Reviewerly/exHarmony huggingface dataset:
Performance Prediction of Sparse Matrix Multiplication on a Distributed BigData Processing Environment

Sparse matrix multiplication (SPMM) is widely used for various machine learning algorithms. With advancements in big-data processing, the importance of distributed SPMM processing becomes important for handling large-scale datasets. We conducted thorough experiments using various distributed SPMM implementations and discovered considerable performance variations for distinct datasets and scenarios. To provide an optimal SPMM execution environment, we propose features that represent SPMM task characteristics. Using these features, we propose building a tree-based nonlinear gradient boosting (GB) regressor model that presents superb prediction accuracy across diverse distributed SPMM implementations and datasets.
Energy-aware and load balancing based dynamic migration strategy for virtual machine

Aiming at the current energy consumption problems of cloud data centers and the running performance of virtual machines, an energy-aware and load balancing based dynamic migration strategy (EALB) is proposed. This strategy selects the host to be migrated, the virtual machine to be migrated, and the target host and completes the migration of the virtual machine, reducing the number of running servers. This strategy uses the gray prediction algorithm to predict the load rate of the hosts. After the migration is completed, the load of each host can be balanced. Experiments prove that the EALB strategy can obviously reduce the energy consumption of the cloud data center and balance the load among the servers.
Blockchain in the Legal Profession: A Boon or a Bane?

Blockchain has created a lot of buzz in the past decade. Despite there being several organizations and corporation's that have their business processes centered solely around the working of Blockchain technology, a large majority of the masses are still not aware of its operability and functioning. Its applications, though they are many, are being adopted at a turtle's pace by parties. One reason this hesitation still exists amongst the people is due to the legal complications and uncertainty associated with this technology. This research paper will specifically focus on the manner in which Block chain will be affecting the Legal Industry in different parts of the world, ponders jurisdictional questions of the technology and addresses the initiative of a body called Global Block Chain Legal Consortium to integrate block chain technology into the legal realm.
Convergence Potential of IT and RTI Act 2005: A Case Study

With the emergence of the digital era, administrative systems depend heavily on ICT-based automation. Gradually, apathy and neglect by the administrative authorities toward data security violations also have come to be in vogue in the academics. This has even led to openly developed and openly used twin cybercriminal organizations (FGoT and JNTUHJAC) ( https://sites.google.com/view/pgfsrk ) in the higher educational institution, viz. JNTUH. Our research work highlights the maladaptive management of an educational institution, JNTUH, wherein massively organized, multiple cybercrimes at university level have prevailed through the cybercriminal website http://jntuhjac.com during 2011–2018. We submitted input data sheets to the RTI authorities, viz. PIOs of various government organizations (e.g., Lok Sabha Secretariat, Principal Secretary to MHRD, AICTE, CSIR, APPSC, Telangana Secretariat, Principal Secretary to the President of India) hinting/indicating/signaling about the unchecked, twin cybercriminal organizations in the JNTUH academic environment. We collected output data sheets of response, to our RTI applications, from various state and central governments of the cybercrime in the JNTUH. In view of noncompliance with RTI Act 2005 of some responses, we first appealed to the Telangana Secretariat and the Ministry of Home Affairs, New Delhi.
History of Mineral Exploration and Exploitation of the Egyptian Nubian Shield

