Reviewerly/exHarmony huggingface dataset:
Machine Learning in Satellites Monitoring and Risk Challenges

The world we are living in is full of challenges such as climate changes, experiencing possible consequences of human-induced environmental change, increasing of population and depleting natural sources. All these challenges demand us to discover and understand comprehensively the natural sources to fulfill our needs. Evolution of sensors for collecting data with high resolution demanded us a new technology to handle the obtained big data to make a better decision. Machine Learning (ML) and Artificial Intelligent (AI) have a vital impact on the evolution of many sectors in economy and human services to develop our daily life. Satellites today service many sectors such as weather services, navigation, space-based telecommunications and direct broadcasting. Health monitoring of Satellites depend mainly on handling the large amount of delivered telemetry data using machine learning techniques for understanding the nature surrounding us to improve our systems and protect our environment. This chapter will show different aspects of satellites systems, the various orbits of satellites between and the risk challenges on satellites operation. Also, it will discuss the uses of satellites in image processing and the importance of machine learning to understand and work out the facing challenges.
On the semantics of Cypher's implicit group-by

Cypher is a popular declarative query language for property graphs. Despite having been adopted by several graph database vendors, it lacks a comprehensive semantics other than the reference implementation. This paper stems from Cypher.PL, a project aimed at creating an executable (and readable) semantics of Cypher in Prolog, and focuses on Cypher's implicit group-by feature. Rather than being explicitly specified in the query, in Cypher the grouping key is derived from the return expressions. We show how this becomes problematic when a single return expression mixes unaggregated property references and aggregating functions, and discuss ways of giving this construct a proper semantics without defying common sense.
Towards Real-Time Web Tracking Detection with T.EX - The Transparency EXtension

Targeted advertising is an inherent part of the modern Web as we know it. For this purpose, personal data is collected at large scale to optimize and personalize displayed advertisements to increase the probability that we click them. Anonymity and privacy are also important aspects of the World Wide Web since its beginning. Activists and developers relentlessly release tools that promise to protect us from Web tracking. Besides extensive blacklists to block Web trackers, researchers used machine learning techniques in the past years to automatically detect Web trackers. However, for this purpose often artificial data is used, which lacks in quality. Due to its sensitivity and the manual effort to collect it, real user data is avoided. Therefore, we present T.EX - The Transparency EXtension, which aims to record a browsing session in a secure and privacy-preserving manner. We define requirements and objectives, which are used for the design of the tool. An implementation is presented, which is evaluated for its performance. The evaluation shows that our implementation can be used for the collection of data to feed machine learning algorithms.
An Efficient Property-Based Authentication Scheme in the Standard Model

In order to solve the problem of platform configuration information leakage that are caused by the traditional platform authentication in the trusted computing environment, this paper proposes a novel property-based authentication (PBA) scheme. In this paper, we design the framework and define the security model of our scheme. Then we give the detail construction of our scheme. Comparing with existing PBA schemes, our PBA scheme is more effective than other schemes. At the aspect of security, this paper proves that our scheme meets correctness, unforgeability and configuration privacy in the standard model.
On exploring factors for enhancing export competitiveness in IT Convergence industries

Purpose - The IT convergence industry, which is the subject of this study, is the main strategy field during the 4th industrial revolution era. Against this background, it is urgent to establish policy measures to survive and spread export products in the global industries. Research, design, data and methodology - In order to achieve this goal, we conducted the Importance - Performance Analysis (IPA) and found that it is necessary to develop tailor - made marketing support for small and medium sized IT exporters and to develop export strategy products with competitive technologies. Results - Above all, customized marketing support for IT export-related SMEs was needed. Next, in the first quadrant, strategic products, qualitative level, global, value added, and information systems were included, and it was found that 'development of export strategic products with competitive technologies' was necessary. In the third quadrant, related variables calculated at present time are not urgent variables. Conclusions - In this study, it would be necessary to calculate the additional implications of the variables that are not considered in this study, including future studies, because the methods considered here as analysis variables are carried out in comparison with the previous studies.
Deep supervised hashing network with integrated regularisation

