Reviewerly/exHarmony huggingface dataset:
Evaluation of a Backup-as-a-Service Environment for Disaster Recovery

Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering low-cost solutions for DR purposes such as the Backup-as-a-service (BaaS) for consumers. Therefore, in this paper, we present an integrated model-experiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and fault-injection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a real-world BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.
Complexities in the process of translating research documents in cross-cultural settings

In multilingual societies, where researchers and participants often do not speak the same language, research is a challenge as a mismatch of understanding between researchers, research instruments and participants often occurs. Reporting on the translation process is crucial because of the potential implications for the validity of the data that follow from it. We aimed to report on the complexities of such a translation process and many considerations that came to our attention. Methodologically, we used a detailed case study to demonstrate that the complexity of translation might be underestimated by researchers who may neglect to report on the challenges that they experience to benefit the wider research community. We emphasise that translating documents, particularly between languages that are not cognate, requires time and financial resources that researchers often do not anticipate or plan for. By discussing what happened to texts that were translated, and how we as researchers were challenged by considerations that were primarily linguistic but also straddled cultural and socio-political domains, we hope to encourage a deeper understanding of the translation task. We conclude that consideration of these complexities is necessary if the aim is the development of translated documents which complement the researchers’ goals.
Sequential Learning for Cross-Modal Retrieval

Cross-modal retrieval has attracted increasing attention with the rapid growth of multimodal data, but its learning paradigm under changing environment is less studied. Inspired by the recent achievement in the field of cognition mechanism on how the human brain acquires knowledge, we propose a new sequential learning method for cross-modal retrieval. In this method, a unified model is maintained to capture the common knowledge of various modalities but are learnt in a sequential manner such that it behaves adaptively according to the evolving distribution of different modalities, and needs no laborious alignment operations among multimodal data before learning. Furthermore, we propose a novel meta-learning based method to overcome the catastrophic forgetting encountered in sequential learning. Extensive experiments are conducted on three popular multimodal datasets, showing that our method achieves state-of-the-art cross-modal retrieval performance without any modal-alignment.
New assistive technology for communicating with disabled people based on gaze interaction

Gaze interaction is a computer access method that allows disabled people to control a computer with their eyes. Gaze interaction only requires the movement of the eye itself-the movement of other muscles is not required, making it a perfect solution for those with rehabilitative disabilities (paralysis, spinal cord injury, repetitive strain injury, severe carpal tunnel) and/or motor disabilities. The paper describes a simple software application of a new assistive device (Tobii eyeX tracker) and its evaluation for communicating with disabled people based on gaze interaction. The proposed communication method is developed as a part of a more complex integrated system for assistance in communicating with and telemonitoring severe neuromotor disabled patients.
Monte Carlo Based Server Consolidation for Energy Efficient Cloud Data Centers

The growing energy consumption of data centers is a compelling global problem and effective server consolidation is at the heart of energy efficient cloud data centers. A variant of bin packing can be used to model the server consolidation problem, where the constraints are multidimensional and heterogeneous vectors rather than scalars and the goal is to satisfy the requested resource allocation using the minimum number physical servers. Since bin packing is NP-hard, we rely on heuristics for practical solutions. Variations of First Fit Decreasing (FFD) based heuristics have been shown to be effective both in theory and practice for the one dimensional homogeneous case. However, the multidimensional and heterogeneous aspects of the server consolidation problem make it more complicated, requiring additional research to adapt FFD to the server consolidation problem. In this paper, we present a new FFD-based server consolidation technique using a Monte Carlo method and Shannon entropy, which considers resource bottlenecks and dynamically adjusts to variance in the utilization of different resources. The proposed heuristic outperforms existing techniques in all scenarios, achieving within 2-5% of optimal on average for medium to high variance in resource utilization, and within 10% worse than optimal on average for all scenarios.
Multimedia Forensic: An Approach for Splicing Detection based on Deep Visual Features

