Reviewerly/exHarmony huggingface dataset:
Investigating One-Time Actions for Domestic Energy Reduction: The Case of District Heating

The design of interactive technology meant to change people's behavior to save energy in the home has occupied HCI researchers in the last decades. In this paper, we extend current HCI literature by investigating one-time actions for long-term energy reduction. We report from an empirical study using district heating as a case. District heating is a way of heating houses where hot water is distributed through a network of pipes from energy supplier to individual buildings. We report from interviews with energy suppliers and households interested in reducing their energy consumption and ask why and how people take one-time actions towards reducing their heat consumption. We present our findings in three themes of; energy literacy as enabler of heat reductions, motivation for heat reductions, and technology supporting heat reduction. We further discuss our findings as to how one-time actions for long-term energy reduction can be supported through interactive technology.
WITHDRAWN: Comparative Research on Active Learning of Big Aata based on Mapreduce and Spark

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Optimal Learning of Joint Alignments with a Faulty Oracle

We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables g <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> ∈ {0,...,k − 1} (up to some global offset) given noisy observations of a set of their pairwise differences {(g <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> − g <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</inf> ) mod k}; specifically, with probability $\frac{1}{k} + \delta $ for some δ > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a pairwise difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\left( {\frac{{n\lg n}}{{k{\delta ^2}}}} \right)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly recent work by Chen and Candés [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
Research on Frequency Conversion Starting Characteristics of Belt Conveyor Motor Based on Simulation

Based on the simulation, the dynamic characteristics of three kinds of starting laws are analyzed. It is found that the system adopts the law of slope curve, the impact of motor speed and electromagnetic torque is too large, and the stability of the system is poor. The system adopts the simple harmonic motion law, the system has a great improvement in the stability, but the response speed is slow, and the stiffness is poor. The system adopts the law of equal acceleration and equal acceleration, with the best stability, rapidity and stiffness.
Service Chaining Security Based on Blockchain

Abstract The SDN (Software Defined Networking) service chaining can take advantage of the separation of control and forwarding in the SDN network, centrally manage and coordinate various physical and virtual resources to dynamically meet users’ complex network service needs. At present, most researches on SDN service chaining are about the service chaining protocol and composition structure, service chaining mapping and service chaining strategy description. However, there is no traceable data storage method for the stability of the performance of the service nodes. This paper proposes to use blockchain technology to ensure the reliable traceability of the service chaining data. Meanwhile, in order to improve the retrieval efficiency, this paper designs and implements the combination of virtual chain and Bloom filter, and through the design of smart contract to achieve the query rate of 584.7KB/s under the condition of using blockchain. Besides, the system can save 28.7% of the retrieval time in the case of a large number of service chaining data storage. These experimental results prove that the system design can be well applied in the service chaining scenario.
Prediction method of equipment maintenance time based on deep learning

In the actual maintenance support application, it is difficult to carry out maintenance work according to the actual environment and conditions of the equipment to be repaired, which leads to the inaccurate timing of the equipment and the low efficiency of the equipment use. In order to study the relationship between the time interval of equipment repair and the geographical environment of the equipment more deeply, a RCNN deep learning model is proposed for training and feature extraction of equipment maintenance service information and geographical environment information, which can also predict the equipment maintenance interval. It also helps to compare among different prediction models and verify the effectiveness of the model, which further provides a method for the optimization of equipment maintenance interval.
Cross-Domain Sentiment Classification Using Topic Attention and Dual-Task Adversarial Training

Cross-domain sentiment classification aims at transferring the knowledge of the source domain with rich annotation resource to the scarcely labeled target domain or even without labels. Existing models fail to automatically capture simultaneously the three related topics, namely sentiment-only topic, i.e. containing domain-independent sentiment words or pivots in literature, domain-only topic, i.e. containing domain-specific words, and function word topic containing such as stop words. We propose a two-stage framework for tackling this problem. The first stage consists of a topic attention network specialized in discovering topics mentioned above. The second stage utilizes the learned knowledge from the first stage for learning a sentiment classification model with the consideration of context. A new sentiment-domain dual-task adversarial training strategy is designed and utilized in both stages. Experiments on a real-world product review dataset show that our proposed model outperforms the state-of-the-art model.
<b>Remote sensing techniques</b> for tracking changes caused by illegal gold mining in Madre de Dios, Peru

