mutable encryption) that ciphertexts can be changed. Unfortunately, even the ideally secure OPE can be vulnerable by inferring the underlying frequency of repeated plaintexts. To solve this problem, in 2015, Kerschbaum designed a frequency-hiding OPE (FH-OPE) scheme based on the notion of a randomized order under the strengthened security model. Later, Maffei et al. has shown that Kerschbaum’s model is imprecise, which means no such OPE scheme can exist. Moreover, they provided a new FH-OPE scheme under the corrected security model. However, their scheme requires the order information of all the encrypted plaintexts as an input; therefore, it causes relatively high overhead during encryption. In this work, we propose a more efficient FH-OPE based on Maffei et al.’ s security model and also present an improved update algorithm suitable for duplicate plaintexts.
VR-Based User Interactions to Exploit Infinite Space in Programming Activities

Virtual reality (VR) devices have now become a commodity, and as such, VR is percolating the traditional working environment of software programmers. Current approaches to use VR as the medium to immerse software programmers essentially project classical IDE windows in the virtual environment: the very same VSCode or IntelliJ window is seen through the VR device. As a consequence, the same constraints imposed by a physical screen are found in the VR environment, thus representing a missed opportunity. VRIDE is a new VR-based environment for object-oriented programming to let software developers carry out their activities in a full VR-based immersed environment. VRIDE innovates by offering interactions based on Code Cubes that are designed to exploit the infinite space in the VR environment. Through code cube interactions our approach disrupts the traditional mapping of windows from desktop to VR by supporting dedicated actions for navigation, inspection, and space management. Our prototype illustrates the feasibility of having a full-immersive virtual environment for software programmers.
Understanding Patient Query With Weak Supervision From Doctor Response

Currently, the need for high-quality dialogue systems that assist users to conduct self-diagnosis is rapidly increasing. Slot filling for automatic diagnosis, which converts medical queries into structured representations, plays an important role in diagnostic dialogue systems. However, the lack of high-quality datasets limits the performance of slot filling. While medical communities like AskAPatient usually have multiple rounds of diagnostic dialogue containing colloquial input and professional responses from doctors. Therefore, the data of diagnostic dialogue in medical communities can be utilized to solve the main challenges in slot filling. This paper proposes a two-step training framework to make full use of these unlabeled dialogue data in medical communities. To promote further researches, we provide a Chinese dataset with 2,652 annotated samples and a large amount of unlabeled samples. Experimental results on the dataset demonstrate the effectiveness of the proposed method with an increase of 6.32% in Micro F1 and 8.20% in Macro F1 on average over strong baselines.
Persona Design Method Based on Data Augmentation by Social Simulation

In the field of marketing, “persona” is widely used as a method to support customer-oriented product and service design. However, in many cases, the persona design process is confined to the classification of customers based on limited attribute information projected from the past to the present using cross-sectional data. In this study, we propose a method, based on attribute information extended by social simulation, to improve the persona design process. The framework consists of the following procedures: 1) treat changes in the external environment explicitly and handle target customers' possible attribute changes (i.e., “virtual life”); 2) classify customers based on a large quantity of attribute information supported and extended by social simulation. As a demonstration of the proposed method, this paper presents a specific case study that focuses on the customer asset situations. We conduct a simulation of customer asset situations at a future point in time, taking into account asset succession and price fluctuations of risky assets, based on individual questionnaire data concerning asset formation and withdrawal in retirement. We design personas using the conventional method and the proposed method, and we compare the two. The results show that the proposed method facilitates a) estimation of future changes in customer states, b) incorporation of possible future events in each persona, and c) consideration of future customer state changes in the state of classification of personas.
Human Activity Recognition in Thermal Infrared Imaging Based on Deep Recurrent Neural Networks

