OpenRaiser/Intern-Atlas huggingface dataset:
Stabilization at upright equilibrium position of a double inverted pendulum with unconstrained bat optimization
Venue: arXiv cs.SY 2015
Year: 2015

A double inverted pendulum plant has been in the domain of control researchers as an established model for studies on stability. The stability of such as a system taking the linearized plant dynamics has yielded satisfactory results by many researchers using classical control techniques. The established model that is analyzed as part of this work was tested under the influence of time delay, where the controller was fine tuned using a BAT algorithm taking into considering the fitness function of square of error. This proposed method gave results which were better when compared without time delay wherein the calculated values indicated the issues when incorporating time delay.
Parameterizing the Permanent: Genus, Apices, Minors, Evaluation mod 2^k
Venue: arXiv cs.CC 2015
Year: 2015

We identify and study relevant structural parameters for the problem PerfMatch of counting perfect matchings in a given input graph $G$. These generalize the well-known tractable planar case, and they include the genus of $G$, its apex number (the minimum number of vertices whose removal renders $G$ planar), and its Hadwiger number (the size of a largest clique minor). To study these parameters, we first introduce the notion of combined matchgates, a general technique that bridges parameterized counting problems and the theory of so-called Holants and matchgates: Using combined matchgates, we can simulate certain non-existing gadgets $F$ as linear combinations of $t=O(1)$ existing gadgets. If a graph $G$ features $k$ occurrences of $F$, we can then reduce $G$ to $t^k$ graphs that feature only existing gadgets, thus enabling parameterized reductions. As applications of this technique, we simplify known $4^g n^{O(1)}$ time algorithms for PerfMatch on graphs of genus $g$. Orthogonally to this, we show #W[1]-hardness of the permanent on $k$-apex graphs, implying its #W[1]-hardness under the Hadwiger number. Additionally, we rule out $n^{o(k/\log k)}$ time algorithms under the counting exponential-time hypothesis #ETH. Finally, we use combined matchgates to prove parity-W[1]-hardness of evaluating the permanent modulo $2^k$, complementing an $O(n^{4k-3})$ time algorithm by Valiant and answering an open question of Björklund. We also obtain a lower bound of $n^{Ω(k/\log k)}$ under the parity version of the exponential-time hypothesis.
Optimal Construction of Regenerating Code through Rate-matching in Hostile Networks
Venue: arXiv cs.IT 2015
Year: 2015

Regenerating code is a class of code very suitable for distributed storage systems, which can maintain optimal bandwidth and storage space. Two types of important regenerating code have been constructed: the minimum storage regeneration (MSR) code and the minimum bandwidth regeneration (MBR) code. However, in hostile networks where adversaries can compromise storage nodes, the storage capacity of the network can be significantly affected. In this paper, we propose two optimal constructions of regenerating codes through rate-matching that can combat against this kind of adversaries in hostile networks: 2-layer rate-matched regenerating code and $m$-layer rate-matched regenerating code. For the 2-layer code, we can achieve the optimal storage efficiency for given system requirements. Our comprehensive analysis shows that our code can detect and correct malicious nodes with higher storage efficiency compared to the universally resilient regenerating code which is a straightforward extension of regenerating code with error detection and correction capability. Then we propose the $m$-layer code by extending the 2-layer code and achieve the optimal error correction efficiency by matching the code rate of each layer's regenerating code. We also demonstrate that the optimized parameter can achieve the maximum storage capacity under the same constraint. Compared to the universally resilient regenerating code, our code can achieve much higher error correction efficiency.
Review-Level Sentiment Classification with Sentence-Level Polarity Correction
Venue: arXiv cs.CL 2015
Year: 2015

We propose an effective technique to solving review-level sentiment classification problem by using sentence-level polarity correction. Our polarity correction technique takes into account the consistency of the polarities (positive and negative) of sentences within each product review before performing the actual machine learning task. While sentences with inconsistent polarities are removed, sentences with consistent polarities are used to learn state-of-the-art classifiers. The technique achieved better results on different types of products reviews and outperforms baseline models without the correction technique. Experimental results show an average of 82% F-measure on four different product review domains.
On Welfare Approximation and Stable Pricing
Venue: arXiv cs.GT 2015
Year: 2015

