Reviewerly/exHarmony huggingface dataset:
Towards a library of usability requirements

1Usability is an essential quality of software products if not an important concern in any business competition. This research discusses an approach to better define and specify reusable usability requirements. This is difficult because requirements specifications tend to be poorly structured, and tend to be introduced late in the development process, which results in major cost and rework. This research starts by identifying concerns related the classification and specification of usability requirements commonly found in literature. Then, it extends the ITLingo RSL language, which is a controlled natural language that allows to specify requirements and tests in a rigorous and structured way, and uses the respective tools to define a library of reusable usability requirements. This research was conducted in a software house operating in the healthcare domain and was applied and evaluated in its family of software products. The most important contribution of this research is the initial proposal of a library of reusable usability requirements, rigorously specified in a language like RSL, which may promote both quality and productivity.
Collision detection based on octree for virtual surgery system

Abstract In the virtual surgery system, the organ is always non-convex and deforms over time which makes the collision detection problem more difficult to handle. In this case, we put forward an innovative collision detection algorithm based on octree. This algorithm can perfectly apply to deal with non-convex organs and organ deformation scenario. In addition, it can achieve high run-time efficiency.
Subaperture stitching interferometry based on the combination of the phase correlation and iterative gradient methods

Subaperture stitching interferometry (SAS) is an important method for map testing of large aperture optical components, in which a mechanical structure is often employed for the testing of each subaperture. By eliminating the phase deviation of the corresponding points in the overlapping regions of every adjacent subaperture, the whole aperture map can be obtained. Accurate subaperture positioning is an important guarantee for precise stitching. In this paper, a hybrid optimization algorithm is proposed to realize subpixel-level positioning accuracy in SAS based on the combination of the phase correlation and iterative gradient methods. The phase correlation method is adopted to calculate the pixel-level positioning deviation first, and the subpixel deviation is derived and then corrected by iterative optimization through the gradient method. The subpixel-level positioning accuracy of the proposed optimization algorithm is verified by simulations and a 76.2 mm off-axis parabolic mirror is chosen as an experimental testing sample. The surface map obtained from the proposed hybrid optimization method is consistent with the full aperture testing result, which also verifies that the proposed optimization algorithm is a powerful tool with subpixel-level positioning accuracy in SAS testing.
Ant colony algorithm based on data classification

Abstract Aiming at the drawbacks of ant colony algorithm applied in the medium and large scale traveling salesman problem (TSP), this paper proposes an ant colony algorithm based on data classification (DACO),in which data classification method takes attraction degree as an important consideration.First,the initial data of the selected data set is divided into two categories - candidate class and elimination class according to the law of attraction degree.Then the elimination class is eliminated to reduce the search time.At the same time, the attraction degree can effectively guide the algorithm to search for the optimal solution when the previous pheromone heuristic effect is not significant.In addition, 3-opt local search algorithm is added to further optimize the solution.The experimental results show that the DACO has a better convergence and solution quality compared with other ant colony algorithms.
An Overview of Data Mining Representation Techniques

Current data mining systems and techniques are used with the objective of finding future values for datasets, leaving aside what could be even more useful information, by not exploring deeper into the outputs of such techniques. This paper presents an overview of the major representation and visualization techniques available and usable to bring forth all the potential associated with data mining techniques, especially those of a more complex nature, like artificial neural networks and support vector machines, with the objective of clarifying and raise awareness of the potential underlaying data mining techniques. The study uncovered a series of methods for this, such as a variety of techniques that help demonstrate artificial neural networks' visual model.
SpojitR: Intelligently Link Development Artifacts

Traceability has been acknowledged as an important part of the software development process and is considered relevant when performing tasks such as change impact and coverage analysis. With the growing popularity of issue tracking systems and version control systems developers began including the unique identifiers of issues to commit messages. The goal of this message tagging is to trace related artifacts and eventually establish project-wide traceability. However, the trace creation process is still performed manually and not free of errors, i. e. developers may forget to tag their commit with an issue id. The prototype spojitR is designed to assist developers in tagging commit messages and thus (semi-) automatically creating trace links between commits and an issue they are working on. When no tag is present in a commit message, spojitR offers the developer a short recommendation list of potential issue ids to tag the commit message. We evaluated our tool using an open-source project hosted by the Apache Software Foundation. The source code, a demonstration, and a video about spojitR is available online: https://github.com/SECSY-Group/spojitr.
Multi‐frame image super resolution using spatially weighted total variation regularisations

