Reviewerly/exHarmony huggingface dataset:
An Efficient Offloading Scheme for Blockchain-Empowered Mobile Edge Computing

Combining computation offloading with Mobile Edge Computing (MEC) is a research hotspot currently. In order to implement a trusted and reliable offloading system with MEC, blockchain technology provides a feasible approach with its features of security, transparency and decentralization. For the traditional blockchain with the linear data structure, the limited throughput seriously hinders the deployment in the practical offloading situation because it is unable to fulfill the requirements of being recorded timely from a tremendous number of offloading transactions. In this paper, we opt to apply a Directed Acyclic Graph (DAG) structure to promote the scalability of the blockchain-empowered offloading system. Specifically, due to the DAG structure, each MEC server can generate blocks consisting of the completed offloading transactions in parallel. In the novel system, an MEC server has to execute not only the offloading but also the mining with the limited computation resource. In this paper, we address the vital issue of how to strike the balance of computing resource allocation between the task offloading and the mining process. To this end, we first propose the Proof-of-Offloading (PoO) mining scheme to motivate the MEC servers to execute more offloading tasks by adjusting the mining difficulty according to its offloading load. Furthermore, we formulate the computing resource allocation as an optimization problem with the objective of minimizing the maximum offloading delay, subject to both the computing capacity and each offloading user’s QoS requirement. Then, a two-layered algorithm is designed to solve this nonlinear problem, in which the original problem is decomposed into two subproblems, including the task selection and computing resource allocation. The simulation results demonstrate that the proposed algorithm has significant improvement over reference algorithms in terms of overall offloading latency.
VQA Model Based on Image Descriptive Paragraph and Deep Integration of BERT

Abstract Visual Question Answering (VQA) is a fast developing field involving multiple disciplines, and it is constantly challenging more complex tasks. The classic combination of CNN+LSTM can effectively extract images and language representation to complete the VQA task, but there are still many problems, such as excessively long sequence processing, etc. In recent years, BERT model has expanded rapidly from the field of natural language processing to a broader multi-modal field with its strong learning ability. In this paper, we propose a novel way to apply BERT model in the VQA field. We use the descriptive paragraph generation technology to transform the picture into a text paragraph description, and integrate question information and image information on BERT model. Our model achieves an excellent performance on the VQA2.0 dataset with an overall accuracy 5% higher than previous models.
MAGNeto: An Efficient Deep Learning Method for the Extractive Tags Summarization Problem

In this work, we study a new image annotation task named Extractive Tags Summarization (ETS). The goal is to extract important tags from the context lying in an image and its corresponding tags. We adjust some state-of-the-art deep learning models to utilize both visual and textual information. Our proposed solution consists of different widely used blocks like convolutional and self-attention layers, together with a novel idea of combining auxiliary loss functions and the gating mechanism to glue and elevate these fundamental components and form a unified architecture. Besides, we introduce a simple but effective data augmentation technique dedicated to alleviate the effect of outliers on the final results. Last but not least, we explore a self-supervised pre-training strategy to further boost the performance of the model by making use of the abundant amount of available unlabeled data. Our model shows the good results as 90% $$F_{1}$$ score on the public NUS-WIDE benchmark, and 50% $$F_{1}$$ score on a noisy large-scale real-world private dataset. Source code for reproducing the experiments is publicly available at: https://github.com/pixta-dev/labteam .
IoT Supported Assistive Network for Elderly People

Internet-of-things (IoT) is playing a very effective role in connecting man to things, man to man and things to things. On the other hand, considerable research and development is taking place on new assistive devices and systems for making the life of independently-living elderly people less painful, if not comfortable. This research paper presents the details of a successful effort to integrate the concept of IoT with personal assistive devices/systems and home assistive systems for elderly persons. Architecture of an IoT supported assistive network is proposed. Several personal assistive devices/systems and home assistive systems are discussed in detail. Suitable communication techniques to connect components with network and user are recommended. Implementation details are presented. The assistive network presented and developed here can be conveniently used by elderly people with zero effort, as most of the actions are taken by the intelligent master controller automatically. Some suggestions are given at the end for further research.
A Block Chain Dynamic Key Sharing and Updating Scheme Based on the (t, n) Threshold

