Reviewerly/exHarmony huggingface dataset:
ECOSTAR - Energy Conservation in Swift through Tiered Storage Architecture

The emergence of modern social media that rapidly deploy web-scale applications combined with growing IoT businesses, is leading to an astronomical increase in unstructured data on the Cloud. This is leading to an increase in the number and size of data centers that consume significant power. Thus, creating energy-efficient storage systems, that reduce the carbon footprint of enterprise data centers is an area of interest and need. We propose ECOSTAR, a cost-effective, tiered object storage architecture modeled for OpenStack Swift, that is aimed at significantly optimizing the energy consumption in data centers while keeping availability constraints in check. There has been work towards using Solid State Drives (SSDs) as a staging layer on top of traditional HDDs, to improve request performance and provide greater energy efficiency. In the proposed hierarchical architecture, we extend the tiering approach by introducing a layer of Tape Drives that will be used to store replicas of partitions that are infrequently accessed, providing a noteworthy reduction in energy consumption. Tape Drives also provide increased durability while significantly reducing the architectural setup costs. Tapes, being a means of offline storage, also increases security against cyber attacks. The simulation results using a Dropbox-like workload show that ECOSTAR was able to reduce total disk energy consumption by 25.83 % and improve performance by 27.34 % when compared to traditional Swift.
Adaptive Navigation Strategies in Evolutionary Robotics Controllers with Location Perception

This research proposed augmenting Evolutionary Robotics controllers with their own robotic simulator. The motivation behind this idea is that the controller could use the simulator to approximate the effects of its actions. This would allow the controller to maintain an approximation of the robot's location and orientation. It was hypothesised that this information could enhance a controller's navigation ability. Both regular and augmented controllers were tested and compared on a task that required them to employ adaptive navigation strategies to efficiently solve the task. Augmented controllers were shown to perform better and possess superior behaviours in comparison to regular controllers.
CaDRoP: Cost Optimized Convergent Causal Consistency in Social Network Systems

Asynchronous geo-replication for data resources is used to provide high availability and lower latency in modern cloud store systems. Convergent causal consistency is the corner-stone to provide useful semantics for online human interaction services. Compared to full replication, partial replication has potential benefit of lower message counts in social network systems. However, static replication is ineffective for time-varying workloads. We propose a causal+ consistency protocol, CaDRoP, to support dynamic replication, and ensure the convergence property for all comments following a post and the causal ordering between posts with explicit causality. We evaluate CaDRoP protocol with realistic workloads by different PUT rates in terms of the practical price of Amazon AWS. The results show that CaDRoP incurs much lower cost than the statically replicated data store in another causal+ algorithm. We further evaluate CaDRoP by comparing it with a clairvoyant optimal replication solution. The findings indicate that with cache, CaDRoP incurs only around 6% ~ 16% extra cost. Without cache, CaDRoP brings around 2% ~ 4.5% extra cost in steady states.
Selection of Class-conditional Filters for Semantic Shifted OOD Detection

Deep neural networks have been deployed in a wide range of applications with remarkable performance but can be easily fooled with data that are out-of-distribution (OOD). Recent works have proposed detection methods for OOD benchmarks consisting of small image datasets from separate domains (i.e. object classification dataset for training samples and digit classification dataset for OOD samples). However, these methods fail for OOD benchmarks consisting of image datasets from the same domain (i.e. Korean food classification dataset for training samples and Italian food classification dataset for OOD samples). To solve this issue, we propose an OOD detection framework that utilizes two simple operations: counting to find class-wise highly activated filters in the last convolution layer, and summation to calculate the confidence score by summation of the activation of highly activated filters. The proposed framework is based on our assumption that given an OOD sample from the same domain, the CNN model will produce similar feature maps through all its filters, while some differences might be found in the feature map from highly activated filters. We show that our method achieves the highest performance for OOD benchmarks consisting of the Food101 dataset, which provides meaningful insights on how this issue, that has been encountered in recent works, can be effectively addressed.
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles*

