Reviewerly/exHarmony huggingface dataset:
MiniVLM: A Smaller and Faster Vision-Language Model

Recent vision-language (VL) studies have shown remarkable progress by learning generic representations from massive image-text pairs with transformer models and then fine-tuning on downstream VL tasks. While existing research has been focused on achieving high accuracy with large pre-trained models, building a lightweight model is of great value in practice but is less explored. In this paper, we propose a smaller and faster VL model, MiniVLM, which can be finetuned with good performance on various downstream tasks like its larger counterpart. MiniVLM consists of two modules, a vision feature extractor and a transformer-based vision-language fusion module. We design a Two-stage Efficient feature Extractor (TEE), inspired by the one-stage EfficientDet network, to significantly reduce the time cost of visual feature extraction by $95\%$, compared to a baseline model. We adopt the MiniLM structure to reduce the computation cost of the transformer module after comparing different compact BERT models. In addition, we improve the MiniVLM pre-training by adding $7M$ Open Images data, which are pseudo-labeled by a state-of-the-art captioning model. We also pre-train with high-quality image tags obtained from a strong tagging model to enhance cross-modality alignment. The large models are used offline without adding any overhead in fine-tuning and inference. With the above design choices, our MiniVLM reduces the model size by $73\%$ and the inference time cost by $94\%$ while being able to retain $94-97\%$ of the accuracy on multiple VL tasks. We hope that MiniVLM helps ease the use of the state-of-the-art VL research for on-the-edge applications.
Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.
Constructive t-secure Homomorphic Secret Sharing for Low Degree Polynomials

This paper proposes t-secure homomorphic secret sharing schemes for low degree polynomials. Homomorphic secret sharing is a cryptographic technique to outsource the computation to a set of servers while restricting some subsets of servers from learning the secret inputs. Prior to our work, at Asiacrypt 2018, Lai, Malavolta, and Schröder proposed a 1-secure scheme for computing polynomial functions. They also alluded to t-secure schemes without giving explicit constructions; constructing such schemes would require solving set cover problems, which are generally NP-hard. Moreover, the resulting implicit schemes would require a large number of servers. In this paper, we provide a constructive solution for threshold-t structures by combining homomorphic encryption with the classic secret sharing scheme for general access structure by Ito, Saito, and Nishizeki. Our scheme also quantitatively improves the number of required servers from $$O(t^2)$$ to O(t), compared to the implicit scheme of Lai et al. We also suggest several ideas for future research directions.
Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy. Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.
Docker-C2A : Cost-Aware Autoscaler of Docker Containers for Microservices-based Applications

This article proposes a cost-aware autoscaler for microservices-based applications deployed with docker containers.This autoscaler decreases the cost of the application deployment as it reduces computing resources.In elastic treatment, microservice resources are scaled when the used metric as the central processing unit (CPU) exceeds the threshold.In case of threshold exceeding, an autoscaler adds many instances of docker containers in order to satisfy the need of the application.In many studies, the autoscaler adds many containers without selecting the appropriate microservices for scaling and without in advance calculation of the adequate number of containers.This may lead to allocating additional resources to inappropriate microservices and a non optimal number of containers.For this reason, we propose our autoscaler "Docker-C2A" which identifies the adequate microservices to add resources.It also calculates the optimal number of needed containers."Docker-C2A" analyses the state of the application, uses the execution history and uses a Particle Swarm Optimization (PSO) algorithm to identify the adequate microservices for scaling resources and to determine the optimal number of containers.As a result, "Docker-C2A" helps to reduce computing resources and to save extra costs.Experimental measurements were conducted on a microservices-based application as a concrete use-case demonstrating the effectiveness of our proposed solution.
INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) MEDIATED EXTENSION SERVICES IN AGRICULTURE IN NEPAL- A REVIEW

