Reviewerly/exHarmony huggingface dataset:
Words as a window: Using word embeddings to explore the learned representations of Convolutional Neural Networks

As deep neural net architectures minimize loss, they accumulate information in a hierarchy of learned representations that ultimately serve the network’s final goal. Different architectures tackle this problem in slightly different ways, but all create intermediate representational spaces built to inform their final prediction. Here we show that very different neural networks trained on two very different tasks build knowledge representations that display similar underlying patterns. Namely, we show that the representational spaces of several distributional semantic models bear a remarkable resemblance to several Convolutional Neural Network (CNN) architectures (trained for image classification). We use this information to explore the network behavior of CNNs (1) in pretrained models, (2) during training, and (3) during adversarial attacks. We use these findings to motivate several applications aimed at improving future research on CNNs. Our work illustrates the power of using one model to explore another, gives new insights into the function of CNN models, and provides a framework for others to perform similar analyses when developing new architectures. We show that one neural network model can provide a window into understanding another.
Introduction to Quantum Computation Reliability

An overview of the quantum computing paradigm with a focus on reliability is provided in a tutorial form intended for practitioners and researchers in the test and reliability community. It is assumed that readers have little prior knowledge of quantum informatics. An introductory description of the mathematical models of a qubit and quantum information processing operations and projective measurement is presented as background. Discussions of quantum error sources and associated fault models are included using the concepts and notation explained in the background section. Topics related to the decoherence problem are also included. The concept of a logical qubit and how quantum error detection and correction can be applied to enhance reliability of quantum computations is formulated using the notions of classical fault models and error detection and correction techniques. The general concept of quantum error detection and correction as applied to enhancing quantum computational reliability is discussed including an example of one of the first such methods originally introduced in 1995.
Private Cloud for Data Storing and Maintain Integrity Using Raspberry Pi

A cloud is a virtual platform that lets you work efficiently. When it comes to deep digging into the cloud, it contains a physical data center which is attached at its backend where no one looks about it. As per the scenario, the concept of storage class can be built from any physical storage device when it combined with powerful microprocessors to form a cloud storage device. Cloud storage is of different levels, like standard level, business level, and enterprise level. We plan to build a standard-level storage class from a microprocessor, where these components are not suitable for higher interaction rates. A storage class depends upon speed, agility, performance which is high in enterprise level. As per the planning of concept, we tend to provide a standard storage class with a combination of a microprocessor to handle all types of storage acquisition from a prototype level. A microprocessor like Raspberry Pi or any other can perform the same type of complex operations, and Raspberry Pi lets you work in a feasible manner where it can be used as a computer with low cost. When it comes to the cloud maintenance level, it handles with insane capabilities and best performance with low latency. Own cloud runs efficiently when it bound with Raspberry Pi and a physical storage device. When these are confined properly, a standard storage class with a good performance level will be the output, where some couple of users can easily work with it. All these operations will smoothly work in local host. When it comes to accessing from a different location, it needs to be Port Forwarded. This lets you access from anywhere around the Globe.
Input Data Characterization Using Machine Learning and Deep Learning

Abstract Seeing the current trends in Information technology, a large volume of heterogeneous data is produced widely across the world by means of social media sites such as Facebook, Instagram, Google plus, etc. and electronic gadgets used by humans for instance sensors. The generated data act as garbage and makes no sense until they are categorized. A strong need of data analytics or data filtering came into existence to filter the data into concrete categories. The generated data contain enormous features with huge dimensions. The high dimension data must be reduced to low dimension data to avoid curse of dimensionality and to build a better machine learning (ML) model. A ML model is built to perform the classification task that result into labeling of the data. In the first phase of work, a framework has proposed to process the data having features belonging to text and images using ML and DL algorithms. In the second phase of work, we have shown data related behavior using ML and DL framework. The proposed work is generic in sense of FS techniques and FE techniques using ML classifiers and DL classifiers. After carefully implementing the algorithms on the datasets we have evaluated the results taking accuracy as our performance metric. After carefully analyzing the accuracy it can be concluded that DL algorithms give better results than ML algorithms on both text and image dataset.
Socio-communication Problems of Distance Education

