Reviewerly/exHarmony huggingface dataset:
Innovative Management of Common-Pool Resources by Smart Contracts

The access to common-pool resources, i.e. to resources in limited common property, are legally distributed in a far more diverse way than limited private property resources. In transportation, a critical case for common-pool resources appear in Green Transport Corridors (GTC), that has been coined by European Union as being «sustainable logistics solutions for cargo transportation’ with a shared pool of resources aiming for multimodal trans-shipment routes with a concentration of freight traffic between significant hubs». Although there are already existing implementations of GTC concepts, there are still a lot of open questions concerning GTC governance and ownership models hindering easy marketing of the GTC approach. This paper discusses how and to which extent smart contracts in combination with blockchain technology as innovative solutions are able to facilitate GTC governance and how smart contracts can be applied to provide legal certainty by managing and allocating distributed access to common-pool resources. Smart contracts can be considered as computerised transaction protocols for the execution of underlying legal contracts, and they do not only target reducing transaction costs by realising trackable and irreversible transactions through blockchain technology for distributed databases, but also show high potential to strengthen cooperative business structures and to facilitate the entrepreneurial collaboration of cross-organisational business processes. From a legal perspective, it is controversial whether the use of smart contracts to distribute access to resources in terms of both general common-pool resources. GTCs implies an added value automatically for legal certainty and fair balance among different forms and degrees of access granted to different members of the cooperative. In cases of incorrect performance, change of circumstances or unduly induced contracts smart contracts fall considerably short on the protection of weaker parties, which the paper illustrates at the example of GTCs to be a decisive detriment of the cooperative members. The paper analyses these potentials and risks of smart contracts for the case of GTCs and showcases from both business and legal perspective in terms of their potential as viable means of distributing access to common-pool resources comprising infrastructure. Keywords common-pool resources, cooperative governance, blockchain, smart contracts, Green Transport Corridors.
Forming a TED talks sphere for convenient search

Purpose TED (Technology, Entertainment, Design, www.ted.com/ ) Talks has been one of the most popular video systems. However, the current TED Talks system expressed its inquired videos as in a two-dimensional (2D) table, which is inconvenient for searching the relationships among videos and tags. This study converts the TED Talks table into a sphere by using optimization techniques to help users search for preferred videos. Design/methodology/approach There are five phases in this study as follows. Phase 1: Reorganize data of 36 tags and 108 videos; Phase 2: Allocate tags on the TED sphere; Phase 3: Allocate videos on the TED sphere; Phase 4: Develop an online interactive TED retrieval system; and Phase 5: Perform survey and evaluation. Findings One survey demonstrated that the TED Talks sphere is more convenient for searching videos, as it is more user-friendly because of its graphical user interface, more convenient to use, more useful for retrieving information and can facilitate a more responsive search for users’ preferred videos. Research limitations/implications The numbers of tags and videos able to be displayed on a sphere is limited by the capacity of an optimization software and hardware. Practical implications The proposed sphere system can be used by a large number of users of TED Talks groups. Social implications This sphere systems can also be applied to other fields which use 2D forms to display the relationships among objects. Originality/value This study uses an optimization method to convert a 2D form into a 3D sphere to highlight the relationships among numerous objects.
Analysis of Filters in Performance Assessment of Principal Component Analysis (PCA) based Face Recognition System

The main challenges in state-of-the-art biometric-based identification systems are accuracy with respect to recognition and robustness with respect to real-time performance. In the past few decades, numerous facial recognition systems have been developed to improve the accuracy and real-time performance of the existing recognition systems. However, the existing systems and techniques could not achieve the required level of performance for recognition. This is due to various factors affecting the performance of these systems. Some of these factors include the availability of limited data sets, a perturbation in the image, lightening, and background color variations, etc. In this article, we provide an analysis of the performance of Principal Component Analysis (PCA) based face recognition system by applying three different linear filters. Experiments have been done by applying different filters over the face images and then a comparative analysis is provided to show the accuracy of each filter using PCA based facial recognition system.
High-Quality Image Security Implementation Using 128-Bit Based on Advanced Encryption Standard algorithm

