Reviewerly/exHarmony huggingface dataset:
Using air-borne gamma ray spectrometry and remote sensing data for detecting alteration zones around Wadi Saqia area, Central Eastern Desert, Egypt

This study presents the air-borne geophysical Gamma Ray Spectrometry data have been used for detecting the alteration zones related to hydrothermal activity. Areas enriched in potassium may indicate the existence of potassic alteration zone that is often associated with hydrothermal alteration accompanied by economic mineral deposits. The radioelements (K (%), eTh (ppm), and eU (ppm)) concentration distribution and the ratios of (K/eTh and K/eU) over the study area were presented. A Potassium Point anomaly map was obtained from the statistical treatment of the K (%), K/eU, and K/eTh spectrometric data. Anomalously High (X+3σ or more) areas were delineated and considered the most preferred areas for potassic alteration. If the maximum values exceed the (X+3σ) threshold, then (X+2σ) or (X+σ) is applied instead. Sixteen areas were delineated and suggested for the presence of potassic alteration. The F-Parameter of Efimov (K*(eU/eTh)) was also implemented as proved approach for delineating the most preferred areas for hydrothermal alteration. The result of the Potassium point anomaly mapping was compared to that of the F-Parameter technique that was used as reference for validating the results. The sixteen anomalous areas revealed from the Potassium point anomaly technique showed perfect correlation with the areas of highly anomalous F-parameter. Remote sensing study supported by field data and laboratory spectral analyses was also implemented in this study as another useful powerful cost-effective approach for alteration mapping. A field study was conducted; samples from the different observed alterations were collected. The spectral signatures of these samples were measured using the "ASD TerraSpec Halo mineral identifier" hand-held spectroradiometer. Spectral analyses were performed to obtain the spectral characteristics of the alteration zones of the study are. The end-members were identified through comparing the measured spectra against the United States Geological Survey (USGS) spectral library in ENVI v.5.1. software. The identified end-members include; Kaolinite, Sericite, Montmorillonite, Chlorite, Epidote and iron oxides. Subpixel Mixture Tuned Matched Filtering (MTMF) mapping technique was finally used for mapping the distribution of these end-members on ASTER nine Visible Near Infra-Red (VNIR) and Short Wave Infra-Red (SWIR) bands. The remote sensing data were validated as Semna gold mine and the identified alterations during the field study were successfully mapped by the remote sensing technique. Finally; Most of the delineated areas using the proposed Potassium point anomaly technique were also evidenced by the approached remote sensing technique. Moreover remote sensing suggested more alteration areas than did the gamma ray spectrometry data.
A Heuristic for Efficient Coordination of Multiple Heterogeneous Mobile Robots Considering Workload Balance

The letter deals with a path planning problem that commonly arises in many applications involving multiple heterogeneous robots called a Multiple Depot Heterogeneous Traveling Salesman Problem (MDHTSP). Specifically, the authors seek to provide good quality of feasible solutions for a path planning problem for a given set of structurally heterogeneous mobile robots located in distinctive depots and a set of targets to visit while minimizing the maximum travel cost (min-max). A solution for MDHTSP with min-max objectives is in great demand for many applications, such as transportation and surveillance, because it is directly related to a significant reduction in the mission completion time. However, no reliable algorithm running in a reasonable computation time has been published for this specific problem. As an extension of our preliminary research on two heterogeneous robots, this letter presents a heuristic approach based on a primal-dual technique for the problem while focusing on the target assignment. The computational results of the implementation verify that the proposed algorithm produces a good quality of feasible solution within a relatively short computation time.
Examining the Psychological State Analysis Relationship Between Bitcoin Prices and COVID-19

The rapid worldwide spread of COVID-19 forced many countries to enforce complete lockdown and strict quarantine policies. The strict lockdown and quarantine affect the psychological state of people toward cryptocurrency. The current research aims to examine the effect of COVID-19 on Bitcoin prices concerning cumulative deaths and confirmed cases. The research comprises daily data from January 20, 2020, to April 30, 2020, during the initial worldwide breakout of COVID-19. This research employed the augmented Dickey-Fuller test to check the stationarity of data, the co-integration test for the interdependency of variables, and the vector error correction model for identifying the direction and long or short-run relationship between Bitcoin prices and COVID-19. The research results show that Bitcoin prices are negatively significant and related to COVID-19 in the short-run. A unidirectional relationship between Bitcoin prices and cumulative deaths is also observed. Investors and the public’s psychological state were positively significant to Bitcoin prices in the long-term because of cashless transactions, unbanked, and less risky virus traveling. The second reason behind the positive psychological relation is un-centralization and easy-to-make payments by Bitcoin. This study’s finding provides timely evidence to decision-makers on Bitcoin price volatility and its impacts on the public’s psychological states regarding COVID-19.
<scp>deGraphCS</scp> : Embedding Variable-based Flow Graph for Neural Code Search

