Reviewerly/exHarmony huggingface dataset:
Evolving Deep Neural Networks for Efficient Image Classification

Image classification or recognition has become base for many real time applications like: object recognition, object tracking, action recognition, drug recovery, video tracking/recognition etc. These applications accuracy depends on the results of image classification system. In literature many researchers have used low level features such as Texture, Gabor, Gradient, SURF and high level features such as CNN for image classification. In recent years, Deep Neural Networks are widely used for image analysis due to its deep feature estimation procedure (i.e. the more no. of features leads to good classification). Thus, this paper proposes three different models based on deep neural networks for efficient Image Classification. The proposed methods are focused on reducing the error rate while increasing the classification accuracy (above 95%).
Pose-guided End-to-end Visual Navigation

Abstract End-to-end visual navigation based on deep reinforcement learning (DRL) has recently attracted much attention. For most existing navigation methods, a robot moves along only fixed directions (e.g., up, down, left and right) on a grid. Obviously, they are not flexible and efficient, which worsens the navigation performance (i.e., the distance of movement and times of rotation). To address this problem, we propose a novel pose-guided end-to-end visual navigation framework, which is flexible and efficient. In the pose-guided navigation framework, a robot can move along arbitrary directions, which are determined by poses between adjacent objects. Further, to select a proper motion and finally form an optimal path, we propose a DRL based action-selected strategy, where a dynamic action select space on the basis of deep siamese actor-critic network is developed. Besides, to validate the proposed method, we propose a novel pose-guided dataset. Experimental results demonstrate that the proposed method outperforms the state of the arts in both flexibility and efficiency.
Controlling Potential Information to Unify Internal Structure for Improved Interpretation and Generalization in Multi-layered Neural Networks

The present paper seems to propose a new type of information-theoretic method to control potential information to be used potentially and possibly for future information processing in neural networks. The new potential information can be substituted for the concept of entropy, because it is much simpler and we can deal with actual network configurations. The potential information can be used to control network configurations directly by controlling the strength of connection weights. Then, we can produce many different types of internal structures for improved generalization as well as interpretation. The new method was applied to a diabetic data set for demonstrating the performance, in particular, for interpretation. The experimental results showed that the new method could control the potential information appropriately by which the method could create the internal structures for improved generalization and interpretation. The results means that we could succeed in unifying internal structures for improved generalization and interpretation.
Secured Tracking and Tracing System Based on Blockchain Technology

Tracking and tracing management is a system which require recording of product's related information associated with product movement, shipping, transition between location until the product reach its final destination. In this management, traceability is a critical element to be satisfied by the business processes. Tracking and tracing of product is important for many purposes from the time product start its order process, prepared, shipping, movement from one delivery stakeholder to other delivery stakeholder until the product reach its destination. This is where we found the effectiveness of technology that is called the blockchain that could increase the safety of all tracking management processes. The blockchain technology since it emerges has contributed to many wide ranges of applications from various fields where safety and trust are critical in the field business process. Through this research, we are willing to present the contribution which can be offered by blockchain that obviously can increase the safety such like other tracking technology such as the use of QR-code, RFID, man-to-man delivery and few others.
Markerless augmented reality: Display Compton scattering model

Abstract Simulations were performed using markerless augmented reality on the concept of Compton scattering. Compton scattering identifies that photons can not only behave as waves but also behave like particles. The process of colliding photons with electrons causes dispersion which shows that the photon has momentum. This article will explain a photon collision simulation to prove the state of light dualism by utilizing augmented reality technology. The development of this simulation was tested using the black-box testing method. Interactive media using augmented reality for Compton scattering simulations can display the concept of collisions between photons and electrons. Our simulation successfully simulates the mechanism of energy release and momentum changes before and after the contact.
Research on the Optimization of Primary School Science Teachers’ Information Technology Literacy under the New Curriculum Standards

With the promulgation of the "Primary School Science Curriculum Standards for Compulsory Education", primary school science education has put forward new requirements for the information technology literacy of primary science teachers. This paper uses interviews as the methods to understand the current status of primary science teachers' information technology literacy and proposes routes to optimize primary science teachers' information technology literacy under practical conditions.
Research and Design of Open Convolutional Neural Network Based on FPGA

