Reviewerly/exHarmony huggingface dataset:
Designing a Cognitive Agent Connector for Complex Environments: A Case Study with StarCraft

The evaluation of cognitive agent systems, which have been advocated as the next generation model for engineering complex, distributed systems, requires more benchmark environments that offer more features and involve controlling more units. One issue that needs to be addressed time and again is how to create a connector for interfacing cognitive agents with such richer environments. Cognitive agents use knowledge technologies for representing state, their actions and percepts, and for deciding what to do next. Issues such as choosing the right level of abstraction for percepts and action synchronization make it a challenge to design a cognitive agent connector for more complex environments. The leading principle for our design approach to connectors for cognitive agents is that each unit that can be controlled in an environment is mapped onto a single agent. We design a connector for the real-time strategy (RTS) game StarCraft and use it as a case study for establishing a design method for developing connectors for environments. StarCraft is particularly suitable to this end, as AI for an RTS game such as StarCraft requires the design of complicated strategies for coordinating hundreds of units that need to solve a range of challenges including handling both short-term as well as long-term goals. We draw several lessons from how our design evolved and from the use of our connector by over 500 students in two years. Our connector is the first implementation that provides full access for cognitive agents to StarCraft: Brood War.
Skeleton Based Temporal Action Detection with YOLO

Detecting actions in untrimmed sequences is an important yet challenging task. In this paper, we innovatively transform the temporal action detection issue into the object detection issue. Our method allows for real-time detection and end-to-end training. It consists of two stages. Firstly, we propose an idea to represent action sequences as images as well as preserving the original temporal dynamics and spatial structure information. Secondly, based on such description, we design a one-dimensional YOLO network to detect human action. In addition, we make a dataset for skeleton based temporal action detection. Experiments on our dataset demonstrate the superiority of our method.
The log-linear cognitive diagnosis modeling (LCDM) in second language listening assessment

This chapter focuses on the log-linear cognitive diagnosis modeling (LCDM), a general diagnostic classification model (DCM) family that allows researchers to model a large group of diagnostic classification models (DCMs) flexibly. Although the LCDM has important advantages over other core DCMs, it remains relatively under-researched in language assessment. This chapter first provides language testers with an introduction to the theoretical and statistical underpinnings of the LCDM. Next, it demonstrates how the LCDM could be applied to a high-stakes listening comprehension test. Finally, it presents guidelines on how to estimate and interpret the model, item, and examinee parameters with readily available software.
Routing in a Delay Tolerant Network with Spiking Neurons

We consider a dynamic reservoir of spiking neurons to generate routing decisions for bundle transmissions in a delay tolerant network (DTN). The reservoir transforms the context, as described by selected system metrics, and the temporal information that is contained in the network's contact list into a high-dimensional feature space, which allows quickly determining the next-hop for bundles whenever needed. The method can be of particular interest for space networks, which are characterized by resource-constrained nodes, known contact opportunities, and dynamic features, such as the state of the channels and buffers. The learning process occurs offline using linear regression and involves a modest computational effort. After training, the spiking neural network can be used to achieve an efficient network operation, allowing to substitute the costly per-bundle shortest path computation with a simple "recall" action of the best next-hop for the current conditions as stored in the reservoir. We investigate different methods to translate the contact list information into a suitable input for the reservoir and evaluate the approach considering the impact of the readout function size applied to GEO-LEO DTN scenarios.
Research on Small Target Recognition Technology Based on Deep Learning

Abstract All small target recognition has always been a difficult problem in the field of target recognition. Traditional target detection methods have met a bottleneck. With the development of deep learning, the field of target recognition has witnessed a boom based on deep learning. According to the characteristics of the small target recognition, this paper briefly introduces the traditional target recognition method, and studies the methods of target detection based on the deep learning. It mainly summarized the development of research on target detection algorithm based on deep learning. Finally, it summarized the influence of the depth study on target recognition and the trend of development in the future.
An effective underwater image enhancement method based on CLAHE-HF

Abstract In order to solve the problem that the unbalanced illumination and noise induce poor contrast, uneven brightness and poor clarity to underwater image, an effective underwater image enhancement method based on spatial and frequency domain is proposed in this paper. Firstly, Contrast Limited Adaptive Histogram Equalization technology is used in spatial domain to enhance the local contrast of different regions of the image according to the distribution of pixel values of each region, which can not only ensure the image texture clarity and detail characteristics, but also suppress the noise. Secondly, Homomorphic Filtering technology is used to enhance the image details and reduce the noise in the image in frequency domain. Two sets of image data that verify the effectiveness of the proposed method were provided. The experimental results show that, compared with the traditional underwater image enhancement methods, the peak signal-to-noise ratio (PSNR), mean square error (MSE) and information entropy of the proposed method are combined optimal.
Association rule learning