With the rapid increase of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning-based approaches that provide end-to-end solutions (i.e., accept natural language as queries and show related code fragments), the performance of code search in the large-scale repositories is still low in accuracy because of the code representation (e.g., AST) and modeling (e.g., directly fusing features in the attention stage). In this paper, we propose a novel learnable de ep G raph for C ode S earch (called deGraphCS ) to transfer source code into variable-based flow graphs based on an intermediate representation technique, which can model code semantics more precisely than directly processing the code as text or using the syntax tree representation. Furthermore, we propose a graph optimization mechanism to refine the code representation and apply an improved gated graph neural network to model variable-based flow graphs. To evaluate the effectiveness of deGraphCS , we collect a large-scale dataset from GitHub containing 41,152 code snippets written in the C language and reproduce several typical deep code search methods for comparison. The experimental results show that deGraphCS can achieve state-of-the-art performance and accurately retrieve code snippets satisfying the needs of the users.
A generalized approach to automotive forensics

In the past years, software became an essential topic in modern vehicles, e.g., with the rise of more and more complex driver assistance systems. The advent of automated driving will drive this trend even further. Today, accident investigation, as well as warranty claim analysis, need to take into consideration an analysis of the rapidly increasing proportion of software and security based implementations as part of modern vehicles, the so-called digital forensics. This paper evaluates the general feasibility of digital forensics on a state-of-the-art vehicle. To do so, we analyzed current digital forensics techniques on a state-of-the-art vehicle to constitute gaps in the automotive forensics process used on in-vehicle systems. We present a general process for automotive forensics to close existing gaps and implemented it on a state-of-the-art vehicle in an in-vehicle device manipulation scenario. The implementation uses the on-board diagnostics interface, the diagnostics over internet protocol, as well as the unified diagnostic services for communication. Our implementation requires automotive Ethernet at the diagnostic interface. Our research shows future directions for efficient automotive forensic as well as the exemplary feasibility of automotive forensic analysis on state-of-the-art vehicles without the need for additional in-vehicle components such as intrusion detection systems or event data recorders.
Multi-Instance Deep Learning Based on Attention Mechanism for Failure Prediction of Unlabeled Hard Disk Drives

Failure of hard disk drives (HDDs) is the most critical reliability issue of data center. Therefore, predicting the failure of the HDD is an important means to ensure the storage security of the data center. However, most current research works had not paid attention to the fact that the self-monitoring, analysis and reporting technology (SMART) data in a returned failed HDD are a long-term sequence that consists of many unlabeled data, as the healthy and faulty data are highly mixed. Because the failure data in the rapid degradation period are less than the health data in the normal state, the mixture of healthy and faulty data results in an extremely data imbalance. This brings a great challenge to find the hidden fault information, and thus failure prediction becomes a difficult task. To cope with the above problems, a multi-instance long-term data classification method based on long short-term memory (LSTM) network and attention mechanism are proposed to predict the failure of HDDs. Regarding long time sequence HDD data as an instance bag, multi-instance learning (MIL) divides it into multiple instances in the subconcept layer, and then studies the connection between instances and bag labels. Based on the analysis of HDD data of a communication company and Backblaze data center, our proposed method can obtain much better results than other methods.
Experimental Verification of the Leader-Follower Formation Control of Two Wheeled Mobile Robots with Obstacle Avoidance

In this work the experimental verification of the control of two wheeled mobile robots with differential configuration under a leader-follower formation is presented, where the leader performs the task of tracking a predefined trajectory, as well as, it is able to avoid obstacles and it sends information of the point-to-point trajectories reached to the follower mobile robot. The follower has the ability to estimate the trajectory of the leader robot only based on the information received from the leader, for arbitrary initial conditions. Experimental verifications with two wheeled mobile robots TurtleBot3 Burger illustrate the performance of the proposed technique, using the robot operating system (ROS) and Python.
Artificial intelligence in critical care: Its about time!

