Reviewerly/exHarmony huggingface dataset:
An Explainable Hybrid Model for Bankruptcy Prediction Based on the Decision Tree and Deep Neural Network

For investors seeking solutions to optimize their portfolio of assets to generate profits and minimize losses, choosing a sophisticated model to evaluate the risk of corporate financial distress will be crucial to support their asset management and investment decisions. Both the machine learning and the newly developed deep learning techniques have been employed to construct bankruptcy prediction models for decades. However, applying the deep learning models might increase the predictive accuracy in exchange for losing model interpretability, because the structure and parameters of the model are not easy to provide accountability for investors. In this study, a hybrid approach integrating the decision tree with the deep neural network was proposed to provide a compromise solution for investors. The decision tree was adopted as the primary model to provide explainable ability, while the deep neural network was chosen to improve predictive accuracy. The decision fusion of two models was designed with the compensatory and non-compensatory approaches. The hybrid model was implemented by concatenating the deep neural network to the selected branches of decision tree that perform poor predictive accuracy during model training. The empirical results showed that the predictive accuracy of the deep neural network and the decision tree were 80% and 87% respectively, and the overall accuracy was improved to 91% by the hybrid model.
Creating community-based tech policy

What are the core ways the field of data science can center community voice and power throughout all the processes involved in conceptualizing, creating, and disseminating technology?? What are the most possible and most urgent ways communities can shape the field of algorithmic decision-making to center community power in the next few years? This interactive workshop will highlight some of the following lessons learned through our combined experience engaging with communities challenging technology in Seattle and Philadelphia, cities in the United States. We will discuss the historical context of disproportionate impacts of technology on marginalized and vulnerable communities; case studies including criminal justice risk assessments, face surveillance technologies, and surveillance regulations; and work in small-group and break-out sessions to engage questions about when and where technologists hold power, serve as gatekeepers, and can work in accountable partnership with impacted communities.
MILP Formulation for Aircraft Path Planning in Persistent Surveillance

Persistent surveillance systems using manned or unmanned aerial vehicles play a crucial role in modern intelligence, surveillance, and reconnaissance missions. One of the crucial aspects that determine the quality of these systems is path planning. Path planning often attempts to optimize one or two objective metrics, such as coverage area, cost, or time, while meeting several constraints that would challenge these missions, such as weather, downtime, and amount of information gathered. A number of approaches have been proposed in the literature to address the path planning problem. However, the majority of these approaches are often based on a single objective measure, such as minimizing cost, maximizing travel distance, or maximizing coverage time. In cases where combined measures are considered, the formulations are often nondeterministic polynomial-time hard, leading to solutions that are computationally intractable. In this article, a mixed integer linear programming model with two conflicting objective functions-namely, maximizing coverage area and coverage time, has been developed. We propose a two-stage method to handle missions that involve multiple periods, multiple vehicles, and multiple dispersed areas while meeting a number of operational constraints, such as refueling and downtimes. Evaluations based on a number of simulated, yet realistic, scenarios show that our formulation leads to very promising outputs and performance.
Parallelly Running k-Nearest Neighbor Classification Over Semantically Secure Encrypted Data in Outsourced Environments

Cloud services with powerful resources are popularly used to manage exponentially increasing data and to carry out data mining to analyze the data.However, a data mining involving query can cause privacy problems by disclosing both the data and the query.One task in data mining, classification, is used in a wide range of applications, and we focus on k-nearest neighbor (kNN) in this study to realize classification.Although several studies have already attempted to address the privacy problems associated with kNN computation in a cloud environment, the results of these studies are still inefficient.In this paper, we propose a very efficient and privacy-preserving kNN classification (PkNC) over encrypted data.While the amount of computation (encryptions/decryptions and exponentiations) and communication of the most efficient kNN classification proposed in prior studies is bounded by O(kln), that of the proposed PkNC is bounded by O(ln), where l is the domain size of data and n is the number of data.When conducting experiments with the same dataset, the prior kNN classification took 12.02 to 55.5 minutes but PkNC took 4.16 minutes.Furthermore, since PkNC allows to be carried out in parallel for each data, its performance can be improved extremely if it is carried out on machine to allow more numerous threads.PkNC protects the privacy of dataset, input query including the kNN result, and does not disclose any data access patterns.We propose several protocols to serve as building blocks to construct PkNC and formally prove their security.In particular, we propose efficient protocols that privately find k largest or smallest elements in array.
Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed

