Reviewerly/exHarmony huggingface dataset:
Current Trends in Cryptography, Steganography, and Metamorphic Cryptography: A Survey

World without Internet is very difficult to recognize. Increasing dependency and popularity of Internet among its users make its user life easier and fast. Some of the information of its users can be public without taking much pain but, on the other hand side, some information cannot be made public. It needs not only privacy but security too. Security at the cost of asset. How much security is enough in terms of cost of asset? Several researcher advocates use cryptography to provide required security to sensitive information. Some popular algorithms which perform cryptography are RSA with AVK, ECC, Hybrid, CE, Blowfish, Twofish, Threefish, etc. Some researchers advocate the use of steganography. Some popular algorithms are LSB, modified LSB, etc. Some researchers advocate the use of both approach, i.e., cryptography and steganography. This approach is popularly known as metamorphic cryptography. Cryptography helps when caught by some unauthorized user and steganography helps in creating less suspect. In this paper, a survey is carried out over popular techniques used by researcher in performing cryptography and steganography. Paper tried to discuss the methodology these algorithms use. Paper also tries to discuss the strength and limitation of various proposed algorithm according to respective authors claims in their research work.
Semi-automated Synthesis of Control System Software Through Graph Search

As the field of automation and robotics develops, control software driving the robotic systems becomes more and more complex. Currently, a predominant approach to modularization and structuring of software in the robotics community, which is embodied by ROS (Robot Operating System) and similar frameworks, is to decompose software into a set of functional modules (i.e. nodes, processes, classes). Particular modules comprising the system are chosen manually by the programmer and they exchange data using loosely coupled publish/subscribe communication mechanisms. While such an approach is helpful and currently highly developed, it seems to lack the means of verifying correctness of the whole control software system (i.e. is there a source of data for all input signals of a given module?, are its outputs actually used by other modules in the system?). Manual specification of system structure and verification of its correctness can be tedious and prone to errors. To help alleviate this, we propose a system composition algorithm utilizing graph search methods. It processes a set of modules implemented by the programmer and produces a graph of modules representing the system with input and output signals specified by the programmer. Such an algorithm can be used with nearly arbitrary control software frameworks.
Protection of User-Defined Sensitive Attributes on Online Social Networks Against Attribute Inference Attack via Adversarial Data Mining

Online social network (OSN) users share various types of personal information with other users. By analysing such personal information, a malicious data miner (or an attacker) can infer the sensitive information about the user which has not been disclosed publicly. This is generally known as attribute inference attack. In this study, we propose a privacy preserving technique, namely 3LP+, that can protect users’ multiple sensitive information from being inferred. We experimentally show that the 3LP+ algorithm can provide better privacy than an existing technique while maintaining the utility of users’ data.
Analyzing Dynamic Social Media Data via Random Projection - A New Challenge for Stream Classifiers

In recent years social media became an important part of everyday life for many people. A big challenge of social media is, to find posts, which are interesting for the user. Many social networks like Twitter handle this problem with so called hashtags. A user can label his own Tweet (post) with a hashtag, while other users can search for posts containing a specified hashtag. But what about finding posts which are not labeled by the creator? We provide a way of completing hashtags for unlabeled posts using classification on a novel real world Twitter data stream. New posts will be created every second, thus this context fits perfectly for non-stationary data analysis. Our goal is to show, how labels (hashtags) of social media posts can be predicted by streaming classifiers. In particular we employ Random Projection (RP) as a preprocessing step in calculating streaming models. Also we provide a novel real world data set for streaming analysis called NSDQ with a comprehensive data description. We show that this dataset is a real challenge for stateof-the-art stream classifiers. While RP has been widely used and evaluated in stationary data analysis scenarios, non-stationary environments are not well analyzed. In this paper we provide a use case of RP on real world streaming data, especially on NSDQ dataset. We discuss why RP can be used in this scenario and how it can handle stream specific situations like concept drift. We also provide experiments with RP on streaming data, using state-of-the-art streaming classifiers like Adaptive Random Forest and concept drift detectors.
Modification of the Firefly Algorithm for Improving Solution Speed

The demand for fast and intelligent optimization methods is constantly growing. Owing to this, new methods based on the behaviour of living organisms are being developed. This article proposes a modification of the classic firefly algorithm based on the acceptance of each movement of a firefly, that provide more accurate values of an objective function. In addition, the algorithm also involves a reduced value of a coefficient, α, in each of its iterations. The effectiveness of the modification is examined using typical test functions. The modification allows for finding the correct solution faster and more accurately. This improvement is achieved at the expense of the algorithm’s sensitivity to a selection parameter, αdamp, which affects the speed at which the value of α decreases.
Do expert surveys underrate lower-income countries?