We study the power of item-pricing as a tool for approximately optimizing social welfare in a combinatorial market. We consider markets with $m$ indivisible items and $n$ buyers. The goal is to set prices to the items so that, when agents purchase their most demanded sets simultaneously, no conflicts arise and the obtained allocation has nearly optimal welfare. For gross substitutes valuations, it is well known that it is possible to achieve optimal welfare in this manner. We ask: can one achieve approximately efficient outcomes for valuations beyond gross substitutes? We show that even for submodular valuations, and even with only two buyers, one cannot guarantee an approximation better than $Ω(\sqrt{m})$. The same lower bound holds for the class of single-minded buyers as well. Beyond the negative results on welfare approximation, our results have daunting implications on revenue approximation for these valuation classes: in order to obtain good approximation to the collected revenue, one would necessarily need to abandon the common approach of comparing the revenue to the optimal welfare; a fundamentally new approach would be required.
Design of an Alarm System for Isfahan Ozone Level based on Artificial Intelligence Predictor Models
Venue: arXiv cs.AI 2015
Year: 2015

The ozone level prediction is an important task of air quality agencies of modern cities. In this paper, we design an ozone level alarm system (OLP) for Isfahan city and test it through the real word data from 1-1-2000 to 7-6-2011. We propose a computer based system with three inputs and single output. The inputs include three sensors of solar ultraviolet (UV), total solar radiation (TSR) and total ozone (O3). And the output of the system is the predicted O3 of the next day and the alarm massages. A developed artificial intelligence (AI) algorithm is applied to determine the output, based on the inputs variables. For this issue, AI models, including supervised brain emotional learning (BEL), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs), are compared in order to find the best model. The simulation of the proposed system shows that it can be used successfully in prediction of major cities ozone level.
A Study of an Modeling Method of T-S fuzzy System Based on Moving Fuzzy Reasoning and Its Application
Venue: arXiv cs.AI 2015
Year: 2015

To improve the effectiveness of the fuzzy identification, a structure identification method based on moving rate is proposed for T-S fuzzy model. The proposed method is called "T-S modeling (or T-S fuzzy identification method) based on moving rate". First, to improve the shortcomings of existing fuzzy reasoning methods based on matching degree, the moving rates for s-type, z-type and trapezoidal membership functions of T-S fuzzy model were defined. Then, the differences between proposed moving rate and existing matching degree were explained. Next, the identification method based on moving rate is proposed for T-S model. Finally, the proposed identification method is applied to the fuzzy modeling for the precipitation forecast and security situation prediction. Test results show that the proposed method significantly improves the effectiveness of fuzzy identification.
LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
Venue: arXiv cs.CV 2015
Year: 2015

Logo detection from images has many applications, particularly for brand recognition and intellectual property protection. Most existing studies for logo recognition and detection are based on small-scale datasets which are not comprehensive enough when exploring emerging deep learning techniques. In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. To facilitate research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes, 10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo classes, 100 brands, and 130,608 logo objects. We describe the ideas and challenges for constructing such a large-scale database. Another key contribution of this work is to apply emerging deep learning techniques for logo detection and brand recognition tasks, and conduct extensive experiments by exploring several state-of-the-art deep region-based convolutional networks techniques for object detection tasks. The LOGO-net will be released at http://logo-net.org/
A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model
Venue: arXiv cs.CV 2015
Year: 2015

This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem. The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme with various face inputting channels, such as the original RGB face image, the detail layer image, and the lighting layer image. With a carefully designed CNN model of deep structure, large input size and small convolutional kernels, we have achieved a high prediction correlation of 0.88. This result convinces us that the problem of facial attractiveness prediction can be solved by deep learning approach, and it also shows the important roles of the facial smoothness, lightness, and color information that were involved in facial beauty perception, which is consistent with the result of recent psychology studies. Furthermore, we analyze the high-level features learnt by CNN through visualization of its hidden layers, and some interesting phenomena were observed. It is found that the contours and appearance of facial features, especially eyes and moth, are the most significant facial attributes for facial attractiveness prediction, which is also consistent with the visual perception intuition of human.
Characteristic specific prioritized dynamic average burst round robin scheduling for uniprocessor and multiprocessor environment
Venue: arXiv cs.OS 2015
Year: 2015

