Reviewerly/exHarmony huggingface dataset:
Diabetic complication prediction using a similarity-enhanced latent Dirichlet allocation model

Diabetes and its complications have been recognized worldwide as a major public health threat. Predicting diabetic complications is regarded as a highly effective technique for increasing the survival rate of diabetic patients. While many studies currently use medical images and structured medical records, very limited efforts have been dedicated to applying data mining techniques for unstructured textual medical records, such as admission and discharge records. Moreover, the similarities among medical records that are overlooked by existing approaches could potentially improve the accuracy of prediction models. In this paper, we propose an approach for diabetic complication prediction based on a similarity-enhanced latent Dirichlet allocation (seLDA) model. Specifically, we first estimate the similarity between textual medical records after data preprocessing, and then we perform seLDA-based diabetic complication topic mining based on similarity constraints. Finally, we construct a prediction model by solving a multilabel classification problem with support vector machines (SVMs). The experimental results show that our approach outperforms the conventional LDA-based approach in similarity indices by 22.49%. Additionally, our approach shows significant improvements in prediction accuracy over four other representative seLDA-based approaches, including random forests (RF), k-nearest neighbors (KNN), logistic regression (LR) and deep neural networks (DNNs).
Restaurant Recommender System Using User-Based Collaborative Filtering Approach: A Case Study at Bandung Raya Region

Culinary becomes one of the needs of today's society. The large number of restaurant choices and also lack of information about the restaurant become an obstacle to people's needs in choosing a restaurant. In this paper, we build a recommender system that can recommend the restaurant in Bandung area. However, today, users want to get a restaurant with a good reputation and fit their tastes, so that restaurant ratings from other users are required in the restaurant recommendation process. We implement a user-based collaborative filtering method for recommend a restaurant personally, based on ratings given by other users. We also implement two similarities, i.e., user rating similarity and user attribute similarity to find the proximity between users. We use Mean Absolute Error (MAE) to evaluate accuration of rating prediction. The best MAE result of each performance is 1.492 for calculation without user attributes and 2.166 for calculation with user attributes.
Active phase error suppression for color phase-shifting fringe projection based on hue pre-correction

Color phase-shifting fringe projection is one of the single-shot three-dimensional shape measurement techniques based on fringe projection profilometry. In practice, the nonlinearity and color crosstalk of the projector-camera system yield undesired phase errors when using phase-shifting algorithm. In this paper, an active phase error suppression method based on hue pre-correction is proposed. Theoretical analysis of phase error is performed taking account of the gamma nonlinearity of the camera. Based on the analysis, the distortion of hue component of the acquired color fringe pattern is employed to represent the overall phase error induced by the combined effect of nonlinearity and color crosstalk. In this method, the hue is pre-corrected to be linear by measuring the function of the combined effect and modifying the computer-generated hue according to the source-distorted relationship. Then, the new modified color fringe pattern can be generated and saved for the further measurement of objects. Experimental results demonstrate the feasibility of the proposed method and it can effectively suppress the induced overall phase error caused by the nonlinearity and color crosstalk of the projector-camera system.
Hardware footprints of S-box in lightweight symmetric block ciphers for IoT and CPS information security systems

The hardware footprint for S-box specification in lightweight block cipher as appropriate to IoT and CPS information security systems is presented in this paper. The S-box Boolean function in the lightweight block cipher is defined using the Reed-Muller structure. A Rule Based–Common Sub-structure Sharing Optimization (RB-CSSO) algorithm has been proposed towards improving the performance efficiency of Reed-Muller structure. This novel hybrid RB-CSSO optimization mechanism first transforms the direct Positive Polarity Reed Muller (PPRM) S-box representation into Mixed Polarity Reed-Muller (MPRM) S-box architecture using local rule based transformation. Secondly, the Common Sub Term (CST) and Common Sub-expression (CSE) merging/elimination are employed over the resulting MPRM structure. The combined rule-based transformation and the common sub-function sharing demonstrate an overall reduction in area, delay and power of the Reed-Muller S-box structure. Both the theoretical analysis and the experimental verification demonstrate reduction in area and delay of S-box. Post synthesis results based on ASIC standard cell based implementations have been used to analyze area, delay and power values across Process, Voltage and Temperature (PVT) corners for a wide range of operating conditions. Extensive comparisons between direct PPRM and optimized MPRM implementations have been carried out. The post layout simulations of S-box structures realized show the advantages of lower area-delay product, power-area product and power-delay product. This work thus authenticates the application of proposed structure for lightweight, resource constrained security systems. Industry standard full suite of Cadence® tools have been employed in the simulations using 65 nm TCBN65GPLUS standard cells of TSMC technology library.
A benchmark image dataset for industrial tools