Perceptions of countries’ policy environments and innovation efforts drive important investment and regulatory decisions, but those perceptions may be biased. One particular concern is that evaluations of richer countries might be inflated compared to evaluations of poorer countries, as general stereotypes of national success spill over into assessments of research capability. Comparison of World Economic Forum survey assessments of businesses’ research spending with official spending figures reveals that, among countries spending comparable amounts on research, richer countries receive higher survey ratings. Richer countries’ educational outcomes are similarly rated higher than similarly performing poorer countries’. These findings suggest that perceptions may systematically disadvantage economically poorer countries, thereby misdirecting real-world investments and policy decisions derived from those rankings.
Optimizing parallel particle tracking in Brownian motion using machine learning

In this paper, we present a generic, scalable and adaptive load balancing parallel Lagrangian particle tracking approach in Wiener type processes such as Brownian motion. The approach is particularly suitable in problems involving particles with highly variable computation time, like deposition on boundaries that may include decay, when particle lifetime obeys exponential distribution. At first glance, Lagranginan tracking is highly suitable for a distributed programming model due to the independence of motion of separate particles. However, the commonly employed Decomposition Per Particle (DPP) method, where each process is in charge of a certain number of particles, actually displays poor parallel efficiency due to the high particle lifetime variability when dealing with a wide set of deposition problems that optionally include decay. The proposed method removes DPP defects and brings a novel approach to discrete particle tracking. The algorithm introduces master/slave model dubbed Partial Trajectory Decomposition (PTD), in which a certain number of processes produce partial trajectories and put them into the shared queue, while the remaining processes simulate actual particle motion using previously generated partial trajectories. Our approach also introduces meta-heuristics for determining the optimal values of partial trajectory length, chunk size and the number of processes acting as producers/consumers, for the given total number of participating processes (Optimized Partial Trajectory Decomposition, OPTD). The optimization process employs a surrogate model to estimate the simulation time. The surrogate is based on historical data and uses a coupled machine learning model, consisting of classification and regression phases. OPTD was implemented in C, using standard MPI for message passing and benchmarked on a model of 220 Rn progeny in the diffusion chamber, where particle motion is characterized by an exponential lifetime distribution and Maxwell velocity distribution. The speedup improvement of OPTD is approximatelly 320% over standard DPP, reaching almost ideal speedup on up to 256 CPUs.
Comment on: “Interaction of the magnetic quadrupole moment of a non-relativistic particle with an electric field in a rotating frame. Ann. Phys. 412 (2020) 168040”

We analyze a recent treatment of the interaction of a magnetic quadrupole moment with a radial electric field for a non-relativistic particle in a rotating frame and show that the derivation of the equations in the paper is anything but rigorous. The authors presented eigenvalues and eigenfunctions for two sets of quantum numbers as if they belonged to the same physical problem when they are solutions for two different models. In addition to it, the authors failed to comment on the possibility of multiple solutions for every set of quantum numbers.
Semantic Similarity Between Adjectives and Adverbs—The Introduction of a New Measure

It is believed that language is the window into the mind, thus a fundamental building brick for the development of artificial intelligence. Understanding natural language is therefore a crucial task for computer systems of the future, and as such it is widely studied. An important tool for the decoding of language are semantic similarity measures, or the description of closeness of the meaning of words. A lot of research on semantic similarity on a conceptual level exists, comparing thus objects and their interconnections, which is typical of similarity between nouns. In this article we propose a novel way of exploring spectral similarity, or the closeness of adjectives and adverbs with respect to the spectrum of all words describing the same feature (e.g., the closeness of sometimes and seldom with respect to the spectrum of words going from never to always). The proposed semantic similarity measure is based on overlaps in second order synonyms of words, and its accuracy is validated in this article thanks to a comparison with human assessment of similarity between words. With a custom accuracy estimate taking in account the fuzziness of the proposed similarity measure, an accuracy of the algorithm of about \(96\%\) is obtained.
3D pose reconstruction with multi-perspective and spatial confidence point group for jump analysis in figure skating

