Reviewerly/exHarmony huggingface dataset:
Investigating spotlight internals to extract metadata

Index based desktop search tools have become the primary means for finding files or launching applications on desktop computer systems. Every major operating system ships with one. Spotlight is the default desktop search app on macOS (formerly OSX) that searches files based on metadata and content. This paper explores the format of the spotlight metadata cache database and opens up another avenue of data previously unavailable to a forensic investigator. With the format now available, and a script to explore and read this database, it is now possible to investigate spotlight metadata on any platform.
Practical Web Spam Lifelong Machine Learning System with Automatic Adjustment to Current Lifecycle Phase

Machine learning techniques are a standard approach in spam detection. Their quality depends on the quality of the learning set, and when the set is out of date, the quality of classification falls rapidly. The most popular public web spam dataset that can be used to train a spam detector—WEBSPAM-UK2007—is over ten years old. Therefore, there is a place for a lifelong machine learning system that can replace the detectors based on a static learning set. In this paper, we propose a novel web spam recognition system. The system automatically rebuilds the learning set to avoid classification based on outdated data. Using a built-in automatic selection of the active classifier the system very quickly attains productive accuracy despite a limited learning set. Moreover, the system automatically rebuilds the learning set using external data from spam traps and popular web services. A test on real data from Quora, Reddit, and Stack Overflow proved the high recognition quality. Both the obtained average accuracy and the F-measure were 0.98 and 0.96 for semiautomatic and full–automatic mode, respectively.
Prediction of Symmetrical and Asymmetrical of Diurnal Global Solar Irradiance Distribution—New Approach

A simple formula to predict the received global solar irradiance q(t), W/m 2 for clear days is suggested on pure theoretical basis.It is expressed in terms of the length of the local day time t d which is well defined in literatures on meteorological basis.The introduced distribution is also a function of the maximum value of the daily received irradiance q max.which in turn is expressed in term of the solar constant.This renders the trial to be a closed system.Thus the obtained distribution is not a semi empirical one.Both cases of symmetrical and asymmetrical distributions for q(t) are considered.For its simplicity it can be easily integrated along the length of the day to get the daily totals of solar energy received by unit horizontal area.This is important for practical applications.Comparison between computed according to the present model and published experimental meteorological data in Barcelona (Spain), Hong Kong (China), Jeddah and Makkah (Saudi Arabia) is given as illustrative examples.Better fitting relative to the published trials for the same locations are obtained.The introduced model itself gives good fitting for the intermediate intervals points of the local day time which is the more effective region.The estimated relative error is 12% for Hong Kong, and it is 7% for Barcelona, Jeddah and Makah.
Assessment of a quantitative mammographic imaging marker for breast cancer risk prediction

The purpose of this study is to assess feasibility of applying a new quantitative mammographic imaging marker to predict short-term breast cancer risk. An image dataset involving 1,044 women was retrospectively assembled. Each woman had two sequential “current” and “prior” digital mammography screenings with a time interval from 12 to 18 months. All “prior” images were originally interpreted negative by radiologists. In “current” screenings, 402 women were diagnosed with breast cancer and 642 remained negative. There is no significant difference of BIRADS based mammographic density ratings between three case groups (p &gt;0.6). A new computer-aided image processing scheme was applied to process negative mammograms acquired from the “prior” screenings and compute image features related to the bilateral mammographic density or tissue asymmetry between the left and right breasts. A group of 30 features related to GLCM texture features and a conventional computer-aided detection scheme generated results are extracted from both CC and MLO views. Using a leave-one-case-out cross-validation method, a support vector machine model was developed to produce a new quantitative imaging marker to predict the likelihood of a woman having mammography-detectable cancer in the next subsequent (“current”) screening. When applying the model to classify between 402 positive and 642 negative cases, area under a ROC curve is 0.70&minus;0.02 and the odds ratios is 6.93 with 95% confidence interval of [4.80,10.01]. This study demonstrated feasibility of applying a quantitative imaging marker to predict short-term cancer risk, which aims to help establish a new paradigm of personalized breast cancer screening.
Recommendation Systems for internship place using artificial intelligence based on competence

