Reviewerly/exHarmony huggingface dataset:
Combining CMMI Specific Practices with Scrum Model to Address Shortcomings in Process Maturity

Software process improvement (SPI) is the approach to understand the software development process lifecycle and implement necessary changes to achieve a high-quality, maintainable product. The software industry is comprised of small and medium-sized enterprises that adopt agile models as their preferred model of development. The Capability Maturity Model Integration (CMMI) model for software process improvement and quality is used to address aspects of software quality and maturity that Scrum was not initially designed to consider. CMMI is used for its reliable practices and consideration of many aspects of software development in process, project and product. After consideration of literature, three process areas are selected to address necessary elements in Scrum used by software developers in offshoring destinations. The practices are selected based on their practicality in small, agile settings and the ability to be incorporated into Scrum activities without disrupting the models sprint cycles. This opens the way for smaller enterprises to create produce quality.
The Thermal and Dynamic Process of Core → Mantle → Crust and the Metallogenesis of Guojiadian Mantle Branch in Northwestern Jiaodong

The Jiaodong gold mineral province, with an overall endowment estimated as &gt;3000 t, located at the eastern segment of the North China Craton (NCC), ranks as the greatest source of Au in China. The structural evolution, magmatic activity and metallogenesis during the Mesozoic played important roles in the large scale regional gold, silver and polymetallic mineralization in this area; among them, the intensive activation of fault structures is the most important factor for metallogenesis. This study takes the regional deep faults as main thread to discuss the controlling role of faults in large scale metallogenesis. The Jiaojia fault and Sanshandao faults in the northwest margin of the Guojiadian mantle branch not only are dominant migration channels for hydrothermal fluid but are very important favorable spaces for ore-forming and ore-hosting during the formation of world-class super large gold deposits in this area. The deep metallogenic process can be summarized as involving intensive Earth’s core, mantle and crust activity → magmatism → uplifting of metamorphic complex → detachment of cover rocks → formation of mantle branch → penetration of hydrothermal fluid along deep faults → concentration of metallogenic materials → formation of super large deposits.
An Extensible, Modular Framework for Classifying YouTube Videos Using Web and Social Media

In this paper, we present an extensible framework that classifies YouTube videos using the videos' metadata and a combination of parsing and searching modules. To obtain classification relative to the contextual environment, the modules interface with web search engines, social media platforms, and third-party classification services.
TAB: Temporally aggregated bag-of-discriminant-words for temporal action proposals

In this work, we propose a new method to generate temporal action proposals from long untrimmed videos named Temporally Aggregated Bag-of-Discriminant-Words (TAB). TAB is based on the observation that there are many overlapping frames in action and background temporal regions of untrimmed videos, which cause difficulties in segmenting actions from non-action regions. TAB solves this issue by extracting class-specific codewords from the action and background videos and extracting the discriminative weights of these codewords based on their ability to discriminate between these two classes. We integrate these discriminative weights with Bag of Word encoding, which we then call Bag-of-Discriminant-Words (BoDW). We sample the untrimmed videos into non-overlapping snippets and temporally aggregate the BoDW representation of multiple snippets into action proposals using a binary classifier trained on trimmed videos in a single pass. TAB can be used with different types of features, including those computed by deep networks. We present the effectiveness of the TAB proposal extraction method on two challenging temporal action detection datasets: MSR-II and Thumos14, where it improves upon state-of-the-art with recall rates of 87.0% and 82.0% respectively at a temporal intersection over union ratio of 0.8.
3D brain tumor segmentation in MRI images based on a modified PSO technique

Abstract Three‐dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional‐order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1‐C modality.
Modal-Dependent Retrieval Based on Mid-Level Semantic Enhancement Space

Cross-media retrieval refers to submitting any kind of media type and obtaining similar results in the form of different media types. The existing cross-media retrieval algorithms typically learn a pair of linear (or non-linear) mappings that project image and textual features from their original heterogeneous feature space into a common homogeneous space, all these traditional methods are simply about utilizing the correlation between the original features and the high-level semantic space. At the same time, due to the ”semantic gap” incurred by the extraction of the visual feature, the feature of text mode is often more distinctive than the visual features in terms of their distribution in the semantic space. Based on the above-mentioned problems, we have established a medium semantic enhancement space (MMSES) to enhance the discrimination capability of textual features. First of all, projecting the original features into the medium meaning semantic enhancement space by utilizing linear discriminant analysis, then projecting the features into a high meaning semantic space by utilizing fixed-distance projection, and finally, studying a different mapping matrix in response to different cross-media retrieval tasks during the retrieval process. In this paper, we employ traditional Euclidean distance to measure the similarity between different modalities in a common space. The effectiveness of MMSES was validated through extensive experiments on three datasets, Wikipedia, Pascal Sentence, and INRIA-Websearch.
Clustering Using Chaotic Circuit Networks with Weighted Couplings

