Reviewerly/exHarmony huggingface dataset:
DenLAC: Density Levels Aggregation Clustering – A Flexible Clustering Method –

This paper introduces DenLAC (Density Levels Aggregation Clustering), an adaptable clustering algorithm which achieves high accuracy independent of the input's shape and distribution. While most clustering algorithms are specialized on particular input types, DenLAC obtains correct results for spherical, elongated and different density clusters. We also incorporate a simple procedure for outlier identification and displacement. Our method relies on defining clusters as density intervals comprised of connected components which we call density bins, through assembling several popular notions in data mining and statistics such as Kernel Density Estimation, the density attraction and density levels theoretical concepts. To build the final clusters, we extract the connected components from each density bin and we merge adjacent connected components using a slightly modified agglomerative clustering algorithm.
Actor Spatiotemporal Relation Networks for Group Activity Recognition

The existing video-based group activity recognition methods do not full use spatiotemporal information, and cannot effectively improve the accuracy of group recognition. This paper proposes an effective group activity recognition model based on Actor Spatial-Temporal Relation Networks (ASRN) to capture potential spatiotemporal features in an end-to-end manner. First, we propose an SRM to get the feature correlation between feature nodes from the temporal dimension and the spatial dimension. Second, Personal Spatiotemporal Feature Module (PSFM) and a Multi-actors Relation Module (MRM) are designed using SRM to extract actor-level spatiotemporal semantic information and the relation features between actors. We conducted experiments on two datasets: volleyball dataset and collective activity dataset. The results on these two datasets show the superiority of our method.
Reliability check via weight similarity in privacy-preserving multi-party machine learning

Multi-party machine learning is a paradigm in which multiple participants collaboratively train a machine learning model to achieve a common learning objective without sharing their privately owned data. The paradigm has recently received a lot of attention from the research community aimed at addressing its associated privacy concerns. In this work, we focus on addressing the concerns of data privacy, model privacy, and data quality associated with privacy-preserving multi-party machine learning, i.e., we present a scheme for privacy-preserving collaborative learning that checks the participants' data quality while guaranteeing data and model privacy. In particular, we propose a novel metric called weight similarity that is securely computed and used to check whether a participant can be categorized as a reliable participant (holds good quality data) or not. The problems of model and data privacy are tackled by integrating homomorphic encryption in our scheme and uploading encrypted weights, which prevent leakages to the server and malicious participants, respectively. The analytical and experimental evaluations of our scheme demonstrate that it is accurate and ensures data and model privacy.
Quantum process tomography of a magneto-optic transformation

We characterize a magneto-optic transformation in the Faraday modality via quantum process tomography (QPT) with quantum light comprising of antibunched single photons generated through heralded spontaneous parametric downconversion. We derive a general process matrix for a unitary transformation which effectively describes the action of the magneto-optic element. This matrix quantifies the polarization attributes of the single photon as it traverses through the magneto-optic element. The magnitude of rotation and ellipticity are extracted through a maximum likelihood based optimization of the process matrix. Measurements are performed for a wide range of temperatures (8–100 K) and applied magnetic fields. The estimated rotation angles are curve fit to determine the Curie-Weiss constant for a terbium gallium garnet crystal. The extracted angles are also utilized to map the rotations on the surface of Bloch sphere.
PDS<sup>2</sup>: A user-centered decentralized marketplace for privacy preserving data processing

We envision PDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a decentralized data marketplace in which consumers submit their tasks to be run within the platform, on the data of willing providers. The goal of PDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is to ensure that users maintain full control on their data and do not compromise their privacy, while being rewarded for the value that their data generates. In order to achieve this, our marketplace architecture employs blockchain technology, privacy-preserving computation and decentralized machine learning.We then compare different potential solutions and identify the Ethereum blockchain, trusted execution environments and gossip learning as the most suitable for the implementation of PDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We also discuss the main open challenges that are left to tackle and possible directions for future work.
DDFPN: Context enhanced network for object detection

Context modeling presents a pioneering approach for confusing object detection. Although FPN has provided features for multi-scale objects, the feature captures limited feature for spatial context and a little feature for semantic context. In this work, we exploit an end-to-end Dilated and Deformable Feature Pyramid Network, namely DDFPN, to jointly extract spatial and semantic context. For the spatial context, we present Dilated and Deformable Convolution (DDC) to generate a more flexible receptive field than the conventional convolution of FPN. We design a Multi-scale DDC module to learn features for the various deformable objects. For the semantic context, we notice semantic context can be extracted from both features and predictions, and we design two modules to estimate two context relationships from them. The Cross Feature Correlation (CFC) module can estimate the contextual attention from other features. The Co-occurrence Inference (CI) module can learn the co-occurrence features from other predictions. Our network can be applied to various baselines of the FPN family and has similar FLOPs, parameters, and inference speed with these baselines. On MSCOCO minival and test-dev datasets, experiments show that our DDFPN is consistently better than various baselines, including RetinaNet, Faster R-CNN, Mask R-CNN, and Cascade R-CNN. Ablation exemplars show that our contexts are complementary to detect various confusing objects.
A New Ensemble Model for Phishing Detection Based on Hybrid Cumulative Feature Selection

