OpenRaiser/Intern-Atlas huggingface dataset:
Tailoring Product Ownership in Large-Scale Agile
Venue: arXiv cs.SE 2018
Year: 2018

In large-scale agile projects, product owners undertake a range of challenging and varied activities beyond those conventionally associated with that role. Using in-depth research interviews from 93 practitioners working in cross-border teams, from 21 organisations, our rich empirical data offers a unique international perspective into product owner activities. We found that the leaders of large-scale agile projects create product owner teams. Product owner team members undertake sponsor, intermediary and release plan master activities to manage scale. They undertake communicator and traveller activities to manage distance and technical architect, governor and risk assessor activities to manage governance. Based on our findings, we describe product owner behaviors that are valued by experienced product owners and their line managers.
Fast and Efficient Bulk Multicasting over Dedicated Inter-Datacenter Networks
Venue: arXiv cs.NI 2018
Year: 2018

Several organizations have built multiple datacenters connected via dedicated wide area networks over which large inter-datacenter transfers take place. This includes tremendous volumes of bulk multicast traffic generated as a result of data and content replication. Although one can perform these transfers using a single multicast forwarding tree, that can lead to poor performance as the slowest receiver on each tree dictates the completion time for all receivers. Using multiple trees per transfer each connected to a subset of receivers alleviates this concern. The choice of multicast trees also determines the total bandwidth usage. To further improve the performance, bandwidth over dedicated inter-datacenter networks can be carved for different multicast trees over specific time periods to avoid congestion and minimize the average receiver completion times. In this paper, we break this problem into the three sub-problems of partitioning, tree selection, and rate allocation. We present an algorithm called QuickCast which is computationally fast and allows us to significantly speed up multiple receivers per bulk multicast transfer with control over extra bandwidth consumption. We evaluate QuickCast against a variety of synthetic and real traffic patterns as well as real WAN topologies. Compared to performing bulk multicast transfers as separate unicast transfers, QuickCast achieves up to $3.64\times$ reduction in mean completion times while at the same time using $0.71\times$ the bandwidth. Also, QuickCast allows the top $50\%$ of receivers to complete between $3\times$ to $35\times$ faster on average compared with when a single forwarding multicast tree is used for data delivery.
Siamese Networks for Semantic Pattern Similarity
Venue: arXiv cs.CL 2018
Year: 2018

Semantic Pattern Similarity is an interesting, though not often encountered NLP task where two sentences are compared not by their specific meaning, but by their more abstract semantic pattern (e.g., preposition or frame). We utilize Siamese Networks to model this task, and show its usefulness in determining SQL patterns for unseen questions in a database-backed question answering scenario. Our approach achieves high accuracy and contains a built-in proxy for confidence, which can be used to keep precision arbitrarily high.
Comparing Linear Width Parameters for Directed Graphs
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we introduce the linear clique-width, linear NLC-width, neighbourhood-width, and linear rank-width for directed graphs. We compare these parameters with each other as well as with the previously defined parameters directed path-width and directed cut-width. It turns out that the parameters directed linear clique-width, directed linear NLC-width, directed neighbourhood-width, and directed linear rank-width are equivalent in that sense, that all of these parameters can be upper bounded by each of the others. For the restriction to digraphs of bounded vertex degree directed path-width and directed cut-width are equivalent. Further for the restriction to semicomplete digraphs of bounded vertex degree all six mentioned width parameters are equivalent. We also show close relations of the measures to their undirected versions of the underlying undirected graphs, which allow us to show the hardness of computing the considered linear width parameters for directed graphs. Further we give first characterizations for directed graphs defined by parameters of small width.
Line-of-Sight and Pilot Contamination Effects on Correlated Multi-cell Massive MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

This work considers the uplink (UL) of a multi-cell massive MIMO system with $L$ cells, having each $K$ mono-antenna users communicating with an $N-$antennas base station (BS). The channel model involves Rician fading with distinct per-user Rician factors and channel correlation matrices and takes into account pilot contamination and imperfect CSI. The objective is to evaluate the performances of such systems with different single-cell and multi-cell detection methods. In the former, we investigate MRC and single-cell MMSE (S-MMSE); as to the latter, we are interested in multi-cell MMSE (M-MMSE) that was recently shown to provide unbounded rates in Rayleigh fading. The analysis is led assuming the infinite $N$ limit and yields informative closed-form approximations that are substantiated by a selection of numerical results for finite system dimensions. Accordingly, these expressions are accurate and provide relevant insights into the effects of the different system parameters on the overall performances.
TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph
Venue: arXiv cs.AI 2018
Year: 2018

