OpenRaiser/Intern-Atlas huggingface dataset:
Transforming Musical Signals through a Genre Classifying Convolutional Neural Network
Venue: arXiv cs.SD 2017
Year: 2017

Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.
The Relationship Between Emotion Models and Artificial Intelligence
Venue: arXiv cs.HC 2017
Year: 2017

Emotions play a central role in most forms of natural human interaction so we may expect that computational methods for the processing and expression of emotions will play a growing role in human-computer interaction. The OCC model has established itself as the standard model for emotion synthesis. A large number of studies employed the OCC model to generate emotions for their embodied characters. Many developers of such characters believe that the OCC model will be all they ever need to equip their character with emotions. This study reflects on the limitations of the OCC model specifically, and on the emotion models in general due to their dependency on artificial intelligence.
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN [1] architecture. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR 2013.
Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines
Venue: arXiv cs.IT 2017
Year: 2017

In the past three decades, many theoretical measures of complexity have been proposed to help understand complex systems. In this work, for the first time, we place these measures on a level playing field, to explore the qualitative similarities and differences between them, and their shortcomings. Specifically, using the Boltzmann machine architecture (a fully connected recurrent neural network) with uniformly distributed weights as our model of study, we numerically measure how complexity changes as a function of network dynamics and network parameters. We apply an extension of one such information-theoretic measure of complexity to understand incremental Hebbian learning in Hopfield networks, a fully recurrent architecture model of autoassociative memory. In the course of Hebbian learning, the total information flow reflects a natural upward trend in complexity as the network attempts to learn more and more patterns.
Composition of Gray Isometries
Venue: arXiv cs.IT 2017
Year: 2017

In classical coding theory, Gray isometries are usually defined as mappings between finite Frobenius rings, which include the ring $Z_m$ of integers modulo $m$, and the finite fields. In this paper, we derive an isometric mapping from $Z_8$ to $Z_4^2$ from the composition of the Gray isometries on $Z_8$ and on $Z_4^2$. The image under this composition of a $Z_8$-linear block code of length $n$ with homogeneous distance $d$ is a (not necessarily linear) quaternary block code of length $2n$ with Lee distance $d$.
Automatic Mapping of French Discourse Connectives to PDTB Discourse Relations
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we present an approach to exploit phrase tables generated by statistical machine translation in order to map French discourse connectives to discourse relations. Using this approach, we created ConcoLeDisCo, a lexicon of French discourse connectives and their PDTB relations. When evaluated against LEXCONN, ConcoLeDisCo achieves a recall of 0.81 and an Average Precision of 0.68 for the Concession and Condition relations.
Complex Networks Analysis for Software Architecture: an Hibernate Call Graph Study
Venue: arXiv cs.SI 2017
Year: 2017

Recent advancements in complex network analysis are encouraging and may provide useful insights when applied in software engineering domain, revealing properties and structures that cannot be captured by traditional metrics. In this paper, we analyzed the topological properties of Hibernate library, a well-known Java-based software through the extraction of its static call graph. The results reveal a complex network with small-world and scale-free characteristics while displaying a strong propensity on forming communities.
Graph Convolution: A High-Order and Adaptive Approach
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the $k$-th order convolution operator and the adaptive filtering module. Importantly, our framework of High-order and Adaptive Graph Convolutional Network (HA-GCN) is a general-purposed architecture that fits various applications on both node and graph centrics, as well as graph generative models. We conducted extensive experiments on demonstrating the advantages of our framework. Particularly, our HA-GCN outperforms the state-of-the-art models on node classification and molecule property prediction tasks. It also generates 32% more real molecules on the molecule generation task, both of which will significantly benefit real-world applications such as material design and drug screening.
Achieving Spectrum Efficient Communication Under Cross-Technology Interference
Venue: arXiv cs.NI 2017
Year: 2017

