Reviewerly/exHarmony huggingface dataset:
An Enhanced Adaptive Block Truncation Coding with Edge Quantization Scheme

Recently, image compression using adaptive block truncation coding based on edge quantization (ABTC-EQ) was proposed by Mathews and Nair. Their approach deals with an image for two types of blocks, edge blocks and non-edge blocks. Different from using the bi-clustering approach on all blocks in previous block truncation coding (BTC)-like schemes, ABTC-EQ adopts tri-clustering to tackle edge blocks. The compression ratio of ABTC-EQ is reduced, but the visual quality of the reconstructed image is significantly improved. However, it is observed that ABTC-EQ uses 2 bits to represent the index of three clusters in a block. We can only use an average of 5/3 bits by variable-length code to represent the index of each cluster. On the other hand, there are two observations on the quantization levels in a block. The first observation is that the difference between the two quantization values is often smaller than the quantization values themselves. The second observation is that more clusters may enhance the visual quality of the reconstructed image. Based on variable-length coding and the above observations, we design variants of ABTC-EQ to enhance the visual quality of the reconstructed image and compression ratio.
Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation

Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.
Data Evaluation for Cassiterite and Coltan Fingerprinting

Within due diligence concepts for raw material supply chains, the traceability of a shipment is a major aspect that has to be taken into account. Cassiterite and coltan are two so-called conflict minerals for which traceability systems have been established. To provide additional credibility to document-based traceability systems the German Federal Institute for Geosciences and Natural Resources (BGR) has developed the analytical fingerprint (AFP) for the minerals coltan, cassiterite, and wolframite. AFP is based on the analysis of a sample from a shipment with a declared origin and evaluates whether the declared origin is plausible or not. This is done by comparison to reference samples previously taken at the declared mine site. In addition to the generation of the analytical data, the data evaluation step, with the aim to state whether the declared origin is plausible or not, is of special importance. Two data evaluation approaches named “Kolmogorov–Smirnov distance (KS-D) approach” and “areas ratio approach” are applied to coltan and cassiterite and result in very low rates of false negative results, which is desired for AFP. The areas ratio approach based on hypothesis testing and a more sophisticated evaluation of the multivariate data structure has some advantages in terms of producing lower rates of false positive results compared to the KS-D approach.
Graph Database Based Knowledge Graph Storage and Query for Power Equipment Management

The knowledge graph is a graph-based data structure that mines the value of data from a large number of associations. Therefore, it is necessary to find a database that can support the expression, storage, and query of associated data as a way of storing knowledge graph. Different than the normally used relational database and RDF tabular storage, the graph database stores data in “vertex” and “edge” as basic storage units, which can realize distributed storage and parallel processing of massive data with complex associations. The graph database utilizes the characteristics of physical “pointing” between nodes to provide “no index” association operations for adjacent nodes and has technical advantages in applications of network-structured data. This paper proposes a knowledge graph storage and query method based on the graph database, and compares the storage and query performance between the general storage format of RDF and graph database based on a constructed power equipment management knowledge graph.
Misleading Repurposing on Twitter

We present the first in-depth and large-scale study of misleading repurposing, in which a malicious user changes the identity of their social media account via, among other things, changes to the profile attributes in order to use the account for a new purpose while retaining their followers. We propose a definition for the behavior and a methodology that uses supervised learning on data mined from the Internet Archive's Twitter Stream Grab to flag repurposed accounts. We found over 100,000 accounts that may have been repurposed. Of those, 28% were removed from the platform after 2 years, thereby confirming their inauthenticity. We also characterize repurposed accounts and found that they are more likely to be repurposed after a period of inactivity and deleting old tweets. We also provide evidence that adversaries target accounts with high follower counts to repurpose, and some make them have high follower counts by participating in follow-back schemes. The results we present have implications for the security and integrity of social media platforms, for data science studies in how historical data is considered, and for society at large in how users can be deceived about the popularity of an opinion. The data and the code is available at https://github.com/tugrulz/MisleadingRepurposing.
K-means Clustering Based Undersampling for Lower Back Pain Data