Abstract Illegal extraction of gold has grown to be a problem in many countries, causing the degradation of the environment. The main purpose of this paper is to investigate changes in tree cover and surface pollution. The development of a mine site has been observed and analysed with images acquired from Landsat and the Sentinel missions. The results of the study showed changes in the state of the environment, strongly suggesting the possibility of ongoing pyrite weathering processes and the transportation of clay materials down watercourses, which can cause not only the further deterioration of the environment but also slow down the natural regeneration of the forest. In addition, research has found disturbing changes in vegetation, showing a loss of tree cover in the Amazon Rainforest as high as 17%. The validity of using remote sensing methods to observe the development of individual mining sites and their characteristics was confirmed.
Semi-Supervised Sentiment Analysis of Portuguese Tweets with Random Walk in Feature Sample Networks

Nowadays, a huge amount of data is generated daily around the world and many machine learning tasks require labeled data, which sometimes is not available. Manual labeling such amount of data may consume a lot of time and resources. One way to overcome this limitation is to learn from both labeled and unlabeled data, which is known as semi-supervised learning. In this paper, we use a positive-unlabeled (PU) learning technique called Random Walk in Feature-Sample Networks (RWFSN) to perform semi-supervised sentiment analysis, which is an important machine learning that can be achieved by classifying the polarity of texts, in Brazilian Portuguese tweets. Although RWFSN reaches excellent performance in many PU learning problems, it has two major limitations when applied in our problem: it assumes that samples are long texts (many features) and that the class prior probabilities are known. We leverage the technique by augmenting the data representation in the feature space and by adding a validation set to better estimate the class priors. As a result, we identified unlabeled samples of the positive class with precision around at 70% in higher labeled ratio, but with high standard deviation, showing the impact of data variance in results. Moreover, given the properties of the RWFSN method, we provide interpretability of the results by pointing out the most relevant features of the task.
Temporal Anomaly Detection by Deep Generative Models with Applications to Biological Data

An approach to anomaly detection is to use a partly disentangled representation of the latent space of a generative model. In this study, generative adversarial networks (GAN) are used as the normal data generator, and an additional encoder is trained to map data to the latent space. Then, a data anomaly can be detected by a reconstruction error and a position in the latent space. If the latent space is disentangled (in a sense that some latent variables are interpretable and can characterize the data), the anomaly is also characterized by the mapped position in the latent space. The present study proposes a method to characterize temporal anomalies in time series using Causal InfoGAN, proposed by Kurutach et al., to disentangle a state space of the dynamics of time-series data. Temporal anomalies are quantified by the transitions in the acquired state space. The proposed method is applied to four-dimensional biological dataset: morphological data of a genetically manipulated embryo. Computer experiments are conducted on three-dimensional data of the cell (nuclear) division dynamics in early embryonic development of C. elegans, which lead to the detection of morphological and temporal anomalies caused by the knockdown of lethal genes.
DR-Tools: a suite of lightweight open-source tools to measure and visualize Java source code

In Software Engineering, some of the most critical activities are maintenance and evolution. However, to perform both with quality, minimizing impacts and risks, developers need to analyze and identify where the main problems come from previously. In this paper, we introduce DR-Tools Suite, a set of lightweight open-source tools that analyze and calculate source code metrics, allowing developers to visualize the results in different formats and graphs. Also, we define a set of heuristics to help the code analysis. We conducted two case studies (one academic and one industrial) to collect feedback on the tools suite, on how we will evolve the tools, as well as insights to develop new tools that support developers in their daily work.Videos: https://bit.ly/30weexX.
Application and Practice of ID3 Algorithms in College Students’ Education

In order to understand the application of ID3 algorithm in college students’ education, relevant analysis will be carried out in practical testing. Firstly, this paper summarizes the ID3 algorithm and its relationship with college students’ education, and then, the application mode and practical effect of ID3 algorithm will be understood through theory and case study. The results show that ID3 algorithm has good performance in college students’ education, can improve the quality of education, optimize the details of work, and has good application value. In the traditional university student education, many problems can not be solved because of the unavoidable influence of human resources. ID3 algorithm can break through the limitations of human resources and achieve good results.
Performance Analysis of Various Trained CNN Models on Gujarati Script

