Reviewerly/exHarmony huggingface dataset:
A generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras

Abstract Categorical compositional distributional semantics is a model of natural language; it combines the statistical vector space models of words with the compositional models of grammar. We formalise in this model the generalised quantifier theory of natural language, due to Barwise and Cooper. The underlying setting is a compact closed category with bialgebras. We start from a generative grammar formalisation and develop an abstract categorical compositional semantics for it, and then instantiate the abstract setting to sets and relations and to finite-dimensional vector spaces and linear maps. We prove the equivalence of the relational instantiation to the truth theoretic semantics of generalised quantifiers. The vector space instantiation formalises the statistical usages of words and enables us to, for the first time, reason about quantified phrases and sentences compositionally in distributional semantics.
Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions.
On extremal double circulant self-dual codes of lengths 90–96

A classification of extremal double circulant self-dual codes of lengths up to 88 is known. We extend this classification to length 96. We give a classification of extremal double circulant self-dual codes of lengths 90, 92, 94 and 96. We also classify double circulant self-dual codes with parameters [90, 45, 14] and [96, 48, 16]. In addition, we demonstrate that no double circulant self-dual [90, 45, 14] code has an extremal self-dual neighbor, and no double circulant self-dual [96, 48, 16] code has a self-dual neighbor with minimum weight at least 18.
Qubits through Queues: The Capacity of Channels with Waiting Time Dependent Errors

We consider a setting where qubits are processed sequentially, and derive fundamental limits on the rate at which classical information can be transmitted using quantum states that decohere in time. Specifically, we model the sequential processing of qubits using a single server queue, and derive explicit expressions for the capacity of such a `queue-channel.' We also demonstrate a sweet-spot phenomenon with respect to the arrival rate to the queue, i.e., we show that there exists a value of the arrival rate of the qubits at which the rate of information transmission (in bits/sec) through the queue-channel is maximised. Next, we consider a setting where the average rate of processing qubits is fixed, and show that the capacity of the queue-channel is maximised when the processing time is deterministic. We also discuss design implications of these results on quantum information processing systems.
Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios

Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.
Personalized Mobile App Recommendation by Learning User's Interest from Social Media

The diversity of personal interest and preference of mobile users results in a wide spectrum of mobile app usage, and it is important to predict such app preference in order to provide personalized services. However, currently available individual app usage data is very limited, which does not cover a large user base. In this paper, we demonstrate that it is possible to make personalized app usage estimation by learning user's app preference from the social media, i.e., public accessible tweets, which can also reflect user's interest and make up for the sparsity of app usage data. By proposing a novel generative model named IMCF+ to transfer user interest from rich tweet information to sparse app usage, we achieve personalized app recommendations via learning the interest's correlation between apps and tweets. Based on a real-world app usage and tweet dataset over a large population, we evaluate the performance of IMCF+ with a variety of scenarios and parameters. With only 10 percent training data, our IMCF+ approach achieves 82.5 percent hitrate in predicting the top ten apps. Moreover, IMCF+ outperforms the other six state-of-the-art algorithms by 4.7 percent and 10 percent in high sparsity case and user cold-start scenario, indicating the effectiveness of our method. All these results demonstrate that our technique can reliably learn user's interest from tweets to help solve the personalized app recommendation problem. Our study is the first step forward for transferring user's interest learned from social media to app preference, which paves the way for providing higher-quality personalized app recommendation and services for mobile users.
Extreme learning machines with expectation kernels

Extreme learning machines (ELMs), especially kernel ELMs (KELMs), have achieved great success in providing efficient and effective solutions to classification applications. This paper proposes a simple but effective expectation kernel ELM (EKELM) to improve the classification abilities of ELMs. EKELM is based on a new family of positive semidefinite (PSD) kernel functions, namely expectation kernels (EKs), to learn similarities between data samples by combining the advances of random feature mapping and conventional kernel functions. EK provides a new insight to model ELMs from the perspective of kernel approximation using random sampling techniques. Particularly, we show that the distribution of random sampling weights, i.e. the input weight of ELM, has deep influences on the classification performance, and we use Gaussian distributions to generate random weights in EKELM. Besides, the number of sampling samples, i.e. the number of hidden neurons in ELM, can be reduced by using a proper nonlinear kernel function. We test the proposed EKELM on 20 benchmark classification datasets, the results prove that using a Gaussian distribution to generate random sampling weights has better performance than uniform distribution. Also, the results show that nonlinear RBF EKELM achieves comparable classification performance compared with the conventional RBF kernel, and requires much less random samples compared with linear EKELM.
Usefulness of linear prediction residual for replay attack detection

