Reviewerly/exHarmony huggingface dataset:
Mobile AMOLED Display Power Model Considering <i>I–R</i> Drop in Smartphones

In order to efficiently manage display power, there is a need for a system that can accurately predict the power consumed according to the displayed image. Existing AMOLED display power models do not consider the effects of I-R drop, which is a voltage drop along the VDD line. Therefore, when the existing power model is applied to a recent AMOLED display panel, which has a large physical size and uses a large current, power consumption cannot be accurately predicted. In this article, we propose a new model to compensate for the I-R drop and present a more accurate method for predicting AMOLED display power consumption by applying it to existing AMOLED display power models. To verify this, we conducted extensive experiments for various image databases on AMOLED display panels of Samsung Galaxy S7 and S9. When comparing the proposed display power model connected with the I-R drop model with conventional power models, it is confirmed that the proposed power model improves the accuracy by up to 70.3% over them.
Generalized Fitch graphs II: Sets of binary relations that are explained by edge-labeled trees

Fitch graphs $G=(X,E)$ are digraphs that are explained by $\{\emptyset, 1\}$-edge-labeled rooted trees $T$ with leaf set $X$: there is an arc $(x,y) \in E$ if and only if the unique path in $T$ that connects the last common ancestor $\mathrm{lca}(x,y)$ of $x$ and $y$ with $y$ contains at least one edge with label "1". In practice, Fitch graphs represent xenology relations, i.e., pairs of genes $x$ and $y$ for which a horizontal gene transfer happened along the path from $\mathrm{lca}(x,y)$ to $y$. In this contribution, we generalize the concept of Fitch graphs and consider trees $T$ that are equipped with edge-labeling $\lambda: E\to \mathcal{P}(M)$ that assigns to each edge a subset $M'\subseteq M$ of colors. Given such a tree, we can derive a map $\varepsilon_{(T,\lambda)}$ (or equivalently a set of not necessarily disjoint binary relations), such that $i\in \varepsilon_{(T,\lambda)}(x,y)$ (or equivalently $(x,y)\in R_i$) with $x,y\in X$, if and only if there is at least one edge with color $i$ from $\mathrm{lca}(x,y)$ to $y$. The central question considered here: Is a given map $\varepsilon$ a Fitch map, i.e., is there there an edge-labeled tree $(T,\lambda)$ with $\varepsilon_{(T,\lambda)} = \varepsilon$, and thus explains $\varepsilon$? Here, we provide a characterization of Fitch maps in terms of certain neighborhoods and forbidden submaps. Further restrictions of Fitch maps are considered. Moreover, we show that the least-resolved tree explaining a Fitch map is unique (up to isomorphism). In addition, we provide a polynomial-time algorithm to decide whether $\varepsilon$ is a Fitch map and, in the affirmative case, to construct the (up to isomorphism) unique least-resolved tree $(T^*,\lambda^*)$ that explains $\varepsilon$.
Broad Forest: A Non-Neural Network Style Broad Model for Streaming Video QoE Evaluation

Currently, video streaming services put more emphasis on user feeling or satisfaction than before. How to design suitable model and algorithm to effectively and efficiently evaluate user quality of experience (QoE) has become a significant technical challenge. To get over this dilemma, this paper proposes the broad forest, a non-neural network style broad model for streaming video QoE evaluation. The design target of broad forest is to take advantage of representation potential of forest and low complexity characteristic of broad learning system. Specifically, we first give the construction of broad forest. Then, the associated incremental learning algorithm for efficiently supporting the added structure and inputs is designed. Finally, we apply the broad forest to streaming video QoE evaluation. Experimental results show that the broad forest can not only guarantee accuracy, but also decrease training time. When subjective features are considered, it can further promote performance of QoE evaluation.
Two Three Step Authentication in ATM Machine to Transfer Money and for Voting Application

Voting plays a major role in electing a right person by the public to rule the country. The main aim of the paper is to perform two operations such as transaction of money and for voting application through ATM machine, by providing the authentication like Biometric – Fingerprint and Face Recognition through the comparison with the Aadhar car for more security and privacy. This voting application through ATM’s makes more easier and faster for the people to increase the percentages of votes.
Highly Efficient Neural Network Language Model Compression Using Soft Binarization Training

