Reviewerly/exHarmony huggingface dataset:
Trivariate gamma regression

Abstract Regression analysis is a method for determining a causal relationship between the response and predictor variables. The regression model has been developed in various ways, one of them is based on the distribution of the response variables. In this study, the response variables follow trivariate gamma distribution, such that the regression model developed is Trivariate Gamma Regression (TGR). The purposes of this study are to obtain the parameter estimators, test statistics, and hypothesis testing on parameters are significance (overall and partial) of the TGR model. The parameter estimators are obtained using the Maximum Likelihood Estimation (MLE). The overall test for the model’s significance is using Maximum Likelihood Ratio Test (MLRT), and the partial test is using the Z test. Based on the results of this study, it can be inferred that the parameter estimators obtained from the MLE are not closed form. Hence a numerical method is needed. In this study, the algorithm of numerical optimization used is BFGS quasi-Newton.
eRollin' On Green

We present a case study on eco-feedback tools in the context of Vienna's Aspern Seestadt. Utilizing an action-research based methodology, we engage the local community in cooperation with the aspern.mobil LAB to co-design a digital interactive tool visualizing the participants' personal CO2-footprint. Following suggestions from related literature and spanning the bow towards critical algorithm studies, we put a special focus on the explainability of the tool and the algorithmic calculations involved in determining the footprint, and evaluate that approach in terms of its transformative potential. Our findings show the educational value and increased awareness resulting from the use of our prototype and shed light on some issues of interface design and trust related to explainable algorithmic processes. Furthermore, in evaluating our research methodology we find action-research based methodologies highly suitable for local communities in relation to sustainable eMobility practices.
The Concept of the Deviant Behavior Detection System via Surveillance Cameras

One of the tasks of a Smart City is an ensuring the safety of its residents. Dangerous situations are often caused by deviant behavior of people: robbery, fight, kidnapping, attack, etc. Nowadays all cities are equipped with video surveillance systems that are used to monitor city life. They also can be used to detect potentially dangerous situation in a real-time and to inform emergency services to taking actions that will help to eliminate them. Due to the huge amount of video material, to solve the problem of detecting dangerous situations, it is necessary to use modern intelligent technologies that allow their automatic analysis. This paper proposes a review of modern technologies, and concept of the deviant behavior detection system based on the reviewed technologies.
SuperOrder: Provider order recommendation system for outpatient clinics

This study aims at developing SuperOrder, an order recommendation system for outpatient clinics. Using the electronic health record data available at midnight, SuperOrder predicts the order contents for each upcoming appointment on a daily basis. A two-level prediction framework is proposed. At the base-level, the predictions are produced by aggregating three machine learning methods. The meta-level predictions are generated by integrating the base-level predictions with the order co-occurrence network. We used the retrospective data between 1 April 2014 and 31 March 2015 in pulmonary clinics from five hospital sites within a large rural health care facility in Pennsylvania to test the feasibility. With a decrease of 6 per cent in the precision, the improvement of the recall at the meta-level is approximately 20 per cent from the base-level. This demonstrates that the proposed order co-occurrence network helps in increasing the performance of order predictions. The implementation will bring a more effective and efficient way to place outpatient orders.
Mining Plausible Hypotheses from the Literature Via Meta-Analysis

Meta-analysis is highly advocated in many fields of empirical research such as medicine and psychology, due to its capability to synthesize quantitative evidence of effects from the literature, based on statistical analysis. However, the adoption of meta-analysis to software engineering is still suffering from inertia, despite the fact that many software engineering researchers have long been arguing the need for it. As an attempt to move beyond the lockstep, we in this paper explore a different use of meta-analysis. Our proposition is that meta-analysis is useful for mining hypotheses because their plausibility is backed by evidence accumulated in the literature, and thus researchers could focus their effort on the areas that are of particular need. We assess our proposition by conducting a lightweight case study on the literature of defect prediction. We found that three out of five hypotheses we extract from our meta-analysis were indeed investigated in separate papers, indicating the usefulness of our approach. We also recognize two uninvestigated hypotheses whose validity we plan to investigate in the future.
From Low Vision to High Quality: Video QoE Enhancement for Visually Impaired Users

In this paper, we investigate the impact of real-time video enhancement techniques on video Quality of Experience (QoE) for users with low vision. To this end, we present the results of a lab-based user study, in which participants with low vision utilize a web-based media player to configure individual video quality enhancement filters (e.g. increased contrast, strong edge enhancement and lowered brightness) depending on types of content. Additionally, for each test participant we measured the visual performance to describe how low vision impacts video QoE. Our results show, that individual video quality filters positively impact the experience of the quality dimensions detail, text and content. Furthermore, we discuss insights from qualitative feedback from participants regarding overall utility and User Experience (UX) aspects of real-time video enhancements.
Study of Sensitivity to Weight Perturbation for Convolution Neural Network

