Reviewerly/exHarmony huggingface dataset:
Modeling execution techniques of inscriptions1

The paper discusses a small ontology to describe the features of the execution techniques of inscriptions, based on a recent contribution discussing the classification methodologies. The modeling is done on the basis of existing recent attempts to model epigraphic documents and not on a general evaluation of existing ontologies. The ontology described is used in parallel to enrich and further structure the EAGLE Vocabularies for Execution Technique, which uses SKOS, with possibly immediate impact on the many projects using the concepts contained there.
Distribution, enrichment, and source of heavy metals in stream sediments from Sg. Koyan District, Pahang, Malaysia

Sg. Koyan district is located in Pahang along N-S Eastern gold belt. It is well known for gold mining activities. Such activities may contribute to heavy metals (HM) contamination that may impact the surrounding environment. While most current studies deal with economic geology research which ignore the impact of HMs contamination. This paper aims to study the distribution, source, and enrichment factor of As, Co, Cr, Cu, Mn, Sn, and Zn using Ti as proxy. Stream Sediments were collected followed by wet sieving, and borate fusion with 15% Nitric Acid for XRF and ICP-OES. These results were transformed into usable data for Cluster Analysis (CA), Principal Component Analysis (PCA), and Enrichment Factor (EF). As the result, it is found that As and Zn having high enrichment factor of 10.2, and 10.4 (significant enrichment 5-20), respectively. From cluster analysis, As, and Zn are grouped together suggesting the source is from human rather than nature. Even though, the highest As concentration is found in Perangih Gold prospect. It is not surprising because As is found associated with gold deposit agreeable with PCA results. In conclusion, As, Co, Cu, Sn, and Zn have anthropogenic sources, Mn is from organic matter with Cr has geogenic origin. Possible sources of anthropogenic may include vehicles, workshop, and even agricultural activities.
Supervised Nested Algorithm for Classification Based on K-Means

The aim of this paper is to present an extension of the k-means algorithm based on the idea of recursive partitioning that can be used as a classification algorithm in the case of supervised classification. Some of the most robust techniques for supervised classification are those based on classification trees that make no assumptions on the parametric distribution of the data and are based on recursively partitioning the feature space into homogeneous subsets of units according to the class the entities belong to. One of the shortcomings of these approaches is that the recursive partitioning of the data, i.e. the growing of the tree, is achieved considering only one variable at a time and, although it makes the tree pretty simple in terms of determining rules to obtain a classification, it also makes them hard to interpret. Building on these ideas we carry the integration of parametric model into trees one step further and propose a supervised classification algorithm based on the k-means routine that sequentially splits the data according to the whole feature vector. Results from applications to simulated data are shown to address the potentiality of the proposed method in different conditions.
Static Detection of File Access Control Vulnerabilities on Windows System

Summary Traditional applications have been developed for decades. Most of the security research around them have focused on the detection of memory corruption vulnerabilities, such as buffer overflow, double fetch, and integer overflow. On the contrary, logic bugs, a kind of flaws caused by unreasonable application logic, attract much less attention. Files are the most common media for programs to persist their data in the system. As the file owners, programs are responsible for protecting their files from malicious users' tampering by leveraging access control mechanisms. However, if a program configures their access control mechanisms in wrong ways and causes evil users to bypass security checks to access files, there exists a file access control vulnerability. As a branch of logic flaws, file access control vulnerabilities are less popular with researchers. Thus, to mitigate the harm of the file access control vulnerabilities on Windows system, our team conducted first‐step research on them. We first classified file access control bugs into two types and codified some bug patterns. Then we formalized file access control vulnerabilities to propose a scalable detection method and implemented a lightweight analysis system StaticFAC. After evaluating StaticFAC in real‐world Windows software, we discovered 15 0‐day bugs.
Creation of an Wearable Startup

The need to understand signals given by our own body is of great interest to most human beings. This quest for self-knowledge is both shared by academic researchers and businesses who want to bring value to consumers in the society. This paper presents a story of how a software engineering researcher who collaborated with hardware engineers and entrepreneurs in an incubator, Simula Garage, hosted by Simula Research Laboratory to create a wearable startup called Sweetzpot. Sweetzpot developed a respiratory inductance plethysomography sensor called Flow to measure breathing signals from ribcage and/or abdominal movements. The team grew to consist of software engineers, students of machine learning and physics, an industrial/interaction designer, a hardware engineer, a lawyer, and an accountant in addition to external collaborators. We present the sequence of events that led to creation and sustainability of the startup and summarize the lessons learnt from it.
An approach to improve detection in scenes with varying object densities in remote sensing

