Reviewerly/exHarmony huggingface dataset:
Deep Learning Does Not Generalize Well to Recognizing Cats and Dogs in Chinese Paintings

Although Deep Learning (DL) image analysis has made recent rapid advances, it still has limitations that indicate that its approach differs significantly from human vision, e.g. the requirement for large training sets, and adversarial attacks. Here we show that DL also differs in failing to generalize well to Traditional Chinese Paintings (TCPs). We developed a new DL object detection method A-RPN (Assembled Region Proposal Network), which concatenates low-level visual information, and high-level semantic knowledge to reduce coarseness in region-based object detection. A-RPN significantly outperforms YOLO2 and Faster R-CNN on natural images (P < 0.02). We applied YOLO2, Faster R-CNN and A-RPN to TCPs with a 12.9%, 13.2% and 13.4% drop in mAP compared to natural images. There was little or no difference in recognizing humans, but a large drop in mAP for cats and dogs (27% & 31%), and very large drop for horses (35.9%). The abstract nature of TCPs may be responsible for DL poor performance.
TPM Based Schema for Reinforcing Security in IBE’s Key Manager

Confidentiality and secure deletion are among the most important security concerns about public cloud storage. Client-side cryptography is widely recognized as a robust approach for addressing these issues. However, managing keys related to encrypted files and their policies is a very challenging task for cloud storage’s users. Studies have shown that even the most achieved solution, namely FADE design, suffers from a leak of cryptographic key due to authentication mechanism and a heavy key management infrastructure. In this paper, we propose a new design that brings the use of Identity Based Encryption (IBE) and Trusted Platform Module (TPM) together. The use of the IBE leads to ease the key management since the security of the entire system is mainly based only on one master key that will be in turn stored and managed securely trough the TPM facilities. The analysis of the prototype implementation of our design have demonstrated that it offers a significant value-added of security with a negligible overhead amount of computing time.
Multi-target Detection for Aerial Images Based on Fully Convolutional Networks

Multi-target detection for aerial images is widely applied for both military and civilian. A novel detection algorithm based on fully convolutional networks with fusion of multilayer convolutional features is proposed in this paper. This method predict directly the classification and bounding boxes of the objects in the aerial images based on pixel information, and improve the network framework by use of Group Normalizer and deformable convolution to promote accuracy of object detection. The experimental results on DOTA dataset with oriented bounding boxes show that the detection algorithm presented in this paper possesses fast detection speed and higher accuracy.
A Novel Multiplicative Substitution Cryptosystem

A cryptosystem primarily used for data encryption and data security. Nowadays the misuse of confidential information by unauthorized parties became a very big issue. Data breach actively increase the necessity of better cryptography algorithms. Various cryptosystems give different levels of encryption. Many encryption methods enhancing security to data. In this paper propose a new cryptosystem called Multiplicative Substitution Cryptosystem (MSC), that encrypts using non-linear functions. This cryptosystem is used to add security for all characters (which also includes extended ASCII characters) where values range from 0 to 255. A non-linear function is used for key whitening. This method uses the same key for both encryption and decryption (symmetric cipher). The Multiplicative Substitution Cryptosystem (MSC) is implemented using python programming. The analysis shows that when compared to other substitution ciphers our multiplicative substitute cryptosystem offers high security, less time complexity, and high throughput.
Bangla DeConverter for Extraction of BanglaText from Universal Networking Language

The people in Bangladesh and two states (i.e., Tripura and West Bengal) in India, which is about 230 million of the world population, use Bengali as their first dialect. However, very few numbers of resources and tools are available for this language. This paper presents a Bangla DeConverter to extract Bangla texts from Universal Networking Language (UNL). It explains and illustrates the different phases of the proposed Bangla DeConverter. The syntactic linearization, the implementation of the results of the proposed Bangla DeConverter, and the extraction of a Bangla sentence from UNL expressions are presented in this paper. The Bangla DeConverter has been tested on UNL expressions of 300 Bangla sentences using a Russian and English Language Server. The proposed system generates 90% syntactically and semantically correct Bangla sentences with a UNL Bilingual Evaluation Understudy (BLEU) score of 0.76.
Image Encryption Algorithm Based on Hyper-chaotic Lorenz Map and Compressed Sensing Theory

