Reviewerly/exHarmony huggingface dataset:
3D Backbone Network for 3D Object Detection.

The task of detecting 3D objects in traffic scenes has a pivotal role in many real-world applications. However, the performance of 3D object detection is lower than that of 2D object detection due to the lack of powerful 3D feature extraction methods. To address this issue, this study proposes a 3D backbone network to acquire comprehensive 3D feature maps for 3D object detection. It primarily consists of sparse 3D convolutional neural network operations in the point cloud. The 3D backbone network can inherently learn 3D features from the raw data without compressing the point cloud into multiple 2D images. The sparse 3D convolutional neural network takes full advantage of the sparsity in the 3D point cloud to accelerate computation and save memory, which makes the 3D backbone network feasible in a real-world application. Empirical experiments were conducted on the KITTI benchmark and comparable results were obtained with respect to the state-of-the-art performance for 3D object detection.
Automated Facial Wrinkles Annotator

This paper presents an automated facial wrinkles annotator for coarse wrinkles, fine wrinkles and wrinkle depth map extraction. First we extended Hybrid Hessian Filter by introducing a multi-scale filter to isolate the coarse wrinkles from fine wrinkles. Then we generate a wrinkle probabilistic map. When evaluated on 20 high resolution full face images (10 from our in-house dataset and 10 from FERET dataset), we achieved good accuracy when the result of coarse wrinkles was validated with manual annotation. Furthermore, we visually illustrate the ability of the annotator in detecting fine wrinkles. This paper advances the field by automate the localisation of the fine wrinkles, which might not be possible to annotate manually. Our automated facial wrinkles annotator will be beneficial to large-scale data annotation and cosmetic applications.
Unsupervised Polyglot Text To Speech

We present a TTS neural network that is able to produce speech in multiple languages. The proposed network is able to transfer a voice, which was presented as a sample in a source language, into one of several target languages. Training is done without using matching or parallel data, i.e., without samples of the same speaker in multiple languages, making the method much more applicable. The conversion is based on learning a polyglot network that has multiple per-language sub-networks and adding loss terms that preserve the speaker's identity in multiple languages. We evaluate the proposed polyglot neural network for three languages with a total of more than 400 speakers and demonstrate convincing conversion capabilities.
An Approach to Discover Malicious Online Users in Collaborative Systems

Collaborative filtering systems employ numerous techniques to provide recommendations for online users. These techniques depend on the collected data about online users’ preferences or ratings of provided items, topics, news, etc. Such systems are vulnerable to malicious users’ attacks in which malicious users carefully target specific profiles in order to boost up or diminish the predictions of some targeted items. In this paper, we suggested an approach to find malicious attacks and remove attackers’ profiles. This leads to placing the user ratings in the region of rejection and thereby affecting his level of trustiness, the impact of specific user ratings is affected by the user calculated trustiness level, where completely untrusted user’s ratings will be neglected by recommendation system. We used a Movie Lens of 1 M rating dataset to perform the required training and test the suggested framework. The suggested method distinguished perfectly between Normal, Excess, Inferiority, and completely dishonest users.
An efficient certificateless multi-receiver threshold decryption scheme

Threshold decryption allows only quorum cooperate users to decrypt ciphertext encrypted under a public key. However, such threshold decryption scheme cannot be applied well in this situation where all users have their public and private key pairs, but do not share any private keys corresponding to the public keys, such as mobile network featured with dynamic character. The direct way to achieve threshold decryption in this case is to divide the message into several pieces and then encrypt these pieces with the public keys of different users. However, this is very inefficient. Multireceiver threshold decryption scheme that could be applied efficiently in the above situation. Recently, some certificateless (ID-based) multireceiver threshold decryption (signcryption) schemes are introduced. But the bilinear pairings are used in most of the existing schemes. In this paper, we propose an efficient certificateless threshold decryption scheme using elliptic curve cryptography (ECC) without bilinear pairing. Performance analysis shows that the proposed scheme has lower computation cost than existing some threshold decryption schemes in both encryption and decryption process. Security analysis shows that our scheme is IND-CCA secure, and no one outside of selected receivers can disclose receivers identities, against the adversaries defined in CL-PKC system under the random oracle model.
A secure storage scheme with key-updating in hybrid cloud

As cloud computing continues to evolve, the role of hybrid cloud security becomes more and more important in the progress of cloud research. In order to improve the multi-user data sharing mechanism in hybrid cloud and enhance the security of hybrid cloud, this paper proposes a new security storage model of hybrid cloud, which combines the public cloud's cost savings and elasticity with the private cloud's security and customisation. In addition, by exploring the security of data storage and interoperability of the hybrid cloud, this paper adopts a forward-secure key-updating encryption scheme, so as to ensure the security of privacy data in hybrid clouds.
A new multiparty identification scheme based on nearring root extraction problem