The long short-term memory language model (LSTM LM) has been widely investigated in large vocabulary continuous speech recognition (LVCSR) task. Despite the excellent performance of LSTM LM, its usage in resource-constrained environments, such as portable devices, is limited due to the high consumption of memory. Binarized language model has been proposed to achieve significant memory reduction at the cost of performance degradation at high compression ratio. In this paper, we propose a soft binarization approach to recover the performance of binarized LSTM LM. Experiments show that the proposed method can achieve a high compression rate of 30 × with almost no performance loss in both language modeling and speech recognition tasks.
M2P2: Movie’s Trailer Reviews Based Movie Popularity Prediction System

Kumar, Krishan Bamrara, Rishab Gupta, Prakhar Singh, NavjotIn today’s world, knowing which movie will be popular among the masses, motivates movie’s crew, before movie comes on the PVR curtain. Trailers and teasers usually come out, before releasing any movie. Moreover, the people are giving comments or feedback about the movie based on the trailer or teaser. These reviews or comments may be used to predict the popularity of the movie in the near future. Therefore, this work focuses for predicting the popularity or success of the movie based on analysis of the trailer comments. This work exposes the importance of the TextBlob and Enchant library. In addition to this, dictionary-based recommendation system has been developed in order to enhance the accuracy of the proposed popularity prediction system. For experimental process, the reviews of the people on trailers before release of the movie have been used to extract the features, and then the same features are mapped on corresponding BookMyShow popularity index after the movie has been released. A percentage of accuracy of the model shows that the proposed model can be used as an accurate movie recommendation system.
Multi-Tier Workload Consolidations in the Cloud: Profiling, Modeling and Optimization

Reducing tail latency becomes increasingly important to improve the user-perceived service experience. User-facing latency-sensitive cloud applications typically contain multiple interactive tiers (e.g., Web, App, Database) running in different virtual machines (VMs) with complex interaction patterns. However, such interactions between VMs in different tiers are often neglected in previous VM consolidation methods, resulting in poor application performance. In this article, we study the consolidation of multi-tier interactive workloads from a new perspective of user-perceived tail latency. We propose a novel profiling-based consolidation methodology to satisfy tail latency requirements while reducing the number of used physical machines. To achieve such a goal, we first perform large-scale profiling experiments under various consolidation settings in a KVM virtualized private cluster to establish the empirical performance values. We consider two key factors that affect the tail latency of multi-tier workloads: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interference</i> with co-located VMs and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interaction</i> between tiers. We model the consolidation of multi-tier workloads as an optimization problem with different objectives and constraints, and derive the consolidation schedule. We implement and evaluate the proposed models, as well as comparing with other methods (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> profiling or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> considering interaction influence). Extensive experimental results show that the proposed method is able to reduce up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5X</i> tail latency, compared with the method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> profiling and up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.3X</i> tail latency, compared with the method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> considering the interaction influence between different tiers.
Blockchain applications beyond the cryptocurrency casino: The Punishment not Reward blockchain architecture

Summary The Bitcoin model originated blockchain architectures and inspired their further development. Blockchain architectures are still most commonly associated with currency applications, and with financial speculation. Bitcoin's rewards for Proof of Work mining became the default consensus technique for blockchains. As an alternative to reward mechanisms for blockchain maintenance, we propose punishment mechanisms for neglecting to maintain the blockchain (provided participants are intrinsically motivated to be beneficiaries of the blockchain application). Punishment not Reward is convincing for enterprise mission critical blockchain applications, and potential punishment mechanisms include denial of service and/or revocation of confidentiality . This obviates the need for reward via cryptocurrencies, along with their attendant volatility, insecure ecosystem, and market manipulation demerits. The privacy concerns of competing entities participating in a blockchain application are prima facie in conflict with the needs of the community to be able to inflict punishment mechanisms. Conflicts of this kind can be addressed via sophisticated cryptographic techniques such as secret sharing, multiparty computation, zero‐knowledge proofs, and so on, which play a vital role. We stress the importance of correctly balancing all application specific interests in the engineering of blockchain applications, so that the mix of incentives and disincentives is stabilizing.
Sigmoidal Spider Monkey Optimization Algorithm

