Reviewerly/exHarmony huggingface dataset:
An Ensemble-based Active Learning for Breast Cancer Classification

Ensembled machine learning paradigms enable base learners to provide more accurate predictions than a standard approach using a single learner. Though the ensemble learning decreases variance or bias, improving predictions, limited literatures have been reported with an active learning strategy narrowing uncertainty in prediction. We present an ensemble based active learning approach for breast cancer detection, averaging predictions from the start of the art machine learning models on histopathology images. We demonstrate that the ensemble based active learning approach outperforms other approaches on breast cancer detection.
MuscleSense

Strength training improves overall health, well-being, physical appearance, and sports performance.There are four major factors that affect training efficacy in a training session: exercise type, number of repetitions, movement velocity, and workload. Prior research has used wearable sensors to detect exercise type, number of repetitions, and movement velocity while training. However, detecting workload remains constrained to instrumented exercise equipment, such as smart exercise machines or RFID-tagged free weights.This paper presents MuscleSense, an approach that estimates exercise workload by using wearable Surface Electromyography (sEMG) sensors and regression analysis. We evaluated the accuracy of several regression models and the effects of sensor placement through a 20-person user study. Results showed that MuscleSense achieved an accuracy of 0.68kg (root mean square error, RMSE) in sensing workload using both forearm and arm sensors and support vector regression (SVR).
Performance Loss Rate Consistency and Uncertainty Across Multiple Methods and Filtering Criteria

As part of our IEA-PVPS Task 13 research on performance loss rate (PLR) determination we report a comparison between several common approaches, including the effects of different data filtering criteria and power predictive models, resulting in 20 distinct PLR calculations. The open data used for the comparisons is from the DOE Regional Test Center Baseline project consisting of 8 identical PV Systems in 4 distinct Köppen-Geiger climate zones, with 1-minute resolution power and weather data. Five filtering methods were compared: three irradiance cutoffs of 0, 200, 800W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> POA, along with two clear sky filters using a clear sky index, and a moving average detection method. Four different power predictive models of varying complexity were compared: the 6K model, the PVUSA model, the data-driven XbX model, and the XbX + UTC model which is comparable to a temperature corrected performance ratio. PLR uncertainty is evaluated through bootstrap iteration of daily predicted power results. PLR comparisons show the XbX + UTC model has the most consistent PLR magnitude across different filtering techniques, with 6K predicting PLR values inconsistent with the other methods in all locations except Florida's Cfa climate zone. System performance by climate zone showed the highest power loss was observed in Florida (Cfa, -1.84%/year), followed by Nevada (BWh, -0.776%/year), then New Mexico (BSk, -0.54%/year), and lastly Vermont (Dfb) showing the best performance (Dfb, 0.063%/year). The 6K model uncertainty is lowest when retaining as much data as possible, and XbX and PVUSA have the highest stability with high irradiance cutoffs, typically used to reduce the temperature variation of the modules. XbX + UTC showed the lowest PLR uncertainty in each filter method. In all cases, the moving average clear sky filter led to the highest PLR uncertainty. Large fluctuations in calculated PLR suggest PLR results are strongly biased by the filtering and method chosen and more validation is needed for individual models.
Exploring the industry's challenges in software testing: An empirical study

Abstract Context Software testing is an important and costly software engineering activity in the industry. Despite the efforts of the software testing research community in the last several decades, various studies show that still many practitioners in the industry report challenges in their software testing tasks. Objective To shed light on industry's challenges in software testing, we characterize and synthesize the challenges reported by practitioners. Such concrete challenges can then be used for a variety of purposes, eg, research collaborations between industry and academia. Method Our empirical research method is opinion survey. By designing an online survey, we solicited practitioners' opinions about their challenges in different testing activities. Our dataset includes data from 72 practitioners from eight different countries. Results Our results show that test management and test automation are considered the most challenging among all testing activities by practitioners. Our results also include a set of 104 concrete challenges in software testing that may need further investigations by the research community. Conclusion We conclude that the focal points of industrial work and academic research in software testing differ. Furthermore, the paper at hand provides valuable insights concerning practitioners' “pain” points and, thus, provides researchers with a source of important research topics of high practical relevance.
Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks

The study proposes an outlier refinement methodology for automatic distortion rectification of wide-angle and fish-eye lens camera models in the context of streamlining vision-based tasks. The line-members sets are estimated in a scene through accumulation of line candidates emerging from the same edge source. An iterative optimization with an outlier refinement scheme was applied to the loss value, to simultaneously remove the extremely curved outliers from the line-members set and update the robust line members as well as estimating the best-fit distortion parameters with lowest possible loss. The proposed algorithm was able to rectify the distortions of wide-angle and fish-eye cameras even in extreme conditions such as heavy illumination changes and severe lens distortions. Experiments were conducted using various evaluation metrics both at the pixel-level (image quality, edge stretching effects, pixel-point error) as well as higher-level use-cases (object detection, height estimation) with respect to real and synthetic data from publicly available, privately acquired sources. The performance evaluations of the proposed algorithm have been investigated using an ablation study on various datasets in correspondence to the significance analysis of the refinement scheme and loss function. Several quantitative and qualitative comparisons were carried out on the proposed approach against various self-calibration approaches.
Experimental demonstration of one-sided device-independent self-testing of any pure two-qubit entangled state

We demonstrate one-sided device-independent self-testing of any pure entangled two-qubit state based on a fine-grained steering inequality. The maximum violation of a fine-grained steering inequality can be used to witness certain steerable correlations, which certify all pure two-qubit entangled states. Our experimental results identify which particular pure two-qubit entangled state has been self-tested and which measurement operators are used on the untrusted side. Furthermore, we analytically derive the robustness bound of our protocol, enabling our subsequent experimental verification of robustness through state tomography. Finally, we ensure that the requisite no-signalling constraints are maintained in the experiment.
Everyday Life Reflection

Reflection is of increasing interest in HCI as it has many potential benefits in design, education and everyday life. In this paper, we explore media-supported reflection through the design and deployment of three concepts. In contrast to prevalent reflective approaches that are based on system-collected data, we explore how user-created media can support personal reflection. Three interactive prototypes were developed, focusing on different modalities: Balance uses audio, Cogito uses text, and Dott uses visual media. We evaluate these concepts in an in-thewild study that is both explorative and comparative. We found that the open-ended systems primarily supported reflection during the creation of media and that the use depended on opportunity and triggers. We conclude the paper with a discussion of our findings regarding the method and the implications of our findings for the broader area of design for reflection.
Performance Comparison of Symmetries Encryption Algorithm AES and DES With Raspberry Pi

This study aims to analyze the performance of symmetrical encryption algorithms between the AES and DES algorithms, and the use of mini PC devices such as Raspberry Pi. It aims to find algorithms that excel in performance. The performance, which is a measure is the time and memory used to encrypt and decrypt with the same key - the method used in research with an experimental approach. Where experimental done on Raspberry PI with PHP programming language, the results of this study indicate that the AES algorithm requires a shorter time in encrypting messages compared to the DES algorithm. The AES algorithm uses a larger memory capacity in encrypting messages compared to the DES algorithm. The AES algorithm is recommended to use because in terms of security AES has a more significant number of keys that is 128 bits while the DES algorithm has a key length of 64 bits or half of the number of keys in the AES Algorithm. The results of the comparison of the two algorithms can be used as a reference where two encryption algorithms are compatible with the applications and devices developed.
Quantifying coherence in terms of the pure-state coherence