In the age of digitization, it’s worthy to preserve ancient scripts, researches, and documents. A digital format of the mentioned documents would help in preservation and replication and therefore, the chief goal of this research is to analyze the performance of various trained CNN (Convolutional Neural Network) models to recognize Gujarati scripts. As the ancient scripts are somewhat degraded in terms of their physical aspects and are sometimes unrecognizable, CNN plays a vital role in digitizing printed texts so that they could be edited in an electronic format, stored more compactly and used in various machine processes. The models used for experimentation includes LeNet (Learning CNN), DenseNet (Dense CNN), and VGGNet (Visual Geometric Group Network), which are used for Optical Character Recognition (OCR), and have obtained the results on our synthetically generated dataset. The dataset comprises more than 400 classes with an 80-20 division of train and test images respectively. The performance parameters include validation accuracy, validation loss and the number of epochs required to train the CNN model.
CCSDS-MHC on Raspberry Pi for Lossless Hyperspectral Image Compression

Abstract This paper is about Consultative Committee for Space Data System for Lossless Multispectral and Hyperspectral Image Compression (CCSDS-MHC) algorithm that is implemented on Raspberry Pi 3 Model B+ system using Open Multi-Processing (OpenMP). CCSDS-MHC algorithm along with the full prediction mode is opted due to its best compression ratio (CR) performance. The issue of Hyperspectral Image Compression is the loss of data when compress, thus CCSDS algorithm is used in this research. Besides, with current technologies that require low-power but high-performance devices, Raspberry Pi was chosen to be tested in terms of its performances while being compared to other platforms. AVIRIS (airborne) and Hyperion (spaceborne) are used to test the performance of the system. OpenMP is introduced to simplify the computational operation through parallelization to take advantage of the multi-core architecture of the hardware system. In term of execution time, CCSDS-MHC algorithm when parallelize using OpenMP gave the best performances about 69.4% for AVIRIS 1997 dataset, 69.3% for AVIRIS 2006 dataset and 67.7% for Hyperion dataset. The execution time of performance CCSDS-MHC in Raspberry Pi 3 Model B+ comparing with other different multicore platform is validate and the mean parallelize is measured. The result of comparison proves that faster performing task gives the best result due to higher speed of CPU-cores performances. From this research, it is proven that Raspberry Pi which is a low-power embedded platform able to compress the hyperspectral images at optimized speed with the implementation of OpenMP through CCSDS algorithm. Therefore, this research will be useful for further studies in lossless of hyperspectral images.
E-Mail Message Encryption Using Advanced Encryption Standard (AES) and Huffman Compression Engineering

E-mail is a means of sending messages electronically based online. Many students, institutions or companies use e-mail as a formal communication tool. However, please note that sending e-mails is done through public channels, namely the internet, making it possible to tap, manipulate data and leak information. Like hacking and theft of millions of unique ID (address) E-mail and passwords that occurred in mid-January 2019 by the digital attackers. E-mail does not have security on the data sent. Implementation of encrypted messages in e-mails with cryptographic methods can be a solution for securing the data sent. In this study used the Advanced Encryption Standard (AES) and Huffman methods. The final result of this study is the occurrence of text messages using the AES and Huffman methods can be done up to 32.200 characters with an accuracy value of 90.62% and the difference in the performance time of encryption and decryption is 1% with the decryption process a little longer than the encryption process.
Fractal Residual Network for Face Image Super-Resolution

Recently, many Convolutional Neural Network (CNN) algorithms have been proposed for image super-resolution, but most of them aim at architecture or natural scene images. In this paper, we propose a new fractal residual network model for face image super-resolution, which is very useful in the domain of surveillance and security. The architecture of the proposed model is composed of multi-branches. Each branch is incrementally cascaded with multiple self-similar residual blocks, which makes the branch appears as a fractal structure. Such a structure makes it possible to learn both global residual and local residual sufficiently. We propose a multi-scale progressive training strategy to enlarge the image size and make the training feasible. We propose to combine the loss of face attributes and face structure to refine the super-resolution results. Meanwhile, adversarial training is introduced to generate details. The results of our proposed model outperform other benchmark methods in qualitative and quantitative analysis.
Quantum key agreement via non-maximally entangled Bell states

