Reviewerly/exHarmony huggingface dataset:
A Review on 3D Image Watermarking and Quality Assessment

The increasing amount of research on watermarking over the past decade has been largely driven by its important applications in digital copyrights management and protection.Digital watermarking is the act of hiding a message related to a digital signal (i.e. an image, song, video) within the signal itself.It is a concept closely related to steganography, in that they both hide a message inside a digital signal.However, what separates them is their goal.Watermarking tries to hide a message related to the actual content of the digital signal, while in steganography the digital signal has no relation to the message, and it is merely used as a cover to hide its existence.With the emerging of low cost 3D display devices, different kinds of 3D applications and the amount of 3D content are booming up, recently.Effective digital watermarking systems for 3D content protection will play an important role for developing and promoting the 3D entertainment industry.The proposed 3D watermarking technique employs the method of slice extraction from a 3D volume.An obj file is used and the wave front object data is obtained.DWT technique is used to embed the secret binary data into one of the extracted slices.The inverse operation is performed at the receiver using the same secret key used at the transmitter.Finally, the watermark is extracted and the quality measures of the reconstructed image is calculated.
Tracing Forum Posts to MOOC Content using Topic Analysis.

Massive Open Online Courses are educational programs that are open and accessible to a large number of people through the internet. To facilitate learning, MOOC discussion forums exist where students and instructors communicate questions, answers, and thoughts related to the course. The primary objective of this paper is to investigate tracing discussion forum posts back to course lecture videos and readings using topic analysis. We utilize both unsupervised and supervised variants of Latent Dirichlet Allocation (LDA) to extract topics from course material and classify forum posts. We validate our approach on posts bootstrapped from five Coursera courses and determine that topic models can be used to map student discussion posts back to the underlying course lecture or reading. Labeled LDA outperforms unsupervised Hierarchical Dirichlet Process LDA and base LDA for our traceability task. This research is useful as it provides an automated approach for clustering student discussions by course material, enabling instructors to quickly evaluate student misunderstanding of content and clarify materials accordingly.
Linear codes over the ring $\mathbb{Z}_4 + u\mathbb{Z}_4 + v\mathbb{Z}_4 + w\mathbb{Z}_4 + uv\mathbb{Z}_4 + uw\mathbb{Z}_4 + vw\mathbb{Z}_4 + uvw\mathbb{Z}_4$

We investigate linear codes over the ring $\mathbb{Z}_4 + u\mathbb{Z}_4 + v\mathbb{Z}_4 + w\mathbb{Z}_4 + uv\mathbb{Z}_4 + uw\mathbb{Z}_4 + vw\mathbb{Z}_4 + uvw\mathbb{Z}_4$, with conditions $u^2=u$, $v^2=v$, $w^2=w$, $uv=vu$, $uw=wu$ and $vw=wv.$ We first analyze the structure of the ring and then define linear codes over this ring. Lee weight and Gray map for these codes are defined and MacWilliams relations for complete, symmetrized, and Lee weight enumerators are obtained. The Singleton bound as well as maximum distance separable codes are also considered. Furthermore, cyclic and quasi-cyclic codes are discussed, and some examples are also provided.
Neural Logic Reinforcement Learning

Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks. However, most DRL algorithms suffer a problem of generalizing the learned policy which makes the learning performance largely affected even by minor modifications of the training environment. Except that, the use of deep neural networks makes the learned policies hard to be interpretable. To address these two challenges, we propose a novel algorithm named Neural Logic Reinforcement Learning (NLRL) to represent the policies in reinforcement learning by first-order logic. NLRL is based on policy gradient methods and differentiable inductive logic programming that have demonstrated significant advantages in terms of interpretability and generalisability in supervised tasks. Extensive experiments conducted on cliff-walking and blocks manipulation tasks demonstrate that NLRL can induce interpretable policies achieving near-optimal performance while demonstrating good generalisability to environments of different initial states and problem sizes.
Impossibility of Strong KDM Security with Auxiliary Input

We show that a strong notion of KDM security cannot be obtained by any encryption scheme in the auxiliary input setting, assuming Learning With Errors (LWE) and one-way permutations. The notion of security we deal with guarantees that for any (possibly inefficient) function f, it is computationally hard to distinguish between an encryption of $$\mathbf {0}$$ and an encryption of $$f(\mathsf {pk}, z)$$ , where $$\mathsf {pk} $$ is the public key and z is the auxiliary input. Furthermore, we show that this holds even when restricted to bounded-length auxiliary input where z is much shorter than $$\mathsf {pk} $$ under the additional assumption that (non-leveled) fully homomorphic encryption exists.
Sequential modeling of Sessions using Recurrent Neural Networks for Skip Prediction