Image super resolution refers to a class of signal processing algorithms to post-process a captured image to obtain its high resolution version. Multi-frame super resolution synthesises high resolution image from multiple low resolution observations. Performance of super resolution algorithms are adversely affected by the noise present in the input images. To develop a noise robust multi-frame image super resolution, an objective function is formulated which contains a weighted data fidelity term and a regularisation term consisting of a bilateral total variation (BTV) term and structure tensor total variation (STV) term. Both BTV and STV are weighted appropriately in a per pixel basis in such a way that the BTV contributes more in smooth regions and STV contributes more on the edges. These terms ensure the continuity of edges and the smoothness of flat regions. An adaptive weighting scheme with the data fidelity term helps to select the reliable pixel alone in the reconstruction process. The proposed method is experimentally evaluated for its performance in real data and different types of noises.
Korat Historical Explorer

Nakhon Ratchasima or Korat is one of the top 7 tourist destinations in Thailand and is also a province with a variety of tourist attractions. Korat is a largest province in Thailand with many historical sites including more than 2000 temples. However, promoting Korat Historical Tourism still lacks the use of modern technology, especially Augmented Reality (AR).
Essential Blockchain Technology Adoption factors in Pharmaceutical Industry

Blockchain the new technology that rapidly develop contemporary beyond the cryptocurrency. Several researches reported the adoption of Blockchain Technology for health, pharmacy, logistic, and education sector. However, the report research in education sector that adopt Blockchain Technology for pharmacy very rare, therefore this research tries to explore on how the Blockchain technology influence the four critical success factors of pharmacy industry which are people, process, technology and organization factors or commonly known as Leavitt diamond model. The systematic literature review used in this research and found a thousand and two hundred ten (1210) papers from eight (8) reputable publishers and an index such as Taylor and Francis, IEEExplore Online Library, MDPI, ACM, Science Direct, Wiley Online, Emerald Inside and Scopus as Index paper publication. Eighteen (18) papers were selected, 23 critical factors found that will influence to people, process, technology, and organization.
Answer Selection Using Multi-Layer Semantic Representation Learning

Abstract Answer selection is the most important module of question answering system. Mining the semantic relevance between questions and answers is the key point of answer selection. However, the answer texts often contain some colloquial expressions and redundant information in some fields. In these cases, some traditional methods of mining text features are complex and the performance improvement is limited. In this paper we investigate using deep learning to enhance the computability of semantics and eliminate the semantic gap for the answer selection module of non-factual question answering system. By integrating different networks to mine the association semantic between questions and answers, a novel multi-layer answer selection model is proposed. The shallow semantic relevance between questions and answers is mined by adding the attention mechanism to CNN, and then the features are input into Bi-LSTM model for further mining to obtain more efficient semantic representation. The experimental results show that compared with other models, our method can achieve the better accuracy in the task of answer selection.
Towards an ontology for personalized hotel room recommendation

This paper presents the design of an ontology based on user profile that allows personalizing guests' hotel rooms and services. The ontology being developed using NeON methodology, takes into consideration the maximum number of concepts associated with hotel guest profile and hotel room. The ontology will also provide a sound representation of comfort metrics for hotel rooms to support recommendation.
An Effective Simulation Analysis of Transient Electromagnetic Multiple Faults

Embedded encryption devices and smart sensors are vulnerable to physical attacks. Due to the continuous shrinking of chip size, laser injection, particle radiation and electromagnetic transient injection are possible methods that introduce transient multiple faults. In the fault analysis stage, the adversary is unclear about the actual number of faults injected. Typically, the single-nibble fault analysis encounters difficulties. Therefore, in this paper, we propose novel ciphertext-only impossible differentials that can analyze the number of random faults to six nibbles. We use the impossible differentials to exclude the secret key that definitely does not exist, and then gradually obtain the unique secret key through inverse difference equations. Using software simulation, we conducted 32,000 random multiple fault attacks on Midori. The experiments were carried out to verify the theoretical model of multiple fault attacks. We obtain the relationship between fault injection and information content. To reduce the number of fault attacks, we further optimized the fault attack method. The secret key can be obtained at least 11 times. The proposed ciphertext-only impossible differential analysis provides an effective method for random multiple faults analysis, which would be helpful for improving the security of block ciphers.
Recovery and interpretation of the 18O/16O of Miocene oolitic goethites in multi-generational mixtures of Fe (III) oxides from a channel iron deposit of Western Australia

