Reviewerly/exHarmony huggingface dataset:
Lifted Codes and Lattices from Codes Over Finite Chain Rings

In this paper we give the generalization of lifted codes over any finite chain ring. This has been done by using the construction of finite chain rings from $p$-adic fields. Further we propose a lattice construction from linear codes over finite chain rings using lifted codes.
Perspective study of Public Cloud: A highly scalable Cloud deployment model

&lt;p&gt;This study paper deals with various Cloud computing and deployment models provided by the Cloud. Cloud computing is the combination of two words, the term Cloud is just an Internet because with the internet we use different type of resources or services like storage, database, networking and server then the term computing is the data processing, storage over the Internet, so Cloud computing is basically the use of different types of services over the internet by individual or by the organization. We can say that the “Cloud computing” is a set of different types of hardware and software that work collectively to deliver many aspects of computing to the end user as online services. Cloud computing is an approach defined as internet based computing where virtual shared servers provide software, infrastructure, platform devices, and other resources and hosting to customers on a pay-as-use basis. These services are offered by shared network and other relevant IT resources on Private, Public, hybrid and community Cloud models. &lt;/p&gt;
Hardness of Approximation of (Multi-)LCS over Small Alphabet

The problem of finding longest common subsequence (LCS) is one of the fundamental problems in computer science, which finds application in fields such as computational biology, text processing, information retrieval, data compression etc. It is well known that (decision version of) the problem of finding the length of a LCS of an arbitrary number of input sequences (which we refer to as Multi-LCS problem) is NP-complete. Jiang and Li [SICOMP'95] showed that if Max-Clique is hard to approximate within a factor of $s$ then Multi-LCS is also hard to approximate within a factor of $\Theta(s)$. By the NP-hardness of the problem of approximating Max-Clique by Zuckerman [ToC'07], for any constant $\delta>0$, the length of a LCS of arbitrary number of input sequences of length $n$ each, cannot be approximated within an $n^{1-\delta}$-factor in polynomial time unless {\tt{P}}$=${\NP}. However, the reduction of Jiang and Li assumes the alphabet size to be $\Omega(n)$. So far no hardness result is known for the problem of approximating Multi-LCS over sub-linear sized alphabet. On the other hand, it is easy to get $1/|\Sigma|$-factor approximation for strings of alphabet $\Sigma$. In this paper, we make a significant progress towards proving hardness of approximation over small alphabet by showing a polynomial-time reduction from the well-studied \emph{densest $k$-subgraph} problem with {\em perfect completeness} to approximating Multi-LCS over alphabet of size $poly(n/k)$. As a consequence, from the known hardness result of densest $k$-subgraph problem (e.g. [Manurangsi, STOC'17]) we get that no polynomial-time algorithm can give an $n^{-o(1)}$-factor approximation of Multi-LCS over an alphabet of size $n^{o(1)}$, unless the Exponential Time Hypothesis is false.
Internet of Things (IoT) Based Video Analytics: a use case of Smart Doorbell

The vision of the internet of things (IoT) is a reality now. IoT devices are getting cheaper, smaller. They are becoming more and more computationally and energy-efficient. The global market of IoT-based video analytics has seen significant growth in recent years and it is expected to be a growing market segment. For any IoT-based video analytics application, few key points required, such as cost-effectiveness, widespread use, flexible design, accurate scene detection, reusability of the framework. Video-based smart doorbell system is one such application domain for video analytics where many commercial offerings are available in the consumer market. However, such existing offerings are costly, monolithic, and proprietary. Also, there will be a trade-off between accuracy and portability. To address the foreseen problems, I'm proposing a distributed framework for video analytics with a use case of a smart doorbell system. The proposed framework uses AWS cloud services as a base platform and to meet the price affordability constraint, the system was implemented on affordable Raspberry Pi. The smart doorbell will be able to recognize the known/unknown person with at most accuracy. The smart doorbell system is also having additional detection functionalities such as harmful weapon detection, noteworthy vehicle detection, animal/pet detection. An iOS application is specifically developed for this implementation which can receive the notification from the smart doorbell in real-time. Finally, the paper also mentions the classical approaches for video analytics, their feasibility in implementing with this use-case, and comparative analysis in terms of accuracy and time required to detect an object in the frame is carried out. Results conclude that AWS cloud-based approach is worthy for this smart doorbell use case.
Sharing ICH Archives: Integration of Online Inventories and Definition of Common Metadata

We present here an ongoing study and experimentation of a system (QueryLab) able to query different inventories at the same time, providing a larger virtual collection that can be explored through intuitive and attractive search tools. The aim is twofold: overcome data silos archives and develop a common metadata able to collect lists of intangible cultural heritage that is not yet included in any online database.
Human Cognitive Models for Heritage Site Monitoring

