Reviewerly/exHarmony huggingface dataset:
OctoBot: An Open-Source Orchestration System for a Wide Range Network Activity Generation

Experimentation in a large-scale testbed environment requires a large amount of emulated traffic to ensure realistic scenario execution and better experiment results. A large number of human-generated network activities can emulate traffic from real network users. Deploying and producing a single activity from an individual user is simple, but emulating and automating it from multiple users with a wide range of activities is challenging. We designed a containerized human agent (i.e., bot) to generate a single activity. Thus, a large number of bots can be deployed and controlled by a single orchestration system. Due to the complexity and wide-range usage of container orchestration systems, we need to develop a simpler system that leverages widely-used open-source container orchestrator. So, researchers and scientists can easily use it to define and execute activity requirements with a few command lines or a single specification file. This work shows the implementation of an orchestration system with several unique bots and their deployment in a production testbed environment. Several activity use cases are also presented to verify their functionality to support large-scale experimentation.
Efficient reconfigurable architecture for moving object detection with motion compensation

The detection and tracking of object in large data surveillance requires a proper motion estimation and compensation techniques which are generally used to detect accurate movement from video stream. In this paper, a novel hardware level architecture involving motion detection, estimation, and compensation is proposed for real-time implementation. The motion vectors are obtained using 16×16 sub-blocks with a novel parallel D flip flop architecture in this work to arrive at an optimised architecture. The sum of absolute difference (SAD) is then calculated by optimized absolute difference and adder blocks designed using kogge-stone adder which helps in improving the speed of the architecture. The controller block is designed by finite state machine model used for synchronization of all the operations. Further, the comparator and compensation blocks are optimized by using basic logical elements and the Kogge-stone adder. Finally, the proposed architecture is implemented on Zynq Z7-10 field-programmable gate array (FPGA) and simulated using System Generator tool for real time traffic signal. The hardware and software parameters are compared with the existing techniques which shows that the proposed architecture is efficient than existing methods of design.
Network Security Situation Prediction Based on Long Short-Term Memory Network

Due to the rapid development of the network, the network security situation is increasingly severe. The network security situation forecast analyzes the past network data and predicts the network situation to the warning of possible network threats in the future. Network security situation prediction can play an important role in network defense, network security warning and network resource allocation. We chose to predict network data first and then evaluate the network situation. We proposed a network security situation prediction method based on LSTM-XGBoost model. We built an improved LSTM neural network model to predict network security data and then used the XGBoost model to conduct situation assessment on the predicted data. The results of comparative experiments show that the model proposed in this paper can complete the task of network security situation prediction more efficiently and accurately.
E-Marketing of Library Resources and Services

Over the years libraries have enjoyed positive and excellent relation with clientele. The new Internet setting and technical upgrading have made libraries to reorder and get used to these innovations for and resourceful performance of the system and services. Improving the usage of library materials and services is indispensable and this is achievable with the help of effective marketing strategies. Librarians should take this virtual age as a great opportunity rather than as a challenge.
Enhancing Cloud Data Security using Multilevel Encryption Techniques

Cloud computing is the provision of IT services over the Internet with the aim of offering faster innovation, flexible resources, and economies of scale. Cloud computing security is in a continuous evolving, it is the one of the main research issues in Cloud. This paper proposes a multilevel encryption technique to secure the transaction of data in public cloud. The proposed technique model is a hybrid method that combines both the symmetric and asymmetric cryptography techniques. Our proposal uses use the Blowfish encryption to encrypt could data and Elliptic Curve Cryptography (ECC) to generate and manage encryption keys. Hence, it ensures a multilevel encryption /decryption process for both the sender and the receiver. Our work ensures Cloud security, transparency and reduces the security threats. Furthermore, we aim to propose multilevel cryptographic schema based on a combination of the Blowfish encryption and Elliptic curve cryptography. Our solution combines both encryption methods: a symmetric method using Blowfish to encrypt data and an asymmetric method using ECC to manage the encryption keys. Our proposition aims to ensure the security of delivered data in public SaaS and prevent non-authorized user to have access to the sensitive information exchanged between the client and the cloud server.
‘Pick-them-Up’, an Android Based Application to Shorten School Shuttle Time