The Nubian Shield rocks in Egypt pertain to the Precambrian only. Mesozoic and Cenozoic intrusives and extrusives are excluded. The age ranges between 1000 and 550 my, i.e., Neoproterozoic. The main target for the shield exploration is the Eastern Desert, the southern part of Sinai and sporadic outcrops in the southern part of the Western Desert. Human started to live in communities especially on the Nile River banks, and searched for certain commodities, the colored ones first followed by gold. Official expeditions were sent to the wild deserts guarded, as they believe, by their beloved deities. The record is fortunately inscribed on the walls to unravel the history of mineral exploration and exploitation during the ancient times. The Egyptian Nubian Shield (ENS) is not only that exposed to the surface but also the Precambrian rocks under the Phanerozoic cover. The nature of the hidden rocks is currently disclosed from the deep oil exploration wells which bottomed in the crystalline rocks. This term is used by the oil geologists for the subsurface igneous and metamorphic rocks. It is fortunate that in Egypt, the Precambrian rocks, act as reservoir rocks for oil in certain places. In the Gulf of Suez oil is seeping from fractured granitic rocks in Gebel El Zeit area. Suez Oil Company is tapping oil from subsurface fractured granitic rocks in Zeit Bay and Megawish Petroleum Company from fractured rhyolitic rocks, north of Hurghada. The nature of the hidden “basement rocks” is sporadically known. Nashfa area west of El Minya is a high granitic basement, encountered in depth to about 600 m. In the vicinity of Assiut, at about 3 km depth mylonitic rocks are encountered and in the subsurface of Matruh basin gneisses prevail. The subsurface data are hosted at the Egyptian Petroleum Organization. The nature of the hidden Precambrian rocks in Libya and Algeria based on the drilled oil wells (Schurmann, The Pre-Cambrian in North Africa, E.J, Brill, Leiden, 1974). These rocks were compared with those exposed in Egypt and Saudi Arabia. It is worthy to mention that basement rocks is a vague term, and in Egypt is considered as Precambrian rocks. This term worldwide is applied to any rocks of any age below the target area in exploration. It is advisable to divide the ENS into northern, central, and southern (Fig. 14.1). The northern includes the Red Sea hills north of Safaga-Qena stretch, and extends to include south Sinai and south Jordan. The central is down to latitude 24 N and the southern is down to latitude 22 N. Gold is rare or nonpresent in the northern part of the ENS, but common in the central and southern parts. Could be the absence or rare occurrence of ultramafic and ophiolitic rocks from the northern sector is the cause?
Algorithms of Posteriori Multi-objective Optimization for Robotic Gripper Design

In this paper, the posteriori methods, such as NSGA-II, MOGWO and MOPSO, are considered in solving multicriteria optimization problems. The main goal of these methods is to find set of non-dominated solutions or Pareto front using Pareto dominance. A kinematic description of the processed four-finger gripper prototype for picking tomatoes, its objective functions and constraint functions are presented. The main advantage of this prototype is that it uses the same driver to simultaneously control the movements of the fingers and the suction nozzle. Three optimization cases are considered: optimization of two objective functions, optimization of objective functions, and optimization of all objective functions simultaneously. To optimize the task of multi-objective functions, a normalized weighted objective function with weightage factors is used. The results of optimizing the kinematic gripper design using posteriori methods method performance measure are presented. Depending on the selected objective functions, we can choose a set of sizes of kinematic gripper elements.
Towards Robust Mission Execution via Temporal and Contingent Planning

In this work, we present a general approach to task planning based on the combination of temporal planning, contingent planning and run-time sensing. The strategy provides a solution for generating plans that can adapt during mission execution by reasoning about the data acquired by the sensory system. The approach detects actions that can change the initial plan and evaluates possible outcomes. We demonstrate the effectiveness of our approach on two different experiments in a maritime environment where the robots have to inspect the state of a valve and execute actions based on the online sensor information.
Supervoxel Segmentation using Spatio-Temporal Lazy Random Walks

Superpixel segmentation has been proved its effectiveness as a preprocessing step for many applications. Similarly, the over-segmentation with temporal consistency of a video can be useful for quite a few researches. In this paper, a novel supervoxel segmentation algorithm based on lazy random walk is proposed. In the proposed framework, a superpixel segmentation is first applied to each frame and the produced superpixels are called special-pixels. Then, we propose a generalized spatio-temporal adjacency matrix, which keeps the information of special-pixels with neighboring relationship, for the generation of supervoxels using lazy random walk approach. In addition, the center relocation and the splitting strategy of supervoxels are proposed to improve the quality of the supervoxels.
Development of an Audio Steganography System Using Discrete Cosine Transform and Spread Spectrum Techniques

In today’s electronic society, security of information is an issue of major concern especially in the communication domain. Indeed, there is a very serious need for schemes that guarantee security of information exchanged between entities, over public networks. Several techniques such as cryptography and/or steganography have been proposed in existing literature. Audio steganography has certain advantages over conventional steganography. Existing audio steganography systems suffer from limitations that includes, their inability to embed information in multiple audio file formats, high distortion rate and low level of robustness of their resultant stego-files. To overcome these limitations, this current research focused on the development of an efficient and robust audio steganography system for the security of information whether in store or on transit across the Internet. This is achieved by first encrypting a given secret information, compressing the resultant cipher-text, then embedding the compressed file in a suitable cover (audio file) using frequency hopping spread spectrum technique, to yield as output, an audio stego file which is not distinguishable from the original audio cover file. To allow for the embedding of the secret compressed file, the selected audio cover file is first broken into frames using one dimensional Discrete Cosine Transform (DCT). Results of performance evaluation of this developed system shows that it has very low level of distortion as revealed by the Signal to Noise Ratio (SNR). The compression ratio obtained is also equal to one (1), which shows that the cover audio file is identical to the resultant stego-file.
Object Tracking Algorithm of UAV Based on Fast Kernel Correlation Filter

