Reviewerly/exHarmony huggingface dataset:
Fog Removal Algorithm for Geographic Images Using Generative Adversarial Nets

Abstract In order to improve the imaging effect of geographic images in a complex weather environment. This paper designs a geographic image defogging algorithm by using the generation countermeasure network to improve the clarity of the image. Firstly, the imaging mode and degradation process of the foggy image is studied in this paper, and the atmospheric scattering model is established to simulate the degradation of the image. Secondly, according to the data characteristics of image defogging, an optimized image defogging model is generated based on Conditional Generation Adversarial Nets (CGAN), so that the image output by the model is closer to the real image. The experimental results show that the image defogging model designed in this paper has good contrast and color richness, which can effectively solve the problem of image degradation in fog. Moreover, the reduction of paired data sets will reduce the measurement index of the geographic image defogging model. This paper provides a reference for the study of geographic image defogging.
An Empirical Case Analysis on Electronic Health Records on Global Perspectives

The maintenance and logging in the health records is always required so that the overall predictive mining can be done on the patient records. In addition, the recording and maintenance of electronic health records is quite mandatory whereby the digital repository related to the patient is very important so that the future based predictions and the analytics can be retained. In addition to this, the patient records are providing the medical practitioners the higher degree of accuracy in the predictions and the aspects related to the knowledge discovery about that particular patient to have the effectiveness. By this way, the overall medical records can be maintained. In this research manuscript, the enormous tools and the vendors are presented usable for the electronic health records. The presented work is having the cavernous analytics on the vendor products associated with the electronic health records whereby the global perspectives and data analytics are cited.
Categorizing Uses of Communications Metadata: Systematizing Knowledge and Presenting a Path for Privacy

Communications metadata can be used to determine a communication’s device, identify the user of the device, and profile the user’s personality and behavior. The current state of affairs is that the increase of attacks against user privacy based on using communications metadata vastly outpaces the ability of users to protect themselves. With few exceptions, protections are point solutions against a specific attack. In the current situation, the user loses.
Spline approximation of unequal aeromagnetic survey data with the use of UAV on spatially inhomogeneous networks when solving problems of archaeological exploration

The article discusses an approach to get a regular cube of data in context of aeromagnetic survey. It is based on spline approximation with nodes on an irregular grid in three-dimensional space. To construct approximation spline, information on measurement errors is additionally used, which makes it possible to reduce the number of parameters as opposed to spline interpolation. This approach was tested on experimental aeromagnetic data from archaeological site Ivanovka-I.
Time series prediction based on fuzzy-membership cerebellar model neural network with differential evolution

In this paper, a fuzzy-membership cerebellar model neural network with differential evolution (DEFM-CMNN) is proposed, which improves the prediction accuracy of the cerebellar model neural network based on fuzzy membership (FM-CMNN) for prediction of time series. Firstly, use the linear quantization function of FM-CMNN input space to quantize the time series into nonlinear state variables. Secondly, the mutation, crossover and selection operations of the differential evolution (DE) algorithm are used to learn the connection weight of the network. Meanwhile, the adaptive mutation operator is introduced to enhance the diversity of individuals in the early stages of evolution. Finally, the effectiveness of the proposed DEFM-CMNN is demonstrated in experiments for time series prediction. The results show that this model has better real-time performance and higher prediction accuracy.
Offline Signature Verification Based on Spatial Pyramid Image Representation with Taylor Series

This paper presents Spatial Pyramid image representation based technique with partial sum of second order Taylor Series Expansion (TSE) for offline signature verification. In this approach, the given signature image is partitioned into sub blocks recursively and the local features in each sub blocks are computed and are represented as histogram. The histograms of the image and subblocks at various levels are concatenated to form a single histogram. This spatial pyramid feature vector is an extension of an orderless bag-of-features consisting of collection of histograms concatenated to form a single histogram. The partial sum of second order Taylor Series Expansion (TSE) is an approximate computation of finite number of terms in a small neighbourhood and hence provides a powerful mechanism to extract the localised structural information from the signature image. We propose kernel structures by extending the sobel operators to compute the higher order derivatives of TSE. Our approach captures both local and global features from the signature. We have used weighted histograms. The weight associated with the histogram is directly proportional to the depth of the level. The Support Vector Machine (SVM) is used for the classification. The classification accuracy of our approach on standard datasets is calculated and the results are compared with a few well known approaches. This shows that the performance of the proposed approach is better than the other approaches in the state of art literature.
Investigating Investment, Inflation, Trade-Openness and Economic Progress Nexus for Pakistan

