Reviewerly/exHarmony huggingface dataset:
Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.
Refined stop-words and morphological variants solutions applied to Hindi-English cross-lingual information retrieval

Cross-Lingual Information Retrieval (CLIR) enables a user to query in a language which is different than the target documents language. CLIR incorporates a machine translation technique, like, Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) which use either a dictionary or a parallel corpus for the training. A Hindi language word may have multiple variations due to the morphological richness of the language, these morphological variants may or may not be present in the dictionary or parallel corpus. The morphological variants which are not present in the dictionary or parallel corpus, are not translated by the state-of-art SMT or NMT translation techniques. Conventional Information Retrieval (IR) technique eliminates the stop-words to improve the IR effectiveness, but there are some significant stop-words whose presence may improve the IR effectiveness. In this paper, a translation induction algorithm, incorporates the refined stop-words list, morphological variants solutions, and translates the words based on the contextual words, is proposed. The proposed algorithm is compared to the manual dictionary, probabilistic dictionary, SMT and NMT based translation techniques for the experimental analysis of Hindi-English CLIR, where it outperforms the other CLIR approaches.
S-Boxes Construction Based on the Cayley Graph of the Symmetric Group for UASNs

Modern block ciphers can be used for designing security schemes to meet the security requirements in underwater acoustic sensor networks. The substitution boxes (S-boxes) used in block ciphers must have good cryptographic properties to resist various attacks. The difficulty in the design of S-boxes is that the cryptographic properties of S-boxes have no obvious distribution. In this paper, we investigate the certain subgraph of the Cayley graph of the symmetric group to get some information about the distribution of the cryptographic properties of S-boxes. Based on the information obtained, an algorithm for designing S-boxes with good cryptographic properties is proposed. The security analysis shows that the preferred S-box constructed by the proposed algorithm has good cryptographic properties.
Attribute Compartmentation and Greedy UCC Discovery for High-Dimensional Data Anonymization

High-dimensional data is particularly useful for data analytics research. In the healthcare domain, for instance, high-dimensional data analytics has been used successfully for drug discovery. Yet, in order to adhere to privacy legislation, data analytics service providers must guarantee anonymity for data owners. In the context of high-dimensional data, ensuring privacy is challenging because increased data dimensionality must be matched by an exponential growth in the size of the data to avoid sparse datasets. Syntactically, anonymising sparse datasets with methods that rely of statistical significance, makes obtaining sound and reliable results, a challenge. As such, strong privacy is only achievable at the cost of high information loss, rendering the data unusable for data analytics. In this paper, we make two contributions to addressing this problem from both the privacy and information loss perspectives. First, we show that by identifying dependencies between attribute subsets we can eliminate privacy violating attributes from the anonymised dataset. Second, to minimise information loss, we employ a greedy search algorithm to determine and eliminate maximal partial unique attribute combinations. Thus, one only needs to find the minimal set of identifying attributes to prevent re-identification. Experiments on a health cloud based on the SAP HANA platform using a semi-synthetic medical history dataset comprised of 109 attributes, demonstrate the effectiveness of our approach.
A self-healing strategy with fault-cell reutilization of bio-inspired hardware

The self-healing strategy is a key component in designing the bio-inspired embryonics circuit with the structure of cell arrays. However, the existing self-healing strategies of embryonics circuits mainly focus on permanent faults inside the modules of cells such as the function module and the configuration register, while little attention is paid to transient faults. From the point of view of obtaining high efficiency of hardware utilization, it would be a huge waste of hardware resources by permanent elimination when a cell only suffers a transient fault which can be repaired by a configuration mechanism. A new self-healing strategy, the Fault-Cell Reutilization Self-healing Strategy (FCRSS) which presents a method for reusing transient fault cells, is proposed in this paper. The circuit structures of all the modules in the cells are described in detail. In the new strategy, two processes of elimination and reconfiguration are combined. Within the process of fault-cell elimination, cells with transient faults in the embryonics circuit array could be reused simultaneously to replace the functions of the cells on their left side in the same row. Therefore, transient fault-cells in a transparent state can be reconfigured to realize the fault-cell reutilization. Finally, a circuit simulation, resource consumption, a reliability analysis and a detailed normalization analysis are presented. The FCRSS can improve the hardware utilization rate and system reliability at the expense of a small amount of hardware resources and reconfiguration time. Following the conclusion, the method of determining the optimal self-healing strategy is presented according to the environmental conditions.
Balance in Demand Driven Acquisitions: The Importance of Mindfulness and Moderation When Utilizing Just in Time Collection Development