Key sharing and updating is the core problem in key management. In order to solve the centralization, security and secret share loss in the process of key management, we propose a decentralized (t, n) threshold dynamic key sharing and updating scheme in the Blockchain system, which makes full use of the advantages of Blockchain decentralization and traceability for key sharing and updating. Firstly, we generated and stored the key based on Diffie-Hellman key exchange protocol to complete the formation of the node Shared key. Secondly, a threshold scheme (t, n) of key sharing is designed based on Lagrange polynomial interpolation function, and the sub-key of each block node is stored in different places to realize key sharing of block chain. Finally, the chameleon hash function is used to update and modify the historical content for node loss in block chain sharing. The analysis shows that this scheme can realize the secure sharing and updating of the key in the Blockchain system.
GFEN: Graph Feature Extract Network for Click-Through Rate Prediction

Click-Through Rate (CTR) is a fundamental task in personalized advertising and recommender systems. It is vital to model different orders feature interactions in click-through rate. There are many proposed methods in this field such as FM and its variants. However, many current methods can not adequately and precisely extract feature interactions. In this paper, we improve an effective light weight method called the Graph Feature Extract Network (GFEN) to further explicitly or implicitly model low-order and high-order feature interactions information via Graph Convolutional Network (GCN) and Global Recombination Network (GRN). GCN explicitly models local high-order feature interactions. GRN automatically captures global high-order feature interactions via the diversity pooling layer and recombines global and local feature interactions via fully connection layer. We conduct extensive experiments on there real-world datasets and show that our model achieves the best performance compared to the existing state-of-the-art methods such as CKE, DKN, Wide&Deep, RippleNet etc. Our proposed GFEN is a very light weight model, which can be applied to other complicate model such as Ripplenet based knowledge graph and deep learning models based CTR. The whole model can be efficiently fit on large-scale raw input feature.
Software Defect Prediction with Spiking Neural Networks

Software defect prediction is one of the most active research areas in software engineering and plays an important role in software quality assurance. In recent years, many new defect prediction studies have been proposed. There are four main aspects of research: machine learning-based prediction algorithms, manipulating the data, effor-softaware prediction and empirical studies. The research community is still facing many challenges in constructing methods, and there are also many research opportunities in the meantime. This paper proposes a method of applying spiking neural network to software defect prediction. The software defect prediction model is constructed by feed-forward spiking neural networks and trained by spike train learning algorithm. This model uses the existing project data sets to predict software defects projects. Extensive experiments on 28 public projects from five data sources indicate that the effectiveness of the proposed approach with respect to the considered metrics.
A Design Method of Reusable Test Case Based on Exploratory Testing for E-Commerce Website

Reusing of test case can reduce the workload of test design and test execution. The research is based on the concept of exploratory testing, and taking the “shopping cart” module of an e-commerce website as an example, this study is devoted to the designing of a feasible method of reusable test case based on exploratory testing and describes the reuse process. Test result shows more than reuse rate of 25% for test case can be achieved and more bugs for functionality and usability are founded through the execution of reusable test cases based on exploratory testing. Finally, further work of development of a reusable test case system based on B/S, and the technology of data mining on extraction and storage of reusable test cases are put forward in the paper.
An efficient dynamic Bayesian network classifier structure learning algorithm: application to sport epidemiology

Abstract Exposing an athlete to intense physical exertion when their organism is not ready for the mobilization of such resources can lead to musculoskeletal injury. In turn, sport practitioners regularly monitor athlete readiness in hopes of mitigating these tragic events. Rapid developments in athlete monitoring technologies has thus resulted in sport practitioners aspiring to siphon meaningful insight from high-throughput datasets. However, revealing the temporal sequence of biological adaptation while yielding accurate probabilistic predictions of an event, demands computationally efficient and accurate algorithms. The purpose of the present study is to create a model in the form of the intuitively appealing dynamic Bayesian network (DBN). Existing DBN approaches can be split into two varieties: either computationally burdensome and thus unscalable, or place structural constraints to increase scalability. This article introduces a novel algorithm ‘rapid incremental search for time-varying associations’ $(Rista)$, to be time-efficient without imposing structural constraints. Furthermore, it offers such flexibility and computational efficiency without compromising prediction performance. The present algorithm displays comparable results to contemporary algorithms in classification accuracy while maintaining superior speed.
Reputation-based Q-Routing for Robust Inter-committee Routing in the Sharding-based Blockchain