Integrating evidence from a single sensor over time is becoming more common due to the Internet of Things (IoT). It can play a critical role in Heritage site monitoring, as networks of sensors are required to catalog and analyze data over extended periods of time. Researchers have often adopted the mechanisms used for multi-source integration, such as Bayesian conditioning and Dempster- Shafer reasoning. Research in human cognitive models provides an interesting alternative insights for accumulating evidence over time. We used this research as a foundation for the current approach which integrates the set theoretic nature of Dempster-Shafer theory with an estimation structure based on Kalman filtering. It is well suited for applications to Wide-area Sensor Networks (WSN) that are commonly found in heritage sites.
A Unified Data Model for ICH Online Inventories: A Proposal

This paper proposes an investigation about problems arising when searching and managing Intangible Cultural Heritage data: how to find out which websites are dealing with them in the world, how to collect and evaluate web services provided to make integrated queries, how to model a common metadata structure able to collect ICH entities coming from different realities and provide them in Linked Open Data format. The study is based on the QueryLab prototype, an ongoing experimentation used to integrate different inventories to provide new and easy ways to query data.
Bulgarian Open Science Digital Library - First Prototype

In this paper we present the process of development of Bulgarian Open Science Digital Library (BOSDL) as the first and main ingredient of Bulgarian Open Science Cloud (BOSC). We introduce and discuss main principles involved in the Open Science movement. We also give a lot of technical details related to BOSDL development.
DER-DIE-DAS Article Play Method in Improving German Vocabulary Mastery

This Community Service activity (PkM) aims to socialize the Der-Die-Das artikel playing method in increasing mastery of foreign language vocabulary, especially German. The location of this service is at SD Negeri 091254 Batu Onom Pematang Siantar. The time of this community service activity is carried out on Wednesday, August 3, 2022 at 10.00-12.00 WIB. The target of this service is its teachers in order to be able to apply a variety of methods in elementary school classes in face-to-face learning. The results of this PkM activity show that the service has succeeded in educating elementary school teachers at SD Negeri 091254 Batu Onom Pematang Siantar on the Der-Die-Das method, which has added a reference to language learning methods at the elementary school level. Simulations have also been carried out on elementary school teachers in order to facilitate teachers' understanding of the implementation steps. The elementary school teachers seemed enthusiastic in participating in community service workshop/training and simulations organized by the PkM team of FKIP lecturers at HKBP Nommensen Pematang Siantar University. This was marked by the presence of teachers on the day of the PkM workshop/training and socialization and the teachers’ wish to always follow the upcoming PkM activities.
The Role of the Educational Multimedia Games to Build Lasting Interest among Young People in the Cultural and Historical Heritage

At present we are witnessing the growth of an entirely digital generation. Young people who, from a very early age, use a computer or tablet and are strong supporters of computer games, can be successfully inspired using that same technologies to love and preserve the cultural and historical heritage of the country.
Sustainable Development of the Practices of Digitization in National Library “Ivan Vazov” – Plovdiv

National Library “Ivan Vazov” in Plovdiv is the second largest library in Bulgaria. It is established as the second national depository of Bulgarian printed output and has contributed significantly to the preservation of the national cultural and historical heritage. This article offers an overview of the library’s history and current developments in the field of automation and digitization.
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training

We investigate the influence of adversarial training on the interpretability of convolutional neural networks (CNNs), specifically applied to diagnosing skin cancer. We show that gradient-based saliency maps of adversarially trained CNNs are significantly sharper and more visually coherent than those of standardly trained CNNs. Furthermore, we show that adversarially trained networks highlight regions with significant color variation within the lesion, a common characteristic of melanoma. We find that fine-tuning a robust network with a small learning rate further improves saliency maps' sharpness. Lastly, we provide preliminary work suggesting that robustifying the first layers to extract robust low-level features leads to visually coherent explanations.
Choice-Aware User Engagement Modeling andOptimization on Social Media

We address the problem of maximizing user engagement with content (in the form of like, reply, retweet, and retweet with comments)on the Twitter platform. We formulate the engagement forecasting task as a multi-label classification problem that captures choice behavior on an unsupervised clustering of tweet-topics. We propose a neural network architecture that incorporates user engagement history and predicts choice conditional on this context. We study the impact of recommend-ing tweets on engagement outcomes by solving an appropriately defined sweet optimization problem based on the proposed model using a large dataset obtained from Twitter.
Memeify: A Large-Scale Meme Generation System

Interest in the research areas related to meme propagation and generation has been increasing rapidly in the last couple of years. Meme datasets available online are either specific to a context or contain no class information. Here, we prepare a large-scale dataset of memes with captions and class labels. The dataset consists of 1.1 million meme captions from 128 classes. We also provide reasoning for the existence of broad categories, called "themes" across the meme dataset; each theme consists of multiple meme classes. Our generation system uses a trained state-of-the-art transformer-based model for caption generation by employing an encoder-decoder architecture. We develop a web interface, called Memeify for users to generate memes of their choice, and explain in detail, the working of individual components of the system. We also perform a qualitative evaluation of the generated memes by conducting a user study. A link to the demonstration of the Memeify system is https://youtu.be/P_Tfs0X-czs.
One-Way Prototypical Networks