This paper presents models of path and control planning for the parking, docking, and movement of autonomous vehicles at low speeds, considering space constraints. Given the low speed of motion, and in order to test and approve the proposed algorithms, vehicle kinematic models are used. Recent works on the development of parking algorithms for autonomous vehicles are reviewed. Bicycle kinematic models for vehicle motion are considered for three basic types of vehicles: passenger car, long wheelbase truck, and articulated vehicles with and without steered semitrailer axes. Mathematical descriptions of systems of differential equations in matrix form and expressions for determining the linearization elements of nonlinear motion equations that increase the speed of finding the optimal solution are presented. Options are proposed for describing the interaction of vehicle overall dimensions with the space boundaries, within which a maneuver should be performed. An original algorithm that considers numerous constraints is developed for determining vehicle permissible positions within the closed boundaries of the parking area, which are directly used in the iterative process of searching for the optimal plan solution using nonlinear model predictive control (NMPC). The process of using NMPC to find the best trajectories and control laws while moving in a semi-limited space of constant curvature (turnabouts, roundabouts) are described. Simulation tests were used to validate the proposed models for both constrained and unconstrained conditions and the output (state-space) and control parameters’ dependencies are shown. The proposed models represent an initial effort to model the movement of autonomous vehicles for parking and have the potential for other highway applications.
Occluded face recognition using NonCoherent dictionary

Today’s world is facing threats from terrorism, for safety concerns system needs to strengthen security. Security is a challenging task and it can be strengthened by technology such as biometric and surveillance cameras. These technologies are deployed everywhere but it is the need of the days a s trong automatic face recognition applications so they can be used to recognize the person in an unconstrained environment. In an unconstrained environment, images are affected by occlusion such as a scarf, goggle, random but these variations decrease the performance of face recognition. Also, the accuracy of face recognition depends on the number of labeled samples and variation available in the training dataset. But some applications of face recognition such as passport verification, identification of these applications have fewer training samples without or with very less occlusion hence, it is not enough to solve the issue of unconstrained conditions. This problem has been targeted by many researchers using an occlusion based training dataset where common variation exists in both training and testing datasets. This paper tackles the occlusion issues by designing a NonCoherent dictionary. The proposed dictionary is designed by two steps firstly it extracts the occlusion from the face image and secondly creates NonCoherent samples. The extensive experimentation is done on benchmark face databases and compared the results on state-of-the-art SRC methods by using NonCoherent and normal dictionary also compared the sparse coefficients of each method. The results show the effectiveness of proposed model.
Neuro-Evolutionary Transfer Learning Through Structural Adaptation

Transfer learning involves taking an artificial neural network (ANN) trained on one dataset (the source) and adapting it to a new, second dataset (the target). While transfer learning has been shown to be quite powerful and is commonly used in most modern-day statistical learning setups, its use has generally been restricted by architecture, i.e., in order to facilitate the reuse of internal learned synaptic weights, the underlying topology of the ANN to be transferred across tasks must remain the same and a new output layer must be attached (entailing removing the old output layer’s weights). This work removes this restriction by proposing a neuro-evolutionary approach that facilitates what we call adaptive structure transfer learning, which means that an ANN can be transferred across tasks that have different input and output dimensions while having the internal latent structure continuously optimized. We test the proposed optimizer on two challenging real-world time series prediction problems – our process adapts recurrent neural networks (RNNs) to (1) predict coal-fired power plant data before and after the addition of new sensors, and to (2) predict engine parameters where RNN estimators are trained on different airframes with different engines. Experiments show that not only does the proposed neuro-evolutionary transfer learning process result in RNNs that evolve and train faster on the target set than those trained from scratch but, in many cases, the RNNs generalize better even after a long training and evolution process. To our knowledge, this work represents the first use of neuro-evolution for transfer learning, especially for RNNs, and is the first methodological framework capable of adapting entire structures for arbitrary input/output spaces.
Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment

The use of a single method of acceleration threshold discrimination cannot fully characterize the change in human fall behavior, which can easily result in misjudgment.In this paper, we propose a human fall detection algorithm that combines human posture, support vector machine (SVM), and quadratic threshold decision.Firstly, a large number of human posture data are collected through a six-axis inertial measurement module (MPU6050).A fall detection model is established through filtering preprocessing, eigenvalue extraction, classification, and SVM training.Secondly, a first-level threshold determination is performed through a wearable wristband device.When a suspected fall occurs, six eigenvalues will be captured and uploaded to a cloud platform to trigger second-level SVM fall judgments.By matching the eigenvalues with the fall detection model, it can be determined accurately whether a fall has taken place.The experimental results show that the fall detection has a recognition rate of 92.2%, a false rate of 3.593%, and missing rate of 2.187%, which can better distinguish other nonfall actions.
Grade 3D Block Modeling and Reserve Estimation of the C-North Iron Skarn Ore Deposit, Sangan, NE Iran