Abstract In this paper, using the fluidity and parallelism of FPGA, we design a set of memory scheduling mechanism to implement an open convolutional neural network. The self-built vehicle database and the cropped and zoomed Daimler Pedestrian Detection Benchmark dataset are each trained under different network structures, and their recognition rates reached 97.8% and 98.6%. An open feedforward network is implemented on the FPGA platform, and experiments show that for small convolutional networks with different structures, the FPGA platform can increase its recognition speed when resources permit.
On Teaching Thinking of "Curriculum Logic" for College Students

Thinking ability refers to the ability to process perceptual materials and transform them into the rational understanding and competence in solving problems through analysis, synthesis, generalization, abstraction, comparison, concretization and systematization. Logic plays an important role in the field of thinking, and the relationship between different disciplines and the connection between knowledge in different disciplines are closely connected by logic, so the application and development of the "curriculum logic" of college students is studied in order to explore the cultivation of their thinking ability. Logic does not have class nature in terms of its attributes. The connotation of "curriculum logic" is to give full play to its instrumental role in logic and improve thinking ability, and to extend it to all disciplines in this way so as to improve the thinking ability of college students.
$$\mathbb {K}$$ and KIV: Towards Deductive Verification for Arbitrary Programming Languages

Deductive program verification is a powerful tool to gain confidence in the correctness of software. However, its application to real programs faces a major hurdle: Each programming language requires development of dedicated verification tool support. In this work, we aim to advance deductive software verification to arbitrary programming languages. We developed a tool that derives algebraic specifications for the deductive proof assistant KIV from the syntax and operational semantics of a programming language specified in the \(\mathbb {K}\) semantic framework. We adapt and implement the generic One-Path Reachability calculus and provide instant tool support for deductive proofs. Through a sophisticated automation approach, we drastically reduce the manual proof steps.
A case study: How did IoT start-up Distronix change its business model to sustain growth in the pay-per-use economy

Industrial Internet of Things emerged as one of the major technologies enabling Industry 4.0 for industries. Multiple start-ups started working in the Industrial Internet of Things field to support this new industrial revolution. Distronix, one such Industrial Internet of Things start-up of India, started operations in 2014, when companies were not even aware of Industrial Internet of Things. Distronix started executing fixed-fee projects for implementation of Industrial Internet of Things. They also started manufacturing sensors to support large customers end-to-end in their Industry 4.0 journey. With the advent of public cloud, companies started demanding pay-per-use model for the solution Distronix provided. This posed a major challenge to Distronix as they had developed technology skills focusing fixed-fee customized project delivery for their clients. The situation demanded that they change their business model from individual project delivery to creation of product sand-box with pre-registered sensors and pre-defined visualization layer to support use cases for Industrial Internet of Things implementation in multiple industry sectors. It forced Rohit Sarkar, the 26 years old entrepreneur and owner of Distronix, to upgrade capabilities of his employees and transform the business model to support pay-per-use economy popularized by public cloud providers. The case discusses the challenges Rohit faced to revamp their business model in such an emerging technology field, like, to develop new skills of the technical people to support such novel initiative, reorienting sales people towards pay as use model, developing new concept of plug and play modular product, devising innovative pricing, better alliance strategy and finding out a super early adopter.
Emotion Classification in Russian: Feature Engineering and Analysis

In this paper, we address the issue of identifying emotions in Russian informal text messages. For this purpose, a new large dataset of text messages from the most popular Russian messaging/social networking services (Telegram, VK) was compiled semi-automatically. Emojis contained in the text messages were used to annotate the data for emotions expressed. This paper proposes an integrated approach to text-based emotion classification combining linguistic methods and machine learning. This approach relies on morphological, lexical, and stylistic features of the text. Furthermore, the level of expressiveness was considered as well. As a result, an emotion classification model demonstrating near-human performance was designed. In this paper, we also report on the importance of different linguistic features of the text messages for the task of automatic emotive analysis. Additionally, we perform error analysis and discover ways to improve the model in the future.
Single Image Super Resolution Reconstruction Algorithm Based on Deep Learning