Published (U-Th)/He ages indicate that an oolitic Miocene channel iron deposit (CID) on Mesa J in the Hamersley province of Western Australia (∼22 °S latitude) contains three predominant generations of Fe (III) oxides – two generations of older hematite and one of younger goethite. Selective dissolution and sequential atom balance calculations were combined with chemical and oxygen isotope analyses to determine the δ18O values of hematite, goethite, and quartz in eight samples from a core drilled in that CID. For the quartz (Q), δ18OQ = +21.7(±0.9)‰. For the younger hematite (Hm1), δ18OHm1 ≥ +3.6(±0.6)‰, whereas for the older hematite (Hm2), δ18OHm2 ≤ +2.1(±0.8)‰. The oolitic CID goethite (G) crystallized in the late Miocene [7(±1) Ma] and is a solid solution represented as Fe(1−Y)AlY(CO3)XO(1−X)OH with values of δ18OG that range from −0.4‰ to +0.7‰. A mixing model for the solid solution expresses the value of δ18OG in terms of Y, X, relevant fractionation factors, and the δ18O of the endmember FeOOH (δ18Ogt). Mixing model-derived values of δ18Ogt range from −2.4‰ to −0.7‰. Paired values of δDgt and δ18Ogt in seven of eight late Miocene oolitic goethites from Mesa J indicate crystallization from meteoric waters at an average temperature of 20(±5) °C. In the late Miocene, Mesa J was in the subtropics at a paleolatitude of ∼29 °S, which invites comparison with the climate of the near-coastal community of Mingenew in Western Australia (presently at 29 °S latitude). The modern MAT at Mingenew is 20 °C – i.e., indistinguishable from the late Miocene Mesa J average of 20 °C. However, this similarity is not matched by the mean annual precipitation (MAP). The modern MAP at Mingenew is about 400 mm, but the lateritic weathering that produces abundant pedogenic Fe (III) oxides appears to require a seasonally contrasting climate with rainfall totals of at least 1300 mm/yr. In the late Miocene, southward-shifted summer season tropical cyclones might have delivered such large amounts of rain (with relatively low δD and δ18O values). If so, the δDgt and δ18Ogt of the oolitic goethites of Mesa J support the idea of a rainfall-driven, possibly microbially mediated, summer season bias in the timing of crystallization. Thermal buffering in the regolith can reconcile summer-dominated goethite crystallization with a temperature that would be analytically indistinguishable from the MAT. Comparison of the δ18Ogt values of the Mesa J goethites with published δ18O values of goethites of similar age from two other sites in the Hamersley province suggest a possible increase in δ18O of late Miocene rainfall with increasing distance from the coast.
A Distributed Multimodal Multi-user Virtual Environment for Visualization and Query of Complex Data

This paper describes an early prototype of a distributed multimodal multi-user virtual environment used for the visualization and query of complex data. The system supports different user interfaces for viewing coloured 3D objects of various sizes representing high-dimensional data to allow visual exploration and pattern detection in the data. Users can navigate and query the environment by using multimodal interaction techniques including speech and gesture recognition. The system serves as a testbed to compare the usability of various interaction techniques for visualization and query of complex data.
Low-Cost Eye Tracking for Foveated Rendering Using Machine Learning

This paper outlines a $50 head-mounted real-time eye tracker to track the user's eye, and uses foveated rendering reduce bandwidth costs and improve immersion. This is accomplished by using two cameras for detecting the iris and locating the beacons around the screen which tracks head movement in three dimensions. A neural net is trained on this data which then predicts where the user is looking based on the inputs from the two cameras. Foveated rendering is performed by only rendering the area of the screen currently being focused and blurring the area that falls under peripheral vision. Maturing this technology could be integrated into virtual reality headsets and for other immersive media experiences while dramatically decreasing bandwidth costs and increasing the overall functionality and capabilities of the devices. This would allow for a more robust as well as wireless virtual reality experience, which would mitigate the largest drawback to mass adoption: clunky wires and lack of a truly realistic experience.
The long and winding road from CT and MRI images to 3D models