This work demonstrates the usefulness of processing linear prediction (LP) residual signal for detecting replay attacks. The playback device having non-flat frequency response modulates the input signal passing through it, resulting spectral distortion in replay signals especially in the low frequency regions (0–300 Hz). In effect, the excitation source information present below 300 Hz gets distorted. The linear prediction (LP) residual signal implicitly contains excitation source information. The excitation source feature, residual mel frequency cepstral coefficients (RMFCC) obtained from the LP residual signal has been proposed for replay detection task. The significance of RMFCC feature has been investigated through Gaussian mixtures model-universal background model (GMM-UBM) ASV experiments and spoof detection experiments using self-developed IITG-MV replay database and standard ASVspoof 2017 database, respectively. For IITG-MV, relative tandem-detection cost function (t-DCF) improvements of 29.78% (male), 7.96% (female) and 24.52% (whole-set) are observed for RMFCC+MFCC combination over MFCC feature. For ASVspoof 2017, relative EER improvements of 37.34% are reported for RMFCC+CQCC combination over CQCC feature. Minimum reported EER is 9.50% for RMFCC+CQCC system. Experimental results shows that the RMFCC feature contains information complementary to MFCC and CQCC features.
Sufficiency of Deterministic Policies for Atomless Discounted and Uniformly Absorbing MDPs with Multiple Criteria

This paper studies Markov decision processes (MDPs) with atomless initial state distributions and atomless transition probabilities. Such MDPs are called atomless. The initial state distribution is considered to be fixed. We show that for discounted MDPs with bounded one-step reward vector-functions, for each policy there exists a deterministic (that is, nonrandomized and stationary) policy with the same performance vector. This fact is proved in the paper for a more general class of uniformly absorbing MDPs with expected total rewards, and then it is extended under certain assumptions to MDPs with unbounded rewards. For problems with multiple criteria and constraints, the results of this paper imply that for atomless MDPs studied in this paper it is sufficient to consider only deterministic policies, while without the atomless assumption it is well-known that randomized policies can outperform deterministic ones. We also provide an example of an MDP demonstrating that if a vector measure is defined on a standard Borel space, then Lyapunov's convexity theorem is a special case of the described results.
From superpixel to human shape modelling for carried object detection

Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background information. A group of superpixels with a high carried object probability and a strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.
Wide context learning network for stereo matching

Binocular stereo matching is a challenging problem in computer vision. Recently, convolutional neural networks (CNNs) have emerged as a promising approach. However, matching ambiguities on ill-posed regions remain an intractable challenge for current methods. In this paper, we propose a novel network model consisting of two main parts: a wide context learning network and stacked encoder–decoder 2D CNNs with a spatial diffusion module. The first part leverages the power of a dilated convolutional layer and spatial pyramid pooling to extract global context information and constitute a matching cost volume. The second part performs contextual aggregation over this matching cost volume to optimize and smooth the matching cost. Finally, we estimate a disparity map by computing the probability of each disparity from the predicted matching cost. The proposed network model allows us to train end-to-end without any further post-processing or refinement. In the experiments, we evaluate our method using the synthesis Scene Flow and real-world KITTI datasets. Our proposed method achieves performance results that are competitive against state-of-the-art methods while maintaining a fast run time.
A Predictive Path-Following Controller for Continuous Replanning With Dynamic Roadmaps

This letter proposes a predictive path-following controller for efficient time parameterization during continuous replanning with dynamic roadmaps (DRMs). DRMs are an approach for fast global path planning in changing environments and are based on a precomputed graph in the configuration space and mappings from workspace voxels to vertices and edges in the graph. However, DRMs are a kinematic path planner and require subsequent steps of smoothing and trajectory generation before execution. This makes it hard to replan while the robot is moving, it is so because the time parameterization should account for the current position and velocity of the robot. To cope with this issue, a predictive path-following controller is presented, which computes the optimal control actions for moving the robot along the planned paths while respecting the bounds on maximum velocities and accelerations. Furthermore, it enables continuous replanning, i.e., the computation of new paths at fixed intervals, which is particularly useful in combination with a prediction of moving obstacles. The real-time capable approach is evaluated in simulation scenarios with moving obstacles and is experimentally validated for a dual-arm robot with 12 degrees of freedom.
A Framework for Adaptive Residual Streaming for Single-Player Cloud Gaming

Applying cloud technology to 3D interactive multimedia applications is a promising way to provide flexible and cost-efficient online high-bandwidth immersive services to a large population of end users. One main reason cloud systems are popular among users is the fact that it relaxes the hardware requirements for high-end interactive visual applications. As most of the computational tasks are done on cloud servers, users no longer need to upgrade their hardware as frequently to keep up with the ever-increasing high-end computing requirements of the latest applications. Moreover, cloud systems make it easier for a user to enjoy applications on different platforms, including mobile devices that are usually not powerful enough to run high-end, memory-intensive services. In short, applying cloud technology to high-end immersive applications has advantages in cost-efficiency and flexibility both for the end users and the service providers. However, there are two main drawbacks to applying cloud technology to 3D interactive multimedia services: (1) high-bandwidth utilization and (2) latency. In this article, we propose a framework that addresses the two problems for single-player cloud gaming by using a combination of collaborative rendering, progressive meshes, and 3D image warping techniques. The experimental results show that the proposed system can reduce the bandwidth usage and improve the visual quality by utilizing local computing power on the client. The results also show that the interaction latency can be reduced somewhat by sacrificing some degree of visual quality in the end system.
Semantic Features for Optimizing Supervised Approach of Sentiment Analysis on Product Reviews