In the last decades, the amount of data obtained from electro-optical sensor systems has been steadily increasing in remote sensing (RS). Manual analysis of remote sensing images is a time-consuming task. Therefore, machine learning methods for detection and classification have become an appealing field of RS. In particular, the family of region convolutional neural networks (R-CNN) shows considerable success in different RS tasks. Advanced RCNN methods are multistage approaches, where first objects are detected and secondly classified with an optional segmentation step. However, the detection performance of advanced R-CNN algorithms suffers in areas with noticeably varying object densities and scales. Advanced R-CNN architectures usually consist of a detector stage and multiple heads. In the detector stage, regions of interest (ROI) are proposed and filtered by a non-maximum suppression (NMS) layer. In an area with a high density of objects, a strictly adjusted NMS may lead to missed detections. In contrast, a low threshold value for NMS can cause multiple overlapping detections for large objects. To address this challenge, we present our approach improving the results of object detector methods in scenes with varying densities of objects. Therefore, we add an encoder-decoder based density estimation network to our detector network to obtain the location of high-density areas. For these locations, additional fine detection of objects is performed. In order to exhibit the effectiveness of our approach, we evaluate our method on common crowd counting and object detection datasets.
Refactoring of Neural Network Models for Hyperparameter Optimization in Serverless Cloud

Machine Learning and Neural Networks in particular have become hot topics in Computer Science. The recent 2019 Turing award to the forefathers of Deep Learning and AI - Yoshua Bengio, Geoffrey Hinton, and Yann LeCun proves the importance of the technology and its effect on science and industry. However, we have realized that even nowadays, the state of the art methods require several manual steps for neural network hyperparameter optimization. Our approach automates the model tuning by refactoring the original Python code using open-source libraries for processing. We were able to identify hyperparameters by parsing the original source and analyzing it. Given these parameters, we refactor the model, add the state of the art optimization library calls, and run the updated code in the Serverless Cloud. Our approach has proven to eliminate manual steps for an arbitrary TensorFlow and Keras tuning. We have created a tool called OptPar which automatically refactors an arbitrary Deep Neural Network optimizing its hyperparameters. Such a transformation can save hours of time for Data Scientists, giving them an opportunity to concentrate on designing their Machine Learning algorithms.
S-Capade: Spelling Correction Aimed at Particularly Deviant Errors

S-capade (spelling correction aimed at particularly deviant errors) is a phonemic distance based spellchecking tool (Source code repository may be found in the references section [].) intended for the correction of misspellings made by children. Whilst typographic misspellings typically deviate from the target by only one or two characters, children’s misspellings tend to be more phonetic. They are influenced both by how the child perceives the pronunciation of a word and by the letters they choose to represent that pronunciation. As such, these misspellings are particularly deviant from the target and can negatively impact the performance of conventional spellcheckers. In this paper we demonstrate that S-capade is capable of correcting a significant portion of misspellings made by children where conventional correction tools fail.
Predictable, Flexible or Correct

Refactoring tools automate tedious and error-prone source code changes. Such tools can improve the speed and accuracy of software development, yet developers frequently eschew automation in favor of manual refactoring. Developers report distrust and lack of predictability as reasons for not using automated tools, but there are no comprehensive explanations of trust and predictability nor guidelines for how to improve these aspects of tools. In this position paper we explore choices and tradeoffs in refactoring tool design.
Prioritized Experience Replay in Multi-Actor-Attention-Critic for Reinforcement Learning

Abstract Experience replay is a significant method of off-policy reinforcement learning (RL), which makes RL reuse the past experience and reduce the correlation between samples. Multi-Actor-Attention-Critic (MAAC) is a successful off-policy multi-agent reinforcement learning algorithm, due to its good scalability. To accelerate convergence, we use prioritized experience replay (PER) to optimize the experience selection in MAAC, and propose the PER-MAAC algorithm. In the PER-MAAC, the priority metric is based on the temporal-difference error during training. The algorithm is evaluated in the scenarios of Multi-UAV Cooperative Navigation and Rover-Tower. The experimental results show that PER-MAAC improves the speed of convergence.
Abnormal Event Detection in Video Based on Sparse Representation

As a research hotspot in intelligent video surveillance system, abnormal event detection has attracted the attention of many researchers in recent years. In order to overcome the shortcoming of the semi-supervised model, that is, the training sample is difficult to contain all possible situations, which leads to the occurrence of error detection, we propose a method based on sparse representation. The principle of this method is to train the model with normal data and abnormal data respectively, get two sparse representation models, and then judge whether there are abnormal events according to the results of the two models. The method has passed the test of the existing data sets and achieved good results.
Blockchain-Based Solution for Proof of Pick-up of a Physical Asset

