Reviewerly/exHarmony huggingface dataset:
On the Use of Requirement Patterns to Analyse Request for Proposal Documents

Requirements reuse is still today a difficult goal to achieve. One particular context in which requirements reuse may give more benefits than costs is that of call for tenders projects, due to the similarity of the requirements documents (which take the form of requests for proposal documents, RfPs) from one project to another. In this paper, we present an approach aimed at making systematic the assessment of RfPs that technology providers need to conduct in order to decide whether they present a bid or not in a call for tenders project. The approach extends a metamodel we already defined for the former PABRE method, which has a similar goal but from the perspective of the organization that issues the call for tenders. The method is illustrated with an exploratory case study in the field of the railway systems domain.
A Logic for Secure Stratified Systems and its Application to Containerized Systems

We present the design and verification of a secure integrity measurement system for containerized systems. Containerization of applications allows fine-graded deployment and management of services and dependencies but also needs fine-graded security mechanisms. In this paper we provide formal abstractions for containerized systems by introducing LS^3, a formal model and logic with sub-domain constructs to represent stratified systems and their interactions. Using our formal model, we prove that the widely used Trusted Computing Group (TCG) based Integrity Measurement Architecture (IMA) securely extends trust measurements from boot to applications. However, IMA is not designed to make domain specific trust measurements and is consequently incapable of creating domain specific integrity reports. Current research aims to improve either trust measurement performance or comprehensiveness but does not improve the measurement function and its semantics to allow remote verification of measurements per domain. We present an enhanced trust measurement architecture design, which produces domain specific integrity measurements suitable for fine-graded remote attestation. Providing domain specific integrity reports eases system and sub-system verification and yields desirable properties such as measurement log stability and constrained disclosure for multi-domain systems. We verify and prove the correctness of our trust measurement architecture using our formal model.
Power of Communication Behind Extreme Cybersecurity Incidents

There are increasing threats for cyberspace. This paper tries to identify how extreme cybersecurity incidents occur based on the scenario of a targeted attack through emails. Knowledge on how extreme cybersecurity incidents occur helps in identifying the key points on how they can be prevented from occurring. The model based on system thinking approach to the understanding how communication influences entities and how tiny initiating events scale up into extreme events provides a condensed figure of the cyberspace and surrounding threats. By taking cyberspace layers and characteristics of cyberspace identified by this model into consideration, it predicts most suitable risk mitigations.
Influence of the angle of fall of light on the photovoltaic panel and its optimization - literature review

Scarce energy sources in today’s world are the cause of meeting energy needs in everyday life through renewable energy sources. Concerns about climate change, rising oil prices and increased government subsidies lead to enlarged investments in renewable energy sources. Today, research focuses on optimizing and improving efficiency of these sources. Renewable energy is derived from natural processes that are constantly renewed. In most of its forms it is delivered directly from the sun or from the heat accumulated in the earth. The purpose of this publication is to present a realistic approach to enhancing efficiency in cases where green energy is extracted from sun through photovoltaic panels. This would lead to a higher power supply for artificial lighting.There are significant energy losses when installing a solar panel on a static structure in the morning and late afternoon due to the fact that the panel is not sun-oriented in east-west direction (daily sun motion). There are losses also when the solar panel is not perpendicular to sun rays (in north-south direction). This publication presents the influence of the sunlight angle falling on photovoltaics and its optimization.
Alphabet usage pattern, word lengths, and sparsity in seven Indo-European languages

Abstract An empirical study on about 1.7 million dictionary words from seven languages viz. English, French, Dutch, Spanish, Italian, Hindi, and German has been conducted. Three intriguing characteristic features have been analyzed. First, the alphabet usage pattern in a language was determined which can be used to give an idea on how alphabets have been employed. For instance, the alphabet ‘e’ is highly used in English, while ‘q’ is least used. Second, the average and range of word lengths in the languages were computed and seen to vary from 1 to 37. Average word lengths were computed in the range (6.665–11.14). For comparison, word lengths have been fitted using Gaussian distribution. Third, a new measure was derived; which we termed ‘Language Sparsity’; computed as one minus ratio of number of words of a particular length already existing to the total number of possible words that can be formed. Sparsity hence gives a measure of the scope of fruition in languages. Two such measures have been defined: a weighted and a nonweighted sparsity. Nonweighted sparsity was found to be minimum (0.877) for English and maximum (0.982) for Dutch. The results obtained can play a significant role in propagating the synergy of language evolution.
Hyper-chaotic Image Encryption using ACM and GBS