Internship program is important to be implemented as it can increase the competence of the university students. In fact, the program gives significant impact to the success of the students in industry. Students freely choose their suitable place for the internship because suitable place will make students feel motivated and as a result they will be more competent. Industrial internship program is learning at the relevant industry, aimed to increase the competence of the students by introducing them with real working condition. Students are still find difficulty in deciding the suitable place for internship because of their low confidence or competence with related issue. Now, students only need to fill the questionnaire and take the test. The data obtained from the test and questionnaires are then processed by using Artificial Neural Network (ANN), one of the Artificial Intelligence (AI) method. The result was that system can execute the process well and provide accurate recommendation for informatics students to determine suitable place for their internship program. Neural Network in the system is using the recurrent architecture to produce accurate optimal training.
Towards Provably Not-at-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments

As autonomous robots increasingly become part of daily life, they will often encounter dynamic environments while only having limited information about their surroundings. Unfortunately, due to the possible presence of malicious dynamic actors, it is infeasible to develop an algorithm that can guarantee collision-free operation. Instead, one can attempt to design a control technique that guarantees the robot is not-at-fault in any collision. In the literature, making such guarantees in real time has been restricted to static environments or specific dynamic models. To ensure not-at-fault behavior, a robot must first correctly sense and predict the world around it within some sufficiently large sensor horizon (the prediction problem), then correctly control relative to the predictions (the control problem). This paper addresses the control problem by proposing Reachability-based Trajectory Design for Dynamic environments (RTD-D), which guarantees that a robot with an arbitrary nonlinear dynamic model correctly responds to predictions in arbitrary dynamic environments. RTD-D first computes a Forward Reachable Set (FRS) offline of the robot tracking parameterized desired trajectories that include fail-safe maneuvers. Then, for online receding-horizon planning, the method provides a way to discretize predictions of an arbitrary dynamic environment to enable real-time collision checking. The FRS is used to map these discretized predictions to trajectories that the robot can track while provably not-at-fault. One such trajectory is chosen at each iteration, or the robot executes the fail-safe maneuver from its previous trajectory which is guaranteed to be not at fault. RTD-D is shown to produce not-at-fault behavior over thousands of simulations and several real-world hardware demonstrations on two robots: a Segway, and a small electric vehicle.
Generative Memory for Lifelong Reinforcement Learning.

Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience. We leverage current understanding of biological memory transfer to arrive at AI algorithms for memory consolidation and replay. In this paper, we propose the use of generative memory that can be recalled in batch samples to train a multi-task agent in a pseudo-rehearsal manner. We show results motivating the need for task-agnostic separation of latent space for the generative memory to address issues of catastrophic forgetting in lifelong learning.
Neural-network-based automatic segmentation of cerebral ultrasound images for improving image-guided neurosurgery

Segmentation of anatomical structures in intraoperative ultrasound (iUS) images during image-guided interventions is challenging. Anatomical variances and the uniqueness of each procedure impede robust automatic image analysis. In addition, ultrasound image acquisition itself, especially acquired freehand by multiple physicians, is subject to major variability. In this paper we present a robust and fully automatic neural-network-based segmentation of central structures of the brain on B-mode ultrasound images. For our study we used iUS data sets from 18 patients, containing sweeps before, during, and after tumor resection, acquired at the University Hospital Essen, Germany. Different machine learning approaches are compared and discussed in order to achieve results of highest quality without overfitting. We evaluate our results on the same data sets as in a previous publication in which the segmentations were used to improve iUS and preoperative MRI registration. Despite the smaller amount of data compared to other studies, we could efficiently train a U-net model for our purpose. Segmentations for this demanding task were performed with an average Dice coefficient of 0.88 and an average Hausdorff distance of 5.21 mm. Compared with a prior method for which a Random Forest classifier was trained with handcrafted features, the Dice coefficient could be increased by 0.14 and the Hausdorff distance is reduced by 7 mm.
Analysis of European Approaches to Improving the Life of the Population Through the Implementation of a Mechanism of Independent Assessment of Qualifications

