Reviewerly/exHarmony huggingface dataset:
Effect of Distance Metrics on Multi-label Classification

Multi-label framework is suitable to represent a lot of data in the real world. Various multi-label classifiers extract information from neighbors and utilize it for further process. However, instances in multi-label data are associated with multiple labels. Therefore, in addition to take into account feature similarity, considering labels also for finding neighbors of known instances may be useful to improve performance of multi-label classifier. This paper presents novel algorithm, called MLFLD-MAXP which is an extension of our algorithm MLFLD. It first searches neighbors using feature similarity as well as label dissimilarity and estimates probabilities using labeled data. It then predicts labels for unlabeled data. In case no label is predicted for an instance, a label with maximum probability is associated with such instances. Algorithms such as MLFLD-MAXP need to make use of certain distance metrics such as Euclidean and choice of distance metric may affect the performance of the classifier. This paper studies the impact of various distance metrics. Empirical evaluation shows improved performance of MLFLD-MAXP when compared with other multi-label classification algorithms. Use of Minkowski distance metric leads to superior performance of MLFLD-MAXP as compared to the use of other distance metrics.
Deep bidirectional neural networks for robust speech recognition under heavy background noise

The Deep Bidirectional LSTM (DBLSTM) with recurrent capability using the old conventional HMM modals performing very well and responding and providing a state-of-the-art technology with very good performance on the on various languages and speech database. Even with the current technologies, the outputs in these works relying on recurrent base neural network schemes. On account of explicit target strategies, it turns out to be extremely hard to coordinate the current day accessible discourse acknowledgment techniques due to the current huge jargon discourse acknowledgment frameworks. This research investigates the possibility of using the DBLSTM as speech acoustic speech miniature in which a calibre neural network scheme with HMM that is a hybrid structure. With few experimental findings we observed that the DBLSTM-HMM hybrid gives better and higher performance and provides better results on various NN schemes and all other previous works the previous works. It is also observed that it outclasses both conventional and deep network benchmarks on a most of the known existing algorithms. The improvements which are occurring in word error rate in the deep network algorithms is modest, considering the fact that a significant elevate in slice level precision. We propose an hybrid technique in which DBLSTM and HMM emerge to be exactly matched for tasks where acoustic modeling and speech prosodies predominates. To add more investigation, need to be conducted on large database and also need to see whether this is working on some specific languages.
Parameter Optimization of a Convolutional Neural Network Using Particle Swarm Optimization

In this paper, parameter optimization of Convolutional Neural Network architectures is proposed by applying the Particle Swarm Optimization algorithm, where the parameters that are optimized are: the number of layers, number of convolution filters, the filter size, and the batch size. The optimized architecture is applied in two sign language databases, the American Sign Language Alphabet and American Sign Language MNIST. This research aims to analyze the performance of the proposed architecture, focusing on obtaining a better recognition rate of the signs to achieve an increase in the recognition and computational performance of the Convolutional Neural Network architecture and subsequently propose tools for assisted communication for the deaf community. The achieved results indicate that the proposed method is effective since a recognition rate above 99% was obtained in both study cases, achieving a reduction in processing times and computational cost.
Financial Fraud Detection using Deep Support Vector Data Description

Nowadays, most financial transactions are virtual all over the world. The rapid usage of credit cards and online transnational applications raises fraudulent activities using these services. So, fraud detection is one of the challenging real-world problems. One of the main challenges in fraud detection is imbalanced datasets, where there are very few cases of fraud in an extremely large amount of non-fraud samples. Also, the behavior of fraud changes frequently making the learning process for the state-of-the-art machine learning binary classifiers complicated. As a result, in this paper, we propose an efficient framework for fraud detection. Our framework consists of a novel preprocessing and subsampling step, which is followed by applying deep support vector data description for fraud detection. We provide a trend analysis based on the size of the training, test datasets, and performance of the model using Area Under the Receiver Operating Characteristic Curve(ROC-AUC) and Average Precision(AP) as metrics. Finally, based on results, our approach outperforms SVM and Random Forest as the state-of-the-art binary classifiers in different scenarios. It achieves a remarkable performance in terms of AP and ROC-AUC equal to 90% and 93%(Best results), respectively.
On Channel Classification by Using DTMB Signal