Since inflation and trade openness rate are considered as critical measure of an economy's health. This article analyze the relation of Economic growth with Investment, Inflation and Trade Openness of Pakistan for 1970- 2019. The policy guide lines from analysis include promotion of policies to increase Investment and Trade-openness in short and long-terms. The study used ARDL bound-testing for long-term and Un-Restricted-Error Correction techniques to discover short-term interrelation amongst a selection of variables. Results of study revealed inflation negatively related to economic performance and positively linked to Investment and Trade-Openness. Findings of enquiry suggested government should focus more on investment friendly policies in the country.
Diagnostics of future directors readiness to solve non-standard tasks in the course of their professional activity

The article deals with the initial formation of professional competencies in the field of managerial decision-making in non-standard situations in the course of professional activity, raises the problem of forming skills and abilities of non-standard thinking in bachelors, determines the conditions and causes of essential changes in bachelor Directors during experimental work. As the main method of diagnostics and testing to determine the initial level of management decision-making, the test method of author's situational tasks was used M.V. Chumakov "On-diagnostics of volitional qualities of the individual", as well as "Orientation test" by Bernard M. Bass. According to the results of the survey, the author revealed an insufficient level of formation of managerial decision-making skills in a non-standard situation for future Directors of theatrical performances. The presence of this problem confirmed the need to implement a specially developed module of pedagogical support for the process of its formation, as well as pedagogical conditions that contribute to its effective functioning. The results of the study may be useful in developing recommendations for teachers of creative universities on the development of professional competencies in the field of decision-making in non-standard situations.
Lossless Image Compression Using Dynamic Block Encoding

In this paper, we present a simple and effective algorithm of lossless image compression to save coding space. Dynamic block encoding is applied, to reduce the coding bit via the reduction of the image spatial correlation. The original image is divided into uniform blocks with a proper size, to achieve a good intra-block correlation, then a reference rule is applied to decorrelate each block and squeeze the extra coding bit of each block, resulting in effective compression. Compared with the traditional unified encoding, the proposed algorithm optimizes the coding space via intra-block correlation, and the iteration compression can be performed further to squeeze inter-block correlation fully. Compression ratios of 1.807 and 1.857 are achieved for a single and three iterations, respectively, with a block size of 4. Due to the simple and efficient reference rule, the proposed algorithm also avoids complex mathematical computation in the traditional image compression methods.
DrtNet: An Improved RetinaNet for Detecting Beverages in Unmanned Vending Machines

With the rapid development of technologies such as mobile payment, deep learning, and cloud computing, the transformation of traditional retail models has become possible. Research on deep learning algorithms has become a hot direction in computer vision. Deep learning techniques have been widely used in face recognition, intelligent transport system, smart city, etc. At present, smart unmanned vending machines (UVMs) are of one of the representative carriers of unmanned retail business models. Compared with traditional vending machines, they have greatly improved user experience and saved implementation costs. One core issue of smart UVMs is how to quickly and accurately identify which and what kind of items a user is taking. In this paper, deep learning is used to solve the problem of object recognition in smart UVMs. To accurately obtain the information of products that a user may take from a UVM, an object detection-based method is used. Specifically, an object detection model, namely DrtNet, is proposed and designed under the smart UVM scenario. The backbone network of DrtNet adopts deformable convolution and group normalization layers. The used loss function contains focal loss function and balance Ll loss function, which can largely improve the recall rate of beverage detection. Experimental results demonstrate the feasibility and effectiveness of applying the deep learning model to smart UVMs.
Research on Person Re-identification Method Based on Data Link Technology

Person re-identification (re-ID) aims to query or match the identical individuals across multiple non-overlapping surveillance cameras and has become an important visual task in various applications such as pedestrian security and video surveillance. Although much recent progress has been made to address the problem of person re-ID, the blind areas without surveillance cameras are not considered, which are usually the hiding places of criminals or suspects. In order to solve this problem, in this paper, we apply data link technology to person re-ID, and propose a person re-ID model based on data link technology, which can realize all-round, continuous and real-time monitoring and tracking of target persons, and even achieve the identifying and attacking of partial key target persons.
Contrast and conditional polarity rule (CCPR) for detection of positivity and negativity in opinionated sentence with indirect polarity