These days, the security of information has become more vital in data transfer through networks and storages, so everyone knows the importance of data encryption to maintain the confidentiality and privacy of information transmitted across different networks. Image ciphering plays a vital role in avoiding eavesdropping or cracking access to the information that is transferred through networks; therefore, to look after the privacy and security of the information that is transferred on the internet, one of the important methods of security is chosen in this paper, which is “Advanced Encryption Standard”. The Advanced Encryption Standard is chosen in this paper based on the capability of it to secure information and the power of it to immunize information from eavesdropping. A Graphical User Interface (GUI) in Matlab is used as software to implement the Advanced Encryption Standard algorithm and to tests various types of High Definition. This research is focused on experiments on different types of pictures (gray, color and black and white) in high quality and seeing the required time for applying the Advanced Encryption Standard algorithm on these pictures. The results of this paper show the time that is required for ciphering and deciphering different types of pictures.
IDMN: A Two-Pass Attention Mechanism in Dynamic Memory Network

For reasoning and reading comprehension tasks, the Dynamic Memory Network, which included attention mechanism, shows strong reasoning and understanding ability for some kinds of problems. But there is more to improve. We believe that in the original iteration structure of the DMN model, even after many iterations, some important information will be missed, or useless information will be considered too much. Based on the above points, we refer to DMN plus model on the basis of DMN model and make improvements in the extraction of attention features with a Two-Pass Attention Mechanism and made some change in output module so that it can improve the accuracy of some kinds of tasks.
Towards a New Metric for Watermarked Image Assessment

To evaluate the watermarking algorithm performance, one of the basic factors used for this purpose is the watermark imperceptibility, which reflects the degradation of watermarked image in compare to the original image. In this paper, the performance of the most commonly used image evaluation metrics, peak signal to noise ratio (PSNR), and structure similarity index (SSIM) are tested in high textured and low textured images, in addition a new metric which considers the texture mapping in assessing the watermarked images has been proposed. Experimental results show better estimation for the watermarked image quality when the texture masking is involved in image assessment in compare to traditional metrics.
Visual Analysis and Mining of Knowledge Graph for Power Network Data Based on Natural Language Processing

Visual analysis and mining of knowledge graph for power network data based on the natural language processing is proposed in this study. Intelligent substation, through the main equipment intelligence, the primary system modularization, the secondary system integration, the communication system network, realizes the remote centralized control to the substation operation adjustment and the electrical operation "one-click" automatic completion. Hence, this paper has 2 core novelties. (1) Under the premise of big data in the power grid, the types, sources, access methods, storage methods and other attributes of the data itself are complex. How to realize the effective data management through visualization of the data itself is one of the important application scenarios. (2) Comprehensive perception refers to not only visualization of the power grid, but also deep perception of the internal operating conditions, and scenario simulation through simulation calculations, assisting analysis and decision-making, and realizing state optimization adjustments. The experimental results have proven the effectiveness.
A Genetic Algorithm for Human Resource Allocation in Software Projects

The search for improvement of the software development process is an endless effort. The human resources allocation is one of the challenging aspects that needs to be investigate, since it brings together many different characteristics including work experience, preferences, domain knowledge, affinities, costs, among others. This work aims to describe a genetic algorithm aimed to provide automated assistance to the allocation of members to a software development project. We also present an evaluation of the efficiency and effectiveness of this algorithm. To evaluate the algorithm, a study was conducted with CMMiDEV assessed software companies. At the end of the study, the participants answered a brief questionnaire about their experience using the algorithm.
The Research of Attacking TOR Network Users

Tor anonymous network is to protect the privacies of the users in the Internet. In this paper, our goal is to research what potential security risks and vulnerabilities we will encounter when using Tor anonymous network. We try to find some determined results from the perspective of an attacker. Using the system architecture of Tor anonymous network and combining the concept of man-in-the-middle (MITM) attack, we design a system for experiments to instigate the attacks in practice. Finally, we discuss and analyze the potential security threats and risks of using Tor anonymous network based on our results of experiments.
Design and Development of EGB Classification Model for predicting Heart Diseases

Prevention is better than cure. The prediction is a very important aspect of Medicinal services. Forecasting of heart disease is one of the most demanding issues ina Medicinal services. Applications of Data Mining Techniques in the Health Care sector are increasing. Neural Network (NN), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), Random Forest (RF) and Linear Discriminant Analysis (LDA) are some of the Data Mining Classification Techniques. Enhanced Gradient Boosting (EGB) is Data Mining Classification Model which is extended from XGB. The proposed research is estimated utilizing the Statistical Metrics (Accuracy, Precision, Recall, and F1-Measure) and ROC (Receiver Operating Characteristic) curve results obtained for performance comparison. The result shows that the ROC (Area Under Curve) obtained for EGB is higher than the ROC (Area Under Curve) obtained for all other Data Mining Classification methods. The Precision value is high when it encounters with EGB.
SIRA - An efficient method for retrieving stereo images from anaglyphs

