Reviewerly/exHarmony huggingface dataset:
A New Belief-based Classification Fusion for Incomplete Data

Reducing the negative impact of estimation on classifier performance in training set is one of the most challenging tasks in incomplete data classification. A new belief-based classification fusion method (BCF) is proposed for incomplete data in this paper and the core idea is to make full use of the existing attributes of incomplete objects in training set to improve the performance of basic classifier without deleting or estimation strategy. Specifically, for a data set with n-dimensional attributes, different attributes generate p (p ≤ n) subsets according to prior knowledge or random combination. Then, p trained basic classifiers (such as SVM) will be obtained with complete objects from corresponding p training subsets, and estimation strategy is used to fill the incomplete objects in the test set. Finally, DS rule is used to fuse p sub-classification results if they do not conflict and a new global fusion method is proposed to fuse the remaining conflict sub-classification results, which can submit the object difficult to be accurately classified into a singleton (special) class to meta-class to reduce error rate and characterize the uncertainly caused by missing values well. Our simulation results illustrate the potential of the proposed method using real data sets, and they show that BCF can improve substantially the classification accuracy.
Academic Articles Recommendation Using Concept-Based Representation

Academic articles recommendation systems have gained a lot of interest as an effective tool to suggest relevant articles for researchers according to their interests. Explicit identification of the topics of interests from the contents of academic articles that the researchers have authored, downloaded or read has been always a challenging task. In this paper, we propose a concept-based method to represent researchers’ interests where the concepts generation process depends on the semantics of the words in the articles related to the researcher. The evaluation results show that the proposed method outperforms the recommendation baseline methods and produces better recommendations for researchers.
Excelsior: Earth observation opportunities for excellence in the Emmena Region

The ERATOSTHENES Centre of Excellence for Earth Surveillance and Space-Based Monitoring of the Environment (ECoE) that will be established through the EXCELSIOR project will provide cutting-edge Earth Observation (EO) research in Cyprus, the Eastern Mediterranean, Middle East and North Africa (EMMENA) region, Europe and Internationally for the benefit of the environment and society. The ECoE will focus on the thematic clusters of Environment and Climate, Resilient Society and Big Earth Data Analytics and conduct research and develop applications within the focus areas of climate change monitoring, disaster risk reduction, access to energy, water resources management and big EO data analytics. The ECoE can serve as an innovation gateway for Cyprus and EMMENA in the areas of Earth observation and remote sensing, thereby providing applications and services for issues that are unique to the EMMENA region. The success indicators to measure the ECoE’s sustainability are also discussed.
A History of the Distance Library Services Conference

The Distance Library Services Conference was founded in 1982 by the librarians of the Off-Campus Library Services department at Central Michigan University. From its inception through its 18th conference in 2018, Central Michigan University planned and administered this conference for the benefit of librarians serving distance learners and faculty at their institutions. This article discusses the founding of the Distance Library Services Conference and its role as the library profession’s preeminent forum for the exchange of ideas on the topic of distance librarianship.
Control Interface of an Automatic Continuous Speech Recognition System in Standard Arabic Language

Human-machine dialogue is still too difficult to set up, but remains an important issue for the Arabic language, which remains a language poorly endowed with communication systems. To this end, we have oriented our work around a control interface for an Automatic Continuous Speech Recognition System (ACSRS), which we have adapted to the Standard Arabic language. For this, a construction of an Automatic Continuous Speech Recognition System was first undertaken and a link was established with a graphic interface based on HMM (Hidden Models of Markov) and the toolbox HTK (Hidden Markov Model Toolkit). The realization of the proposed interface will avoid the dependency constraint, common in most of the ACSRS (s), and make the system independent of the speakers. On the one hand, the transcription of the sentences recognized by this system will be executed in real time; and on the other hand, the display will be on a screen independent of the system.
An Ideal Secret Sharing Scheme Realizing an Access Structure Based on a Finite Projective Plane of Order 3

A secret sharing scheme is one of cryptographic primitives to share a secret among participants so that only qualified subsets of participants can access the secret. In this paper, we give a new access structure with 13 participants based on a finite projective plane of order 3 and construct an ideal secret sharing scheme realizing the access structure. The minimal qualified set of the access structure consists of all the lines of the finite projective plane as well as all the collection of 7-point sets satisfying a certain property. The soundness of the scheme is established theoretically together with exhaustive search by computer.
ARP: asexual reproduction programming