Russian higher education, under the influence of globalization and the need for integration into the global scientific community, is faced with such problems as preservation quality of information flows (fundamental education), training a creative specialist, and the problem of an effective organizational form of education. Distance education as an opportunity to gain knowledge has existed for a very long time and there has always been a dismissive distrust of them in universities. However, directly or indirectly, the crisis caused stakeholders of education, as the most massive part of the world's population, to the necessity of making and testing distance forms of learning and communication to the full extent. The study analyzed the opinions of students about the possibilities and limitations of online learning, the degree of trust in the learning outcomes, and the quality and applicability of gain knowledge. The sociopsychological conditions for completing educational programs are also evaluated.
Evaluation of The Application of Hospital Management Information System (SIMRS) in RSUD Dr. Kanujoso Djatiwibowo Using The HOT-Fit Method

Abstract Hospital Management Information System (SIMRS) is a system that can process and integrate the entire hospital service process flow in the form of a network of coordination, reporting and administrative procedures to obtain accurate data and information. At the RSUD Dr. Kanujoso Djatiwibowo (RSKD) of Balikpapan City, the application of SIMRS experienced problems such as the high number of troubleshooting cases and the redundancy of patient medical record numbers. This case is considered to inhibit the performance of hospital services related to efficiency. Based on this problem, the evaluation of SIMRS in the RSKD was carried out by using the constructed Human, Organization, Technology-Fit (HOT-Fit) model. This model involves eight variables which consists of System Quality, Information Quality, and Service Quality, System Use, User Satisfaction, Structure, and Environment, and Net Benefits. The results indicate that the proposed model either directly or indirectly affects the benefits obtained from implementing the SIMRS. Further implications of the results toward improvement of the system are provided.
Challenges in Smart Healthcare for Physical Rehabilitation

In the current medical practice, personal physical physiotherapy is expensive, the therapist’s time of service is limited, and training equipment is available in a clinical environment. So, the need to provide low-cost and convenient smart health assistance services are growing tremendously. ePHoRt is a web-based platform created for helping patients after hip replacement surgery. We discuss and present the techniques, methods, and strategies used throughout the execution of the project.
Improved BP Neural Network to Predict the Comprehensive Performance of Concrete

In order to improve the feasibility and accuracy of the concrete comprehensive performance prediction model, the factor analysis method and the BP neural network method are combined to propose an improved BP neural network concrete comprehensive performance prediction model. Example data was selected to test the improved BP neural network concrete comprehensive performance prediction model. The test results: the relative average errors of the 28d strength and slump expansion of the 14 groups of training sample predicted values and actual values were 3.398% and 1.712%, respectively. The average relative errors of 28d strength and slump expansion of each predicted sample are 4.33%, 3.80%, 5.77%, 2.07%, 1.02% and 3.55%, 0.94%, 1.48%, 0.59%, 2.00%, all of which are less than 10%, Which proves that the improved BP neural network prediction model has better prediction accuracy.
Conversational Agents for Complex Collaborative Tasks

Dialogue is a very active area of research currently, both in developing new computational techniques for robust dialogue systems and in the active fielding of commercial conversational assistants such as Apple's Siri and Amazon's Alexa. This article argues that, while current techniques can be used to design effective dialogue‐based systems for very simple tasks, they are unlikely to generalize to conversational interfaces that enhance human ability to solve complex tasks by interacting with artificial intelligence reasoning and modeling systems. We explore some of the challenges of tackling such complex tasks and describe a dialogue model designed to meet these challenges. We illustrate our approach with examples of several implemented systems that use this framework.
Empowering Citizen-Environment Interaction vs. Importunate Computer-Dominated Interaction: Let’s Reset the Priorities!

This paper starts out by extending the scope of traditional human-computer interaction research towards human-environment interaction and in the context of smart cities to citizen-environment interaction. This is complemented by a critical evaluation of technology-driven approaches in the development and deployment of smart environments, especially for smart cities. It argues to reset the priorities by putting humans and citizens first and computer technologies with non-transparent AI-based mechanisms second in contrast to current developments resulting in increased automation in all kinds of applications. The evaluation of this ‘smart-everything paradigm’ implies to reflect and to redefine the basic assumptions. Based on the concept of ‘human-technology symbiosis’, the paper discusses several design trade-offs as, e.g., ‘human control and empowerment vs. automated and autonomous systems’ and ‘usable privacy vs. importunate smartness’. The resulting proposal for urban environments is to move beyond ‘smart-only’ cities and to establish the goal of humane, sociable, and cooperative hybrid cities, reconciling people and technology by providing a balance between human control and automation as well as privacy and smartness. This can be achieved by viewing the city and its citizens as mutual cooperation partners, where a city is ‘smart’ in the sense of being ‘self-aware’ and ‘cooperative’ towards its citizens by supporting them in their activities. The goal is to enable citizens to exploit their individual, creative, social, and economic potential and to live a self-determined life, thus meeting some of the challenges of the urban age by enabling people to experience and enjoy a satisfying life and work.
Towards formally verified compilation of tag-based policy enforcement