Hashing has been widely deployed to approximate nearest neighbour search for large-scale multimedia retrieval tasks due to storage and retrieval efficiency. State-of-the-art supervised hashing methods for image retrieval construct deep structures to simultaneously learn image representation and generate good hash codes, and the key step among them is simultaneously learned feature representation and binary hash code. Existing methods use similarity and regularity loss to train deep hashing systems, but these two functions usually work together but not cooperative, which may lead to inadequate performance of the whole system. In this study, a new method for training deep hashing system to learn compact binary codes is presented. The deep supervised hashing network with integrated regularisation (DSHIR) system develop the zero division restriction as a new part of the loss function, which settles the problem of cooperatively guiding the system generate similarity preserving binary codes. DSHIR system also modifies the similarity handling loss to better extract features from image data, which promotes the performance compared to existing end-to-end deep hashing systems. Experiments show that DSHIR yields about 10 per cent higher mean average precision on CIFAR-10 dataset, and also promote on other evaluation indexes compared with state-of-the-art systems.
Multimedia Learning Modules Development based on Android Assisted in Light Diffraction Concept

Abstract This paper develops learning tools in the form of lesson implementation plans (RPP), modules, and assessment sheets. RPP has been developed with a model of guided inquiry as a means of learning activities. The modules are developed in the form of Multimedia Learning Modules (MLMs) which can attract the interest and motivation of students. The assessment sheet developed is an assessment of mathematical and verbal abilities. The results of the validation of the products developed show that the criteria are very feasible to be used in learning activities.
Traceable CP-ABE for Outsourced Big Data in Cloud Storage

Ciphertext Policy Attribute Based Encryption (CP-ABE) is a promising cryptographic solution for the unauthorized access to the outsourced big data in the cloud environment. However, most of the existing CP-ABE schemes do not have the provision to trace the users who misuse their secret key for profit intention which indeed reduces the CP-ABE schemes security. Thus, in this paper, we propose traceable CP-ABE (T-CP-ABE) for outsourced big data in cloud storage. Our scheme has the provision to dynamically trace who is decrypting the ciphertext during outsourced proxy decryption, which helps to identify the malicious users who misuse their secret key for profit intention. Furthermore, our scheme has an efficient key sanity check, which ensures that only the well-formed secret key is used for decryption. Security analysis proves that our scheme resist against the secret key forging and chosen-plaintext attacks. Performance evaluation proves that our T-CP-ABE scheme is efficient than other traceability schemes.
Consistency of the Elastic Net under a finite second moment assumption on the noise

Elastic Net regularization is a powerful tool to do prediction as well as variable selection. De Mol et al. (2009) developed a theoretical framework to analyse the Elastic Net and proved important properties as the consistency of the Elastic Net estimator under certain model assumptions. In this paper, these assumptions are relaxed and extended to a wider class of noise distributions. It is shown that the consistency of the Elastic Net still holds true under a finite second moment assumption on the noise term.
Persuading Risk-Conscious Agents: A Geometric Approach

We consider a persuasion problem between a sender and a receiver whose utility may be nonlinear in her belief; we call such receivers risk-conscious. Such utility models arise when the receiver exhibits systematic biases away from expected-utility-maximization, such as uncertainty aversion (e.g., from sensitivity to the variance of the waiting time for a service). Due to this nonlinearity, the standard approach to finding the optimal persuasion mechanism using revelation principle fails. To overcome this difficulty, we use the underlying geometry of the problem to develop a convex optimization framework to find the optimal persuasion mechanism. We define the notion of full persuasion and use our framework to characterize conditions under which full persuasion can be achieved. We use our approach to study binary persuasion, where the receiver has two actions and the sender strictly prefers one of them at every state. Under a convexity assumption, we show that the binary persuasion problem reduces to a linear program, and establish a canonical set of signals where each signal either reveals the state or induces in the receiver uncertainty between two states. Finally, we discuss the broader applicability of our methods to more general contexts, and illustrate our methodology by studying information sharing of waiting times in service systems.
Chinese News Keyword Extraction Algorithm Based on TextRank and Word-Sentence Collaboration

TextRank always chooses frequent words as keywords of a text. However, some infrequent words may also be keywords. To solve the problem, a keyword extraction algorithm based on TextRank is proposed. The algorithm takes the importance of sentences into consideration and extracts keywords through word-sentence collaboration. Two text networks are built. One network’s nodes are words where the diffusion of two words is defined to calculate the correlation between words. Another’s nodes are sentences where BM25 algorithm is used to calculate the correlation between sentences. Then a sentence-word matrix is constructed to extract the keywords of a text. Experiments are conducted on the Chinese news corpus. Results show the proposed algorithm outperforms TextRank in Precision, Recall and F1-measure.
Learning to Select User-Specific Features for Top-N Recommendation of New Items