Fabrication of an image by utilizing a wide range of image altering tasks can lead to fraud. Since a measurable inspector must consider each of these. numerous techniques have emerged for calculation to identifying picture tampering activities and algorithms. In this paper, we propose a convolution neural network based network to deal with tampering and segmentation of visual data. Network is designed to preserve local and global characteristics of data to detect tampering. Inception module based CNN network is proposed intended to identify a picture's content and tampered part using mask R-CNN. Through a progression of examinations, we showed that our proposed methodology can consequently figure out how to distinguish numerous picture tampering methods without depending on pre-chosen highlights or any pre-preparing and segment out tampered region. Evaluation results demonstrate that our proposed methodology can distinguish tampering over CASIA v1.0 and v2.0 with an accuracy of 98.76% and 97.92%, respectively.
QA4IE: A Question Answering Based System for Document-Level General Information Extraction

Information Extraction (IE) is the task of distilling structured information from unstructured texts by identifying references to named entities as well as relationships between such entities. Existing IE solutions, including Relation Extraction and Open IE, can hardly take cross-sentence information like coreferences into account and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation triples). In order to overcome the weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering approaches to produce high-quality relation triples across sentences. Based on this framework, we develop a real-time IE system, which can perform general IE throughout the entire document. For training and evaluating our system, we build a large-scale IE benchmark using distant supervision under human evaluation. We deploy both component analyses and pipeline experiments to evaluate our system. The results show that our system can generalize on unseen entities and relations, as well as achieve significant improvements over existing IE systems.
Kaa: Evaluating Elasticity of Cloud-Hosted DBMS

Auto-scaling is able to change the scale of an application at runtime. Understanding the application characteristics, scaling impact as well as the workload, an auto-scaler aligns the acquired resources to match the current workload. For distributed Database Management Systems (DBMS) forming the backend of many large-scale cloud applications, it is currently an open question to what extent they support scaling at run-time. In particular, elasticity properties of existing distributed DBMS are widely unknown and difficult to evaluate and compare. This paper presents a comprehensive methodology for the evaluation of the elasticity of distributed DBMS. On the basis of this methodology, we introduce a framework that automates the full evaluation process. We validate the framework by defining significant elasticity scenarios for a case study that comprises two DBMS for write-heavy and read-heavy workloads of different intensities. The results show that scalable distributed DBMS are not necessarily elastic and that adding more instances to a cluster at run-time may even decrease the experienced performance.
Intelligent System for Health Monitoring Applications

The percentage of the aged people in the world's population is rising continuously. Hence, there is a need to provide better technological support to elderly. A modern global objective with the purpose of providing better help to the aged people is seen wherein a good deal of research has been taking place in the areas of ambient intelligence.We propose an ambient intelligent model circuit for the elderly by utilizing sensors, GSM module and a speaker interfaced with Arduino Uno. This model provides assistance to the elderly by alerting the person himself or care taker in the vicinityusing a speaker. This also helps old people with poor vision to get to know about an abnormal condition immediately. At the same time, a message is sent to alert the friends, family or caregiver in the situation of emergency.
Towards Generalizing Sensorimotor Control Across Weather Conditions

The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data. Labeling large amounts of data for all possible scenarios that a model may encounter would not be feasible; if even possible. We propose a framework to deal with limited labeled training data and demonstrate it on the application of vision-based vehicle control. We show how limited steering angle data available for only one condition can be transferred to multiple different weather scenarios. This is done by leveraging unlabeled images in a teacher-student learning paradigm complemented with an image-to-image translation network. The translation network transfers the images to a new domain, whereas the teacher provides soft supervised targets to train the student on this domain. Furthermore, we demonstrate how utilization of auxiliary networks can reduce the size of a model at inference time, without affecting the accuracy. The experiments show that our approach generalizes well across multiple different weather conditions using only ground truth labels from one domain.
An Empirical Investigation on the Effect of Shape Exaggeration in Face Sketch to Photo Matching

Facial sketch to photo retrieval system has useful application in forensic investigation. The main problem identified in the system is that the sketch and photo are from different modality. In order to address this problem, intra-modality and inter-modality matching approaches have been introduced by many researchers. However, the proposed methods mostly ignore the effects of shape exaggeration of the facial sketch. In this paper, we attempt to investigate empirically and highlight the importance of considering the shape exaggeration effects in any of the two aforementioned matching approaches. This investigation is limited to the Chinese University of Hong Kong (CUHK) Face Sketch Database (CUFS) and CUHK Face Sketch FERET Database (CUFSF) datasets. The results demonstrate that the shape exaggeration effects may reduce retrieval rate accuracy.
Multi-frame Image Super-Resolution by Interpolation and Iterative Backward Projection