The realities of budgetary constraints, and a transformative digital world, point to a future in which academic libraries continue to expand demand driven acquisitions in order to maintain necessary access. However, legitimate concern has arisen regarding the affect this shift might have on the coverage and scope of library collections. The gathering of diverse perspectives, and subjects that support minorities may fall to the way side through the further embrace of DDA, buried in fulfilling the needs of the majority. This paper investigates the University of Utah’s demand-driven acquisitions program and the potential for imbalance in the collection. Data regarding campus demographics and the DDA pool itself are considered. Rather than discouraging the use of DDA, the primary purpose of this research is to put forth tangible ways in which librarians can deliberately counterbalance the focus of DDA purchases in order to ensure collections remain inclusive and diverse.
Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Recently, Mashup is becoming a promising software development method in the mobile service computing environment, which enables software developers to compose existing mobile services to create new or value-added composite RESTful web application. Due to the rapid increment of mobile services on the Internet, it is difficult to find the most suitable services for building user-desired Mashup application. In this paper, we integrate word embeddings enhanced hierarchical Dirichlet process and factorization machines to recommend mobile services to build high-quality Mashup application. This method, first of all, extends the description documents of Mashup applications and mobile services by using Word2vec tool and derives latent topics from the extended description documents of Mashup and mobile services by exploiting the hierarchical Dirichlet process. Secondly, the factorization machine is applied to train these latent topics to predict the probability of mobile services invoked by Mashup and recommend mobile services with high-quality for Mashup development. Finally, the performance of the proposed method is comprehensively evaluated. The experimental results indicate that compared with the existing recommendation methods, the proposed method has significant improvements in MAE and RMSE.
Research on Learning OWL Ontology from Relational Database

Ontology learning is the pivotal technology to construct knowledge base efficiently, and the data of existing knowledge systems are mostly organized in relational mode. In order to reuse and share knowledge in existing knowledge systems, it is necessary to construct ontology for existing knowledge systems. After comparing and analyzing the formal definitions of relational data schema and ontology, this paper proposes a method of extracting knowledge ontology from relational data model. This method builds OWL ontology knowledge base architecture based on metadata of relational data schema, maps different data elements into components of OWL ontology, and implements with prototype tool based on Java platform. As a case study, this method is applied to transform the database of online examination system into OWL ontology model. The method proposed in this paper can be applied to the practice of transferring the widely existing relational-based knowledge base system to the knowledge base system based on OWL ontology framework.
An almost complete curvature scale space representation: Euclidean case

Here, we intend to propose local shape curve features which are invariant under planar Euclidean transformations and independent with respect to the original curve parameterization. The present work generalizes the family of Curvature Scale Space descriptors in order to increase the shape information quantity to tend to the completeness property. For this, a more pragmatic criterion is introduced in this paper which we call the almost completeness. We define it as a pre-completeness for a given resolution of features. Such descriptors are formed by the curvatures on the set of curve points obtained from the antecedents of different curvature levels. This level set is fixed with a given rule. The idea of the almost completeness is to make a compromise between the cardinal of the set of curvature's levels and the optimal number of scales. The rule is submitted to an unsupervised statistical study and the scales are obtained with a spectral analysis. Experiments are conducted on several known datasets. Promising results in the sense of shape retrieval and shape recognition rates are demonstrated.
A Governing Framework for Data-Driven Small Organizations in Colombia

