Reviewerly/exHarmony huggingface dataset:
An Efficient Approach for Geo-location Accuracy Improvement of Legacy IRS-1C/1D Data Products

IRS-1C/1D remote sensing datasets from Indian Space Research organization (ISRO) is of significant importance till now for change detection analysis in the field of agriculture growth, wetland mapping, urban sprawl monitoring, snow/glacier studies and ingestion of historical digital sensor data in data cube framework for doing data analytics and generation of value added information. Currently, IRS-1C/1D georeferenced data products have system level location accuracy go up to 1 Km. In addition, the location error of IRS-1C/1D datasets are increased to many kilometers especially in hilly terrain scenes. Such higher location accuracy error makes legacy IRS-1C/1D data not directly usable for any kind of space borne applications. In this paper, an efficient approach is presented to improve the geo-location accuracy of IRS-1C/1D data by incorporating inflight geometric calibration exercises. The rigorous sensor model is fed with primary image, ancillary orbit and attitude data, digital elevation model (DEM) and a reference image to produce ortho-rectified product. The major challenge is to do automatic feature matching of IRS-1C/1D data with current RS-2A reference image database to achieve sub-pixel level geometric error. As datasets are more than two decades older, the number of outliers in matched feature points are more. Also geo-location error of 1C/1D is varying a lot which makes it difficult to set the search window radius for feature matching. To handle this scenario, mode seeking based pruning mechanism is added with robust SIFT feature matching technique which is independent of search window size. Additionally, SIFT parameters are also fine-tuned to get more stable feature points. The approach developed is tested in many multi temporal IRS-1C/1D LISS-3 datasets. The geo-location error of the products generated is at sub-pixel level accuracy with respect to the reference. The IRS-1C/1D data products generated with this new updated chain can be used directly as analysis ready datasets.
TensorFlow-Based Semantic Techniques for Multi-cloud Application Portability and Interoperability

Cloud computing permits plentiful access to shared pools of resources that are configurable and provide higher-level services to the user, which can be easily and hastily granted with minimal management effort. With the advancements in cloud computing, many cloud service providers have started using the distributed high-end servers to provide services to its users. However, while designing an efficient cloud environment, it has been found that the application portability is a key issue. Portability is usually offered to mitigate supplier lock-in. Nevertheless, shifting from a single method to an alternative for a minimum of work, seeing that is quite possible together with box companies, which can also improve the durability along with scalability. Therefore, in this paper, a novel TensorFlow-based semantic technique is designed and implemented to significantly port the applications between high-end servers. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique. Extensive experiments reveal that the proposed technique outperforms the existing techniques.
Lossless Video Compression Using Bayesian Networks and Entropy Coding

Lossless video compression algorithms are used in applications ranging from archival of video records to the field of medicine. In this paper, we propose a simple yet effective encoding technique for lossless video compression. Our technique automatically learns Bayesian Networks to discover Conditional Dependencies in a video stream through Stochastic Hill Climbing. Utilizing this network, variable length codes are generated using Entropy Coding procedure achieved by Huffman Coding. This structured data is used to encode the video stream. The algorithm has been tested and compared alongside H.264, FF Video Codec 1 (FFV1) and Gzip. The proposed compression model performs on average at least as good and at times better than the aforementioned techniques.
Let the Evidence Speak—Using Bayesian Thinking in Law, Medicine, Ecology and Other Areas

"Let the Evidence Speak—Using Bayesian Thinking in Law, Medicine, Ecology and Other Areas." Technometrics, 62(1), pp. 137–138
Design of Solar Irradiance Measurement Based On Analitycal Data Using Microcontroller

Based on the 2018 RUPTL, Indonesia targets the development of renewable energy plants reach 23% on 2025. With a tropical climate, Indonesia is a country that has considerable potential to use photovoltaic as a renewable energy. But in its use, photovoltaic has an obstacle that can be detrimental namely intermittent properties. These constraints can be overcome by means of planning that aims to optimize photovoltaic performance. Planning in this study is in the form of actual measurement results from solar irradiance with data based on surrounding environmental conditions such as temperature, relative humidity, rainfall, and UV intensity. Then the data is processed using the Ordinary Least Square (OLS) method. The purpose of this study is to obtain accurate solar irradiance values so that they can be used in planning photovoltaic optimization. This measurement has an error value of 18.9% which is calculated using the MAPE method by using Solar Power Meter as the actual value.
Using Immersive Virtual Reality in Ancient Egypt’s Giza Plateau