Robots and Artificial Intelligence (AI) play an increasingly important role in manufacture. One of the tasks is to identify tools in the scene so that the tools can be applied to different assembly purposes. In the AI community, many datasets have been generated and deployed to train robots to recognize individual items, however, these datasets are scene-specific and lack generic background. In this paper, we report our dataset contains photos of 8 objects types that would be easily recognized by qualified workers. This is achieved by gathering images of common tools in a typical factory. The ground truth categories of our dataset are manually labeled by experienced workers, which would be worthy evaluation tools for the intelligence industrial systems. The equipment used and the image collection process are discussed, along with the data format. The mean average precisions range from 64.37% to 78.20%, which bring the possibility for future improvement. The dataset is ideal to evaluate and benchmark view-point variant, vision-based control algorithm for industry robots. It is now public available from https://github.com/tools-dataset/Industrial-Tools-Detection-Dataset.
Generation of synthetic solar datasets for risk analysis

In this paper, we present a method for the synthetic generation of long-term time series of coupled 1-min global horizontal solar irradiance (GHI) and direct normal solar irradiance (DNI). This method requires an input of 10–15 annual time series of hourly DNI and GHI values that can be retrieved from satellite-based irradiance databases, and produces 100 years of 1-min solar radiation values that can be used for risk analysis or as input for solar plants performance simulation in a wide range of scenarios. The method consists of the conjunction of three steps. The first one, based on a stochastic procedure, is used to generate 100 years of monthly DNI and GHI values. The second step consists of the subsequent generation of daily irradiation values. To that end we have used a bootstrapping technique. The synthetic daily sequences have the same serial correlation structure as the observed data. The last step consists of the generation of 100 years of 1-min solar irradiance data out of the daily values based on the non-dimensionalization of the daily profiles by the clear sky envelope approach. The method has been applied for the location of Seville showing satisfactory results in terms of cumulative distribution functions (CDFs) of the synthetic data. We obtain an average monthly KSI (Kolmogorov-Smirnov test integral) index of 0.11 kWh/m2 for GHI and 0.26 kWh/m2 for DNI. The minimum KSI value is 0.07 kWh/m2 for GHI and 0.15 kWh/m2 for DNI obtained in January. The maximum KSI value is 0.19 kWh/m2 for GHI and 0.34 kWh/m2 for DNI obtained in June and August respectively.
Automated Functional Dependency Detection Between Test Cases Using Doc2Vec and Clustering

Knowing about dependencies and similarities between test cases is beneficial for prioritizing them for cost-effective test execution. This holds especially true for the time consuming, manual execution of integration test cases written in natural language. Test case dependencies are typically derived from requirements and design artifacts. However, such artifacts are not always available, and the derivation process can be very time-consuming. In this paper, we propose, apply and evaluate a novel approach that derives test cases' similarities and functional dependencies directly from the test specification documents written in natural language, without requiring any other data source. Our approach uses an implementation of Doc2Vec algorithm to detect text-semantic similarities between test cases and then groups them using two clustering algorithms HDBSCAN and FCM. The correlation between test case text-semantic similarities and their functional dependencies is evaluated in the context of an on-board train control system from Bombardier Transportation AB in Sweden. For this system, the dependencies between the test cases were previously derived and are compared to the results our approach. The results show that of the two evaluated clustering algorithms, HDBSCAN has better performance than FCM or a dummy classifier. The classification methods' results are of reasonable quality and especially useful from an industrial point of view. Finally, performing a random undersampling approach to correct the imbalanced data distribution results in an F1 Score of up to 75% when applying the HDBSCAN clustering algorithm.
Multi-Channel Embedding Convolutional Neural Network Model for Arabic Sentiment Classification

With the advent of social network services, Arabs’ opinions on the web have attracted many researchers in recent years toward detecting and classifying sentiments in Arabic tweets and reviews. However, the impact of word embeddings vectors (WEVs) initialization and dataset balance on Arabic sentiment classification using deep learning has not been thoroughly studied. In this article, a multi-channel embedding convolutional neural network (MCE-CNN) is proposed to improve Arabic sentiment classification by learning sentiment features from different text domains, word, and character n-grams levels. MCE-CNN encodes a combination of different pre-trained word embeddings into the embedding block at each embedding channel and trains these channels in parallel. Besides, a separate feature extraction module implemented in a CNN block is used to extract more relevant sentiment features. These channels and blocks help to start training on high-quality WEVs and fine-tuning them. The performance of MCE-CNN is evaluated on several standard balanced and imbalanced datasets to reflect real-world use cases. Experimental results show that MCE-CNN provides a high classification accuracy and benefits from the second embedding channel on both standard Arabic and dialectal Arabic text, which outperforms state-of-the-art methods.
Strategic planning for utility-scale solar photovoltaic development – Historical peak events revisited