This paper proposes machine learning algorithms to classify channels by using Digital Terrestrial Multimedia Broadcast (DTMB) signal. Channel state information (CSI) usually reflects the environment where a receiver is in. In this paper, the DTMB signal is adopted to extract the CSI features, including cross-correlation of the PN sequence in frame header and the baseband DTMB signal and the high order cumulants (HOCs) of the DTMB signal. Machine Learning algorithms, K-nearest neighbor (KNN), supported vector machine (SVM), Random Forest and Neural Network with one hidden layer, are employed respectively to classify and recognize ten typical broadcasting channel models. Simulations illustrate that the accuracy of the scheme based on the PN correlation features outweighs the HOCs features; and the adopted classification algorithms all show good performance in terms of accuracy; moreover, KNN has the lowest complexity compared to the other three. The accuracy of KNN based on PN correlation is over 95% even when the SNR is below -5dB if the correlation gains of two neighbour frame headers are combined.
Critical Pedagogy in Digital Era: Understanding the Importance of Arts &amp;amp; Humanities for Sustainable IT Development

The paper defines the critical pedagogy as the root cause of a cross-disciplinary method of delivery of teaching and research in Higher Education. The paper explicates the lexicographical meaning of the term ‘critical’ in the research context of Arts and Humanities, its conceptual and para-conceptual paradigms that urges us to intersect the specific areas of Information Technology and vice-versa. The premise of the paper is that the IT does not take over Arts and Humanities, rather Arts and Humanities helps the IT make a better society of human civilization where the machine and human co-exists; and understand (we used the word for the machine deliberately) the valued existence of each other. The development of IT without aesthetics of human existence results irrecoverable disaster. The history states so. The development of IT under the shadow of Arts and Humanities results enrichment of both the unending development of IT and the human society to a progressive and sustainable direction. Critical Pedagogy is defined in terms of the development of transdisciplinary education and research intersected with digital studies.
Retaliation in Bitcoin networks

Due to counterparty risks, some Bitcoin trading platforms allow users to rate the level of trust they have in others. We examine users’ feedback behavior on two Bitcoin trading platforms and provide statistically strong evidence that the feedback behavior of Bitcoin users is dependent on how they are rated themselves, that is, they retaliate. In addition, user’s reputation is strongly and positively associated with the scores they deliver, and there is a certain persistence in the scores a user gives to others. We find that peers deliver negative feedback relatively quickly to users with bad reputation. Moreover, well-reputed users withhold negative feedback longer and give positive feedback faster than users with bad reputation.
Using Graph Edit Distance for Noisy Subgraph Matching of Semantic Property Graphs

The subgraph matching problem is a fundamental problem in graph theory that is known to be NP-complete. In this study, performers were asked to develop algorithms to search for semantic property graphs that were subgraphs of a large knowledge graph. The templates provided contained structural information about the subgraphs and some attributes for each node and edge. There also exists a similarity measure between a set of attribute values that occurs on every node and edge. Algorithms performed well in the case where an exact match existed, but performers were also provided templates that had noise added such that there existed no match in the knowledge graph. Performers were asked to find the closest matches to those noisy subgraphs. To evaluate performance on this task, we developed a version of the graph edit distance algorithm to measure the cost of editing the template graph so that it is isomorphic in structure and attributes to the performer submission.
Tracking Unspecified Detected Targets By Propagating Instantly Created Self-Organizing Maps

We propose a novel method for the real time tracking of moving objects, where target features are not known in advance. Recognition models are instantly created when the target needs to be tracked based on events or triggers and is propagated to nearby areas. We introduce a technique to minimize the explosive increase in the number of recognition models and active servers used for tracking.
Bagua: A NFSR-Based Stream Cipher Constructed Following Confusion and Diffusion Principles