In wireless communication, heterogeneous technologies such as WiFi, ZigBee and BlueTooth operate in the same ISM band.With the exponential growth in the number of wireless devices, the ISM band becomes more and more crowded. These heterogeneous devices have to compete with each other to access spectrum resources, generating cross-technology interference (CTI). Since CTI may destroy wireless communication, this field is facing an urgent and challenging need to investigate spectrum efficiency under CTI. In this paper, we introduce a novel framework to address this problem from two aspects. On the one hand, from the perspective of each communication technology itself, we propose novel channel/link models to capture the channel/link status under CTI. On the other hand, we investigate spectrum efficiency from the perspective by taking all heterogeneous technologies as a whole and building crosstechnology communication among them. The capability of direct communication among heterogeneous devices brings great opportunities to harmoniously sharing the spectrum with collaboration rather than competition.
Harmonic State Space Modeling of a Three-Phase Modular Multilevel Converter
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents the harmonic state space (HSS) modeling of a three-phase modular multilevel converter (MMC). MMC is a converter system with a typical multi-frequency response due to its significant harmonics in the arm currents, capacitor voltages, and control signals. These internal harmonic dynamics can have a great influence on the operation characteristics of MMC. However, the conventional modeling methods commonly used in two-level voltage-source converters (VSCs), where only the fundamental-frequency dynamic is considered, will lead to an inaccurate model that cannot accurately reflect the real dynamic characteristics of MMC. Therefore, the HSS modeling method, in which harmonics of state variables, inputs, and outputs are posed separately in a state-space form, is introduced in this paper to model the MMC in order to capture all the harmonics and the frequency couplings. The steady-state and small-signal dynamic HSS models of a three-phase MMC are developed, respectively. The validity of the developed HSS model of a three-phase MMC has been verified by the results from both the nonlinear time domain simulation model in MATLAB/Simulink and the laboratory prototype with 12 submodules per arm.
Fine-Grained Reliability for V2V Communications around Suburban and Urban Intersections
Venue: arXiv cs.IT 2017
Year: 2017

Safe transportation is a key use-case of the 5G/LTE Rel.15+ communications, where an end-to-end reliability of 0.99999 is expected for a vehicle-to-vehicle (V2V) transmission distance of 100-200 m. Since communications reliability is related to road-safety, it is crucial to verify the fulfillment of the performance, especially for accident-prone areas such as intersections. We derive closed-form expressions for the V2V transmission reliability near suburban corners and urban intersections over finite interference regions. The analysis is based on plausible street configurations, traffic scenarios, and empirically-supported channel propagation. We show the means by which the performance metric can serve as a preliminary design tool to meet a target reliability. We then apply meta distribution concepts to provide a careful dissection of V2V communications reliability. Contrary to existing work on infinite roads, when we consider finite road segments for practical deployment, fine-grained reliability per realization exhibits bimodal behavior. Either performance for a certain vehicular traffic scenario is very reliable or extremely unreliable, but nowhere in relatively proximity to the average performance. In other words, standard SINR-based average performance metrics are analytically accurate but can be insufficient from a practical viewpoint. Investigating other safety-critical point process networks at the meta distribution-level may reveal similar discrepancies.
Preference-based performance measures for Time-Domain Global Similarity method
Venue: arXiv cs.LG 2017
Year: 2017

For Time-Domain Global Similarity (TDGS) method, which transforms the data cleaning problem into a binary classification problem about the physical similarity between channels, directly adopting common performance measures could only guarantee the performance for physical similarity. Nevertheless, practical data cleaning tasks have preferences for the correctness of original data sequences. To obtain the general expressions of performance measures based on the preferences of tasks, the mapping relations between performance of TDGS method about physical similarity and correctness of data sequences are investigated by probability theory in this paper. Performance measures for TDGS method in several common data cleaning tasks are set. Cases when these preference-based performance measures could be simplified are introduced.
The application of data mining techniques to support customer relationship management: the case of ethiopian revenue and customs authority
Venue: arXiv cs.CY 2017
Year: 2017

The application of data mining technique has been widely applied in different business areas such as health, education and finance for the purpose of data analysis and then to support and maximizes the organizations customer satisfaction in an effort to increase loyalty and retain customers business over their lifetimes . The researchers primary objective, in this paper is to classify customers based on their common attributes since customer grouping is the main part of customer relationship management. In this study, different characteristics of the ERCA customers data were collected from the customs database called ASYCUDA. Once the customers data were collected, the necessary data preparation steps were conducted on it and finally a data set consisting of 46748 records was attained. The classification modeling was built by using J48 decision tree and multi layer perceptron ANN algorithms with 10-fold cross-validation and splitting (70% training and 30% testing) techniques. Among these models, a model which was built using J48 decision tree algorithm with default 10-fold cross-validation outperforms 99.95% of overall accuracy rate; while the classification accuracy of ANN is 99.71%. So decision tree has better accuracy than ANN for classifying ERCA customers data.
Navigate, Understand, Communicate: How Developers Locate Performance Bugs
Venue: arXiv cs.SE 2017
Year: 2017