The motion process of multi-dimensional chaotic system is complex and variable, the randomness of motion state is stronger, and the motion state is more unpredictable within a certain range. This feature of multi-dimensional chaotic system can effectively improve the security performance of digital image encryption algorithm. In this paper, the hyper-chaotic Lorenz map is used to design the encryption sequence to improve the random performance of the encryption sequence, thus optimizing the performance of the digital image encryption algorithm. In this paper, the chaotic sequence is used to randomly select the row vector of the Hadamard matrix to form the Hadamard matrix to determine the measurement matrix, which simplifies the computational difficulty of the algorithm and solves the problem of the discontinuity of the key space in the random matrix design.
Scaling simulation-to-real transfer by learning a latent space of robot skills

We present a strategy for simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation–reality gap, we propose a method for increasing the sample efficiency and robustness of existing simulation-to-real approaches which exploits hierarchy and online adaptation. Instead of learning a unique policy for each desired robotic task, we learn a diverse set of skills and their variations, and embed those skill variations in a continuously parameterized space. We then interpolate, search, and plan in this space to find a transferable policy which solves more complex, high-level tasks by combining low-level skills and their variations. In this work, we first characterize the behavior of this learned skill space, by experimenting with several techniques for composing pre-learned latent skills. We then discuss an algorithm which allows our method to perform long-horizon tasks never seen in simulation, by intelligently sequencing short-horizon latent skills. Our algorithm adapts to unseen tasks online by repeatedly choosing new skills from the latent space, using live sensor data and simulation to predict which latent skill will perform best next in the real world. Importantly, our method learns to control a real robot in joint-space to achieve these high-level tasks with little or no on-robot time, despite the fact that the low-level policies may not be perfectly transferable from simulation to real, and that the low-level skills were not trained on any examples of high-level tasks. In addition to our results indicating a lower sample complexity for families of tasks, we believe that our method provides a promising template for combining learning-based methods with proven classical robotics algorithms such as model-predictive control.
A Tool for Creating Educational Resources Through Content Aggregation

A problem that students find in some subjects is to find online educational resources on their content that have adequate quality. This paper presents a tool that allows a teacher to create in a simple way educational resources using their own material or automatically extracted from repositories of open data or linked data. In addition, the tool offers a user management system that allows a student to register with a specific teacher, so that they can access all the resources published by a specific teacher. Educational resources can be downloaded from the tool in order to be used non-online. In the article, the proposal has been illustrated in the context of the subject of Software Engineering.
An Efficient Semiautomatic Active Contour Model of Liver Tumor Segmentation from CT Images

Computed tomography (CT) image is one of the most extensively used imaging modalities for revealing and analyzing tumors as it has a higher spatial resolution, faster imaging speed relatively lower cost compared to MRI. The liver tumor volumetry requires the tumor segmentation in three dimensions which is performed manually tracing the tumor regions on slices which are tiresome and prolonged also, and the volume of manual demarcation is subjective. The major challenging task in liver tumor segmentation is due to the significant variation in location, shape, intensity and texture. This makes it difficult to develop a universal computer algorithm that is applicable for all cases. In this paper, an efficient semiautomatic technique for segmentation of liver tumor from CT images is proposed. The technique is based on semiautomated segmentation approach based on active contour segmentation using the level set method. The proposed approach consists of mainly four stages. In the first stage, the region of interest (ROI) image is initialized, which contains the liver tumor region in the CT image extraction using seed points; in the second stage, resampling and segmentation of ROI is done for reducing the noise and enhancing the boundaries; in the third stage, threshold values adjustment and seed point calculations are carried out; and in the fourth stage, the post-processing is done to extract and refine the liver tumor boundaries. The proposed technique is compared with the region growing algorithms that require filling holes and removing small connected components that can be achieved by using binary morphological operations: opening to remove small connected components and closing to fill holes. The liver tumors detected by the scheme were compared with those manually traced by experts, used as the ground truth results. The study was evaluated on two datasets of tumors. The proposed scheme obtained the Dice similarity coefficient 0.938 and the Jaccard similarity coefficient 0.883. The mean surface distance, the median surface distance and the maximal surface distance were 0.025, 0.712 and 2.828 mm, respectively. With the proposed scheme, the time requisite is reduced significantly and proves to be helpful to the observer in better visualizing the probable tumors inside the liver. The proposed method represents the 3D reconstruction of segmented tumors within the liver.
A highly secured and streamlined cloud collaborative editing scheme along with an efficient user revocation in cloud computing