Recently meta-heuristic techniques have attracted more attention. Algorithms based on Bio-inspired problems are among the most popular techniques of this field. In meta-heuristic algorithms, Genetic algorithm is one of the most useful. GA uses chromosome representation and operates on the chromosome with crossover and mutation operators. Genetic programming is a form of GA with tree representation for its chromosomes. GP was developed to evolve programming in computers and is a population-based algorithm. But GP is very slow and needs a long time for converging. On the other hand, asexual reproduction optimisation (ARO) is another variant of meta-heuristic algorithms in which convergence to the global optima is done at a fast time. In this paper, we introduced a new method, which is inducted by asexual reproduction with combination to GP. This algorithm is named Asexual Reproduction Programming (ARP). ARP has advantages of both ARO and GP together i.e. the fast convergence time of ARO and the power and flexibility of GP. ARP has fast convergence to global optimum while its error is less than GP. By mathematically analysing and proving, we show the ARP convergence to the global optimum. To assay the efficiency of the ARP, two algorithms were compared on some real-valued symbolic regression problems. Perusing the experimental results demonstrate that ARP outperforms GP in performance and convergence time.
Randomness Analysis for GSS-sequences Concatenated

Binary sequences produced by a generator should appear as random as possible, that is, have no logical pattern to be used in cryptographic applications. In this paper, we give a detailed analysis of the randomness of a family of binary sequences obtained from generalized self-shrinking generator, an element in the class of decimation-based sequence generators. We have applied the most important batteries of statistical tests to the sequence resulting from the concatenation of the family of generalized sequences obtained from a PN-sequence. This complete study provides good results and allow us to construct a new binary sequence with good cryptographic properties from a family of generalized self-shrunken sequences.
A method for document image enhancement to improve template-based classification

Document classification is one of the significant procedure in paper document recognition. This article proposed a method for document image enhancement to improve the performance of classification in the convolutional neural network. An enhanced document image was generated by extracting the table frame, text region, and shape of the raw document. The template-based classification experiment on 414 customs documents and more than one thousand generated images showed the enhanced image could help CNN model achieve higher accuracies compared to the original images. It could also diminish the interference of noise and unrelated features in document classification optimizing the robustness of networks. The proposed method also demonstrated the channels of the image could provide more information except for color in deep neural networks. As the similarity in the whole image classification tasks, the conclusion might provide ideas for the training of the neural networks in other fields such as street view recognition, medical image recognition, etc.
Design of an efficient VCMA controlled spintronic random number generator

Random number generators play an essential role in stochastic circuits, neuromorphic computing and cryptographic systems. Existing CMOS based true random number generators (TRNGs) incur a significant performance, power and area overheads. The systems based on magnetic tunnel junctions (MTJs) offer favorable features such as high resilience, low energy consumption, good performance, and less complexity. Compared with the conventional MTJ switching approaches, voltage controlled magnetic anisotropy switching (VCMA) features low switching current, minimal control circuitry and calibration process, and insensitivity to the write pulse duration. In this paper an efficient design for TRNG based on VCMA using MTJ is presented. The proposed magnetic TRNG consists of a standard two-terminal MTJ connected to the drain terminal of a PMOS transistor which controls the pulse width of the applied bias voltage. MTJ is used as a noise source device and its precessional switching characteristics are exploited to design the TRNG. The resistance of the device fluctuates while operating in precessional switching regime and the magnetization state is selected randomly via thermal fluctuations. The voltage developed across the MTJ corresponding to its random state results into a random bit. A 64-bit parallel random number generator has been implemented and evaluated using Spice simulating tool (with 45 nm CMOS technology node). The proposed circuit has an advantage of less circuit complexity and high energy efficiency. It achieves a high throughput of 64Gbps with an energy consumption of 5.6 fJ/bit.
Learning Single-Shot Detector with Mask Prediction and Gate Mechanism

Detection efficiency plays an increasingly important role in object detection tasks. One-stage methods are widely adopted in real life because of their high efficiency especially in some real-time detection tasks such as face recognition and self-driving cars. RetinaMask achieves significant progress in the field of one-stage detectors by adding a semantic segmentation branch, but it has limitation in detecting multi-scale objects. To solve this problem, this paper proposes RetinaMask with Gate (RMG) model, consisting of four main modules. It develops RetinaMask with a gate mechanism, which extracts and combines features at different levels more effectively according to the size of objects. It firstly extracted multi-level features from input image by ResNet. Secondly, it constructed a fused feature pyramid through feature pyramid network, then gate mechanism was employed to adaptively enhance and integrate features at various scales with the respect to the size of object. Finally, three prediction heads were added for classification, localization and mask prediction, driving the model to learn with mask prediction. The predictions of all levels were integrated during the post-processing. The augment network shows better performance in object detection without the increase of computation cost and inference time, especially for small objects.
Cost Minimization for Music Uploading to a Cloudlet

