Reviewerly/exHarmony huggingface dataset:
Edge-to-fog computing for color-assisted moving object detection

Future Internet-of-Things (IoT) will be featured by ubiquitous and pervasive vision sensors that generate enormous amount of streaming videos. The ability to analyze the big video data in a timely manner is essential to delay-sensitive applications, such as autonomous vehicles and body-worn cameras for police forces. Due to the limitation of computing power and storage capacity on local devices, the fog computing paradigm has been developed in recent years to process big sensor data closer to the end users while it avoids the transmission delay and huge uplink bandwidth requirements in cloud-based data analysis. In this work, we propose an edge-to-fog computing framework for object detection from surveillance videos. Videos are captured locally at an edge device and sent to fog nodes for color-assisted L1-subspace background modeling. The results are then sent back to the edge device for data fusion and final object detection. Experimental studies demonstrate that the proposed color-assisted background modeling offers more diversity than pure luminance based background modeling and hence achieves higher object detection accuracy. Meanwhile, the proposed edge-to-fog paradigm leverages the computing resources on multiple platforms.
d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding

Training a deep neural network often requires enormous amount of labeled data, while data annotation is usually an expensive and tedious task. Domain adaptation is a popular way to solve this problem by leveraging data source from a different but related domain. In this chapter, we introduce a domain adaptation algorithm (d-SNE) that uses stochastic neighborhood embedding for aligning the source and target data. Extensive experiments demonstrate the efficacy of proposed method compared to conventional domain adaptation approaches.
Some results on linearized trinomials that split completely

Linearized polynomials over finite fields have been the subject of many papers over the last several decades. Recently, there has been a renewed interest in linearized polynomials because of new connections to coding theory and finite geometry. We consider the problem of calculating the rank or nullity of a linearized polynomial L(x) = Σdi =0 aixqi (where ai ∈ Fqn ) from the coefficients ai. The rank and nullity of L(x) are the rank and nullity of the associated Fq-linear map Fqn → Fqn . McGuire and Sheekey [6] defined a d × d matrix AL with the property that nullity(L) = nullity(AL − I). We present some consequences of this result for some trinomials that split completely, i. e., trinomials L(x) = xqd −bxq −ax that have nullity d. We give a full characterization of these trinomials for n ≤ d2 − d + 1.
Rate-Optimizing Compilers for Continuously Non-malleable Codes

We study the rate of so-called continuously non-malleable codes, which allow to encode a message in such a way that (possibly adaptive) continuous tampering attacks on the codeword yield a decoded value that is unrelated to the original message. Our results are as follows: Continuously non-malleable codes are useful for protecting arbitrary cryptographic primitives against related-key attacks, as well as for constructing non-malleable public-key encryption schemes. Our results directly improve the efficiency of these applications.
Mobile learning for high-school mathematics as a path to better sustainability in a fast-changing society: an exploratory study from Vietnam

The use of mobile learning, or M-learning, has been increasingly appreciated by educators due to its sustainability potential in different facets such as finance (i.e., affordable cost) and flexibility (i.e., time and pace of learning). However, it may not be effective in all situations. This study explored the feasibility of using M-learning for students’ self-study of mathematics in the context of Vietnamese high schools. Using 542 student and 40 teacher responses to two surveys, the study showed that the use of M-learning might not be feasible for students’ self-study of mathematics due to difficulties related to accessing mathematics websites, the quality of mathematics website content, students’ low level of self-learning ability and learning disengagement. This study suggests that the use of M-learning may contribute to the sustainability of education; adopting it should be based on a critical examination of contextual factors, especially students’ self-learning ability and engagement. M-learning can be promising and beneficial to students due to its capability to equip students to prepare for the fast-changing and technological-driven world. Educators have increasingly appreciated the use of M-learning, because it becomes more affordable and flexible. Nonetheless, there is still a question about near-future adoptions of M-learning due to unavailability of and inaccessibility to quality contents from trusted maths websites. The propensity of student engagements in M-learning is also an important issue for future research.
Blockchains and International Business

Discusses how blockchain is transforming international business practices and relatioships. Blockchain and smart contracts are transforming international trade activities. Proof-ofconcepts (PoCs), prototypes, pilot projects, and actual deployments indicate that smart contracts can bring benefits to those involved in trading.
Structured-chromosome GA optimisation for satellite tracking

