OpenRaiser/Intern-Atlas huggingface dataset:
Crosstalk based Fine-Grained Reconfiguration Techniques for Polymorphic Circuits
Venue: arXiv cs.AR 2018
Year: 2018

Truly polymorphic circuits, whose functionality/circuit behavior can be altered using a control variable, can provide tremendous benefits in multi-functional system design and resource sharing. For secure and fault tolerant hardware designs these can be crucial as well. Polymorphic circuits work in literature so far either rely on environmental parameters such as temperature, variation etc. or on special devices such as ambipolar FET, configurable magnetic devices, etc., that often result in inefficiencies in performance and/or realization. In this paper, we introduce a novel polymorphic circuit design approach where deterministic interference between nano-metal lines is leveraged for logic computing and configuration. For computing, the proposed approach relies on nano-metal lines, their interference and commonly used FETs, and for polymorphism, it requires only an extra metal line that carries the control signal. In this paper, we show a wide range of crosstalk polymorphic (CT-P) logic gates and their evaluation results. We also show an example of a large circuit that performs both the functionalities of multiplier and sorter depending on the configuration signal. Our benchmarking results are presented in this paper. For CT-P, the transistor count was found to be significantly less compared to other existing approaches, ranging from 25% to 83%. For example, CT-P AOI21-OA21 cell show 83%, 85% and 50% transistor count reduction, and MultiplierSorter circuit show 40%, 36% and 28% transistor count reduction with respect to CMOS, genetically evolved, and ambipolar transistor based polymorphic circuits respectively.
On the Feasibility of Integrating mmWave and IEEE 802.11p for V2V Communications
Venue: arXiv cs.NI 2018
Year: 2018

Recently, the millimeter wave (mmWave) band has been investigated as a means to support the foreseen extreme data rate demands of emerging automotive applications, which go beyond the capabilities of existing technologies for vehicular communications. However, this potential is hindered by the severe isotropic path loss and the harsh propagation of high-frequency channels. Moreover, mmWave signals are typically directional, to benefit from beamforming gain, and require frequent realignment of the beams to maintain connectivity. These limitations are particularly challenging when considering vehicle-to-vehicle (V2V) transmissions, because of the highly mobile nature of the vehicular scenarios, and pose new challenges for proper vehicular communication design. In this paper, we conduct simulations to compare the performance of IEEE 802.11p and the mmWave technology to support V2V networking, aiming at providing insights on how both technologies can complement each other to meet the requirements of future automotive services. The results show that mmWave-based strategies support ultra-high transmission speeds, and IEEE 802.11p systems have the ability to guarantee reliable and robust communications.
Polarity and Intensity: the Two Aspects of Sentiment Analysis
Venue: arXiv cs.CL 2018
Year: 2018

Current multimodal sentiment analysis frames sentiment score prediction as a general Machine Learning task. However, what the sentiment score actually represents has often been overlooked. As a measurement of opinions and affective states, a sentiment score generally consists of two aspects: polarity and intensity. We decompose sentiment scores into these two aspects and study how they are conveyed through individual modalities and combined multimodal models in a naturalistic monologue setting. In particular, we build unimodal and multimodal multi-task learning models with sentiment score prediction as the main task and polarity and/or intensity classification as the auxiliary tasks. Our experiments show that sentiment analysis benefits from multi-task learning, and individual modalities differ when conveying the polarity and intensity aspects of sentiment.
Conditional Neural Processes
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.
Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer's Disease
Venue: arXiv cs.LG 2018
Year: 2018

Most machine learning classifiers give predictions for new examples accurately, yet without indicating how trustworthy predictions are. In the medical domain, this hampers their integration in decision support systems, which could be useful in the clinical practice. We use a supervised learning approach that combines Ensemble learning with Conformal Predictors to predict conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is to enhance the classification performance (Ensemble learning) and complement each prediction with a measure of credibility (Conformal Predictors). Our results showed the superiority of the proposed approach over a similar ensemble framework with standard classifiers.
Implementing SCRUM to develop a connected robot
Venue: arXiv cs.SE 2018
Year: 2018