Driven by recent computer vision applications, recovering 3D pose in the field of figure skating has become increasingly important. However, conventional works have suffered because of getting 3D information based on the corresponding 2D information directly or leaving the specificity of sports out of consideration. Issues such as restriction from self-occlusion, abnormal pose, limitation of venue and so on will result in poor results. Motivated by these problems, this paper proposes a multitask architecture based on a calibrated multi-camera system to facilitate jointly 3D jump pose of figure skater in the presence of the 2D Part Confidence Map. The proposals consist of three key components: Temporal smoothness and likelihood distribution based discrete probability points selection; Multi-perspective and combinations unification based large-scale venue 3D reconstruction; Spatial confidence point group and multiple constraints based human skeleton estimation. This work can be applied to 3D animated display and video motion capture of figure skating competition. The accuracy rate on the test sequences is 82.32% in body level and 92.96% in joint level.
Fuzzy path planning of unmanned underwater vehicle based on adaptive fireworks algorithm optimization

Aiming at the typical problem of unmanned underwater vehicle (UAV) path planning, this paper proposes a fuzzy optimization algorithm based on adaptive fireworks. Firstly, the track performance index with a multi-objective fuzzy optimization strategy based on energy consumption and buoy threat was established. Then, the adaptive fireworks search algorithm was used for track planning. Finally, three groups of simulation and comparison experiments with different levels of complex environment were carried out. The results showed that the fuzzy optimization algorithm based on adaptive fireworks can effectively solve the multi-objective fuzzy optimization problem. The method proposed in this paper provided a useful reference for unmanned underwater vehicle path planning.
Assessment of Solar Energy Potential using GIS and AHP approach: A Case Study of Bumthang Valley

This paper evaluates the Solar Energy Potential for Bumthang valley in central Bhutan using geographic information system (GIS) and Analytic Hierarchy Process (AHP) approach. In order to find the suitable catchment for solar PV installation, the analysis have been carried out based on three broad factor; climatological, environmental and economic factors namely; solar irradiance, elevation, aspect, slope, LULC, proximity to town and proximity from road besides exclusion criteria; Protected Area and Biological Corridor (PA&BC). The weights of the criteria are calculated using MCDM-AHP and applied on the individual criteria raster to generate the final suitability map. The highly suitable area for placing solar farm has been assessed applying exclusion criteria and yielded a potential area of 6.54 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the valley. The solar energy potential based on different efficiencies of PV modules indicated a potential of more than 1000 GWh/Year, while current energy consumption of the valley indicated only 11.5 GWh/Year.
Experimental Research on Geographic Information System Interface Map Information Layout

The Geographic Information System (GIS) has played an important role in urban planning management, transportation, military and other fields. In order to explore the effective method of GIS interface layout, this paper conducts an experimental study on the pop-up window layout of detailed information of GIS interface targets, and analyzes the reaction time of the participants. At the same time, a questionnaire was issued to collect subjective feeling data. The research in this article shows that when a pop-up window appears in the interface, it should be displayed in the center of the interface or around the click position. Secondly, you can choose to display it in the right half of the interface, and try not to display it in the bottom-left of the interface. In addition, if the information in the pop-up is two columns, and the information can be divided into two categories, the left-right distribution is better than the up-down distribution.
User Behavioral Biometrics and Machine Learning Towards Improving User Authentication in Smartphones

Smartphone and smart devices, in general, have penetrated modern life, accompanying humans in the majority of their daily activities, realizing the era of IoT. This tight bond between mobile devices and humans has introduced numerous solutions and automation in people’s everyday living, however, it also comes with a cost, since we are more exposed to security risks and private data leakages. This work extends our previous study on improved user authentication mechanism for smartphones. More specifically, we present a biometric-based approach which utilizes machine learning and the development of a mobile application. The application’s evaluation results have shown very large success rates, implying its effectiveness in enhancing user’s privacy.
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference.

Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinatorial optimization problem to find the most likely configuration of the discrete-valued model. Linear programming (LP) relaxations in the Sherali-Adams hierarchy are widely used to attempt to solve this problem, and smooth message passing algorithms have been proposed to solve regularized versions of these LPs with great success. This paper leverages recent work in entropy-regularized LPs to analyze convergence rates of a class of edge-based smooth message passing algorithms to $\epsilon$-optimality in the relaxation. With an appropriately chosen regularization constant, we present a theoretical guarantee on the number of iterations sufficient to recover the true integral MAP solution when the LP is tight and the solution is unique.
Black-Box Attacks via the Speech Interface Using Linguistically Crafted Input