Estimation of ore grade is a time and cost consuming process that requires laboratory-based and exploratory information to present the shape and the ore grade distribution of ore deposit in three dimensional space. The block size is one of the most important parameters which impacts the quality of grade estimates in a resource model. This study aims at spatial modeling of iron ore deposit using geostatistical estimation methods such as Ordinary Kriging based on error estimation, selection of the appropriate size for mining blocks using Vlse Kriterijumsk Optimizacija Kompromisno Resenje method, and performing a three-dimensional block modeling along a grade estimation study for the resource estimation in the C-North iron ore deposit, NE Iran. The variogram that was used in OK estimation was cross validated. Cross validation results showed that compared with the local model, OK with the global model was the most appropriate model for the ore body. Detailed distribution maps of total iron contents in the C-North ore deposit showed a close relationship between structural features and higher iron contents, relative to other areas of the ore deposit. Structural features included the major faults and fault zones along the axial plane. These structures are interpreted to have played a significant role in (re) mobilisation and concentration of the metals, in agreement with observations made elsewhere in the Sangan iron ore complex. Based on the estimation results, 83 million tons of resource was estimated at an average grade of 41.86 % Fe using OK method. The C-North ore deposit has been classified based on the relative estimation error variance and the Australasian Code for Reporting of Mineral Resources and Ore Reserves. It is hoped that this example, taken from very different application fields, will encourage practitioners in applying an OK method with variety of ore deposits.
De los entornos virtuales de aprendizaje: hacia una nueva praxis en la enseñanza de la matemática

This theoretical-reflective study proposed an analysis of the theories that support virtual learning environments for the praxis of teaching Mathematics, the use of Information and Communication Technologies (ICT), their impact and the challenges in Mathematics teacher training. Learning situations that are linked to the use of ICT and the insertion of technologies in Mathematics classroom were explored. This analysis shows the urgent need to rethink teaching praxis in light of the implications and challenges of this type of environment.
vCanteen: A Smart Campus Solution to Elevate University Canteen Experience

The persistent problem circulating around various university canteens has always been about high crowd density during lunch hours. To efficiently tackle this issue, a platform called “vCanteen” has been developed that integrates an online food ordering system, a virtual queuing system, together with a machine learning-based crowd estimation system. vCanteen aims at reducing queuing time when ordering food, and allowing users to know the estimated crowd density in a university canteen in real-time. The crowd estimation system has been developed using a multi-column convolutional neural network (MCNN). This paper discusses the vCanteen prototype that was developed and tested at the canteen in the Faculty of Engineering, Chulalongkorn University, Thailand. The description of the crowd estimation system is provided in details including error evaluation and lessons learned.
Incremental Analysis of Logic Programs with Assertions and Open Predicates

Generic components are a further abstraction over the concept of modules, introducing dependencies on other (not necessarily available) components implementing specified interfaces. They have become a key concept in large and complex software applications. Despite undeniable advantages, generic code is also anti-modular. Precise analysis (e.g., for detecting bugs or optimizing code) requires such code to be instantiated with concrete implementations, potentially leading to expensive combinatorial explosion. In this paper we claim that incremental, whole program analysis can be very beneficial in this context, and alleviate the anti-modularity nature of generic code. We propose a simple Horn-clause encoding of generic programs, using open predicates and assertions, and we introduce a new incremental, multivariant analysis algorithm that reacts incrementally not only to changes in program clauses, but also to changes in the assertions, upon which large parts of the analysis graph may depend. We also discuss the application of the proposed techniques in a number of practical use cases. In addition, as a realistic case study, we apply the proposed techniques in the analysis of the LPdoc documentation system. We argue that the proposed traits are a convenient and elegant abstraction for modular generic programming, and that our preliminary results support our thesis that the new incrementality-related features added to the analysis bring promising advantages in this context.
Mon$$\mathbb {Z}_{2^{k}}$$a: Fast Maliciously Secure Two Party Computation on $$\mathbb {Z}_{2^{k}}$$