Hardware-assisted reference monitoring is receiving increasing attention as a way to improve the security of existing software. One example is the PIPE architecture extension, which attaches metadata tags to register and memory values and executes tag-based rules at each machine instruction to enforce a software-defined security policy. To use PIPE effectively, engineers should be able to write security policies in terms of source-level concepts like functions, local variables, and structured control operators, which are not visible at machine level. It is the job of the compiler to generate PIPE-aware machine code that enforces these source-level policies. The compiler thus becomes part of the monitored system’s trusted computing base---and hence a prime candidate for verification.
Research on the Application of Vocabulary Relation Extractio n Method of Demand Entity Based on Bi-GRU

Abstract Designing and mining the entity vocabulary relationship of requirements engineering to form a knowledge base of requirements engineering has a positive effect on providing the intelligent service of demand and software development in the future. But there are very few targeted extractions of the relationship between demand vocabulary in the current demand field. Therefore, this paper proposes a demand entity vocabulary relationship extraction model consists of the Bi-GRU, the attention mechanism and the labeling and filtering layer of the demand entity. This paper discribes how to use the data from actual projects to verify the methods above. The experimental results show that the method of relationship extraction combining the attention mechanism and the labeling and filtering layer of the demand entity in the Bi-GRU neural network has a significant effect in the demand vocabulary relationship extraction.
Smart Surveillance and Tracking System

This paper presents an intelligent system that can identify and recognize faces with applications, including a person's tracking, home surveillance, and private security. An automatic face recognition intrinsically handles security issues with flexibility - in the case of an unrecognized or unknown person, the real-time video stream is processed, motion is detected, and dual-axis pan-tilt servos track that person with a camera. Furthermore, such peculiar activities are video recorded with synchronization from cloud storage, and mobile alerts are generated. In the absence of the internet, a database file is developed with an audio notification to the security room if the anonymous face detected is not present in the database. Speech recognition and relay are also added for voice transmission and activation of surrounding lights. This project intends to substitute costly security systems using Raspberry pi 3B+ as the microcomputer.
Automatic Term Extraction on Turkish Scientific Texts

In order for a text or collection to be understood, it is very important to understand the terms contained in it. In this study, it is aimed to detect terms in a domain-specific (Cyber Security) corpus. A two-layer method is suggested for the determination of the terms used in single words or phrases. Term candidate words are determined by statistical methods in the first layer. In the second layer, the possibility of using these words in phrases with semantic approaches is checked. In the study, Word2Vec approach was used to determine semantic affinity and 3 different datasets were used. The results show that the terms used in singular or binary patterns were successfully determined using the proposed method.
Increasing the Confidence of Deep Neural Networks by Coverage Analysis

The great performance of machine learning algorithms and deep neural networks in several perception and control tasks is pushing the industry to adopt such technologies in safety-critical applications, as autonomous robots and self-driving vehicles. At present, however, several issues need to be solved to make deep learning methods more trustworthy, predictable, safe, and secure against adversarial attacks. Although several methods have been proposed to improve the trustworthiness of deep neural networks, most of them are tailored for specific classes of adversarial examples, hence failing to detect other corner cases or unsafe inputs that heavily deviate from the training samples. This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model robustness against different unsafe inputs. In particular, four coverage analysis methods are proposed and tested in the architecture for evaluating multiple detection logic. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs, introducing limited extra-execution time and memory requirements.
SumBot: Summarize Videos Like a Human