This paper presents the results of experiments demonstrating novel black-box attacks via the speech interface. We demonstrate two types of attack that use linguistically crafted adversarial input to target vulnerabilities in the handling of speech input by a speech interface. The first attack demonstrates the use of nonsensical word sounds to gain covert access to voice-controlled systems. This attack exploits vulnerabilities at the speech recognition stage of handling of speech input. The second attack demonstrates the use of crafted utterances that are misinterpreted by a target system as a valid voice command. This attack exploits vulnerabilities at the natural language understanding stage of handling of speech input.
Ontology Evolution in the Context of Model-Based Secure Software Engineering

Ontologies as a means to formally specify the knowledge of a domain of interest have made their way into information and communication technology. Most often, such knowledge is subject to continuous change, which demands for consistent evolution of ontologies and dependent artifacts. In this paper, we study ontology evolution in the context of a model-based approach to engineering of secure software, where ontologies are used to formalize the security context knowledge which is needed to come up with software systems which can be considered secure. In this application scenario, techniques for detecting ontology changes and determining their semantic impact are faced with a couple of challenging requirements which are not met by existing solutions. To overcome these shortcomings, we adapt a state-based approach to model differencing to OWL ontologies. Our solution is capable of detecting semantic editing patterns which may be customly defined using graph transformation rules, but it does not depend on information about editing processes such as persistently managed change logs. We showcase how to leverage semantic editing patterns for the sake of system model co-evolution in response to changing security context knowledge, and demonstrate the feasibility of the approach using a realistic medical information system.
On the Use of Fuzzy Sets Weighted Subsethood Indicators in a Text Categorization Problem

The paper studies the problem of fuzzy sets subsethood in a new framework inspired by our previous work on textual information processing. The proposed algorithms for categorizing textual documents are expressed in terms of the (approximate) subsethood of representing them fuzzy sets. Here we focus on the novel aspect of such a subsethood which consists in assuming that the elements of the universe are assigned with some importance degrees. Thus when the degree of approximate subsethood of two fuzzy sets is to be determined then not only membership degrees are taken into account but also the mentioned importance degrees. We study how these importance degrees may be taken into account by a class of approximate subsethood indicators based on the calculus of linguistically quantified propositions with a special emphasis on the Kosko’s indicator.
Right of Way Rules based Collision Avoidance Approach Using Model Predictive Control

This paper presents a Model Predictive Control (MPC) based collision avoidance scheme for unmanned aerial vehicles (UAVs) in civilian airspace consisting of manned and unmanned aerial vehicles. The MPC formulation takes the Federal Aviation Regulations for collision avoidance mid-air collision scenarios into account. The optimal control inputs to the UAV in the form of angular velocities are computed by optimizing the MPC cost function for a finite prediction horizon. The algorithm is evaluated for pairwise and multi-UAV conflict scenarios and compared against inverse proportional navigation (IPN) collision avoidance approach. The results show that MPC has lower control effort than the IPN while achieving similar performance of IPN.
A QoE Evaluation of an Augmented Reality Procedure Assistance Application

Augmented reality (AR) is a key technology to enhance worker effectiveness during increasing automation of repetitive jobs in the workplace. AR will achieve this by assisting the user to successfully perform complex and frequently changing procedures. The design of AR applications for these roles is critical to their acceptability and utility. User quality of experience (QoE) will inform these design decisions. A user arrives at a quality judgment upon post-experience reflection of their degree of delight or annoyance relating to the degree of fulfilment of pragmatic and hedonic needs and expectations of the medium under consideration. QoE researchers have largely depended upon post-experience subjective reports to determine the user's QoE. Subjective reports have been shown to be biased by primacy, recency and maxima of experience stimuli. Recent research involves the identification of implicit metrics that can be used to determine user QoE continuously during a multimedia experience. This work evaluates head rotation frequency as an objective metric of user QoE. The literature shows that emotion is expressed in the frequency of a person's head rotation around three axes of movement (pitch, yaw and roll). Low frequency head rotation has been shown to include expression of happy emotion while high frequency exclusively expresses anger emotion. These emotions are analogous to those reflected on by a user during the quality formation process. This demo paper analyses the amount of high frequency head rotation exhibited by the user upon task completion using an AR procedure assistance application or a paper-based control. An optimal Rubik's Cube solving AR application was used as a proof of concept for AR-based procedure assistance. Preliminary results showed that the AR environment yielded higher task success rates and significantly shorter task completion durations. The AR users exhibited significantly lower amplitudes of anger frequencies in their head rotations than the control group.