In this paper we present a new 2-party protocol for secure computation over rings of the form $$\mathbb {Z}_{2^k}$$ . As many recent efficient MPC protocols supporting dishonest majority, our protocol consists of a heavier (input-independent) pre-processing phase and a very efficient online stage. Our offline phase is similar to BeDOZa (Bendlin et al. Eurocrypt 2011) but employs Joye-Libert (JL, Eurocrypt 2013) as underlying homomorphic cryptosystem and, notably, it can be proven secure without resorting to the expensive sacrifice step. JL turns out to be particularly well suited for the ring setting as it naturally supports $$\mathbb {Z}_{2^k}$$ as underlying message space. Moreover, it enjoys several additional properties (such as valid ciphertext-verifiability and efficiency) that make it a very good fit for MPC in general. As a main technical contribution we show how to take advantage of all these properties (and of more properties that we introduce in this work, such as a ZK proof of correct multiplication) in order to design a two-party protocol that is efficient, fast and easy to implement in practice. Our solution is particularly well suited for relatively large choices of k (e.g. $$k=128$$ ), but compares favorably with the state of the art solution of SPD $$\mathbb {Z}_{2^k}$$ (Cramer et al. Crypto 2018) already for the practically very relevant case of $$\mathbb {Z}_{2^{64}}$$ .
Domain Adaptation for Object Detection via Style Consistency

We propose a domain adaptation approach for object detection. We introduce a two-step method: the first step makes the detector robust to low-level differences and the second step adapts the classifiers to changes in the high-level features. For the first step, we use a style transfer method for pixel-adaptation of source images to the target domain. We find that enforcing low distance in the high-level features of the object detector between the style transferred images and the source images improves the performance in the target domain. For the second step, we propose a robust pseudo labelling approach to reduce the noise in both positive and negative sampling. Experimental evaluation is performed using the detector SSD300 on PASCAL VOC extended with the dataset proposed in arXiv:1803.11365 where the target domain images are of different styles. Our approach significantly improves the state-of-the-art performance in this benchmark.
Visual Cryptography Scheme with Essential Participants

Visual cryptography scheme (VCS) shares a binary secret image into multiple shadows printed on transparencies. Stacking shadows can visually decode the secret image without computational resources. Specifically, a (k, n) threshold VCS ((k, n)-VCS) shares a secret image into n shadows, stacking any k shadows can reveal the secret image by human visual system, while any less than k shadows cannot decode any information regarding the secret image. In practice, some participants (essentials) play more important roles than others (non-essentials). In this paper, we propose a (t, s, k, n) VCS with essential participants (so called (t, s, k, n)-EVCS). The secret image is shared into n shadows with s essentials and n-s non-essentials. Any k shadows, including at least t essentials, can reveal the secret image. The proposed scheme is constructed from a monotonic (K, N)-VCS. The condition and optimal choice of (K, N)-VCS to construct (t, s, k, n)-EVCS are given by solving integer programming model. The experimental results are conducted to verify the feasibility of our scheme.
Imbalanced Data Classification Algorithm Based on Clustering and SVM

The class-imbalance learning is one of the most significant research topics in the data mining and machine learning. Imbalance problem means that one of the classes has much more samples than that of other classes. To deal with the issues of low classification accuracy and high time complexity, this paper proposes an novel imbalance data classification algorithm based on clustering and SVM. The algorithm suggests under-sampling in majority samples based on the distribution characteristics of minority samples. First, specific clusters are detected by cluster analysis on the minority. Second, a cluster boundary strategy is proposed to eliminate the bad influence of noise samples. To structure a balanced dataset for imbalance data, this paper proposes three principles of under-sampling on majority samples according to the characteristic of samples in the cluster. Finally, the optimal classification model from the linear combination of hybrid-kernel SVM is obtained. The experiments based on datasets in UCI and KEEL database show that our algorithm effectively decreases the interference of noise samples. Compared with the SMOTE and Fast-CBUS, the proposed algorithm not only reduces the feature dimension, but also improves the precision of the minor classes under the different labeled sample rates generally.
Transfer learning in deep convolutional neural networks for detection of architectural distortion in digital mammography

Deep learning models have reached superior results in various fields of application, but in many cases at a high cost of processing or large amount of data available. In most of them, specially in the medical field, the scarcity of training data limits the performance of these models. Among the strategies to overcome the lack of data, there is data augmentation, transfer learning and fine-tuning. In this work we compared different approaches to train deep convolutional neural network (CNN) to automatically detect architectural distortion (AD) in digital mammography. Although several computer vision based algorithms were designed to detect lesions in digital mammography, most of them perform poorly while detecting AD. We used the VGG-16 network pre-trained on ImageNet database with progressive fine-tuning to evaluate its performance on AD detection over a database of 280 images of clinical mammograms. Finally, we compared the results with a custom CNN architecture trained from scratch for the same task. Results indicated that a network with transfer learning and certain level of fine-tuning reaches the best results for the task (AUC = 0.89) compared with the other approaches, but no statistically significant difference was found between the best results using different amount of data augmentation and also compared to the custom CNN.