The growth of ecommerce has triggered online reviews as a rich source of product information. Revealing consumer sentiment from the reviews through Sentiment Analysis (SA) is an important task of online product review analysis. Two popular approaches of SA are the supervised approach and the lexicon-based approach. In supervised approach, the employed machine learning (ML) algorithm is not the only one to influence the results of SA. The utilized text features also handle an important role in determining the performance of SA tasks. In this regard, we proposed a method to extract text features that takes into account semantic of words. We argue that this semantic feature is capable of augmenting the results of supervised SA tasks compared to commonly utilized features, i.e., bag-of-words (BoW). To extract the features, we assigned the correct sense of the word in reviewing the sentence by adopting a Word Sense Disambiguation (WSD) technique. Several WordNet similarity algorithms were involved, and correct sentiment values were assigned to words. Accordingly, we generated text features for product review documents. To evaluate the performance of our text features in the supervised approach, we conducted experiments using several ML algorithms and feature selection methods. The results of the experiments using 10-fold cross-validation indicated that our proposed semantic features favorably increased the performance of SA by 10.9%, 9.2%, and 10.6% of precision, recall, and F-Measure, respectively, compared with baseline methods.
Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower–upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage–width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.
Russian Sign Language Dactyl Recognition

In this paper, we compare several real-time sign language dactyl recognition systems and present a new model based on deep convolutional neural networks. These systems are able to recognize Russian alphabet letters presented as static signs in Russian Sign language used by people from deaf community. In such an approach, we recognize words from Russian natural language presented by consequent hand gestures of each letter. We evaluate our approach on Russian (RSL) sign language, for which we collect our own dataset and evaluate dactyl recognition.
Lightweight Identity-Based Group Key Agreements Using Extended Chaotic Maps for Wireless Sensor Networks

Group key agreements (GKA) enable a group of sensor nodes in wireless sensor networks (WSNs) to negotiate a common secret key and to establish a secure communication channel using this secret key. Due to hardware limitations, sensor nodes cannot perform heavy computations. Therefore, lightweight authentication schemes for GKA are required in WSNs. This paper presents a novel lightweight identity-based round-efficient GKA scheme for WSNs, which is designed using extended chaotic maps and does not require a trusted authentication server online. Furthermore, based on the proposed GKA scheme, an extended scheme with perfect forward secrecy is developed using the extended chaotic map-based Diffie-Hellman key exchange. Both of the proposed schemes are not only more efficient than related schemes, but also provide more functionality.
Optimization of security issues in adoption of cloud ecosystem

This paper presents a concise preface for the Cloud ecosystem, logical cloud security challenges and Confidence Mechanism concept, (REO) architecture and opportunities are presented briefly. To noticeably and resourcefully discuss security intimidation, risks, controls, agreement and other safety desires within the cloud framework, this paper also discuss cloud service providers, cloud vendors and cloud tenants with a framework. Role of security in cloud service ecosystem is further elaborate in the paper.
Interview Study on the Agile Development of Mechatronic Systems

Abstract In order to be as responsive as possible to changes in the dynamic context of mechatronic system development, companies are increasingly integrating agile approaches into their development processes. They are confronted with the challenges of adapting approaches that originate in software development to the conditions of physical development, without neglecting the experiences gained over many years regarding product and process knowledge. In addition, agile development approaches must be integrated into existing processes through a systematic implementation strategy. In order to gain an initial understanding of the current situation in mechatronic companies with regard to agile development approaches, an interview study was conducted with 18 participants from real development practice. This could show that the companies in mechatronic system development are currently at the beginning of agile transformation and need approaches that are modelled on the basis of real development projects and are best possible tailored to the needs of these companies through a clear technical orientation. The findings gained are not universally valid, but represent a basis for further research work.
Greedy AutoAugment

A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentation policies to ensure their effectiveness in covering the search space. In this paper, we propose Greedy AutoAugment as a highly efficient search algorithm to find the best augmentation policies. We use a greedy approach to reduce the exponential growth of the number of possible trials to linear growth. The Greedy Search also helps us to lead the search towards the sub-policies with better results, which eventually helps to increase the accuracy. The proposed method can be used as a reliable addition to the current artifitial neural networks. Our experiments on four datasets (Tiny ImageNet, CIFAR-10, CIFAR-100, and SVHN) show that Greedy AutoAugment provides better accuracy, while using 360 times fewer computational resources.