Few-shot models have become a popular topic of research in the past years. They offer the possibility to determine class belongings for unseen examples using just a handful of examples for each class. Such models are trained on a wide range of classes and their respective examples, learning a decision metric in the process. Types of few-shot models include matching networks and prototypical networks. We show a new way of training prototypical few-shot models for just a single class. These models have the ability to predict the likelihood of an unseen query belonging to a group of examples without any given counterexamples. The difficulty here lies in the fact that no relative distance to other classes can be calculated via softmax. We solve this problem by introducing a "null class" centered around zero, and enforcing centering with batch normalization. Trained on the commonly used Omniglot data set, we obtain a classification accuracy of .98 on the matched test set, and of .8 on unmatched MNIST data. On the more complex MiniImageNet data set, test accuracy is .8. In addition, we propose a novel Gaussian layer for distance calculation in a prototypical network, which takes the support examples' distribution rather than just their centroid into account. This extension shows promising results when a higher number of support examples is available.
Employing distributional semantics to organize task-focused vocabulary learning

How can a learner systematically prepare for reading a book they are interested in? In this paper,we explore how computational linguistic methods such as distributional semantics, morphological clustering, and exercise generation can be combined with graph-based learner models to answer this question both conceptually and in practice. Based on the highly structured learner model and concepts from network analysis, the learner is guided to efficiently explore the targeted lexical space. They practice using multi-gap learning activities generated from the book focused on words that are central to the targeted lexical space. As such the approach offers a unique combination of computational linguistic methods with concepts from network analysis and the tutoring system domain to support learners in achieving their individual, reading task-based learning goals.
TG-PSM: Tunable Greedy Packet Sequence Morphing Based on Trace Clustering

Common privacy enhancing technologies fail to effectively hide certain statistical aspects of encrypted traffic, namely individual packets length, packets direction and, packets timing. Recent researches have shown that using such attributes, an adversary is able to extract various information from the encrypted traffic such as the visited website and used protocol. Such attacks are called traffic analysis. Proposed countermeasures attempt to change the distribution of such features. however, either they fail to effectively reduce attacker's accuracy or do so while enforcing high bandwidth overhead and timing delay. In this paper, through the use of a predefined set of clustered traces of websites and a greedy packet morphing algorithm, we introduce a website fingerprinting countermeasure called TG-PSM. Firstly, this method clusters websites based on their behavior in different phases of loading. Secondly, it finds a suitable target site for any visiting website based on user indicated importance degree; thus providing dynamic tunability. Thirdly, this method morphs the given website to the target website using a greedy algorithm considering the distance and the resulted overhead. Our evaluations show that TG-PSM outperforms previous countermeasures regarding attacker accuracy reduction and enforced bandwidth, e.g., reducing bandwidth overhead over 40% while maintaining attacker's accuracy.
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift

We tackle the task of adapting event extractors to new domains without labeled data, by aligning the marginal distributions of source and target domains. As a testbed, we create two new event extraction datasets using English texts from two medical domains: (i) clinical notes, and (ii) doctor-patient conversations. We test the efficacy of three marginal alignment techniques: (i) adversarial domain adaptation (ADA), (ii) domain adaptive fine-tuning (DAFT), and (iii) a novel instance weighting technique based on language model likelihood scores (LIW). LIW and DAFT improve over a no-transfer BERT baseline on both domains, but ADA only improves on clinical notes. Deeper analysis of performance under different types of shifts (e.g., lexical shift, semantic shift) reveals interesting variations among models. Our best-performing models reach F1 scores of 70.0 and 72.9 on notes and conversations respectively, using no labeled data from target domains.
Ternary and Binary Quantization for Improved Classification

Dimension reduction and data quantization are two important methods for reducing data complexity. In the paper, we study the methodology of first reducing data dimension by random projection and then quantizing the projections to ternary or binary codes, which has been widely applied in classification. Usually, the quantization will seriously degrade the accuracy of classification due to high quantization errors. Interestingly, however, we observe that the quantization could provide comparable and often superior accuracy, as the data to be quantized are sparse features generated with common filters. Furthermore, this quantization property could be maintained in the random projections of sparse features, if both the features and random projection matrices are sufficiently sparse. By conducting extensive experiments, we validate and analyze this intriguing property.
BITCOIN PRICES PREDICTION USING DEEP LEARNING