Smart contracts, a by-product of Blockchain technology are a set of programs that are digitally verifiable and facilitate the automation of agreements between two or more parties without the intervention of a Trusted Third Party (TTP) service. The integration of smart contracts and Blockchain systems could help us in self-verifiable agreements between physical parties or processes. The paper deals with the application of an online delivery system, predominantly a food delivery system where the items ordered by a customer, when sent to the restaurant is verified by a smart contract that triggers automatically when the carrier picks up the order from the requested restaurant or seller. Since the smart contracts are distributed and transparent across a blockchain network the customer is aware and has definitive proof of where he has received his order from. In this paper, we give an implementation of how the smart contract would execute when the carrier picks up the order from the requested restaurant or seller and how this is reflected on the customer's side with the help of Blockchain systems.
Applying information technologies to mathematical education of IT specialists in English-speaking academic groups

Knowledge of the basic theoretical foundations and possession of the skills of applying Mathematical Analysis, Differential Equations, Linear Algebra, Analytic Geometry, Discrete Mathematics, Numerical Methods, Theory of Probability and Mathematical Statistics are important for the professional training of future specialists in the field of information technologies. Since future specialists in the field of information technology require a deep mathematical training, the curricula of IT specialties usually contain various mathematical disciplines from this list. A specialist of any IT profile must have specific professional features and competencies. These characteristics of future IT professionals should preferably be formed in the process of studying both special and general scientific disciplines. The result of the educational process is formation of both hard and soft skills of students. This article is devoted to the study of some features of the use of information technology in the process of teaching some questions of mathematical disciplines in English to students of IT profile who are not native speakers of this language.
The Impact of Distance, Cardinal-direction and Time on Solar Irradiance Estimation: A Case-study

Long term Global Horizontal Irradiance (GHI) data sets are essential to assess the local solar resource and estimate the potential power production of photovoltaic systems. Statistical models are found to be very effective in estimating the GHI. In this study we examine to what extent the performance of such models is affected by the distance, direction and temporal difference between the training and testing period. To quantify these factors three machine learning models are considered: Random Forest, Extreme Gradient Boosting, and Artificial Neural Network. These models estimate the GHI at 15 weather stations in the Netherlands by considering 11 meteorological variables. The paper demonstrates that GHI estimation is more accurate when the model is trained on a station that is located closer to the target station, where an increased error of 3% and 7% is found up to a distance of respectively 40 and 120 km. In addition, in the case study it is found that the accuracy of GHI estimation improves when the test station is located in a northeast, east, southeast or south direction from the training station. This partly correlates with the prevailing wind direction. Finally, the testing period selected is found to significantly affect the obtained model performance, whereas the influence of the training period is found to be minimal.
A Novel Approach to Collectively Determine Cybersecurity Performance Benchmark Data

How do we determine cybersecurity performance benchmark data across organizations without the organizations revealing data points involving their frequency of information compromises? Disclosures of underlying data points are fundamentally inhibited by the need for privacy. It is a responsible organizational action to prevent the risk of expected or unexpected damages through data disclosure. Obtaining the data, especially valid and reliable data, necessary to calculate benchmarks, was thus an unsolvable wicked problem. The problem was therefore redefined as: How do we enable a distributed power-base of cybersecurity managers across organizations to collectively determine their benchmark data without actually disclosing their own data points? The core of the solution is a simple creative idea of having a protocol for a network of organizations to calculate benchmarks by distributing such calculations starting with some obfuscating data instead of centrally collecting the constituent data points of each organization. In this way, the confidential data of the organization would never be released beyond organizational systems. The fuller development of the protocol faced the issues of establishing trust in the network and preventing statistical compromises that were addressed through creative use of available technology, leading to the final solution, a distributed peer-to-peer architecture called Trusted Query Network (TQN). Distributed processing can induce trust and privacy into computer networks. In addition: (1) A research group representing multiple strengths and different but complementary backgrounds can be a very powerful asset in solving difficult problems. (2) Use of creativity is central to design science research but is particularly needed in solving apparently intractable problems. A group format can encourage free flow of ideas and brainstorming that are useful in spurring creativity. (3) It is very useful to be visionary in finding and solving challenging problems. Research groups provide the psychological strength to confront existing design challenges and to visualize their out-of-the-box solutions.
MLMVN in Speckle Noise Filtering