In this paper, we propose a Multiparty Identification Scheme based on Nearring root Extraction Problem. This scheme enables more than two or three parties to prove their identity among themselves. We have chosen Nearring root extraction problem as a basis for our scheme. An adversary finds difficult to break the scheme as he/she finds very hard to extract the nth root for a given near polynomial ring.
Combining Model-Driven Architecture and Software Product Line Engineering: Reuse of Platform-Specific Assets

Reuse automation is a main concern of software engineering to produce high quality applications in a faster and cheaper manner. Some approaches define cross-platform model-driven software product lines to systematically and automatically reuse generic assets in software development. They improve the product line assets reusability by designing them according to the Model-Driven Architecture specifications. However, their reuse of platform-specific assets is limited due to an inefficient platform-specific variability management. This issue interfere with gains in productivity provided by reuse. In this paper, we define platform-specific variability by identifying variation points in different software concerns based on the well-known “4+1” view model categorization. Then, we fully manage platform-specific variability by structuring the Platform-Specific Model using two sub-models: the Cross-Cutting Model, obtained by transformation of the Platform-Independent Model; and the Application Structure Model, obtained by reuse of variable platform-specific assets. This structure is supported by a framework, based on a Domain-Specific Modeling Language, helping developers to build an application model. Experiments on three concrete applications confirmed that our approach significantly improves product lines productivity.
Enhanced Classification of Sentiment Analysis of Arabic Reviews

Sentiment analysis is the process of mining textual data in order to extract the author’s opinion, typically expressed as a positive, neutral, or negative attitude towards the written text. It is of great interest and has been extensively studied in the English language. However, sentiment analysis in the Arabic language has not received wide attention and most of the research done on Arabic either focuses on introducing new datasets or new sentiment lexicons. In this paper, we introduce a preprocessing suite that includes morphological processing, emoticon extraction, and negation processing to improve the sentiment analysis. Furthermore, we conduct experiments on sentiment analysis of hotel reviews that target two classification tasks: positive/negative and positive/negative/neutral. Our experimental results using various supervised learning algorithms, including deep learning algorithm, demonstrate the effectiveness of the proposed techniques.
Policy for Managing Technology in Nursing Education

Health science teacher competence is multifaceted and continuously changing according to national and international healthcare standards. Organizational restructuring and emphasis on cost effectiveness is changing the scope of health science teachers' practical work and their role in healthcare (worldwide).This study aimed to describe student teachers' perceptions of the competencies needed to work as an educator in the healthcare field. Objective of study was to gain new knowledge which can be used in the development of teacher education programs in nursing science and to define a broader definition of the health science educators.A qualitative study was conducted. Data were collected from 23 Finnish students completing a master's degree in teaching in the healthcare context using focus group interviews. The data were analyzed by inductive content analysis.The student teachers identified eight main categories of teacher competence: leadership and management competence; evidence-based practice competence; subject competence; ethical competence; pedagogical competence; collaboration competence; internationalization competence; and continuous professional development competence.This study identified essential teacher competencies that can be evaluated among students to develop health science teacher curricula. The findings can be used in follow-up studies or comparative research to investigate competence differences between novice and experienced teachers.
DLT/Blockchain as a Building Block for Enterprise Transformation

DLT holds enormous promise for re imagining and streamlining much of the enterprise process automation we have built over the past three decades. It is not, however, a silver bullet. We must apply the same skepticism, solution focus, architectural rigor, and creative problem-solving we have honed over those decades to ensure that the next generation of solutions are both fit for-purpose and worthy of our trust. For clarity's sake, my focus will be on private, permissioned DLT deployments where the parties are known to one another within an enterprise ecosystem.
Domain Adaptive Semantic Segmentation Through Structure Enhancement

Although fully convolutional networks have recently achieved great advances in semantic segmentation, the performance leaps heavily rely on supervision with pixel-level annotations which are extremely expensive and time-consuming to collect. Training models on synthetic data is a feasible way to relieve the annotation burden. However, the domain shift between synthetic and real images usually lead to poor generalization performance. In this work, we propose an effective method to adapt the segmentation network trained on synthetic images to real scenarios in an unsupervised fashion. To improve the adaptation performance for semantic segmentation, we enhance the structure information of the target images at both the feature level and the output level. Specifically, we enforce the segmentation network to learn a representation that encodes the target images’ visual cues through image reconstruction, which is beneficial to the structured prediction of the target images. Further more, we implement adversarial training at the output space of the segmentation network to align the structured prediction of the source and target images based on the similar spatial structure they share. To validate the performance of our method, we conduct comprehensive experiments on the “GTA5 to Cityscapes” dataset which is a standard domain adaptation benchmark for semantic segmentation. The experimental results clearly demonstrate that our method can effectively bridge the synthetic and real image domains and obtain better adaptation performance compared with the existing state-of-the-art methods.
CONSCIOUSNESS AS INTERFACE IN COMMUNICATION STRUCTURE