A New Ensemble Model for Phishing Detection Based on Hybrid Cumulative Feature Selection(PDCFS) proposes a model that partitions the main dataset into n partitions based on the features available in the dataset. This process is done by feeding the dataset into multiple feature selection methods: Chi-Square, Gain Ratio, Information Gain, Pearson Correlation Coefficient, and Principal Components Analysis, and arranging the dataset into n datasets taking top-n features including the class label, given by the filter method, and discarding the remaining ones each time. Then each partitions of the dataset are used for training and testing using 5-fold cross validation, applying a number of classifiers: Support Vector Machine, Naïve Bayes, C4.5, Random Forest, JRip, PART, and k-Nearest Neighbors. In the next, the results are voted to get the best possible result. Majority voting is applied on both reduced feature subsets gained through feature selection steps, and full feature set to draw a comparison between the feature sets followed. The overall speculation suggests that Random Forest with reduced feature set of 32 tops the result chart with and accuracy of 98.36 %, while proposed PDCFS scores 98.24 % of accuracy. PDCFS also demonstrates a comparative performance when compared with other hybrid models.
A Comparison between AttnGAN and DF GAN: Text to Image Synthesis

Nowadays conversion from text to high resolution image is a challenging task due to its wide variety of application area. For text to image conversion almost all systems use Generative Adversarial Networks as the basic part of the system and GAN guarantees semantic consistency between the text input and the generated image output. In this paper we are comparing two algorithms that is used for generating image from text. The first algorithm is the AttnGAN and the second one is the DF-GAN. AttnGAN builds on top of StackGAN by using attention network which allows it to capture word level information along with the broader sentence level information. The second algorithm is the DF-GAN, which uses single generator and discriminator model to synthesize high resolution images and also uses Matching-Aware Gradient Penalty (MA-GP) to get real images with real description. The model contains a Deep text-image Fusion Block (DFBlock) to generate image features from text. Both algorithms work efficiently for image generation from text but DF-GAN generates the perfect output than AttnGAN. The AttnGAN always focus on the textual part to generate output image but DF-GAN also focuses on background of image.
Inferring Association Between Alcohol Addiction and Defendant's Emotion Based on Sound at Court

Alcohol addiction can lead to health and social problems. It can also affect people's emotions. Emotion plays a key role in human communications. It is important to recognize the people's emotions at the court and infer the association between the people's emotions and the alcohol addiction. However, it is challenging to recognize people's emotions efficiently in the courtroom. Furthermore, to the best of our knowledge, no existing work is about the association between alcohol addiction and people's emotions at court. In this paper, we propose a deep learning framework for predicting people's emotions based on sound perception, named ResCNN-SER. The proposed model combines several neural network-based components to extract the features of the speech signals and predict the emotions. The evaluation shows that the proposed model performs better than existing methods. By applying ResCNN-SER for emotion recognition based on people's voices at court, we infer the association between alcohol addiction and the defendant's emotion at court. Based on the sound source data from 54 trial records, we found that the defendants with alcohol addiction tend to get angry or fearful more easily at court comparing with defendants without alcohol addiction.
Comparison Between Random Forest and Recurrent Neural Network for Photovoltaic Power Forecasting

Objectives : Photovoltaic power generation which significantly depends on meteorological conditions is intermittent and unstable. Therefore, accurate forecasting of photovoltaic power generation is a challenging task. In this research, random forest (RF), recurrent neural network (RNN), long short term memory (LSTM), and gated recurrent unit (GRU) are proposed and we will find an efficient model for forecasting photovoltaic power generation of photovoltaic power plants.Methods : We used photovoltaic power generation data from photovoltaic power plants at Gamcheonhang-ro, Saha-gu, Busan, and meteorological data from Busan Regional Meteorological Administration. We used solar irradiance, temperature, atmospheric pressure, humidity, wind speed, wind direction, duration of sunshine, and cloud amount as input variables. By applying the trial and error method, we optimized hyperparameters such as estimators in RF, and number of hidden layers, number of nodes, epochs, and validation split in RNN, LSTM, and GRU. We compared proposed models by evaluation indexes such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE).Results and Discussion : The best RF at 1,000 of number of decision tree achieved test R2=0.865, test RMSE=16.013, and test MAE=9.656. The best choice of RNN was 6 hidden layers and the number of nodes in each layer was 90. We set the epochs at 450. RNN achieved test R2=0.942, test RMSE=10.530, and test MAE=6.390. To find the best result of LSTM, we used 3 hidden layers, and the number of nodes was 600. The epochs were set to 200. LSTM achieved test R2=0.944, test RMSE=10.29, and test MAE=6.360. GRU was set to 3 hidden layer and the number of nodes was 450. The epochs were set to 500. GRU achieved test R2=0.945, test RMSE=10.189, and test MAE=5.968.Conclusions : We found RNN, LSTM, and GRU performed better than RF, and GRU model showed the best performance. Therefore, GRU is the most efficient model to predict photovoltaic power generation in Busan, Korea.
Image Caption Generation in Telugu