Recommending new items to users remains a challenge due to the absence of user's past preferences for these items. Item features from side information are typically leveraged to tackle the problem. Existing methods formulate regression models, taking as input item features and as output user ratings. Availing of high dimensional item features, these methods are confronted with the issue of overfitting, which greatly impedes recommendation experience. In this work, we opt for feature selection to solve the problem of recommending top-N new items with high-dimensional side information. Existing feature selection methods find a common set of features for all users, which fails to differentiate user preferences over item features. To achieve personalization for feature selection, we propose to select item features specifically for users. The refined features filtered out the dimensions that are irrelevant to recommendations or unappealing to users. The experiment results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features crucial to top-N recommendations and hence boosting the performance.
Sustainable Design Based on User Research

A “sustainable solution” is a mix of products and services that enables people to live better, consuming less environmental resources and improving the quality of their physical and social contexts of life. It is necessary to rethink sustainable design approaches with user research. It is adopted cases study in user lifestyle, user behavior and user experience to discuss how user research can help designers understand user needs in sustainable context and conducting sustainable concept by design. It is indicate that by conducting user research, sustainable design can break through experimental ideas to real combination of design, industry and eco-culture.
Data Driven Priority Scheduling on a Spark Streaming System

Big data has become essential for businesses as it enables companies and organizations to gather insights from their data and use it to determine marketing opportunities, assist decision-making or even to find new business opportunities. Companies spend a great deal of effort collecting large amounts of data, which in some cases must be processed in real-time in order to capitalize on business opportunities. Predicting the expected input load at a given point in time can be very difficult and sometimes impossible. As a result, a great deal of effort is put into creating techniques to address varying input loads. A widely used approach is dynamic resource provisioning, but resource provisioners may not react in time to address the resource shortage which can result in increased processing latencies. This paper presents a priority scheduling technique that can be used in conjunction with dynamic and static resource provisioning. This approach allows users to assign a priority to input data items. The scheduler ensures that higher priority data items are given precedence over lower priority data items. This means that when resources become constrained the higher priority data items receive a greater share of resources and experience lower queueing delays in comparison to low priority items. A prototype for the data driven priority scheduler is implemented on the Spark Streaming system.
Exploitation and Comparison of Computational Intelligence Techniques on the Feature Selection Problem

Feature selection aims to gain a minimal feature subset in a problem domain while conserving the accuracy of the original data. Feature selection is a process for making more efficient data analysis by selecting more relevant features for the related problem solving. Feature selection increases prediction probability of algorithms by decreasing the dimensionality, eliminating irrelevant features. In this study, four computational intelligence techniques are implemented and compared on the well-known data instances taken from UCI. This is the first time that Migrating Birds Optimization (MBO) is used for the feature selection problem. Specifically, the exploited algorithms are (i) MBO, which is a recently proposed but successful technique, (ii) particle swarm optimization, which has originated from the simulation of behavior of biological organisms, (iii) simulated annealing, which is a well-known and frequently used as a benchmark algorithm and (iv) differential evolution. In our filter-based approach, we also implemented the inconsistency based subset evaluator to evaluate the performance of a given feature subset. Performance comparison is done with k-nearest neighbor, as the classifier where all features are used in the benchmark. Results show that the MBO algorithm presents the best performance in terms of number of winning cases.
Understanding the Role of Language in Conflict

The purpose of this article is to illustrate innovations in text analysis associated with understanding conflict-related communication events. Two innovations will be explored: LIWC (Linguistic Inquiry and Word Count), the text modeling program from the open-source data analysis software program R, and SPSS Modeler. The LIWC analysis revisits the 2009 study by Donohue and Druckman and the 2014 study by Donohue, Liang, and Druckman focusing on text analyses of the Oslo I Accords between the Palestinians and Israelis to illustrate this approach. The R and SPSS modeling of text analysis use the same data set as the LIWC analysis to provide a different set of pictures associated with each leader’s rhetoric during the period in which the Oslo I accords were being negotiated. Each innovation provides different insights into the mind-set of the two groups of leaders as the secret talks were emerging. The implications of each approach in establishing an understanding of the communication exchanges are discussed to conclude the article.
The Ground Target Detection and Tracking Method in Surveillance Video

With the development of image processing towards deep learning, the detection and tracking technology of ground objects such as vehicles and pedestrians in real scenes is becoming more and more advanced. However, there are still many difficulties in the detection and tracking of multiple objects in real scenes due to the blocking between vehicles and pedestrians and background changes. In this paper, an improved haar-like feature was used in the vehicle and it was compressed. Adaboost classifier was used to detect the feature and Kalman filter was used to track the feature. In combination with the neural network method in pedestrian detection, YOLOv3 network is used to combine deep_sort, and the depth feature is used to fuse LOMO feature for trajectory connection in many complex scenes. The experimental results show that the proposed algorithm reduces training time in vehicle detection and tracking, connects partially disconnected tracks in pedestrian detection and tracking, and improves the integrity of track tracking.
A Radiomic feature–based Nipple Detection Algorithm on Digital Mammography

