Reviewerly/exHarmony huggingface dataset:
Efficient Representation and Counting of Antipower Factors in Words

A k-antipower (for $$k \ge 2$$ ) is a concatenation of k pairwise distinct words of the same length. The study of antipower factors of a word was initiated by Fici et al. (ICALP 2016) and first algorithms for computing antipower factors were presented by Badkobeh et al. (Inf. Process. Lett., 2018). We address two open problems posed by Badkobeh et al. Our main results are algorithms for counting and reporting factors of a word which are k-antipowers. They work in $$\mathcal {O}(nk \log k)$$ time and $$\mathcal {O}(nk \log k\,+\,C)$$ time, respectively, where C is the number of reported factors. For $$k=o(\sqrt{n/\log n})$$ , this improves the time complexity of $$\mathcal {O}(n^2/k)$$ of the solution by Badkobeh et al. Our main algorithmic tools are runs and gapped repeats. We also present an improved data structure that checks, for a given factor of a word and an integer k, if the factor is a k-antipower.
A Switch in Time Saves the Dime: A Model to Reduce Rental Cost in Cloud Computing

With the rapid growth of cloud computing, firms face a dizzying array of choices and pricing structures for performing their computing tasks on the cloud. Unlike captive computing resources, cloud computing occurs as a pay-as-you-go contract, similar to the provision of electricity. We develop a method to reduce the rental cost of completing a given computing task with a certain deadline. The current practice is to use a single computing resource that can get the task done in the cheapest possible manner. Instead, costs can be significantly reduced if the task is switched between multiple resources, some more powerful and others less powerful. We apply our method to a real computing task at Cidewalk and show that costs can be significantly reduced.
Exploring Character Shapes for Unsupervised Reconstruction of Strip-Shredded Text Documents

Digital reconstruction of mechanically shredded documents has received increasing attention in the last years mainly for historical and forensics needs. Computational methods to solve this problem are highly desirable in order to mitigate the time-consuming human effort and to preserve document integrity. The reconstruction of strips-shredded documents is accomplished by horizontally splicing pieces so that the arising sequence (solution) is as similar as the original document. In this context, a central issue is the quantification of the fitting between the pieces (strips), which generally involves stating a function that associates a pair of strips to a real value indicating the fitting quality. This problem is also more challenging for text documents, such as business letters or legal documents, since they depict poor color information. The system proposed here addresses this issue by exploring character shapes as visual features for compatibility computation. Experiments conducted with real mechanically shredded documents showed that our approach outperformed in accuracy other popular techniques in the literature considering documents with (almost) only textual content.
Verifiably Multiplicative Secret Sharing

A d-multiplicative secret sharing (d-MSS) scheme allows the players to multiply d shared secrets without recovering the secrets by converting their shares locally into an additive sharing of the product. It has been proved that the d-MSS among n players is possible if and only if no d unauthorized sets of players cover the whole set of players (type Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> ). Although this result implies some limitations on SS in the context of MPC, the d-multiplicative property is still useful for simplifying complex tasks of MPC by computing the product of d field elements directly and non-interactively without any setup. This paper aims to improve the usefulness of the d-MSS by enhancing the security against malicious adversaries. First, we introduce the notion of verifiably multiplicative SS, verifiably MSS for short, which is mainly formalized for detecting malicious behaviors. Informally, an SS scheme is verifiably d-multiplicative if the scheme is d-multiplicative and further enables the players to locally generate a share of a proof that the summed value is correct (i.e., the product of d shared secrets). Secondly, we prove that there is no error-free verifiably MSS scheme whose decoder of the proof is additive, and that by accepting an error probability that can be chosen arbitrarily, there exists a verifiably d-MSS scheme realizing a given access structure if and only if the access structure is of type Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> . In the proposed construction, each share of a proof consists of only two field elements. This result means that we can efficiently achieve the optimal resiliency of the standard d-MSS even against malicious adversaries. We note that by allowing a general class of decoders that includes a linear one, there is an error-free verifiably d-MSS scheme if the access structure is of type Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d+1</sub> . Finally, we generalize the d-multiplicative property to a d-or-less version where the number d' of multiplied secrets with d' ≤ d is not known in advance. We show that a d-or-less MSS scheme can be constructed from any d-MSS scheme of the same access structure with a constant overhead, and the feasibility of (verifiably) d-MSS implies that of (verifiably) d-or-less MSS.
MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.
Distributed Bernoulli Filtering Using Likelihood Consensus