Summary These days with the expanded fame of cloud computing, the interest for cloud‐based collaborative editing service is rising. The encryption method is utilized to ensure and secure the data, during the collaborative editing process. In the encryption process, the cloud requires more time to work the collaborative editing. This paper proposes an efficient scheme for reducing the encryption burden over the cooperative users, as the possibilities of cooperative users read and write data by means of any gadget. In the proposed scheme, the encrypted file sent by the data owner is split into smaller segments and stored in the cloud by the cloud service provider (CSP) along with specific tags. Once the cooperative user receives and decrypts the file from the CSP, it modifies and encrypts only the modified segment and resends to the CSP. The CSP after verifying the signature replace the original file segment in the cloud with the modified segment based on the tag information. The scheme that is put forward is performed based on the modified ciphertext‐policy hierarchical attribute–based encryption, and the security process is done based on the attribute‐based signature schemes. This work employs a proficient attribute updating method to accomplish the dynamic change of users' attributes, consisting granting new attributes, revoking previous attributes, and regranting formerly revoked attributes. A writer's attributes and keys have been revoked, and the stale information cannot be written.
Mapping digital information security management in the university libraries of Pakistan

ABSTRACT Maintaining digital information and protecting data assets remains a principal concern in libraries. Digital information security awareness is a major tool in overcoming incidents about data loss in libraries. This paper focuses on the awareness level of librarians and current status of digital information security management (DISM) in academic libraries of Lahore, Pakistan. The results also highlight the policies at individual or institutional level about DISM in selected libraries. The data were collected through structured interviews from 15 public and private sector academic librarians. Findings revealed that presently, digital resources are the integral part of university libraries such as OPAC's, official records and digital resources. However, information professionals did not show interest in security management of digital resources, as they least considered this role as part of their jobs. Therefore, they attended limited training programs to update their knowledge. A general trend indicates that institutional information technology departments were responsible for maintaining library server, network and hardware.
Towards Novel Video Steganography Approach for Information Security

Communication security has taken vital role with the advancement in digital communication. The universal use of internet for communication has increased the attacks to users. The security of information is the present issue related to privacy and safety during storage and communication. This paper deals with the proposition of a multilayered secure channel to transfer sensitive data/video over an unreliable network. The secret video is first encrypted using the NOLSB algorithm. The cipher video produced is hidden in a video file with more size. This video file is in turn encrypted following the (m,k) firm technique to maximize resource utilization and to optimize the bandwidth. Then video shares are sent over many channels in the network in order to assure security. This method guarantee that, even if some shares got lost over network, video files could be recovered at the end receiver without need to resend the video file by the sender.
Contribution of Solar Photovoltaic on the Reduction of Greenhouse Gas Emission in the Indonesia Power Sector

Abstract Indonesia has released the target of reducing Greenhouse gas (GHG) emissions. The strategy includes enhancement renewable energy utilization. One of the most potential renewable energy resources in Indonesia is solar energy. State Electricity Company of Indonesia has planned to develop solar Photo voltaic (PV) power plan in the document planning 2019-2028. The objective of this study is to explore the contribution of solar PV to the reduction GHG of Indonesia. The potency of solar irradiance and solar PV output in several big cities in Indonesia are discussed. The results show that by using the PV technology of Crystalline silicon with mounting position building integrated and the slope and azimuth is 0°, the average solar PV output in Indonesia is 1361.2 kWh/kWp. The planned solar PV installed capacity by the end of 2028 is 985.5 MW. The electricity resulted by the system in a year will be 1341.47 GWh or it is only 0.31% of national electricity consumption. The contribution of solar PV to the GHG emission reduction is 1.341 million tons CO 2eq in the year 2028.
Simultaneous Recognition of Hand Shape and Two-Axis Wrist Bending Using Wearable Wrist Contour Measuring Device

Hand gestures can express various kinds of information. However, existing techniques present difficulties for practical use such as environmental limitations and obstructions to natural gestures. To address these difficulties, our earlier work has developed a wrist contour measuring device that recognizes a hand shape and pronation angle. An earlier report has described the necessity of increasing the number of recognizable hand motions to realize a highly usable screen operation. This paper reports a trial of simultaneous recognition of hand shape and wrist-bending state. After observing the wrist contour and then clarifying the phenomenon, we design features, and construct a recognizer. The simultaneous recognition rate of four hand shapes and 14 wrist-bending states achieved, in this research, is 79.7%. As described in this paper, a trial of a user interface using hand gestures recognized by the device is also conducted. Several problems related to practical use are discussed.
EVALD – a Pioneer Application for Automated Essay Scoring in Czech