Recommender systems play an essential role in music streaming services, prominently in the form of personalized playlists. Exploring the user interactions within these listening sessions can be beneficial to understanding the user preferences in the context of a single session. In the 'Spotify Sequential Skip Prediction Challenge', WSDM, and Spotify are challenging people to understand the way users sequentially interact with music. We describe our solution approach in this paper and also state proposals for further improvements to the model. The proposed model initially generates a fixed vector representation of the session, and this additional information is incorporated into an Encoder-Decoder style architecture. This method achieved the seventh position in the competition, with a mean average accuracy of 0.604 on the test set. The solution code is available at https://github.com/sainathadapa/spotify-sequential-skip-prediction.
Proposing a new method of image classification based on the AdaBoost deep belief network hybrid method

Image classification has different applications. Up to now, various algorithms have been presented for image classification. Each of these method has its own weaknesses and strengths. Reducing error rate is an issue which much researches have been carried out about it. This research intends to optimize the problem with hybrid methods and deep learning. The hybrid methods were developed to improve the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In fact, this method is anunsupervised method, in which all layers are one-way directed layers except for the last layer. So far, various methods have been proposed for image classification, and the goal of this research project was to use a combination of the AdaBoost method and the deep belief network method to classify images. The other objective was to obtain better results than the previous results. In this project, a combination of the deep belief network and AdaBoost method was used to boost learning and the network potential was enhanced by making the entire network recursive. This method was tested on the MINIST dataset and the results were indicative of a decrease in the error rate with the proposed method as compared to the AdaBoost and deep belief network methods.
IMPROVE THE SPEED OF WEB APPLICATION PAGE GENERATION BY TRANSFORMING A DATABASE QUERY INTO A SET OF LOOPS

Currently, despite the emergence of new storage technologies, the use of relational databases is dominant. In almost all areas of human life there are information systems with such databases, and often they are implemented in the form of web-applications. It is important to quickly receive a response from the user to his request. However, in the case of a complex system, the query can access a large number of tables, which will take a long time. There are various ways to speed up the information system: indexing, denormal-ization, the use of materialized views, configuring the DBMS parameters. However, the use of such methods to improve system performance requires careful study and analysis of the system for a long period of time, otherwise you can get the opposite result. The paper proposes a formal way to transform the query text with the selection of partial queries in it to execute them inside or outside the cycle of viewing query records in the formation of the resulting table. The view of the query, which formalizes the rules of transformation of the text of the original query in order to eliminate resource-intensive connection operations, is considered. The algorithm of transformation of query for use of partial queries is presented. Various options for using key and non-key attributes from the tables that are accessed by the query are described, which determines the type of transformation of the query. It is shown how the time of obtaining the result after the performed transformations is reduced, by the example of a real system of a medical institution, which is based on a da-tabase with a large number of tables related to each other, and requires a large number of queries per unit of time, while the queries refer to many tables.
EEG Data Fusion for Improving Accuracy of Binary Classification.

The paper refers to the problem of classification for multiple medical data. The proposed methodology for EEG data processing consists of seven stages and assumes different variations of the Dempster-Shafer technique as a base instrument for data fusion. Attained accuracy is comparable to other more popular algorithms and can be a promising further basis for real-time data classification.
SISTEM INFORMASI PROMOSI DAN PENJUALAN KERAJINAN SULAMAN BERBASIS WEB PADA SULAMAN INDAH MAYANG PARIAMAN

This reseach was conducted at the handycrafts Sulaman Indah Mayang Pariaman. Sulaman Indah Mayang is a bussiness that sells various kinds of minang embroidered crafts. The effort craft Sulaman Indah Mayang still use conventional way in processing sales data and do promotion. For that required an information system that will assist in the process of promotion and sales. Information system application of promotion and sale of web based embroidery craft developed using PHP programming language and MySql as database. Application design using object oriented methodology with UML design tool. The end result of this research is the creation of application of Information System Promotion and Web- Based Sales that can help Sulaman Indah Mayang in the process of promotion and sales.
Comment on "Hidden truncation hyperbolic distributions, finite mixtures thereof and their application for clustering" Murray, Browne, and \McNicholas

We comment on the paper of Murray, Browne, and McNicholas (2017), who proposed mixtures of skew distributions, which they termed hidden truncation hyperbolic (HTH). They recently made a clarification (Murray, Browne, McNicholas, 2019) concerning their claim that the so-called CFUST distribution is a special case of the HTH distribution. There are also some other matters in the original version of the paper that were in need of clarification as discussed here.
A Fuzzy Approach Based for Document Datasets Clustering