Due to the rise of mainstream cloud storage adoption in the past ten years, the service reached has reached a wide audience, including the music industry. When saving a music file to a multi-cloud storage system (cloudlet), there are several tasks to be performed. At each step, there may be several clouds to select from to execute the task required. The performance of each cloud may vary in time and cost in executing each task due to differences in hardware quality or energy usage. This article will demonstrate a solution previously used to solve the Heterogeneous Assignment Problem from computer systems and apply it to this specific problem of uploading music to a cloudlet in order to find the best assignment of clouds to tasks. The algorithm minimizes the total cost under a given timing constraint in an efficient manner. Then, using programming, a computer can calculate the best solution for large problems. Experiments will show the effectiveness of the algorithm in this application.
An Automatic Design of Camouflage Patterns Based on CNNs

In order to get with the environmental changes of battlefield quickly, the military camouflage should be changeable. If the camouflage patterns of the clothes and vehicles like tanks are different from the environment, it's very easy for cameras of enemies to find. As we all know that the same patterns is used in the most of current military all over the world. In this paper, we propose a novel feature-extraction method from an image using convolutional neural networks. Then the pattern will be combined with the environmental style pattern. The composite image is mapped onto the surface of the actual 3D clothes and vehicles finally. In this paper, the Eye-Movement equipment is applied to evaluate the results for better comparison. We can produce the proper pattern even the different and complicated environment.
SentiRank: A System to Integrate Aspect-Based Sentiment Analysis and Multi-criteria Decision Support

This study proposes a novel ranking method, called SentiRank, that integrates data coming from aspect-level sentiment analysis into a multi-criteria decision aiding procedure. The novelty of the work is the combination of the evaluation of the features of a set of products (based on user preferences) with the reviews in social networks about the same products. The contribution of this work is twofold: theoretical and practical. From the theoretical side, we propose an automatic method to extract the aspect categories and their evaluations from the online opinions of users. Next, we describe how to merge that information with the decision maker’s preferences about the features of a set of products. The ELECTRE method is used afterwards to rank the products with both types of input data. From the practical side, we have implemented a tool that can be used to rank a set ofs restaurants in Tarragona, using available data and reviews on the Web. The tests show that the ranking is effectively modified when online reviews are included in the analysis.
Efficient Real-time Query of Multimedia Data Based on Local Sensitive Hash

In recent years, monitoring of modern metropolitan events and civil infrastructure has been identified as an important part of the management of public services. Through rapid information from citizens, intelligent equipment for collecting multimedia information can pass from a terminal initialization, application, examination, provide useful information to deduce such emergency situations. To this end, in this paper, we proposed a realtime query framework in IoT sensing data streams for efficient realtime query of multimedia with hash method. More specifically, the proposed method is featured by: (i) the Batch Orthogonal Locality Sensitive Hashing is adopted to deal with the image and video data with hash code. (ii) a comprehensive flow processing is designed for realtime data query based on storm and kafka. Finally, a series of experiments are presented to evaluate the efficiency and satisfactory performance of the proposed methods.
On Limitation of Plausible Deniability

Recently, a new security definition, named plausible deniability, for synthetic records has been proposed by Bindschaedler et al. Intuitively, the synthetic record r is said to satisfy plausible deniability if there are multiple “original” records that may become r. In this paper, we show that even if each synthetic record satisfies the plausible deniability, there is a possibility that the collection of these records will not have the plausible deniability, i.e., some of these records are re-identifiable.
A Novel Big Data Index Architecture for Programming Environment

Abstract With the rapid development of computer information technology and the upgrading of programming software, the types and number of project codes are growing rapidly, showing typical characteristics of large data such as massive, instantaneous, diverse and variable. The distributed column storage database HBase based on the Hadoop big data platform, has the characteristics of high reliability, high performance, column-oriented, and scalability. It also has good scalability, can store more than ten billions of data, and is suitable for large-scale data reading and writing, which takes advantages in processing large-scale unstructured and semi-structured software data on the programming site. So, this paper studies the big data index architecture in the programming field. In view of the low efficiency of HBase non-primary key attribute query, the inverted index is a natural platform for cloud-based big data storage and query in this large development site. Based on this advantage, this paper designs and optimizes the secondary index architecture based on the HBase for programming field big data inverted index.
Detecting double compression and splicing using benfords first digit law