The aim of this research study is to adopt an innovative IT means for presenting the ancient Egyptian heritage to both the academics and tourists, remotely. Academic staff and students, tourists, tour agents/guides, and historians can all benefit from the online access to major Egyptian monuments using an advanced Immersive Virtual Reality (IVR) package, such as Second Life (SL). IVR is a form of technology that creates computer-generated worlds or immersive environments which people can explore and interact with. SL is a virtual game environment that has been used successfully in education and tourism. We had reported on the preliminary results of this research project in [1] where we compared what we did using SL with other tourism packages and games. With the success of the first stage of the research in the Giza plateau, we expect it to lead the way for further integration between IT and cultural heritage. It may very well be replicated for other historical locations in Egypt.
Quantum speed limit time of a spin qubit in noninteracting spin bath

We study the dynamic of quantum speed limit (QSL) time of a qubit coupled to a bath of noninteracting spins. The investigations are carried under both exact and approximate solutions of the model and the results of the two approaches are compared with each other. Under the exact solution and in the low temperature limit, QSL time becomes independent of the number of spins in the environment with potential capacity for quantum speed up. Beyond a critical value of temperature, there exists a frequency of the spins that reduces the capacity for speeding up quantum evolution. The effects of driving time, nonuniform frequencies of the bath’s spins and coupling strength on QSL time are also investigated.
WIDER Face Challenge using Multi-Task Cascading Neural Network

Nowadays, face detection is common. Its used in many areas. With the help of face detection benchmark data-sets, many signs of progress have been made. Face detection methods used nowadays is not matching the real-world requirements. With new advancing technologies and services, we need to upgrade our existing systems. By using a data-set called as WIDER FACE which is very large in size than already existing data-sets, we can improve the performance. WIDER FACE data-set has many faces in it which may be challenging as it includes faces under different conditions. Moreover, we can see that in face detection task, WIDER FACE data-set is best for training and testing the accuracy of the model. But existing face detection algorithms and models are not up-to the mark. They have major limitations. So we created a WIDER FACE detection system which will help us overcome all those issues.
Anonymous IoT Mutual Inter-Device Authentication Scheme Based on Incremental Counter (AIMIA-IC)

Cyber attackers are shifting their attention from traditional computers to IoT devices for malignant activities like exposing smart homeowner private information and/or to launch botnet attacks. Like for conventional networks, the security of IoT networks rests on how properly the authentication process is done. However, unlike conventional networks, IoT infrastructure faces an uphill battle in deploying and operating strong authentication schemes because of inherent limitations on the underlying storage and computation capability. In this paper, we propose a new anonymous mutual Inter-device authentication protocol based on transient identities, incremental counter and temporary secret keys for IoT. The proposed protocol is based on symmetric cryptography and somehow follows the ZigBee protocol. It allows IoT devices to anonymously and mutually authenticate in an unlinkable and untraceable manner, and implements essential security requirements for IoT devices. By analyzing the protocol, we evaluate and demonstrate its efficiency and its relatively limited computational and storage overhead. Furthermore, the security of the protocol is assured through informal security analysis and formally by using the automated validation of Internet security protocols and applications (AVISPA) toolkit.
The Inclusion of Students from high Level Education with Disabilities through ICT's

Nowadays, the challenges that teachers face on different levels of education are varied and some are more complex than others, such is the case of the inclusion of students who present some disability in high level education with the rest of the class. The role of teacher is of the utmost importance, as this is the first contact with the student, it should be the support and bridge which will best manage the relationships between students, as well as finding the best path that allows all students to advance at the same pace and above all to make possible a better development in the classroom by the students. The Information and Communication Technologies (ICT's) play an important role in education, since they're one of the primary technological tools which the students count with to comprehend, communicate and exchange information between them, socialize and develop better and better as much as outside and inside the classroom.
An Adaptive Wavelet Multilevel Soft Threshold Algorithm for Denoising Partial Discharge Signals

For denoising of partial discharge (PD) signals in electrical equipment, the traditional wavelet threshold method is affected by the empirical and subjective selection methods of wavelet base and decomposition layer, which will cause the waveform to be distorted and produce large errors in the process of denoising. In order to reduce the influence of the above factors, this paper proposes a new adaptive wavelet multilevel soft threshold denoising algorithm based on waveform similarity parameters and energy distribution analysis. Firstly, the waveform similarity method is used to determine the optimal wavelet base. Secondly, multi-scale wavelet decomposition is performed on the simulated ideal partial discharge signal and noise, and the signal is processed by the multilevel threshold method. Finally, the energy analysis of the PD signal and the noise signal is performed to determine the optimal number of decomposition layers. The proposed method and the traditional wavelet threshold method are used to denoise the simulated discharge signal, and the denoising effects of the two algorithms are compared quantitatively from four indexes: signal-to-noise ratio, root mean square error, waveform similarity coefficient and ascending trend parameter. After calculation, the index of the new algorithm is improved, which indicates that the new algorithm has certain practicability and provides a new reference for the denoising of PD signals.
Research on Anomaly Detection Method for electro-data