In large Computers; the huge volume of files actually generate disorder to analyze it. So, it desires to design a clustering techniques which reduce the costs of analysts.  Document clustering is an essential process in text mining, which retrieve the information with an acceptable accuracy, which can be achieved by fuzzy clustering.Reuters 21578 dataset is used for experimental purpose, the proposed system was tested by using Reuters 21578 datasets according to the time required to cluster data. The proposed system improves data clustering algorithms by construct required fuzzy clusters.  The proposed system showed a good result compared with clustering techniques in comparing with other clustering techniques in time efficiency.
Axiomatizing first-order consequences in inclusion logic

Inclusion logic is a variant of dependence logic that was shown to have the same expressive power as positive greatest fixed-point logic. Inclusion logic is not axiomatizable in full, but its first-order consequences can be axiomatized. In this paper, we provide such an explicit partial axiomatization by introducing a system of natural deduction for inclusion logic that is sound and complete for first-order consequences in inclusion logic.
University of Colorado, Colorado Springs—Ent center for the arts

The UCCS Ent Center for the Arts is a transformational five-venue center; an innovative collaboration involving the university, six community arts partners and three local school districts. Jaffe Holden provided acoustic and audio/video systems design services for a wide range of spaces including a multi-use hall, recital hall, art gallery, drama theaters, art lab, recording studio, music rehearsal room, teaching studios and practice rooms. Our acoustic focus within the Shockley-Zalabak Theater was on making a successful multi-use hall to support a wide range of programming including full orchestra ensembles and small chamber groups. The acoustic response can be adjusted by a motorized system of drapes and acoustic banners concealed above a partially sound-transparent ceiling. A stage lift allows changes to be made to seating configuration and provides a stage extension. An acoustical shell can be configured to accommodate both large and small group performance. The Chapman Foundation Recital Hall’s acoustics can also be tailored to suit various musical ensembles, thanks to an adjustable acoustic curtain system hidden above the partially sound-transparent ceiling and slatted wood wall at the rear of the stage. One of the biggest challenges was to successfully isolate a dance studio directly above a recording studio control room.
How do computer scientists use google scholar?: A survey of user interest in elements on SERPs and author profile pages

In this paper, we explore user interest in elements on Google Scholar’s search engine result pages (SERPs) and author profile pages (APPs) through a survey in order to predict search behavior. We investigate the effects of different query intents (keyword and title) on SERPs and research area familiarities (familiar and less-familiar) on SERPs and APPs. Our findings show that user interest is affected by the respondents’ research area familiarity, whereas there is no effect due to the different query intents, and that users tend to distribute different levels of interest to elements on SERPs and APPs. We believe that this study provides the basis for understanding search behavior in using Google Scholar.
On the Study of Teacher’s Question Types and Students’ Answers in Primary School English Teaching

Teachers’ questions have been regarded as an important component in foreign language teaching context. The present paper aims to present a brief investigation into teachers’ question types and students’ answers in primary school English teaching, and tries to draw some implications for primary school English teachers. The video was transcribed and analyzed by the researcher. According to what is surveyed in the study, some questioning strategies were put forward for primary English teaching in the future.
An Improved Group Similarity-Based Association Rule Mining Algorithm in Complex Scenes

Association rule (AR) mining in complex scene has attracted extensive attention of researchers in recent years. Typically, many researchers focused on an algorithm itself and ignored a generalization method to improve the performance of AR mining. Tuna et al., presented a general data structure Speeding-Up AR Structure with Inverted Index Compression (SAII) which could be utilized in most of the existing algorithms to improve their performance IEEE Trans. Cybern. 46(12) (2016) 3059–3072. However, we found that this algorithm consumes a lot of time in re-ordering data because a one-to-one comparison method is used in this process, which is the main reason that the speeding-up structure is difficult to establish when coping with much more large amount of data. To overcome these problems, this paper aims to propose an improved speeding-up AR algorithm based on group similarity and Apache Spark framework to further reduce the memory requirements and runtime. Our simulation results on the police business big dataset make clear that our improved approach performs well and is more suitable for a big data environment.
Dealing with Errors

It is inevitable when producing any non-trivial software project that we will encounter errors, unexpected eventualities and erroneous data. This is just a fact of life and we need to develop strategies for dealing with it.
Confinement of wave-function in Fractal geometry, a detection using DFT