Association rule learning is a rule-based and unsupervised machine learning method, which is widely used to discover interesting relations between items (i.e., records) in a database and also to explore how and why these items are connected. A widely known application of association rule learning is to analyze the purchased items in market basket transactions, which aims to identify what goods are often bought together in one transaction so as to adjust sales strategies to increase sales. For example, a famous story is that on Friday nights, the sales of diapers and beer were correlated in Walmart: a bottle of beer was often bought when diapers were bought. The explanation was that working men were asked to pick up diapers on their way home from work, and they would also buy a bottle of beer for themselves at the same time. Based on this finding, Walmart put the shelves of these two goods close to each other on Friday nights, and the sales volume of both increased greatly. In the above example, diapers and beer are correlated in the following way: diapers → beer, meaning that if diapers are bought, beer is highly like to be bought in the same transaction. This is a basic form of association rule we are going to cover in this section. Actually, association rule learning is widely used to mine the relations of items from transaction databases, and the rules generated are used to guide the activities of personalized product recommendation, shelf placement, combined coupon dispatching, and bundle sales in the retailing industry.
Payload Scaling for Adaptive Steganography: An Empirical Study

Payload-scaling laws of imperfect steganography inform the steganographer about how the size of secret payload should grow with cover size for constant statistical detectability. In this letter, we carry out an empirical study for the case when the steganographer and the steganalyst operate at a game-theoretic equilibrium. We first explore the possibility to leverage a generalization of the square root law to content-adaptive steganography due to Ker. Since this result does not appear to be tight enough for realistic cover sizes, we instead work with a detectability limited sender in image sources with a forced model as well as real images in both spatial and JPEG domain. The scaling is observed in practice when the images are carefully cropped to preserve the distribution of costs across scales.
Nested Virtualization Without the Nest

With the increasing popularity of containers, managing them on top of virtual machines becomes a common practice, called nested virtualization. This paper presents BrFusion and Hostlo, two solutions that address each of two networking issues of nested virtualization: network virtualization duplication and virtual machine-bounded pod deployments. The first issue lengthens network packet paths while the second issue leads to resource fragmentation. For instance, in respect with the first issue, we measured a throughput degradation of about 68% and a latency increase of about 31% in comparison with a single networking layer. We prototype BrFusion and Hostlo in Linux KVM/QEMU, Docker and Kubernetes systems. The evaluation results show that BrFusion leads to the same performance as a single-layer virtualization deployment. Concerning Hostlo, the results show that more than 11% of cloud clients see their cloud utilization cost reduced by down to 40%.
Decentralized Collaborative Knowledge Management using Git

Apart from documents, datasets are gaining more attention on the World Wide Web. An increasing number of the datasets on the Web are available as Linked Data, also called the Linked Open Data Cloud1 or Giant Global Graph2. Collaboration of people and machines is a major aspect of the World Wide Web and as well of the Semantic Web. Currently, the access to RDF data on the Semantic Web is possible by applying the Linked Data principles3, and the SPARQL specification4, which enables clients to access and retrieve data stored and published via SPARQL endpoints. RDF resources in the Semantic Web are interconnected and often correspond to previously created vocabularies and patterns. This way of reusing existing knowledge facilitates the modeling and representation of information and may optimally reduce the development costs of a knowledge base. As a result of the collaborative reuse process, structural and content interferences as well as varying models and contradictory statements are inevitable.
Distributed algorithms made secure: a graph theoretic approach

