Reviewerly/exHarmony huggingface dataset:
ALTM: Adaptive learning-based thermal model for temperature predictions in data centers

To design effective control schemes for energy efficiency in data centers, it is crucial to have a thermal model of the system. Constructing thermal models of data centers for temperature prediction is extremely challenging, due to inherent complexity. Computational fluid dynamics (CFD) simulations or physical heat transfer equations are conventionally used to construct such thermal models. More recent approaches combine physical heat transfer rules and data-driven methods in an effort to obtain more accurate models. Our proposed adaptive learning-based thermal model (ALTM) is fast, adapts to thermal changes in the data center environment, and does not require prior knowledge of heat transfer rules between data center entities. Unlike other methods, ALTM is a holistic thermal model that predicts temperature of critical zones using data center operational variables as inputs. The operational variables are the controllable parameters and easily obtained measurements from IT and cooling units. A key use case for ALTM is that it can be effectively used for thermal-aware workload schedulers or cooling system controllers. Our results confirm the accuracy and adaptability of the model.
What Topics Do Images Say: A Neural Image Captioning Model with Topic Representation

Image captioning aims to generate descriptions of images with natural language sentences automatically. Most methods tackle this problem in an end-to-end fashion in recent years, which generates captions directly from image level features but ignores high-level semantic information. The method that introduced attribute concept into the CNN-RNN framework made a considerable improvement while the performance depended on the manually selected attributes heavily. In this paper, we propose a topic-guided neural image captioning model which incorporates a topic model into the CNN-RNN framework. Our model represents each image as a set of topics and each topic as various words with relevant distributions. We conduct experiments on Microsoft COCO dataset. The results show that our model outperforms the baselines and achieves promising performance. It verifies that the topic features are effective to represent high-level semantic information of images.
A comparison of Satellite Image Compression methods in the Wavelet Domain

Abstract Better resolution of remotely sensed satellite images will make images clearer and interpretation easier but will increase the total volume of data that has to be managed. In order to reduce data volume for easier satellite communication transmission and reduce the total volume of data needed to be stores, the images should be compressed. Image compression in wavelet domain can be used for both lossy or lossless compression. Four major compression methods are available using the wavelet domain, i.e. CCSDS, Wavelet, Bandelet, and JPEG 2000. Some optical satellite images, were used as input data in simulation software which analyzed and compared the four compression methods in the wavelet domain. The result showed that the CCSDS method yielded the fastest compression and decompression time, but the Bandelet method retained better image quality when reconstructing original images or approximations of them compared to CCSDS. The JPEG 2000 method delivered better quality images than CCSDS for low bit rate. In summary at a rate of 0.25 bpp, CCSDS is 15 times faster than Bandelet and 3 times faster than JPEG2000. However, CCSDS quality is lower by up to 8.77% compared with Bandelet and up to 13.64% compared with JPEG2000.
Object detection algorithm based AdaBoost residual correction Fast R-CNN on network

The rapid development of computer hardware has promoted the prosperity of computer vision. Target object detection is widely used in various industrial and commercial fields, and contour detection is the core of target object detection. In order to realize the object target contour recognition method based on computer vision with high accuracy, this paper takes the cattle face position determination as an example, Fast R-CNN as the object contour detection algorithm, and uses AdaBoost as the residual detector to improve the accuracy of the results. In the experiment, the LabelImg tool marks the positional coordinates of the facial contours of 1000 cows, and at the same time, SURF algorithm was used to extract image features. The AdaBoost cascade classifier trained 900 positive images and 100 negative images. Fast R-CNN used the original images and the labeled images as training sets respectively. The results show that in the image set with resolution of 866*652 (pixels), the target detection accuracy of using Fast R-CNN is 91.6%, and AdaBoost as the residual detector will improve the accuracy to 96.76%. Meanwhile, by comparing the two training data sets of Fast r-cnn, the image labeled by LabelImg is used as the Fast r-cnn training set to obtain the optimal accuracy of 96.9% and the optimal recognition time of single picture of 0.35s.
Distributed ledger technology and European Union General Data Protection Regulation compliance in a flexible working context