Sharma, Basudev Sharma, Vivek Kumar Kumar, SandeepSpider monkey optimization (SMO) algorithm is a recently developed optimizer that is stimulated by the extraordinary social activities of spider monkeys known as fission–fusion social structure. The SMO is developed to find solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. During the solution search process in SMO, perturbation rate plays very important role. The convergence rate of SMO is highly affected by it. Usually, perturbation rate is defined by a simple function that is linearly in nature. But some application has nonlinear nature, thus a nonlinear function may improve the outcomes of SMO. For that reason, a non linear function, namely sigmoidal function used to decide perturbation in SMO and proposed strategy named as sigmoidal SMO. The investigational outcomes show the superiority of the anticipated technique over other meta-heuristics.
Matrix adaptation evolution strategies for optimization under nonlinear equality constraints

This work concerns the design of matrix adaptation evolution strategies for black-box optimization under nonlinear equality constraints. First, constraints in form of elliptical manifolds are considered. For those constraints, an algorithm is proposed that evolves itself on that manifold while optimizing the objective function. The specialty about the approach is that it is possible to ensure that the population evolves on the manifold with closed-form expressions. Second, an algorithm design for general nonlinear equality constraints is presented. For those constraints considered, an iterative repair approach is presented. This allows the evolution to happen on the nonlinear manifold defined by the equality constraints for this more general case as well. For both cases, the algorithms are interior point methods, i.e., the objective function is only evaluated at feasible points in the parameter space, which is often required in the area of simulation-based optimization. For the experimental evaluation, different test problems are introduced. The proposed algorithms are evaluated on those providing insights into the working principles of the different approaches. It is experimentally shown that correcting the mutation vectors after the repair step is important for an effective evolution strategy. Additional experiments are conducted for providing a comparison to other evolutionary black-box optimization methods, which show that the developed algorithms are competitive.
Brain Tumor Semi-automatic Segmentation on MRI T1-weighted Images using Active Contour Models

Brain tumor is a collection of abnormal growth in brain tissue. One of the methods to diagnose brain tumor is using magnetic resonance imaging (MRI) to produce images of brain tissue, on which the radiologist will perform manual segmentation of the tumor boundary. Manual segmentation poses a challenge in a large number of images. A Computer Aided Diagnosis (CAD) system can be designed to perform an automated segmentation of tumor boundary, thus providing more efficient and objective results. In this work, we compared and analyze the performance of snake active contour (SAC), morphological active contour without edge (MACWE), and morphological geodesic active contour (MGAC) segmentation algorithms on 3049 brain MRI T1-weighted images containing glioma, meningioma, or pituitary tumor. The performance of these algorithms quantified using the Jaccard Similarity Index (JSI) and the Hausdorff Distance (HD). The best segmentation results were obtained by the MGAC with the average JSI and HD of 71.18% and 4.04 pixels, respectively. The JSI of MGAC segmentation was highest for meningioma (77.94%) and lowest for glioma (66.31%) while a random shift in contour initialization affected the glioma and pituitary tumors more than the meningiomas, as shown by the respective post-shift JSI of 76.42%, 76.84%, and 85.98% accuracy for glioma, pituitary, and meningioma.
A Text Normalization Method for Speech Synthesis Based on Local Attention Mechanism

This paper proposes a deep learning model based on a recurrent neural network (RNN) to solve the problem of text normalization for speech synthesis.Traditional rule-based models cannot take advantage of contextual information and do not handle text outside of rules well, while deep learning-based models can handle these problems better.Based on the seq2seq neural network, we construct a new textnormalized deep learning model that considers the context of words in sentences by using the gated recurrent unit (GRU) and local attention mechanism.In recent years, seq2seq has made many achievements in different fields of natural language processing.Our research proves that well-constructed small network models in specific application fields can also achieve meaningful results.In our small network, the local attention mechanism is used to reduce the computational complexity without decreasing accuracy.In the experiments, we compared our model with the attention-based model proposed by Sproat, the without attention model and the attention model.Experimental results show that our method reduces the network scale while gathering the main context information, overcomes the defect of high complexity of the traditional attention mechanism model, and achieves higher accuracy.It can also be seen from experiments that the most important related words are relatively close to the target word in text normalization.
MLPPT-MHS: Multi-Layered Privacy Preserving and Traceable Mobile Health System