Purpose Customer reviews are one important source that contains valuable information for quality evaluation of products or services. Review sentences contain sentiment words that show whether a user’s opinion is positive or negative. When review sentence has mix opinions, having sentiment words of both polarities, it is difficult to conclude whether it is positive or negative opinion. The purpose of this study is to improve the detection of polarity in such situation. Design methodology approach In this research, methods such as part-of-speech tagging, polarity analysis and rules selection are used to identify the polarity. A set of rules called contrast and conditional polarity rules (CCPR) has been created to improve the polarity detection in cases when there is mixture of sentiment words used in contrast and conditional type of review sentences. The experiment is conducted with data sets from three domains, i.e. restaurant, electronic and Tripadvisor. Findings The experimental result confirms that CCPR rules have higher baseline of the polarity aggression. In restaurant domain, CCPR rules (62.07%) have increased 13.79% compared with the Pol_Agg_MPQA baseline (48.28%) and 13.79% compared with Pol_Agg_Senti baseline (48.28%). In electronic domain, CCPR rule (79.17%) is higher by 12.50% compared with the Pol_Agg_MPQA baseline (66.67%) and 16.67% compared with Pol_Agg_Senti baseline (62.50%). Another one, CCPR rule (70.83%) is higher by 8.33% compared with the Pol_Agg_MPQA baseline (62.50%) and 12.50% compared with Pol_Agg_Senti baseline (58.33%). In conclusion, result of experiment shows promising outcome with improvement in detecting the positivity and negativity of indirect sentence, especially for the case of sentence with indirect polarity. Originality value To address the problem of mix opinions in terms of polarities, this paper presents a rule-based approach to improve the result of identifying positivity and negativity in sentence with indirect polarities.
Bibliometric Study of CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT) during 1990–2019

The study examines the pattern of growth of papers published by CSIR-Institute of Himalayan Bioresource Technology (IHBT) during 1990-2019; identifies most prolific authors and their citation impact, communication pattern of scientists in terms of journal publishing country and impact factor of these journals as well as the pattern of domestic and international collaboration. Analysis of data indicates a consistent growth of output during the study period except a decline in 2019-2020. Publications appeared in journals published from the advanced countries of the West and more than half of the papers were published in medium, high and very high impact factor journals. The output is highly scattered in terms of journal publication as one-third of the papers were published in 28 journals. Only about 8% papers remained uncited and rest were cited one or more times. Of the 20 highly cited papers, six women scientists acted as first author. The study found that more than half of the papers were published in domestic and international collaboration. USA was the most collaborating country and academic institutions were found to be most domestic collaborating institutions in India as well as abroad. Among all the Indian institutions, Guru Nanak Dev University (Amritsar) was most collaborating domestic institution and University of Vienna, Austria from abroad.
Chess ADC – An Automated Aide-De-Camp

Various types of tools and techniques are used to analyse chess games. The existing most successful and accredited method is, electronic boards where it is able to track and extract the movement data with the help of electronic equipment and pressure detecting sensors [1] . But that solution is expensive. Chess ADC is a comprehensive framework that can be used by anyone for practicing and developing chess skills. It allows users to play chess games on a real chessboard and measure their level of skill. Although chess is a very complicated game that has many different patterns of piece movements, all the number of states that a game can have is finite. We can solve chess with just math if we have unlimited amount of computing power [2] . Deep learning models have already been used in research on various board games such as backgammon, checkers, Go and chess [3] . Chess ADC also utilizes these technologies to give a better user experience for the players. We call this system "Chess ADC - An Automated Aide-De-Camp" because it functions as an aide-de-camp for chess.
Strategy for the generation of personalized recommendations

Ubiquitous learning is a new educational model, identifying by the mobility of students, who have devices to access learning resources through wireless networks. Also, the instructional processes are related to the situation that involves the student, allowing access to adapted educational resources according to the profile and learning context. To offer personalized services to students in ubiquitous contexts we have developed an Architecture directed by Ontological Models and a Personalization Strategy to generate recommendations. This article presents the implementation of the strategy to create personalized recommendations in the learning services provided by a u-learning application for the university entrance course. We conclude that the proposed strategy, it may be appropriate to guide the personalization of recommendations when looking for apps to support ubiquitous learning since it considers different types of adaptation, different personalization techniques and situational parameters that characterize the context where the student learns.
RETROSPECTIVE ANALYSIS OF MAMMOGRAM, SONOMAMMOGRAM AND ITS HISTOPATHOLOGICAL CORRELATION AS AN AID FOR DIAGNOSIS OF BREAST LESIONS