A school shuttle service required to arrive at the destination home on time for the students will not come late to school. However, the shuttle service often gets the incident delays to pick up the students, because the driver must wait for the students who were not ready at the previous pick up. There are many reasons that make those students unready to be picked up. For example, when the driver arrived they were still taking a bath, having their breakfast, getting dressed, and other reasons. The idea to create a mobile application which is able to shorten student shuttle time hoped to solve these problems, we called the application is ‘pick-them-up’. We designed the analysis of business process using Use Case and Activity Diagram, before we built the application. This application is installed on the android mobile phone for the parents, students and the shuttle driver. When the students are ready to be picked up, parents or students will press the ready button on the application. On the drivers screen application will appear the notification explain that the student is ready to be picked up. When the students will be back at their home, they will notice the location of the car through this application to make easier find the shuttle car service. The parents will also get a notification when the shuttle car service will start delivering the students at their home. This application does not only shorten the shuttle time and minimize student delays, but also make the students, parents, and driver more convenient. The test result of this application prototype achieved 100% of suitable features for the users.
소비자 리뷰 텍스트마이닝을 이용한 신생 산업 시장 구조 분석: 국내 수제 맥주 시장의 경쟁 관계 및 시장 구조를 중심으로

PurposeThis paper aims to effectively utilize user-generated content (UGC) and analyze the market structure of a relatively new market which lacks rich user review information. Specifically, we propose a domain-specific text mining tool for the domestic craft beer market and visualize the market structure by incorporating how individual beer products are positioned in the perceptual map of consumers.Design/methodology/approachWe collect user review information from Naver blogs, and extract words that describe beers. We identify semantic relationships between beer products through text mining, and then depending on these semantic relationships, construct a graph representing the market structure of the domestic craft beer market based on the consumer''s perceptual map.FindingsFirst, beer products produced in the same brewery are perceived as very similar to consumers. Second, only two products, ‘Heukdang Milky Stout’ and ‘Gompyo’, was noticeably distinguishable from other products. Third, even though ‘Gyeongbokgung’ is from a different brewery, it is located very close to the products of ‘Jeju Beer’ brewery such as ‘Jeju Baeknokdam Ale’ and ‘Seongsan Ilchulbong Ale’, which suggests the influence of ‘landmark series.’ We successfully show that our methodology effectively describes the market structure of the craft beer market.
Effective and practical neural ranking

Supervised machine learning methods that use neural networks ("deep learning") have yielded substantial improvements to a multitude of Natural Language Processing (NLP) tasks in the past decade. Improvements to Information Retrieval (IR) tasks, such as ad-hoc search, lagged behind those in similar NLP tasks, despite considerable community efforts. Although there are several contributing factors, I argue in this dissertation that early attempts were not more successful because they did not properly consider the unique characteristics of IR tasks when designing and training ranking models. I first demonstrate this by showing how large-scale datasets containing weak relevance labels can successfully replace training on in-domain collections. This technique improves the variety of queries encountered when training and helps mitigate concerns of over-fitting particular test collections. I then show that dataset statistics available in specific IR tasks can be easily incorporated into neural ranking models alongside the textual features, resulting in more effective ranking models. I also demonstrate that contextualized representations, particularly those from transformer-based language models, considerably improve neural ad-hoc ranking performance. I find that this approach is neither limited to the task of ad-hoc ranking (as demonstrated by ranking clinical reports) nor English content (as shown by training effective cross-lingual neural rankers). These efforts demonstrate that neural approaches can be effective for ranking tasks. However, I observe that these techniques are impractical due to their high query-time computational costs. To overcome this, I study approaches for offloading computational cost to index-time, substantially reducing query-time latency. These techniques make neural methods practical for ranking tasks. Finally, I take a deep dive into better understanding the linguistic biases of the methods I propose compared to contemporary and traditional approaches. The findings from this analysis highlight potential pitfalls of recent methods and provide a way to measure progress in this area going forward.
Mitigating Lifetime-Energy-Makespan Issues in Reliability-Aware Workflow Scheduling for Big Data

The emergence of cloud computing in big data era has exerted a substantial impact on our daily lives. The conventional reliability-aware workflow scheduling (RWS) is capable of improving or maintaining system reliability by fault tolerance techniques such as replication and checkpointing based recovery. However, the fault tolerant techniques used in RWS would inevitably result in higher system energy consumption, longer execution time, and worse thermal profiles that would in turn lead to a decreased hardware lifespan. To mitigate the lifetime-energy-makespan issues of RWS in cloud computing systems for big data, we propose a novel methodology that decomposes the complicated studied problem. In this methodology, we provide three procedures to solve the energy consumption, execution makespan, and hardware lifespan issues in cloud systems executing real-time workflow applications. We implement numerous simulation experiments to validate the proposed methodology for RWS. Simulation results clearly show that the proposed RWS strategies outperform comparative approaches in reducing energy consumption, shortening execution makespan, and prolonging system lifespan while maintaining high reliability. The improvements on energy saving, reduction on makespan, and increase in lifespan can be up to 23.8%, 18.6%, and 69.2%, respectively. Results also show the potentiality of the proposed method to develop a distributed analysis system for big data that serves satellite signal processing, earthquake early warning, and so on.
Values: A mirror image of individual