Currently, most critical care information is not expressed automatically at a granular level, rather is continually assessed by overindulged Intensive Care Unit (ICU) staff. Furthermore, due to different confounding morbidities and the uniqueness of the ICU setting, it is difficult to protocolize treatment regimens in the ICU. In highly complex ICU setting where man and resource management becomes extremely challenging, definite advancements are required to implement Artificial Intelligence (AI) for prognosticating the course of the disease to aid in informed decision-making. AI is the intelligence of a computer or computer-supervised robot to execute a piece of work commonly associated with intelligent beings, wherein the machines go beyond the realms of normal information processing by adding the characteristics of learning, sound reasoning, and weighting of the inputs. AI recognizes circuitous, relational time-series blueprint within datasets and this reasoning of analysis transcends conventional threshold-based analysis adapted in ICU protocols. AI works on the principle of a more complex form of Machine Learning by Artificial Neural Networks (ANN). These information-processing paradigms use multidimensional arrays called tensors which aid in 'learning' and 'weighting' all the information made available to it, thereby converting normal machine learning into Deep Learning. Here, the use of AI for data mining in complex ICU settings for protocol formulation and temporal representation and reasoning is discussed.
EM/Power Side-Channel Attack: White-Box Modeling and Signature Attenuation Countermeasures

This article presents analysis methods to pin-point the cause of sidechannel leakage in integrated circuits and proposes a number of techniques for leakage attenuation. -Rosario Cammarota, Intel Labs -Francesco Regazzoni, University of Amsterdam and Università della Svizzera Italiana.
Fast dehazing method for improving the image quality in pellet size measurement

Images captured during the manufacturing of iron green pellets may be seriously degraded by haze, which greatly affects the performance of machine vision systems for pellet size measurement. Hence, to improve the quality of green pellet images and pellet size measuring accuracy, a fast image dehazing method is proposed in this study. By analyzing the different features between haze and haze-free image patches, a new and simple prior, which is that haze-free image patches have the maximal local image entropy, is proposed. The transmission map is then calculated using the proposed prior and the haze-free image is obtained. The dehazing effect of the proposed algorithm was assessed using qualitative and quantitative criteria, and compared with six current image dehazing algorithms on two datasets of hazy green pellet images established by ourselves. The results demonstrate that the proposed algorithm achieves better dehazing performance than the other algorithms and can improve the accuracy of pellet size measurement. Moreover, the computing efficiency of the proposed method meets the requirements for real-time industrial applications when used as a pre-processing step. This proposed dehazing method will enable machine vision monitoring systems to operate in industrial environments where dust and steam are common.
Long-Tailed Recognition Using Class-Balanced Experts

Deep learning enables impressive performance in image recognition using large-scale artificially-balanced datasets. However, real-world datasets exhibit highly class-imbalanced distributions, yielding two main challenges: relative imbalance amongst the classes and data scarcity for mediumshot or fewshot classes. In this work, we address the problem of long-tailed recognition wherein the training set is highly imbalanced and the test set is kept balanced. Differently from existing paradigms relying on data-resampling, cost-sensitive learning, online hard example mining, loss objective reshaping, and/or memory-based modeling, we propose an ensemble of class-balanced experts that combines the strength of diverse classifiers. Our ensemble of class-balanced experts reaches results close to state-of-the-art and an extended ensemble establishes a new state-of-the-art on two benchmarks for long-tailed recognition. We conduct extensive experiments to analyse the performance of the ensembles, and discover that in modern large-scale datasets, relative imbalance is a harder problem than data scarcity. The training and evaluation code is available at https://github.com/ssfootball04/class-balanced-experts .
An Efficient Method for No-Reference Video Quality Assessment

Methods for No-Reference Video Quality Assessment (NR-VQA) of consumer-produced video content are largely investigated due to the spread of databases containing videos affected by natural distortions. In this work, we design an effective and efficient method for NR-VQA. The proposed method exploits a novel sampling module capable of selecting a predetermined number of frames from the whole video sequence on which to base the quality assessment. It encodes both the quality attributes and semantic content of video frames using two lightweight Convolutional Neural Networks (CNNs). Then, it estimates the quality score of the entire video using a Support Vector Regressor (SVR). We compare the proposed method against several relevant state-of-the-art methods using four benchmark databases containing user generated videos (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC). The results show that the proposed method at a substantially lower computational cost predicts subjective video quality in line with the state of the art methods on individual databases and generalizes better than existing methods in cross-database setup.
Differentially Private Triangle Counting in Large Graphs

Triangle count is a critical parameter in mining relationships among people in social networks. However, directly publishing the findings obtained from triangle counts may bring potential privacy concern, which raises great challenges and opportunities for privacy-preserving triangle counting. In this paper, we choose to use differential privacy to protect triangle counting for large scale graphs. To reduce the large sensitivity caused in large graphs, we propose a novel graph projection method that can be used to obtain an upper bound for sensitivity in different distributions. In particular, we publish the triangle counts satisfying the node-differential privacy with two kinds of histograms: the triangle count distribution and the cumulative distribution. Moreover, we extend the research on privacy preserving triangle counting to one of its applications, the local clustering coefficient. Experimental results show that the cumulative distribution can fit the real statistical information better, and our proposed mechanism has achieved better accuracy for triangle counts while maintaining the requirement of differential privacy.
Multi-face Recognition for the Detection of Prisoners in Jail using a Modified Cascade Classifier and CNN