This paper presents a novel optimisation approach, called Structured-Chromosome Genetic Algorithm (SCGA), that addresses the issue of handling variable-size design space optimisation problems. This is based on variants of standard genetic operators able to handle structured search spaces. The potential of the presented methodology is shown by solving the problem of defining observation campaigns for tracking space objects from a network of tracking stations. The presented approach aims at supporting the space sector in response to the constantly increasing population size in the around-Earth environment. The test case consists in finding the observation scheduling that minimises the uncertainty in the final state estimation of a very low Earth satellite operating in a highly perturbed dynamical environment. This is evaluated by coupling the optimiser with an estimation routine based on a sequential filtering approach that estimates the satellite state distribution conditional on received indirect measurements. The solutions found by employing SCGA are finally compared to the ones achieved using more traditional approaches. Namely, the problem has been reformulated to be faced using standard Genetic Algorithm and another variable-size optimiser, the "Hidden-genes" Genetic Algorithm variant.
A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc.Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used.One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model.Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods.With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values.The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark.The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time.Afterwards, the proposed approach is compared with other recent methods in the literature review.Based on the computation results, the proposed hybrid approach outperforms the other methods.
Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. In video games, agents of different types often form teams. In this paper, we demonstrate the usefulness of MAPFalgorithms from artificial intelligence for moving such non-homogeneous teams in congested video game environments.
The minimum size of a linear set

In this paper, we first determine the minimum possible size of an Fq-linear set of rank k in PG(1,qn). We obtain this result by relating it to the number of directions determined by a linearized polynomial whose domain is restricted to a subspace. We then use this result to find a lower bound on the number of points in an Fq-linear set of rank k in PG(2,qn). In the case k=n, this confirms a conjecture by Sziklai in [9].
Experimental demonstration of single-shot quantum and classical signal transmission on single wavelength optical pulse

Abstract Advances in highly sensitive detection techniques for classical coherent communication systems have reduced the received signal power requirements to a few photons per bit. At this level one can take advantage of the quantum noise to create secure communication, using continuous variable quantum key distribution (CV-QKD). In this work therefore we embed CV-QKD signals within classical signals and transmit classical data and secure keys simultaneously over 25 km of optical fibre. This is achieved by using a novel coherent displacement state generator, which has the potential for being used in a wide range of quantum optical experiments. This approach removes the need for separate channels for quantum communication systems and allows reduced system bandwidth for a given communications specification. This demonstration therefore demonstrates a way of implementing direct quantum physical layer security within a conventional classical communications system, offering a major advance in term of practical and low cost implementation.
Optimizing Parameters for Uncertain Execution and Rescheduling Robustness

We describe use of Monte Carlo simulation to optimize schedule parameters for execution and rescheduling robustness in the face of execution uncertainties. We search in the activity input parameter space where a) the onboard scheduler is a one shot non-backtracking scheduler and b) the activity input priority determines the order in which activities are considered for placement in the schedule. We show that simulation driven search for activity parameters outperforms static priority assignment. Our approach can be viewed as using simulation feedback to determine problem specific heuristics e.g. Squeaky Wheel Optimization. These techniques are currently baselined for use in the ground operations of NASA’s next planetary rover, the Mars 2020 rover.
Two New Results About Quantum Exact Learning

We present two new results about exact learning by quantum computers. First, we show how to exactly learn a k-Fourier-sparse n-bit Boolean function from O(k^{1.5}(log k)^2) uniform quantum examples for that function. This improves over the bound of Theta~(kn) uniformly random classical examples (Haviv and Regev, CCC'15). Our main tool is an improvement of Chang's lemma for sparse Boolean functions. Second, we show that if a concept class {C} can be exactly learned using Q quantum membership queries, then it can also be learned using O ({Q^2}/{log Q} * log|C|) classical membership queries. This improves the previous-best simulation result (Servedio-Gortler, SICOMP'04) by a log Q-factor.
Effort Prediction in Agile Software Development with Bayesian Networks

