Reviewerly/exHarmony huggingface dataset:
Detection, Localization and Retrieval of tampering/Alteration in Microsoft Word Documents for Authentication and Copyright Protection using Multiple Techniques

The Covid-19 Pandemic has changed the nature of work culture and made everything digitized. While this has many advantages, making everything online brings with it security threats such as ensuring authenticity of the documents. It is often seen that the documents transferred from one source to another may undergo changes which tampers the original work of the author. We have taken this challenge as an opportunity to develop techniques to Detect, Localize and Retrieve any forgery that may be attempted to an original document wherein there is removal, replacement or addition of a word in the document without the need of original document. We have incorporated the method of hashing along with encryption and decryption to accomplish the techniques to its fullest efficiency.
Pengembangan Media Augmented Reality pada Benda-benda Kebudayaan dalam Prosesi Ritual Pembuatan Perahu Pinisi di Desa Ara, Kecamatan Bontobahari, Kabupaten Bulukumba

This research aims to determine the development of Augmented Reality media in the Pinisi Boat Making Ceremony in Ara Village, Bulukumba Regency. The main problems raised in this research are what are the cultural objects and meanings of these cultural objects in the ritual procession of making Pinisi boats in Ara Village, Bontobahari District, Bulukumba Regency, and how to apply Augmented Reality media as a medium for cultural development based on cultural objects that have been analyzed. This research uses descriptive research and development. This study uses a qualitative approach and RnD (Research &amp; Development). The instruments in this study use interview techniques, documentation and voice recording. The data analysis technique used is descriptive qualitative data analysis and development techniques. Pinisi is one of the cultural assets belonging to the Indonesian nation that is unique, and in its manufacturing ritual there are several cultural objects that have moral values and spiritual messages that must be preserved and preserved. Augmented Reality media is an alternative to cultural development in the modern era. Therefore, technology-based cultural development media is needed in an effort to preserve culture from generation to generation.&#x0D; Keywords: Augmented Reality, Pinisi Boat, Bulukumba, Building Ceremony.
Reading Skill of Elementary School Students and Relationship to Foreign Language (German and Japanese) Contained in The Text

This study aims to analyze students' interest in foreign languages such as German or Japanese. Descriptive qualitative research method. The data collection technique used descriptive qualitative and gave some reading texts to the children. The results of the study showed that the relationship between students to foreign languages other than the language that was introduced was very high, and they increasingly wanted to learn the foreign language even though they could not pronounce foreign vocabulary correctly, the foreign language contained in the Indonesian text did not make them stop reading or reading.
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident variations caused by the stochastic nature of our world. These outcomes are governed by the characteristic kinematics of objects that dictate their overall motion caused by a local interaction. Conversely, the movement of an object provides crucial information about its underlying distinctive kinematics and the interdependencies between its parts. This two-way relation motivates learning a bijective mapping between object kinematics and plausible future image sequences. Therefore, we propose iPOKE - invertible Prediction of Object Kinematics - that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis. In contrast to previous works, we do not generate arbitrary realistic videos, but provide efficient control of movements, while still capturing the stochastic nature of our environment and the diversity of plausible outcomes it entails. Moreover, our approach can transfer kinematics onto novel object instances and is not confined to particular object classes. Project page is available at this https URL
A New Public-Key Cryptosystem Based on LCD Codes

A cryptosystem is a structure or scheme consisting of a set of algorithms that converts plaintext to ciphertext to encode or decode messages securely.The cryptosystem points at a computer system that employs cryptography.Cryptosystems are classified by the method they use to encrypt data.One of them is symmetric key encryption.While the symmetric key algorithm uses the same key for encryption and decryption, asymmetric key encryption or public-key encryption uses the different keys.So it is more reliable than symmetric cipher algorithm.In this paper, we propose a new public-key cryptosystem by using LCD codes and explain the signature protocol based on this system which is reliable.
On learning an interpreted language with recurrent models.

Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.
Demonstration-Guided Reinforcement Learning with Learned Skills

Demonstration-guided reinforcement learning (RL) is a promising approach for learning complex behaviors by leveraging both reward feedback and a set of target task demonstrations. Prior approaches for demonstration-guided RL treat every new task as an independent learning problem and attempt to follow the provided demonstrations step-by-step, akin to a human trying to imitate a completely unseen behavior by following the demonstrator's exact muscle movements. Naturally, such learning will be slow, but often new behaviors are not completely unseen: they share subtasks with behaviors we have previously learned. In this work, we aim to exploit this shared subtask structure to increase the efficiency of demonstration-guided RL. We first learn a set of reusable skills from large offline datasets of prior experience collected across many tasks. We then propose Skill-based Learning with Demonstrations (SkiLD), an algorithm for demonstration-guided RL that efficiently leverages the provided demonstrations by following the demonstrated skills instead of the primitive actions, resulting in substantial performance improvements over prior demonstration-guided RL approaches. We validate the effectiveness of our approach on long-horizon maze navigation and complex robot manipulation tasks.
An Automated Smart Healthcare System for COVID: Safe in Post Pandemic Life

At the beginning of COVID-19 there is no cure or vaccine and it became more dangerous as the virus symptoms were kept concealed.The only way to get cure of this virus is self-isolation and maintain social distancing.Because of the COVID-19 rate of death and COVID-19 cases are increases day by day.The virus had infected over 168.9 million people worldwide.Because of the careless behaviour of people and complacency are leading to spike in COVID-19 cases.So in this work, the application of internet of things (IOT) in healthcare and social distance monitoring the patients in the pandemic situations from COVID-19 is proposed.this project consists of three parts: A lightweight and low cost IoT node for data analysis and give treatment to those who exceed the limit.The IoT node is used to track the health parameter such as body temperature, blood oxygen level and pulse rate and update this to cloud and notify the user by message.Here is the two way to communicate between IoT node and smart phone is by Wi-Fi or Bluetooth and we use GPS tracker to track the patient.A smart health monitoring system maintains the contact between doctors and patient and it helps in daily monitoring the vital parameters and it is user friendly.
Perception of Students on the Use of Mobile Phones in EFL Learning: A Phenomenological Study

This research study focuses on the students’ perception on the use of mobile phones in EFL learning in the context of Nepal. It aims to investigate how the students are taking smart phones as a means for EFL learning in their educational experience. The study was carried out among the students of the bachelor level at Dhankuta Multiple Campus. It was largely informed by a phenomenological approach in order to examine the students’ experiences of using mobile phones in EFL learning. The semi- structured interview was held to collect the required data. Results indicate that all participants have positive attitudes on the use of mobile phones in learning English. However, they feel a few difficulties in understanding the text through the mobile phones. They expect supportive guidance and reliable network quality for effective EFL learning. Mobile phones can serve as a useful language learning tool if it is utilized properly for specific purposes. All curriculum developers, syllabus designer and policy makers should consider the space of mobile assisted English language learning in the present curriculum.
Digital Media and Information Literacy: A way to Paperless Society

Purpose - The study's main objective was to find out the possibility of a paperless library and society with particular reference to Top 60 Universities from QS world University ranking 2021 and their library professionals. ICT knowledge and skills of these LIS professionals and evaluated their digital literacy skills was another aim of this study. Design/methodology/approach - The researchers used the survey method for this study using a structured questionnaire, distributed through the google form to library professionals of world-famous universities, ranked as top 60 in QS World University Ranking. 206 responses were received. The information collected from the respondents has been analyzed using an Excel sheet and SPSS software. Findings - Most professionals are interested in digital learning and adopting paperless learning to contribute to a paperless society. They go for online ways to answer reference queries of users and work in a refined atmosphere. They are learning from digital resources and have support from online platforms if they suffer. Also, they are actively engaging with the digital environment and promoting it too.
Gaussian Belief Space Path Planning for Minimum Sensing Navigation

We propose a path planning methodology for a mobile robot navigating through an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts. The desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a novel information-geometric distance function. The distance function we introduce is shown to be an asymmetric quasi-pseudometric and can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to solve the formulated shortest-path problem. To gain insight into the asymptotic optimality of the proposed algorithm, we show that the considered path length function is continuous with respect to the topology of total variation. Simulation results demonstrate that the proposed method is effective in various robot navigation scenarios to reduce sensing costs, such as the required frequency of sensor measurements and the number of sensors that must be operated simultaneously.
Analysis of the Performance Evaluation Method of University Informationization Based on Teacher Satisfaction