Many people are usually suffered from low back pain(LBP). It is very important to identify the LBP in the early stage. The classification algorithm in machine learning can help us to predict whether a person is suffered from low back pain, but class imbalance is often a problem in various real-world datasets including the LBP dataset. In this paper, LBP diagnosis based on a k-means clustering combined with undersampling has been proposed. The first strategy is to combine k-means and stratified random sampling to undersample(KSS). The second strategy is to combine k-means and Manhattan distance to undersample(KMD). Experiments have been conducted on LBP dataset by classification systems. The performance of the method is evaluated using the area under curve(AUC) metric. The results show that the highest classification accuracy (0.92) is obtained for the KSS is combined with logistic regression on LBP dataset. The KSS combine with linear SVM has higher accuracy and stability.
INTERNETOWA PLATFORMA DO NAUKI JĘZYKÓW DUOLINGO – OPINIE UŻYTKOWNIKÓW

A recent increase in the introduction and integration of information and communication technologies in education has prompted researchers to investigate the functioning and effectiveness of various digital tools. In the first part of the article, the authors present the benefits of using online platforms in language learning, with special focus on Duolingo. The second part describes the major assumptions, course design and language tasks offered by the platform. The final part concentrates on the results of a small-scale study in the form of interviews, conducted with 32 Duolingo users.
An image fusion dehazing algorithm based on dark channel prior and retinex

The dehazing algorithm for image processing is widely used to improve image quality, which aims to reduce the impact of hazy weather. The typical dehazing algorithm using dark channel prior (DCP) can carry out image dehazing efficiently. However, the processed image only by using DCP will lead to the low lightness and the weak colour of the image, which cannot meet the visual characteristics of the human eye in some extent. To solve this problem, this paper proposes an image fusion dehazing algorithm based on DCP and retinex. First, the original image is processed by the DCP dehazing algorithm to get the dehazed image. Then, the dehazed image is processed by using the retinex algorithm to get the enhanced image. Finally, the dehazed image and the enhanced image are fused using a linear fusion method, which is expected to improve the lightness and colour of the image after dehazing. Experimental results show that the algorithm proposed in this paper not only achieves good results for image dehazing, but also enhances its lightness and colour.
Where Can I Help? Human-Aware Placement of Service Robots

As service robots are entering more and more homes it gets evermore important to find behavior strategies that ensure a harmonic coexistence between those systems and their users. In this paper, we present a novel approach to enable a mobile robot to provide timely assistance to a user moving in its environment, while simultaneously avoiding unnecessary movements as well as interferences with the user. We developed a framework that uses information about the last object interaction to predict possible future movement destinations of the user and infer where they might need assistance based on prior knowledge. Given this prediction, the robot chooses the best position for itself that minimizes the time until assistance can be provided as well as avoids interferences with other activities of the user. We evaluated our approach in comparison to state-of-the-art methods in simulated environments and performed a user study in a virtual reality environment. Our evaluation demonstrates that our approach is able to decrease both the time until assistance is provided and the travel distance of the robot as well as increases the average distance between the user and the robot in comparison to state-of-the-art systems. Additionally, the robot behavior generated by our method is rated as more pleasant by our study participants than comparable literature approaches.
Ancient Text Character Recognition Using Deep Learning

Ancient scripts provide a captivating insight into the knowledge of ancestors which needs to be preserved for future generations.Therefore, there is a need to convert the digital script available in degraded format into textual format.To accomplish this model is being proposed in the paper that comprises of binarization using selection encoder decoder techniques.The results indicate the binarization accuracy as 74.24% approximately and F-measure is 75% (approximately) which comes out to be greater than other previously developed model.The binarized images are being further segmented using Seam Carbel method at character level and are manually compared with the vocabulary, the segmentation accuracy (A s) comes out to be 70% approximately.Further, characters are recognized using a three layer Convolutional Neural Network and the recognition accuracy (Ar) is found to be 73% approximately, the recognized images are further converted into text using one to one mapping, to be further used for translation into universally acceptable language like English.
Modelling User Behavior Dynamics with Embeddings