Abstract Compliance of distributed ledger technology (DLT) based solutions with the European Union General Data Protection Regulation (EU GDPR) is currently a hot topic both from a technical point of view, as well as from a legal and normative one. Flexible working is a new working model where employees can perform their work activity without specific time and space constraints. In this context privacy and information trustworthiness are relevant and, therefore, on the one hand it can gain benefit from using DLT based solutions, on the other hand it must comply with the GDPR. This paper describes a solution that combines DLTs and the new Ciphertext‐Policy Attribute Based Encryption cryptographic techniques to support compliance with the EU GDPR in the context of flexible working. The devised solution is being concretely experimented, in the context of the EU H2020 CITADEL project, by the Municipality of Bari (Italy).
Towards Explaining Deep Neural Networks Through Graph Analysis

Due to its potential to solve complex tasks, deep learning is being used across many different areas. The complexity of neural networks however makes it difficult to explain the whole decision process used by the model, which makes understanding deep learning models an active research topic. In this work we address this issue by extracting the knowledge acquired by trained Deep Neural Networks (DNNs) and representing this knowledge in a graph. The proposed graph encodes statistical correlations between neurons' activation values in order to expose the relationship between neurons in the hidden layers with both the input layer and output classes. Two initial experiments in image classification were conducted to evaluate whether the proposed graph can help understanding and explaining DNNs. We first show how it is possible to explore the proposed graph to find what neurons are the most important for predicting each class. Then, we use graph analysis to detect groups of classes that are more similar to each other and how these similarities affect the DNN. Finally, we use heatmaps to visualize what parts of the input layer are responsible for activating each neuron in hidden layers. The results show that by building and analysing the proposed graph it is possible to gain relevant insights of the DNN's inner workings.
Ensuring Cybersecure Telemetry and Telecommand in Small Satellites: Recent Trends and Empirical Propositions

With the advent of a large number of small satellites in the orbit performing critical missions, securing ground/space infrastructure against cyberattacks has become a blazing issue amongst operators and policymakers. In this paper, we provide up-to-date analysis on the recent advances, existing, and future trends in small satellite cybersecurity, considering the advent of new policies and architectures, implementation of modern cryptographic algorithms, and prospects of quantum cryptographic techniques. We explore the performance of selected encryption schemes in the cyberphysical space of small satellites, using throughput, memory footprint, security, and energy consumption as key performance metrics. We propose an open-source cybersecurity analysis platform in the context of small satellites and have found out that the 128 bit Advanced Encryption Standard (AES) is most suitable for securing small satellite telemetry/telecommand channel. We have also investigated possibilities of telemetry compression, proposing a modified Consultative Committee for Space Data Systems telemetry/telecommand packet format to incorporate compression and encryption, and have found simple Huffman coding to be suitable. In order to improve the resilience of ground infrastructure against cyberattacks, a redundant array of ground station elements is recommended to be architected.
Gaze-Dependent Distortion Correction for Thick Lenses in HMDs

Common VR headsets require lenses that increase the field of view and allow the user to focus the display. In most systems, thick lenses are used that generate strong pincushion distortions. To account for this, the content is warped by a corresponding barrel distortion before displaying, resulting in undistorted images for the viewer. This approach assumes that the eye is exactly positioned, typically on the optical axis of the lens. However, in real systems the eye's location deviates from this optimal position even at rest; moreover, the pupil - and thus the optical center of the eye - moves by several millimeters when the user looks around. Thus, eye movement results in additional distortion, which is ignored in current VR headsets. Also in literature on head-mounted displays, the effect is most often not considered, or at least badly documented. The contribution of this paper are experiments that emphasize the importance of this mostly ignored effect. To this end, we have built a simple setup with a camera at variable eye positions in a standard VR headset, that allows us to directly measure the variation of distortion during eye movement. Our experiments show that distortion varies by several dozens of pixels within the full range of eye movements, which emphasizes that the effect is definitely significant. We also demonstrate how in a headset with built-in eye tracker, the knowledge of the eye position can be used to achieve a view-dependent lens correction with only minimal additional effort at run-time.
Unsupervised Anomaly Detection for Seasonal Time Series

We extend eBay's Atlas algorithm to automatically detect anomalies in unlabeled, seasonal time series data. Named MULDER, the algorithm involves deriving a "surprise" metric from the time series, which is then analysed statistically for anomalies. We evaluate the efficacy of MULDER via the Numenta Anomaly Benchmark, and calibrate it for deployment with injected anomalies on production data. We find that MULDER can be used to create alerts with a low false positive rate, and outperforms several popular open source implementations.
Blockchain in Industry: Review of key use cases and lessons learned