Knowledge graph is a kind of valuable knowledge base which would benefit lots of AI-related applications. Up to now, lots of large-scale knowledge graphs have been built. However, most of them are non-Chinese and designed for general purpose. In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented. It is built automatically from massive technical papers that are published in Chinese academic journals of different research domains. Some carefully designed heuristic rules are used to extract high quality entities and relations. Totally, it comprises of over 260 million triplets that are built upon more than 52 million entities which come from 38 research domains. Our preliminary ex-periments indicate that TechKG has high adaptability and can be used as a dataset for many diverse AI-related applications. We released TechKG at: http://www.techkg.cn.
Heuristics for Efficient Sparse Blind Source Separation
Venue: arXiv cs.LG 2018
Year: 2018

Sparse Blind Source Separation (sparse BSS) is a key method to analyze multichannel data in fields ranging from medical imaging to astrophysics. However, since it relies on seeking the solution of a non-convex penalized matrix factorization problem, its performances largely depend on the optimization strategy. In this context, Proximal Alternating Linearized Minimization (PALM) has become a standard algorithm which, despite its theoretical grounding, generally provides poor practical separation results. In this work, we propose a novel strategy that combines a heuristic approach with PALM. We show its relevance on realistic astrophysical data.
Properties and constructions of constrained codes for DNA-based data storage
Venue: arXiv cs.IT 2018
Year: 2018

We describe properties and constructions of constraint-based codes for DNA-based data storage which account for the maximum repetition length and AT/GC balance. We present algorithms for computing the number of sequences with maximum repetition length and AT/GC balance constraint. We describe routines for translating binary runlength limited and/or balanced strings into DNA strands, and compute the efficiency of such routines. We show that the implementation of AT/GC-balanced codes is straightforward accomplished with binary balanced codes. We present codes that account for both the maximum repetition length and AT/GC balance. We compute the redundancy difference between the binary and a fully fledged quaternary approach.
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
Venue: arXiv cs.LG 2018
Year: 2018

We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal model representations not to encode sensitive information, which has the effect of pushing downstream predictors towards exhibiting demographic parity with respect to the sensitive information. The net result is that these BriarPatches provide an intervention mechanism available at user level, and complements prior research on fair representations that were previously only applicable by model developers and ML experts.
User Association and Load Balancing for Massive MIMO through Deep Learning
Venue: arXiv cs.IT 2018
Year: 2018

This work investigates the use of deep learning to perform user cell association for sum-rate maximization in Massive MIMO networks. It is shown how a deep neural network can be trained to approach the optimal association rule with a much more limited computational complexity, thus enabling to update the association rule in real-time, on the basis of the mobility patterns of users. In particular, the proposed neural network design requires as input only the users' geographical positions. Numerical results show that it guarantees the same performance of traditional optimization-oriented methods.
Protocol for Energy-Efficiency using Robust Control on WSN
Venue: arXiv cs.SY 2018
Year: 2018

The present work analyzes the feasibility of obtaining a single controller (robust), with theoretical guarantees of stability and performance, valid for a total set of network configurations in designed the controller for an uncertain success probability obtain the protocol for Energy-Efficiency in Networked Control System NCS. In particular, this work investigates the performance degradation, in terms of the $\mathcal{H}_{\infty}$ guaranteed cost, between optimal controller design (precisely known probability) and the sub-optimal controller design (robust to probability uncertainties). The feasibility of the proposed methodology is validated by a numerical example.
ICNLoWPAN -- Named-Data Networking for Low Power IoT Networks
Venue: arXiv cs.NI 2018
Year: 2018

Information Centric Networking is considered a promising communication technology for the constrained IoT, but NDN was designed only for standard network infrastructure. In this paper, we design and evaluate an NDN convergence layer for low power lossy links that (1) augments the NDN stateful forwarding with a highly efficient name eliding, (2) devises stateless compression schemes for standard NDN use cases, (3) adapts NDN packets to the small MTU size of IEEE 802.15.4, and (4) generates compatibility with 6LoWPAN so that IPv6 and NDN can coexist on the same LoWPAN links. Our findings indicate that stateful compression can reduce the size of NDN data packets by more than 70% in realistic examples. Our experiments show that for common use cases ICNLoWPAN saves 33% of transmission resources over NDN, and about 20% over 6LoWPAN.
Fuzzy Controller of Reward of Reinforcement Learning For Handwritten Digit Recognition
Venue: arXiv cs.LG 2018
Year: 2018