This study is conducted to know Information and Communication Technology (ICT) mediated extension services in agriculture in Nepal. In this report, different ICT tools that are used for extension of agriculture in Nepal, organizations and divisions working for an extension, extension system of country, benefits and advantages from the use of ICT tools, constraints in the use of ICT tools in agriculture extension and their solutions are included. This report is prepared by studying different journal articles, reports, books and newspapers. Among ICT tools mobile phone is the most used ICT tools by farmers and laptop is the most used ICT tools used by the organizations for data processing. Radio and television are more popular in rural areas. This study shows that most of the ICT users are confined in urban areas. There are many advantages of the use of ICT tools in the field of agriculture. ICT tools can cover a large number of the population with high speed. ICT tools increase productivity, sustainability and efficiency of farms by providing relevant information timely and more easily. ICT tools provide information about quality seeds, transportation, marketing, new technologies and weather condition that facilitate the farmers for making the right decision. There are many agriculture-related mobile applications available at Google play store. These mobile apps give knowledge about cropping methods, crop management practices, crop protection, the market price of inputs and outputs etc. These mobile apps are managed in Nepali language for facilitating the Nepali farmers. Agriculture-related programs are broadcasted through national television channels and FM radio stations. There are technical, socio-economic, institutional and infrastructural constraints that limit the use of ICTs in agriculture fields.
Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework

We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial structures to evaluate, in order to identify high-performing structures as quickly as possible. Our main contribution is to introduce and evaluate a new learning-to-search framework for this problem called L2S-DISCO. The key insight is to employ search procedures guided by control knowledge at each step to select the next structure and to improve the control knowledge as new function evaluations are observed. We provide a concrete instantiation of L2S-DISCO for local search procedure and empirically evaluate it on diverse real-world benchmarks. Results show the efficacy of L2S-DISCO over state-of-the-art algorithms in solving complex optimization problems.
Applying Machine Learning Techniques for Performing Comparative Opinion Mining

Abstract In recent times, comparative opinion mining applications have attracted both individuals and business organizations to compare the strengths and weakness of products. Prior works on comparative opinion mining have focused on applying a single classifier, limited comparative opinion labels, and limited dataset of product reviews, resulting in degraded performance for classifying comparative reviews. In this work, we perform multi-class comparative opinion mining by applying multiple machine learning classifiers using an increased number of comparative opinion labels (9 classes) on 4 datasets of comparative product reviews. The experimental results show that Random Forest classifier has outperformed the comparing algorithms in terms of improved accuracy, precision, recall and f-measure.
EWNStream+: Effective and Real-time Clustering of Short Text Streams Using Evolutionary Word Relation Network

The real-time clustering of short text streams has various applications, such as event tracking, text summarization and sentimental analysis. However, accurately and efficiently clustering short text streams is challenging due to the sparsity problem (i.e., the limited information comprised in a single short text document leads to high-dimensional and sparse vectors when we represent short texts using traditional vector space models), topic drift and the fast generated text streams. In this paper, we provide an effective and real-time Evolutionary Word relation Network for short text streams clustering (EWNStream[Formula: see text]) method. The EWNStream[Formula: see text] method constructs a bi-weighted word relation network using the aggregated term frequencies and term co-occurrence statistics at corpus level to overcome the sparsity problem and topic drift of short texts. Better still, as the query window in the stream shifts to the newly arriving data, EWNStream[Formula: see text] is capable of incrementally updating the word relation network by incorporating new word statistics and decaying the old ones to naturally capture the underlying topic drift in the data streams and reduce the size of the network. The experimental results on a real-world dataset show that EWNStream[Formula: see text] can achieve better clustering accuracy and time efficiency than several counterpart methods.
A Distributed Framework of Autonomous Drones for Planning and Execution of Relief Operations during Flood Situations