In this paper, we focus on clustering phenomena in a network composed of coupled chaotic circuits. In this investigation, the coupling strength is reflected by the distance information when the chaotic circuits are placed in a two-dimensional grid. We observe various clustering phenomena in the network of coupled chaotic circuits when we vary the scaling parameters, including the coupling strength, the distance between coupled chaotic circuits and the density of the chaotic circuits.
Detecting Machine-Translated Paragraphs by Matching Similar Words

Machine-translated text plays an important role in modern life by smoothing communication from various communities using different languages. However, unnatural translation may lead to misunderstanding, a detector is thus needed to avoid the unfortunate mistakes. While a previous method measured the naturalness of continuous words using a N-gram language model, another method matched noncontinuous words across sentences but this method ignores such words in an individual sentence. We have developed a method matching similar words throughout the paragraph and estimating the paragraph-level coherence, that can identify machine-translated text. Experiment evaluates on 2000 English human-generated and 2000 English machine-translated paragraphs from German showing that the coherence-based method achieves high performance (accuracy = 87.0%; equal error rate = 13.0%). It is efficiently better than previous methods (best accuracy = 72.4%; equal error rate = 29.7%). Similar experiments on Dutch and Japanese obtain 89.2% and 97.9% accuracy, respectively. The results demonstrate the persistence of the proposed method in various languages with different resource levels.
An Application Specific Processor Architecture with 3D Integration for Recurrent Neural Networks

Deep learning using recurrent neural networks has broadened the horizon of artificial intelligence. It can process a massive amount of multimodal natural data (video, audio) and learn useful join representations in various applications. However, implementation of recurrent neural networks in hardware and learn representations requires high throughput and memory bandwidth of that hardware platform. This work offers a 3D hardware architecture with application-specific instruction set processor, 3D-stacked memory, and sized on-chip memory for training and inference of recurrent neural networks. It also implemented a set of short instructions after analyzing different complex, time-consuming, special operations into high-level function blocks. The accelerator also performed state-of-the-art mixed precision training using custom instructions. A high-level programming environment is developed to generate Very Long Instruction Word (VLIW) instructions for this accelerator and processed a popular and successful variant of the recurrent neural network. At 28nm, this work achieved 8.5 x processing speedup, 47.5 x energy efficiency per sequence, and 2.71 x reduction in silicon area against a GPU.
Liquid-solid interaction sound synthesis

Liquid-solid interaction produces a characteristic sound which is different from the sound of bubbly water flows. This paper explores liquid-solid interaction sound (LSIS) synthesis. The resulting LSIS consists of two components: bubble sound and impact sound, which corresponding to liquid-liquid collision and liquid-solid collision, respectively. To improve the quality of LSIS, we propose a novel sound enrichment method called Feature Transfer Synthesis (FTS), which is designed to compensate for the differences between the real-world recording and the synthesized sound. We also greatly resolve the synchronization problem during blending the two components of LSIS through a key frame algorithm with normal force and grid surface. Moreover, a generalized dipole model for sound radiation is performed to estimate the LSIS pressure at a listener position. We illustrate our approach through a series of experiments and a perceptual user study, demonstrating the utility of our LSIS synthesis pipeline in producing realistic sounds at practical computational times.
Current Mathematics Curriculum of South Korea and Its Embodiment into Textbooks