Abstract Identifying the identity of a prisoner in a detention cell, through facial recognition automatically is a big, exciting problem and there are many different approaches to solve this problem because it must detect multiple faces (multi-face). Especially in uncontrolled real-life scenarios, faces will be seen from various sides and not always facing forward, which makes classification problems more difficult to solve. In this research, one method is combined deep neural networks that is Convolutional Neural Networks (CNN) and Haar Cascade Classifier as real-time facial recognition, which has proven to be very efficient in face classification. Methods are implemented with assistance library Open-CV for multi-face detection and 5MP CCTV camera devices. In preparing the architectural model Convolutional Neural Networks Do configuration parameter initialization to speed up the network training process. Test results on 51 test data using constructs Convolutional Neural Networks VGG16 models up to a depth of 16 layers of convolution layers with input from the extraction of the Haar Cascade Classifier resulting in facial recognition system performance reaching an accuracy rate of about 87%.
Verified Hardware/Software Co-Assurance: Enhancing Safety and Security for Critical Systems

Experienced developers of safety-critical and security-critical systems have long emphasized the importance of applying the highest degree of scrutiny to a system's I/O boundaries. From a safety perspective, input validation is a traditional “best practice.” For security-critical architecture and design, identification of the attack surface has emerged as a primary analysis technique. One of our current research focus areas concerns the identification of and mitigation against attacks along that surface, using mathematically-based tools. We are motivated in these efforts by emerging application areas, such as assured autonomy, that feature a high degree of network connectivity, require sophisticated algorithms and data structures, are subject to stringent accreditation/certification, and encourage hardware/software co-design approaches. We have conducted several experiments employing a state-of-the-art toolchain, due to Russinoff and O'Leary, and originally designed for use in floating-point hardware verification, to determine its suitability for the creation of safety-critical/security-critical input filters. We focus first on software implementation, but extending to hardware as well as hardware/software co-designs. We have implemented a high-assurance filter for JSON-formatted data used in an Unmanned Aerial Vehicle (UAV) application. Our JSON filter is built using a table-driven lexer/parser, supported by mathematically-proven lexer and parser table generation technology, as well as verified data structures. Filter behavior is expressed in a subset of Algorithmic C, which defines a set of C++ header files providing support for hardware design, including the peculiar bit widths utilized in that discipline, and enables compilation to both hardware and software platforms. The Russinoff-O'Leary Restricted Algorithmic C (RAC) toolchain translates Algorithmic C source to the Common Lisp subset supported by the ACL2 theorem prover; once in ACL2, filter behavior can be mathematically verified. We describe how we utilize RAC to translate our JSON filter to ACL2, present proofs of correctness for its associated data types, and describe validation and performance results obtained through the use of concrete test vectors.
A Waste Management Technique to detect and separate Non-Biodegradable Waste using Machine Learning and YOLO algorithm

This research Paper proposes an application of Image Processing that works on the principle of machine learning and YOLO (You Only Look Once) algorithm was used to detect the custom object. The proposed application is a method to detect non-biodegradable waste from the bins so that the non-biodegradable waste can easily be separated from the bins. Data was divided into three major categories of non- biodegradable waste that were glass, metal and plastic. 450 -500 images of each category were collected in order to train the model. Each image was labelled using a labelling tool in the format of yolo. A machine learning model was created that was trained on the data set of all three categories of the images of non- biodegradable waste. After completion of training a number of files were obtained that were further used in testing model of the application. After completion of testing and checking the accuracy of the model on the webcam all the files were finally imported on the raspberry pi camera. Final testing was done on raspberry pi camera and results were verified accordingly and accuracy was calculated on the basis of the output received.
Commentary: The Role of Geodetic Algorithms for Earthquake Early Warning in Cascadia

Abstract The ShakeAlert earthquake early warning (EEW) system issues public alerts in California and will soon extend to Oregon and Washington. The Cascadia subduction zone presents significant new challenges and opportunities for EEW. Initial publications suggested that EEW algorithms based on Global Navigation Satellite System (GNSS) data could provide improved warning for intraslab events and dramatically improved warning for offshore megathrust events, both of which contribute significantly to hazard in Cascadia. We find that some expectations in these publications were unrealistic, and we demonstrate that in general geodetic algorithms would not produce timely warnings for intraslab events nor warning times of two minutes or more for severe shaking from megathrust earthquakes. Nonetheless, lessons from recent earthquakes in Japan and California, for which alerts from seismic algorithms suffered from magnitude saturation and high data latencies, demonstrate the urgent need for rigorous testing of geodetic EEW as a potential complement to seismic EEW.