UAV (Unmanned Aerial Vehicle) is serving as a major platform for developing and testing the artificial intelligence technology. However, how to develop the technology of UAV visual object tracking encounters many numerous difficulties given that the further development of UAV technology is based on it. Firstly, based on the kernel correlation filtering algorithm, this paper uses multiple features to train the regressor respectively, and then fuses the feature map adaptively. Secondly, the template update strategy is changed according to the peak to sideline ratio of the feature map and the similarity between the templates. Next, through the verification on OTB100, the algorithm proposed in this paper is greatly improved compared with others seeing that tracking speed exceeds 30 fps, meeting the real-time requirements. Last but not least, the simulation system of UAV object tracking is built under the ROS (Robot Operating System) platform, and further verified the feasibility of the algorithm.
Magic: The Gathering is Turing Complete

Magic: The Gathering a popular and famously complicated trading card game about magical combat. In this paper we show that optimal play in real-world Magic at least as hard as the Halting Problem. This provides a positive answer to the question is there a real-world game where perfect play undecidable under the rules in which it typically played?, a question that has been open for a decade [David Auger and Oliver Teytaud, 2012; Erik D. Demaine and Robert A. Hearn, 2009]. To do this, we present a methodology for embedding an arbitrary Turing machine into a game of Magic such that the first player guaranteed to win the game if and only if the Turing machine halts. Our result applies to how real Magic played, can be achieved using standard-size tournament-legal decks, and does not rely on stochasticity or hidden information. Our result also highly unusual in that all moves of both players are forced in the construction. This shows that even recognising who will win a game in which neither player has a non-trivial decision to make for the rest of the game undecidable. We conclude with a discussion of the implications for a unified computational theory of games and remarks about the playability of such a board in a tournament setting.
Motion Pattern Recognition from Crowded Video

During the previous decades, in the era of video surveillance, an immense consideration had been given to crowd video control and crowd management. Among the undertakings for video surveillance, retrieval of crowd video is a very challenging task. Motion Pattern Recognition from Crowded Video generally faces the problem of occlusion, low resolution, cluttering sometimes object social interaction is not present then find the abnormal behavior is quite tough. We handle these pattern recognition problems by introducing the social force model approach in which we calculate the social force between pedestrians presented in the crowd which helps in recognizing the behavior of the crowd in an abnormal situation. In our approach, we find some well-known types of motion pattern these are: lane, ring, fountainhead, and bottleneck [1]. In this paper, our proposed approach focused on some steps these are: preprocessing of frames, calculate optical flow, calculate the social force model among pedestrians, Clustering after Jacobian matrix calculation and in the last step behavior recognition with the help of KNN classifier.
Synchronized Forward-Backward Transformer for End-to-End Speech Recognition

Recently, various approaches utilize transformer networks, which apply a new concept of self-attention, in end-to-end speech recognition. These approaches mainly focus on the self-attention mechanism to improve the performance of transformer models. In our work, we demonstrate the benefit of adding a second transformer network during the training phase, which is optimized on time-reversed target labels. This new transformer receives a future context, which is usually not available for standard transformer networks. We have access to future context information, which we integrate into the standard transformer network by proposing two novel synchronization terms. Since we only require the newly added transformer network during training, we are not changing the complexity of the final network and only adding training time. We evaluate our approach on the publicly available dataset TEDLIUMv2, where we achieve relative improvements of 9.8% for the dev and 6.5% on the test set, respectively, if we employ synchronization terms with euclidean metrics.
An Anonymization Approach for Dynamic Dataset with Multiple Sensitive Attributes

In recent days, the personal health information is collected in different fields. When the data is shared for different reasons, it poses a major danger to the field of health care. A number of anonymization methods are implemented to maintain person’s privacy. The existing methods of anonymization support only single sensitive and low-dimensional data. In our recent experiment, a method of anonymization is expected in order to anonymize high-dimensional data with multiple sensitive attributes. In line with the concept of k-anonymity and l-diversity, it combines anatomization and improved slicing strategy. The experimental findings show that it is limited to the static discharge of information only. In dynamic situations, the current technique may produce poor quality or high data loss. Hence, in this proposed approach, an anonymization model is designed in such a way to anonymize continuously growing dataset while assuring high utility.
An r2rml-based Approach to Map Dengue Patient Database to Ontology

