Reviewerly/exHarmony huggingface dataset:
Development of a Predictive Model for a Photovoltaic Module’s Surface Temperature

PV (photovoltaic) systems are receiving the spotlight in Korea due to the Renewable Energy 3020 Implementation Plan (RE3020), which has the goal of reaching 20% for the proportion of renewable energy generation by 2030. Accordingly, the actual performance evaluation of PV systems to achieve the RE3020 has become more important. PV efficiency is mainly determined by various weather conditions (e.g., solar radiation) that affect the power generation of PV systems. However, the efficiency is also affected by changes in module surface temperature. In particular, the efficiency decreases when the module surface temperature rises. That is, the actual PV efficiency falls short of the rated efficiency. The estimation of module surface temperature is critical for evaluating the actual performance of PV systems. Many studies have been conducted to calculate the surface temperature. However, most of the previous studies focused on calculations of current surface temperatures using current environment data, which means that the previous studies have limitations related to timestep. That is, there is a lack of predictive models that calculate the future surface temperatures by using the current measured data. Therefore, this study developed a predictive model using an ANN (artificial neural network) algorithm to determine the surface temperature of PV modules for a future period of time. Then, this study evaluated the actual performance (i.e., power generation) with the predicted surface temperatures.
CUSTOMER PREDICTIVE ANALYTICS USING ARTIFICIAL INTELLIGENCE

This conceptual paper exclusively focused on how artificial intelligence (AI) serves as a means to identify a target audience. Focusing on the marketing context, a structured discussion of how AI can identify the target customers precisely despite their different behaviors was presented in this paper. The applications of AI in customer targeting and the projected effectiveness throughout the different phases of customer lifecycle were also discussed. Through the historical analysis, behavioral insights of individual customers can be retrieved in a more reliable and efficient way. The review of the literature confirmed the use of technology-driven AI in revolutionizing marketing, where data can be processed at scale via supervised or unsupervised (machine) learning.
ResNet: Detection of Invasive Ductal Carcinoma in Breast Histopathology Images Using Deep Learning

Detection of Invasive Ductal Carcinoma (IDC) is a difficult, time taking and mandatory job. Moreover, the precise detection of IDC is important to give proper medical treatment to the patients. In this medical situation, Deep Learning (DL) approach is much reliable. The evolution of Deep Learning is taken off in the past few years due to a rise in more and more data, better hardware and it supports with robust computational modelling algorithms to train a framework. Based on the advantage of DL, a framework is introduced to automatically detect IDC in Breast Histopathology Images (BHI) of Breast Cancer (BC) either as a malign or benign. This ResNet is provided with labelled BHI data to extract common features and trained to classify the future images thus able to detect the presence of IDC in breast tissue lesion image. It takes less effort, gives better accuracy within less time. Thus our framework helps to speed up a pathologist's work and provides diagnosis support.
Stealthy DGoS Attack: DeGrading of Service under the Watch of Network Tomography

Network tomography is a powerful tool to monitor the internal state of a closed network that cannot be measured directly, with broad applications in the Internet, overlay networks, and all-optical networks. However, existing network tomography solutions all assume that the measurements are trust-worthy, leaving open how effective they are in an adversarial environment with possibly manipulated measurements. To understand the fundamental limit of network tomography in such a setting, we formulate and analyze a novel type of attack that aims at maximally degrading the performance of targeted paths without being localized by network tomography. By analyzing properties of the optimal attack, we formulate novel combinatorial optimizations to design the optimal attack strategy, which are then linked to well-known problems and approximation algorithms. Our evaluations on real topologies demonstrate the large damage of such attacks, signaling the need of new defenses.
PAM &amp; PAL: Policy-Aware Virtual Machine Migration and Placement in Dynamic Cloud Data Centers

We focus on policy-aware data centers (PADCs), wherein virtual machine (VM) traffic traverses a sequence of middleboxes (MBs) for security and performance purposes, and propose two new VM placement and migration problems. We first study PAL: policy-aware virtual machine placement. Given a PADC with a data center policy that communicating VM pairs must satisfy, the goal of PAL is to place the VMs into the PADC to minimize their total communication cost. Due to dynamic traffic loads in PADCs, however, above VM placement may no longer be optimal after some time. We thus study PAM: policy-aware virtual machine migration. Given an existing VM placement in the PADC and dynamic traffic rates among communicating VMs, PAM migrates VMs in order to minimize the total cost of migration and communication of the VM pairs. We design optimal, approximation, and heuristic policyaware VM placement and migration algorithms. Our experiments show that i) VM migration is an effective technique, reducing total communication cost of VM pairs by 25%, ii) our PAL algorithms outperform state-of-the-art VM placement algorithm that is oblivious to data center policies by 40-50%, and iii) our PAM algorithms outperform the only existing policy-aware VM migration scheme by 30%.
Emotion Attention-Aware Collaborative Deep Reinforcement Learning for Image Cropping