The Korean-revised school mathematics curriculum 2011 focuses on nurturing students’ mathematical creativity and mathematical attitude. This new curriculum emphasizes contextual learning so that students can grasp basic mathematical concepts and make connections with their everyday lives. This curriculum also encourages the use of activities through which students may attain an intuitive idea of what they are learning and enhances their creativity and reasoning to justify mathematical results based on their knowledge and experience. On the other hand, the Ministry of Education, Science and Technology (MEST) announced a policy on advanced mathematics education on January in 2012 to improve Korean students’ low interest, value, and confidence in mathematics sharply compared with their highest grades at international mathematics tests like TIMSS and PISA. This policy has led textbook revision procedure of 2012 and 2013 to emphasize activities using various educational tools and technology, to carry out STEAM to promote interest and understanding and to develop integrated thinking ability, to create friendly and fun mathematics textbooks based on materials of real life and storytelling. This paper provides a detailed overview of the main challenges of the current high school curriculum and discusses how these challenges have been embodied into textbooks both successfully in some parts and unsatisfactorily in the other parts.
A Darwinian Genetic Algorithm for State Encoding Based Finite State Machine Watermarking

We propose an extended Darwinian Genetic Algorithm that builds upon the traditional genetic algorithm in the efforts to more accurately depict the process of natural selection We demonstrate the capabilities of this algorithm by employing it with an existing state encoding based watermarking system for sequential circuits that was previously hindered by attempting to naïvely solve the subgraph isomorphism problem that arises during the watermark embedding process. Wherein using the proposed approach we are able to demonstrate maximal savings for a 64-bit watermark of 26%, 23%, and 20%, in addition to the 128-bit watermark where maximal savings were 30%, 29%, and 20% over the current state-of-the-art in terms of literals, area, and delay required for synthesizing the watermarked finite state machines, respectively. Additionally, average savings for the 64-bit watermark were 1%, and 2% with 4% overhead and for the 128-bit watermark average savings were 12%, 7%, and 2%, in terms of literals, area, and delay.
CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream

In recent years, data stream has been considered as one of the primary sources of big data. Data stream has grown very rapidly in the last decades. Data stream environment has many features distinguishing the batch learning data which arrives on the fly with high speed. Data stream mining has attracted research focus due to its presence in many real-time applications such as telecommunication, networking, and banking. One of the most important challenges in data stream is the distribution of data is changing continuously which is leading to the phenomenon called "concept drift." Another issue for streaming data is dealing with imbalanced class in the dataset. Many classification algorithms have been made to cope with the concept drift; however, many of them are dealing with the drift from the balanced data. In this paper, we propose a model called "CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream" which aims to handle the imbalanced data and detect the concept drift and behave equally with different types of drift. The algorithm was evaluated on real and synthetic dataset and compared with leading edge methods AWE, SMOTE, SERA, and OOB. Our method performs significantly better average prediction accuracy than the other compared methods.
Web Camera Primarily based Face Recognition Victimization VIOLA JONE’S Rule with Arduino Uno

In this paper, we've a twisted to propose topology securing outline organizing (VIOLA/JONES) procedure for midway face affirmation. Regardless, faces in evident free is moreover ceased up by articles or totally very surprising appearances, that can't offer the full faced film to portrayal. Key reason based on absolute fragmentary face affirmation manners by which inside which. to deal with this, our VIOLA/JONES procedure assesses a non-resolute alteration mystery composing the second sort out geometric structure of the graph, with the objective that a ton of exact spot along, incredible correspondence visiting be spotted with the topological data in this way camera catch the face regardless of whether coordinated or unmatched the picture. on the off chance that the coordinated the individual you get the ringer generally u will get semiconductor unit light-weight watch the Arduino Uno. We propose a topology safe guarding helper coordinating (VIOLA/JONES) methodology to build up the following solicitation structure for each face.
Detection of Nasalized Voiced Stops in Cleft Palate Speech Using Epoch-Synchronous Features

The presence of velopharyngeal dysfunction in individuals with cleft palate (CP) nasalizes the voiced stops. Due to this, voiced stops (/b/, /d/, /g/) tend to be perceive like nasal consonants (/m/, /n/, /ng/). In this work, a novel algorithm is proposed for the detection of nasalized voiced stops in CP speech using epoch-synchronous features. Speech regions corresponding to consonant and consonant-vowel transitions are segmented using the knowledge of glottal activity, syllable nucleus, low-frequency spectral dominance, and vowel onset point. The segmented regions are epoch-synchronously processed to analyze the spectral, spectro-temporal, excitation source, and periodicity characteristics of normal and nasalized voiced stops. Spectral and spectro temporal features are computed using single pole filter based time-frequency representation. The amplitude of Hilbert envelope of linear prediction residual, measured around the epoch is used to analyze the effect of nasalization on excitation source. Comparison of speech frames of successive inter-epoch intervals is carried out to analyze the periodicity characteristics. The proposed features are used to develop a support vector machine classifier for the classification of normal and nasalized voiced stops. Segmentation accuracy for the proposed knowledge based method is found to be better than the hidden Markov model based force-alignment approach. The detection rate of nasalized voiced stops is found to be high for the proposed epoch synchronous features than the conventional Mel-frequency cepstral coefficients.
Closing the Verification Gap with Static Sign-off