Fractals are self-repeating patterns which have dimensions given by fractions rather than integers. While the dimension of a system unambiguously defines its properties, a fractional dimensional system can exhibit interesting properties. The recent work on confinement of the electronic wavefunction in fractal dimensions by creating artificial lattice has given rise to new possibilities of designing artificial lattice. In this study, we demonstrate that the first principle methods can be effectively employed to investigate, design and characterize electrons in Hausdorff dimension. We apply the method to study the molecular graphene and Lieb lattices which lead to fractal lattices based on these fractals. We construct the hexaflake and Vicsek fractals by adsorption of CO molecules on Cu(111) surface. This opens up the possibility of using high throughput techniques for screening and discovering such lattices from currently known crystals or developing them altogether.
MINI: Multidimensional Integrated Neural Interface

Data science today, have reached up to great heights and it has achieved many milestones with great innovation and ideas. On the basis of that have come with an idea where it’s a help to mankind. In today’s world with such a growing technology we have many people with different disabilities in them but with the help of technology, they are able to recover it to a great extent. Increasing the level of technologies alternately put an effect on the increase of its uses in the real world which helps the human being in various aspects. With such hope, to help mankind we have come up with an idea where we can help the people who cannot speak due to various reasons. So, we can come up with a technology where it will help them to express their feelings to the world without speaking.
Library Facilities, Sources and Services in the Medical and Allied Colleges in Goa State: A Study

Purpose: The purpose of this paper is to determine the true complexion of the present status of the libraries, existing facilities, resources and electronic services available in the government medical, dental, nursing, pharmacy colleges and private nursing, pharmacy, homoeopathic and Ayurveda medical colleges in the state of Goa. Methodology: Survey method adopted for the present study. A well-structured questionnaire was designed keeping the objectives in mind and distributed to the respective college librarians and collected the required primary data. Collected data were analyzed using statistical tools with the help of SPSS software. Findings: It is clear from the study that, 87.5% of college libraries under the study are having less than 5 library staff. 12.5% college library (i.e. GMC) is having 16 to 20 library staff respectively. The study reveals that five colleges out of eight colleges under the study are having full-time librarians. 12.5% librarians belonged to less than 30 years, 37.5% librarians belonged to 31 to 35 years age group and 25% librarians each is aged between 45 to 50 and more than 50 years age group respectively. The majority of the librarians have experienced between 16 to 30 years of experience. All the colleges under the study are not having scientific and technical reports, electronic databases, standards, patents and audiovisual collection in their college libraries.
A REVIEW PAPER ON ATTRIBUTE-BASED ENCRYPTION FOR MESSAGE PRIVACY IN CLOUD

The notion of attribute-based encryption (ABE) was proposed as an economical alternative to public-key infrastructures.It is the set of descriptive attributes, used as an identity to generate a secret key, as well as serving as the access structure that performs access control.ABE is also a useful building block in various cryptographic primitives such as searchable encryption. .It successfully integrates Encryption and Access Control and is ideal for sharing secrets among groups, especially in a Cloud environment.Most developed ABE schemes support key-policy or ciphertextpolicy access control in addition to other features such as decentralized authority, efficient revocation and key delegation.This paper surveys mainstream papers, analyzes main features for desired ABE systems, and classifies them into different categories.With this high-level guidance, future researchers can treat these features as individual modules and select related ones to build their ABE systems on demand.For ABE, it is not realistic to trust a single authority to monitor all attributes and hence distributing control over many attribute-authorities is desirable.A multi-authority ABE scheme can be realized with a trusted central authority (CA) which issues part of the decryption key according to a user's global identifier (GID).However, this CA may have the power to decrypt every cipher text, and the use of a consistent GID allowed the attribute-authorities to collectively build user's attributes.Decentralized ABE scheme can eliminate the burden of heavy communication and collaborative computation.It is observed that privacypreserving decentralized key-policy ABE scheme has claimed to achieve better privacy for users and is provably secure in the standard model
A Novel Re-weighting Method for Connectionist Temporal Classification

The connectionist temporal classification (CTC) enables end-to-end sequencelearning by maximizing the probability of correctly recognizing sequencesduring training. With an extra $blank$ class, the CTC implicitly convertsrecognizing a sequence into classifying each timestep within the sequence. Butthe CTC loss is not intuitive for such classification task, so the classimbalance within each sequence, caused by the overwhelming $blank$ timesteps,is a knotty problem. In this paper, we define a piece-wise function as thepseudo ground-truth to reinterpret the CTC loss based on sequences as the crossentropy loss based on timesteps. The cross entropy form makes it easy tore-weight the CTC loss. Experiments on text recognition show that the weightedCTC loss solves the class imbalance problem as well as facilitates theconvergence, generally leading to better results than the CTC loss. Besidethis, the reinterpretation of CTC, as a brand new perspective, may bepotentially useful in some other situations.