Spatial puzzles composed of rigid objects, flexible strings and holes offer interesting challenges for reasoning about spatial entities that are common in the human daily-life’s activities. This motivates the use of spatial puzzles as domains of study in this work. The goal of this paper is to investigate the automated solution of this kind of problems by extending an algorithm that combines Answer Set Programming (ASP) with Markov Decision Process (MDP) and Reinforcement Learning (RL), called oASP(MDP). This method is capable of constructing the set of domain states online, i.e., while the agent interacts with a changing environment. The aim of the extension proposed in this work is to add heuristics as a mechanism to accelerate the learning process, resulting in the main contribution of this paper: the Heuristic oASP(MDP) (HoASP(MDP)) algorithm. Experiments were performed on deterministic, non-deterministic and non-stationary versions of the puzzles. Results show that the proposed approach can considerably accelerate the learning process, outperforming other state-of-the-art methods.
Image Enhancement Performance of Fuzzy Filter and Wiener Filter for Statistical Distortion

Image enhancement is a process by which we can enhance (or otherwise modify, warp, and mutilate) images. Image enhancement methods are applied to images to remove the different types of distortion that are either present in the image during capturing or injected into the image during transmission. The goal in image restoration is to remove the distortion from the image in such a way that the “original” image is visible. One of the most popular methods is Fuzzy filter and Wiener filter. In this work, statistical distortion used and image enhancement performed by Fuzzy (Blurred) filter and Wiener (statistically designed) filter. Further results have been compared for different images.
A New Approach for an Intuitionistic Fuzzy Sugeno Integral Using Morphological Gradient Edge Detector

In this paper, an extension to the Sugeno integral using intuitionistic fuzzy sets for morphological gradient edge detection is presented. The proposed method is used as an aggregation operator to combine the four gradients of the morphological gradient edge detector and enables the calculation of the Sugeno integral for combining multiple sources of information with a membership degree and non-membership using intuitionistic fuzzy sets. In this paper, the focus is on aggregation operators that use measures with intuitionistic fuzzy sets, in particular, the Sugeno integral. The performance of the proposed method is compared with the traditional Sugeno integral and other aggregation operators, using images of the Berkeley database (BSDS) and with synthetic images.
Training Samples Set Simplified Method based on Key Frame Extraction

The requirement of huge amount of training set data has restricted the development of the deep learning his paper designs a system which uses the differential key frame extraction method to extract the key images from the original video sequences. And these key images will be used as the training set of the deep learning model. The system can simplify the training set while ensuring the recognition accuracy of the model. The experimental results show that the proposed method can reduce the manually annotate without accuracy loss as well as improving the training efficiency.
English billboard text recognition using deep learning

Abstract English billboards are common in our daily work and life, and how to effectively recognize them is a problem worthy of study. This paper mainly uses Progressive Scale Expansion Network (PSENet) and Convolutional Recurrent Neural Network (CRNN) to conduct text recognition experiments on English billboards. The English billboards are divided into four categories: text distortion, larger background, special text font, and normal format. PSENet is used for text detection, and CRNN is used for text recognition. The identification results of PSENet and CRNN revealed differences among the four groups of English billboards: normal format text recognition is the best, and large background text recognition is the worst, and the recognition effect of text distortion and special text font is less accurate. Finally, the work of this paper is analyzed and summarized, and the future work is prospected.
A dynamic characterization of thermal stimulation for residual stresses using digital photoelasticity

Evaluation of residual and thermal stresses using temporal analysis of color in photoelasticity images was applied to three discs with residual stresses in different zones. The stress field generated by a compressive load is deformed under residual stress presence. 3D color trajectories for interest pixels show behavior differences between locations with and without residual stress. Finally, k-means analysis for three experiments shows the presence of residual stresses and relates their temporal behavior with a high stress level zone.
The Effect Analysis of Thermal Infrared Colorization

Due to the advantage that a thermal camera robustly works in various illumination conditions, it has become a crucial sensor in real-world applications, such as self-driving, advanced driver assistance as well as a surveillance system. However, unlike RGB images with color information, thermal images do not contain abundant information. This disadvantage makes it users difficult to recognize thermal scenes. In this paper, we aim to make pseudo-RGB that can be used at day and night by receiving thermal images as inputs and to show the necessity of pseudo-RGB research through colorization that synthesizes chromaticity of RGB in thermal images. Furthermore, we evaluate models to see whether the produced colorized image can be interpreted by perceptual models for tasks, such as pedestrian detection and depth estimation by feeding colorized images to the models that are trained with original RGB images. These experiments explicitly show the limitation and possibility of thermal colorization.
Application of SPOC Based Online and offline Hybrid Teaching Design in University Computer Foundation