Abstract Super resolution image reconstruction technology is the core technology in image processing, which provides technical support for most of the intelligent devices for target tracking and target detection. However, the existing image acquisition stage will cause extra high cost to improve the image resolution. Optimization algorithm is one of the best ways to solve this kind of problem. At present, there is no substantial breakthrough in this field in China. Therefore, this paper studies single image super-resolution reconstruction algorithm based on deep learning. In this paper, the application of super-resolution reconstruction algorithm in single image is discussed. The analysis shows that the application of deep learning in this field is shallow and has a large optimization space. Especially for the existing main problems, the traditional algorithm is optimized and improved. According to the demand of resolution reconstruction, combined with the latest deep learning method, the accuracy and robustness of the algorithm are further improved. At the same time, it effectively improves the comprehensive performance of the model. In order to further verify the actual performance of the proposed algorithm, the corresponding comparative experiments are established. The experimental results show that the proposed algorithm has obvious advantages over the traditional fsrcnn algorithm, especially when the PSNR of dataset 4 is 2, 3, and 4 times, it can increase 0.41 dB, 0.58 dB and 0.52 DB respectively. Analysis shows that this algorithm has obvious advantages and achieves ideal results.
Voice Controlled Robotic Car

Our paper focuses on development of voice controlled robotic vehicle for unmanned environment for selecting up and dropping an individual. Our goal is to assist the people by assisting them in their daily activities with the assistance of robotic car. The voice controlled provides a convenient and effortless thanks to control the robotic car which may also aid people that cannot walk. Voice controlled robotic system is also valuable in areas where there is high risk for humans to enter. The robotic vehicle operates as per the command received from android device, for this Arduino is integrated in the system.
Computational Resource Allocation Strategy in a Public Blockchain Supported by Edge Computing

Blockchain, as an emerging distributed data management technology, has attracted extensive attention in recent years. In particular, a public blockchain network can ensure data security by addressing computationally intensive cryptographic tasks. Therefore, for node devices, sufficient computing power is required. However, mobile devices with limited computing power do not meet the conditions required by public blockchain network applications (OZEX, CoininAsia, BitRewards, etc.). To copy with the mentioned problems, nodes can offload computing tasks to edge computing services with low latency. This paper mainly focuses on the trade between edge computing providers (ECP) and nodes. We build a computational resource market model based on auction. Meanwhile, we propose two strategies to deal with two methods of offloading to achieve higher system profit. We also prove that the proposed strategy has individual rationality, authenticity under resource constraints. The simulation results have significance for administrators of a public blockchain network to improve the efficiency of computing resource allocation.
Impact of Randomization on Ensembles for Streams with Concept Drift

Abstract In the present era many real world applications are built from streaming data. The distribution of underlying data in such streams tend to change with course of time called as concept drift. A lot of algorithms are proposed in machine learning and data mining domain which are used to handle this drifting scenario. Ensembles form an integral component of such algorithms. In this paper, randomization is added using online bagging to the existing four drift handling approaches and its effect is analysed over multiple patterns of concept drift such as gradual, abrupt, recurring etc. Experimental work has been conducted over artificially generated data streams and real datasets to validate the impact of bagging in learning process.
Performance Evaluation and Improvement of Real-Time Computer Vision Applications for Edge Computing Devices

Advances in deep neural networks have provided a significant improvement in accuracy and speed across a large range of Computer Vision (CV) applications. However, our ability to perform real-time CV on edge devices is severely restricted by their limited computing capabilities. In this paper we employ Vega, a parallel graph-based framework, to study the performance limitations of four heterogeneous edge-computing platforms, while running 12 popular deep learning CV applications.
Vision-Based Analysis for Queue Characteristics and Lane Identification

This paper presents a vision-based approach to lane identification and estimation of service rate, arrival rate, and queue saturation. The method is based on analyzing object trajectories produced. Experiments are demonstrated by applying the proposed method to different traffic scenarios: light, moderate, and heavy. The accuracy of the test is examined by comparing the queue analysis results against the ground truth. Results show that the approach is able to yield satisfactory results when the vehicle movement stays within the lane. However, the error increases when vehicle movement overlaps or switches lanes. In conclusion, the algorithm works to identify the lane membership of trajectories under different conditions. The proposed method could also be used to automate the estimation of traffic congestion levels at sections covered by surveillance cameras.
A survey on single server private information retrieval in a coding theory perspective