Image Super-Resolution is the well-known technique of improving the quality of images by sharpening the resolution of images. Multi- frame Super- Resolution is procedure of developing an image with high resolution by combining a series of poor resolution images of the look-alike scene. Here, an Improved Super- Resolution technique is implemented using interpolation and iterative backward projection (IBP) registration algorithm. Initially, bilinear interpolation is carried out for a sequence of four images with poor resolution. These resultant images are then registered using Iterative Backward Projection algorithm to yield a high-resolution image. This Improved Super- resolution reconstruction algorithm is simulated on MATLAB and compared with Interpolation Reconstruction algorithm. Improved Super Resolution technique proves to be excellent than Interpolation Reconstruction techniques by means of visuality, Mean_square_error (MSE), Peak-Signal to Noise ratio (PSNR).
Design of Distributed Academic-record System Based on Blockchain

As a distributed system, blockchain is currently an attractive choice for the back-end of public services because of its benefits. Decentralized and carries a consensus mechanism making data stored into irreversible ledger is an advantage of the blockchain. This study discusses the architecture design of academic recording systems in Indonesia, which is currently centralized to be decentralized in permitted networks. Participants involved in the network system, namely institutions as academic staff, teachers, students, and external parties, which each participant has the authority and arrangement with smart-contracts. Evaluation results show less time on the proposed system to complete business processes than the currently system. The task of validation activities can be eliminated and perform smart-contract functions inside to become a "trust-less operation", in which academic data are ensured to be protected from abused or damage due to human error. The data that has been included in the ledger consists of personal performance from the history of academic activities required by students that can be traced by interested parties such as organizations wishing to recruit employees, as well as educational institutions that require the achievement of specific information needed by prospective applicants.
Understanding and controlling financial fraud in the drug industry

Purpose This study aims to assess the fraud cases, factors and control measures of financial fraud in the drug industry with evidence from Ghana. Drug industry and pharmaceutical are the same, and they are used interchangeably in this study. Design/methodology/approach Data from questionnaires were collected from 412 manufacturers, wholesalers and retailers of the drug industry. Data were presented and analysed with descriptive statistics and probit regression. Findings Results show that, in general, stealing of drugs, stealing of cash, usage of fake cheques, falsified documents and dubious accounting practices are some of the fraud cases in the industry. Factors such as gender, educational level, religious beliefs, regulatory 7measures, pressure, rationalization and opportunities influence financial fraud in the drug industry. Control measures such as thorough assessment of products, regular review of fraud policies, installation of fraud-detection software and effective internal systems could reduce the menace. Research limitations/implications The paper addresses a number of theoretical and systemic issues on financial fraud in the drug industry but with limited specific quantitative data or calculations as well as limited sample size. Further studies could offer a more quantitative approach with a larger sample size in an attempt, for instance, to estimate the financial costs of financial fraud to the drug industry. Practical implications This paper openly tackles various attempted frauds and financial malfeasances from stakeholder perspectives in the drug industry. Practical measures have been given to tackle the consequences of the menace. Originality/value This paper is geared towards providing valuable learning points for stakeholders in the drug industry to handle daily operations to assist them in detecting and preventing similar occurrence of financial fraud.
On the Improvement of Recognizing Single-Line Strings of Japanese Historical Cursive

Transcribing historical Japanese document is the first step to preserve them as cultural assets. These historical documents can be directly useful not only for disaster prevention but also for enriching Japanese culture. Indeed, there is even an ongoing national project for more than 100 years with the aim of comprehensively compiling old documents. However, it is difficult to read Japanese historical cursive even for modern Japanese without training. In this paper, we report on research to recognize a single-line text of Japanese historical cursive. Our result has more than 95% accuracy for the text consisting of only 46 Hiragana characters, while 84.08% accuracy for the text including thousands of Kanji characters. Both of them are state-of-the-art accuracy. That is, our result on 46 Hiragana characters significantly outperformed the previous state-of-the-art. And this is the first research to recognize thousands of cursive Kanji characters. Furthermore, we had various experiments to improve the recognition accuracy of historical cursive, which includes data augmentation for rare characters, enhancement by language model, and fine-tuning with samples written by the same author as the test data. As a result, because of various handwriting styles, it is practically effective to fine-tune with samples written by the same author as the test data. It easily outperforms the improvements by data augmentation and language model.
Matching Novelty While Training: Novel Recommendation Based on Personalized Pairwise Loss Weighting