Understanding user interaction behaviors remains a challenging problem. Quantifying behavior dynamics over time as users complete tasks has only been done in specific domains. In this paper, we present a user behavior model built using behavior embeddings to compare behaviors and their change over time. To this end, we first define the formal model and train the model using both action (e.g., copy/paste) embeddings and user interaction feature (e.g., length of the copied text) embeddings. Having obtained vector representations of user behaviors, we then define three measurements to model behavior dynamics over time, namely: behavior position, displacement, and velocity. To evaluate the proposed methodology, we use three real world datasets: (i) tens of users completing complex data curation tasks in a lab setting, (ii) hundreds of crowd workers completing structured tasks in a crowdsourcing setting, and (iii) thousands of editors completing unstructured editing tasks on Wikidata. Through these datasets, we show that the proposed methodology can: (i) surface behavioral differences among users; (ii) recognize relative behavioral changes; and (iii) discover directional deviations of user behaviors. Our approach can be used (i) to capture behavioral semantics from data in a consistent way, (ii) to quantify behavioral diversity for a task and among different users, and (iii) to explore the temporal behavior evolution with respect to various task properties (e.g., structure and difficulty).
High-Capacity Reversible Data Hiding in Encrypted Images by Information Preprocessing

With the development of cloud computing, high-capacity reversible data hiding in an encrypted image (RDHEI) has attracted increasing attention. The main idea of RDHEI is that an image owner encrypts a cover image, and then a data hider embeds secret information in the encrypted image. With the information hiding key, a receiver can extract the embedded data from the hidden image; with the encryption key, the receiver reconstructs the original image. In this paper, we can embed data in the form of random bits or scanned documents. The proposed method takes full advantage of the spatial correlation in the original images to vacate the room for embedding information before image encryption. By jointly using Sudoku and Arnold chaos encryption, the encrypted images retain the vacated room. Before the data hiding phase, the secret information is preprocessed by a halftone, quadtree, and S-BOX transformation. The experimental results prove that the proposed method not only realizes high-capacity reversible data hiding in encrypted images but also reconstructs the original image completely.
Policy Entropy for Out-of-Distribution Classification

One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent’s policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.
New results on modular Golomb rulers, optical orthogonal codes and related structures

We prove new existence and nonexistence results for modular Golomb rulers in this paper. We completely determine which modular Golomb rulers of order k exist, for all k ≤ 11, and we present a general existence result that holds for all k ≥ 3. We also derive new nonexistence results for infinite classes of modular Golomb rulers and related structures such as difference packings, optical orthogonal codes, cyclic Steiner systems and relative difference families.
Many-objective Search-based Selection of Software Product Line Test Products with Nautilus

The Variability Testing of Software Product Lines (VTSPL) concerns the selection of the most representative products to be tested according to specific goals. Works in the literature use a great variety of objectives and distinct algorithms. However, they neither address all the objectives at the same time nor offer an automatic tool to support this task. To this end, this work introduces Nautilus/VTSPL, a tool to address the VTSPL problem, created by instantiating Nautilus Framework. Nautilus/VTSPL allows the tester to experiment and configure different objectives and categories of many-objective algorithms. The tool also offers support to visualization of the generated solutions, easing the decision-making process.
Fall Detection Using Neural Network Based on Internet of Things Streaming Data

Fall event has become a critical health problem among elderly people. We propose a fall detection system that analyzes real-time streaming data from the Internet of Things (IoT) to detect irregular patterns related to fall. We train a deep neural network model using accelerometer data from an online physical activity monitoring dataset named, MobiAct. An IBM Cloud-based IoT data processing framework is used to manage streaming data. About 96.71% of accuracy is achieved in assessing the performance of the proposed model.
The bibliostatistical analysis of the first 25 volumes of a collection of academic papers «Factors in experimental evolution of organisms»

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Parallel Raster Scan for Euclidean Distance Transform

