Reviewerly/exHarmony huggingface dataset:
Cluster-Based Input Weight Initialization for Echo State Networks

Echo state networks (ESNs) are a special type of recurrent neural networks (RNNs), in which the input and recurrent connections are traditionally generated randomly, and only the output weights are trained.Despite the recent success of ESNs in various tasks of audio, image, and radar recognition, we postulate that a purely random initialization is not the ideal way of initializing ESNs.The aim of this work is to propose an unsupervised initialization of the input connections using the K -means algorithm on the training data.We show that for a large variety of datasets, this initialization performs equivalently or superior than a randomly initialized ESN while needing significantly less reservoir neurons.Furthermore, we discuss that this approach provides the opportunity to estimate a suitable size of the reservoir based on prior knowledge about the data.
The Case for Agile Methodologies against Traditional Ones in Financial Software Projects

Agile methodologies have emerged to challenge traditional ones and overcome their limitations. Increasingly, software development organizations are scaling agile practices in order to meet the requirements of the quickly changing and regularly developing business environment. The main objectives of this study are to investigate the main differences between the traditional project management methodologies and agile methodologies, as well as to identify some key advantages and challenges of applying agile development in a financial software environment. The findings clearly show that using Agile methodologies in a financial software development environment increases the efficiency and transparency of the development process, as well as the stakeholders’ satisfaction, but the project managers must know how to adapt different Agile frameworks to the given context of their organization.
Curating #AanaJaana [#ComingGoing]: gendered authorship in the ‘contact zone’ of Delhi’s digital and urban margins

This paper examines the curation of a month-long public exhibition titled #AanaJaana [#ComingGoing] in one of New Delhi’s busiest metro stations, as a form of self-authorship by young women from its digital and urban margins. #AanaJaana [#ComingGoing] is a metaphor for journeys, communications, connections, associations, interceptions, social networks and individual/collective behaviours, that is curated as women ‘see’ and ‘speak’ with/through their mobile phones. Using Marie Louise Pratt’s notion of ‘contact zone’, we examine #AanaJaana as a space of encounters that emerges by visually ‘composing-with’ as well as ‘learning-with’ the realities and constraints of space, technology and power. Based on self-authorship over a period of 6 months within a ‘safe space’ of a WhatsApp group of young women living in the urban margins, we draw attention to #AanaJaana as a set of crosscutting networks of power dynamics over women’s bodies across the home, mobile phone and the city. #AanaJaana refers to how young women in the margins negotiate the ‘freedoms’ of moving (aana) in online space with the ‘dangers’ of going out (jaana) into the city, or the restrictions of entering (aana) online space with the freedom of leaving (jaana) home. We argue first, that #AanaJaana is a space of confinement because of the infrastructural paralysis in the peripheries. Second that it is also at the same time translocally produced by referencing several textual, digital and material spaces of self-realisation. Finally, we argue that #AanaJaana is a space of intertextuality through encounters between emojis, shorthand, voice notes on the mobile phone, with parody and dark humour of their gendered experiences that can transform shame, humiliation and fear into reflection, resistance and agency. The paper concludes that as a polycentric practice, #AanaJaana offers an appropriate metaphor to expand the ‘contact zone’ in order to decolonise gendered knowledge and power across digital-analogue margins.
Changes in clinical trials of endocrine disorder and metabolism and nutrition disorder drugs in mainland China over 2010–2019

With the improvements in relevant policies, laws, and regulations regarding drug clinical trials in China, the quantity and quality of drug clinical trials have gradually improved, and the development prospects of drug clinical trials for endocrine disorders and metabolism and nutrition disorders are promising. Based on information from the clinical trials from the online drug clinical trial registration platform of the National Medical Products Administration, we aimed to review and evaluate the development of clinical trials of drugs for endocrine disorders and metabolism and nutrition disorders in mainland China from 2010 to 2019, as well as the trends over time. A total of 861 trials were carried out on 254 types of drugs for endocrine disorders and metabolism and nutrition disorders, among which 531 (61.67%) involved endocrine disorders, and 330 (38.33%) addressed metabolism and nutrition disorders. The annual number of clinical trials has been increasing gradually, with a significant increase in 2017. Among them, the proportion of clinical trials with Chinese epidemiological characteristics was relatively large (Wu, Annual Report on Development Health Management and Health Industry in China, 2018). The largest number of trials were for diabetes drugs (55.63%), followed by trials of drugs for hyperlipidemia (19.4%) and those for hyperuricemia (7.9%). It was found that the geographical area of the leading units also showed obvious unevenness according to the analysis of the test unit data. Based on the statistics and evaluation of the data, comprehensive information is provided to support the cooperation of global pharmaceutical R&D companies and research units in China and the development of international multicenter clinical trials in China. This work additionally provides clinical trial units with a self-evaluation of scientific research competitiveness and hospital development strategies. At the same time, it provides a reference with basic data for sponsors and stakeholders in these trials to determine their development strategy goals.
A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization

This paper proposes a phase angle-modulated bat algorithm (P-AMBA) for high-dimensional binary optimization. The idea was to reduce the optimization time by introducing angle modulation technology to reduce the optimization dimensions. Different from the original angle-modulated bat algorithm (AMBA), the control of the trigonometric generating function cosine wave is by introducing new parameters, thereby improving the perturbation ability of the function curve near the x-axis. P-AMBA can explore more 0/1 solutions, and it has advantages in optimizing convergence speed and global search capabilities. The numerical results of the 0–1 knapsack problem tests show that P-AMBA is superior to the contrast algorithms on optimization ability and optimization time. Finally, the experimental result of a compact dual-band planar monopole antenna design showed the effectiveness of P-AMBA in engineering applications.
Detecting Hate Speech using Deep Learning Techniques

Social networking sites saw a steep rise in terms of number of users in last few years.As a result of this, the interaction among the users also increased considerably.Along with these posting racial comments based on cast, race, gender, religion, etc. also increased.This propagation of negative messages is collectively known as hate speeches.Often these posts containing negative comments in social networking sites create law and order situations in the society, leading to loss of human life and properties.Detecting hate speech is one of the major challenges faced in recent time.In recent past, there have been a considerable amount of research going on the field of detection of hate speech in the social networking sites.Researchers in the fields of Natural Language Processing and Machine Learning have done considerable amount research in in this area.This paper uses a simple up sampling method to make the data balanced and implements deep learning models like Long Short Term Memory (LSTM) and Bi-directional Long Short Term Memory (Bi-LSTM) for improved accuracy in detecting hate speech in social networking sites.LSTM was found to have better accuracy that Bi-LSTM for the data set considered.LSTM also had better values for precision and F1 score.Bi-LSTM only for higher values for recall.
IoT System Based on parameter optimization of Deep Learning using Genetic Algorithm

Nowadays, more and more human activity recognition (HAR) tasks are being solved with deep learning techniques because it's high recognition rate.The architectural design of deep learning is a challenge because it has multiple parameters which effect on the result.In this work, we propose a novel method to enhance deep learning architecture by using genetic algorithm and adding new statistical features.Genetic algorithm is utilized as an enhancing method to get the optimal value parameters of deep learning.Also new statistical features are appended to the features that are extracted automatically from CNN technique.Because the spread of the internet and its significance in our life, we developed Internet of Things (IoT) system.Therefore, we evaluated the performance of the proposed method in its system and found satisfactory results.Moreover, the proposed method was trained on two benchmark datasets (WISDM and UCI) and tested on the dataset, which was collected from IoT system.The results showed that the proposed model improved the accuracy up to 93.8% and 86.1% for user-dependent and independent.
A White-Box Masking Scheme Resisting Computational and Algebraic Attacks

White-box cryptography attempts to protect cryptographic secrets in pure software implementations. Due to their high utility, white-box cryptosystems (WBC) are deployed by the industry even though the security of these constructions is not well defined. A major breakthrough in generic cryptanalysis of WBC was Differential Computation Analysis (DCA), which requires minimal knowledge of the underlying white-box protection and also thwarts many obfuscation methods. To avert DCA, classic masking countermeasures originally intended to protect against highly related side-channel attacks have been proposed for use in WBC. However, due to the controlled environment of WBCs, new algebraic attacks against classic masking schemes have quickly been found. These algebraic DCA attacks break all classic masking countermeasures efficiently, as they are independent of the masking order.In this work, we propose a novel generic masking scheme that can resist both DCA and algebraic DCA attacks. The proposed scheme extends the seminal work by Ishai et al. which is probing secure and thus resists DCA, to also resist algebraic attacks. To prove the security of our scheme, we demonstrate the connection between two main security notions in white-box cryptography: probing security and prediction security. Resistance of our masking scheme to DCA is proven for an arbitrary order of protection, using the well-known strong non-interference notion by Barthe et al. Our masking scheme also resists algebraic attacks, which we show concretely for first and second-order algebraic protection. Moreover, we present an extensive performance analysis and quantify the overhead of our scheme, for a proof-of-concept protection of an AES implementation.
A Similarity based K-Means Clustering Technique for Categorical Data in Data Mining Application