With the development of information technology, modern colleges and universities are generally carrying out information construction work, aiming to provide convenience to teachers through information technology and improve teaching quality and efficiency. However, there are differences in the informatization teaching systems constructed by different universities, and teachers may not be satisfied. At this time, in order to promote the further development of the informatization teaching system as much as possible, universities can evaluate the performance of informatization construction from the perspective of teacher satisfaction. Know the current defects according to the evaluation results, and finally make targeted improvements. This paper will carry out research work based on this logic, mainly constructing a teacher informatization satisfaction evaluation system, and then launching an evaluation to obtain the performance level of various aspects of college informatization construction, and finally put forward relevant strategies based on the performance level.
Adversarial Attacks on Machinery Fault Diagnosis

Despite the great progress of neural network-based (NN-based) machinery fault diagnosis methods, their robustness has been largely neglected, for they can be easily fooled through adding imperceptible perturbation to the input. For fault diagnosis problems, in this paper, we reformulate various adversarial attacks and intensively investigate them under untargeted and targeted conditions. Experimental results on six typical NN-based models show that accuracies of the models are greatly reduced by adding small perturbations. We further propose a simple, efficient and universal scheme to protect the victim models. This work provides an in-depth look at adversarial examples of machinery vibration signals for developing protection methods against adversarial attack and improving the robustness of NN-based models.
A hybrid ensemble method with negative correlation learning for regression

Hybrid ensemble, an essential branch of ensembles, has flourished in the regression field, with studies confirming diversity's importance. However, previous ensembles consider diversity in the sub-model training stage, with limited improvement compared to single models. In contrast, this study automatically selects and weights sub-models from a heterogeneous model pool. It solves an optimization problem using an interior-point filtering linear-search algorithm. The objective function innovatively incorporates negative correlation learning as a penalty term, with which a diverse model subset can be selected. The best sub-models from each model class are selected to build the NCL ensemble, which performance is better than the simple average and other state-of-the-art weighting methods. It is also possible to improve the NCL ensemble with a regularization term in the objective function. In practice, it is difficult to conclude the optimal sub-model for a dataset prior due to the model uncertainty. Regardless, our method would achieve comparable accuracy as the potential optimal sub-models. In conclusion, the value of this study lies in its ease of use and effectiveness, allowing the hybrid ensemble to embrace diversity and accuracy.
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning

Cooperative multi-agent reinforcement learning is a decentralized paradigm in sequential decision making where agents distributed over a network iteratively collaborate with neighbors to maximize global (network-wide) notions of rewards. Exact computations typically involve a complexity that scales exponentially with the number of agents. To address this curse of dimensionality, we design a scalable algorithm based on the Natural Policy Gradient framework that uses local information and only requires agents to communicate with neighbors within a certain range. Under standard assumptions on the spatial decay of correlations for the transition dynamics of the underlying Markov process and the localized learning policy, we show that our algorithm converges to the globally optimal policy with a dimension-free statistical and computational complexity, incurring a localization error that does not depend on the number of agents and converges to zero exponentially fast as a function of the range of communication.
Rancang Bangun Aplikasi Toko Bunga Berbasis Web Menggunakan Waterfall dan Pieces

Along with the current development of digitalization technology, almost all aspects have adapted to these changes, including the Indonesian people, who are mostly traders, also take part in this development. The website is one of the digitalization models that is often used in promoting products, both goods and Services, in this case the author takes one example, namely the sale of a flower shop "Bunds Flowers" where in this case the flowers that are sold are not like flowers in general, but there are flowers in the type of paper, plastic, to imported and local flowers, making this flower shop very worthy of having more promotional value, both in terms of conventional (face-to-face) and modern (online/digitalized) sales. This study uses two methods where the waterfall method is applied to the system development process and the PIECES method to analyze the user's response to the system that has been built. In this journal the author aims to build a system where sales and ornamental plant enthusiasts still use traditional media, with the websites help consumers find what they need in an easy way by opening a website. In the analysis process, the respondents have produced several assessments related to the flower shop application, including 1) performance, respondents rated 3.73 (Satisfied) 2) Information, respondents rated 4.33 (Satisfied), 3) Economic, respondents rated 4.33 (Satisfied) , 4) Control and Security, respondents rated 4.10 (Satisfied) and 5 Efficiency, respondents rated 3.73 (Satisfied) and Service, respondents rated 3.73 (satisfied).
Compressed Oblivious Encoding for Homomorphically Encrypted Search.