This paper proposes a collaborative deep reinforcement learning model for automatic image cropping (called CDRL-IC). By modeling image cropping as a decision-making process of reinforcement learning, our model could generate optimal cropping result in a few moving and zooming steps. An image with good composition is a comprehensive result by considering the relative importance of objects and also the spatial organization of visual elements. Therefore, emotion attention information which indicates the relationship and importance between objects is applied together with contextual information of color image for image cropping. In order to sufficiently use the emotion attention map and the color image, they are processed by two collaborative agents. The two agents make their primary learning separately and then share information through an information interaction module for making joint action prediction. In order to efficiently evaluate the cropping quality in the reward function, weighted Intersection Over Union (WIoU) is designed by integrating emotion attention map in the traditional IoU. Our CDRL-IC model is tested on a variety of datasets for both image cropping and thumbnail generation. The experiments show that our CDRL-IC model outperforms state-of-the-art methods on these benchmark datasets.
Doctoral Colloquium—Towards a Better User Interface of Augmented Reality Based Indoor Navigation Application

Navigation service is a widespread geoinformation service and can be embedded in an augmented reality (AR). In this work-in-progress, we aim at a user interface of AR-based indoor navigation system, which could not only guide users to destinations quickly and safely, but also improve users' spatial learning. We designed an interface for indoor navigation on HoloLens, gathered feedback from users, and found that arrows are an intuitive aid of orientation. Semantic meanings embedded in icons are not self-explaining, but icons with text can serve as virtual landmarks and help with spatial learning.
Quantum algorithm for the multicollision problem

The current paper presents a new quantum algorithm for finding multicollisions, often denoted by ℓ-collisions, where an ℓ-collision for a function is a set of ℓ distinct inputs that are mapped by the function to the same value. In cryptology, it is important to study how many queries are required to find an ℓ-collision for a random function of which domain is larger than its range. However, the problem of finding ℓ-collisions for random functions has not received much attention in the quantum setting. The tight bound of quantum query complexity for finding a 2-collisions of a random function has been revealed to be Θ(N1/3), where N is the size of the range of the function, but neither the lower nor upper bounds are known for general ℓ-collisions. The paper first integrates the results from existing research to derive several new observations, e.g., ℓ-collisions can be generated only with O(N1/2) quantum queries for any integer constant ℓ. It then provides a quantum algorithm that finds an ℓ-collision for a random function with the average quantum query complexity of O(N(2ℓ−1−1)/(2ℓ−1)), which matches the tight bound of Θ(N1/3) for ℓ=2 and improves upon the known bounds, including the above simple bound of O(N1/2). More generally, the algorithm achieves the average quantum query complexity of O(cN⋅N(2ℓ−1−1)/(2ℓ−1)), and runs over O˜(cN⋅N(2ℓ−1−1)/(2ℓ−1)) qubits in O˜(cN⋅N(2ℓ−1−1)/(2ℓ−1)) expected time for a random function F:X→Y such that |X|≥ℓ⋅|Y|/cN for any 1≤cN∈o(N1/(2ℓ−1)), where it is assumed that QRAM is available. With the same query complexity, it is actually able to find a multiclaw for random functions, which is harder to find than a multicollision.
Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting.
Performance Analysis of Merge Sort Algorithms

Sorting of data is employed in numerous applications and plays a vital role in determining the overall performance, speed and power. There is much sorting technique's like the Bubble Sort, Quick Sort, Selection Sort, Insertion Sort, Merge Sort, Heap Sort etc. Out of these, the Merge sort works the best when sorting huge data sets. In this paper, five of these merge sorting algorithms namely serial merge sort, parallel merge sort, bitonic merge sort, odd-even merge sort and the modified merge sort are analyzed and comparison based on resource utilization, delay and area are made. These algorithms are designed and developed to work on FPGA's. They can be parallelized on the FPGA easily to get better performance. Based on the results the best algorithm for an application or problem instance can be selected.
A Case Study: Students’ Perception of a Collaborative Game-Based Virtual Learning Environment