This work presents an image encryption technique using a four dimensional (4D) hyper-chaotic system. A user defined secret key of 32 character and an information from plain image are used as input initial conditions of the hyper-chaotic system to generate a hyper-chaotic sequence. To increase the complexity of the encryption mechanism or robustness against chosen plain-text attack, Global Bit Scrambling (GBS) based on the hyper-chaotic sequence and pixel scrambling based on Arnold Cat Map (ACM) is used. The key dependency along with the plain image dependency of the highly sensitive hyper-chaotic sequence reduces the inaccessibility of the encrypted image and ensures the abridgment and hideout of information between a sender and authorized receiver only. The fragility and dependability of the algorithm is tested on various type of images. The quality is tested by the parameters like NPCR, UACI, SSIM, entropy and differential test, and key sensitivity test are done to ensure the reliablity of this work. The NIST statistical tests are also done to check the randomness of the encrypted results. A comparative study is done for reassuring the authors claim.
Restructuring extended Lexical elaborate Language

Requirements engineering is a process that explores the objectives of the actors and the activities to achieve their objectives, meeting requirements. The use of the natural language oriented requirement model as eLEL is interesting in order to specify the requirements and represent the conceptual level of a system. However, the typing of attributes and the representation of the method of a system that the eLEL model describes do not conform to the rule imposed by Object-Oriented Programming to type an attribute and construct a method. But we thought that the information that constitutes them is already described in the universe of discourse (UofD). Our strategy is to extend eLEL in its conceptual representation and the new extended lexicon is called ReLEL. To achieve this, we have added new method concepts to the ReLEL metamodel and extended the attribute format to obey the rule imposed by Object Oriented Programming (POO). Nevertheless, to validate the proposed approach, we evaluated the attribute and method performance of both eLEL and ReLEL requirements models. As a result, we have found that the ReLEL model has more attribute and method level elements than the eLEL model. Therefore, we have proposed an approach to derive a UML class diagram conceptual model from the ReLEL requirement model.
Polynomial Code with Detecting the Symmetric and Asymmetric Errors in the Data Vectors

Paper contains results of researches in polynomial codes field at error detecting in data vectors. The authors established previously unknown polynomial codes properties, which consideration is expedient when organizing systems with fault detection. A separable binary codes qualitative classification is given, it is shown that special code classes can be distinguished that are focused on the certain types errors detection arising in the data vectors (unidirectional, symmetric or asymmetric). Examples of codes included in each special codes class, as well as methods for their use in the automation and computing systems construction, are given. The generator polynomial types that allow detecting all symmetric and asymmetric errors in the polynomial code data vectors are established. Some experimental studies result with a combinational circuits benchmarks LGSynth'89 set, confirming the results of theoretical studies are given.
Upstream Corporate Database Management Collaboration Using GIS

Abstract Today's exploration, production and operation in the oil and gas industry dealing with data in various types of format. Integrating these diverse types of data are difficult and some of the data might be scattered in the employee's personal computer. The initial concept of collaboration database management is to create comprehensive upstream database system in an integrated portal to handle upstream data information in a relevance format based on historical and up-to-date representation data in the company. The core of data visualization is mainly supported by map and web services that is connected to multi-database including Professional Petroleum Data Management (PPDM), geospatial, and lynx application. To distribute web and map services, a comprehensive script was created to transfer information within the web portal and optimize its functionality. There are some challenges of building an integrating platform of lynx and geographic information system. From the phase of development, dealing with data duplication, consolidate between data and application, and integrating web and map services are the main challenges of our methodology. To handle these issues, a primary key between technical data and database is defined to correlate a relation between database and application reading and deploying metadata from the source to the web portal. As the result, the collaboration data mangement was successfully developed and deployed into company system. Map, table, and report section are well established and optimized with functions that we built during the development. Both map and upstream data web services were linked to the web portal to open seismic and well section. Users now have a portal to find their data information and visualize it in the same window.
CalorieKiller: Burning Calories using Mobile Exergame with Wearables