Values are determinants of human behavior. They play a very important role in the lives of every individual. It is the challenge for the 21st century. Values are related to nature by birth. No one is born with a set of values relating to events, situations, practices, individuals or institutions. These are shaped, natured and developed at home and educational institutions. “Education without values is of no use” Today’s present scenario, our education is only for the heads and hands and not the hearts. - Lack of Value Education has been an important part in the present scenario of growing violence and terrorism, pollution and ecological imbalances. School has to play a major role to inculcate the values. A teacher who lives by values exercises a great influence on students. It can be introduced through curricular as well as co-curricular activities. Our educational system should therefore aim not merely at information based knowledge, but on the inculcation of values that guides the students, in the desirable use of the acquired knowledge and skills, thus molding them into ideal / positive human beings and useful members of the society. The teachers only can do the positive global changes in the society. The present article is insist the importance of value  for the future generation and save our nation.
Recognition of Dysfluency in Speech: A Bidirectional Long-Short Term Memory Based Approach

Speech recognition plays a significant role in the human-machine interaction process, in which the first machine will search suitable keywords to act with help of Automatic Speech Recognition (ASR) system. But, if there is any discontinuity or dysfluency in speech then the Automatic Speech Recognition (ASR) system considers those words as out of vocabulary words, for such irregular structured speech machines cannot be able to perform the task as per the commands. In this paper, recognition of such speech discontinuity or dysfluency is done by using Bidirectional Long-Short Term Memory (Bi-LSTM) Recurrent Neural Network (RNN). The proposed work concentrates on recognition of dysfluency based on Mel-frequency Cepstral Coefficients- Shifted Delta Cepstral (MFCC-SDC) feature vectors. The datasets consist of 14 female speakers and 6 Male speakers taken from UCLASS database for testing and training the neural network. The proposed algorithm can recognize dysfluencies like prolongation, repetition, and blockage effectively with the highest accuracy of 96%.
Meet Meets Join: The Interaction between Private and Common Knowledge

This paper explores the lattice-theoretic properties of the complete lattice of the family of all partitions of an arbitrary state space. I discuss a duality correspondence connecting meets (finest common coarsening) and joins (coarsest common refinement) of families of partitions, confirming their natural order-theoretic characterizations. The partition lattice turns out to fail to be a distributive, or even a modular, lattice. I provide intuitive interpretations of these negative results in terms of the interaction between common knowledge and pooling multiple sources of information, with applications to information exchange within and between different populations and to criminal procedure law.
IoT and the role of cyber forensics1

The Internet of Things (“IoT”) refers to the ability of everyday objects to connect to the Internet and to send and receive data. Many definitions include functionality examples to better clarify how the systems operate and the interrelation between devices and entities within and outside the IoT. The things, or devices, are designed to operate independent of the need for user input to start a process or function. In the early 1980s several graduate students and an engineer at Carnegie Mellon University set about the task of modifying a Coke machine with sensors that would monitor the stock and temperature of soda. A toaster is more widely believed to be the first IoT device. In 1989, Interop President Dan Lynch challenged John Romkey to connect a toaster to the Internet. IoT devices change this paradigm by offering an ever-growing list of functions that can be performed outside the need of user intervention to start, or continue, an operation.
Regularization of nouns due to drift, not selection: An artificial-language experiment

Author(s): Ventura, Rafael; Plotkin, Joshua; Roberts, Gareth | Abstract: Corpus data suggests thatnfrequent wordsnhave lower rates of replacement and regularization. It is not clear, however, whether this holds due to stronger selection against innovation among high-frequency words or due to weaker drift at high frequencies. Here, we report two experiments designed to probe this question. Participants were tasked with learning a simple miniature language consisting of two nouns and two plural markers. After exposing plural markers to drift and selection of varying strengths, we tracked noun regularization. Regularization was greater for low- than for high-frequency nouns, with no detectable effect of selection. Our results therefore suggest that lower rates of regularization of more frequent words may be due to drift alone.
Building Capacity for Reproducible Evidence Synthesis: Training Opportunities for Librarians