The use of medical images (i.e., radiograph, CT-MR scans, etc.) acquired by different instruments has been one of the greatest inventions in the field of both diagnostic and interventional medicine since it was able, from a very early stage, to present as a transparency the state of human tissues either as a projection at one vertical level or as a series of section images derived from tomography (CT or MR) images. The term medical imaging refers mainly to X-rays, ultrasound, CT, magnetic resonance imaging (MRI and functional MRI), and endoscopic images (usually operating in the visible spectrum of electromagnetic radiation). Additionally, medical images can also be taken from microscopes, capsule endoscopy, etc. and give one or more images that can even visualize the molecular structure of human tissues. All medical images have as their main purpose the extraction of the qualitative information of the existing state, for example, if a human organ has normal findings or not, and in the case of illness to evaluate (extracting quantitative-numerical information) by appropriate measurements the image or all of the image series. This chapter is trying to introduce the concept of 3D model generation of internal human body organs for medical applications. Two best practice approaches are described and additionally a real pilot case study of liver cancer is demonstrated.
A Nested MCMC Method Incorporated With Atmospheric Process Decomposition for Photovoltaic Power Simulation

Large-scale photovoltaic (PV) generation's uncertainties significantly affect power system planning and operations. Thus, a stochastic PV power simulation method, which can accurately capture such uncertainties, is urgently needed to provide a foundation for further uncertain studies on power systems with PV stations. This paper proposes a nested Markov chain Monte Carlo (MCMC) method incorporated with atmospheric process decomposition (APD) for PV power simulation. First, an imaginary clear-sky model matching the local actual clear-sky atmosphere is designed to convert PV power to an attenuation coefficient (AC). Second, a nested AC Markov chain (MC) is proposed based on APD to distinguish the macroscale and meso-microscale ACs while consider their coupling relationship. Third, an improved MCMC method is developed to simulate this MC's each layer in a nested manner for stochastically synthesizing AC time series; this method can improve synthesizing accuracy thanks to the adoption of an optimal state number decision-making model to ensure the MCMC model's quality and a 3D transition probability matrix to capture the dynamics of transition probabilities with respect to state duration. Finally, synthetic AC time series are reconverted to PV power time series. The results validate the proposed method's accuracy over previous ones in reproducing PV power characteristics.
Semantic Role Labeling System for Persian Language

In this article, we present an automatic semantic role labeling system in Persian consisting of two modules: argument identification for specifying argument spans and argument classification for categorizing their semantic roles. Our modules have been trained on Persian Proposition Bank in which predicate-argument information is manually added as a layer on top of Persian Dependency Treebank with about 30,000 sentences. Therefore, our system was trained on 216,871 verbal predicates and 42,386 nonverbal ones consisting of 40,813 nouns and 1,573 adjectives with 33 semantic classes. As a supervised method, we used maximum entropy for building an argument identifier that results in human-level accuracy of 99% and support vector machine for an argument classifier with an F1 of 84. Regarding both verbal and nonverbal predicates with an expanded role set, we achieved reasonable results.
A Minimal Neural Network Model to Predict Power Loss due to Soiling in Stable Environments

One key obstacle to high penetration of solar energy into electric grids is the intermittency of the energy source. Solar power forecasting can be the key to overcome intermittency by allowing energy providers to plan for fluctuations in advance and avoid disruptions to the grid. Over the years, various forecasting models have been presented in the literature that makes use of environmental variables such as temperature, humidity, soiling, and other parameters to forecast the output of solar panels in a given environment. In regions with stable environmental conditions, many parameters may not be necessary for accurate prediction. This paper presents a minimalistic prediction model based on feedforward artificial neural networks that predicts the power output of a soiled panel using only time of the day and the number of days the solar panel has not been cleaned for as inputs. This model can be used to determine a cleaning schedule to prevent losses due to soiling. The model was trained using six months' worth of hourly data measured from a 100-Watt monocrystalline solar panel. The model was able to predict maximum power with an RMSE of 0.069. Two models are presented in this paper. One used random discrete grid search for the hyperparameters selection and other was a hand-crafted model; both performed similarly.
Complex key generation with secured seed exchange for Vernam cipher in security applications

The randomness of a number used for coding the data with a complex algorithm that makes more difficult for the middle-man or the intruder or an unauthorized person to decrypt. The paper is about the three different methods of pseudorandom generation and analysis by comparing the number of occurrences of digits with respect to length of the different keys. The methods are Vulgar fractions, 3n + 1 algorithm, Magic square nxn and the combination of this numbers generated sequences. The conclusion is based on the analysis of number of occurrences of digits; hence, the cryptographer can select and adopt any appropriate key for data encryption. The three different methods are for number generation; of sequences of numbers (keys) used for encryption with strong algorithm in combination with existing cryptosystems such as Vernam cipher or seed exchange Massey-Omura protocol. Applications are in banks, OTP generation, and authentication and in other domains like online shopping etc.