Historically, data has been seen as the result of business transactions, with a little value beyond them, but they were not treated as a valuable shared asset. From the early 1990s, business decisions and processes started to be driven increasingly by data and data analysis. Further investment in data management was the approach taken to tackle the increasing volume, velocity, and variety of data, such as complex data repositories, data warehouses, and complex information systems. Data needed to be shared between multiple systems, which gave origin to the master data management. The first attempts of Data Management and Data Governance were failed, owing that, they were conducted by Information Technology division; today, Data Governance is still an under-researched area. In Colombia, very few organizations have started to implement Data Governance programs. There are IT Governance initiatives, within which there may be some particular linings for Data Quality or Metadata Management, as well as for Information Security. In the year 2014, the Ministerio de Tecnologías de la Información (MinTIC, a national government organization) designed a guide for Data Governance, which must be followed by every public institution, as part of the national policies such as Gobierno Digital Colombia. Small organizations, generally, know little or nothing about this initiative, but are aware of the importance of exploiting the data to obtain useful knowledge for the development of their businesses.
Improved Statistically Based Retrievals via Spatial-Spectral Data Compression for IASI Data

In this paper, we analyze the effect of spatial and spectral compression on the performance of statistically based retrieval. Although the quality of the information is not completely preserved during the coding process, experiments reveal that a certain amount of compression may yield a positive impact on the accuracy of retrievals. We unveil two strategies, both with interesting benefits: either to apply a very high compression, which still maintains the same retrieval performance as that obtained for uncompressed data; or to apply a moderate to high compression, which improves the performance. As a second contribution of this paper, we focus on the origins of these benefits. On the one hand, we show that a certain amount of noise is removed during the compression stage, which benefits the retrievals performance. On the other hand, we analyze the effect of compression on spectral/spatial regularization (smoothing). We quantify the amount of information shared among the spatial neighbors for the different methods and compression ratios. We also propose a simple strategy to specifically exploit spectral and spatial relations and find that, when these relations are taken into account beforehand, the benefits of compression are reduced. These experiments suggest that compression can be understood as an indirect way to regularize the data and exploit spatial neighbors information, which improves the performance of pixelwise statistics-based retrieval algorithms.
Framework of Big data Analysis about IoT-Home-device for supporting a decision making an effective strategy about new product design

This paper introduces a framework of big data analysis about IoT home devices which are delivered to the consumer through several distribution channels, are used by a home user in the smart home, and are repaired in A/S center (repair shop). We collect big data and make an analysis at three major stages that are distribution stage, customer-usage stage, and A/S stage. The ultimate purpose of the presented framework is to help the small/medium companies to make an elastic strategy for the new product. Therefore they can make a more effective decision at three major stages. For example, they can reduce redundancy about a distribution channel, they can adjust a quantity of warehousing, release, stock. They can make a decision on what to upgrade the new next device, how to increase durability, and so on. For these purposes, this framework consists of three subsystems. 1) A data crawler that collects and stores big data about IoT-home devices at three major stages, 2) A big data analyzer about IoT-home device with an appreciate analytic model, 3) A visualization of insights, which help a user to understand the analytic output.
Estimating Productivity in a Scrum team

Scrum is an agile framework within which people can address complex problems, while productively and creatively delivering products of the highest possible value. There are many factors that may affect the completion of a project including the team dynamics, how tasks and user stories are organized by the team members in each sprint, the capabilities of the team members and the complexity of each task. For this reason, it is often difficult to estimate how much work can be completed in a sprint as this is dependent on how much work can be done by the team within a fixed time. A delay in a single sprint may affect subsequent sprints which in turn may delay the delivery of the project. This paper describes a multi-agent based simulation that can be used to estimate the completion of a software project based on the composition of the team, the complexity of the tasks and potential problems that may arise during the software development process.
Gabor-oriented local order feature-based deep learning for face annotation

Face annotation, a modern research topic in the area of image processing, has useful real-life applications. It is a really difficult task to annotate the correct names of people to the corresponding faces because of the variations in facial appearance. Hence, there still is a need for a robust feature to improve the performance of the face annotation process. In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved.
Published incidents and their proportions of human error