We consider the detection and tracking of a target in a decentralized sensor network. The presence of the target is uncertain, and the sensor measurements are affected by clutter and missed detections. The state-evolution model and the measurement model may be nonlinear and non-Gaussian. For this practically relevant scenario, we propose a particle-based distributed Bernoulli filter (BF) that provides to each sensor approximations of the Bayes-optimal estimates of the target presence probability and the target state. The proposed method uses all the measurements in the network while requiring only local intersensor communication. This is enabled by an extension of the likelihood consensus method that reaches consensus on the likelihood function under both the target presence and target absence hypotheses. We also propose an adaptive pruning of the likelihood expansion coefficients that yields a significant reduction of intersensor communication. Finally, we present a new variant of the likelihood consensus method that is suited to networks containing star-connected sensor groups. Simulation results show that in challenging scenarios, including a heterogeneous sensor network with significant noise and clutter, the performance of the proposed distributed BF approaches that of the optimal centralized multisensor BF. We also demonstrate that the proposed distributed BF outperforms a state-of-the-art distributed BF at the expense of a higher amount of intersensor communication.
Parallel Motion Planning: Learning a Deep Planning Model against Emergencies

To handle the issue of preventing emergencies for motion planning in autonomous driving, we present a novel parallel motion planning framework. Artificial traffic scenes are firstly constructed based on real traffic scenes. A deep planning model which can learn from both real and artificial scenes is developed and used to make planning decisions in an end-to-end mode. To prevent emergencies, a generative adversarial networks (GAN) model is designed and learns from the artificial emergencies from artificial traffic scenes. During deployment, the well-trained GAN model is used to generate multiple virtual emergencies based on the current real scene, and the well-trained planning model simultaneously makes different planning decisions for both virtual scenes and the current scenes. The final planning decision is made by comprehensively analyzing observations and virtual emergencies. Through parallel planning, the planner can timely make rational decision without a large number of calculations when an emergency occurs.
Demodulation of a single closed-fringe interferogram with symmetric wavefront and tilt

A phase recovery method from a single fringe pattern with closed fringes is developed. The interferogram is first normalized and then rotated to create a new fringe pattern. Both interference images are multiplied which results in the sum of the cosines of the symmetric and asymmetric components of the phase respect to the rotating axis. If the original phase contains tilt and also possesses a high degree of symmetry respect to the rotating axis, the cosine of the asymmetric term will be composed of tilt and a small asymmetric term. In this case, the Fourier transform of the multiplication will be given by two high intensity peaks localized symmetrically respect to the origin and a broader lobule centered at the origin. The asymmetric component of the phase is then recovered with a Fourier method and used to calculate the phase of the original interferogram. The above procedure may serve as an alternative to other methods that works iteratively to recover the phase from a single interferogram with closed fringes as it is demonstrated in experimental and simulated data.
Enabling the use of a planning agent for urban traffic management via enriched and integrated urban data

Improving a city's infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment is that this may enable the use of intelligent processes to autonomously manage urban facilities such as traffic signal controls. In this paper we document an attempt to integrate sets of sensor and historical data using a data hub and a set of ontologies for the data. We argue that access to such high level integrated data sources leads to the enhancement of the capabilities of an urban transport operator. We demonstrate this by documenting the development of a planning agent which uses such data as inputs in the form of logic statements, and when given traffic goals to achieve, outputs complex traffic signal strategies which help transport operators deal with exceptional events such as road closures or road traffic saturation. The aim is to create an autonomous agent which reacts to commands from transport operators in the face of exceptional events involving saturated roads, and creates, executes and monitors plans to deal with the effects of such events. We evaluate the intelligent agent in a region of a large urban area, under the direction of urban transport operators.
Blind Quality Assessment of 3-D Synthesized Views Based on Hybrid Feature Classes