Clustering plays a major role in the data mining application, because it divides and groups the data effectively.In the pattern analysis, two major challenges occur in real-life applications that includes handling the categorical data and the availability of correctly labeled data.According to the characteristics of homogeneity, the clustering techniques are designed to group the unlabeled data.Some important issues such as high memory utilization, time consumption, overhead, computation complexity and less effective results are present in various existing algorithms of numerical data.Therefore, the research study implemented clustering techniques based on the similarity of categorical data.Simultaneously, the attributes of inter and intra-clusters' similarities are identified, and then the performance of proposed method is improved by integrating those similarities.The noises are also removed by performing the pre-processing techniques, so the similarity between noise-free elements are estimated.Once these similarities are identified, the insignificant attributes are removed and the relevant attributes are chosen from the preprocessed elements.The overhead is reduced by developing the Similarity-based K-means Clustering (SKC) approach for clustering the attributes that depends on divergence distance.The efficiency of SKC is tested in the experimental analysis by means of precision, f-measure, accuracy, error rate of clustering and recall.The results state that the developed study achieved 98.45% accuracy for the publicly available dataset when comparing with the existing techniques: variations of Particle Swarm Optimization (PSO) and semi-supervised clustering system.
ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain

Chemical patents contain rich coreference and bridging links, which are the target of this research.Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents.We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.
CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

Image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Hence, the goal here is to enhance the embedding capacity while preserving the visual quality of the stego-image. It is also intended to ensure that the scheme is resistant to steganalysis attacks. This paper proposes a compressed sensing image steganography (CSIS) scheme to achieve these goals. In CSIS, the cover image is sparsified block-wise, linear measurements are obtained, and then permissible measurements are selected. Next, the secret data is encrypted, and 2 bits of this encrypted data are embedded into each permissible measurement. For the reconstruction of the stego-image, ADMM and LASSO are used for the resultant optimization problem. Experiments are performed on several standard greyscale images and a colour image. Higher embedding capacity, 1.53 times more compared to the most recent scheme, is achieved. An average of 37.92 dB PSNR value, and average values close to 1 for both the mean SSIM index and the NCC coefficients are obtained, which is considered good. These metrics show that CSIS substantially outperforms existing similar steganography schemes.
Formation of Student Management Activities in the Higher Education Management System

The article describes the concept of educational management, the scientific work of scientists studying management problems, the principles of modern management, the organization of experimental work in the research process and the implementation of the results in practice.
Mapping Alteration Mineralogy in Eastern Tsogttsetsii, Mongolia, Based on the WorldView-3 and Field Shortwave-Infrared Spectroscopy Analyses

This study produces alteration mineral maps based on WorldView-3 (WV-3) data and field shortwave-infrared (SWIR) spectroscopy. It is supported by conventional analytical methods such as X-ray diffraction, X-ray fluorescence, and electron probe X-ray micro analyzer as an initial step for mineral exploration in eastern Tsogttsetsii, Mongolia, where access is limited. Distributions of advanced argillic minerals (alunite, dickite, and kaolinite), illite/smectite (illite, smectite, and mixed-layered illite-smectite), and ammonium minerals (buddingtonite and NH4-illite) were mapped using the decorrelation stretch, band math, and mixture-tuned-matched filter (MTMF) techniques. The accuracy assessment of the WV-3 MTMF map using field SWIR data showed good WV-3 SWIR data accuracy for spectrally predominant alteration minerals such as alunite, kaolinite, buddingtonite, and NH4-illite. The combination of WV-3 SWIR mineral mapping and a drone photogrammetric-derived digital elevation model contributed to an understanding of the structural development of the hydrothermal system through visualization of the topographic and spatial distribution of surface alteration minerals. Field SWIR spectroscopy provided further detailed information regarding alteration minerals such as chemical variations of alunite, crystallinity of kaolinite, and aluminum abundance of illite that was unavailable in WV-3 SWIR data. Combining WV-3 SWIR data and field SWIR spectroscopy with conventional exploration methods can narrow the selection between deposit models and facilitate mineral exploration.
Development of Separation Technique for the Production of Vanadium from Domestic Vanadium Titanomagnetite Ore