Most works of recommender system seek to provide highly accurate item prediction while having potentially great bias to popular items. Both users and items' providers will suffer if their system has strong preference for monotonous popular items. A better system should consider also item novelty. Previous works of novel recommendation focus mainly on re-ranking a top-N list generated by an accuracy-focused base model. As a result, these frameworks are 2-stage and essentially limited to the base model. In addition, when training the base model, the common BRP loss function treats all pairs in the same manner, consistently suppresses interesting negative items which should have been recommended. In this work, we propose a personalized pairwise novelty weighting for BPR loss function, which covers the limitations of BPR and effectively improves novelty with marginal loss in accuracy. Base model will be guided by the loss weights to learn user preference and to generate novel suggestion list in 1 stage. Comprehensive experiments on 3 public datasets show that our approach effectively promotes novelty with almost no decrease in accuracy.
Model-based Systems Engineering Process for Supporting Variant Selection in the Early Product Development Phase

Combining the benefits of model-based systems engineering (MBSE) and product line engineering (PLE), model-based product line engineering (MB-PLE) is applied for the design and development of complex product line systems with variants. However, due to limitations such as visualization complexity and lack of support for variant selection, MB-PLE is still not widely applied in the manufacturing industry. In this paper, we propose a model-based systems engineering process in the early development phase to promote the application of MB-PLE in the manufacturing industry. Through the integration of PSS planning model, SysML4Mechatronics modeling language and an optimal variant selection approach, the proposed process facilitates the modeling of a product line system and supports variant selection as well as architecture verification. The feasibility of the proposed systems engineering process is demonstrated with a case study.
Software Protection Decision Support and Evaluation Methodologies (Dagstuhl Seminar 19331).

This report documents the program and the outcomes of Dagstuhl Seminar 19331 ``Software Protection Decision Support and Evaluation Methodologies''. The seminar is situated in the domain of software protection against so-called man-at-the-end attacks, in which attackers have white-box access to the software that embeds valuable assets with security requirements such as confidentiality and integrity. The attackers try to compromise those by reverse-engineering the software and by tampering with it. Within this domain, the seminar focused mainly on three aspects: 1) how to evaluate newly proposed protections and attackers thereon; 2) how to create an appropriate benchmark suite to be used in such evaluations; 3) how to build decision support to aid users of protection tool with the selection of appropriate protections. The major outcomes are a structure for a white-paper on software protection evaluation methodologies, with some concrete input collected on the basis of four case studies explored during the seminar, and a plan for creating a software protection benchmark suite.
The Improvement of LDR Based Solar Tracker’s Action using Machine Learning

Solar energy is a clean, renewable and sustainable energy source that has the potential of becoming one of the major constituents of the total global energy mix. The achievement of maximum possible photovoltaic (PV) energy conversion requires that the sun's direct beam directly illuminates the photovoltaic panel's surface as long as possible. This can possibly be achieved through solar tracking techniques. Light Dependent Resistor (LDR) based tracking systems face multiple predicaments such as temporary clouding, fog, man-made blockage and others that cause impaired tracking. The objective of this research is to develop an LDR (Light Dependent Resistor) based solar tracker that has the ability to make accurate decisions and ignore faulty sensor readings. To satisfy this objective, Machine Learning techniques are implemented to produce a tracker that has the ability to learn and improve its action from daily interaction with the environment. A Machine Learning based algorithm is developed and implemented in a MATLAB environment. The algorithm has the functionality of continuously testing the consistency of input LDR sensor values, hence making the decision of either approval of the values and usage for further learning or alternatively, discarding of the values and replacing it by a more logical value from the previously learned information. The developed system is tested using Root Mean Square Error (RMSE). The system has showed a lowered and converging error response in varying environmental conditions (Sunny, Semi-Cloudy and Cloudy), thus the proposed system has proved its effectiveness in tackling the temporary blockage problem, resulting in a cheap, robust and noise tolerant solar tracking system.