In this paper, we use the multilayer neural network with multi-valued neurons (MLMVN) as an intelligent tool for speckle noise filtering. MLMVN is a complex-valued feedforward neural network, which was successfully used for solving various problems including classification, prediction and additive noise filtering. Here, we apply MLMVN containing a single hidden layer for speckle noise filtering by processing overlapping patches taken from a noisy image. A resulting image is obtained by averaging the resulting intensities over all overlapping pixels. To train MLMVN, a learning set created of randomly selected patches from various images is used. A network is trained to transform a noisy patch into a clear one. To make a learning process more efficient, an incremental approach is used. A learning set is divided into batches, and a network is trained by sequentially going over all of them. In terms of PSNR, this approach significantly outperforms Lee filter - traditionally used for speckle noise filtering, and the BM3D filter - commonly recognized as one of the best nonlinear filters ever.
A Privacy and Authentication Scheme for IoT Environments Using ECC and Fuzzy Extractor

The internet of things (IoT) is consisting of many complementary elements which have their own specificities and capacities. These elements are gaining new application and use cases in our lives. Nevertheless, they open a negative horizon of security and privacy issues which must be treated delicately before the deployment of any IoT. Recently, different works emerged dealing with the same branch of issues, like the work of Yuwen Chen et al. that is called LightPriAuth. LightPriAuth has several drawbacks and weakness against various popular attacks such as Insider attack and stolen smart card. Our objective in this paper is to propose a novel solution which is “authentication scheme with three factor using ECC and fuzzy extractor” to ensure security and privacy. The obtained results had proven the superiority of our scheme's performances compared to that of LightPriAuth which, additionally, had defeated the weaknesses left by LightPriAuth.
Consideration on Data Conformance Toward Building Trust in Supply Chain

The new threats and risks to supply chains are increasing, and countermeasures to these threats are required. In Cross-ministerial Strategic Innovation Promotion Program (SIP) "Cyber Physical Security for IoT Society", a framework to enhance the trustworthiness of the supply chain is considered. In this paper, we focus on the data transmitted between organisations and propose a framework to enhance the trustworthiness of data supply chain by validating the conformance of its management and usage. The framework consists of 1) profile that defines the requirements of data management and usage, 2) conformance validation logic that ensures that the management and usage comply with the profile and 3) Trust Store that manages results of validation results through the entire supply chain.
Semantic Query Expansion based on Entity Association in Medical Question Answering

Abstract Query expansion technology is a common method to solve the semantic deviation problem between questions and answers in the question answering system. In medical question answering field, the current semantic-based query expansion method has the following two shortcomings. (1) In the stage of obtaining candidate extended terms, only the medical entity’s concept in the query is used, but the medical entity’s association between questions and answers is ignored (2) During the extended terms filtering stage, the interference of negative medical entities to the mutual information is ignored. Given the above-mentioned deficiencies, this paper proposes a semantic query expansion method based on entity association in medical question answering. This method first combines the query intent with the inference association between medical entities to obtain candidate extended terms from the medical knowledge graph. Then filter candidate extended terms using the selection method based on negative medical entity recognition and mutual information and thus get the final extended terms according to the co-occurrence association between medical entities. Experimental results show that our proposed method outperforms the query expansion method based on the thesaurus which commonly used in the current medical question and answering system.
A combination of classification based methods for recommending tweets

A 3 month long RecSys 2020 challenge1 was organized by Twitter[1]. Various kinds of implicit feedback with varying levels of sparsity were considered. In subsequent sections, I describe my approach to address this task, which helped me land on the 4th place on the final leaderboard with the final score of 20. My team name was learner_recsys and link to my code is present on GitHub2. This is detailed in Table 1. I used a combination of classification-based approaches, content-based recommendations, and non-personalized baselines.
An Improved A* Algorithm Applying to Path Planning of Games

Abstract Pathfinding is the core issues in the artificial intelligence field of games, and how to establish an effective method of pathfinding is still focused on. A* algorithm is the most widespread type of game pathfinding at present but the problems still exist such as the curve path and the time-consuming. Based on the analysis and research of traditional A* algorithm, this paper proposes an A* algorithm based on region search. Firstly, the algorithm divides the game map into several connected regions where the role can move freely and without barriers. Then the algorithm uses region search instead of node search. It reduces the search path and the search time. According to the portal between regions, the optimal feasible path is extracted from the region paths. It can effectively solve the zigzag path problem. Experimental simulation results show that the algorithm has shorter search time and can generate smoother path and meet the real-time and real experience requirements of pathfinding in the game.