In this paper, we propose one new quantum key agreement (QKA) protocol using non-maximally entangled Bell states with positive operator-valued measurement (POVM). It is designed for multi-party QKA by non-maximally entangled Bell states with POVM. Since Bell states and single particle can be obtained by various physical systems, thus, our protocol is feasible based on the current technology. It is secure against the outsider and participant attack. Further, it is shown that the shared key is decided by all participants. Therefore, it could guarantee the security and fairness.
A Semantic Inference Based Method for Privacy Measurement

In the era of Internet and big data, an increasing number of intelligent applications have been developed. As the result, a lot of user data can be collected and stored by Internet companies as well as by ordinary users through various media platforms such as Facebook, WeChat, etc. that may contain information related to personal privacy. Even though privacy protection has been declared by Internet service providers, after collecting enough amount of seemingly less relevant data, an attacker can still infer user privacy via one means or another, e.g., by running a data mining algorithm. This can undoubtedly bring high risk of privacy disclosure to users under such an attack model. So, accurately measuring the leakage of privacy becomes an urgent issue. Although many privacy measurement and protection methods have been proposed in recent years, they mainly target at structured datasets and are thus inadequate to the measurement of the disclosure of specific privacy information. In addition, most of the methods have failed to consider the internal connections and relationships between privacy information and thus cannot be used to measure the implicit privacy disclosure risk on unstructured data. In this paper, we propose a semantic inference method based on the WordNet ontology to measure privacy disclosure in which we employ an information content (IC) based method to determine the weight of attributes to describe the inference preferences in the process of inferring privacy. Experiment was performed to verify the effectiveness of the IC based inference weight assignment method and to compare the proposed measurement method to some privacy disclosure behavior learned through a data mining algorithm and some existing privacy measurement methods to demonstrate the advantages of the proposed method for measuring privacy disclosure.
Maintainability Measurement and Evaluation of myITS Mobile Application Using ISO 25010 Quality Standard

Academic Information System (AIS) has become a mandatory application for universities nowadays. AIS is an academic information system that was built to provide convenience to users in campus's academic administration activities by online. Therefore, AIS must be a system that has good service quality. The many software quality standards that exist today show the importance of achieving software quality. The purpose of this study is to evaluate FRS module's maintainability quality measurements results in myITS application which can be used as a reference in further development. Quality software information that can be measured such as the amount of functions, the amount of lines of code, complexity, the amount of errors, and trials used to support management planning, organizing, implementing, and controlling. The research methods used consist of reverse engineering, quality matrix analysis, system quality testing, and evaluation. This research focuses on maintainability quality standards based on ISO 25010. The results of this study stated that the FRS module in the myITS application has good maintainability quality, this is evidenced by the quality score of the myITS Lecturer at 2.670 and myITS Student at 2.083.
Improving Adversarial Learning with Image Quality Measures for Image Deblurring

Generative adversarial networks (GANs) have become popular and powerful models for solving a wide range of image processing problems. We introduce a novel component based on image quality measures in the objective function of GANs for solving image deblurring problems. Such additional constraints can regularise the training and improve the performance. Experimental results demonstrate marked improvements on generated or restored image quality both quantitatively and visually. Boosted model performances are observed and testified on three test sets with four image quality measures. It shows that image quality measures are additional flexible, effective and efficient loss components to be adopted in the objective function of GANs.
Squeezer - A Tool for Designing Juicy Effects

This paper introduces Squeezer, a tool for designing juicy effects inthe Unity game engine. Drawing upon inspiration from sound effect synthesizers and description languages, Squeezer can "synthesize" common types of juice effects, by combining simple effects into effect sequences. These effect sequences can be edited and executed even while playing the game in the editor, lowering iteration times and easing the exploration of effects. We make a preliminary usability test to verify the functionality and scope of the tool. Squeezer is available at: https://github.com/pyjamads/Squeezer
Application of social networks users digital fingerprints to predict their information image

This paper is devoted to detection of correlations between the information image of users in social network and their real psychological personality traits and personal details. The ongoing research consists of psychodiagnostic testing of social media users, as well as further collection and processing of their open data in the social media profile. The interim results of our ongoing research allow us to conclude that there are significant patterns that allow us to determine age, gender or level of education by user' avatar and other public data in their profiles.