The main focus of this project is to promote and encourage a healthy lifestyle through the means of a mobile exergame with wearable sensors. It is crucial to create a fun and social environment to perform exercises so that it will attract more people to work out and stay fit. Our project uses features of gaming application - animated characters performing the exercises as a means for multiple people to exercise together and create a friendly environment. Along with this gaming aspect the calories burnt by the players will be calculated during every exercise session and displayed. This information of calories burnt will be calculated through conducting case studies where the movement detected by the accelerometer sensors when particular exercises are performed will be recorded along with the heart rate of the person after every exercise. This paper will showcase the design and application of the CalorieKiller project that calculates and displays the calories burnt by the user while they exercise through the means of our game.
Distributed Multi-Agent Preference Learning for An IoT-enriched Smart Space

There have been several efforts on preference learning in a smart space by means of multi-agent collaborations. Each agent captures a user action or handles part of learning but decision makings are done in a centralized manner. This makes it difficult for a smart space to deal with learning complexity due to the increase and reconfiguration of smart devices. While the complexity is relieved by articulating the learning space, it is not flexible because the articulation procedure needs to be resumed whenever a smart space reconfiguration occurs. In this paper, we propose a distributed multi-agent preference learning architecture which allows a group of physically separate agents to collaborate with each other for learning a user's task preference efficiently in an IoT enriched smart space. For this, the proposed scheme provides four key features: ontology-based knowledge structure for task-driven agent collaboration, knowledge exchange protocol for task-aware causality among agents, Q-learners for observing and learning from user behaviors, and negotiation and acknowledgement protocol for preventing agents from performing disorganized actions. Evaluation results show that the proposed scheme allows smart device agents to learn user preferences in a fully distributed way and outperforms existing approaches in terms of learning speed and system overhead.
Continuous Variable Binning Algorithm to Maximize Information Value Using Genetic Algorithm

Binning (bucketing or discretization) is a commonly used data pre-processing technique for continuous predictive variables in machine learning. There are guidelines for good binning which can be treated as constraints. However, there are also statistics which should be optimized. Therefore, we view the binning problem as a constrained optimization problem. This paper presents a novel supervised binning algorithm for binary classification problems using a genetic algorithm, named GAbin, and demonstrates usage on a well-known dataset. It is inspired by the way that human bins continuous variables. To bin a variable, first, we choose output shapes (e.g., monotonic or best bins in the middle). Second, we define constraints (e.g., minimum samples in each bin). Finally, we try to maximize key statistics to assess the quality of the output bins. The algorithm automates these steps. Results from the algorithm are in the user-desired shapes and satisfy the constraints. The experimental results reveal that the proposed GAbin provides competitive results when compared to other binning algorithms. Moreover, GAbin maximizes information value and can satisfy user-desired constraints such as monotonicity or output shape controls.
Numerical Simulation of Neural Fields with Finite Transmission Speed and Random Disturbance

This paper aims at presenting an efficient and accurate numerical method for treating both deterministic and stochastic Neural Field Equations(NFEs) with a finite signal transmission speed and in the presence of external stimuli input. The numerical integration tool devised belongs to the class of Galerkin-type spectral approximations, and our particular effort focuses on an efficient implementation of the solution technique because of the partial integro-differential fashion of the NFEs in use. This method is intended for implementation in MATLAB. Its performance and efficiency is investigated on an NFE model with external stimuli inputs. We study both the deterministic case of the mentioned model and its stochastic counterpart to observe important differences in the solution behavior when the assumption of finite/infinite transmission speed implemented.
Data Acquisition in Determining Lab Work Assesment Ranking Using Fuzzy Analytic Hierarchy Process (FAHP)

In the implementation of lab work, there are many assessment criteria which affect the final scoring of a lab work. A university student must go through several stages in carrying out lab work. The problem arising is at the assessment stage, where lab work lecturers find it difficult to determine the ranking of lab work assessment for each student. The more criteria and alternative assessments arise, the more difficult it will be for the lab work lecturers to determine the priority of lab work participants' scores. This study focuses on making a fuzzy-based acquisition system which is the measurement result data in the lab work process. Furthermore, the data which have been determined by fuzzy with the provisions of the specified criteria, the entire criteria in the lab work process were used by analytic hierarchy process (AHP) method which was integrated in the web server application. The hierarchy levels represented the criteria for the lab work process, namely: pretest, lab work implementation, post test and lab work report-making. The implementation of lab work drawn from fuzzy output was one of the highest weight input criteria. The alternative used was a number of students attending the lab work, using AHP stages, including: decomposition, comparative judgment, synthesis of priority and logical consistency. This system was able to show the accuracy level with fuzzy and to select priorities from the assessment criteria and alternative lab work participants. The findings showed that the fuzzy-based data acquisition was able to provide recommendations for measurements on lab work based on the scores of membership degree and fire strength chosen with an error of less than 5%. The use of AHP was able to give the assessment priority to a number of criteria and alternative lab work assessments with the priority score from student 1 was 0.292, student 2 was 0.233, student 3 was 0.214 and student 4 was 0.261.
Recovering Fine Grained Traceability Links Between Software Mandatory Constraints and Source Code