Background: Performance bugs can lead to severe issues regarding computation efficiency, power consumption, and user experience. Locating these bugs is a difficult task because developers have to judge for every costly operation whether runtime is consumed necessarily or unnecessarily. Objective: We wanted to investigate how developers, when locating performance bugs, navigate through the code, understand the program, and communicate the detected issues. Method: We performed a qualitative user study observing twelve developers trying to fix documented performance bugs in two open source projects. The developers worked with a profiling and analysis tool that visually depicts runtime information in a list representation and embedded into the source code view. Results: We identified typical navigation strategies developers used for pinpointing the bug, for instance, following method calls based on runtime consumption. The integration of visualization and code helped developers to understand the bug. Sketches visualizing data structures and algorithms turned out to be valuable for externalizing and communicating the comprehension process for complex bugs. Conclusion: Fixing a performance bug is a code comprehension and navigation problem. Flexible navigation features based on executed methods and a close integration of source code and performance information support the process.
A reliability-based approach for influence maximization using the evidence theory
Venue: arXiv cs.SI 2017
Year: 2017

The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through social networks for example. In this paper, we propose an influence measure that combines many influence indicators. Besides, we consider the reliability of each influence indicator and we present a distance-based process that allows to estimate the reliability of each indicator. The proposed measure is defined under the framework of the theory of belief functions. Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network. Finally, we present a set of experiments on a dataset collected from Twitter. These experiments show the performance of the proposed solution in detecting social influencers with good quality.
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
Venue: arXiv cs.CV 2017
Year: 2017

It is challenging to recognize facial action unit (AU) from spontaneous facial displays, especially when they are accompanied by speech. The major reason is that the information is extracted from a single source, i.e., the visual channel, in the current practice. However, facial activity is highly correlated with voice in natural human communications. Instead of solely improving visual observations, this paper presents a novel audiovisual fusion framework, which makes the best use of visual and acoustic cues in recognizing speech-related facial AUs. In particular, a dynamic Bayesian network (DBN) is employed to explicitly model the semantic and dynamic physiological relationships between AUs and phonemes as well as measurement uncertainty. A pilot audiovisual AU-coded database has been collected to evaluate the proposed framework, which consists of a "clean" subset containing frontal faces under well controlled circumstances and a challenging subset with large head movements and occlusions. Experiments on this database have demonstrated that the proposed framework yields significant improvement in recognizing speech-related AUs compared to the state-of-the-art visual-based methods especially for those AUs whose visual observations are impaired during speech, and more importantly also outperforms feature-level fusion methods by explicitly modeling and exploiting physiological relationships between AUs and phonemes.
Rule-Mining based classification: a benchmark study
Venue: arXiv cs.LG 2017
Year: 2017

This study proposed an exhaustive stable/reproducible rule-mining algorithm combined to a classifier to generate both accurate and interpretable models. Our method first extracts rules (i.e., a conjunction of conditions about the values of a small number of input features) with our exhaustive rule-mining algorithm, then constructs a new feature space based on the most relevant rules called "local features" and finally, builds a local predictive model by training a standard classifier on the new local feature space. This local feature space is easy interpretable by providing a human-understandable explanation under the explicit form of rules. Furthermore, our local predictive approach is as powerful as global classical ones like logistic regression (LR), support vector machine (SVM) and rules based methods like random forest (RF) and gradient boosted tree (GBT).
Storage, Communication, and Load Balancing Trade-off in Distributed Cache Networks
Venue: arXiv cs.IT 2017
Year: 2017