The article discusses the role of consciousness as interface in thinking, interpersonal communication and network communication. Communication is verbal communication in the shell of meta-communication. Framing and categorization of messages are necessary sides of meta-communication. Basing on coherence, recursion and hierarchy, communication is similar to the processes occurring in the neocortex of the brain. The development of digital technologies generates neuronet - the global information environment, in which human consciousness is interface between human thinking and the exocortex of virtual reality.
SOCIAL NETWORKS USE IN FOREIGN LANGUAGE TEACHING PRACTICE

The article deals with the issues related to the process of the formation of foreign-language information competence through social networks. The author cites a fragment of the research conducted among students and foreign language teachers. Students, who participated in the research, are of the direction “Pedagogical Education”, the specialisation - “Foreign Language”. This research was aimed at identifying the readiness of students and teachers to social networks inclusion in educational process. The study was carried out by the method of questioning. The opinions and expectations of teachers and students regarding social networks use in foreign language teaching practice within blended learning are considered and analysed. The analysis of opinions on the issue of the effectiveness of social networks use for the development of various types of speech activity is conducted. The presented research allows evaluating the pedagogical applicability of social networks in foreign language teaching practice.
Distillation Strategies for Proximal Policy Optimization.

Vision-based deep reinforcement learning (RL) typically obtains performance benefit by using high capacity and relatively large convolutional neural networks (CNN). However, a large network leads to higher inference costs (power, latency, silicon area, MAC count). Many inference optimizations have been developed for CNNs. Some optimization techniques offer theoretical efficiency, such as sparsity, but designing actual hardware to support them is difficult. On the other hand, distillation is a simple general-purpose optimization technique which is broadly applicable for transferring knowledge from a trained, high capacity teacher network to an untrained, low capacity student network. DQN distillation extended the original distillation idea to transfer information stored in a high performance, high capacity teacher Q-function trained via the Deep Q-Learning (DQN) algorithm. Our work adapts the DQN distillation work to the actor-critic Proximal Policy Optimization algorithm. PPO is simple to implement and has much higher performance than the seminal DQN algorithm. We show that a distilled PPO student can attain far higher performance compared to a DQN teacher. We also show that a low capacity distilled student is generally able to outperform a low capacity agent that directly trains in the environment. Finally, we show that distillation, followed by fine-tuning in the environment, enables the distilled PPO student to achieve parity with teacher performance. In general, the lessons learned in this work should transfer to other modern actor-critic RL algorithms.
Integrated Reconfigurable 4×4 Optical Unitary Converter Using Multiport Directional Couplers

We demonstrate novel silicon photonic 4×4 reconfigurable optical unitary converter, comprising multiport directional couplers and phase shifter arrays. By optimizing the phase shift with simulated annealing algorithm, reconfigurable mode sorting and switching are experimentally realized.
Formalizing Enrichment Mechanisms for Bibliographic Ontologies in the Semantic Web

This paper presents an analysis of current limitations to the reuse of bibliographic data in the Semantic Web and a research proposal towards solutions to overcome them. The limitations identified derive from the insufficient convergence between existing bibliographic ontologies and the principles and techniques of linked open data (LOD); lack of a common conceptual framework for a diversity of standards often used together; reduced use of links to external vocabularies and absence of Semantic Web mechanisms to formalize relationships between vocabularies, as well as limitations of Semantic Web languages for the requirements of bibliographic data interoperability. A proposal is advanced to investigate the hypothesis of creating a reference model and specifying a superontology to overcome the misalignments found, as well as the use of SHACL (Shapes Constraint Language) to solve current limitations of RDF languages.
Space lower bounds for linear prediction.

We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natural streaming setting. This implies that such problems cannot be solved (at least in this setting) by scalable memory-efficient streaming algorithms. Our results build on a memory lower bound for a simple linear-algebraic problem -- finding orthogonal vectors -- and utilize the estimates on the packing of the Grassmannian, the manifold of all linear subspaces of fixed dimension.
Graph Neural Processes: Towards Bayesian Graph Neural Networks.