Quantifying quantum coherence is a key task in the resource theory of coherence. Here we establish a good coherence monotone in terms of a state conversion process, which automatically endows the coherence monotone with an operational meaning. We show that any state can be produced from some input pure states via the corresponding incoherent channels. It is found that the coherence of a given state can be well characterized by the least coherence of the input pure states, so a coherence monotone is established by only effectively quantifying the input pure states. In particular, we show that our proposed coherence monotone is the supremum of all the coherence monotones that give the same coherence for any given pure state. We also prove that our coherence monotone is continuous. Considering the convexity, we prove that our proposed coherence measure is a subset of the coherence measure based on the convex roof construction. The similarities and differences between our coherence monotone and coherence cost are studied in detail. As applications, we give a concrete expression of our coherence measure by employing the geometric coherence of a pure state. We also give a thorough analysis of the states of the qubit and finally obtain a series of analytic coherence measures. The numerical examples are also given to show the difference between our coherence monotone and that based on the convex roof construction.
Map-Z: Exposing the Zcash Network in Times of Transition

Zcash is a privacy-preserving cryptocurrency that provides anonymous monetary transactions. While Zcash's anonymity is part of a rigorous scientific discussion, information on the underlying peer-to-peer network are missing. In this paper, we provide the first long-term measurement study of the Zcash network to capture key metrics such as the network size and node distribution as well as deeper insights on the centralization of the network. Furthermore, we present an inference method based on a timing analysis of block arrivals that we use to determine interconnections of nodes. We evaluate and verify our method through simulations and real-world experiments, yielding a precision of 50% with a recall of 82% in the real-world scenario. By adjusting the parameters, the topology inference model is adaptable to the conditions found in other cryptocurrencies and therefore also contributes to the broader discussion of topology hiding in general.
RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance

For enterprise, personal and societal applications, there is now an increasing demand for automated authentication of identity from images using computer vision. However, current authentication technologies are still vulnerable to presentation attacks. We present RoPAD, an end-to-end deep learning model for presentation attack detection that employs unsupervised adversarial invariance to ignore visual distractors in images for increased robustness and reduced overfitting. Experiments show that the proposed framework exhibits state-of-the-art performance on presentation attack detection on several benchmark datasets.
Gender Recognition from Facial Images using Convolutional Neural Network

Gender is one of the facial attributes whose identification serves crucial step in systems including smart billboards, emotion recognition and surveillance systems. This paper explores a deep learning based solution for automatic detection of gender from face images of a well-balanced dataset. The solution involves a transfer learning framework where the knowledge is reused out of a deep learning model which performed quite well in classification task of other domain. We investigate the reusability of Visual Geometry Group-16 (VGG16), an off-the-shelf Convolutional Neural Network (CNN) model which is pre-trained upon large dataset of natural images. Fine-tuning a segment of the CNN upon the moderately sized dataset yields better performance than obtained from the state-of-the-art approaches when implemented upon the public LFW-Gender dataset.
Inter-UAV Collision Avoidance using Deep-Q-Learning in Flocking Environment

In the past twenty years, Unmanned Aerial Vehicles (UAVs) have demonstrated their ability in supporting both military and civilian applications. It has proved itself useful in tasks which are dangerous or too costly with usual methods. Certain applications also contains tasks which can be processed faster by using multiple UAVs that work together and perform smaller tasks which can be further combined to get the intended result. However, to achieve this, we need autonomous coordination among the multi UAVs. Multiple UAVs flying as a swarm, incorporates a pattern or a shape that has to be maintained through out the flight. But there can be cases where the pattern or shape of swarm has to be changed. Under such circumstances, collision has to be avoided between the UAVs as they travel to their new position forming a new shape. We have proposed an algorithm which helps in finding optimal goal positions for the UAVs to flock and to find the trajectory which supports collision free movement to its assigned position. We also ensure that the total distance travelled by individual UAV is minimized. The optimal goal position assignment is done using Hungarian Algorithm. Deep Q Learning method is used to find the optimal flight parameters for a collision free trajectory for the UAVs to reach its goal. Results from simulation show that the algorithm is sufficiently fast for practical applications as optimal assignments and flight parameters were computed for 50 UAVs in less than 1s.
Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.
Improvements On Bicubic Image Interpolation