In order to overcome the problem of low computational efficiency caused by data mining anomaly detection method and insensitivity to abnormal data with clustered characteristics, a new anomaly detection method for electro-data is proposed, this method uses an electric anomaly detection model based on SCiForest algorithm to test whether the user's electricity consumption data is abnormal or not, and combines with daily load curve data for verification. The result shows that the method can not only effectively detect scattered anomalies, but also detect the clustered anomalies. In the detection scenario of electricity consumption data with clustered characteristics, the method is more effective in testing whether the user's electricity consumption data is abnormal or not.
Transfer Leaming in Automatic Speech Recognition for Serbian

In automatic speech recognition systems the training data used for system development and data expected to be obtained during the practical use of the system do not have to fit each other perfectly, but other similar data may be available. Transfer learning can help to exploit such similar data for training in order to boost the speech recognizer's performance in a certain domain. In this context, the paper presents the first application of transfer learning in speech recognition for the Serbian language. Several methods are proposed, with the goal of optimizing system performance on a specific part of the existing speech database for Serbian. The experimental results evaluated on a test set from the desired domain show significant improvement in both word error rate and character error rate.
Video Manipulation Detection via Recurrent Residual Feature Learning Networks

Over the past decade, the increased adoption of Internet-connected technology has led to a substantial growth in the amount of videos produced and digested by people around the world. Subsequently, manipulated content within videos has become far more common and difficult to detect. Material of this nature poses a significant problem as it provides a way to falsely affect viewers' beliefs. In this work, we first develop a pipeline to generate manipulated video segments from preexisting videos and then develop a deep learning architecture to detect unique video manipulations on a frame-by-frame basis. Specifically, we develop a model which analyzes manipulated video segments represented as sequences of images via a joint Residual Network (ResNet) feature extractor and Long Short-Term Memory (LSTM) network to detect video frames that exhibit signs of manipulation and classify which type of manipulation was applied. We train our model on a modified subset of the UCF101 Action Recognition Dataset [1] which we alter with a set of four manipulation types: object insertion, compression, frame blackout, and blurring. We evaluate the classification accuracy of our model by creating manipulation "blocks", or sets of consecutive manipulated video frames, varying block length, distance between blocks, and manipulation distribution within blocks. Experimental results demonstrate that our model achieves high (>90%) classification accuracy in the presence of increased frame class transitions.
A ROS based framework for Multi-floor navigation for unmanned ground robots

This paper presents use of a cost graph as a representation of a multi-floor building to enable the multi-floor autonomous navigation capability for a team of robot(s). A method for global path planning on this cost graph have been presented. A navigation stack provides a framework for building autonomous navigation capabilities. A navigation stack which enables use of the proposed approach for navigation in a multi-floor building and enables multi-robot operations has been detailed. The improvements provided by the proposed navigation stack over the existing ROS (Robot Operating System) navigation stack have been explained. A way to integrate multiple local path-execution nodes which can combine together to execute the planned global path has also been explained. The paper also demonstrates the reuse of existing ROS compliant source codes for implementation of the proposed navigation stack, thereby optimizing the use of proven and established technology. Further, the extensions to different components of the existing ROS navigation stack, definition of new ROS messages and action definitions, to enable interaction between the components of the stack has been explained. The paper concludes with a brief study on how the proposed stack can be used for multi-robot operations.
An encryption algorithm for grey-scale image based on bit-plane decomposition and diffuse representation

In this work, the authors construct a new algorithm to encrypt images of grey-scale type. After the decomposition of the image into 8 bit-planes, the key-image for the encryption and decryption algorithm are got by the representation of the 8 bit-planes in the new introduced basis. The decryption is done by subtraction between each encrypted image and key-image to obtain the 8 bit-planes from which we can restitute the original image without loss of information. The effectiveness of our proposed scheme is confirmed by some experimental results.
Adaptive Cloud Application Tuning with Enhanced Structural Bayesian Optimization

