Reviewerly/exHarmony huggingface dataset:
The expense of neuro-morpho functional machines

An unsolved problem in both natural and artificial evolutionary systems is determining the exact environmental and evolutionary conditions that enable complexity to evolve [16]. This is especially pertinent in evolutionary robotics [4] where possible problem-solving behavior is constrained by brain (controller) and body (morphology) complexity [12]. We evaluate the impact of environments and complexity costs on robotic controller and morphology evolution across various evolutionary robotics task scenarios. This study uses evolutionary robotics as an experimental platform to investigate the arrow of complexity hypothesis [3], previously demonstrated to hold in artificial evolutionary systems given an imposed complexity cost [2]. Specifically, we test whether energy costs imposed on evolving robot controller and morphology complexity enables the evolution of increasingly complex controller and morphological designs concomitant with increasing environment complexity. Morphological complexity was equated with possible sensor configurations for a physical counterpart Khepera III mobile robot [8], and neural complexity was equated to artificial neural network topological configurations that coupled with a robot's evolved morphology.
Online Economy on the Move: The Future of Blockchain in the Modern Banking System

The Indian banking system is a complex hierarchical structure engaged in financial intermediation. In its role as a financial intermediary, it performs several transactions like cash deposits, cash withdrawals and investments on a daily basis and is an important institution to assess a nation’s economic growth. However, with the constantly growing amount of data and the increasing susceptibility of banks to online fraud, banks now need to shift to a more robust, efficient and secure platform to carry out their functions. One such platform is provided by the blockchain structure. Blockchain is a decentralized, public ledger system which has gained immense popularity and recognition for its efficiency, security and transparency since its inception in 2008. This paper aims to make required changes in the blockchain model prevalent today to make it relevant for use in the banking system and evaluates the advantages of the blockchain system over the current system employed in consortium banking and payments through an assessment framework. This paper also tries to devise types of blockchain platforms which can encompass most of the functionalities of the banking system existing today. The paper is divided into three parts. The first part aims to devise a suitable consensus protocol which can be used to make blockchain suitable for banks, the second part discusses types of blockchain platforms applicable in the banking sector and the third part employs use cases in payments and consortium banking to demonstrate the advantages of blockchain.
Research on mixed Teaching Mode of Deep Learning based on MOOC+SPOC Environment

In recent years, with the continuous improvement of information-based teaching facilities in colleges and universities, many colleges and universities open MOOC and SPOC platforms to the society free of charge to guide learners to improve their knowledge and skills by way of autonomous learning, which has been widely promoted. However, this kind of autonomous learning lacks systematic supervision and management to the learners. Therefore, the design of an effective mixed teaching model is the premise to ensure the learning effect. Based on the theory of deep learning and combining the characteristics of MOOC and SPOC, this paper designs a mixed teaching model of deep learning based on “MOOC+SPOC” environment, which not only makes full use of the characteristics of information resources, but also retains the advantages of traditional course teaching. The effectiveness of this model is proved by the author's teaching practice and questionnaire analysis, which is helpful to improve the quality of information-based subject teaching in colleges and universities.
A Smart Discussion Forum Website

Sentiment Analysis deals with understanding the context of textual data and further forming an opinion based on the piece of text. The Sentiment Analysis further classifies the user’s emotions and opinions in various categories such as positive, negative, or neutral. Applications of Sentiment Analysis in various research areas are quite abundant and clearly visible across the literature. Here in this paper, we also accomplished the application of Sentiment Analysis. To be specific, we developed a discussion forum website that allows a user to post questions, answers, and comments or feedback of their choice along with to like and dislike answers. This discussion forum then automatically performs the Sentiment Analysis on the feedback or comments posted by the users. This performed Sentiment Analysis categorizes the answers written on various topics on discussion forum website and then presents the emotional quotient of people, i.e., whether the users are happy, angry, or sad, etc. with the answers. Therefore, in our discussion forum, we ranked the answers based on the sentiment score and no. of likes and dislikes, which makes our discussion forum unique as compared to other available discussion forums in the market. In order to realize the effectiveness of our work, dummy data entries were made on the discussion forum website in order to cross verify the ranking of answers based on the sentiment score and no. of likes and dislikes.
An Empirical Study on the Factors of Virtual Simulation Teaching Effect