Fully homomorphic encryption (FHE) enables a simple, attractive framework for secure search. Compared to other secure search systems, no costly setup procedure is necessary; it is sufficient for the client merely to upload the encrypted database to the server. Confidentiality is provided because the server works only on the encrypted query and records. While the search functionality is enabled by the full homomorphism of the encryption scheme. For this reason, researchers have been paying increasing attention to this problem. Since Akavia et al. (CCS 2018) presented a framework for secure search on FHE encrypted data and gave a working implementation called SPiRiT, several more efficient realizations have been proposed. In this paper, we identify the main bottlenecks of this framework and show how to significantly improve the performance of FHE-base secure search. In particular, 1. To retrieve $\ell$ matching items, the existing framework needs to repeat the protocol $\ell$ times sequentially. In our new framework, all matching items are retrieved in parallel in a single protocol execution. 2. The most recent work by Wren et al. (CCS 2020) requires $O(n)$ multiplications to compute the first matching index. Our solution requires no homomorphic multiplication, instead using only additions and scalar multiplications to encode all matching indices. 3. Our implementation and experiments show that to fetch 16 matching records, our system gives an 1800X speed-up over the state of the art in fetching the query results resulting in a 26X speed-up for the full search functionality.
Effectiveness of Optimization Algorithms in Deep Image Classification

Adam is applied widely to train neural networks. Different kinds of Adam methods with different features pop out. Recently two new adam optimizers, AdaBelief and Padam are introduced among the community. We analyze these two adam optimizers and compare them with other conventional optimizers (Adam, SGD + Momentum) in the scenario of image classification. We evaluate the performance of these optimization algorithms on AlexNet and simplified versions of VGGNet, ResNet using the EMNIST dataset. (Benchmark algorithm is available at \hyperref[https://github.com/chuiyunjun/projectCSC413]{https://github.com/chuiyunjun/projectCSC413}).
The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

Scatterplots can encode a third dimension by using additional channels like size or color (e.g. bubble charts). We explore a potential misinterpretation of trivariate scatterplots, which we call the weighted average illusion, where locations of larger and darker points are given more weight toward x- and y-mean estimates. This systematic bias is sensitive to a designer's choice of size or lightness ranges mapped onto the data. In this paper, we quantify this bias against varying size/lightness ranges and data correlations. We discuss possible explanations for its cause by measuring attention given to individual data points using a vision science technique called the centroid method. Our work illustrates how ensemble processing mechanisms and mental shortcuts can significantly distort visual summaries of data, and can lead to misconceptions like the demonstrated weighted average illusion.
Limbo: Efficient Zero-knowledge MPCitH-based Arguments.

This work introduces a new interactive oracle proof system based on the MPC-in-the-Head paradigm. To improve concrete efficiency and offer flexibility between computation time and communication size, a generic proof construction based on multi-round MPC protocols is proposed, instantiated with a specific protocol and implemented and compared to similar proof systems. Performance gains over previous work derive from a multi-party multiplication check optimized for the multi-round and MPC-in-the-Head settings. Of most interest among implementation optimizations is the use of identical randomness across repeated MPC protocol executions in order to accelerate computation without excessive cost to the soundness error. The new system creates proofs of SHA-256 pre-images of 43KB in 53ms with 16 MPC parties, or 23KB in 188ms for 128 parties. As a signature scheme, the non-interactive variant produces signatures, based on the AES-128 circuit, of 18KB in about 4ms; this is 20% faster and 32% larger than the Picnic3 scheme (13kB in 5.3ms for 16 parties) which is based on the 90% smaller LowMC circuit.
Private Hierarchical Clustering and Efficient Approximation

In collaborative learning, multiple parties contribute their datasets to jointly deduce global machine learning models for numerous predictive tasks. Despite its efficacy, this learning paradigm fails to encompass critical application domains that involve highly sensitive data, such as healthcare and security analytics, where privacy risks limit entities to individually train models using only their own datasets. In this work, we target privacy-preserving collaborative hierarchical clustering. We introduce a formal security definition that aims to achieve balance between utility and privacy and present a two-party protocol that provably satisfies it. We then extend our protocol with: (i) an optimized version for single-linkage clustering, and (ii) scalable approximation variants. We implement all our schemes and experimentally evaluate their performance and accuracy on synthetic and real datasets, obtaining very encouraging results. For example, end-to-end execution of our secure approximate protocol for over 1M 10-dimensional data samples requires 35sec of computation and achieves 97.09% accuracy.