Agile methods are receiving a growing interest from industry and these approaches are nowadays well accepted and deployed in software engineering. However, some issues remain to introduce agility in systems engineering. The objective of this paper is to show an agile management implementation in an educational project consisting in developing a connected mobile robot, and to evaluate the issues and benefits of adopting an agile approach. Among the most famous agile management methods, SCRUM has been chosen to lead this experiment. This paper first presents the project and how students traditionally manage it, then it describes how Scrum could be used instead. It evaluates the difficulties and interests to introduce agility in this project, and concludes on the ability of Scrum to design, test and progressively integrate the system, thus providing an operational prototype more quickly.
A Convolutional Neural Network for Aspect Sentiment Classification
Venue: arXiv cs.CL 2018
Year: 2018

With the development of the Internet, natural language processing (NLP), in which sentiment analysis is an important task, became vital in information processing.Sentiment analysis includes aspect sentiment classification. Aspect sentiment can provide complete and in-depth results with increased attention on aspect-level. Different context words in a sentence influence the sentiment polarity of a sentence variably, and polarity varies based on the different aspects in a sentence. Take the sentence, 'I bought a new camera. The picture quality is amazing but the battery life is too short.'as an example. If the aspect is picture quality, then the expected sentiment polarity is 'positive', if the battery life aspect is considered, then the sentiment polarity should be 'negative'; therefore, aspect is important to consider when we explore aspect sentiment in the sentence. Recurrent neural network (RNN) is regarded as a good model to deal with natural language processing, and RNNs has get good performance on aspect sentiment classification including Target-Dependent LSTM (TD-LSTM) ,Target-Connection LSTM (TC-LSTM) (Tang, 2015a, b), AE-LSTM, AT-LSTM, AEAT-LSTM (Wang et al., 2016).There are also extensive literatures on sentiment classification utilizing convolutional neural network, but there is little literature on aspect sentiment classification using convolutional neural network. In our paper, we develop attention-based input layers in which aspect information is considered by input layer. We then incorporate attention-based input layers into convolutional neural network (CNN) to introduce context words information. In our experiment, incorporating aspect information into CNN improves the latter's aspect sentiment classification performance without using syntactic parser or external sentiment lexicons in a benchmark dataset from Twitter but get better performance compared with other models.
The Systems of Computer Mathematics in the Cloud-Based Learning Environment of Educational Institutions
Venue: arXiv cs.CY 2018
Year: 2018

The article highlights the promising ways of providing access to the mathematical software in higher educational institutions. It is emphasized that the cloud computing services implementation is the actual trend of modern ICT pedagogical systems development. The analysis and evaluation of existing ex-perience and educational research of different types of mathematical software packages use are proposed. The methodological guidance on the organization of the cloud-based learning environment in higher educational institutions using the systems of computer mathematics (SCM) is outlined. The advantages and disadvantages of different cloud service models of access to SCM are de-scribed. The cloud-based learning component with the use of Maxima system is described and evaluated.
Uncertainty Quantification of Electronic and Photonic ICs with Non-Gaussian Correlated Process Variations
Venue: arXiv cs.NA 2018
Year: 2018

Since the invention of generalized polynomial chaos in 2002, uncertainty quantification has impacted many engineering fields, including variation-aware design automation of integrated circuits and integrated photonics. Due to the fast convergence rate, the generalized polynomial chaos expansion has achieved orders-of-magnitude speedup than Monte Carlo in many applications. However, almost all existing generalized polynomial chaos methods have a strong assumption: the uncertain parameters are mutually independent or Gaussian correlated. This assumption rarely holds in many realistic applications, and it has been a long-standing challenge for both theorists and practitioners. This paper propose a rigorous and efficient solution to address the challenge of non-Gaussian correlation. We first extend generalized polynomial chaos, and propose a class of smooth basis functions to efficiently handle non-Gaussian correlations. Then, we consider high-dimensional parameters, and develop a scalable tensor method to compute the proposed basis functions. Finally, we develop a sparse solver with adaptive sample selections to solve high-dimensional uncertainty quantification problems. We validate our theory and algorithm by electronic and photonic ICs with 19 to 57 non-Gaussian correlated variation parameters. The results show that our approach outperforms Monte Carlo by $2500\times$ to $3000\times$ in terms of efficiency. Moreover, our method can accurately predict the output density functions with multiple peaks caused by non-Gaussian correlations, which is hard to handle by existing methods. Based on the results in this paper, many novel uncertainty quantification algorithms can be developed and can be further applied to a broad range of engineering domains.
MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming, requiring a trained pathologist to manually examine histopathological images in order to identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a novel region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F-measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.
A New Look at the Refund Mechanism in the Bitcoin Payment Protocol
Venue: arXiv cs.CR 2018
Year: 2018