Detecting image forgeries in JPEG encoded images has been a research topic in the field of media forensics for a long time. Until today, it still holds a high importance as tools to create convincing manipulations of images have become more and more accessible to the public, which in return might be used to e.g. generate fake news. In this paper, a passive forensic detection framework to detect image manipulations is proposed based on compression artefacts and Benfords First Digit Law. It incorporates a supervised approach to reconstruct the compression history as well as provides an un-supervised detection approach to detect double compression for unknown quantization tables. The implemented algorithms were able to achieve high AUC values when classifying high quality images exceeding similar state-of-the-art methods.
Big Data Tools for Tasks

As the use of data mining with its application becomes more predominant in academe and in industry, there is a growing need to develop needed tools and rely on their outputs for meaningful information. As the various aspects of big data developed, different tools developed to assist with the utilisation of these aspects. Various types of users had different requirements for the same aspect of data mining. Furthermore, these tools began as tools for simple data mining tasks among software designed companies while eventually evolving into more specialised and niche roles within smaller business/groups. In this chapter, we will cover the various types of considerations that might form a customer base for a given tool. At the end, we will describe various tools in terms of the tasks that they perform and the user market that they are designed for.
Learning a Generalized Matrix from Multi-graphs Topologies Towards Microservices Recommendations

This paper presents a methodology that combines latent factor models with graph-based models. The proposed recommendation system identifies a recommended item as a node of a graph. More specifically, the topology of the graph and the paths between the nodes are considered as critical features regarding the associations between them. Furthermore, in the current approach, these structural features are considered as feedback. These structural features are extracted from a pool of several application graphs which are afterwards generalized into a unified matrix of proximities. The main reason for the use of this structural feedback is to generate recommendations and discover unobserved relations using matrix factorization techniques. The approach is tested on a data set that consists of cloud-native microservices graphs.
A Customized Children Friendly and Secure Search Engine

In current technological era Internet is playing a vital role in acquiring knowledge base. WWW is a huge collection of web-pages and providing suitable content to the users is quite expected but in case of children as internet users it is highly demanding because all the web pages may not be suitable and readable by them. As per age and interest kids are using internet is increasing every day. So our work proposes a customized search engine technique which will provide secure browsing with a children friendly environment. This technique will automatically identify web pages suitable for children on the Web and show them in a simple way and making surfing easy and child friendly.
A Method Based on Clustering to Suppress DRFM Dense False Targets

The technology of interrupted sampling repeater jamming (ISRJ) based on digital radio frequency memory (DRFM) can generate the signal being coherent with the radar signal to cause range deception jamming. The paper proposes a method based on clustering to suppress DRFM dense false targets (DFTs). First, analyze the statistic properties of echo to obtain the threshold of the noise and improve the signal-noise ratio of echo. Next, considering the characteristic of the jamming created by DRFM, apply the algorithm of clustering to gain two parameters composed of the estimated range of DFTs interval and the location of the false target nearest to radar. Then the jamming data, a two-dimensional dataset, can be reconstructed based on these parameters. Finally, optimal result of the elimination between the echo data and the reconstructed jamming data can be determined through the cost function and the targets can be marked. Simulation results show that the proposed method suppresses DFTs effectively.
Practical Issues and Guidelines in Handling Erroneous Secondary Radar Measurements in Multi Target Tracking

This paper develops methods to circumvent the issues in track initiation and track maintenance that can occur in radars with primary and secondary measurements. In a combined Primary and Secondary radar surveillance applications the measurements from secondary radar (Identification of Friend or Foe IFF) along with primary radar constitute another category of measurements referred as associated measurements. This paper describes the possible types of erroneous measurement that can occur under the combined Primary and Secondary radar surveillance applications. Ideally one target should produce only one measurement (Either primary, secondary or associated) from radar for a given time and also with unique IFF code. But, in practice due to various reasons there can be (a) primary and secondary measurements for same target, - due to the failure of combining logic (b) multiple secondary and associated plots for same target - due to the issues in secondary radar side lobe reflections. The methods developed in this paper first identify the presence of multiple measurements from same target and then utilize these measurements to improve the estimation accuracy. The developed methods shows that by incorporating measurements from primary and secondary radars improved estimation accuracy can be obtained while in case of multiple secondary radar measurements the using only the nearest measurements yields best results. The developed logics are explained in this paper using detailed flow charts. The abnormal detections and the root cause analysis carried out in this paper are based on the practical knowledge obtained by handling various real time radar recordings collected from the field.