Exploring underlying properties of a neural network contributes to pursuing its internal behavior and functionality. For convolution neural networks (CNNs), a sensitivity measure to weight perturbation is introduced in this paper to reflect the extent of the network output variation, which could evaluate the effect of the weights on the network. The sensitivity is defined as the mathematical expectation of absolute output variation due to weight perturbation with respect to all possible inputs. Assuming that the conditional distribution of input obeys the normal, the sensitivity is iteratively computed layer to layer until the entire network. Without loss of generality, the paper proposes an approximate algorithm to compute a theoretical sensitivity, which is actually a function of mapping between the network's output variation and its weight perturbation. The experimental results demonstrate the coincidence of the computed theoretical sensitivity with the simulated actual output variation of the network. Thus a criterion can be established to evaluate the influence of weights on CNNs' output.
Political Economy Assessment of the Educational System Promotion in Russia in the Conditions of Digital Economy Formation

The article reviews a political-economic analysis of the growing dynamics of the education system in Russia, using the example of higher education in the creative solutions of the digital economy. The author examines the economics of education from the political economy view. The evolution of targets in higher education is presented. When comparing definitions of higher education targets, they provide two mutually related, but competing functions of higher school: the role of higher education in the teaching workforce for all economic sectors and the general intellectual growing of a creative, socialized personality. The nature of economic interests in the higher education system is determined. Market economy and democratic society principles imply models of competitive harmony in economic interests of higher education. The educational product is analyzed from the principle of diglossia as a public and private benefit. In the near future, a combination of fee and free higher education in Russia is inevitable. However, the share of fee-paying educational services of universities will dramatically increase. The analysis confirms academic steps to create a scientific product in the digital economy. Competition of universities in the market of scientific products for receiving profitable contracts with firms and corporations stimulates universities to increase their rankings. The conclusion shows the inconsistency of the dynamics of the development of the education system of Russia in the digital economy. An important contradiction is in the prevalence of political decisions free from long-term socio-economic consequences.
Analyzing the Effect of Moving Target Defense for a Web System

Moving target defense (MTD) is a feasible idea for reducing the ratio of successful attacks by altering or diversifying the attributes or parameters of a protected system. As a result of applying MTD techniques to a system, an attacker would have more difficulties in launching attacks. Although several MTD techniques have been proposed for different types of attack, estimating the effectiveness of combining these MTDs remains a challenge. With the aim of setting up a method for evaluating MTDs, we first propose a model composed of two MTD diversification techniques to compare an attack success ratio between theoretical and experimental probability. To validate the proposed model, we conducted an experiment involving an actual attack and then analyzed how our MTD model can adequately estimate a binary-code injection attack. Results show that the rate of attack success is 100% when MTD diversification is not implemented, while the rate is reduced depending on how many variants can be diversified in a target system. Our method is an important first step toward establishing a method for evaluating MTDs, as well as predicting an MTD’s defensive abilities.
Fast Verified BCD Subtyping

A decision procedure for the Barendregt-Coppo-Dezani subtype relation on intersection types (“BCD subtyping”) is presented and formally verified in Coq. Types are extended with unary, covariant, distributing, preordered type constructors and binary products. A quadratic upper bound on the algorithm runtime is established. The formalization can be compiled to executable OCaml or Haskell code using the extraction mechanism of Coq.
Random Orthogonal Projection Based Enhanced Bidirectional Extreme Learning Machine

Bidirectional extreme learning machine (B-ELM) divides the learning process into two parts: At odd learning step, the parameters of the new hidden node are generated randomly, while at even learning step, the parameters of the new hidden node are obtained analytically from the parameters of the former node. However, some of the odd-hidden nodes play a minor role, which will have a negative impact on the even-hidden nodes, and result in a sharp rise in the network complexity. To avoid this issue, we propose a random orthogonal projection based enhanced bidirectional extreme learning machine algorithm (OEB-ELM). In OEB-ELM, several orthogonal candidate nodes are generated randomly at each odd learning step, only the node with the largest residual error reduction will be added to the existing network. Experiments on six real datasets have shown that the OEB-ELM has better generalization performance and stability than B-ELM, EB-ELM, and EI-ELM algorithms.
Improved Meet-in-the-Middle Attacks on Reduced-Round Kiasu-BC and Joltik-BC