Bicubic algorithm has the advantages of more accurate image magnification and faster processing speed. Nowadays, bicubic amplification technology is adopted in many image processing software, such as Adobe, printer driver, etc. In this paper, an improved bicubic interpolation algorithm is proposed. Unlike the traditional algorithm, which sets the key parameter a to - 0.5 in order to guarantee Taylor's third order approximation, we remove such requirement because this value is only applicable to the interpolation of flat area while the change of image pixel value is often irregular By directly optimizing the mean square deviation of the image, we obtain the higher-order optimization function. At the same time, a new variable is introduced to replace the a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> term of the optimization function, so that the higher-order optimization problem can be transformed into the least squares problem. Experiments show that the optimized parameters have better reconstruction effect, and the acquisition of the optimized parameters is directly related to the magnification of the image, and the image itself has little influence.
Taxonomy of Attacks on Web Based Applications

Computers are being reasonably important part of our day to day lives. Important information is being shared and received via web, which are now a day's very much vulnerable to attacks. Web based attacks are considered as most important aspect in breaching network security. Various applications of web comprise of health care, banking and e-business operations. Key elements of security include confidentiality, integrity, availability which must be preserved in all aspects. Some common attacks include Malformed XML, SQL Injection, XML Bomb, XPath Injection. During transmission of data attacker impersonates itself as an authorize user and try to conceal the confidential information. In this paper we will discuss various web-based attacks along with security measures to decrease the impact of attack in the network.
Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model

End-to-end dialogue generation has achieved promising results without using handcrafted features and attributes specific to each task and corpus. However, one of the fatal drawbacks in such approaches is that they are unable to generate informative utterances, so it limits their usage from some real-world conversational applications. In order to tackle this issue, this paper attempts to generate diverse and informative responses with a variational generation model, which contains a joint attention mechanism conditioning on the information from both dialogue contexts and extra knowledge. The experiments on benchmark DSTC7 data show that the proposed method generates responses with more grounded knowledge and improve the diversity of generated language.
A Privacy-Protected Image Retrieval Scheme for Fast and Secure Image Search

With the development of multimedia technology, the secure image retrieval scheme has become a hot research topic. However, how to further improve algorithm performance in the ciphertext needs to be further explored. In this paper, we propose a secure image retrieval scheme based on a deep hash algorithm for index encryption and an improved 4-Dimensional(4-D)hyperchaotic system. The main contributions of this paper are as follows: (1) A novel secure retrieval scheme is proposed to control data transmission. (2) An improved 4-D hyperchaotic system is proposed to preserve privacy. (3) We propose an improved deep pairwise-supervised hashing (DPSH) algorithm and secure kNN to perform index encryption and propose an improved loss function to train the network model. (4) A secure access control scheme is shown, which aims to achieve secure access for users. The experimental results show that the proposed scheme has better retrieval efficiency and better security.
Degree Tables for Secure Distributed Matrix Multiplication

We consider the problem of secure distributed matrix multiplication (SDMM) in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers. We construct polynomial codes for SDMM by studying a recently introduced combinatorial tool called the degree table. Maximizing the download rate of a polynomial code for SDMM is equivalent to minimizing N, the number of distinct elements in the corresponding degree table. We propose new constructions of degree tables with a low number of distinct elements. These new constructions lead to a general family of polynomial codes for SDMM, which we call GASP,. (Gap Additive Secure Polynomial codes) parametrized by an integer r. GASP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> outperforms all previously known polynomial codes for SDMM. We also present lower bounds on N and show that GASP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> achieves the lower bounds in the case of no server collusion.
Precise building deformation measurement using holographic multipoint replication

For the active control of large-scale structures, especially high-rise buildings and bridges, fast and accurate measurement of local deformations is required. We present a highly accurate and fast vision-based measurement technique and, to the best of our knowledge, first experimental results for the control of an adaptive-structures prototype frame, equipped with hydraulic actuators. Deformations are detected at multiple discrete points, based on a photogrammetric approach with additional holographic spot replication. The replication leads to effective averaging of most error contributions, especially discretization and photon noise. Measurements over a distance of 11.4 m result in a measurement uncertainty of 0.0077 pixel (corresponding to 0.055 mm in object space).