In the area of distributed graph algorithms a number of network's entities with local views solve some computational task by exchanging messages with their neighbors. Quite unfortunately, an inherent property of most existing distributed algorithms is that throughout the course of their execution, the nodes get to learn not only their own output but rather learn quite a lot on the inputs or outputs of many other entities. This leakage of information might be a major obstacle in settings where the output (or input) of network's individual is a private information (e.g., distributed networks of selfish agents, decentralized digital currency such as Bitcoin).While being quite an unfamiliar notion in the classical distributed setting, the notion of secure multi-party computation (MPC) is one of the main themes in the Cryptographic community. The existing secure MPC protocols do not quite fit the framework of classical distributed models in which only messages of bounded size are sent on graph edges in each round. In this paper, we introduce a new framework for secure distributed graph algorithms and provide the first general compiler that takes any natural non-secure distributed algorithm that runs in r rounds, and turns it into a secure algorithm that runs in O(r · D · poly(Δ)) rounds where Δ is the maximum degree in the graph and D is its diameter. A natural distributed algorithm is one where the local computation at each node can be performed in polynomial time. An interesting advantage of our approach is that it allows one to decouple between the price of locality and the price of security of a given graph function f. The security of the compiled algorithm is information-theoretic but holds only against a semi-honest adversary that controls a single node in the network.This compiler is made possible due to a new combinatorial structure called private neighborhood trees: a collection of n trees T(u1), . . . , T(un), one for each vertex ui ϵ V(G), such that each tree T(ui) spans the neighbors of ui without going through ui. Intuitively, each tree T(ui) allows all neighbors of ui to exchange a secret that is hidden from ui, which is the basic graph infrastructure of the compiler. In a (d, c)-private neighborhood trees each tree T(ui) has depth at most d and each edge e ϵ G appears in at most c different trees. We show a construction of private neighborhood trees with d = O(Δ · D) and c = O(D), both these bounds are existentially optimal.
Convergence rates of latent topic models under relaxed identifiability conditions

In this paper we study the frequentist convergence rate for the Latent Dirichlet Allocation (Blei, Ng and Jordan, 2003) topic models. We show that the maximum likelihood estimator converges to one of the finitely many equivalent parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without assuming separability or non-degeneracy of the underlying topics and/or the existence of more than three words per document, thus generalizing the previous works of Anandkumar et al. (2012, 2014) from an information-theoretical perspective. We also show that the $n^{-1/4}$ convergence rate is optimal in the worst case.
Obesity Entity Extraction from Real Outpatient Records: When Learning-Based Methods Meet Small Imbalanced Medical Data Sets

The postoperative health status of an obesity patient indicates the outcome of the surgical treatment. By each postoperative revisit, physicians need to go through the previous patient records to recall the patient status and to evaluate the postoperative risk of readmission. In order to support in this process, we develop a method to extract indicators and to analyse weight changes, so that potential complications and risks of clinical readmission can be recognized timely. In this paper, we will compare two approaches that are based on traditional machine learning and neural networks. Relevant aspects referring to a health status change or treatment-relevant aspects are extracted from the outpatient medical records as they are generated for each postoperative revisit. The performance of traditional machine learning on the task of obesity-related entity extraction is compared with one variation of attentive recurrent neural networks. The ensemble classifier of binary attentive bi-LSTM with the data balancing using conditional generative adversarial networks (CGAN) has achieved F1 measure of 86.5% on the task of classification of eight classes of obesity-related entities. We conclude that for processing a small data set using neural networks, a data balancing method should firstly be applied to achieve an extended corpus and a general representation, which can apparently increase the differentiability of the input data. A fine-tuning in the networks can provide further enhancement of the performance.
Online Documentation Portals in Library Technical Services: Shedding Light on Local Practices and Procedures

The creation, sharing, and maintenance of documentation are critical to support the often detailed and complex work of library technical services units. This column examines documentation portals that share technical services policies and procedures, with a focus on library-based portals that are accessible to personnel within the associated libraries while also being fully or partially open to external users. A review of the literature is provided, as well as links to and descriptions of selected portal examples.
Multilevel Thresholding for Coastal Video Image Segmentation Based on Cuckoo Search Algorithm

In the coastal video image segmentation, images are partitioned into land and sea classes, and each of these classes could have different segmentation qualities. In order to cope with variations in image quality and opaque areas, this paper has proposed a multilevel threshold technique based on the Cuckoo Search (CS) algorithm as an optimization algorithm for selecting optimum threshold values. The optimum threshold values are determined by maximizing Otsu's or Kapur's objective function using CS algorithm. The CS algorithm uses McCulloch's method for Lévy flight generation and combined with Otsu's and Kapur's objective functions to analyze CS algorithm performance. Based on the evaluations of PSNR, MSE, FSIM and CPU time parameters, the McCulloch's method based on CS algorithm with Otsu's objective function is the most promising and computationally efficient for segmenting coastal video images.
Authentication of medical images using passive approach