Abstract Kiasu-BC and Joltik-BC are internal tweakable block ciphers of authenticated encryption algorithms Kiasu and Joltik submitted to the CAESAR competition. Kiasu-BC is a 128-bit block cipher, of which tweak and key sizes are 64 and 128 bits, respectively. Joltik-BC-128 is a 64-bit lightweight block cipher supporting 128 bits tweakey. Its designers recommended the key and tweak sizes are both 64 bits. In this paper, we propose improved meet-in-the-middle attacks on 8-round Kiasu-BC, 9-round and 10-round Joltik-BC-128 by exploiting properties of their structures and using precomputation tables and the differential enumeration. For Kiasu-BC, we build a 5-round distinguisher to attack 8-round Kiasu-BC with $2^{109}$ plaintext–tweaks, $2^{112.8}$ encrytions and $2^{92.91}$ blocks. Compared with previously best known cryptanalytic results on 8-round Kiasu-BC under chosen plaintext attacks, the data and time complexities are reduced by $2^{7}$ and $2^{3.2}$ times, respectively. For the recommended version of Joltik-BC-128, we construct a 6-round distinguisher to attack 9-round Joltik-BC-128 with $2^{53}$ plaintext–tweaks, $2^{56.6}$ encryptions and $2^{52.91}$ blocks, respectively. Compared with previously best known results, the data and time complexities are reduced by $2^7$ and $2^{5.1}$ times, respectively. In addition, we present a 6.5-round distinguisher to attack 10-round Joltik-BC-128 with $2^{53}$ plaintext–tweaks, $2^{101.4}$ encryptions and $2^{76.91}$ blocks.
A Knowledge-Based Computational Environment for Real-World Data Processing

The data contains knowledge. Researchers working in various fields are struggling to extract information from specific data sets. But data processing is a complex process. Real-world data must be converted multiple times to become useful knowledge. This process is very unfriendly to non-computer professional researchers. The lack of knowledge related to data processing makes their work unsatisfactory. The main purpose of this paper is to build a computational environment based on ontology technology, which provides selection and description of data processing algorithms to help the users extract information from the real-world data. This article provides a real case to indicate the rationality of this computational environment. The authors extracted significant conclusions from a data set of the acid-base state at patients.
Image Contrast Enhancement Based on Laplacian-of-Gaussian Filter Combined with Morphological Reconstruction

In the paper, a method of contrast enhancement is presented. It combines linear and non-linear techniques. A non-linear approach based on morphological reconstruction is applied to select essential regions on the of Laplacian-of-Gaussian (LoG) of the input. The morphologically modified result of LoG is next added to the initial image. Thanks to the morphological processing, only the meaningful region boundaries are used to enhance the image contrast. The usage of morphological processing allows for selecting more precisely the image regions that are subject to contrast enhancement. The method performs well on textured images, allowing for adjusting the level of visible details in the output image while increasing the sharpness of meaningful image regions. A couple of examples illustrates the performance of the proposed method.
Computational Color Naming for Human-Machine Interaction

In this paper, we present two simple methods for automatic color naming, the first one learns the pixel-wise color name annotations from the color-chips and later method learns the image-wise color names from the real-world weakly labelled images. Color information is an important feature for many computer vision applications. Color name as a descriptor finds it's applications in many real-world tasks such as Color Blind assistance, image retrieval and scene understanding. Color naming in images is a challenging problem due to shadows, view angles, illumination conditions and surface reflections. Manual labelling of color names for real-world images in applications like search engines, fashion parsing and tracking is a tedious task and time consuming. The proposed systems for color naming automates the process and avoids human labelling. These methods are based on superpixels in the CIELAB color space. We trained Random Forests classifier with color-chip dataset for pixel-wise color naming and for image-wise color naming it is trained on weakly color labelled image dataset. Both models are tested on real-world images for color name judgments. Experimental results shows that color names learned through these proposed systems have advantages in terms of implementation costs, speed of execution and can be used in real-time applications with lowcost hardware.
Improving the Quality of Clustering-Based Diagnostic Rules by Lowering Dimension of the Cluster Prototypes

The study concerns an evaluation of rule premise conditions generated from cluster prototypes. Different clustering methods are used as the first step of training the rule-based classifiers. If rules are generated directly from cluster prototypes, their premise complexity (the number of conditions) is equal to the dimension of considered training data. This paper describes an idea of detecting an appropriate part of the clustering-based rule premise and refinement of the rule set. It is especially valuable in medical domain applications of rule-based classifiers in the view of their generalization quality and interpretability.
Nonce-Based Key Agreement Protocol Against Bad Randomness