Bayesian optimization has been widely applied on the performance tuning for workloads such as: large web applications running on Java Virtual Machine (JVM), parallel databases such as Hive / HBase, and Hive on Spark. However, with the rapidly expanding search space of these complex and large applications on cloud, the evaluation phase takes inordinate amount of time, rendering Bayesian optimization ineffective. In this paper, we propose a novel Bayesian optimization based framework, called, Adaptive Cloud Application Optimization Framework (ACAOF) to efficiently and optimally tune the performance of cloud workloads via significantly pruning the search space. We conducted extensive evaluations on ACAOF to compare with non-optimized Bayesian optimization on multiple categories of cloud workloads. The results demonstrate that the ACAOF outperforms approximately by up to 218%. The comparison with other machine learning techniques such as Random Search, Neural Network, Genetic Algorithm and Hill Climb also shows the significant effectiveness of ACAOF.
A Multiple Feature Fusion Based Image Retrieval Algorithm

With the rapid development of information technology, how to quickly retrieve the target image from massive image data has become a research hotspot in the field of image processing. In this paper, we propose an image retrieval algorithm based on multiple feature fusion. Our idea is to improve existing image retrieval algorithms that are only done with a single feature extraction. First, we combine the global features and local features of the image by setting different weights, and then further retrieve based on the adaptive coefficients of the fusion features. Experimental results show that our retrieval speed and accuracy of the algorithm is better than single feature extraction, which improves the query efficiency. It has a good P-R curve and has good application value in the field of image retrieval.
On metric regularity of Reed–Muller codes

In this work we study metric properties of the well-known family of binary Reed–Muller codes. Let A be an arbitrary subset of the Boolean cube, and $${\widehat{A}}$$ be the metric complement of A—the set of all vectors of the Boolean cube at the maximal possible distance from A. If the metric complement of $${\widehat{A}}$$ coincides with A, then the set A is called a metrically regular set. The problem of investigating metrically regular sets appeared when studying bent functions, which have important applications in cryptography and coding theory and are also one of the earliest examples of a metrically regular set. In this work we describe metric complements and establish the metric regularity of the codes $${{\mathcal {R}}}{{\mathcal {M}}}(0,m)$$ and $${{\mathcal {R}}}{{\mathcal {M}}}(k,m)$$ for $$k \geqslant m-3$$ . Additionally, the metric regularity of the codes $${{\mathcal {R}}}{{\mathcal {M}}}(1,5)$$ and $${{\mathcal {R}}}{{\mathcal {M}}}(2,6)$$ is proved. Combined with previous results by Tokareva (Discret Math 312(3):666–670, 2012) concerning duality of affine and bent functions, this establishes the metric regularity of most Reed–Muller codes with known covering radius. It is conjectured that all Reed–Muller codes are metrically regular.
PV Output Forecasting by Deep Boltzmann Machines with SS-PPBSO

This paper proposes an efficient method for photovoltaic (PV) system output forecasting by Deep Boltzmann Machines (DBM) with Scatter Search-Predator-Prey Brain Storm Optimization (SS-PPBSO). DBM plays a key role to extract features of input variables while SS-PPBSO is a new evolutionary computation that combines PPBSO with Scatter Search. In recent years, as renewable energy, PV systems are positively introduced into power network in Japan so that power system operation becomes complicated due to the uncertainty. To overcome this challenge, it is required to forecast PV outputs that are influenced by weather conditions significantly. This paper proposes a new efficient PV output forecasting method with DBM that makes use of SS-PPBSO in learning. The effectiveness of the proposed method is demonstrated for real data of a PV system.
Data Set Synthesis Based on Known Correlations and Distributions for Expanded Social Graph Generation

Nowadays, data created through the usage of different services are most commonly not available to the average researcher. Security and privacy have become a top concern, which has further restricted access to certain real-life data, especially holding true for social networks. This is why synthetic data generators have become a very important area of research, particularly synthetic social graph generators. However, even today, such generators mostly create graphs that contain just the information whether two nodes are connected. Fortunately, there is an existing conceptual solution for an expanded social graph generator that aims to generate synthetic graphs containing multiple weighted edges between nodes, thus showing various types of relationships among those nodes, all based on known real-life data characteristics. One of its proposed steps is the generation of necessary data according to provided distributions and correlations. This paper focuses on the generation of such data by adapting an existing iterative algorithm for non-normal multivariate data simulation to generate synthetic data based on the publicly available distributions and correlations of Facebook interaction parameters. It is shown that the characteristics of the generated synthetic data are similar to the known characteristics of the real-life data, proving that the chosen algorithm, along with the accompanying alterations, can be used as one of the steps within the process of generating a synthetic expanded social graph.