CPU scheduling is one of the most crucial operations performed by operating systems. Different conventional algorithms like FCFS, SJF, Priority, and RR (Round Robin) are available for CPU Scheduling. The effectiveness of Priority and Round Robin scheduling algorithm completely depends on selection of priority features of processes and on the choice of time quantum. In this paper a new CPU scheduling algorithm has been proposed, named as CSPDABRR (Characteristic specific Prioritized Dynamic Average Burst Round Robin), that uses seven priority features for calculating priority of processes and uses dynamic time quantum instead of static time quantum used in RR. The performance of the proposed algorithm is experimentally compared with traditional RR and Priority scheduling algorithm in both uni-processor and multi-processor environment. The results of our approach presented in this paper demonstrate improved performance in terms of average waiting time, average turnaround time, and optimal priority feature.
Towards Structured Deep Neural Network for Automatic Speech Recognition
Venue: arXiv cs.CL 2015
Year: 2015

In this paper we propose the Structured Deep Neural Network (structured DNN) as a structured and deep learning framework. This approach can learn to find the best structured object (such as a label sequence) given a structured input (such as a vector sequence) by globally considering the mapping relationships between the structures rather than item by item. When automatic speech recognition is viewed as a special case of such a structured learning problem, where we have the acoustic vector sequence as the input and the phoneme label sequence as the output, it becomes possible to comprehensively learn utterance by utterance as a whole, rather than frame by frame. Structured Support Vector Machine (structured SVM) was proposed to perform ASR with structured learning previously, but limited by the linear nature of SVM. Here we propose structured DNN to use nonlinear transformations in multi-layers as a structured and deep learning approach. This approach was shown to beat structured SVM in preliminary experiments on TIMIT.
Improved Approximation Algorithms for Relay Placement
Venue: arXiv cs.DS 2015
Year: 2015

In the relay placement problem the input is a set of sensors and a number $r \ge 1$, the communication range of a relay. In the one-tier version of the problem the objective is to place a minimum number of relays so that between every pair of sensors there is a path through sensors and/or relays such that the consecutive vertices of the path are within distance $r$ if both vertices are relays and within distance 1 otherwise. The two-tier version adds the restrictions that the path must go through relays, and not through sensors. We present a 3.11-approximation algorithm for the one-tier version and a PTAS for the two-tier version. We also show that the one-tier version admits no PTAS, assuming P $\ne$ NP.
Wireless Multihop Device-to-Device Caching Networks
Venue: arXiv cs.IT 2015
Year: 2015

We consider a wireless device-to-device (D2D) network where $n$ nodes are uniformly distributed at random over the network area. We let each node with storage capacity $M$ cache files from a library of size $m \geq M$. Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical "protocol model" of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions including Zipf distributions with exponent less than one. In the parameter regimes of interest, we show that a decentralized random caching strategy with uniform probability over the library yields the optimal per-node capacity scaling of $Θ(\sqrt{M/m})$, which is constant with $n$, thus yielding throughput scalability with the network size. Furthermore, the multihop capacity scaling can be significantly better than for the case of single-hop caching networks, for which the per-node capacity is $Θ(M/m)$. The multihop capacity scaling law can be further improved for a Zipf distribution with exponent larger than some threshold $> 1$, by using a decentralized random caching uniformly across a subset of most popular files in the library. Namely, ignoring a subset of less popular files (i.e., effectively reducing the size of the library) can significantly improve the throughput scaling while guaranteeing that all nodes will be served with high probability as $n$ increases.
Data-driven Workflows for Microservices
Venue: arXiv cs.PL 2015
Year: 2015

Microservices is an architectural style inspired by service-oriented computing that has recently started gaining popularity. Jolie is a programming language based on the microservices paradigm: the main building block of Jolie systems are services, in contrast to, e.g., functions or objects. The primitives offered by the Jolie language elicit many of the recurring patterns found in microservices, like load balancers and structured processes. However, Jolie still lacks some useful constructs for dealing with message types and data manipulation that are present in service-oriented computing. In this paper, we focus on the possibility of expressing choices at the level of data types, a feature well represented in standards for Web Services, e.g., WSDL. We extend Jolie to support such type choices and show the impact of our implementation on some of the typical scenarios found in microservice systems. This shows how computation can move from a process-driven to a data-driven approach, and leads to the preliminary identification of recurring communication patterns that can be shaped as design patterns.
Approximation Algorithms for Finding Maximum Induced Expanders
Venue: arXiv cs.DS 2015
Year: 2015