In this paper, a novel quality metric to evaluate depth-based synthesized views is proposed. This metric relies on a hybrid approach that uses features extracted in different phases of the image synthesis procedure, namely from the bitstream, from intermediate data produced during the synthesis process, and from the final synthesized view; these features are then combined using support vector regression. A new data set of synthesized images, with compression and rendering artifacts, was built and used to develop and assess the proposed metric. The metric performance is compared with conventional full-reference two-dimensional image quality assessment metrics and with quality assessment metrics developed specifically for synthesized images. The experimental results showed that the proposed solution outperforms the considered benchmark metrics, being able to predict the subjective quality scores of the synthesized images with a Pearson correlation coefficient close to 0.9.
Automatic Detection of Malaria Parasites Using Unsupervised Techniques

The focus of this paper is towards comparing the computational paradigms of two unsupervised data reduction techniques, namely Auto encoder and Self-organizing Maps. The domain of inquiry in this paper is for automatic malaria identification from blood smear images, which has a great relevance in healthcare informatics and requires a good treatment for the patients. Extensive experiments are performed using the microscopically thick blood smear image datasets. Our results reveal that the deep-learning-based Auto encoder technique is better than the Self-organizing Maps technique in terms of accuracy of 87.5%. The Auto encoder technique is computationally efficient, which may further facilitate its malaria identification in the clinical routine.
Exploring the Challenges Towards Lifelong Fact Learning

So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups. Here, we introduce a new large-scale alternative. What makes the proposed setup more natural and closer to human-like visual systems is threefold: First, we focus on concepts (or facts, as we call them) of varying complexity, ranging from single objects to more complex structures such as objects performing actions, and objects interacting with other objects. Second, as in real-world settings, our setup has a long-tail distribution, an aspect which has mostly been ignored in the LLL context. Third, facts across tasks may share structure (e.g., $$\langle $$ person, riding, wave $$\rangle $$ and $$\langle $$ dog, riding, wave $$\rangle $$ ). Facts can also be semantically related (e.g., "liger" relates to seen categories like "tiger" and "lion"). Given the large number of possible facts, a LLL setup seems a natural choice. To avoid model size growing over time and to optimally exploit the semantic relations and structure, we combine it with a visual semantic embedding instead of discrete class labels. We adapt existing datasets with the properties mentioned above into new benchmarks, by dividing them semantically or randomly into disjoint tasks. This leads to two large-scale benchmarks with 906,232 images and 165,150 unique facts, on which we evaluate and analyze state-of-the-art LLL methods.
Common Semantic Representation Method Based on Object Attention and Adversarial Learning for Cross-Modal Data in IoV

With the significant development of the Internet of Vehicles (IoV), various modal data, such as image and text, are emerging, which provide data support for good vehicle networking services. In order to make full use of the cross-modal data, we need to establish a common semantic representation to achieve effective measurement and comparison of different modal data. However, due to the heterogeneous distributions of cross-modal data, there exists a semantic gap between them. Although some deep neural network (DNN) based methods have been proposed to deal with this problem, there still exist several challenges: the qualities of the modality-specific features, the structure of the DNN, and the components of the loss function. In this paper, for representing cross-modal data in IoV, we propose a common semantic representation method based on object attention and adversarial learning (OAAL). To acquire high-quality modality-specific feature, in OAAL, we design an object attention mechanism, which links the cross-modal features effectively. To further alleviate the heterogeneous semantic gap, we construct a cross-modal generative adversarial network, which contains two parts: a generative model and a discriminative model. Besides, we also design a comprehensive loss function for the generative model to produce high-quality features. With a minimax game between the two models, we can construct a shared semantic space and generate the unified representations for cross-modal data. Finally, we apply our OAAL on retrieval task, and the results of the experiments have verified its effectiveness.
Low-Shot Learning From Imaginary 3D Model

Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-art approaches are largely unsuitable in scarce data regimes. To address this shortcoming, this paper proposes employing a 3D model, which is derived from training images. Such a model can then be used to hallucinate novel viewpoints and poses for the scarce samples of the few-shot learning scenario. A self-paced learning approach allows for the selection of a diverse set of high-quality images, which facilitates the training of a classifier. The performance of the proposed approach is showcased on the fine-grained CUB-200-2011 dataset in a few-shot setting and significantly improves our baseline accuracy.
Supervised Sparse Coding With Decision Forest