We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes. A Graph Neural Process is defined as a Conditional Neural Process that operates on arbitrary graph data. It takes features of sparsely observed context points as input, and outputs a distribution over target points. We demonstrate graph neural processes in edge imputation and discuss benefits and drawbacks of the method for other application areas. One major benefit of GNPs is the ability to quantify uncertainty in deep learning on graph structures. An additional benefit of this method is the ability to extend graph neural networks to inputs of dynamic sized graphs.
Threading the Arduino with Haskell

Programming embedded microcontrollers often requires the scheduling of independent threads of execution, specifying the interaction and sequencing of actions in the multiple threads. Developing and debugging such multi-threaded systems can be especially challenging in highly resource constrained systems such as the Arduino line of microcontroller boards. The Haskino library, developed at the University of Kansas, allows programmers to develop code for Arduino-based microcontrollers using monadic Haskell program fragments. This paper describes our efforts to extend the Haskino library to translate monadic Haskell code to multi-threaded code executing on Arduino boards.
Some Reflections On Styrene Suspension Polymerization Experiment Teaching

The results of suspension polymerization of styrene were studied by changing the dosage of initiator, stirring speed, size of stirring paddle, content of dispersant and reaction temperature. The results showed that when using 500 ml four flasks, styrene 40 ml and polyvinyl alcohol solution (3 g polyvinyl alcohol dissolved in 1000 ml deionized water) 200 ml, initiator 0.5 g, stirring speed 300-450 r/min, temperature range 86-95°C, using the stepped heating method and using the impeller with a spreading blade diameter of 43 mm can ensure the success of the experiment, and the yield is relatively high.
Motivasi Mahasiswa Usia Dewasa Mengikuti Perkuliahan pada Program Studi Pendidikan Guru Paud di FKIP Uninus Bandung

This research is based on data from observation data of students of PG PAUD Uninus Bandung, which in conclusion about 40% of students of PG PAUD study program from the age side including middle age or other term is included in middle age. This condition attracts the attention of researchers so it is important to examine the background of students, motivation, goals and reasons of the students of the adult age group to attend the lectures in the PG PAUD Uninus FKIP. It is expected to obtain data for the stakeholders both at the level of Prodi, Faculty and University level. The method used in this research use descriptive analytical research method with qualitative approach. This method is supported by data collection techniques such as observation, documentation and interviews. The sample of this research is students of PG PAUD Uninus FKIP study program which is in the adult age range or about 25 and up to 60 years old. Technique of data analysis this research use three step analysis, that is data reduction, data display and data verification. The expected output of this research is in the form of scientific articles published in accredited national journals. The results showed that adult students who attended lectures at Prodi PG PAUD mostly aged between 51 to 55 years from the employment side of most of the students is a non-civil servant or honorary teachers and most have not been certified. adult students attending or continuing lectures in Prodi PG PAUD, among others, because the demands of their professions as PAUD teachers for the benefit of linearity or for students who have not previously had a diploma equivalent to some other scholars who have civil servants but the diploma is not linear with PAUD so that they have to go to college or PAUD field so that the diploma in accordance with the profession. The results conclude that economic factors are not the dominant factor for students to follow or continue their lectures in the area of early childhood is more dominated by linearity with their work not because of economic factors. As for economic reasons, certification. or civil servants that only a small part of one of the factors of adult students continue studying in Prodi PG PAUD.
Augmented Reality (AR)-Based Application to Introduce Nagasepaha North Bali Puppet Style Character

Tourism is a mainstay of Bali's original source of income. The object of tourist attraction in Bali is nature, culture, and a combination of nature and culture. Buleleng is located in the north of Bali. Besides being famous for its stunning underwater scenery, it also has cultural richness in the form of shadow puppet craft. The purpose of this study was to create an Augmented Reality (AR) -based application of shadow puppet characters which were the handicrafts from Nagasepaha village, Buleleng. This AR application uses smartphones as shadow puppet recognition media for children or young generation virtually. So, the process of introducing shadow puppet becomes more interesting and easy to use. Users can run the application, then the application performs marker tracking. If the marker matches the reference data contained in the application, the application displays the shadow puppets in 3D on the smartphone screen. The information displayed is the name of the shadow puppet character along with the accompanying attributes, animated shadow puppet, the voice of the puppeteer who describes the puppet character. The shadow puppet characters shown are from the Ramayana epic, namely Rama, Shita, Hanoman, Laksmana, and Rahwana. The presence of AR, it is expected to increase the love of the younger generation for shadow puppet artwork from the village of Nagesepaha which is an icon of cultural heritage from North Bali. The usability test is categorised good with the percentage is 84%. It's mean the program is running well and ready to use by end user.