Purpose This paper aims to provide an understanding of the proportions of incidents that relate to human error. The information security field experiences a continuous stream of information security incidents and breaches, which are publicised by the media, public bodies and regulators. Despite the need for information security practices being recognised and in existence for some time, the underlying general information security affecting tasks and causes of these incidents and breaches are not consistently understood, particularly with regard to human error. Design/methodology/approach This paper analyses recent published incidents and breaches to establish the proportions of human error and where possible subsequently uses the HEART (human error assessment and reduction technique) human reliability analysis technique, which is established within the safety field. Findings This analysis provides an understanding of the proportions of incidents and breaches that relate to human error, as well as the common types of tasks that result in these incidents and breaches through adoption of methods applied within the safety field. Originality/value This research provides original contribution to knowledge through the analysis of recent public sector information security incidents and breaches to understand the proportions that relate to human error.
Determination of the optimal drill path sequence using bat algorithm and analysis of its optimization performance

Generation of circular holes with varying dimensions in diverse parts/products is one of the most common operations in any manufacturing industry. Determination of the optimal drill path sequence is an important problem in hole-drilling operation using CNC machines. This problem's structure is quite analogous to travelling salesman problem and hence, is NP-complete belonging to both NP and NP-hard complexity classes. Due to exponential increase in number of possible solutions when the number of holes to be drilled increases, various evolutionary algorithms are seemed to be the viable choices in solving this type of optimization problem. In this paper, an almost unexplored swarm-based algorithm, in the form of bat algorithm, is applied to determine the optimal path sequences for different layouts consisting of 5 × 5, 7 × 7, 9 × 9 and 11 × 11 matrices of holes, and a 14-hole benchmark model, taking into account machining time, machining cost and non-productive cost of the related drilling operation.
The CASE Dataset of Candidate Spaces for Advert Implantation

With the advent of faster internet services and growth of multimedia content, we observe a massive growth in the number of online videos. The users generate these video contents at an unprecedented rate, owing to the use of smart-phones and other hand-held video capturing devices. This creates immense potential for the advertising and marketing agencies to create personalized content for the users. In this paper, we attempt to assist the video editors to generate augmented video content, by proposing candidate spaces in video frames. We propose and release a large-scale dataset of outdoor scenes, along with manually annotated maps for candidate spaces. We also benchmark several deep-learning based semantic segmentation algorithms on this proposed dataset.
Key is in the Air: Hacking Remote Keyless Entry Systems

A Remote Keyless Systems (RKS) is an electronic lock that controls access to a building or vehicle without using a traditional mechanical key. Although RKS have become more and more robust over time, in this paper we show that specifically designed attack strategies are still effective against them. In particular, we show how RKS can be exploited to efficiently hijack cars’ locks. Our new attack strategy—inspired to a previously introduced strategy named jam-listen-replay—only requires a jammer and a signal logger. We prove the effectiveness of our attack against six different car models. The attack is successful in all of the tested cases, and for a wide range of system parameters. We further compare our solution against state of the art attacks, showing that the discovered vulnerabilities enhance over past attacks, and conclude that RKS solutions cannot be considered secure, calling for further research on the topic.
Application of 3D Digital Image Correlation for Development and Validation of FEM Model of Self-Supporting Arch Structures

Many building structures, due to a complex geometry and non-linear material properties, are cumbersome to analyze with finite element method (FEM). A good example is a self-supporting arch-shaped steel sheets. Considering the uncommon geometry and material profile of an arch (due to plastic deformations, cross section of a trough, a goffer pattern), the local loss of stability can occur in unexpected regions. Therefore, the hybrid experimental-numerical methodology of analysis and optimization of arch structures have been proposed. The methodology is based on three steps of development and validation of a FEM with utilization of a digital image correlation (DIC) method. The experiments are performed by means of 3D DIC systems adopted sequentially for each measurement step conditions from small size sections, through few segment constructions up to full scale in situ objects.