The success rate of software projects has been increased since agile methodologies were adopted by many companies. Due their flexibility and continuous communication with clients, the main reason for the failure has shifted from the formulation and understanding of the requirements to inaccurate effort estimation. In recent years, several researchers and practitioners have proposed different estimation techniques. However, some projects are still failing because the budget and/or schedule are not accurately estimated since there still are numerous uncertain variables in software development process. Previous team collaborations, expertise and experience of team members, frequency of changing requirements or priorities are just a few examples. To improve the accuracy of effort estimation, this research proposes a model for agile software development project prediction using Bayesian networks. Based on literature review and practitioners’ knowledge, we identified two major categories of factors that influence effort needed: teamwork quality and user stories characteristics. We identified the sub-factors for each category and inter-dependencies between them. In our model, these factors are the nodes of the directed acyclic graph. The model can help agile teams to obtain a better software effort estimation.
SOCCOMAS: a FAIR web content management system that uses knowledge graphs and that is based on semantic programming

Abstract We introduce Semantic Ontology-Controlled application for web Content Management Systems (SOCCOMAS), a development framework for FAIR (‘findable’, ‘accessible’, ‘interoperable’, ‘reusable’) Semantic Web Content Management Systems (S-WCMSs). Each S-WCMS run by SOCCOMAS has its contents managed through a corresponding knowledge base that stores all data and metadata in the form of semantic knowledge graphs in a Jena tuple store. Automated procedures track provenance, user contributions and detailed change history. Each S-WCMS is accessible via both a graphical user interface (GUI), utilizing the JavaScript framework AngularJS, and a SPARQL endpoint. As a consequence, all data and metadata are maximally findable, accessible, interoperable and reusable and comply with the FAIR Guiding Principles. The source code of SOCCOMAS is written using the Semantic Programming Ontology (SPrO). SPrO consists of commands, attributes and variables, with which one can describe an S-WCMS. We used SPrO to describe all the features and workflows typically required by any S-WCMS and documented these descriptions in a SOCCOMAS source code ontology (SC-Basic). SC-Basic specifies a set of default features, such as provenance tracking and publication life cycle with versioning, which will be available in all S-WCMS run by SOCCOMAS. All features and workflows specific to a particular S-WCMS, however, must be described within an instance source code ontology (INST-SCO), defining, e.g. the function and composition of the GUI, with all its user interactions, the underlying data schemes and representations and all its workflow processes. The combination of descriptions in SC-Basic and a given INST-SCO specify the behavior of an S-WCMS. SOCCOMAS controls this S-WCMS through the Java-based middleware that accompanies SPrO, which functions as an interpreter. Because of the ontology-controlled design, SOCCOMAS allows easy customization with a minimum of technical programming background required, thereby seamlessly integrating conventional web page technologies with semantic web technologies. SOCCOMAS and the Java Interpreter are available from (https://github.com/SemanticProgramming).
An efficient incremental strongly connected components algorithm for evolving directed graphs

都采用强连通分量算法将有向图转换成 DAG, 以 提高其执行效率, 被广泛应用于社交分析, 道路导航和软件分析等多种场景
Context Attention Heterogeneous Network Embedding

Network embedding (NE), which maps nodes into a low-dimensional latent Euclidean space to represent effective features of each node in the network, has obtained considerable attention in recent years. Many popular NE methods, such as DeepWalk, Node2vec, and LINE, are capable of handling homogeneous networks. However, nodes are always fully accompanied by heterogeneous information (e.g., text descriptions, node properties, and hashtags) in the real-world network, which remains a great challenge to jointly project the topological structure and different types of information into the fixed-dimensional embedding space due to heterogeneity. Besides, in the unweighted network, how to quantify the strength of edges (tightness of connections between nodes) accurately is also a difficulty faced by existing methods. To bridge the gap, in this paper, we propose CAHNE (context attention heterogeneous network embedding), a novel network embedding method, to accurately determine the learning result. Specifically, we propose the concept of node importance to measure the strength of edges, which can better preserve the context relations of a node in unweighted networks. Moreover, text information is a widely ubiquitous feature in real-world networks, e.g., online social networks and citation networks. On account of the sophisticated interactions between the network structure and text features of nodes, CAHNE learns context embeddings for nodes by introducing the context node sequence, and the attention mechanism is also integrated into our model to better reflect the impact of context nodes on the current node. To corroborate the efficacy of CAHNE, we apply our method and various baseline methods on several real-world datasets. The experimental results show that CAHNE achieves higher quality compared to a number of state-of-the-art network embedding methods on the tasks of network reconstruction, link prediction, node classification, and visualization.
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models