At this moment, the staff is the most important intellectual and professional resource that ensures the socioeconomic development of the country, region, and enterprise. Every year the Russian Federation approaches the global standards of qualification assessment. Presidential Decree № 249 of April 16, 2014 established the National Council for professional qualifications under the President of the Russian Federation, which coordinates the work aimed at improving the quality of vocational education, as well as the formation of a system of independent assessment of professional qualifications. The expediency of an objective independent confirmation of a person’s qualifications necessary to perform certain work or solve specific tasks is due to the dynamically changing labor market. The basis of certification is professional standards. Independent assessment of qualifications allows the employee to confirm their qualifications, skills, and competencies, thereby contributing to a higher position, improving their well-being and quality of life. This contribution deals with the European experience of joint activities of business and education in the organization and evaluation of professional competencies of employees, allowing to create optimal conditions for their inclusion in the system of continuing education. All this strengthens the role of professional communities in the development of professional qualifications, procedures for confirming compliance of the applicant’s qualification with the provisions of the professional standard. The developed framework of professional qualifications is based on the results of training, and this approach allows you to structure and compare professional qualifications. In addition, based on the results of training, it is possible to formulate educational standards and programs of additional professional education that meet the needs of the labor market and the citizens themselves in order to improve the quality of life of the population.
Hash functions from superspecial genus-2 curves using Richelot isogenies

Last year Takashima proposed a version of Charles, Goren and Lauter's hash function using Richelot isogenies, starting from a genus-2 curve that allows for all subsequent arithmetic to be performed over a quadratic finite field Fp2. In a very recent paper Flynn and Ti point out that Takashima's hash function is insecure due to the existence of small isogeny cycles. We revisit the construction and show that it can be repaired by imposing a simple restriction, which moreover clarifies the security analysis. The runtime of the resulting hash function is dominated by the extraction of 3 square roots for every block of 3 bits of the message, as compared to one square root per bit in the elliptic curve case; however in our setting the extractions can be parallelized and are done in a finite field whose bit size is reduced by a factor 3. Along the way we argue that the full supersingular isogeny graph is the wrong context in which to study higher-dimensional analogues of Charles, Goren and Lauter's hash function, and advocate the use of the superspecial subgraph, which is the natural framework in which to view Takashima's Fp2-friendly starting curve.
A Transfer Learning Exploited for Indexing Protein Structures from 3D Point Clouds

In this paper, we propose a transfer learning-based methodology that can be exploited for indexing protein structures from associated 3D point clouds. Such a methodology can be particularly useful for biologists that are searching automated solutions to find family members of a query protein or even to label new structures by directly using input raw 3D point clouds. Comparative study and performance evaluation show the efficiency and the potential of the proposed methodology.
Unmanned Patrol System Based on Kalman Filter and ZigBee Positioning Technology

In this paper, a security patrol method using UAV (Unmanned Aerial Vehicle) [1] as a carrier was proposed to solve the problems caused by traditional patrol methods such as low patrol frequency, low patrol efficiency and high security patrol cost. The ZigBee[2] module (the chip using CC2530 of ti) was adopted to locate the UAV. Also, the Kalman filter algorithm was used to reduce the influence of noise on the airborne patrol system to improve the positioning accuracy. This system was equipped with a ground station. and users could select nodes at the ground station for planned flight. The data exchanged between the ground station and the UAV through the XBee S3B module[3]. The UAV transmitted the images captured by the camera to the ground station in real time through the Athreros AR9271 WIFI launcher for security personnel to check in time. The ground station also handled the situation on the spot. As the UAV reached the area with poor GPS signal, we used ZigBee networking to locate and enhance the accuracy of positioning. Security patrols with UAVs(Unmanned Aerial Vehicles) as carriers improved traditional patrol methods to some extent. After a certain amount of data analysis, the results show that the ZigBee positioning data processed by Kalman filter are reliable.
Trade-offs in Protecting Keccak Against Combined Side-Channel and Fault Attacks