Human activity recognition (HAR) is a vast branch of research that focuses on determining the specific action of a person according to sensor data. However, predicting human activity at night is still challenging due to the lack of sufficient accuracy of sensor data. A new model for multimodal thermal infrared data-based HAR (MTIR-HAR) is presented in this paper which can enhance the HAR accuracy by automatically learning the human features from the raw data. Six extra deep layers are added to the recurrent neural network (RNN) to improve the performance of the HAR system at night. These layers extract the most complex features from thermal infrared imaging for classification. The sequence classification technique is applied to separately merged data. The experimental results showed that the proposed method outperformed (up to 98.0%) on the MHAD dataset than the SVM and LSTM methods. Furthermore, the method has achieved the highest accuracy rates (up to 80.2%) compared with other related results in the same DHU Night Dataset under different walking conditions.
Embedded Smart Glove using Ultrasonic and Flame Sensors for Helping Visually Impaired People

Visually impaired is someone’s condition of lacking visual observation due to neurological and physiological factors. Also, wildfire and coronavirus cases made the situation worst for visually impaired people. A helping tool is very needed to overcome these problems. This paper proposes a prototype of an embedded glove using ultrasonic and flame sensors to provide a convenient and safe method for visually impaired people to overcome difficulties in daily life. Smart glove system prototype based on Arduino UNO microcontroller, ultrasonic and flame sensors, SIM800L GSM module for communication, and buzzer for alarm notification. Then, the complete device prototype was tested using both the acrylic box and embedded at one’s hand as a proof of concept for emphasizing the reliability and usefulness of our work. The results show that the ultrasonic sensor works well for detecting an object with a coverage of 40 cm from the blind. The fire/flame sensor also works well when using fire detection mode for detecting the small fire from matches or lighter at a distance of about 15 cm or big fire from a burnt paper at a distance of about 20 cm and will be sending a short message to the respected person through SIM800L GSM. The only drawback for this device is a bit heavy when used on hand and still lacks an IoT system. However, further research will be conducted to overcome those problems soon.
An empirical investigation of the effect of corruption on domestic savings in Nigeria

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Visual Analysis of Two‐Phase Flow Displacement Processes in Porous Media

Abstract We developed a new visualization approach to gain a better understanding of the displacement of one fluid phase by another in porous media. This is based on a recent experimental parameter study with varying capillary numbers and viscosity ratios. We analyse the temporal evolution of characteristic values in this two‐phase flow scenario and discuss how to directly compare experiments across different temporal scales. To enable spatio‐temporal analysis, we introduce a new abstract visual representation showing which paths through the porous medium were occupied and for how long. These transport networks allow to assess the impact of different acting forces and they are designed to yield expressive comparability and linking to the experimental parameter space both supported by additional visual cues. This joint work of porous media experts and visualization researchers yields new insights regarding two‐phase flow on the microscale, and our visualization approach contributes towards the overarching goal of the domain scientists to characterize porous media flow based on capillary numbers and viscosity ratios.
An Optimized FPGA Based Box-Muller Gaussian Random Number Generator Architecture for Communication Applications

This paper presents an efficient Box-Muller GRNG (Gaussian Random Number Generator) structure to reduce the FPGA (Field Programmable Gate Array) logic utilization. The proposed approach is mainly targeting communication applications. We focus on optimizing the throughput. The FPGA Block RAMs (BRAMs) are associated to multiplexer for reducing the required memory size and producing the Box-Muller logarithmic function. The uniform random variables and the multiplexer selector are produced by an LFSR (Linear Feedback Shift Register) based unit. According to this approach, we can reduce the required memory size along with keeping the conventional accuracy. A Xilinx FPGA device is used for validation. The obtained results show that the proposed Box-Muller GRNG structure offers reductions of memory size from 4M to 6K and from 8G to 10K for tail of 5.52σ and 7.81σ, respectively when implemented on a Xilinx FPGA device. Furthermore, a similar high accuracy for the conventional Box-Muller GRNG is achieved with a reduced FPGA slice utilization.