We consider load balancing in a network of caching servers delivering contents to end users. Randomized load balancing via the so-called power of two choices is a well-known approach in parallel and distributed systems. In this framework, we investigate the tension between storage resources, communication cost, and load balancing performance. To this end, we propose a randomized load balancing scheme which simultaneously considers cache size limitation and proximity in the server redirection process. In contrast to the classical power of two choices setup, since the memory limitation and the proximity constraint cause correlation in the server selection process, we may not benefit from the power of two choices. However, we prove that in certain regimes of problem parameters, our scheme results in the maximum load of order $Θ(\log\log n)$ (here $n$ is the network size). This is an exponential improvement compared to the scheme which assigns each request to the nearest available replica. Interestingly, the extra communication cost incurred by our proposed scheme, compared to the nearest replica strategy, is small. Furthermore, our extensive simulations show that the trade-off trend does not depend on the network topology and library popularity profile details.
Challenges to Keeping the Computer Industry Centered in the US
Venue: arXiv cs.CY 2017
Year: 2017

It is undeniable that the worldwide computer industry's center is the US, specifically in Silicon Valley. Much of the reason for the success of Silicon Valley had to do with Moore's Law: the observation by Intel co-founder Gordon Moore that the number of transistors on a microchip doubled at a rate of approximately every two years. According to the International Technology Roadmap for Semiconductors, Moore's Law will end in 2021. How can we rethink computing technology to restart the historic explosive performance growth? Since 2012, the IEEE Rebooting Computing Initiative (IEEE RCI) has been working with industry and the US government to find new computing approaches to answer this question. In parallel, the CCC has held a number of workshops addressing similar questions. This whitepaper summarizes some of the IEEE RCI and CCC findings. The challenge for the US is to lead this new era of computing. Our international competitors are not sitting still: China has invested significantly in a variety of approaches such as neuromorphic computing, chip fabrication facilities, computer architecture, and high-performance simulation and data analytics computing, for example. We must act now, otherwise, the center of the computer industry will move from Silicon Valley and likely move off shore entirely.
Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English
Venue: arXiv cs.CY 2017
Year: 2017

We highlight an important frontier in algorithmic fairness: disparity in the quality of natural language processing algorithms when applied to language from authors of different social groups. For example, current systems sometimes analyze the language of females and minorities more poorly than they do of whites and males. We conduct an empirical analysis of racial disparity in language identification for tweets written in African-American English, and discuss implications of disparity in NLP.
Better than Real: Complex-valued Neural Nets for MRI Fingerprinting
Venue: arXiv cs.CV 2017
Year: 2017

The task of MRI fingerprinting is to identify tissue parameters from complex-valued MRI signals. The prevalent approach is dictionary based, where a test MRI signal is compared to stored MRI signals with known tissue parameters and the most similar signals and tissue parameters retrieved. Such an approach does not scale with the number of parameters and is rather slow when the tissue parameter space is large. Our first novel contribution is to use deep learning as an efficient nonlinear inverse mapping approach. We generate synthetic (tissue, MRI) data from an MRI simulator, and use them to train a deep net to map the MRI signal to the tissue parameters directly. Our second novel contribution is to develop a complex-valued neural network with new cardioid activation functions. Our results demonstrate that complex-valued neural nets could be much more accurate than real-valued neural nets at complex-valued MRI fingerprinting.
Exploring the Imposition of Synaptic Precision Restrictions For Evolutionary Synthesis of Deep Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

A key contributing factor to incredible success of deep neural networks has been the significant rise on massively parallel computing devices allowing researchers to greatly increase the size and depth of deep neural networks, leading to significant improvements in modeling accuracy. Although deeper, larger, or complex deep neural networks have shown considerable promise, the computational complexity of such networks is a major barrier to utilization in resource-starved scenarios. We explore the synaptogenesis of deep neural networks in the formation of efficient deep neural network architectures within an evolutionary deep intelligence framework, where a probabilistic generative modeling strategy is introduced to stochastically synthesize increasingly efficient yet effective offspring deep neural networks over generations, mimicking evolutionary processes such as heredity, random mutation, and natural selection in a probabilistic manner. In this study, we primarily explore the imposition of synaptic precision restrictions and its impact on the evolutionary synthesis of deep neural networks to synthesize more efficient network architectures tailored for resource-starved scenarios. Experimental results show significant improvements in synaptic efficiency (~10X decrease for GoogLeNet-based DetectNet) and inference speed (>5X increase for GoogLeNet-based DetectNet) while preserving modeling accuracy.