Integrating Dengue patients' data in semantic web from “deep web” facilitates interoperability amongst healthcare systems. Mapping dengue patients' data that exist in relational databases with the existing machine-readable and interoperable Dengue knowledge-base is crucial towards providing interoperability through linked data network amongst Electronic Health Record(EHR) systems. An RDB to RDF mapping language(R2RML) based approach that maps dengue patients' information maintained in legacy system(Relational database) to an existing dengue patients' ontology is proposed in this paper. Mapping file is generated using R2RML for the database which is subsequently customized and mapped with the existing ontology. Finally RDF model is generated using R2RML processor based on the customized ontology and Simple Protocol and RDF Query Language (SPARQL) query is also executed that demonstrates ontology mapped patients triples.
Answer Selection Based on Mixed Embedding and Composite Features

With the rapid growth of text information, intelligent question answering has gained more attention than ever. In this paper, we focus on answer selection, one kind of question answering tasks. In this field, deep neural networks and attention mechanism have brought encouraging results. To improve the performance further, we investigate mixed embedding (word embedding and character embedding) representation for sentences to encode rich meaning. At the same time, we introduce a convolutional neural network (CNN) to compensate the loss of the max pooling layer in our attention based bidirectional Long Short-Term Memory (biLSTM) model. CNN features and the features from max pooling form final composite features, which are employed to select correct answers. Experimental results show that we can obviously improve the Mean Reciprocal Rank (MRR) performance by 6.0% with the help of mixed embedding and composite features. The MRR and ACC@1 score are 79.63% and 69.60% respectively.
Analysing the interrelationship between online reviews and sales: the role of review length and sentiment index in electronic markets

With the advent of electronic commerce, online customer reviews (OCRs) have become a significant source of information related to a product/service. Nowadays, lot of research is being carried out to understand the role played by OCRs on the buying intentions and product evaluation of the prospective customers. In this paper, we use log linear regression models to include the effects of product price, review length, review volume and sentiments (positive or negative words) on the sales of a product. Using Amazon.com review dataset, we show that in addition to the price, the customers are influenced by review volume, length, star rating and sentiment of reviews' text. We have also discussed the managerial as well as theoretical implication of the regression-based sales modelling integrated with information processing technique of text mining and sentiment analysis.
Multi-level Encryption Based on Fractional Fourier Transform, Double Random Phase Encoding Combined with Chaos, and Arnold Transform

A novel scheme for colour image encryption using fractional Fourier transform, random phase encoding, and chaotic map combined with Arnold transform is proposed using software MATLAB R2016a. We use the logistic chaotic map for diffusion. Scrambling along each layer is processed with replacement matrices by chaotic system and Arnold transform. The three scrambled images are converted into one-encrypted image by using double random phases. Proposed method provides multi-layer security of the colour image in both intermediate (fractional) and time domains. Simulation result verifies its efficiency and superiority in comparison to the other related schemes. The various analysis results display that the keys used are highly sensitive to their values, which is the indication to the high level security of the algorithm. Moreover, additional keys used in the proposed scheme provide more flexibility. While, statistical analysis result verifies its robustness against the statistical attacks.
Optimizing Picnic for Limited Memory Resources

Picnic is a post-quantum digital signature scheme, where the security is based on the difficulty of inverting a symmetric block cipher and zero-knowledge proofs. However, generating a Picnic signature to a specific message requires up to 300 kB content depending RAM. As the memory of an IoT device is limited this can lead to issues at the implementation. Our target is bringing post-quantum cryptography to IoT systems. We propose three structural adjustments of the Picnic algorithm to reduce the memory usage. Two adjustments are compatible with the reference implementation, one of them breaks backward compatibility. We show analytically that the content depending memory for generating a signature can be decreased to under 10 kB.With these adjustments, Picnic becomes suitable for IoT devices with little RAM. Since our approach also aims at easier parallelization, a speed-up depending on the number of instances is possible.
On the Prospects of Optimization Theory

the situation resulted from the solution of Shannon problem by optimization theory of error-correcting coding in the applied studies of coding theory is analyzed. Unresolved problems of classical (algebraic) theory are discussed. The results for main clusters of decoding algorithms built on the basis of Optimization Theory (OT) are considered. Further tasks for OT development are given, the methods for their solution are offered. Broad possibilities shown by new versions of Viterbi algorithm are mentioned. Advantages of main OT paradigms including the advantages of symbolic codes and divergent coding are shown. A special group of decoding algorithms with direct metrics control (DDMC) is singled out. The main ways to develop coding theory for the coming years and for the future are listed.
Klasifikasi Sampah Daur Ulang Menggunakan Support Vector Machine Dengan Fitur Local Binary Pattern

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.