When deployed in a potentially hostile environment, security-critical devices are susceptible to physical attacks. Consequently, cryptographic implementations need to be protected against side-channel analysis, fault attacks and attacks that combine both approaches. CAPA (CRYPTO 2018) is an algorithm-level combined countermeasure, based on MPC, with provable security in a strong attacker model. A key challenge for combined countermeasures, and CAPA in particular, is the implementation cost. In this work, we use CAPA to obtain the first hardware implementations of Keccak (SHA-3) with resistance against combined side-channel and fault attacks. We systematically explore the speed-area trade-off and show that CAPA, in spite of its algorithmic overhead, can be very fast or reasonably small. In fact, for the standardized Keccak-f[1600] instance, our low-latency version is nearly twice as fast as the previous implementations that only consider side-channel security, at the cost of area and randomness consumption. For all four presented designs, the protection level for side-channel and fault attacks can be scaled separately and to arbitrary order. To evaluate the physical security, we assess the side-channel leakage of a representative second-order secure implementation on FPGA. In addition, we experimentally validate the claimed fault detection probability.
Reversible Jump Probabilistic Programming

In this paper we present a method for automatically deriving a Reversible Jump Markov chain Monte Carlo sampler from probabilistic programs that specify the target and proposal distributions. The main challenge in automatically deriving such an inference procedure, in comparison to deriving a generic Metropolis–Hastings sampler, is in calculating the Jacobian adjustment to the proposal acceptance ratio. To achieve this, our approach relies on the interaction of several different components, including automatic differentiation, transformation inversion, and optimised code generation. We also present Stochaskell, a new probabilistic programming language embedded in Haskell, which provides an implementation of our method.
The future of anatomic pathology: deus ex machina?

This editorial is in response to the article on digital pathology published by Van Es (1), which is in turn a response to the articles published by Eric F. Glassy (2) and Thomas James Flotte (3). Our goal is to add what we feel are pertinent historical details and offer our perspective concerning the emerging role of digital pathology in anatomic pathology.
Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection.&#x0D; This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection.&#x0D; According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used.
Semi-automatic Training Data Generation for Semantic Segmentation using 6DoF Pose Estimation

In this research, we propose a method using a low cost process to generate large volumes of real images as training data for semantic segmentation. The method first estimates the six-degree-of-freedom (6DoF) pose for objects in images obtained using an RGB-D sensor, and then maps labels that have been pre-assigned to 3D models onto the images. It also captures additional input images while the camera is moving, and is able to map labels to these other input images based on the relative motion of the viewpoint. This method has made it possible to obtain large volumes of ground truth data for real images. The proposed method has been used to create a new publicity available dataset for affordance segmentation, called the NEDO Part-Affordance Dataset v1, which has been used to benchmark some typical semantic segmentation algorithms.
Study the Possibility of Creating Self-Diagnosis and First Aid System

The article analyzes the existing methods of self-diagnosis and first aid techniques. The authors investigated the existing systems in the presented area, identified the main problems of such systems. A comparative analysis of Russian and foreign analogues revealed that the main advantage of the system being developed is the combination of first aid functionality and self-diagnostic methods. Also, the article describes the generalized structure of the system using a number of case studies for a mobile application based on the Android operating system. The basic algorithms of the modules of the system of self-diagnosis and first aid and their interconnection are described. An assumption is made on the formation of recommendations and a preliminary forecast on the data received from the user.
A Survey on Digital Image Steganography Approaches

Steganography is the science of secret communication. It assumes if message is visible then it can be accessed. If it is invisible then it can never be in front of attack. So it hides the existence of message in an embedded image. As the awareness of privacy and security is increased by an individual, in government sector, security agencies and in some secret personal communication, the importance of steganography is also increasing. The characteristics of steganography like imperceptibility, robustness, high capacity separate it from other relevant methods like cryptography and watermarking. Here is the review of different techniques and state of the art methods done by the different scholars. These methodologies have described in different domains of steganography like spatial domain, frequency domain, discrete courier transform in frequency domain and adaptive domain. Every domain has its own characteristics. We analyzed various approaches in our survey and described their characteristics.
Echo state network-based spatio-temporal model for solar irradiance estimation

In this study, echo state network-based spatio-temporal (STESN) model is proposed to predict the solar irradiance of a given target station, depending on the historical data of its surrounding sites. Hourly solar irradiance datasets for the whole year 2017, obtained from California Irrigation Management Information System (CIMIS) at eight different stations are used. Comparison studies show that SPESN could achieve more accurate prediction, compared with the conventional persistence (PSS) benchmark model.