In this paper key applications of blockchain (and smart contracts) are reviewed coming from across different industries around the world, and lessons learned are pulled from this review with a specific focus on what can be learned regarding the energy industry and in relation to the energy transition in particular. While it is difficult to obtain clear conclusions based on such early stage applications in other industrial sectors, like the financial sector, some basic insights emerged that could provide general lessons learned to inform players in the energy industry (in particular utilities and the innovation ecosystem around them) about the potential for blockchain in the energy industry, considering of course that stakeholders in this industry operate and innovate under a highly regulated market.
Çorba: crowdsourcing to obtain requirements from regulations and breaches

Modern software systems are deployed in sociotechnical settings, combining social entities (humans and organizations) with technical entities (software and devices). In such settings, on top of technical controls that implement security features of software, regulations specify how users should behave in security-critical situations. No matter how carefully the software is designed and how well regulations are enforced, such systems are subject to breaches due to social (user misuse) and technical (vulnerabilities in software) factors. Breach reports, often legally mandated, describe what went wrong during a breach and how the breach was remedied. However, breach reports are not formally investigated in current practice, leading to valuable lessons being lost regarding past failures. Our research aim is to aid security analysts and software developers in obtaining a set of legal, security, and privacy requirements, by developing a crowdsourcing methodology to extract knowledge from regulations and breach reports. We present Çorba, a methodology that leverages human intelligence via crowdsourcing, and extracts requirements from textual artifacts in the form of regulatory norms. We evaluate Çorba on the US healthcare regulations from the Health Insurance Portability and Accountability Act (HIPAA) and breach reports published by the US Department of Health and Human Services (HHS). Following this methodology, we have conducted a pilot and a final study on the Amazon Mechanical Turk crowdsourcing platform. Çorba yields high quality responses from crowd workers, which we analyze to identify requirements for the purpose of complementing HIPAA regulations. We publish a curated dataset of the worker responses and identified requirements. The results show that the instructions and question formats presented to the crowd workers significantly affect the response quality regarding the identification of requirements. We have observed significant improvement from the pilot to the final study by revising the instructions and question formats. Other factors, such as worker types, breach types, or length of reports, do not have notable effect on the workers’ performance. Moreover, we discuss other potential improvements such as breach report restructuring and text highlighting with automated methods.
Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis

One can either use machine learning techniques or lexicons to undertake sentiment analysis. Machine learning techniques include text classification algorithms like SVM, naive Bayes, decision tree or logistic regression, whereas lexicon-based sentiment analysis uses either general or domain-based lexicons. In this paper, we investigate the effectiveness of domain lexicons vis-à-vis general lexicon, wherein we have performed aspect-level sentiment analysis on data from three different domains, viz. car, guitar and book. While it is intuitive that domain lexicons will always perform better than general lexicons, the actual performance however may depend on the richness of the concerned domain lexicon as well as the text analysed. We used the general lexicon SentiWordNet and the corresponding domain lexicons in the aforesaid domains to compare their relative performances. The results indicate that domain lexicon used along with general lexicon performs better as compared to general lexicon or domain lexicon, when used alone. They also suggest that the performance of domain lexicons depends on the text content; and also on whether the language involves technical or non-technical words in the concerned domain. This paper makes a case for development of domain lexicons across various domains for improved performance, while gathering that they might not always perform better. It further highlights that the importance of general lexicons cannot be underestimated — the best results for aspect-level sentiment analysis are obtained, as per this paper, when both the domain and general lexicons are used side by side.
Immersive Job Taste: a Concept of Demonstrating Workplaces with Virtual Reality

This paper presents a new concept of `Immersive Job Taste' - interactive virtual reality demonstration of a workplace that aims to give a feeling of going through an average workday of a professional with elements of basic training. The main target audiences of Job Taste simulations are young job seekers who can be aided in selecting a career path at school or a welfare center, choosing the first or a new occupation, often after a period of being unemployed. The design methodology behind the Immersive Job Taste concept includes presentation of a workplace, typical tasks, feedback on performance, and advice on applying for jobs in the specific industry. We developed several scenarios and applied different virtual and augmented reality concepts to build prototypes for different types of devices. The prototypes were evaluated by several groups of primary users and experts. The results indicate a generally very positive attitude towards the concept. In this paper, we discuss the potential impact of applying the concept and directions for future work.
Authentication using alignment of the graphical password