Every year, flood hits the world economy by billions of dollars, costs thousands of human and animal lives, destroys a vast area of land and crops, and displaces large populations from their homes.The flood affected require a time-critical help, and a delay may cause the loss of precious human lives.The ground rescue operations are difficult to carry out because of the unavailability of transport infrastructure.However, drones, Unmanned Vehicles, can easily navigate to the areas where road networks have been destroyed or become ineffective.The fleet participating in the rescue operation should have drones with different capabilities in order to make the efforts more successful.A majority of existing systems in the literature offered a centralized system for these drones.However, the performance of the existing system starts decreasing as the required number of tasks increases.This research is based on the hypothesis that a distributed intelligent method is more effective than the centralized technique for relief operations performed by multiple drones.The research aims to propose a distributed method that allows a fleet of drones with diverse capabilities to communicate and collaborate, so that the task completion rate of rescue operations could be increased.The proposed solution consists of three main modules: 1) communication and message transmission module that enables collaboration between drones, 2) realignment module that allows drones to negotiate and occupy the best position in the air to optimize the coverage area, 3) situation monitoring module that identifies the ground situation and acts accordingly.To validate the proposed solution, we have performed a simulation using AirSim simulator and compared the results with the centralized system.The proposed distributed method performed better than legacy systems.In the future, the work can be extended using reinforcement learning and other intelligent algorithms.
Overview of the Importance of Corporate Security in Business

This article gives an overview of the importance of corporate security in today’s business. Information security has stood out as paramount importance to organizations. The security of any system and people continues to remain a top priority for the company. In every organization or business, the employees can possibly represent their organizations to enormous amounts of cyber risks. Every employee in the organization have accountability to remain vigilant and properly respond to security threats as they arise. In today’s busy World, every corporate security is focusing on ensuring that they secure their customers, facilities, systems and ensure that they are designing security into their client solutions. Few key initiatives include collaborating with the IT organization on foundational projects such as securing remote access, transforming the identity and access administration services and improving the identification and remediation of vulnerabilities. As we seek to protect the company’s brand name and the client’s trust, it is vitally important for every organization to consider security a key responsibility. When organizations choose to disrespect security policies & procedures, the organization is at danger. Through the organizational control lens, tried to explain the corporate security precaution-taking behavior. In this article, we will discuss how the security attack happens and how can it be prevented, data security and cybersecurity.
Are requirements elicitation sessions influenced by participants' gender? An empirical experiment

Requirements elicitation is a crucial phase in the software development life cycle. During requirements elicitation sessions, requirements engineers capture software requirements, and motivate stakeholders to express needs and expected software functionalities. In this context, there is a lack of extensive empirical research reporting the extent to which elicitation sessions can be influenced by participants' gender. This paper presents our research endeavour to investigate requirements engineers' effort and elicited requirements' accuracy based on participants' gender. We conducted an experiment in two rounds with a total of 59 students who played the role of requirements engineers. In the first experimental task, the participant watched two videos where men and women stakeholders expressed software requirements. Later on, the participants specified software requirements in the shape of Business Process Model and Notation (BPMN) and next they generated Graphical User Interfaces (GUIs) from those models. We observed two significant differences. One between men and women requirements engineers in terms of dedicated effort during requirements specification: men took less effort. Other between stakeholders' gender in terms of accuracy resulted of BPMN models: models built from men stakeholders yield more accuracy. On the contrary, accuracy of resulted GUIs models did not show significant differences regarding requirements engineers or stakeholders' gender. Analysing descriptive data, women spent more time both as stakeholders and as requirements engineers but their accuracy is better.
Information flow and error scaling for fully quantum control

The optimally designed control of quantum systems is playing an increasingly important role to engineer novel and more efficient quantum technologies. Here, in the scenario represented by controlling an arbitrary quantum system via the interaction with an another optimally initialized auxiliary quantum system, we show that the quantum channel capacity sets the scaling behavior of the optimal control error. Specifically, by fitting the model to numerical data, we verify that the minimum control error is ensured by maximizing the quantum capacity of the channel mapping the initial control state into the target state of the controlled system, i.e., optimizing the quantum information flow from the controller to the system to be controlled. Analytical results, supported by numerical evidences, are provided when the systems and the controller are either qubits or single Bosonic modes.
Quantum Key Distribution Over Quantum Repeaters with Encoding: Using Error Detection as an Effective Postselection Tool