We initiate the study of approximating the largest induced expander in a given graph $G$. Given a $Δ$-regular graph $G$ with $n$ vertices, the goal is to find the set with the largest induced expansion of size at least $δ\cdot n$. We design a bi-criteria approximation algorithm for this problem; if the optimum has induced spectral expansion $λ$ our algorithm returns a $\fracλ{\log^2δ\exp(Δ/λ)}$-(spectral) expander of size at least $δn$ (up to constants). Our proof introduces and employs a novel semidefinite programming relaxation for the largest induced expander problem. We expect to see further applications of our SDP relaxation in graph partitioning problems. In particular, because of the close connection to the small set expansion problem, one may be able to obtain new insights into the unique games problem.
A Socio-Technical approach to address the Information security: Using the 27001 Manager Artefact
Venue: arXiv cs.CR 2015
Year: 2015

In general, the perspective customer / supplier followed by organizations, regarding information security management, is based mainly on management controls based on standards such as ISO / IEC 27001: 2015, resulting in the production of especially technical analysis reports, rather than a socio-technical approach. This leads to the perception by the customer of the delivery of a product instead of a service.The product concerned is reduced to a set of prescriptions, sometimes unrelated, which materialize in a descriptive and static view of client security management. As a result, the client can hardly use the product continuously, following the dynamics of changes in their organization, therefore recognizing value in the provision made by the supplier. The use of the paradigm Service Dominant Logic (LDS), in the development of a range of security management information, helps to change the focus of tangible resources to the intangible assets. The aspects of tangibility, materialized in a document that describes the client's vulnerabilities and attack vectors are referred to a secondary level, given the importance of the intangible aspects, such as the interaction that is established between the customer specialists and supplier. In this article we propose to analyze in the perspective of a socio-technical theory, the Activity Theory, the service provided by an artifact called 27001 Manager, designed to assist the entire cycle of analysis, development and maintenance of an information security management system (ISMS). The analysis aims at observing the existing interaction between customer / supplier, considering that the service is inherently dynamic and inter-subjective, ie the result of a compromise between the customer and the supplier.
Inter-Frame Coding For Broadcast Communication
Venue: arXiv cs.IT 2015
Year: 2015

A novel inter-frame coding approach to the problem of varying channel-state conditions in broadcast wireless communication is developed in this paper; this problem causes the appropriate code-rate to vary across different transmitted frames and different receivers as well. The main aspect of the proposed approach is that it incorporates an iterative rate-matching process into the decoding of the received set of frames, such that: throughout inter-frame decoding, the code-rate of each frame is progressively lowered to or below the appropriate value, prior to applying or re-applying conventional physical-layer channel decoding on it. This iterative rate-matching process is asymptotically analyzed in this paper. It is shown to be optimal, in the sense defined in the paper. Consequently, the data-rates achievable by the proposed scheme are derived. Overall, it is concluded that, compared to the existing solutions, inter-frame coding presents a better complexity versus data-rate tradeoff. In terms of complexity, the overhead of inter-frame decoding includes operations that are similar in type and scheduling to those employed in the relatively- simple iterative erasure decoding. In terms of data-rates, compared to the state-of-the-art two-stage scheme involving both error-correcting and erasure coding, inter-frame coding increases the data-rate by a factor that reaches up to 1.55x.
Improvised Salient Object Detection and Manipulation
Venue: arXiv cs.CV 2015
Year: 2015

In case of salient subject recognition, computer algorithms have been heavily relied on scanning of images from top-left to bottom-right systematically and apply brute-force when attempting to locate objects of interest. Thus, the process turns out to be quite time consuming. Here a novel approach and a simple solution to the above problem is discussed. In this paper, we implement an approach to object manipulation and detection through segmentation map, which would help to desaturate or, in other words, wash out the background of the image. Evaluation for the performance is carried out using the Jaccard index against the well-known Ground-truth target box technique.
Diversity and Adjudication
Venue: arXiv cs.SE 2015
Year: 2015

This paper takes an axiomatic and calculational view of diversity (or "N-version programming"), where multiple implementations of the same specification are executed in parallel to increase dependability. The central notion is "adjudication": once we have multiple, potential different, outcomes, how do we come to a single result? Adjudication operators are explicitly defined and some general properties for these explored.
Virtual-MIMO-Boosted Information Propagation on Highways
Venue: arXiv cs.IT 2015
Year: 2015

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme, in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range and therefore accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the \emph{Information Propagation Speed} (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.