Confusion and diffusion are important design principles in block ciphers. The famous structures in block ciphers such as SPN, Feistel and Misty are proposed based on them and towards provable security against differential and linear cryptanalyses. There is few structure based on the two principles in stream ciphers except for Trivium. In this paper, we generalize the design ideas of Trivium to propose a new construction of Galois structure nonlinear feedback shift registers based on confusion and diffusion principles. As an application of this construction, a stream cipher named Bagua is proposed, which is a hardware-oriented primitive of 128-bit initialization vector and 128-bit or 256-bit key. It can be implemented in parallel up to 32 iterations at once, and the maximum throughout can be up to 8 Gbps. One can choose the parallel degree in implementation according to the requirement of throughput and hardware overhead in different application environments. Its resistances against differential and linear cryptanalyses are estimated theoretically and experimentally.
Fuzzy Identity-Based Ring Signature from Lattices

In this paper, a construction of a fuzzy identity-based ring signature scheme (LFIBRS) is proposed. Our LFIBRS combines the characteristics of both the fuzzy identity-based signature (FIBS) and the ring signature. On the one hand, a signature issued under an identity <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>ID</mtext> </math> can be verified by any identity <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mtext>ID</mtext> <mrow> <mo>′</mo> </mrow> </msup> </math> that is “close enough” to the identity <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mtext>ID</mtext> </math> . Since biometric identification is the well-known most popular and reliable identification method, our LFIBRS can be applied in such a situation whenever it is required for official audit or supervision that the signer’s real identity is needed to be authenticated. On the other hand, LFIBRS provides anonymity under the random oracle model. In addition, LFIBRS provides unforgeability under the small integer solution (SIS) lattice hardness assumption which can resist large-scale quantum computer attacks in the future.
Verifying cache architecture vulnerabilities using a formal security verification flow

Caches form a key feature of modern processors as they help to improve memory access timing by exploiting temporal and spatial data locality. However, the timing differences between cache hits and misses may lead to security vulnerabilities as they can be exploited by logical side channels. In this paper, we propose an innovative and coherent verification methodology to formally verifies cache designs against the potential existence of cache side channel vulnerabilities. Our methodology has been applied to three different cache models, i.e., a conventional cache, a static partitioned cache and a dynamic partitioned cache. The vulnerabilities of these caches have been evaluated using 28 different cache attack types. Results show that our proposed verification method can be applied successfully in verifying whether a cache is vulnerable to side channel attacks or not.
Traffic Sign Recognition Using Histogram of Oriented Gradients and Convolutional Neural Networks

This paper presents the design of the hybrid system that uses the HOG (Histogram of oriented gradients) with convolutional neural networks (CNN) for traffic sign recognition. The system implements two basic operations- feature extraction and classification. Using HOG and convolutional layers the features of images are extracted. The extracted images are used for classification. This is implemented using fully-connected neural networks. GTSRB (German Traffic Sign Recognition) is used to design the recognition system. During designing stochastic gradient descent algorithm is applied for the learning of CNN parameters. The accuracy of the CNN based traffic recognition system was obtained 99.87% for training data, 99.74% for validation data and 98.2% for test data. The obtained results demonstrate the applicability of the designed system in traffic sign recognition.
An Approach for Representing and Storing RDF Data in Multi-model Databases

The emergence of NoSQL multi-model databases, natively supporting scalable and unified storage and querying of various data models, presents new opportunities for storing and managing RDF data. In this paper, we propose an approach to store RDF data in multi-model databases. We identify various aspects of representing the RDF data structure into a multi-model data structure and discuss their advantages and disadvantages. Furthermore, we implement and evaluate the proposed approach in a prototype using ArangoDB—a popular multi-model database.
Sonicprint

Although fingerprint technology holds great promise for user authentication, commercial scanners face significant challenges in terms of security (e.g., fake finger) and adoptability (e.g., wearables). SonicPrint pushes the boundary of fingerprint biometrics beyond smartphones to any smart devices without the need for specialized hardware. To achieve this, it listens for fingerprintinduced sonic effect (FiSe) caused when a user swipes his/her fingertip on smart device surface. Compared to other biometrics including physiological patterns and passive sensing, SonicPrint is a low-cost, privacyoriented and secure approach to identify users across smart devices of unique form-factors.
Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials

Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a noncontiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models.
Examining Technology and Teaching Gaps in Russian Universities Amid Coronavirus Outbreak