BIP70 is the Bitcoin payment protocol for communication between a merchant and a pseudonymous customer. McCorry et al. (FC~2016) showed that BIP70 is prone to refund attacks and proposed a fix that requires the customer to sign their refund request. They argued that this minimal change will provide resistance against refund attacks. In this paper, we point out the drawbacks of McCorry et al.'s fix and propose a new approach for protection against refund attacks using the Bitcoin multi-signature mechanism. Our solution does not rely on merchants storing refund requests, and unlike the previous solution, allows updating refund addresses through email. We discuss the security of our proposed method and compare it with the previous solution. We also propose a novel application of our refund mechanism in providing anonymity for payments between a payer and payee in which merchants act as mixing servers. We finally discuss how to combine the above two mechanisms in a single payment protocol to have an anonymous payment protocol secure against refund attacks.
On the Menezes-Teske-Weng's conjecture
Venue: arXiv cs.IT 2018
Year: 2018

In 2003, Alfred Menezes, Edlyn Teske and Annegret Weng presented a conjecture on properties of the solutions of a type of quadratic equation over the binary extension fields, which had been convinced by extensive experiments but the proof was unknown until now. We prove that this conjecture is correct. Furthermore, using this proved conjecture, we have completely determined the null space of a class of linear polynomials.
Privacy-preserving Machine Learning through Data Obfuscation
Venue: arXiv cs.CR 2018
Year: 2018

As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications. While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties. Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs. In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications. Specifically we introduce an obfuscate function and apply it to the training data before feeding them to the model training task. This function adds random noise to existing samples, or augments the dataset with new samples. By doing so sensitive information about the properties of individual samples, or statistical properties of a group of samples, is hidden. Meanwhile the model trained from the obfuscated dataset can still achieve high accuracy. With this approach, the customers can safely disclose the data or models to third-party providers or end users without the need to worry about data privacy. Our experiments show that this approach can effective defeat four existing types of machine learning privacy attacks at negligible accuracy cost.
Evaluating the impact of the 2012 Olympic Games policy on the regeneration of East London using spatio-temporal big data
Venue: arXiv cs.SI 2018
Year: 2018

For urban governments, introducing policies has long been adopted as a main approach to instigate regeneration processes, and to promote social mixing and vitality within the city. However, due to the absence of large fine-grained datasets, the effects of these policies have been historically hard to evaluate. In this research, we illustrate how a combination of large-scale datasets, the Index of Deprivation and Foursquare data (an online geo-social network service) could be used to investigate the impact of the 2012 Olympic Games on the regeneration of East London neighbourhoods. We study and quantify both the physical and socio-economic aspects of this, where our empirical findings suggest that the target areas did indeed undergo regeneration after the Olympic project in some ways. In general, the growth rate of Foursquare venue density in Olympic host boroughs is higher than the city's average level since the preparation period of the Games and up to two years after the event. Furthermore, the deprivation levels in East London boroughs also saw improvements in various aspects after the Olympic Games. One negative outcome we notice is that the housing affordability becomes even more of an issue in East London areas with the regeneration gradually unfolding.
Cultural Influences on Requirements Engineering Process in the Context of Saudi Arabia
Venue: arXiv cs.SE 2018
Year: 2018