With the rapid development of information technology, the general courses of “University Computer Foundation” in the non-computer major in western universities are facing many difficulties and higher requirements and challenges. In the context of teaching reform in Colleges and universities, blended teaching has become one of the effective ways to solve the above problems. In this regard, the teaching team of “University Computer Foundation” of Yunnan Normal University, based on the modern education concept of students as the center, adopts the online and offline mixed teaching mode of “MOOC +SPOC+ physical classroom”, and obtains good teaching effect in the post epidemic period.
Transform-based quality assessment for enhanced image

Images captured in bad weather suffer from low contrast and faint color. Recently, plenty of enhanced algorithms have been proposed to improve visibility and restore color. The goal of image quality assessment is to predict the perceptual quality for improving imaging systems' performance. We proposed a no-reference image quality enhancement measure using hypercomplex Fourier transform for color images. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than the original image. An increase in the magnitude of higher frequency coefficients indicates an enhancement in contrast to the image's luminance content. To test the performance of the proposed algorithm, the public database TID2013 is used. The Pearson rank-ordered correlation coefficient is utilized to measure and compare the proposed quality measure's performance with state-of-the-art approaches.
A Criminal Smart Contract for Distributed Denial of Service Attacks

The rapid growth in the number of services invites exponentially increasing malicious traffic which aims to overwhelm a server. This attempt to exhaust the server of its resources is called a Distributed Denial-of-Service (DDoS) attack. The pseudonymity (pseudo-anonymity) of Blockchain can be either desirable or undesirable to society and this unique feature put together with the elimination of trusted intermediaries that smart contracts offer, appeals to the illegal side of the world. This paper sheds light on the “dark side” of smart contracts, i.e., how they are used to stimulate criminal activity, specifically, a contract to allow the terms and execution of a DDoS to take place in a secure community. Though this proposal endeavors to release a DDoS attack, it can be extended to other real-world crimes as well. Apart from this, our work also covers how this framework could be used for Selfish Mining.
A Method of Measuring Wrist Angle of Underwater Manipulators Based on Machine Vision

For the underwater manipulators based on visual positioning, it is impossible to calculate the wrist angle through the inverse kinematics solution, because the camera is installed in the front of the wrist. This paper presents a method of measuring wrist angle of most underwater manipulators without installing angle sensor. For this method, this paper carries on the detailed mathematical derivation, and gives error analysis combined with the real machine test.
A unified FFT-based approach to maximum assignment problems related to transitive finite group actions

This paper studies the cross-correlation of two real-valued functions whose common domain is a set on which a finite group acts transitively. Such a group action generalizes, e.g., circular time shifts for discrete periodic time signals or spatial translations in the context of image processing. Cross-correlations can be reformulated as convolutions in group algebras and are thus transformable into the spectral domain via Fast Fourier Transforms. The resulting general discrete cross-correlation theorem will serve as the basis for a unified FFT-based approach to maximum assignment problems related to transitive finite group actions. Our main focus is on those assignment problems arising from the actions of symmetric groups on the cosets of Young subgroups. The "simplest" nontrivial representatives of the latter class of problems are the NP-hard symmetric and asymmetric maximum quadratic assignment problem. We present a systematic spectral approach in terms of the representation theory of symmetric groups to solve such assignment problems. This generalizes and algorithmically improves Kondor's spectral branch-and-bound approach (SODA, 2010) to the exact solution of the asymmetric maximum quadratic assignment problem.
A Classification Based Ensemble Pruning Framework with Multi-metric Consideration

Machine learning techniques have been an essential driving force for Internet companies while handling daily tasks like fraud detection. Given a pre-designed model, several efforts, such as fine-tuning and ensemble techniques, can be taken to enhance its performance. However, more effective methods, with consideration of task-specific requirements, are always highly demanded. In this paper, we aim at efficiently obtaining an effective framework with consideration of multiple metrics, based on a pre-designed model. To achieve this, we build a framework which utilizes the ensemble pruning technique and adapts a classification based optimization method to perform the model pruning procedure. On one hand, to maintain the performance consistency of the pruned ensemble model between the selection and deployment stage, a calibration strategy is introduced to the model pruning (weight optimization) procedure. On the other hand, to meet the business requirement for multi-metric consideration, multiple evaluation metrics can be equipped in this framework, and we improve the optimization method to simultaneously optimize these metrics. Experiments are first conducted on several benchmark datasets, and the results show that this framework can effectively enhance the performance. We further apply the proposed framework to large-scale fraud detection tasks, which also validates its effectiveness.
PROCEDURE MODEL FOR STRUCTURED RELATIONAL MODELING OF REQUIREMENTS TO SUPPORT REQUIREMENTS-ORIENTED DECISION MAKING