Anaglyph reversion aims to recover the best possible approximation of a stereo pair of images from an anaglyph. Possible applications include a range of practical situations like enabling visualization of legacy anaglyphs on the Web, saving storage/transmission bandwidth by encoding stereo pairs as anaglyphs before stereo visualization or enabling users to enjoy stereo visualization using any available device. The recovering process faces a challenging issue: the anaglyphic stereo matching. Different from regular stereo images, corresponding pixels in the left and right views of an anaglyph have dissimilar intensity values, lowering photometric consistency and thus turning the usual stereo matching algorithms not suitable. In this work we propose SIRA, an efficient method for anaglyph reversion, introducing a novel approach to find stereo correspondences based on a pixel descriptor developed to deal with anaglyphic photometric differences. The descriptor core idea is to model stereo pairs as time series, extracted from both views of an anaglyph. The series are then compared through a time series matching algorithm, providing a faster, yet accurate, pixels alignment. Occlusions are dealt with using a colorization strategy based on the nearest neighbor search. We evaluate SIRA’s computational efficiency and both objective and subjective image quality on the well-known Middlebury dataset. We also compared SIRA with state of the art related methods. The results show SIRA achieves equivalent image quality while consuming 26 times less computational resources, on average. Therefore, SIRA shows up as an effective and efficient method to convey anaglyph reversion, advantaging the aforementioned applications.
Stochastic Dispatching of Wind Power Considering Errors of Power Prediction

Abstract Aiming at the stochastic dispatch problem brought by wind power, the expected model of wind power considering the errors of power prediction is established according to stochastic programming theory. And the calculation steps based on Monte Carlo stochastic simulation and genetic algorithm are proposed. An IEEE 30-bus example with wind farms is given to verify the feasibility and effectiveness of the method. The result shows that the stochastic dispatch method based expected value model can quantify the uncertainty caused by the prediction error of wind power well and optimize the expected value of wind power accommodation. It plays a positive role in improving dispatching level and promoting wind power accommodation.
OSPAci: Online Sentiment-Preference Analysis of User Reviews for Continues App Improvement

Detecting user’s sentiment and preference (e.g., complain or new feature wanted) timely and precisely is crucial for developers to improve their apps correspondingly to win the competitive mobile-app market. In this paper, we propose a novel and automated framework OSPAci, which aims to identify user’s sentiment and preference effectively based on online user reviews. OSPAci uses sentiment analysis and natural language processing techniques to obtain sentence-level sentiment scores and fine-grained user preference from mobile app reviews. Then, it analysis the evolution of user’s sentiment trend and preference. Finally, the user sentiment trend and preference correlation is analyzed along the time dimension, thus this model can be used to monitor user’s sentiment tendency and preference almost in time. We evaluate the feasibility and performance of OSPAci by using real Google play’s user reviews. The experimental results show that OSPAci can effectively and efficiently identify the user’s sentiment tendency and detect user preference timely and precisely.
Breaking the Password Security Standards Using Offline Attacks and Public User Attributes

Password security standards based on the Shannon Entropy were made to ensure that users are protected from attacks. This research investigates the possibility of breaking the password security standards using offline attacks and public user attributes. The idea here is to reduce the keyspace by collecting a character set that is likely to be chosen as a password. Ten million compromised passwords were used to establish the Dictionary Character Set, while 1.4 billion compromised email accounts were used to establish the User-Attribute Character Set. Based on the experiments conducted, we proved that there is a high chance of breaking 8 - 10 character length passwords in a reasonable time. Dictionary and Attribute-Based attacks were proved to work in the wild based on cracking time and the number of compromised passwords in the datasets. Results show that it is possible to break the password standards using offline attacks and public user attributes in a reasonable time. A Machine Sensitive Key Based Password Strength Metric is then proposed based on the ratio of risk and safe probabilities of the password. The metric is based on the measurements gathered from the Brute Force, Dictionary and Attribute-Based Password Attacks.
Face Emotion Classification using AMSER with Artificial Neural Networks