Abstract In the paper, we present EVALD applications (Evaluator of Discourse) for automated essay scoring. EVALD is the first tool of this type for Czech. It evaluates texts written by both native and non-native speakers of Czech. We describe first the history and the present in the automatic essay scoring, which is illustrated by examples of systems for other languages, mainly for English. Then we focus on the methodology of creating the EVALD applications and describe datasets used for testing as well as supervised training that EVALD builds on. Furthermore, we analyze in detail a sample of newly acquired language data – texts written by non-native speakers reaching the threshold level of the Czech language acquisition required e.g. for the permanent residence in the Czech Republic – and we focus on linguistic differences between the available text levels. We present the feature set used by EVALD and – based on the analysis – we extend it with new spelling features. Finally, we evaluate the overall performance of various variants of EVALD and provide the analysis of collected results.
A comparative study of the use of electronic resources by LIS and computer science students in two Nigerian universities

Purpose The purpose of this study is to compare the use of electronic resources (e-resources) by undergraduate students in library and information science (LIS) and computer science in two Nigerian universities. Design/methodology/approach This study adopted a comparative method. The questionnaire was designed to elicit data from 680 undergraduate students in two universities. Simple percentages and chi square ( χ 2 ) statistical tool were used to analyse and test the hypotheses. The results are presented in tables and charts. Findings The findings show that the undergraduate students use some of the e-resources and do not use others probably because of lack of awareness or skills to effectively use the e-resources. Overall, almost half of the undergraduate students rated their skills to use e-resources to be high. It also emerged that there is significant difference between LIS students and computer science students use of e-resources. Practical implications The findings of this study on e-resources and their use by different subject areas in the university will establish an important foundation for selecting and providing e-resources and services. Such a foundation allows for the development of both useful theory and practical application. Originality/value The study reported findings on e-resources used particularly by different user groups in universities in Nigeria. The study will add to the body of knowledge on use of e-resources in Nigeria.
DynGRAR: A dynamic approach to mining gradual relational association rules

Relational Association Rules (RARs) capture generic relations between attributes values in possibly large data sets. Due to their ability to uncover underlying semantically relevant patterns, they are of particular interest in data mining research and applicable in both unsupervised and supervised learning scenarios. With the aim of increasing the stability and expressiveness of the classical, non-gradual RARs, Gradual Relational Association Rules (GRARs) have been introduced. By generalizing the boolean relations to gradual relations, GRARs also capture the degrees to which generic relations are satisfied. In the current paper we introduce a new approach called DynGRAR (Dynamic Gradual Relational Association Rules Miner) for uncovering interesting GRARs in dynamic data sets which are incrementally extended with both new data instances and new data attributes. DynGRAR dynamically adjusts the set of all interesting GRARs. Through multiple experiments performed on publicly available software defect prediction data sets, we have evaluated DynGRAR versus applying the standard GRARs mining algorithm from scratch on the extended data. The results obtained emphasize the superior performance of the dynamic approach we propose.
A FPGA Based Parallel Multivariate Gaussian Random Number Generation Framework

Multivariate Gaussian distribution random numbers are widely used in the field of machine learning and financial engineering computation. Based on the Fast Jump Ahead algorithm, a parallel hardware acceleration architecture for generating multivariable Gaussian vectors is proposed in this paper. The structure uses WELL19937 algorithm as the basic uniformly distributed random number generator and is capable of generating arbitrary number of parallel uncorrelated multivariate Gaussian vector sequences. The experimental results show that the parallel acceleration structure with P parallelism can achieve the throughput of generating P / N random vectors per cycle for dimension N, which is superior to the related work and the implementation of CPU and GPU.
Development of an Interactive Training Support System for Drawing and Painting Skill on a 3D Object with AR and Haptic Interface

The purpose of this research is to propose a new drawing learning support environment to replace the conventional learning environment. In conventional drawing learning, it is customary to sketch 3D object on a plane. Therefore, there are many research cases in which a system assists a learner to learn drawing skill, when the learner draw a sketch on a plane. On the other hand, in this research, we aimed at design and development of the environment that can support the acquisition of the drawing skill when the learner is drawing in three dimensions. A virtual 3D object is superimposed on a real 3D object using augmented reality, and the positional relationship between the real 3D object and the virtual 3D object is linked. As a result, we have realized a system that allows students to easily compare pictures in real 3D object with virtual 3D object when painting.