Remarkable achievements have been attained by deep neural networks in various applications. However, the increasing depth and width of such models also lead to explosive growth in both storage and computation, which has restricted the deployment of deep neural networks on resource-limited edge devices. To address this problem, we propose the so-called SCAN framework for networks training and inference, which is orthogonal and complementary to existing acceleration and compression methods. The proposed SCAN firstly divides neural networks into multiple sections according to their depth and constructs shallow classifiers upon the intermediate features of different sections. Moreover, attention modules and knowledge distillation are utilized to enhance the accuracy of shallow classifiers. Based on this architecture, we further propose a threshold controlled scalable inference mechanism to approach human-like sample-specific inference. Experimental results show that SCAN can be easily equipped on various neural networks without any adjustment on hyper-parameters or neural networks architectures, yielding significant performance gain on CIFAR100 and ImageNet. Codes will be released on github soon.
Computer-Aided English Teaching Platform Based on Secure Shell Framework

With the aid of information-based teaching platform, the traditional classroom English teaching mode can be improved to provide students with a realistic linguistic environment and enhance their learning interest. As a result, the web-based computer aided English teaching has become the mainstream in college English education. Based on the browser/server (B/S) structure, this paper designs a computer-aided English teaching platform of the model–view–controller (MVC) architectural pattern. The programming was carried out using the MySQL database, and the functions of the computer-aided English teaching platform were realized securely and stably by the popular Secure Shell (SSH) framework. The system test results show that the proposed platform supports the open teaching of college English, solves the lack of interaction in traditional classroom teaching, and arouses the students’ learning interest. The research further promotes the reform of college English teaching, and enriches the theories and practices on computer-aided English teaching.
A GAMING APPROACH FOR CULTURAL HERITAGE KNOWLEDGE AND DISSEMINATION

Abstract. In these last years, video games have become one of the most popular entertainment for children/teenagers/adults thanks to their appealing and seductive features and, in this context, the Serious Games (SG) have become an important research field. The most popular SGs in Cultural Heritage (CH) used the historical building like scenario where the game is playing. In this paper we show the procedure to achieve a CH video game where the Cultural Heritage is the main actor and not the scenario of the game. Furthermore, the game is not a SG but an Action-Adventure Game (AAG) or Survival Game (SuG), in a largest heading it can be classified as Entertainment Games (EGs). The novelty of this study is not only in the original application of the CH within the AAG sector but also consists of the experimentation of the Virtual Reality (VR) algorithm and of the application of Augmented Reality (AR) within the VR scenario used in the form of an avatar. Furthermore, in this paper we overcome the technical problems due to the different size of the environment and the work art.
Nonlocal Means Image Denoising With Minimum MSE-Based Decay Parameter Adaptation

Nonlocal means (NLM), a patch-based nonlocal recovery paradigm, has attracted much attention in recent decades. The decay parameter will greatly affect restoration performance of the NLM method. However, the existing NLM methods with decay parameter adaptation cannot determine this parameter effectively. To address this problem, we have proposed the minimum mean square error (MSE) based decay parameter adaptation method for the NLM denoising. In the proposed method, the globally optimal decay parameter is determined to produce the pre-denoised image based on the derived relation between the global minimum MSE and the decay parameter. Then, the pixel-wise MSE is estimated based on the pre-denoised result and the corresponding method noise. Finally, the optimal pixel-wise decay parameter is obtained by minimizing the pixel-wise MSE to produce the estimated restored image and the boosting strategy is implemented on this image to generate the final denoised result. Extensive simulations on the standard test images and real images corrupted with Gaussian noise and speckle noise demonstrate that the proposed method significantly outperforms some compared NLM methods in noise reduction and detail preservation and can provide better restoration performance than other state-of-the-art denoising methods in most cases in terms of objective metrics and human vision.