Information specialists (IS) and librarians provide expertise as evidence synthesis collaborators, including database and gray literature searching, and support for information management, documentation and data archiving. A growing body of literature suggests that when IS collaborate with researchers, the quality and reproducibility of evidence syntheses improves. Training opportunities specifically for IS in these methods are scarce, however, particularly outside of the health sciences. This poster will describe two initiatives that fill this critical training gap: Evidence Synthesis Beyond the Health Sciences: A Training for Librarians, and the Library Carpentries Introduction to R and litsearchr lesson. The programs were piloted in August 2020 to over 60 librarians and IS in North America, Europe and Africa. We will elaborate on our approach to hands-on training in an online environment, highlight ways these programs build capacity for reproducible evidence synthesis and teach data science skills to support emerging methods, and discuss plans for future training.
On QoE Impact of DoH and DoT in Africa: Why a User’s DNS Choice Matters

Internet security and Quality of Experience (QoE) are two antagonistic concepts that the research community has been attempting to reconcile. Internet security has of late received attention due to users’ online privacy and security concerns. One example is the introduction of encrypted Domain Name System (DNS) protocols. These protocols, combined with suboptimal routing paths and offshore hosting, have the potential to negatively impact the quality of web browsing experience for users in Africa. This is particularly the case in edge access networks that are far away from essential infrastructures such as DNS and content servers. In this paper, we analyse the QoE impact of using open public DoH and DoT resolvers when resolving websites that are hosted in Africa versus those hosted offshore. The study further compares the performance of DoT and DoH under different network conditions (mobile, community network, Eduroam and Campus wired network). Our results show that high latency and circuitous DNS resolution paths amplify the performance impact of secure DNS protocols on DNS resolution time and page load time. The study further shows that users’ DNS resolver preferences hugely determine the level of QoE. This study proposes wider adoption of Transport Layer Security version 1.3 (TLSv1.3) to leverage its latency-reduction features such as false start and Zero or One Round Trip Time (0/1-RTT). The study further proposes the localisation of content and secure DNS infrastructure. This, coupled with peering and cache sharing recommended by other works, will further minimise the impact of secure DNS protocols on Quality of Experience.
CMAP-LAP: Configurable Massively Parallel Solver for Lattice Problems

Lattice problems are a class of optimization problems that are notably hard. There are no classical or quantum algorithms known to solve these problems efficiently. Their hardness has made lattices a major cryptographic primitive for post-quantum cryptography. Several different approaches have been used for lattice problems with different computational profiles; some suffer from super-exponential time, and others require exponential space. This motivated us to develop a novel lattice problem solver, CMAP-LAP, based on the clever coordination of different algorithms that run massively in parallel. With our flexible framework, heterogeneous modules run asynchronously in parallel on a large-scale distributed system while exchanging information, which drastically boosts the overall performance. We also implement full checkpoint-and-restart functionality, which is vital to high-dimensional lattice problems. Through numerical experiments with up to 103,680 cores, we evaluated the performance and stability of our system and demonstrated its high capability for future massive-scale experiments.
Multi-word Lexical Units with Repetition of Lexemes in Czech and Identification of Their Variants

This paper is part of the project Between Lexicon and Grammar (2016–2018), supported by the Grant Agency of the Czech Republic, reg. no. 16-07473S. This project is a follow-up of the project entitled The Grammar-Based Treebank of Czech (2013–2015,cf. Skoumalova et al. 2014; Petkevic et al. 2015a, 2015b) and devoted to automatic parsing driven by a formal HPSG-like grammar of Czech.
Information Technology During COVID-19 Pandemic

Information technology is serving us finished the coronavirus pandemic, it is essential to classify what IT includes. Information technology (IT) is a broad field that covers all functions and processes relating to computers or technology in an organisation or business.
PENGEMBANGAN APLIKASI WISATA BERBASIS ANDROID DI KABUPATEN MANDAILING NATAL MENGGUNAKAN METODE PROTOTYPE

In this day and age technology has been very developed, advanced and rapid. Since the discovery of the internet, all information has become very easy to access. With the development of technology and information that is increasingly developing it will be easier and more effective if the data on tourism are combined with technology with sophisticated smartphone devices, utilizing smartphones, especially Android, will help tourists to get information on interesting places to visit. The purpose of this study is to find solutions to overcome the above problems by building an Android-based travel application. The method used in making this application is the Prototyping method. The study was conducted based on the development of applications that have been made by previous researchers. From the results of these testers, Prototyping was perfected until the level of user needs was met.