Analyzing the factors of virtual simulation teaching effect is helpful to find better teaching method. This paper selects the students in Heyuan Polytechnic as the research sample, combined with the relevant literature research, aiming at the factors that affect virtual simulation teaching effect, and uses the structural equation model to make empirical analysis. The result shows that: usefulness, feedback and reflection, operation and application all have positive effects on participation. Students' participation and exploration will affect teaching effect, and have significant positive impact on improving students' outcome of knowledge mastery, operation skill and communication skill. suggestions were put forward to improve the effect of virtual simulation teaching base on research result.
Improving the Correlation Filter-based Tracking Using Low-illumination Enhancement Method

Correlation filter-based trackers are commonly used nowadays and has shown good results in both precision rate and frame per second rate, and have been developed from working on raw pixels, to features of the image, ending on applying them on layers of convolutional neural networks. However, those trackers have not shown good results dealing with complex object appearances such as blur caused by fast motion, deformation, and difficult illumination cases. In this paper, we present a method that treats tracking objects in low illumination case. It makes a low illumination enhancement to each dark image before each treatment of a sequence using two different enhancers: Light Image Enhancement via Illumination Map Estimation (LIME) and Contrast Enhancement (CE) applied on two different trackers: tracking with Kernelized Correlation Filters (KCF) and Hierarchical Convolutional Features Tracker (HCFT).
Community-Aware Photo Quality Evaluation by Deeply Encoding Human Perception

Computational photo quality evaluation is a useful technique in many tasks of computer vision and graphics, for example, photo retaregeting, 3-D rendering, and fashion recommendation. The conventional photo quality models are designed by characterizing the pictures from all communities (e.g., "architecture" and "colorful") indiscriminately, wherein community-specific features are not exploited explicitly. In this article, we develop a new community-aware photo quality evaluation framework. It uncovers the latent community-specific topics by a regularized latent topic model (LTM) and captures human visual quality perception by exploring multiple attributes. More specifically, given massive-scale online photographs from multiple communities, a novel ranking algorithm is proposed to measure the visual/semantic attractiveness of regions inside each photograph. Meanwhile, three attributes, namely: 1) photo quality scores; weak semantic tags; and inter-region correlations, are seamlessly and collaboratively incorporated during ranking. Subsequently, we construct the gaze shifting path (GSP) for each photograph by sequentially linking the top-ranking regions from each photograph, and an aggregation-based CNN calculates the deep representation for each GSP. Based on this, an LTM is proposed to model the GSP distribution from multiple communities in the latent space. To mitigate the overfitting problem caused by communities with very few photographs, a regularizer is incorporated into our LTM. Finally, given a test photograph, we obtain its deep GSP representation and its quality score is determined by the posterior probability of the regularized LTM. Comparative studies on four image sets have shown the competitiveness of our method. Besides, the eye-tracking experiments have demonstrated that our ranking-based GSPs are highly consistent with real human gaze movements.
Certificate Management System Using Blockchain

Overcoming the problems of existing file storage and sharing approaches, this paper gives an efficient way of storing and sharing the files using blockchain and smart contracts. As an example, Managing student’s information and sharing them or giving access to unauthorized people in a much secured way is one of the major responsibilities of any university or organization. Instead of storing the certificates in a normal database, blockchain technology can be used to store the certificates. In our approach, the certificates and other documents will be uploaded to the blockchain and will be shared securely when any third party needs to access the certificates. The university administrator has ultimate rights to upload student’s certificates and managing them. Students can view their documents and certificates; other unauthorized people (third party/organization/employer/others) can access the files after taking permission of the college administrator. These types of approaches are very useful in this era where efficient storage and sharing the files plays a crucial role. The feature which differentiates our approach of certificate management system from existing certificate management system is, our system is decentralized, cryptographically secure, immutable and efficient.
Characterize and Evaluate Scientific Domain and Domain Context Knowledge Map