Software development requires intensive communication between the requirements engineers and software stakeholders, particularly during the Requirements Engineering (RE) phase. Therefore, the individuals' culture might influence both the RE process and the result. Our aims are to investigate the extend of cultural influences on the RE process, and to analyze how the RE process can be adapted to take into account cultural aspects. The model we present is based on Hofstede's cultural theory. The model was applied on a pilot case study in the context of the conservative Saudi Arabian culture. The results reveal 6 RE aspects and 10 cultural factors that have a large impact on the RE practice.
Incremental Relational Lenses
Venue: arXiv cs.PL 2018
Year: 2018

Lenses are a popular approach to bidirectional transformations, a generalisation of the view update problem in databases, in which we wish to make changes to source tables to effect a desired change on a view. However, perhaps surprisingly, lenses have seldom actually been used to implement updatable views in databases. Bohannon, Pierce and Vaughan proposed an approach to updatable views called relational lenses, but to the best of our knowledge this proposal has not been implemented or evaluated to date. We propose incremental relational lenses, that equip relational lenses with change-propagating semantics that map small changes to the view to (potentially) small changes to the source tables. We also present a language-integrated implementation of relational lenses and a detailed experimental evaluation, showing orders of magnitude improvement over the non-incremental approach. Our work shows that relational lenses can be used to support expressive and efficient view updates at the language level, without relying on updatable view support from the underlying database.
Subpixel-Precise Tracking of Rigid Objects in Real-time
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel object tracking scheme that can track rigid objects in real time. The approach uses subpixel-precise image edges to track objects with high accuracy. It can determine the object position, scale, and rotation with subpixel-precision at around 80fps. The tracker returns a reliable score for each frame and is capable of self diagnosing a tracking failure. Furthermore, the choice of the similarity measure makes the approach inherently robust against occlusion, clutter, and nonlinear illumination changes. We evaluate the method on sequences from rigid objects from the OTB-2015 and VOT2016 dataset and discuss its performance. The evaluation shows that the tracker is more accurate than state-of-the-art real-time trackers while being equally robust.
Partitioning Vectors into Quadruples: Worst-Case Analysis of a Matching-Based Algorithm
Venue: arXiv cs.DS 2018
Year: 2018

Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm, and prove that this algorithm is a (3/2)-approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem, and prove that, in one particular case, the algorithm is a (5/4)-approximation algorithm. Our analysis is tight in all cases except one.
The Cloud Technologies and Augmented Reality: the Prospects of Use
Venue: arXiv cs.CY 2018
Year: 2018

The article discusses the prospects of the augmented reality using as a component of a cloud-based environment. The research goals are the next: to explore the possibility of the augmented reality using with the involvement of the cloud-based environment components. The research objectives are the next: to consider the notion of augmented reality; to analyze the experience the augmented reality using within the cloud environment / system; to outline the prospects of the augmented reality using in educational institutions; to consider the technical conditions of the augmented reality use. The object of research is: the educational process in educational institutions of Ukraine of different levels of accreditation. The subject of research is: the educational process in a cloud-based environment in educational institutions of Ukraine. The research methods used are the next: analysis of scientific publications, observations. The results of the research are the next: on the basis of the analysis of scientific works, it has been established that the experience of the augmented reality using in the systems based on cloud technologies already exists. However, the success of such a combination has not yet been proven. Currently, laboratory tests are known, while the experiment was not carried out under natural conditions in control and experimental groups. It is revealed that the attraction of the augmented reality for the educators requires the development of new methodologies, didactic materials, updating and updating of the curriculum. The main conclusions and recommendations: the main principles of augmented reality use in the learning process are: designing of the environment that is flexible enough, attention should be paid to the teaching and didactic issues; adjusting the educational content for mastering the material provided by the curriculum.
Towards a simplified ontology for better e-commerce search
Venue: arXiv cs.IR 2018
Year: 2018

Query Understanding is a semantic search method that can classify tokens in a customer's search query to entities such as Product, Brand, etc. This method can overcome the limitations of bag-of-words methods but requires an ontology. We show that current ontologies are not optimized for search and propose a simplified ontology framework designed specifically for e-commerce search and retrieval. We also present three methods for automatically extracting product classes for the proposed ontology and compare their performance relative to each other.