This study was carried out magnetic separation experiments for vanadium recovery and mineralogical characterization on domestic Vanadium Titanomagnetite (VTM).Additionally, a beneficiation process was developed to recover not only vanadium but also titanium.The head ore used in this study was 0.54% based on V2O5, and it was analyzed that more than 80% of magnetite and ilmenite were contained.As a result of the magnetic separation, it was found that the magnetic intensity of 0.05 T is effective in improving the grade of vanadium and 0.1 T is effective in improving the recovery.Based on this, a magnetic separation process was developed to recover concentrates with a grade of 1% and a recovery of 82.39% based on V2O5.Also, based on the previous experimental results, the development of a process capable of recovering titanium as well as vanadium was developed to simultaneously recover vanadium concentrates with V2O5 grade of 1.06% and 88.41% recovery, and titanium concentrates with TiO2 grade of 42.20% and 73.39% recovery.
An in-depth performance analysis of the oversampling techniques for high-class imbalanced dataset

Class imbalance occurs when the distribution of classes between the majority and the minority classes is not the same. The data on imbalanced classes may vary from mild to severe. The effect of high-class imbalance may affect the overall classification accuracy since the model is most likely to predict most of the data that fall within the majority class. Such a model will give biased results, and the performance predictions for the minority class often have no impact on the model. The use of the oversampling technique is one way to deal with high-class imbalance, but only a few are used to solve data imbalance. This study aims for an in-depth performance analysis of the oversampling techniques to address the high-class imbalance problem. The addition of the oversampling technique will balance each class’s data to provide unbiased evaluation results in modeling. We compared the performance of Random Oversampling (ROS), ADASYN, SMOTE, and Borderline-SMOTE techniques. All oversampling techniques will be combined with machine learning methods such as Random Forest, Logistic Regression, and k-Nearest Neighbor (KNN). The test results show that Random Forest with Borderline-SMOTE gives the best value with an accuracy value of 0.9997, 0.9474 precision, 0.8571 recall, 0.9000 F1-score, 0.9388 ROC-AUC, and 0.8581 PRAUC of the overall oversampling technique.
A Novel Pyramid Network with Feature Fusion and Disentanglement for Object Detection

In order to alleviate the scale variation problem in object detection, many feature pyramid networks are developed. In this paper, we rethink the issues existing in current methods and design a more effective module for feature fusion, called multiflow feature fusion module (MF3M). We first construct gate modules and multiple information flows in MF3M to avoid information redundancy and enhance the completeness and accuracy of information transfer between feature maps. Furtherore, in order to reduce the discrepancy of classification and regression in object detection, a modified deformable convolution which is termed task adaptive convolution (TaConv) is proposed in this study. Different offsets and masks are predicted to achieve the disentanglement of features for classification and regression in TaConv. By integrating the above two designs, we build a novel feature pyramid network with feature fusion and disentanglement (FFAD) which can mitigate the scale misalignment and task misalignment simultaneously. Experimental results show that FFAD can boost the performance in most models.
Security Analysis of Bit plane Level Image Encryption Schemes

A selective bit-plane encryption scheme was proposed for securing the transmission of image data in mobile environments with a claim that it provides a high security viz. the encryption of the four most significant bit-planes is sufficient for a high image data security. This paper presents the security analysis of the said encryption scheme and reports new important results. We perform the security analysis of the bit-level encryption by considering the normal images and their histogram equalised enhanced images. We consider different bit-plane aspects to analyse the security of the image encryption, and show that the encryption of the four most significant bit-planes is not adequate. The contents of the images can be obtained even when all the bit-planes except one least significant bit-plane are encrypted in the histogram equalised images as shown in the results. The bit-plane level security analysis seems very useful for the analysis of the bit-plane level image encryption schemes.