Domain knowledge map, a.k.a., scholarly network, construction as an important method can describe the significant characters of a selected domain. In this research, we will address three fundamental problems for scholarly network generation. Firstly, two different methods will be investigated to associate keywords on the graph: Co-occur Domain Distance and Citation Probability Distribution Distance. Secondly, this paper will construct domain (core journals and conference proceedings) knowledge and domain referral (domain citation) scholarly networks, and propose a novel method to integrate those graphs by optimizing the nodes and their linkage. Finally, the paper will propose an innovative method to evaluate the accuracy and coverage of scholarly networks based on training keyword oriented Labeled-LDA model and validate different domain or domain referral graphs.
Accuracy Analysis of Similarity Measures in Surprise Framework

Recommender Systems (RS) are growing technologies, which can be very useful for consumers in finding items of their interest on the web. Collaborative filtering(CF), a popular approach of Recommender Systems, recommends items to users based on other users with the same taste. The key step of the collaborative filtering method is to compute the similarity between users and items. The success of any recommender model hugely depends on how accurately the notion of similarity has been modelled. There are many open-source Python frameworks, which are useful in building and experimenting with RS models in the industry as well as academics such as Surprise, Python-recsys, Case Recommender, Polara, Spotlight, and RecQ. This work provides a brief introduction to the Surprise, a Python library for Recommender System, explaining its architecture, implementation and main features. Furthermore, a comparative study of the Surprise framework with other related frameworks is provided to demonstrate the fact why it is better than other frameworks in terms of implementing and handling new and complex recommender models. We have evaluated the accuracy of various built-in similarity measures provided by the Surprise framework using the real-world benchmark MovieLens datasets (100 k and 1 M). Thereafter, the accuracy of the recommendation is measured in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The empirical results demonstrate that Pearson-baseline outperforms other built-in similarity measures available in the Surprise framework.
Portable Cardioanalyzer with Registration of Multiply Leads of Electrocardiac Signals

In this study, a portable cardioanalyzer is considered for assessment of heart conditions without limiting patient movement including an ECG recorder with external electrodes, wireless transmitter for work in networks of the third and fourth generation and a computing module. Distinctive features based on methods of protected patents of non-invasive cardiodiagnosis by portable cardioanalyzer suggested are the following: calculating of equivalent electric generator of the patient’s heart (EEGH); determination of patient’s critical condition in real-time and automatic call of ambulance in critical condition of patient’s heart.
Analysis of the Cryptography Methods for Design of Crypto-Processor

With the fast progression of the internet, information security is becoming more important every day in data transmission and storage. Protecting digital data and maintaining information security have been achieved through the use of cryptographic algorithms. Most popular cryptographic algorithms are the Advanced Encryption Standard (AES) and Data Encryption Standard (DES). Encryption operation changes a plain text data to distorted data called ciphertext. The original text can be retrieved with a special key from the ciphertext. This study aims to analyze cryptographic algorithms on hardware solutions. Application-Specific Integrated Circuits (ASICs) and Field Programmable Gate Arrays (FPGAs) have been used to produce crypto-processors. In this study, the chosen algorithms are implemented on FPGAs due to their fast and easy programming capabilities. We have used the Xilinx ISE synthesis tool, ModelSim SE simulation tool, and Xilinx Spartan-6 FPGA platforms for implementation and functionality verification of the designed circuits. FPGA based implementation of AES and DES algorithms are compared in terms of Speed, Memory, and Processing Time. Study results show that AES needs less processing time than DES does for embedded and portable applications.
High-level image representation-based on Gestalt theory for image classification