Telemedicine is getting a lot of popularity. It is because of the way that the medical images are sent over the Internet to share the restorative state of a patient, making the analysis simple for the medical staff. There is, however, a good possibility that the contents of a medical image may be altered due to the openness of the Internet. The watermarking techniques are utilised to keep the integrity of medical images intact. These techniques, however, need some additional information (watermark) to be embedded into the medical images, which may deteriorate their visual quality, leading to false diagnosis. In this study, a non-intrusive procedure for verification of the medical images is proposed in which the polar cosine transform is used for feature extraction and the PatchMatch algorithm to locate the forged regions. Experimentally, the authors demonstrate that the proposed technique is robust against the noise, blurring, scaling, rotation and JPEG compression.
Concurrent Self-Identification Applying QR Code to Record Class Attendance (QRClass)

The traditional way of recording student attendance is that an instructor calls student names one by one and records their absence or presence status. It usually consumes much time and sometimes causes errors. Therefore, some instructors may choose not to record student attendance, and this could affect academic learning and outcome. Although many self-identification methods have been explored, the entire processing time is similar to the traditional one. Hence, to solve such the problem, this research proposes a methodology to develop a concurrent class attendance system applying QR code, called QRClass. The results show that the processing time of QRClass is reduced by approximately 93.14% for 49 students compared to the traditional way. Therefore, QRClass is not only require less processing time, but also significantly increase the effectiveness of the class attendance recording method.
A Design of Reward Function in Multi-Target Trajectory Recovery with Deep Reinforcement Learning

It attracts a lot of attention in the field of object trajectory detection that detectors always receive several geographical locations without any information about the targets, and furthermore it comes into a problem to use the geographical location information received by the sensors to reconstruct the trajectory of each targets as well as to distinguish the targets in each frame, which is called multi-target trajectory recovery and can be solved by the Deep Reinforcement Learning (DRL). A mathematically model of the direction and curvature of the target trajectory according to the peculiarity of trajectories is proposed. Then, a reward function based on Trajectory Osculating Circle (TOC) is designed based on this mathematical model. Firstly, the issue of the recovery of multi-target trajectories is introduced and it can be switched into a model which can be implemented by DRL. Secondly, a structure of DRL on this issue is come up with, and is tested with the proposed reward function. Finally, a mathematical derivation and physical interpretation of the proposed reward function is implemented. The experimental result shows that with the guidance of the TOC reward function, DRL can reverse the trajectory more effectively than the state-of-the-art clustering method, and the trace is corresponding with the actual trajectory.
Moving object detection based on high-speed video sequence images

Moving object detection is one of the key technologies in the field of target extraction, pattern recognition and motion measurement. The problem of moving object detection based on high-speed video sequence images is investigated in this paper. First, the high-speed video tracking system is introduced. Second, an improved hybrid detection method combined background subtraction with frame difference is proposed, and the moving object is post-processed by using the threshold area de-noising and morphology filter. Finally, the experiment results show that the proposed method detects the moving object from the high-speed video sequence images precisely and effectively.
Bloch vector norms of separable multi-partite quantum systems

In this letter, we find an upper bound for the norms of Bloch vectors for quantum systems comprised of an arbitrary number of qudits. We generalize a recent result of Li et al . for four-partite quantum systems. We apply our result to provide an upper bound on the entanglement measure given by the Bloch vector norm and we provide a necessary algebraic condition for separability of a general multi-partite quantum system under any arbitrary partition function. Finally we show that the finer the partitioning is, the smaller our upper bound becomes.
Learn to Select via Hierarchical Gate Mechanism for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained task. Recurrent Neural Network (RNN) model armed with attention mechanism seems a natural fit for this task, and actually it achieves the state-of-the-art performance recently. However, previous attention mechanisms proposed for ABSA may attend irrelevant words and thus downgrade the performance, especially when dealing with long and complex sentences with multiple aspects. In this paper, we propose a novel architecture named Hierarchical Gate Memory Network (HGMN) for ABSA: firstly, we employ the proposed hierarchical gate mechanism to learn to select the related part about the given aspect, which can keep the original sequence structure of sentence at the same time. After that, we apply Convolutional Neural Network (CNN) on the final aspect-specific memory. We conduct extensive experiments on the SemEval 2014 and Twitter dataset, and results demonstrate that our model outperforms attention based state-of-the-art baselines.
Optimum Design for Drone Highway Network

This paper describes a design method for drone highway networks to eliminate the risk of conflict between drones and to improve the overall flight efficiency. Many flight path designing methods have been proposed; however, none of them addresses the issue of flight efficiency. We optimize each path using ant colony optimization and optimize the position of the terminal connecting the paths using particle swarm optimization. Experimental results show that the proposed method improves flight efficiency by 15.6% on average.