Recognition of human environment with computer systems always was a big deal in artificial intelligence. In this area handwriting recognition and conceptualization of it to computer is an important area in it. In the past years with growth of machine learning in artificial intelligence, efforts to using this technique increased. In this paper is tried to using fuzzy controller, to optimizing amount of reward of reinforcement learning for recognition of handwritten digits. For this aim first a sample of every digit with 10 standard computer fonts, given to actor and then actor is trained. In the next level is tried to test the actor with dataset and then results show improvement of recognition when using fuzzy controller of reinforcement learning.
Scalable Block-Wise Product BCH Codes
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we comprehensively investigate block-wise product (BWP) BCH codes, wherein raw data is arranged in the form of block-wise matrix and each row and column BCH codes intersect on one data block. We first devise efficient BCH decoding algorithms, including reduced-1-bit decoding, extra-1-bit list decoding, and extra-2-bit list decoding. We next present a systematic construction of BWP-BCH codes upon given message and parity lengths that takes into account for performance, implementation and scalability, rather than focusing on a regularly defined BWP-BCH code. It can easily accommodate different message length or parity length at minimal changes. It employs extended BCH codes instead of BCH codes to reduce miscorrection rate and an inner RS code to lower error floor. We also describe a high-speed scalable encoder. We finally present a novel iterative decoding algorithm which is divided into three phases. The first phase iteratively applies reduced BCH correction capabilities to correct lightly corrupted rows/columns while suppressing miscorrection, until the process stalls. The second phase iteratively decodes up to the designed correction capabilities, until the process stalls. The last phase iteratively applies the proposed list decoding in a novel manner which effectively determines the correct candidate. The key idea is to use cross decoding upon each list candidate to pick the candidate which enables the maximum number of successful cross decoding. Our simulations show that the proposed algorithm provides a significant performance boost compared to the state-of-the-art algorithms.
Real Time 3D Indoor Human Image Capturing Based on FMCW Radar
Venue: arXiv cs.CV 2018
Year: 2018

Most smart systems such as smart home and smart health response to human's locations and activities. However, traditional solutions are either require wearable sensors or lead to leaking privacy. This work proposes an ambient radar solution which is a real-time, privacy secure and dark surroundings resistant system. In this solution, we use a low power, Frequency-Modulated Continuous Wave (FMCW) radar array to capture the reflected signals and then construct to 3D image frames. This solution designs $1)$a data preprocessing mechanism to remove background static reflection, $2)$a signal processing mechanism to transfer received complex radar signals to a matrix contains spacial information, and $3)$ a Deep Learning scheme to filter broken frame which caused by the rough surface of human's body. This solution has been extensively evaluated in a research area and captures real-time human images that are recognizable for specific activities. Our results show that the indoor capturing is clear to be recognized frame by frame compares to camera recorded video.
On effective human robot interaction based on recognition and association
Venue: arXiv cs.CV 2018
Year: 2018

Faces play a magnificent role in human robot interaction, as they do in our daily life. The inherent ability of the human mind facilitates us to recognize a person by exploiting various challenges such as bad illumination, occlusions, pose variation etc. which are involved in face recognition. But it is a very complex task in nature to identify a human face by humanoid robots. The recent literatures on face biometric recognition are extremely rich in its application on structured environment for solving human identification problem. But the application of face biometric on mobile robotics is limited for its inability to produce accurate identification in uneven circumstances. The existing face recognition problem has been tackled with our proposed component based fragmented face recognition framework. The proposed framework uses only a subset of the full face such as eyes, nose and mouth to recognize a person. It's less searching cost, encouraging accuracy and ability to handle various challenges of face recognition offers its applicability on humanoid robots. The second problem in face recognition is the face spoofing, in which a face recognition system is not able to distinguish between a person and an imposter (photo/video of the genuine user). The problem will become more detrimental when robots are used as an authenticator. A depth analysis method has been investigated in our research work to test the liveness of imposters to discriminate them from the legitimate users. The implication of the previous earned techniques has been used with respect to criminal identification with NAO robot. An eyewitness can interact with NAO through a user interface. NAO asks several questions about the suspect, such as age, height, her/his facial shape and size etc., and then making a guess about her/his face.
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Venue: arXiv cs.CV 2018
Year: 2018