The Facial expression recognition phases provides a leading challenges and issues which are volatile while representing the different facial expression. The test sample complexity is not measured during the training phase of classification model. In certain parts of the faces the emotions are detected by the physiological changes that are expressed externally on face. The human cognition laws like principle of simplicity are unreliable in finding out the suitable test samples. To overcome, a new algorithm is proposed Advanced Maximally Stable Extremal Regions (AMSER) method for extracting the features. With the corresponding dataset and Artificial Neural Networks (ANN), the classification process is extracted with better facial expressions. The experimental result shows the perfectness and correct accuracy in classifying the facial expressions.
Introduction to Data Structures

Now that you are comfortable discussing algorithms and their computational complexity, it is time to introduce data structures. As we mentioned before, the two topics are often taught in tandem. This is because many of the basic methods in data structures illustrate basic algorithms, and your choice of algorithm can often be helped by employing the right data structure. Depending on our goal, a certain data structure could make our problem very simple or intractable.
A Fuzzy Logic Approach for Course Outcomes-based Assessment

Assessment of course outcome is essential to gauge how much knowledge and skills did a student gained at the end of a term; which is another concern of Outcomes-Based Education. Survey questionnaires were distributed to both low and middle-level management of Don Honorio Ventura State University to determine the importance of the study, 91.7% answered that the practice of assessing the course outcomes is seldom performed. This paper presents a fuzzy logic approach for evaluating the course outcome of Computer-Aided Design laboratory course among five sections of the Electronics Engineering program. There are three input variables and one output variable. Triangular Membership function (MF) was used to both variables, the Mamdani System was used in the Fuzzy Inference System, and to obtain the crisp output the centroid defuzzification was considered. The outcome scores show that only one section achieved satisfactory performance, and the rest are under poor performances, although the instructor had completed the course outline. The result of fuzzy approach provides lower values than the classical method if an assessment is low, and gives a higher value than the classical method if the assessment is high.
Data Mining Technique based Ambient Assisted Living for Elderly People

Ambient assisted living plays a vital role in assisting the elderly people. There will be a significant amount of rise in the population of the elderly people in the future. Elderly people must need an assistance to enrich their lifestyle. To provide such an opportunity, human activity recognition (HAR) will be of great use. Monitoring day to day activities are essential to assist independent elders. In this paper, we have collected a dataset of 5 daily activities using accelerometer and gyroscope from elders and used data mining techniques such as KNN and Random forest for classification. We have achieved an accuracy of 88 % using KNN and 89% using random forest. The results obtained are favorable in recognizing the activities to the greater extent.
Listwise learning to rank with extreme order sensitive constraint via cross‐correntropy

Summary In information retrieval, learning to rank was originally proposed for ranking retrieved documents according to their relevance by machine learning techniques. In general, it can effectively improve the performance of critical ranking tasks, where the listwise approach is one of the most popular approaches adopted for ranking. However, considering that documents with more relevance receive greater attention, current ranking function based listwise approaches discriminate correctly ranked sequences with incorrect sequences; however, they are incapable of recognizing which incorrect sequences are relatively more satisfied. In this study, we propose a listwise approach based on an extreme order sensitive constraint to overcome the aforementioned drawback. We first define this constraint and then design a novel listwise loss function, ListXOS, based on the constraint via cross‐correntropy to improve the performance of ranking tasks. Experimental results on three public datasets present improved performance of learning to rank by 6% compared with conventional methods, which demonstrate the superiority of the proposed approach over related state‐of‐the‐art approaches.
Optical flow estimation combining with objects edge features

Optical flow estimation has been concerned with biological plausibility since the optical flow was drawn from psychology. In this Letter, optical flow estimation combining with objects edge features is proposed. Inspired of functional division and collaboration of edge detection in primary visual cortex and motion information acquisition in a motion-selective area in the human visual cortex, a neural sub-network is introduced to extract motion edge information as primary visual cortex does, and the other neural sub-network is utilised to perceive motion information as a motion-selective area in visual cortex does. The inference sub-network in the authors’ model used augmentation features composed of objects, edge features and general features as inputs to predict the optical flow. In experiments on large displacements benchmark datasets, their approach showed promising results to improve the performance of optical flow comparing with the other models based on the visuomotor perception of visual cortex processing mechanism.