The English education course in China aims to help students establish the English skills necessary to enhance their international competitiveness. However, traditional English classes often lack the linguistic environment to enable students to apply the English skills they have learned from their textbook. Virtual reality (VR) technology can create an immersive English language environment and then encourage the learners to use English by presenting different collaborative communication tasks. Thus, to promote learners in using the target language skills this study applied a spherical video-based virtual reality technology to build a linguistic environment, and a collaborative learning strategy was adopted to promote their communication. Additionally, a mixed-methods research approach was used to analyze students' achievement in a traditional classroom and a virtual reality supported collaborative classroom and to evaluate their perception of the two approaches. The experimental results revealed that the collaborative classroom, supported by virtual reality, was able to enhance students' achievements. Moreover, analysis of student interviews identified their attitudes towards the virtual-reality-supported collaborative class and the use of language learning strategies in those classes. These findings could be a valuable reference for those who intend to create opportunities for students to collaborate and communicate in the target language in their classroom and then improve their language skills.
Flexible bivariate correlated count data regression

Multivariate count data are common in many disciplines. The variables in such data often exhibit complex positive or negative dependency structures. We propose three Bayesian approaches to modeling bivariate count data by simultaneously considering covariate‐dependent means and correlation. A direct approach utilizes a bivariate negative binomial probability mass function developed in Famoye (2010, Journal of Applied Statistics ). The second approach fits bivariate count data indirectly using a bivariate Poisson‐gamma mixture model. The third approach is a bivariate Gaussian copula model. Based on the results from simulation analyses, the indirect and copula approaches perform better overall than the direct approach in terms of model fitting and identifying covariate‐dependent association. The proposed approaches are applied to two RNA‐sequencing data sets for studying breast cancer and melanoma (BRCA‐US and SKCM‐US), respectively, obtained through the International Cancer Genome Consortium.
Geometry consistency aware confidence evaluation for feature matching

Most existing approaches prune wrong matches via estimating an image transformation or solving a graph-based global matching optimization problem, which usually suffers from varying local transformations and outliers. Inspired by the insight that neighboring true matches usually hold consistent local topological structures across images, in this paper we propose a new approach to evaluate the confidence of each putative match based on how well its two keypoints can predict each other by exploring the geometric constraint with its neighboring matches. With the evaluation, a two-stage approach combining recursively false match pruning and correct match incrementing is presented to obtain the reliable matches. Experiments on various image pairs demonstrate that our approach can conduct robust feature matching in challenging conditions and outperform state-of-the-art approaches.
Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm

Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements provided by one or multiple sensors. Additional information, such as imperfect estimates of target classes provided by a classifier, can facilitate the target-measurement association and thus improve MTT performance. In this letter, we describe how a recently proposed MTT framework based on the sum-product algorithm can be extended to efficiently exploit class information. The effectiveness of the proposed approach is demonstrated by simulation results.
Machine Learning on Volatile Instances

Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes. However, running distributed SGD can be prohibitively expensive because it may require specialized computing resources such as GPUs for extended periods of time. We propose cost-effective strategies to exploit volatile cloud instances that are cheaper than standard instances, but may be interrupted by higher priority workloads. To the best of our knowledge, this work is the first to quantify how variations in the number of active worker nodes (as a result of preemption) affects SGD convergence and the time to train the model. By understanding these trade-offs between preemption probability of the instances, accuracy, and training time, we are able to derive practical strategies for configuring distributed SGD jobs on volatile instances such as Amazon EC2 spot instances and other preemptible cloud instances. Experimental results show that our strategies achieve good training performance at substantially lower cost.
An Effective Bone Fracture Detection using Bag-of-Visual-Words with the Features Extracted from SIFT