This paper is based on current market prediction value of cryptocurrency Bitcoin.The Bitcoins is like shareable as piece of cakes but whenever, the price of Bitcoins will be fluctuated more.It will be difficult to predict.This problem discussed is based on computer science and delivered through Bitcoin market frequence, project using machine learning high level computer concept and predict highly closed accurate Bitcoin price using various real world dataset.
DATA SANITY AND HOME BASED CLUSTERING

The customers receive their monthly electricity unit consumption and the cost.The bill report does not contain any details of electricity consumption or solutions to efficiently utilize their consumption.Hence there is a need to provide appliance level breakdown of energy consumption and also provide tips or recommendations to cut down on unnecessary consumption and help save energy also provide similar house comparison to motivate customers save energy, high budget alerts and monthly bill projection.
Measuring Solar Irradiance for Photovoltaics

In recent years, solar energy technology has emerged as one of the leading renewable energy technologies currently available. Solar energy is enabled by the solar irradiance reaching the earth. Here we describe the characteristics of solar irradiance as well as the sources of variation. The different components of the solar irradiance and the instruments for measurement of these components are presented. In photovoltaics, the measurement of solar irradiance components is essential for research, quality control, feasibility studies, investment decisions, plant monitoring of the performance ratio, site comparison, and as input for short-term irradiance forecasting. Some more details are also provided related to physics of measuring instruments, their calibration, and associated uncertainty.
Hardware Implementation of an Improved Hybrid Cryptosystem for Numerical Image Encryption and Authenticity

Cryptography is the science that concerns protecting information by transforming its comprehensible form into an incomprehensible one. The conception of a robust cryptosystem is a challenge. In this paper, an improved hybrid cryptosystem for numerical image protection is presented. First, the initial secret key is generated by a secure hash function (keccak). Secondly, the plain image is encrypted through the advanced encryption standard (AES) with CTR mode. Finally, a Rivest-Shamir-Adleman (RSA) algorithm is used to secure the symmetric key transmitted over the insecure channel and owner signature. Our cryptosystem is implemented in hardware and evaluated by different tools mainly identified from the image cryptography community using numerous kinds of standard images. The experimental and analytical findings prove that our framework security gives a trade-off between robustness and performance, which can be used in several domains like medicine, military, and community privacy.
Fast and Efficient Parallel Breadth-First Search with Power-law Graph Transformation

In the big data era, graph computing is widely used to exploit the hidden value in real-world graphs in various scenarios such as social networks, knowledge graphs, web searching, and recommendation systems. However, the random memory accesses result in inefficient use of cache and the irregular degree distribution leads to substantial load imbalance. Breadth-First Search (BFS) is frequently utilized as a kernel for many important and complex graph algorithms. In this paper, we describe a preprocessing approach using Reverse Cuthill-Mckee (RCM) algorithm to improve data locality and demonstrate how to achieve an efficient load balancing for BFS. Computations on RCM-reordered graph data are also accelerated with SIMD executions. We evaluate the performance of the graph preprocessing approach on Kronecker graphs of the Graph500 benchmark and real-world graphs. Our BFS implementation on RCM-reordered graph data achieves 326.48 MTEPS/W (mega TEPS per watt) on an ARMv8 system, ranking 2nd on the Green Graph500 list in June 2020 (the 1st rank uses GPU acceleration).
The Effect of Guided Question in Enhancing The Students’ Writing Skill

Andhiny Angelia Vitriani, 2020. The Effect of Guided Question in Enhancing The Students’ Writing Skill at the Second Grade of SMPN 1 Parepare (Supervised by Amzah and Mujahidah).&#x0D; This research was aimed to find out the effect of guided question to enhancing the students’ writing skill at the second grade of SMPN 1 Parepare. The purpose of this research was to know is guided question is effective to to enhance students’ writing skill and to know how students’ achievement in writing skill after taught by using guided question. This research took the second grade of SMPN 1 Parepare with 37 students as sample.&#x0D; &#x0D; This research was classified as quantitative research, where the instrument this research were writing test consist pre-test and post-test . The sampling technique of this research was cluster sampling. In this research used pre-experimental design with one group pre-test and post-test&#x0D; &#x0D; The researcher found that guided question was effective to enhance students writing skill, the students were able to show a sign progress especially: well began to construct sentences, showed a fairly good link between topic and whole content, and, showed a fairly good connected between sentences each other. Based on the calculation, the result of the data analysis by using t-test showed the value of t-test (to) was higher than ttable (tt), to &gt; tt = (11.54 &gt; 1.69) in significant degree of 0.05% (5%). It means that the Alternative Hypothesis (Ha) was accepted and the Null Hypothesis (Ho) is rejected. In conclusion, There is significant effect of Guided Question to enhance student’s writing skill or Guided Question Technique was effective to enhance students’ wrting skill.&#x0D; &#x0D; &#x0D; Keywords: Teaching Writing Skill, Guided Question&#x0D; &#x0D;