It is commonly held that because SOCs are extremely complex, the gap between the ability to design and to verify is beyond manageable proportions. Recent reality, though, has been more positive. The recent increase in SOC scale has been enabled by design-style advances, and the increase in SOC scale has been much more rapid than that of constituent IP. Consequently, verification complexity and SOC failure is now more correlated to SOC implementation schemes than IP functionality. In this milieu, SOC sign-off appears more manageable when broken into verification problems posed by the individual SOC implementation schemes, each scheme characterized by a narrow range of failure risks that can be modeled precisely. This tends to be a sweet spot for “static analysis”, which is a general term for methods that are based on algorithmically analyzing design attributes rather than testing a design by evaluating input stimuli through them. In fact, the dissection of SOC verification into the individual verification problems and the development of failure-specific static analysis methods for them has been the verification revolution of the past decade, without which the SOC revolution would have stalled.
An Implementation of DBF and CFAR Models in OTHR Signal Processing

In this paper an implementation of signal processing techniques used in over the horizon radar (OTHR) signal processing are presented. The paper describes all signal processing steps suitable for OTHR based on frequency modulated continuous wave (FMCW) principle. Main focus is given to the Digital Beamforming (DBF) and Constant False Alarm Ration (CFAR) models, but the other steps are presented as well. All steps are demonstrated on real data obtained from the sites located in the Gulf of Guinea.
Learning a Mixture of Gaussians via Mixed-Integer Optimization

We consider the problem of estimating the parameters of a multivariate Gaussian mixture model (GMM) given access to n samples that are believed to have come from a mixture of multiple subpopulations. State-of-the-art algorithms used to recover these parameters use heuristics to either maximize the log-likelihood of the sample or try to fit first few moments of the GMM to the sample moments. In contrast, we present here a novel mixed-integer optimization (MIO) formulation that optimally recovers the parameters of the GMM by minimizing a discrepancy measure (either the Kolmogorov–Smirnov or the total variation distance) between the empirical distribution function and the distribution function of the GMM whenever the mixture component weights are known. We also present an algorithm for multidimensional data that optimally recovers corresponding means and covariance matrices. We show that the MIO approaches are practically solvable for data sets with n in the tens of thousands in minutes and achieve an average improvement of 60%–70% and 50%–60% on mean absolute percentage error in estimating the means and the covariance matrices, respectively, over the expectation–maximization (EM) algorithm independent of the sample size n. As the separation of the Gaussians decreases and, correspondingly, the problem becomes more difficult, the edge in performance in favor of the MIO methods widens. Finally, we also show that the MIO methods outperform the EM algorithm with an average improvement of 4%–5% on the out-of-sample accuracy for real-world data sets.
A Brief Review on: Implementation of Lossless Color Image Compression

This paper describes a lossless image compression techniques on colour images with the help of Huffman and CALIC coding. In this approach, an RGB image is first transformed into a gray scale image. In a gray color space, the color components are downsampled and we estimate the value between original image and the downsampled image. These values are substracted from an original counterpart to obtain prediction error. At the decoder end, the exact reverse method is used to obtain the original color image. Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are the two basic parameters used to obtain a good quality image as same as the original image. As compaired to other existing techniques, Huffman method provide a good compression results.
Metaconcepts: Isolating Context in Word Embeddings

Word embeddings are commonly used to measure word-level semantic similarity in text, especially in direct word-to-word comparisons. However, the relationships between words in the embedding space are often viewed as approximately linear and concepts comprised of multiple words are a sort of linear combination. In this paper, we demonstrate that this is not generally true and show how the relationships can be better captured by leveraging the topology of the embedding space. We propose a technique for directly computing new vectors representing multiple words in a way that naturally combines them into a new, more consistent space where distance better correlates to similarity. We show that this technique works well for natural language, even when it comprises multiple words, on a simple task derived from WordNet synset descriptions and examples of words. Thus, the generated vectors better represent complex concepts in the word embedding space.