Personal Health Record (PHR) is a transpiring patient-centric embodiment of patient information trade that is customarily contracted out to third parties, for illustration, cloud service providers. There were far-reaching privacy apprehensions as individual medical records may possibly be unveiled to some third-party servers in addition to unconstitutional parties. On the road to warrant the patients' charge to access their medical records, it is a hopeful procedure to encrypt the medical records prior to outsourcing. Hitherto, questions like exposure to privacy, scalability during key management, in addition to proficient user revocation, have lingered the most imperative tests in the direction of accomplishing fine-grained access control. This manuscript puts forward a fresh multi-layer patient-centric scaffold in addition to an anthology of techniques in support of data access control to medical records amassed in honest-but-curious servers. In the direction of accomplishing fine-grained as well as scalable access control, a new-fangled modification of Cipher-text Policy Attribute-Based Encryption (CP-ABE) technique is persuaded with random key length in the offline encryption phase to encrypt each patient's PHR file. In the online phase, the CP-ABE encrypted data is transmitted to the cloud through a secure tunnel and then the data in rest is encrypted again by the 256-bit Advanced Encryption Standard (AES-256). In order to eliminate the problem aroused from leaked keys, both attribute and user revocation is done. The proposed system achieves data confidentiality, access control, user revocation, random key length, resists collusion attack, tracks the user location periodically, and preserves forward and backward secrecy. Within the MLPPT-MHS scheme, the computation time to perform Key Generation, Encryption, and Decryption would be 823.75ms, 152.43ms, 82.54ms and the storage overhead incurred to record the Public Parameters, Secret Key and Cipher Text would be 6098 bits, 59,653 bits, and 26,712 bits. These investigational outcomes demonstrate the security, scalability, as well as efficiency of this MLPPT-MHS scheme.
Tensor latent block model for co-clustering

With the exponential growth of collected data in different fields like recommender system (user, items), text mining (document, term), bioinformatics (individual, gene), co-clustering, which is a simultaneous clustering of both dimensions of a data matrix, has become a popular technique. Co-clustering aims to obtain homogeneous blocks leading to a straightforward simultaneous interpretation of row clusters and column clusters. Many approaches exist; in this paper, we rely on the latent block model (LBM), which is flexible, allowing to model different types of data matrices. We extend its use to the case of a tensor (3D matrix) data in proposing a Tensor LBM (TLBM), allowing different relations between entities. To show the interest of TLBM, we consider continuous, binary, and contingency tables datasets. To estimate the parameters, a variational EM algorithm is developed. Its performances are evaluated on synthetic and real datasets to highlight different possible applications.
On Online Hate Speech Detection. Effects of Negated Data Construction

In the era of social media and mobile internet, the design of automatic tools for online detection of hate speech and/or abusive language becomes crucial for society and community empowerment. Nowadays of current technology in this respect is still limited and many service providers are still relying on the manual check. This paper aims to advance in this topic by leveraging novel natural language processing, machine learning, and feature engineering techniques. The proposed approach advocates a classification-like technique that makes use of a special data design procedure. The latter enforces a balanced training scheme by exploring the negativity of the original dataset. This generates new transfer learning paradigms, Two classification schemes using convolution neural network and LSTN architecture that use FastText embeddings as input features are contrasted with baseline models constituted of Logistic regression and Naives' Bayes classifiers. Wikipedia Comment dataset constituted of Personal Attack, Aggression and Toxicity data are employed to test the validity and usefulness of the proposal.
Batch Processing through Particle Swarm Optimization for Target Motion Analysis with Bottom Bounce Underwater Acoustic Signals

A target angular information in 3-dimensional space consists of an elevation angle and azimuth angle. Acoustic signals propagating along multiple paths in underwater environments usually have different elevation angles. Target motion analysis (TMA) uses the underwater acoustic signals received by a passive horizontal line array to track an underwater target. The target angle measured by the horizontal line array is, in fact, a conical angle that indicates the direction of the signal arriving at the line array sonar system. Accordingly, bottom bounce paths produce inaccurate target locations if they are interpreted as azimuth angles in the horizontal plane, as is commonly assumed in existing TMA technologies. Therefore, it is necessary to consider the effect of the conical angle on bearings-only TMA (BO-TMA). In this paper, a target conical angle causing angular ambiguity will be simulated using a ray tracing method in an underwater environment. A BO-TMA method using particle swarm optimization (PSO) is proposed for batch processing to solve the angular ambiguity problem.
A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise

Remotely sensed images are widely used in many imaging applications.Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy.This study is intended to address these defects of remotely sensed data efficiently.A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory.The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article.The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures.The comparative study reveals the efficiency of the model.