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. It is a challenging task to have real-time, efficient, and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations. A key limitation of the prior works is the lack of a systematic design optimization framework of RNN model and hardware implementations, especially when the block size (or compression ratio) should be jointly optimized with RNN type, layer size, etc. In this paper, we adopt the block-circulant matrix-based framework, and present the Efficient RNN (E-RNN) framework for FPGA implementations of the Automatic Speech Recognition (ASR) application. The overall goal is to improve performance/energy efficiency under accuracy requirement. We use the alternating direction method of multipliers (ADMM) technique for more accurate block-circulant training, and present two design explorations providing guidance on block size and reducing RNN training trials. Based on the two observations, we decompose E-RNN in two phases: Phase I on determining RNN model to reduce computation and storage subject to accuracy requirement, and Phase II on hardware implementations given RNN model, including processing element design/optimization, quantization, activation implementation, etc. Experimental results on actual FPGA deployments show that E-RNN achieves a maximum energy efficiency improvement of 37.4$\times$ compared with ESE, and more than 2$\times$ compared with C-LSTM, under the same accuracy.
Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing
Venue: arXiv cs.GR 2018
Year: 2018

Edge-preserving smoothing is a fundamental procedure for many computer vision and graphic applications. This can be achieved with either local methods or global methods. In most cases, global methods can yield superior performance over local ones. However, local methods usually run much faster than global ones. In this paper, we propose a new global method that embeds the bilateral filter in the least squares model for efficient edge-preserving smoothing. The proposed method can show comparable performance with the state-of-the-art global method. Meanwhile, since the proposed method can take advantages of the efficiency of the bilateral filter and least squares model, it runs much faster. In addition, we show the flexibility of our method which can be easily extended by replacing the bilateral filter with its variants. They can be further modified to handle more applications. We validate the effectiveness and efficiency of the proposed method through comprehensive experiments in a range of applications.
Globalness Detection in Online Social Network
Venue: arXiv cs.SI 2018
Year: 2018

Classification problems have made significant progress due to the maturity of artificial intelligence (AI). However, differentiating items from categories without noticeable boundaries is still a huge challenge for machines -- which is also crucial for machines to be intelligent. In order to study the fuzzy concept on classification, we define and propose a globalness detection with the four-stage operational flow. We then demonstrate our framework on Facebook public pages inter-like graph with their geo-location. Our prediction algorithm achieves high precision (89%) and recall (88%) of local pages. We evaluate the results on both states and countries level, finding that the global node ratios are relatively high in those states (NY, CA) having large and international cities. Several global nodes examples have also been shown and studied in this paper. It is our hope that our results unveil the perfect value from every classification problem and provide a better understanding of global and local nodes in Online Social Networks (OSNs).
Explaining Neural Networks Semantically and Quantitatively
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically. The analysis of the specific rationale of each prediction made by the CNN presents a key issue of understanding neural networks, but it is also of significant practical values in certain applications. In this study, we propose to distill knowledge from the CNN into an explainable additive model, so that we can use the explainable model to provide a quantitative explanation for the CNN prediction. We analyze the typical bias-interpreting problem of the explainable model and develop prior losses to guide the learning of the explainable additive model. Experimental results have demonstrated the effectiveness of our method.
Smart Contracts for Multiagent Plan Execution in Untrusted Cyber-physical Systems
Venue: arXiv cs.CR 2018
Year: 2018

Intelligent Cyber-physical systems can be modelled as multi-agent systems with planning capability to impart adaptivity for changing contexts. In such multi-agent systems, the protocol for plan execution must result in the proper completion and ordering of actions in spite of their distributed execution. However, in untrusted scenarios, there is a possibility of agents not respecting the protocol either due to faults or due to malicious reasons thereby resulting in plan failure. In order to prevent such situations, we propose to implement the execution of agents through smart contracts. This points to a generic architecture seamlessly integrating intelligent planning-based CPS and smart-contracts.
Sparse mmWave OFDM Channel Estimation Using Compressed Sensing in OFDM Systems
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes and analyzes a mmWave sparse channel estimation technique for OFDM systems that uses the Orthogonal Matching Pursuit (OMP) algorithm. This greedy algorithm retrieves one additional multipath component (MPC) per iteration until a stop condition is met. We obtain an analytical approximation for the OMP estimation error variance that grows with the number of retrieved MPCs (iterations). The OMP channel estimator error variance outperforms a classic maximum-likelihood (ML) non-sparse channel estimator by a factor of approximately $2\hat{L}/M$ where $\hat{L}$ is the number of retrieved MPCs (iterations) and $M$ the number of taps of the Discrete Equivalent Channel. When the MPC amplitude distribution is heavy-tailed, the channel power is concentrated in a subset of dominant MPCs. In this case OMP performs fewer iterations as it retrieves only these dominant large MPCs. Hence for this MPC amplitude distribution the estimation error advantage of OMP over ML is improved. In particular, for channels with MPCs that have lognormally-distributed amplitudes, the OMP estimator recovers approximately 5-15 dominant MPCs in typical mmWave channels, with 15-45 weak MPCs that remain undetected.