Purpose In the diagnosis and detection of breast lesions, the nipple is an important anatomical landmark which can be used for the registration on multiview mammograms. In this study, we propose a new detection algorithm for nipples on digital mammography (DM) by applying pixel classification based on geometric and radiomic features extracted from breast boundary regions. Methods The imaging characteristics of nipples are closely related to the visibility on mammograms. To locate the nipple on mammogram, a searching area is first determined based on the breast boundary and chest wall orientation. Two different approaches are developed for obvious and subtle nipples, respectively. For obvious nipples, top hat transformation is employed to detect the nipple region, whose geometric center is regarded as the nipple position. For subtle nipples, the curved searching area near the breast boundary is mapped onto a Cartesian plane through a revised rubber band straightening transformation. On the straightened searching area, the geometric and radiomic features are calculated along the normal direction of the breast boundary, and a random forest classifier is trained for subtle nipple localization. Results Seven hundred and twenty‐one DMs were collected for the evaluation of the proposed algorithm. The locations of nipples are manually identified by an experienced radiologist as the reference standard. The average Euclidean distance between the computed nipple position and the reference standard was 2.69 mm (obvious) and 7.81 mm (subtle), respectively. A total of 97.61% of the obvious nipples (613/628) and 88.17% of the subtle nipples (82/93) were detected within a 10‐mm radius centered from the reference standard. Conclusions The evaluation results show that the proposed method is effective for nipple detection on DM, especially for subtle nipple detection.
Online Adaptation of Language Models for Speech Recognition

Hybrid models of speech recognition combine a neural acoustic model with a language model, which rescores the output of the acoustic model to find the most linguistically likely transcript. Consequently the language model is of key importance in both open and domain specific speech recognition and much work has been done in adapting the language model to the speech input. We present an efficient pipeline for hybrid speech recognition where a domain-specific language model is selected for each utterance based on the result of domain classification. Experiments on public speech recognition datasets in the Vietnamese language show improvements in accuracy over the baseline speech recognition model for little increase in running time.
A Bot for Suggesting Questions That Match Each User's Expertise

In this extended abstract, we present our approach to the expert recommendation based on PMF (Probabilistic Matrix Factorization) and term expansion for CQA services.
Privacy Centric Collaborative Machine Learning Model Training via Blockchain

This paper tackles the issue of data siloing, where organisations are unable to share data with each other because of privacy concerns. Machine Learning models, which could benefit greatly from larger data sets shared between organisations, suffer in this era of data isolation. To solve this problem, a blockchain based implementation is proposed that allows training of machine learning models in a privacy compliant way. Instead of using blockchain in a typical database-style manner, the proposed solution uses blockchain as a means to handle joint ownership and joint control over a computer system known as the Training Machine. The Training Machine, set-up jointly by consortium members, serves as a secure, independent container that accepts data sets and an untrained model as inputs from different entities, trains the model internally, and outputs the trained model without revealing any data to other entities. Data is then deleted automatically. Blockchain ensures that this machine is not under the control of any one entity but is rather controlled transparently by all data-sharing parties. By placing sensitive information in an isolated system, and establishing blockchain based access control, the solution ensures that data is not accessible to any party other than the owner. The paper also shares use cases of this technology, along with a risk analysis and proof of concept.
User Experience Improvement Design of Shopping Mall Based on Crowd Classification Research

With the rising of the e-commerce, consumers nowadays prefer to shop online out of its efficiency, flexibility and abundance of choice. That is to say, hundreds of traditional offline shopping malls are now going through the imperative urgency of transformation. This paper uses a variety of research methods and classifies the people in the malls based on their needs. Secondly, this paper proposes several design strategies for different groups, meanwhile summarizes the effective ways of user experience improvements. In general, this research is dedicated to suggesting mighty directions for the transformation of offline shopping malls.
Phase contrast Zernike method with dynamic transparent application

A new approach is proposed to implement phase contrast Zernike filter with dynamic transparent for phase objects visualization. We investigated the effect of the radius size of the filter at the low and high phase distribution of objects in the input plane to the spatial intensity distribution. A significant increase in contrast is shown when using the proposed dynamic filter. The experimental results of the dynamic phase-contrast filter Zernike are consistent with the numerical simulation.