Rapidly growing utility-scale solar photovoltaic (PV) holds promise for addressing the energy and environmental challenges posed by high electricity demand days (HEDDs). We assessed the implications on strategic planning of future solar development in an emerging solar market, New York State (NYS) in the U.S., by synthesizing information on electrical infrastructure, tax assessment, geographical constraints and measured meteorological data. Considering three solar PV penetration scenarios (4500 MW, 6000 MW, and 9000 MW), we examined the impact of distributed utility-scale solar farms on peak demand reduction and ramping requirements during historical peak events. Our results reiterate that, across NYS, a wealth of low-value land exists to support utility-scale solar PV and that generation from these resources can reduce peak demand by up to 9.6% under the 9000 MW scenario. In addition, peak demand reduction displays locational and temporal dependency. Contingent upon local meteorology, a solar farm can reduce demand during the peak hour by anywhere between 10 and 74% of its rated capacity during summer HEDDs. However, the highest ramping requirements are more likely to occur during winter than summer. Furthermore, because developers cannot predict performance during the annual system peak, current capacity valuation methodologies for solar projects may not be adequate to promote a healthy competitive market for solar. Incorporating a broader spectrum of peak demand conditions into variable resource capacity valuation would improve strategic planning, not only in NYS, but across growing solar markets worldwide.
Combining Multiple Criteria Decision Making with Vector Manipulation to Decide on the Direction for a Powered Wheelchair

The application of the Weighed Aggregated Sum Product ASsessment (WASPAS) method to suggest a direction for a powered wheelchair is described. The method considers five directions as alternatives. The ultrasonic sensors readings are used as criteria weights. The overall score of the alternatives is converted to vector magnitudes and the directions of the alternatives are considered as vector angles. The suggested direction is represented as a resultant vector formed from the vector manipulation of the vectors magnitudes and angles. The suggested direction tends to move a powered wheelchair away from obstacles. The entire system supports disabled wheelchair users to maneuver their powered wheelchairs using a decision-making technique. This research presents that technique along with a new method of selecting the best compromised route. Three cases are considered, and a best compromised direction is suggested in each case. The suggestions from WASPAS could be over-ridden by the user through holding a steering joystick in a set position.
The structure of musical harmony as an ordered phase of sound: A statistical mechanics approach to music theory

Music, while allowing nearly unlimited creative expression, almost always conforms to a set of rigid rules at a fundamental level. The description and study of these rules, and the ordered structures that arise from them, is the basis of the field of music theory. Here, I present a theoretical formalism that aims to explain why basic ordered patterns emerge in music, using the same statistical mechanics framework that describes emergent order across phase transitions in physical systems. I first apply the mean field approximation to demonstrate that phase transitions occur in this model from disordered sound to discrete sets of pitches, including the 12-fold octave division used in Western music. Beyond the mean field model, I use numerical simulation to uncover emergent structures of musical harmony. These results provide a new lens through which to view the fundamental structures of music and to discover new musical ideas to explore.
PrOS: Light-Weight Privatized Se cure OSes in ARM TrustZone

TrustZone is a hardware security technique in ARM mobile devices. Using TrustZone, software components running within the secure world can be completely isolated from the normal world, which ensures hardware-enforced security access control over the underlying computing resources. In order to support multiple trusted applications, TrustZone runs its own operating system, called the secure OS, within the secure world. Unfortunately, attackers have been exploiting privilege escalation vulnerabilities in a secure OS, as reported in most of major secure OSes from product vendors including Samsung, Huawei, and Qualcomm. More critically, as all trusted applications are running on the same secure OS instance, compromising the secure OS leads to compromising all trusted applications, rendering the secure OS as a single point of failure endangering the entire TrustZone's security. This paper presents PrOS, our mechanism to privatize secure OSes through direct virtualization of TrustZone. PrOS allows each trusted application to run with its own secure OS such that the secure OS is no longer a single point of security failure. One particular challenge for PrOS lies in how efficiently to implement software-only virtualization for TrustZone for a practical deployment in real systems despite the condition that the current ARM architectures do not support hardware-assisted virtualization for TrustZone. As opposed to the common belief that software-only virtualization is inefficient and sluggish, we have found several common design features inherent in the secure OS to leverage for optimally tailoring the TrustZone virtualization scheme. We implemented PrOS on a 64-bit ARM development board. According to our evaluation, PrOS incurs 0.02 and 1.18 percent performance overheads on average in the normal and secure worlds, respectively, demonstrating its effectiveness in the field.
The Internet shutdown muzzle(s) freedom of expression in Ethiopia: competing narratives