We propose a post-selection technique, based on quantum error detection, for quantum key distribution (QKD) systems that run over quantum repeaters with encoding. In such repeaters, quantum error correction techniques are used for entanglement distillation. By developing an analytical approach to study such quantum repeaters, we show that, in the context of QKD, it is often more efficient to use the error detection, rather than the error correction, capability of the underlying code to sift out cases where an error has been detected. We implement our technique for three-qubit repetition codes by modelling different sources of error in crucial components of the system. We then investigate in detail the impact of such imperfections on the secret key generation rate of the QKD system, and how one can use the information obtained during entanglement swapping and decoding stages to maximize the rate. For benchmarking purposes, we specify the maximum allowed error rates in different components of the setup below which positive key rates can be obtained.
Transformation of Pedagogical Communicative Competence during Creation Digital Online Courses

The purpose of this work is to identify the transformations of communicative functions. These functions are associated with the subjects of communication in both fields: in pedagogical and organizational, also in the instrumental and design once during the process of creating digital video recordings for online courses. Questionnaires, observation, and factor analysis were used as research methods. It helped to formulate a popularity rating of digital tools for future teachers. The study was conducted at the Institute of Psychology and Education of Kazan (Volga region) Federal University. The study involved 120 people: teachers developing online courses and students studying at the pedagogical educational programs. Transforming indicators of pedagogical communicative competence are highlighted. These indicators are needed to be emphasized when improving teacher training. It will help future teachers to be able to effectively carry out their activities in digital educational environment. These indicators are: the ability to organize interactive cooperation during training, the ability to organize mutual exchange and development of students, the ability to create an open environment for analysis and improvement. The methodological aspects of the development of pedagogical communication functions in the dynamic environment of digital education and an interactive environment are disclosed on the basis of work with videos. Some recommendations for future teachers’ skills improving and development of training experience organization in the digital environment are given. These are a clearly-set-goal for educational video materials, adequate video content, skills for digital tools using, also the skill to choose adequate digital tools for a task fulfillment. The tool that can enhance enthusiasm, and stimulate students for productive work in a digital environment. The study allows improving video content and increasing the effectiveness of its use in the educational process. Also, the results of the experiment can become a basis for teacher training programs improving. Especially it can be useful for future teachers’ pedagogical communicative and digital competences development. The tasks developed within the frame of the research will be included in the assignments of pedagogical internship for pedagogical Master’s degree courses at the Institute of Psychology and Education of Kazan Federal University. These tasks for students include development and use of online courses with video materials.
Model Extraction Attacks on Recurrent Neural Networks

Model extraction attacks are an attack in which an adversary utilizes a query access to the target model to obtain a new model whose performance is equivalent to the target model efficiently, i.e., fewer datasets and computational resources than those of the target model. Existing works have dealt with only simple deep neural networks (DNNs), e.g., only three layers, as targets of model extraction attacks, and hence are not aware of the effectiveness of recurrent neural networks (RNNs) in dealing with time-series data. In this work, we shed light on the threats of model extraction attacks on RNNs. We discuss whether a model with a higher accuracy can be extracted with a simple RNN from a long short-term memory (LSTM), which is a more complicated and powerful type of RNN. Specifically, we tackle the following problems. First, in case of a classification task, such as image recognition, extraction of an RNN model without final outputs from an LSTM model is presented by utilizing outputs halfway through the sequence. Next, in case of a regression task such as weather forecasting, a new attack by newly configuring a loss function is presented. We conduct experiments on our model extraction attacks on an RNN and an LSTM trained with publicly available academic datasets. We then show that a model with a higher accuracy can be extracted efficiently, especially through configuring a loss function and a more complex architecture different from the target model.