Numerous methods are available to describe images by words and image captioning is a well-examined and researched topic. Further, the subject was much studied to work with English. Our model functions to generate descriptive explanations for general images in Telugu using Deep Learning techniques. Trained on images having Telugu captions, the features of an image are initially extracted using InceptionV3, a pre-trained Convolutional Neural Network model whereas the syntactic and semantic features are processed by using pre-trained fastText word embeddings of Telugu on a Recurrent Neural Network of Long short-term memory architecture. This concept can spread the benefit of well-developed models to native Telugu speakers.
Density Weighted Diversity Based Query Strategy for Active Learning

Deep learning has made remarkable achievements in various domains. Active learning, which aims to reduce the budget for training a machine-learning model, is especially useful for the Deep learning tasks with the demand of a large number of labeled samples. Unfortunately, our empirical study finds that many of the active learning heuristics are not effective when applied to Deep learning models in batch settings. To tackle these limitations, we propose a density weighted diversity based query strategy (DWDS), which makes use of the geometry of the samples. Within a limited labeling budget, DWDS enhances model performance by querying labels for the new training samples with the maximum informativeness and representativeness. Furthermore, we propose a beam-search based method to obtain a good approximation to the optimum of such samples. Our experiments show that DWDS outperforms existing algorithms in Deep learning tasks.
Static Image Action Recognition with Hallucinated Fine-Grained Motion Information

Static image action recognition is a challenging task due to the lack of motion information in a static image. Some previous works have attempted to hallucinate the motion information in a static image using a generator learnt from freely available unlabeled videos. However, their hallucinated motion information is either low-level or coarse-grained, which may contain lots of noise or lose motion details. In contrast, we propose to hallucinate fine-grained high-level motion information, which is more robust and detail-preserving. Specifically, we hallucinate motion feature map which encodes the motion details of human body parts. We also hallucinate motion attention map to focus on motion-related regions. Our hallucinated motion information can greatly facilitate static image action recognition, which is confirmed by the experiments on two static action image datasets and two video datasets.
Multi-Bit Blinding: A Countermeasure for RSA Against Side Channel Attacks

Asymmetric algorithms such as RSA are considered secure from an algorithmic point of view, yet their implementations are typically vulnerable as they are used by attackers to comprise the secret key. Many countermeasures have been proposed to thwart these attacks. However, they are typically broken as the key can be easily compromised when attackers succeed figuring out which part of the traces belong to the square and multiply operations. In this paper, a new countermeasure is proposed against side channel attacks, referred to as multi-bit blinding. The proposed method provides a constant execution behavior regardless of the key value without additional cost (i.e., dummy/extra operations). It realizes this by considering multiple bits of the key (i.e., two in this paper) simultaneously and always perform the same operations on them independent of the two-bit value. This makes attacks much harder as the attacker cannot retrieve the key simply by identifying the operations. Instead, the attackers need to guess the correct values of the operations as well. As a case study, the security of an RSA algorithm implementation based on the proposed method is evaluated. Our experimental results show that the new method is secure against profiled and non-profiled side channel attacks with less overhead than currently published countermeasures.
Healthcare Domain in IoT with Blockchain Based Security- A Researcher's Perspectives