The fault-tolerant consensus problem has been extensively studied in the distributed systems era since the late 1970s. It has regained popularity with the rise of blockchain. However, the scalability and efficiency issues remain to be the challenges in the consensus. One of the solutions is using sharding algorithms to partition networks into several committees. However, previous work does not consider Byzantine problem during the inter-committee routing. In this paper, we propose a Reputation-based Q-routing algorithm using reinforcement learning to assist the communication procedure between different shards. Through the modification of Q-routing protocol, our algorithm can efficiently withstand some of the Byzantine failures. Moreover, to resist routing information tampering, we design a hierarchical structure that uses BFT to validate the routing tables. The experiments show the robustness of routing algorithm brought by reinforcement learning in the verification procedure of sharding-based consensus.
Generalized Algorithms for Stream Encryption of Signals in Communication Channels with the UAV

Synthesis of algorithms of stream data encryption based, as a rule, on principles of construction of LFSR generators of pseudo-random numbers on Galois and Fibonacci schemes. Each pseudo-random number generator assigned to a matrix, let's call it the Galois (Fibonacci) matrix, using which a sequence of binary numbers, which coincides with the series produced by a register number generator. In classical pseudo-random numbers generators, single-circuit register feedback formed exclusively primitive polynomials. In generalized generators, feedback is, firstly, multi-circuit and, secondly, can be created by polynomials, not necessarily primitive (enough that they were irreducible polynomial). But at the same time, the following condition must be met: the forming element of the matrix, which meets the generator of the pseudo-random numbers, must be a primitive element of the Galois field, generated by the irreducible polynomial chosen to build the generator of pseudo-random numbers. The main advantage of the generalized generators is that: the generalized generators of pseudo-random numbers, unlike the classical ones, are free from attack by Berlekamp-Messi. The proposed algorithms are applicable in many areas, including cryptographic protection of command and video information in the UAV.
Software Risk Management Process Improvement Experience in Enterprise Projects

Software projects that are growing and getting more complex require more effective risk management for project success. Continuous improvement of risk management process can be achieved by utilizing emerging technologies, standards and experiences. In the scope of this study, risk management process improvement activities are implemented and maintained for a year within a sampled set of software development projects carried out by the TÜBITAK-BİLGEM Cyber Security Institute. Our achievements, at the end of those activities, include the integration of risk management process with software development life cycle tools, a constantly visible risk treatment plan, while ensuring a proactive risk management approach. In addition, we setup an infrastructure to increase the involvement of stakeholders into the risk management process. The risk management process, which was improved as a result of this study, was implemented in other cyber security projects within TÜBITAK-BİILGEM Cyber Security Institute. In this study, we share our experiences during the adaptation of the process, preparation of management plan and establishment of infrastructure.
An Automatic Evaluation Method for Modal Logic Combination Formula

Modal logic is widely used in the knowledge representation of intelligent knowledge systems, but the strict deduction of dependency axioms and inference rules of compound formulas in the world hinders its application in practical systems.This paper presents an automatic evaluation algorithm of modal logic compound formula based on Haskell functional programming to solve the problem of evaluating modal logic compound formula in the possible world. In this paper, an $$M^+$$ model is presented, and on this basis, the automatic evaluation of the compound formula in the possible world is realized.Experimental and case results verify the effectiveness and accuracy of the proposed algorithm, and its automatic solution obviously reduces the manual dependence.
Seiðr: Dataplane Assisted Flow Classification Using ML

Real-time, high-speed flow classification is fundamental for network operation tasks, including reactive and proactive traffic engineering, anomaly detection and security enhancement. Existing flow classification solutions, however, do not allow operators to classify traffic based on fine-grained, temporal dynamics due to imprecise timing, often rely on sampled data, or only work with low traffic volumes and rates. In this paper, we present Seior, a classification solution that: (i) uses precision timing, (ii) has the ability to examine every packet on the network, (iii) classifies very high traffic volumes with high precision. To achieve this, Seior exploits the data aggregation and timestamping functionality of programmable dataplanes. As a concrete example, we present how Seior can be used together with Machine Learning algorithms (such as CNN, k-NN) to provide accurate, real-time and high-speed TCP congestion control classification, separating TCP BBR from its predecessors with over 88-96% accuracy and F1-score of 0.864-0.965, while only using 15.5 MiB of memory in the dataplane.
Spontaneous Expression Recognition Based on Visual Attention Mechanism and Co-salient Features