Distance transform (DT) and Voronoi diagrams (VDs) have found many applications in image analysis. Euclidean distance transform (EDT) can generate forms that do not vary with the rotation, because it is radially symmetrical, which is a desirable characteristic in distance transform applications. Recently, parallel architectures have been very accessible and, particularly, GPU-based architectures are very promising due to their high performance, low power consumption and affordable prices. In this paper, a new parallel algorithm is proposed for the computation of a Euclidean distance map and Voronoi diagram of a binary image that mixes CUDA multi-thread parallel image processing with a raster propagation of distance information over small fragments of the image. The basic idea is to exploit the throughput and the latency in each level of memory in the NVIDIA GPU; the image is set in the global memory, and can be accessed via texture memory, and we divide the problem into blocks of threads. For each block we copy a portion of the image and each thread applies a raster scan-based algorithm to a tile of m×m pixels. Experiment results exhibit that our proposed GPU algorithm can improve the efficiency of the Euclidean distance transform in most cases, obtaining speedup factors that even reach 3.193.
PENGEMBANGAN E-RAPORT KURIKULUM 2013 BERBASIS WEB PADA SMA TUNAS MEKAR INDONESIA

SMA Tunas Mekar Indonesia is one of Lampung Province schools that uses the 2013 curriculum. The assessment reporting process of student learning skills and achievement by teachers uses report cards that are distributed to parents at the end of each semester. In the managing the report card data process, there are several obstacles, namely the obstruction of grade recapitulation because the subject teacher is late in sending student grades, it takes a long time in managing grades because the processing value data done repeatedly from the attendance recap report then the application of Microsoft Excel and the report books are recapitulated by staff allows data writing errors. The solution developed for this problem is to build a Web-Based E-Report Card Application that will help SMA Tunas Mekar Indonesia by simplifying and accelerating teachers and homeroom teachers in assessing student learning outcomes reports process on inputting student scores. In this application, teachers and homeroom teachers no longer need to send assessment data via email, because the data entered is already integrated with other data. So that it doesn't take a long time to enter student grades, and minimize the damage and loss of report card data. The application built has provided complete information regarding student report card information and helps schools in reporting student grades based on the testing results using ISO 25010 standart with a percentage of success with a total average of 92.82%.
Fracking bad language – hydraulic fracturing and earthquake risks

Abstract. Hydraulic fracturing, or fracking, is a borehole stimulation technique used to enhance permeability in geological resource management, including the extraction of shale gas. The process of hydraulic fracturing can induce seismicity. The potential to induce seismicity is a topic of widespread interest and public concern, particularly in the UK where seismicity induced by hydraulic fracturing has halted shale gas operations and triggered moratoria. Prior to 2018, there seemed to be a disconnect between the conclusions of expert groups about the risk of adverse impacts from hydraulic-fracturing-induced seismicity and the reported level of public concern about hydraulic fracturing induced seismicity. Furthermore, a range of terminology was used to describe the induced seismicity (including tremors, earthquakes, seismic events, and micro-earthquakes) which could indicate the level of perceived risk. Using the UK as a case study, we examine the conclusions of expert-led public-facing reports on the risk (likelihood and impact) of seismicity induced by hydraulic fracturing for shale gas published between 2012 and 2018 and the terminology used in these reports. We compare these to results from studies conducted in the same time period that explored views of the UK public on hydraulic fracturing and seismicity. Furthermore, we surveyed participants at professional and public events on shale gas held throughout 2014 asking the same question that was used in a series of surveys of the UK public in the period 2012–2016, i.e. “do you associate shale gas with earthquakes?”. We asked our participants to provide the reasoning for the answer they gave. By examining the rationale provided for their answers, we find that an apparent polarisation of views amongst experts was actually the result of different interpretations of the language used to describe seismicity. Responses are confounded by the ambiguity of the language around earthquake risk, magnitude, and scale. We find that different terms are used in the survey responses to describe earthquakes, often in an attempt to express the risk (magnitude, shaking, and potential for adverse impact) presented by the earthquake, but that these terms are poorly defined and ambiguous and do not translate into everyday language usage. Such “bad language” around fracking has led to challenges in understanding, perceiving, and communicating risks around hydraulic-fracturing-induced seismicity. We call for multi-method approaches to understand the perceived risks around geoenergy resources and suggest that developing and adopting a shared language framework to describe earthquakes would alleviate miscommunication and misperceptions. Our findings are relevant to any applications that present – or are perceived to present – the risk of induced seismicity. More broadly, our work is relevant to any topics of public interest where language ambiguities muddle risk communication.