The impact of Internet of Things (IoT) is most predominant and leading over the world. IoT is used to gather information/data from various sensors and other smart devices. The beneficiary of using technology of IoT can exchange information and enable to communicate with smart devices among themselves. In healthcare environment, IoT brings more convenient to doctors, nurses and patients. It can be more efficient for monitoring the illness patients and diagnosing very effective with reducing the cost. There are various security breaches and malignant attacks in healthcare system which are extremely vulnerable such as forgery, privacy leakage etc. Therefore these challenges and issues in security based on authentication and interoperability which bring to deteriorate. Recently, one of the key development in security is created by Blockchain (BC) technology which can exchange the data in secure manner. In many existing studies can be improved by enabling BC technology in the healthcare system. The main scope of BC technology has to convey effectively exchanging information between patient and other medical service parties based on some of the advantages such as authentication, immutability, decentralized storage, interoperability, distributed ledger, trustworthy and provides opportunity in a reliable manner and increase in productive. This paper presents challenges and problems for BC based IoT healthcare and provide with the security requirements of such domains due to traditional security measures. The aim of this paper is simply investigate how Blockchain will improve the healthcare environment within the IoT context. It also amalgamates the potential of blockchain technology as a promising security measure, highlights potential challenges in the healthcare domain, and provides an analysis of different blockchain based security solutions.
Personal Vocabulary Recommendation to Support Real Life Needs

The vocabulary taught in language classes or through digital language learning tools is disconnected from the real-life needs of many language learners. Immigrants, refugees, students abroad learn a language to navigate through their daily lives and often need words that are missing from their curricula they study. Today's language learners rely heavily on digital translators and dictionaries, creating a database of words they need in their everyday life. The availability of this data could allow personal vocabulary suggestions that meet real-life needs. To show the unsuitability of commonly provided vocabulary lists, we compare them to the vocabulary needed by 37 Syrian refugees living in Lebanon and Germany. We show that the vocabulary provided by the Cambridge English List and Duolingo has low usefulness and low efficiency and discuss future directions for personal vocabulary recommendations.
A tile-fusion method for accelerating Winograd convolutions

Compared with fast convolution methods such as im2col and the fast Fourier transform, Winograd-based convolution, which has been widely applied to accelerate convolutional neural networks (CNNs), can provide high performance with smaller filters. Although there are several reported studies on the algorithmic optimization of CNNs, most of them are targeted at hardware architectures. The existing implementations of the Winograd method perform well below what one would expect, due to the fact that the tile size of Winograd-based convolution is usually empirical and the features of each convolution layer are ignored. This study aims to fill this gap and focuses on the efficient implementation of Winograd-based convolution in the CNN model. Specifically, we discuss the causes of poor performance, calculate the coefficient of computation complexity model and demonstrate a speedup in the inference process using an elaborate tile-fusion method, which derives the optimal tile size for each convolution layer in a CNN model. Compared with the representative existing implementations of CuDNN with a 4 × 4 tile, Arm Compute Library with a 6 × 6 tile, and NNPACK with an 8 × 8 tile, the results show significant performance improvements on of up to 1.89 × , 1.29 × and 1.17 × , respectively.
An Approach for Document Clustering Using Semantic Similarity and Whale Optimization

Document ontology matching is one of the fastest-growing fields in the world of Semantic Web Technologies. Clustering documents based on their contents is a difficult task due to the under-optimized algorithms. In the proposed method, TF-IDF is used to select the features in order to analyse the documents and the word occurrences in the document. Multiple variations of TF-IDF are used as feature selection methods to analyse the best method clubbed with other measuring techniques and optimization algorithms to devise a high precision and accurate algorithm for Discovering Patterns in the documents. The Dataset used in the proposed method is CHIC heritage dataset in the English language which contains over 1000 documents. WebPMI and KL divergence is used as a measuring technique to measure semantic similarity between two queries. At last Whale Optimization Algorithm (WOA) is used to optimize the whole framework and produce high accuracy results.
Enhancing data quality to mine credible patterns

The importance of big data is widely accepted in various fields. Organisations spend a lot of money to collect, process and mine the data to identify patterns. These patterns facilitate their future decision-making process to improve the organisational performance and profitability. However, among discovered patterns, there are some meaningless and misleading patterns which restrict the effectiveness of decision-making process. The presence of data discrepancies, noise and outliers also impacts the quality of discovered patterns and leads towards missing strategic goals and objectives. Quality inception of these discovered patterns is vital before utilising them in making predictions, decision-making process or strategic planning. Mining useful and credible patterns over social media is a challenging task. Often, people spread targeted content for character assassination or defamation of brands. Recently, some studies have evaluated the credibility of information over social media based on users’ surveys, experts’ judgement and manually annotating Twitter tweets to predict credibility. Unfortunately, due to the large volume and exponential growth of data, these surveys and annotation-based information credibility techniques are not efficiently applicable. This article presents a data quality and credibility evaluation framework to determine the quality of individual data instances. This framework provides a way to discover useful and credible patterns using credibility indicators. Moreover, a new Twitter bot detection algorithm is proposed to classify tweets generated by Twitter bots and real users. The results of conducted experiments showed that the proposed model generates a positive impact on improving classification accuracy and quality of discovered patterns.