Software traceability is a necessary process to carry out source code maintenance, testing and feature location tasks. Despite its importance, it is not a process that is strictly conducted since the creation of every software project. Over the last few years information retrieval techniques have been proposed to recover traceability links between software artifacts in a coarse-grained and middle-grained level. In contexts where it is fundamental to ensure the correct implementation of regulations and constraints at source code level, as in the case of HIPAA, proposed techniques are not enough to find traceability links in a fine-granular way. In this research, we propose a fine-grained traceability algorithm to find traces between high level requirements written in human natural language with source code lines and structures where they are implemented.
A Robust and Reversible Watermarking Technique for Relational Dataset Based on Clustering

With rapid information development, data sharing becomes a crucial part in the Internet. In the process of sharing, data ownership protection and data traceability are two key issues that need to be solved urgently. To address these problem, digital watermarking technology can be a solution. Digital watermarking is used to guard the rights of owners of digital products. Many robust and reversible watermarking techniques are proposed recently to ensure the rights and recover original data set. But most methods require primary keys of the data as a required parameter, resulting in original data not recovered and partial data not traceable against data structure attack. In this paper, a cluster-based robust and reversible watermarking (RRWC) technique for relational data has been proposed that provides a solution to two major function: ownership rights protection and partial data traceability. The unsupervised classification algorithm is used to group dataset, where the primary key of the data will not be used and the watermarks can be embedded with low distortion and high capacity. RRWC addresses malicious attacks, such as subset insertion attack, deletion attack, alteration attack and data structure attack. Experimental results demonstrate the effectiveness and robustness of RRWC against attacks.
Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice

Dense prediction tasks typically employ encoder-decoder architectures, but the prevalent convolutions in the decoder are not image-adaptive and can lead to boundary artifacts. Different generalized convolution operations have been introduced to counteract this. We go beyond these by leveraging guidance data to redefine their inherent notion of proximity. Our proposed network layer builds on the permutohedral lattice, which performs sparse convolutions in a high-dimensional space allowing for powerful non-local operations despite small filters. Multiple features with different characteristics span this permutohedral space. In contrast to prior work, we learn these features in a task-specific manner by generalizing the basic permutohedral operations to learnt feature representations. As the resulting objective is complex, a carefully designed framework and learning procedure are introduced, yielding rich feature embeddings in practice. We demonstrate the general applicability of our approach in different joint upsampling tasks. When adding our network layer to state-of-the-art networks for optical flow and semantic segmentation, boundary artifacts are removed and the accuracy is improved.
A framework for segregating solid waste by employing the technique of image annotation

The way to proficient waste management is to guarantee appropriate segregation of waste to ensure its proper reuse. The objective of this paper is to identify types of wastes generated in India, the nature of waste coming out from different cities, the current disposal method being employed there, amount of waste that gets dumped at landfill. These statistics are useful for identifying efficient methods to segregate waste to enhance the efficiency of reusing and recycling process. The paper may justify the reason behind expanding number of landfills in India. Valuable insights can be obtained after classifying waste into biodegradable/ non-biodegradable classes, and its suitability for disposal or not. They can help in choosing proper disposal techniques for different categories of waste.
Verification and Recognition of Fractal Characteristics of Communication Modulation Signals

With the rapid development of software radio and communication technologies, wireless communication environment is becoming more complicated. How to accurately identify communication modulation signals under low SNR environment has become a hot topic in current research. Fractal is an effective tool to describe the geometric irregularity and geometric scale characteristics, and feature extraction of signals has become possible by fractal theory. However, whether the communication signals have fractal characteristics, and whether the fractal feature can be used to achieve accurate feature extraction of signals is still a problem worth exploring. This paper first took QPSK signal as an example, and used mathematical methods to prove that the communication modulation signals have fractal characteristics. Then, an improved fractal box dimension algorithm was used to extract and recognize five signals to verify the effectiveness of fractal theory based feature extraction. Simulation results illustrate that the recognition result can achieve 97.8% even under the SNR of 10dB environment. This provides a theoretical basis for the wide application of fractal theory in the field of signal identification.