The fractures which are non-detectable and which are not diagnosed properly are the two major challenges to the orthopedicians and radiologists in the field of orthopaedics. In many countries the count of patients with bone-related fracture issues is increasing very rapidly, especially many are meeting with an accident. Additionally, due to a shortage in rural areas where the medical facility is very poor and unavailability of orthopaedics makes the introduction of Computer-based systems. In this paper, a Computer-Aided Fracture Detection technique is proposed using X-rays which detects and locate the fractures very efficiently and lessens the burden of Orthopedicians. Bag Of Visual words(BOVW) technique is proposed where features are labelled as fractured and non-fractured with the training of Back Propagation Neural Network(BPNN) classifier with the features extracted from SIFT(Scale Invariant Feature Transform). The proposed method experiments over 300 X-rays images which are collected as a dataset from KIMS hospital, Hyderabad. The experimental results give 90% accuracy compared to HTBFD (Hough transform Based Fracture Detection and ANN( Artificial Neural Network) which can be used for better location of fractures.
Towards a Deep Learning Model for Vulnerability Detection on Web Application Variants

Reported vulnerabilities have grown significantly over the recent years, with SQL injection (SQLi) being one of the most prominent, especially in web applications. For these, such increase can be explained by the integration of multiple software parts (e.g., various plugins and modules), often developed by different organizations, composing thus web application variants. Machine Learning has the potential to be a great ally on finding vulnerabilities, aiding experts by reducing the search space or even by classifying programs on their own. However, previous work usually does not consider SQLi or utilizes techniques hard to scale. Moreover, there is a clear gap in vulnerability detection with machine learning for PHP, the most popular server-side language for web applications. This paper presents a Deep Learning model able to classify PHP slices as vulnerable (or not) to SQLi. As slices can belong to any variant, we propose the use of an intermediate language to represent the slices and interpret them as text, resorting to well-studied Natural Language Processing (NLP) techniques. Preliminary results of the use of the model show that it can discover SQLi, helping programmers and precluding attacks that would eventually cost a lot to repair.
Stylometry as a Reliable Method for Fallback Authentication

Over the decades with advancement in artificial intelligent systems, stylometry has proven to be crucial in authorship attribution. It is an evident that by natural development, writing styles between individuals are unique and cannot be fabricated. Stylometric analysis research has been done for author identification and there is significant progress to recognize an author based on their written texts. This paper aims to evaluate the efficiency o f u sing s tylometry a s a fall back authentication method. We proposed to detect differences between writings on the same topic provided by a set of users and tested whether these differences are enough to use for an authentication system. We observed 74% accuracy in detecting the actual authors and concluded that with additional features the accuracy can be pushed to above 90%. Moreover, we deviced a threshold for authentication of a particular user. We observed that the combination of textual features can support authenticity of the user. We also analyzed the impact of some data cleaning systems like removing stop words and punctuation marks, and how they affected the overall detection outcome.
Bitcoin and Blockchain: Audit Implications of the Killer Bs

ABSTRACT This case examines auditing implications of Bitcoin and blockchain, and is intended for either undergraduate or graduate auditing and assurance courses. Students are asked to engage in aspects of planning and risk assessment for the audit of an online retailer. The case provides an interesting setting in which to achieve the learning objectives of: (1) identifying risks of material misstatement, (2) linking risks of material misstatement to relevant financial statement assertions, (3) explaining changes in audit procedures for responding to risks, (4) understanding a financial statement auditor's use of service auditor reports, and (5) understanding management specialists and the role they play in an audit. The modular case can be used for in-class discussion or as an out-of-class assignment and requires minimal advance preparation by the instructor. The case may be assigned in part or in its entirety. Student responses indicate the case is interesting and offers a positive learning experience.
Discovery and diagnosis of wrong SPARQL queries with ontology and constraint reasoning

The discovery and diagnosis of wrong queries in database query languages have gained more attention in recent years. While for imperative languages well-known and mature debugging tools exist, the case of database query languages has traditionally attracted less attention. SPARQL is a database query language proposed for the retrieval of information in Semantic Web resources. RDF and OWL are standardized formats for representing Semantic Web information, and SPARQL acts on RDF/OWL resources allowing to retrieve answers of user’s queries. In spite of the SPARQL apparent simplicity, the number of mistakes a user can make in queries can be high and their detection, localization, and correction can be difficult to carry out. Wrong queries have as consequence most of the times empty answers, but also wrong and missing (expected but not found) answers. In this paper we present two ontology and constraint reasoning based methods for the discovery and diagnosis of wrong queries in SPARQL. The first method is used for detecting wrongly typed and unsatisfiable queries. The second method is used for detecting mismatching between user intention and queries, reporting incomplete, faulty queries as well as counterexamples. We formally define the above concepts and a list of examples to illustrate the methods is shown. A Web online tool has been developed to analyze SPARQL queries according to the proposed methods.