INTRODUCTION: Breast cancer is the most common cancer impacting 2.1 million women each year and also relates to the most cancer related deaths in women. In 2018, it was estimated that 627,000 women died from breast cancer which approximates to 15 % of all cancer related deaths among women [1]. The triple test– clinical examination, mammography and core biopsy helps in differentiating benign and malignant lesions. Histopathological examination is considered being the gold standard test for confirming malignant lesions and forms the basis of management. AIM: To assess sensitivity of mammogram with ultrasonography in diagnosing various breast lesions and to correlate the categorized breast lesions (BI-RADS) with histopathology reports and thereby obtain specificity and NPV of evaluation using Mammogram and ultrasonography. STUDY DESIGN: Retrospective analytical study. Study Period: July 2018 – July 2019. METHODS: The results of ultrasonography and mammography of 72 cases diagnosed clinically with breast lesions over the period of one year in tertiary health care hospital were compared with histopathology reports. RESULTS: The mean age of the patients was 45.65 ± 3.19. Our results showed that in histopathology reports in 20 patients (27.78%) were malignant, 51 cases (70.83%) had benign disease and 1 case 1.39% was borderline malignant. Fibroadenoma was the commonest benign lesion whereas infiltrating ductal carcinoma was the most common malignant lesion. Breast Imaging – Reporting and Data System (BIRADS) by mammogram revealed category II in 54.1%, III in 20.8%, IV in 16.6% and V in 8.3%. The specificity of mammography alone in diagnosing malignant breast lesions was 90.1%. When combined (ultrasound and mammogram), the specificity in diagnosing malignant breast lesion was 98.5% CONCLUSION: Mammography and sono-mammogram plays an important role in the diagnostic and surgical management of breast lesions with correlative histopathology evaluation. The diagnostic accuracy shows significant improvement when mammogram was combined with ultrasound correlation and thereby improving sensitivity and specificity of diagnosing malignant breast lesions.
RPM: Additive Stream Ciphers for Lightweight Communication Security

As research of lightweight cryptographic primitives keeps widening, so does the need for different approaches towards rigorous analysis of their properties. In this paper we investigate the use of suitable variations of the ideal cipher analysis methodology to a class of lightweight stream ciphers. Our case study is a suite of lightweight cryptographic techniques proposed in the industry domain to efficiently target communication security while meeting use cases such as interconnected devices working in Low Power Wide Area networks. First of all, we isolate these lightweight techniques as non-linear, additive, stream ciphers. Then, we describe theoretical analysis to support some evidence of plausible security: (a) high-period stream ciphers rule out certain classes of attacks; (b) idealized versions of the analyzed lightweight stream ciphers have high period. Finally, we show empirical performance results, demonstrating effective cipher methods that easily fit into the limited resources of constrained environments, while outperforming state-of-the-art methods in unconstrained environments (e.g., the AES block cipher) by almost one order of magnitude.
Constructing Covert Channel on Intel CPU-iGPU platform

Most modern multi-core CPUs are implemented in System-on-a-Chip (SoC), where CPU and integrated Graphics Processing Unit (iGPU) share physical memory and some microarchitectures. iGPUs provide horsepower required by computeintensive tasks, taking off CPU's heavy workload. These tasks include rendering graphics, executing cryptographic or machine learning algorithms. While iGPU utilization has been expanded to various fields, now covering security-sensitive applications as well, possible security threats that could follow has not received enough attention yet. In this paper, we present a novel covert channel attack on heterogeneous processor platforms (CPU-iGPU) by using cache activity on LLC as a communication channel. To successfully mount this attack, iGPU-side trojan finds LLC eviction set on Shared Virtual Memory (SVM), which will create contention on target cache line of CPU-side spy. We successfully construct covert channel between a CPU process and an iGPU kernel, making it the first micro-architectural timing attack on Intel CPU-iGPU platform. We also evaluate performance of the attack in terms of channel bandwidth and bit error rate.