The current study facilitates an evidence-based discussion on teaching during the period of an abrupt shift to virtual classrooms caused by coronavirus spread. There has been a massive investment to bring technology to universities; however, there are some significant gaps that must be addressed. The first is the availability of university online platforms and the second is the digitalization of the curriculum combined with teachers' confusion and anxiety around the ability to adjust to the new digital environment. The study is aimed to research the current situation at two Russian universities: RUDN (People's Friendship University of Russia) and PNRPU (Perm National Research Polytechnic University) and describe what changes have been implemented into teaching by the university administration and professors. The research questions include two groups: 1) Learning management system (LMS): Did the university offer a teaching platform to be used by all teachers? Or was it an individual choice of every teacher? 2) Curriculum: Did teachers have to modify the content of their course? In what way? Our study contributes several meaningful findings regarding understanding the advantages, disadvantages, and challenges in changing the education landscape.
Prediction of the number of Women Population in Medan City 2025 by using The Leslie Matrix

Abstract The Leslie matrix model is a model used to predict the number and rate of female population growth. This study aims to determine the number and rate of female population growth in the city of Medan in 2025 based on data on birth rates and life expectancy. The eigenvector is used to determine the number of female populations at each age interval, while the eigenvalues are used to determine the population growth rate. To determine the number of female populations from each age interval, a positive eigenvalue is sought. Based on positive eigenvalues, three cases occur, namely: 1) the population will tend to increase if; 2) the population will tend to decline if; 3) the population will tend to be stable if. Based on the research results, the eigenvalues were obtained λ = 1,3673 which means the number of the female population in the city ofMedan tends to increase
Misery and Economic Growth Nexus in Nigeria; Implications for Electrical Energy Management

Abstract At first glance, misery seems unquantifiable but has been established to be an aggregation of unemployment and inflation. Nigeria is in a pitiable condition as she is ranked the 6th most miserable country in the world. This study aims to ascertain the effect of economic growth on Misery in Nigeria, that is, to determine whether economic growth rate has strengthened or weakened the misery of Nigerians. This study adopts Autoregressive Distributed Lag (ARDL) model because it considers policy lags of economic phenomena and allows combined order of integrations. The study finds an inverse nexus between economic growth and misery. Hence, recommendations were made in form of measures to ensure the need for economic growth to increase at faster and higher pace to combat high misery levels in Nigeria.
An Algorithm of Data Skew in Spark Based on Partition

To solve the problem of data skew, many algorithms have been proposed at present. Due to different operating mechanisms, many advantages of hadoop-based algorithms cannot be fully realized in spark. However, most proposed algorithms are hadoop-based. Tang zhuo et al. proposed SKRSP, an adaptive partitioning method to deal with data skew in spark application. Compared with previous researches, this algorithm can more effectively alleviate the problems of data skew. Moreover, with the increase of data skew, the effect of this algorithm to deal with data skew is more and more significant. However, the research of this algorithm is based on the same hardware and software configuration of the nodes in the cluster. This paper presents a load balancing and key redistribution algorithm based on Spark (LBKRS) which optimizes the SKRSP algorithm from the point of view of load balancing. By monitoring the CPU utilization, memory utilization and other information of the calculation nodes, the LBKRS algorithm has a better effect on the data skew of different configuration nodes and is more adaptable to the actual production situation.
Multiparty quantum communication protocol based on quantum stabilizer codes with identity authentication

Abstract Quantum communication has been extensively studied and continuously developed due to its theoretical absolute security. In this work, a novel multiparty quantum communication protocol based on quantum stabilizer codes (QSC) is proposed. Firstly, a trusted third party provides quantum identity authentication (QIA) for users. Secondly, users prepare quantum multiparticle entangled states for transmitting secret information. Thirdly, the secret information is encoded by the QSC to resist channel noise. A part of the encoded qubits is selected to detect the security of the communication. In this protocol, QIA prevents active attacks such as impersonation attacks; the physical laws of quantum entangled state ensure information security; the theory of QSC corrects the flipping errors of qubits, and improves the communication efficiency. In addition, we prove the security of the protocol, and further verify the communication efficiency. Compared with existing protocols, this protocol has better performance on communication efficiency.