By jointly conducting sparse coding and classifier training, supervised sparse coding has shown its effectiveness in a variety of recognition tasks. However, the existing supervised sparse coding methods often consider linear classification, which limits their discrimination in handling highly nonlinear data. In this letter, we propose a new supervised sparse coding model by incorporating decision tree classifiers. Since decision trees can well deal with the non-linear properties of data, the introduction of decision trees to sparse coding can noticeably improve the discrimination of coding. Meanwhile, sparse coding is able to produce sparse de-correlated features that decision tree is in favor of. For further improvement, we close the loop of sparse coding and decision tree learning with an ensemble framework, which alternatively learns a dictionary for sparse coding and a decision tree for classification. The resulting series of decision trees as well as series of dictionaries are used to construct a decision forest for classification. The proposed method was applied to face recognition and scene classification, and the experimental results have demonstrated its power in comparison with recent supervised sparse coding methods.
Mining Maximal Dynamic Spatial Colocation Patterns

A spatial co-location pattern represents a subset of spatial features whose instances are prevalently located together in a geographic space. Although many algorithms of mining spatial co-location pattern have been proposed, there are still some problems: 1) they miss some meaningful patterns (e.g., {Ganoderma_lucidumnew, maple_treedead} and {water_hyacinthnew(increase), algaedead(decrease)}), and get the wrong conclusion that the instances of two or more features increase/decrease (i.e., new/dead) in the same/approximate proportion, which has no effect on prevalent patterns. 2) Since the number of prevalent spatial co-location patterns is very large, the efficiency of existing methods is very low to mine prevalent spatial co-location patterns. Therefore, first, we propose the concept of dynamic spatial co-location pattern that can reflect the dynamic relationships among spatial features. Second, we mine small number of prevalent maximal dynamic spatial co-location patterns which can derive all prevalent dynamic spatial co-location patterns, which can improve the efficiency of obtaining all prevalent dynamic spatial co-location patterns. Third, we propose an algorithm for mining prevalent maximal dynamic spatial co-location patterns and two pruning strategies. Finally, the effectiveness and efficiency of the method proposed as well as the pruning strategies are verified by extensive experiments over real/synthetic datasets.
A Vision-Based Posture Monitoring System for the Elderly Using Intelligent Fall Detection Technique

Elderly monitoring systemsMonitoring systems are the major applications of care for elderly and the disabled who live alone. Falls are the leading factor to be detected in the elderly monitoring system to avoid serious injuries and even death. The detection systemsDetection systems often use ambient sensorsAmbient sensors , wearable sensor, and vision-based technologiesVision-based technologies . In case of sensor-based devices, the elderly are required to wear the detection devices, however, quite often, they forget to wear these or do not wear them correctly. Moreover, the sensors need to be charged and maintained regularly. Also, the ambient sensors need to be installed in all the rooms to cover the whole actuationActuation . The additional difficulty is that they are complex in circuitry and sensitive to temperature. Vision-based devices are the only plausible solution that can replace the aforementioned sensors. Besides, the cost of vision-based implementation is much lower and related devices are better than wearable devices in activity recognition. Much like Ambient sensors, cameras can also be installed in all the rooms; the cost and maintenance of these are less as compared to ambient sensors. This chapter proposes a vision-based posture monitoring system using infrared cameras connected to a digital videoDigital Video recorder and a fall detection mechanism to classify the falls. In the chapter, we observe the behavior of the elderly through the specially designed clothing fabricated with retroreflective radium tape (red in color) for posture identification. The proposed fall detection technique comprises various modules of operations such as image segmentation, rescaling, and classification. The infrared cameras observe the movement of the elderly people and signals are transmitted to a digital video recorder. The digital video recorder snaps only the motion frames from the signal. The motion images are segmented to red band using image segmentation and further rescaled for better classification using k-Nearest Neighbor-Nearest Neighbor (k-NN) and decision tree classifiers. The tests have been conducted on 10 different subjects to identify the falls during various motions such as supine, sitting, sitting with knee extension, and standing. We have shown a detection rate of 94% for the proposed model with k-nearest neighbor classifier.