Abstract The development of complex technical systems is characterized by a large number of system elements as well as their interactions. With regard to requirements management, many requirements have to be considered, which can have different relations to each other. If these requirements are used as basis for criteria in the decision making process, these relations must also be considered in the multi-criteria evaluation of product alternatives. Therefore, a computer-aided approach is presented in this paper, which allows the systematic modeling of requirement interactions focusing on multi-criteria decision making. For this purpose, basic relation types are identified, which are used to model submatrices in order to derive the Requirement Relation Matrix (RRM). Matrix-based as well as graph-based visualization methods are used for the RRM in order to improve the alternatives with the knowledge about the relational linkage. In addition, the effects of changes in requirements can be transferred to the decision making process. The approach is exemplarily applied to the extension of a test laboratory by a test bench.
Chinese Word Sense Disambiguation Based on Classification

Word sense disambiguation (WSD) is a well-known task in the field of natural language processing. It attempts to determine a meaning of a word that has a couple of senses. This paper studies the Chinese word sense disambiguation by employing supervised classification method. Initially, feature selection is performed based on feature windows. Three types of features are extracted in this research: part-of-speech (POS), words, and 2-gram collocation. Further, we make a comparison of different classification algorithms, including sequential minimal optimization (SMO), naïve bayes, and multilayer perceptron (MLP). Different parameters are tested in order to obtain best precision of dataset. Additionally, a punctuation optimization approach is proposed to refine the final classification precision. Experimental results show that our method can achieve a good effect. The proposed approach contributes a lot to the WSD task by exploring feature selection as well as punctuation optimization.
Analysis the Traces Identified in the Case of Violent Crimes

The studies and research show that in almost all countries of the world, violent crimes are on the rise. Against this background, the criminal prosecution authorities - the police and the prosecutor's office, as institutions of the rule of law, improve investigative procedures, using the scientific means and methods necessary to prove guilty and to prosecute the perpetrators. Human-environment interaction gives rise to the most different traces that can be useful in determining the time or succession of occurrence events, as well as determining human behavior in this process. Any human activity deployed in time and space, through physical movements, leaves in that place different changes from the previous situation, which is preserved for an appreciable period of time. Depending on all the elements presented, you can reconstitute the dynamic picture of the deed. The traces come to occupy the central place in science of forensics, so that at present no one can imagine a crime committed by physical movements of the person, without the proper traces being made at the place where it was committed.
Interpretation and Pragmatics – Legal Ambiguity

This definition is only apparently clear and simple. In fact, in order to understand what it means exactly, it is necessary to analyze first the concept of [§1.1] interpretation and then the related concepts of [§1.2] ambiguity, [§1.3] ordinary meaning, and [§1.4] vagueness.
Designing e-complaint tenant mobile application to XYZ mall

Nowadays, services based on technology is the fastest solution to know and get the information or to decide in the serving activities. XYZ mall has a good complaint management as the best services for the tenant property. Previously, the complaint management already existed based on the website. Nowadays, website is not effective anymore. According to the survey result, users are rarely use website to know the information, that's why developing the mobile application is needed as an information media. Mobile application is the great solution because of the smart phone is used in a various party in the world. So, on of the solution for XYZ mall for this problem is designing mobile e-complaint tenant for XYZ mall. This application is designed with Grounded Research method and UML. This application can have accessed with android smart phone so can be handled faster.
Restaurant Review System based on Sentimental Analysis using R Language

This work aims at understanding the exact sentiments of each review to know where the restaurant is lacking, and analyze the sentiments of the user which we do not understand from the ratings provided by customers. The feedback provided through sentiments in comments or in form of opinions act as a good indicator for various purposes. These responses can be grouped into positive set of class or negative class. This scenario can be interpreted as a typical classification problem, where the respective groups indicate a sentiment. Sentimental analysis, thus provides critical insight to companies in analyzing the success or failure of a product when launched, and further helps in improving the quality of the product. It also benefits the management in taking key business or organizational decisions. In this article, user reviews of different restaurants from various online sources are extracted, sentiment analysis is performed on this data to obtain a consolidated rating which helps in understanding the opinion of the users.