Spontaneous facial expression recognition has gained much attention from researchers in recent years, however most of the existing algorithms still encounter bottlenecks in performance due to too big redundant images data in the video. In this paper, we propose a novel co-salient facial feature extraction algorithm, combined with human visual attention mechanism and group data co-processing technology, which would largely reduce the redundant information in the original images and effectively improve the recognizing accuracy of facial expressions. Firstly, based on human visual mechanism, key frames of expression are dynamically derived from the original videos to capture the temporal dynamics of facial expressions. Secondly, using key sequence frames, salient regions are obtained by multiplicative fusion algorithm and in multi-images co-operative manner. Thirdly, we get rid of these salient regions due to their little deformation and low-correlation to facial expressions, and reduce the number of facial features data. At last, we extract Local Binary Pattern (LBP) features from the remainder of facial features and use Support Vector Machine (SVM) classifier to classify them respectively. Experimental results on dataset Cohn-Kanade plus and MMI showed that our proposed method can effectively improve the recognizing accuracy of spontaneous expression sequence.
Cultural Control Room: CETRA addresses and exploits the IoT to innovate Cultural Heritage promotion

Abstract CETRA - Cultural Equipment with Transmedial Recommendation Analytics - is a comprehensive combination of IoT (Internet of Things), machine learning, recommendation, content adaptivity and Big Data Analytics applications, in line with a new concept of transmediality, aiming to close the gap between tourist-cultural promotion requirements and the need to deliver personalized, dynamic and accessible heritage content to users. The paper will describe the research results related to new technological solutions addressing the heritage experience in an era of: massive content production and massive participation by visitors who demand personalized involvement.
Management of Context in Mobile Environments

Mobility becomes a mode of life or work, ubiquitous access to information becomes necessary. At the same time, technological advances in wireless networks and the success of handheld computers and mobile phones are encouraging this movement. The diversity of modes of interaction in mobile environments must allow users to access information anywhere and at any time. This flexibility makes users more demanding and brings new challenges to user interfaces. For this, the context must be well understood and managed in an appropriate form to promote sharing between the different devices and offer a high level of abstraction. Several architectures have been developed to allow context management in mobile environments. To face the challenges of context management in mobile environments, and taking into account the limits of existing solutions, we offer a modular and flexible architecture, which takes the context as the most important element to reason, adapt and provide a service in a appropriate form.
Semantic-Aware Chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser

Deep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well integrated linguistic information into the deep learning-based Chinese zero pronoun resolution model. This paper adopts the idea based on the pre-trained model, and integrates the semantic representations in the pre-trained Chinese semantic dependency graph parser into the Chinese zero pronoun resolution model. The experimental results on OntoNotes-5.0 dataset show that our proposed Chinese zero pronoun resolution model with pre-trained Chinese semantic dependency parser improves the F-score by 0.4% compared with our baseline model, and obtains better results than other deep learning-based Chinese zero pronoun resolution models. In addition, we integrate the BERT representations into our model so that the performance of our model was improved by 0.7% compared with our baseline model.
Speeding up Common Hyperparameter Optimization Methods by a Two-Phase-Search

Hyperparameter search concerns everybody who works with machine learning. We compare publicly available hyperparameter searches on four datasets. We develop metrics to measure the performance of hyperparameter searches across datasets of different sizes as well as machine learning algorithms. Further, we propose a method of speeding up the search by using subsets of data. Results show that random search performs well compared to Bayesian methods and that a combined search can speed up the search by a factor of 5.
A Model-based approach to assist Android Activity Lifecycle Development

In Android app development, conforming to the activity lifecycle model is imperative to maintain app robustness and reliability as well as avoiding many issues tied to lifecycle state transitions, such as memory leaks, data preservation, and app crashes. Previous studies have shown that Android developers possess limited understanding and awareness of the activity lifecycle model and the current state-of-the-art Android app development tools and methods provide developers with little support during activity lifecycle development. In this study, we present an approach and a framework that provides a dynamic visual view for the activity lifecycle state transitions during implementation. The approach follows model-based development utilizing DSVL (Domain Specific Visual Language) and is implemented as a proof-of-concept Android Studio plugin. We evaluated our approach through experimentation by real Android developers. Initial results show that our approach can be useful and effective in assisting Android developers.
Fluent Session Programming in C#

We propose SessionC#, a lightweight session typed library for safe concurrent/distributed programming.The key features are (1) the improved fluent interface which enables writing communication in chained method calls, by exploiting C#'s out variables, and (2) amalgamation of session delegation with async/await, which materialises session cancellation in a limited form, which we call session intervention.We show the effectiveness of our proposal via a Bitcoin miner application.