Graphical password authentication is an alternate and yet evolving type of authentication in this current era, it is either recall or recognition based. User will either reproduce the image or recognize the images which was used or produced during the registration phase. Passwords guards the resources and information by allowing only designated person to access. Graphical password is new, trending and seems to be very promising method of authentication.it exists as an alternative to the text password. Alphanumeric usernames and passwords are the most common technique put in use. This technique showed to have some of it's advantages over simple text password but also with some cons too. When it comes to alphanumeric passwords, user either will try to keep a simple strength password which stands a chance to get cracked, but it gets inconvenient for the users to remember the strong strength passwords. With the graphical password, user will keep images or points-of-interest of images as password. Most advanced and secure type of authentication for these days is bio-metric, but it is too expensive to be used by all. Therefore, graphical password which is less expensive, secure and easy to be used by all can be considered as a perfect alternative method of authentication.
Monolithic image decomposition

Image decomposition is one of the essential image processing techniques in computer vision and computational photography because it can be applied to various areas, such as image smoothing, detail enhancement, image abstraction, and high-dynamic-range compression. The main goal of image decomposition is to successfully separate structure from a given image by preserving edge-like structure components and removing fine-scale details without prior information. This paper proposes an effective image decomposition technique called monolithic image decomposition that considers both local and global features using RGB color channels simultaneously by exploiting low-rank approximation and total variation-based minimization. Our approach is different to previous approaches in that previous approaches use either local or global features, and perform the image decomposition process channel by channel and combine the decomposition result of each channel. Using monolithic parameter update, we successfully separate the texture and structure from a given image while preventing artifacts such as staircase effects present in traditional filter based approaches. The experiment results prove the effectiveness of our approach in image decomposition. We also show the usefulness of our approach by presenting successful applications in structure–texture–noise decomposition, detail enhancement and image abstraction.
A Resource Aware Parallelized Back Propagation Neural Network in Enabling Efficient Large-Scale Digital Health Data Processing

Along with the development of digital health, efficient machine learning is anxiously needed to handle the growing health data. Among various machine learning algorithms, back propagation neural network (BPNN) shows great effectiveness in both academia and industrial fields. However, it is frequently reported that the conventional BPNN algorithm encounters low efficiency issue in dealing with large-scale digital health data. Therefore this paper presents a Hadoop based parallelized BPNN algorithm which is able to process the large-scale data efficiently. In order to complement the potential accuracy loss issue for the parallelized data processing, ensemble learning techniques are also involved. Additionally although Hadoop supplies a number of default schedulers, the heterogeneous distributed computing environment may still impact the efficiency of the parallelized BPNN. Consequently, this paper also presents a gene expression programming (GEP) algorithm based load balancing approach, which enables the computing resource awareness and the optimal scheduling of the parallelized BPNN. The experiments employ the classification task as the underlying testing basis. Two types of the experiments are carried out, in which the first one focuses on evaluating the accuracy of the presented algorithm with classifying the benchmark dataset; the second one focuses on evaluating the efficiency of the presented algorithm with classifying the large-scale dataset. The experimental results show the effectiveness of the presented resource aware parallelized BPNN algorithm.
RAD Design and Data Management Systems of Natural Resources and Local Wisdom

Abstract This research proposed the Rapid Application Development (RAD) design and data management systems of natural resources and local wisdom from the real data. The objectives were to: 1) collect local resources such as biological of local plant, animal, and the wisdom in the community; 2) study, organize and create an information system collection; 3) create a web-based application to present information of the data. The research used Rapid Application Development to plan and combining process from existed data to the web-based application for managing natural resources and local wisdom for data management. Natural resources and local wisdom data management at Tambon Ramdang is a case study of the successful implementation from the RAD design. The conclusion showed the lesson learn from this research and extended future work in this area.
Logistic Regression Region Weighting for Weakly Supervised Object Localization

In this paper, we address the problem of weakly supervised object localization using region weighting. For a weakly labelled image/video, the inside regions have different relevance to its semantic label. We first over-segment an image/video to get super-pixel/voxel regions, and assign each region with a latent weight to represent its support to the semantic label, then regress the weights to right values by optimizing the classification according to the weak labels. We adopt logistic regression as our base model due to its good performance in multiple-instance setting. The latent region weights are incorporated into the objective function as an interpretation of region combination at feature-level. The weights and the model parameters are optimized in an alternate manner. With the updates of the weights, the model is trained on the semantic regions independently of the background, therefore the learned model is capable of distinguishing object and non-object regions, and generating irregular-shape object localization. The method overcomes the limitations of applying multiple-instance learning to visual object localization. Experimental results on three datasets validates the effectiveness of the proposed method.