Most of the existing cryptographic schemes, e.g., key agreement protocol, call for good randomness. Otherwise, the security of these cryptographic schemes cannot be fully guaranteed. Nonce-based cryptosystem is recently introduced to improve the security of public key encryption and digital signature schemes by ensuring security when randomness fails. In this paper, we first investigate the security of key agreement protocols when randomness fails. Then we define the security model for nonce-based key agreement protocols and propose a nonce-based key agreement protocol that protects against bad randomness. The new protocol is proven to be secure in our proposed security model.
Architectural reasoning in the conceptual phase - a case study in the oil and gas industry

This paper evaluates the use of architectural reasoning to explore the problem space in a system development project in the oil and gas industry. The suppliers in this industry have traditionally been tailor-making their systems for each delivery project. To improve the systems offering across the client and project portfolio, the suppliers must put more effort in the conceptual phase to explore the design space. Architectural reasoning is the process of transferring problem and solution know-how into a new systems architecture. In this paper we review literature on architectural reasoning in the conceptual phase, and on application in the oil and gas industry. To evaluate the use of architectural reasoning in the industry, we perform a case study in a subsea supplier company. From the case study, we are identifying a work-flow for architectural reasoning, utilizing the market needs, design, and domain knowledge to evolve the system. Evaluating the tools and working methods, we find that working in a multi-disciplinary team is key to support the reasoning process. We find that the team is utilizing the design and domain knowledge to improve the system architecture. However, the team lacks methods to make this knowledge explicit and to quantify the issues they are identifying.
Virtual keyboard based on a brain-computer interface

Abstract This paper proposes a novel LabVIEW based algorithm regarding the implementation of an eye-blinks strength controlled virtual keyboard aimed to help people with neuromotor disabilities. The application can be considered a brain-computer interface which is useful for the assistance of patients suffering from Locked-In syndrome or amyotrophic lateral sclerosis. A precise, repeatable and easily detectable control signal could be the eye-blink strength. The LabVIEW based virtual keyboard provides the following features: detection and counting the voluntary eye-blinks, switch command, select command, highlight action running in parallel with the previously mentioned processes, enabling cancel, delete and space commands. The working principle underlying the implementation of the proposed virtual keyboard is Divide and Conquer paradigm. Thus, it is possible the switching task across the rows, half rows and keys (characters) in order to select a row, half row, respectively key associated to the character that should be introduced in the text box.
Krill herd algorithm with chaotic time interval and elitism scheme

We propose a new chaotic krill herd (CKH) in terms of the recently developed krill herd (KH) algorithm, to solve global numerical optimization problems. In CKH, chaos characteristics are introduced into the KH so as to further enhance its global search ability. The elitism scheme is also applied to store the best krill during the process when updating the krill. This new approach can speed up the global convergence, while preserving the advantage of the standard KH, thus making the approach more feasible for a wider range of practical applications. Here, thirteen different chaotic maps are used to tune the time interval of the krill in the KH algorithm. Twenty-four standard benchmark functions are utilized to verify the effects of the CKH and it has been demonstrated that, in most cases, the performance of CKH with a proper chaotic map is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods.Highlights A new meta-heuristic algorithm, namely CKH is proposed for global optimization.13 different chaotic maps are applied to tune the main parameter of the KH algorithm.The elitism scheme is applied to keep the best fitness krill.The CKH algorithm is compared with ten well-known methods.
On the Identification of Decision Boundaries for Anomaly Detection in CPPS

In this work, a new data-driven approach to anomaly detection in Cyber-Physical-Production-Systems (CPPS) is developed, which uses the geometric structure non-convex hull to build a decision boundary for the classification of new observations. A novel algorithm based on Mixture-of-Experts model is presented to estimate n-dimensional non-convex hulls. Furthermore, a new method is proposed to solve the point in non-convex hull problem. With these two methods in hand, a novel algorithm for anomaly detection is developed. Compared with convex hulls based anomaly detection methods, our approach can handle data sets with arbitrary shapes. Since the presented geometric approach does not make any assumption about either the probability density or the structure of given data, it can be combined with different machine learning algorithms. The effectiveness of this approach is evaluated with real world data collected from wind turbines.
Spatial Average Pooling for Computer Go

Computer Go has improved up to a superhuman level thanks to Monte Carlo Tree Search (MCTS) combined with Deep Learning. The best computer Go programs use reinforcement learning to train a policy and a value network. These networks are used in a MCTS algorithm to provide strong computer Go players. In this paper we propose to improve the architecture of a value network using Spatial Average Pooling.