Video currently accounts for 70% of all internet traffic and this number is expected to continue to grow. Each minute, more than 500 hours worth of videos are uploaded on YouTube. Generating engaging short videos out of the raw captured content is often a time-consuming and cumbersome activity for content creators. Existing ML- based video summarization and highlight generation approaches often neglect the fact that many summarization tasks require specific domain knowledge of the video content, and that human editors often follow a semistructured template when creating the summary (e.g. to create the highlights for a sport event). We therefore address in this paper the challenge of creating domain-specific summaries, by actively leveraging this editorial template. Particularly, we present an Inverse Reinforcement Learning (IRL)-based framework that can automatically learn the hidden structure or template followed by a human expert when generating a video summary for a specific domain. Particularly, we propose to formulate the video summarization task as a Markov Decision Process, where each state is a combination of the features of the video shots added to the summary, and the possible actions are to include/remove a shot from the summary or leave it as is. Using a set of domain-specific human-generated video highlights as examples, we employ a Maximum Entropy IRL algorithm to learn the implicit reward function governing the summary generation process. The learned reward function is then used to train an RL-agent that can produce video summaries for a specific domain, closely resembling what a human expert would create. Learning from expert demonstrations allows our approach to be applicable to any domain or editorial styles. To demonstrate the superior performance of our approach, we employ it to the task of soccer games highlight generation and show that it outperforms other state-of-the-art methods, both quantitatively and qualitatively.
Exploring Energy-Accuracy Tradeoffs in AI Hardware

Artificial intelligence (AI) is playing an increasingly significant role in our everyday lives. This trend is expected to continue, especially with recent pushes to move more AI to the edge. However, one of the biggest challenges associated with AI on edge devices (mobile phones, unmanned vehicles, sensors, etc.) is their associated size, weight, and power constraints. In this work, we consider the scenario where an AI system may need to operate at less-than-maximum accuracy in order to meet application-dependent energy requirements. We propose a simple function that divides the cost of using an AI system into the cost of the decision making process and the cost of decision execution. For simple binary decision problems with convolutional neural networks, it is shown that minimizing the cost corresponds to using fewer than the maximum number of resources (e.g. convolutional neural network layers and filters). Finally, it is shown that the cost associated with energy can be significantly reduced by leveraging high-confidence predictions made in lower-level layers of the network.
On the Knowledge Graphs of Postgraduate Entrance English Examination Based on WordNet and D3.js

To reveal visually the semantic composition of high-frequency words in postgraduate entrance English examination for the students to have better understanding and memorization of the words in the examination outline and the high-frequency words, this paper presents the network structure of these high-frequency words via visualized knowledge graphs and the network structure of the words in the examination outline in WordNet, optimizes further WordNet to draw a concise and lucid semantic network by means of such corpus sources as WordNet (a semantic dictionary), D3.js (a visualized library) and British National Corpus. In the meantime, the paper gives consideration to the time-effectiveness of the content and the examination outline and adopts the test papers of postgraduate entrance English (1) examination during 2015-2020. In addition, a secondary function including the retrieval of long and difficult sentences for the quicker learning of the key content of the examination by the students is derived from the main function of the semantic visualization of the words.
A Comparative study of machine learning models for breast cancer prediction

Abstract The enormous growth in the medical technologies and the availability of clinical data has motivated researchers to progress much towards predictive analytics. Integrating machine learning techniques to healthcare domain has a progressive outcome called Computer Aided Diagnosis. A comparative analysis is presented to study the suitability of machine learning algorithms for benchmark breast cancer data. Statistical non-parametric evaluation is also carried out to indicate the integrity of the framework.
Research on Driver's Gaze Zone Estimation Based on Transfer Learning

With the increasing importance of gaze tracking technology in the detection of driver's attention and fatigue, the estimation of the driver's gaze zone has become a key point and a hot issue for safe driving. Although there has been significant improvement in the research of the driver's gaze area at present, a general gaze zone estimation system for different subjects and perspectives is still lacking. In order to better accomplish this task, this paper uses the “Columbia Gaze Data Set” that contains 56 subjects with different gaze directions and head poses to estimate the driver's gaze zone. After pre-processing the pictures such as normalization and data augmentation, the image dataset is modeled and predicted using the improved VGG16 convolutional neural network structure constructed by Keras, and the 5 head poses and 21 gaze points of each head pose in the dataset are reclassified and divided into 8 gaze areas, corresponding to the gaze zone of the driver.
Digital Transformation of Education: Technology Strengthens Creative Methodological Productions in Master's Programs in Education

Teachers who enter postgraduate training programs in the field of education get prepared for the use of technology to create better conditions for learning. The objective of this work is to identify the potentialities of research and pedagogical training in the use of technologies as a form of professionalization of teachers in Postgraduate Programs in Education. A bibliometric analysis of the degree works reports on the high involvement of postgraduate teaching staff in creative methodological productions, using different technologies to bring children, adolescents and young people closer to learning, but also on the differences in their application in the face of multiple learning needs and knowledge areas. In future degrees, it is necessary to move towards the training of teachers in the applications of artificial intelligence in the educational field.