Abstract In this paper, we present a new perspective of single server private information retrieval (PIR) schemes by using the notion of linear error-correcting codes. Many of the known single server schemes are based on taking linear combinations between database elements and the query elements. Using the theory of linear codes, we develop a generic framework that formalizes all such PIR schemes. This generic framework provides an appropriate setup to analyze the security of such PIR schemes. In fact, we describe some known PIR schemes with respect to this code-based framework, and present the weaknesses of the broken PIR schemes in a unified point of view.
The Construction of Educational Model for Business English Programme Based on Discipline Core Competencies

Core competency has become the keynote of curriculum reform in education field both at home and abroad. The integration of core competency with various disciplines has become increasingly developing. In order to meet the needs of social development and employers for Business English professionals, this paper adopts theoretical deduction method to summarize and refine the educational model of Business English discipline based on the core competency. The educational model constructed in this study, to some extent, is the first time to integrate core competency with Business English discipline, which has a certain innovative significance in the development of Business English programme. Hopefully, this educational model can provide a new pathway for the teaching reform of Business English discipline.
Contour Method for Ultrasound Images

Medical ultrasonography is widely used because it is a safe, non-invasive and non-ionizing diagnosis method. For these reasons, medical ultrasound is recommended for monitoring parts of body. This paper proposes a new contour methodology for ultrasound images, based on the Dijkstra algorithm. It will is used to detect structures or boundaries regions. It belongs to Live wire segmentation techniques group, for extracting quickly and accurately regions of interests in images. This can be done through a set of seed points, generated by an automatic process or selected manually by the user. Experimental results show that this method is robust and efficient in segmentation of fetal ultrasound images. It is less sensible to noise and, shadows and to imprecise boundary regions.
Development of Student Community at Gorky State Pedagogical Institute named after A. M. Gorky in the 1960s

The purpose of the study is to determine the features of the student community’s development at one of the oldest Russian pedagogical universities in the 1960s during scientific and technological revolution. The article examines the main aspects of student life at that time, studies contribution of students of Gorky State Pedagogical Institute named after A. M. Gorky (GSPI named after A. M. Gorky) to social life development in the country. The study is novel in that it introduces into scientific use new historical sources, including documents stored in Nizhny Novgorod’s archives - the State Socio-Political Archives of Nizhny Novgorod Region and the Central Archives of Nizhny Novgorod Region. As a result, the author reconstructed the history of the student community at GSPI named after A. M. Gorky in the 1960s, which is an important step in studying the history of the Soviet student community.
Mutual Attention Learning Network for Domain Adaptive Person Re-identification

Abstract Owing to its important role in video surveillance, person re-identification(re-ID) has become a hot research field of computer vision. The purpose of person re-ID is to identify the same person under different cameras. Domain adaptive person re-ID is to transfer the training model from labeled dataset to unlabeled dataset. Due to the neglect of key channels and key regions, the performance of most existing domain adaptive person re-ID methods is limited. To solve the above problem, this paper proposed a mutual attention learning network model (MALNet). It is based on channel attention and spatial attention mechanism. It consists of two channel-spatial attention convolutional neural networks (channel-spatial attention CNNs). Two networks are pre-trained in a source domain. They adapt to the target domain by the mutual attention learning strategy afterward. They distill and supervise each other to enhance the complementarity of MAL-Net. Moreover, a channel-spatial attention loss (CSAL) function is proposed to constrain and approximate the classification prediction distributions and feature triplet distributions. Extensive comparative experiments are carried out on two large-scale re-ID datasets (Market-1501 and DukeMTMC-reID). The performance of MAL-Net is better than other state-of-the-art methods, which proves the superiority and effectiveness of our method.
Modeling of Operational Aspects for the Autonomous Vehicle Development

The development of systems with highly automated or autonomous functions leads to high demands on the consideration of dependencies to external actors. In particular, the utilization phase of the systems must be considered closely, since the dynamic, operative context manifests itself only within utilization. Additionally, when considering involved sensors and actors for the utilization of functions of Advanced Driver Assistance Systems (ADAS) for commercial vehicles, a precise and clear communication between different stakeholders in vehicle development is essential. In Model-Based Systems Engineering (MBSE), this can be achieved by modeling so-called operational aspects. Operational aspects are model elements and their relationships to each other, which are represented in suitable views. In this contribution, modeling of operational aspects will be demonstrated exemplary on a case study related to the development of an Evasive Maneuver Assistant (EMA) for vehicles. Currently, modeling operational aspects is not common in the civil sector and therefore not always considered in the development phase of commercial vehicles.