Freedom of expression is a foundational stone for democracy and embraces a number of other rights, including – freedom to seek, receive, and impart information using any medium. Around 16 million Ethiopians, comprising 15.4% of the total population, use the Internet. The Ethiopian Internet shutdown saga has been marked by competing narratives. On one hand, the government has been using two governing narratives, i.e. national security narrative and ‘economic growth’ narrative to justify shutdowns of the Internet. But, the individuals’ self-expressions on social media find itself as competing narrative. Since 2016, the Internet has been closed more than six times under the cloak of national security, quelling rising protests, controlling strikes, and exam cheating. As such, freedom of expression of millions has been muzzled. The article argues blanket Internet shutdowns do not meet the caveats of human rights law.
The Distance-Based Balancing Ensemble Method for Data With a High Imbalance Ratio

Many classification tasks suffer from the class imbalance problem that seriously hinders the precision of classifiers.The existing algorithms frequently incorrectly categorize new instances into the majority class.The ensemble learning is an effective method to address the imbalance problem, as is the Splitting Balancing Ensemble (SBE) method that learns the unbalanced dataset by converting it into multiple balanced subsets on which sub-classifiers are built.However, the SBE generates balanced subsets that are too small when learning a highly unbalanced dataset and lead to under-fitting.We propose the Distance-based Balancing Ensemble (DBE) method to deal with this issue and improve the generalization performance of the classification algorithm.The DBE divides highly unbalanced learning set into multiple unbalanced subsets with a much lower imbalance ratio and then applies a modified adaptive semi-unsupervised weighted oversampling method to each subset to obtain balanced subsets for the sub-classifiers.We further propose our Distance-based Combination Rule (DCR) as a more effective method for combining the ensemble results.Tests with 48 public unbalanced datasets from public repositories are performed to demonstrate the effectiveness of the DBE model with the DCR.The results show that the DBE-DCR model outperforms other ensemble models.
Extracting Quality Attributes from User Stories for Early Architecture Decision Making

Software quality attributes (e.g., security, performance) influence software architecture design decisions, e.g., when choosing technologies, patterns or tactics. As software developers are moving from big upfront design to an evolutionary or emerging design, the architecture of a system evolves as more functionality is added. In agile software development, functional user requirements are often expressed as user stories. Quality attributes might be implicitly referenced in user stories. To support a more systematic analysis and reasoning about quality attributes in agile development projects, this paper explores how to automatically identify quality attributes from user stories. This could help better understand relevant quality attributes (and potential architectural key drivers) before analysing product backlogs and domains in detail and provides the “bigger picture” of potential architectural drivers for early architecture decision making. The goal of this paper is to present our vision and preliminary work towards understanding whether user stories do include information about quality attributes at all, and if so, how we can identify such information in an automated manner.
Bayesian WIV Estimators for 3-D Bearings-Only TMA With Speed Constraints

This paper develops Bayesian weighted instrumental variable (WIV) estimators under equality and inequality speed constraints for the three-dimensional (3-D) bearings-only target motion analysis (TMA) problem, which show improved estimation accuracies compared to their unconstrained counterpart. Incorporating the speed constraint information into our previously proposed Bayesian WIV estimator imposes a quadratic constraint on this problem. Finding an optimal solution for the resulting non-convex problem is by no means straightforward. The main problem with iterative optimization methods such as the maximum a posteriori is that they require initialization and may converge to a local minimum if poorly initialized. Using advanced linear and nonlinear algebra techniques, non-iterative accurate estimators under equality and inequality constraints are developed for this problem. Furthermore, it is proven that the proposed equality constrained estimator is approximately asymptotically unbiased and that the inequality constrained estimator is the optimal solution. An approximate covariance matrix has also been developed for the constrained WIV under equality constraint. Simulation results indicate that the equality and inequality constrained estimators outperform their unconstrained counterpart in the simulated scenarios.
Pre-processing approaches for collaborative filtering based on hierarchical clustering

Recommender Systems (RS) support users to find relevant contents, such as movies, books, songs, and other products based on their preferences. Such preferences are gathered by analyzing past users' interactions, however, data collected for this purpose are typically prone to sparsity and high dimensionality. Clustering-based techniques have been proposed to handle those problems effectively and efficiently by segmenting the data into a number of similar groups based on predefined characteristics. Although such techniques have gained increasing attention in the recommender systems community, they are usually bound to a particular recommender system and/or require critical parameters, such as the number of clusters. In this paper, we present three variants of a general-purpose method to optimally extract users' groups from a hierarchical clustering algorithm, specifically targeting RS problems. The proposed extraction methods do not require critical parameters and enable any recommender algorithm to be used at the recommendation step. Our experiments have shown promising recommendation results in the context of nine well-known public datasets from different domains.