Feature extraction and representation are crucial stages for image classification. However, the derived features might not be robust discriminators, which radically alter image classification results. In this paper, we present an enhanced Bag of visual Words representation for better image classification. We are inspired by the Gestalt laws of grouping. So, our design goals are: (1) Introduce the spatial information into Bag of Words representation by grouping the aligned visual words (high order features), (2) Preserve the spatial relationships among the visual words groups against different geometric transformations, (3) Achieve the high-level representation of visual words by extracting visual phrases. Theses phrases convey the semantic meaning of object parts. The proposed approach has been evaluated, in terms of classification accuracy, on Caltech101 image database. Experimental results indicate that the classification performances are improved by using visual phrases.
Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

Abstract With the availability of an abundance of data through the Internet, the premises to solve some intelligence analysis tasks have changed for the better. The study presented herein sets out to examine whether and how a data-driven approach can contribute to solve intelligence tasks. During a full day observational study, an ordinary military intelligence unit was divided into two uniform teams. Each team was independently asked to solve the same realistic intelligence analysis task. Both teams were allowed to use their ordinary set of tools, but in addition one team was also given access to a novel text analysis prototype tool specifically designed to support data-driven intelligence analysis of social media data. The results, obtained from the case study with a high ecological validity, suggest that the prototype tool provided valuable insights by bringing forth information from a more diverse set of sources, specifically from private citizens that would not have been easily discovered otherwise. Also, regardless of its objective contribution, the capabilities and the usage of the tool were embraced and subjectively perceived as useful by all involved analysts.
Structural Decision Theory

Judging an act’s causal efficacy plays a crucial role in causal decision theory. A recent development appeals to the causal modeling framework with an emphasis on the analysis of intervention based on the causal Bayes net for clarifying what causally depends on our acts. However, few writers have focused on exploring the usefulness of extending structural causal models to decision problems that are not ideal for intervention analysis. The thesis concludes that structural models provide a more general framework for rational decision makers.
Improving Temporal Stability in Inverse Tone-Mapped High Dynamic Range Video

Inverse tone mapping (ITM) is widely used to convert a standard dynamic range (SDR) image to its high dynamic range (HDR) version. While using frame-specific ITMs for a video sequence, temporal stability needs to be maintained not to cause visual artifacts such as flashing, flickering and delayed intensity-change. In this paper, we propose a weighted temporal filtering method to smooth look-up table (LUT)-based ITM to avoid such artifacts. Our proposed method uses a LUT-similarity measure and a histogram-based content-aware weighing for filtering LUTs in the time-domain. Our method is highly portable and can be applied to any set of LUT-based ITMs, agnostic to the way of their generation. Experiments exhibit effectiveness of our method.
Federated Adaptive Bitrate Live Streaming over Locality Sensitive Playback Coalitions

Live video broadcasts to particular communities or targeted audiences many-at-a-times indulge a cluster of localities from where the end-users are interested in participating. In this paper, we leverage this idea to develop a system called Federated Live Streaming over DASH (FLiDASH) which forms end-users coalitions based on the locality of the network connectivity (like a set of end-users behind a common core-network service gateway). In FLiDASH, the coalition members stream the live data collectively based on a federated adaptive bitrate streaming mechanism where the coalition as a whole decides the optimal bitrate for video streaming and distributes the download overhead among its members. We have thoroughly evaluated FLiDASH in an emulated setup, and observe a 40% improvement in the live streaming QoE with a 20% reduction in the network traffic usage.
On the numerical range of matrices defined over a finite field

Let q be a prime power. For u=(u1,…,un),v=(v1,…,vn)∈Fq2n let 〈u,v〉:=∑i=1nuiqvi be the Hermitian form of Fq2n. Fix an n×n matrix M over Fq2. Set Num(M):={〈u,Mu〉|u∈Fq2n,〈u,u〉=1} (the numerical range of M introduced by Coons, Jenkins, Knowles, Luke and Rault (case q a prime q≡3(mod4)) and by the author (arbitrary q)). When n=2 we prove an upper bound for |Num(M)|. We describe Num(M) for several classes of matrices, mostly for n=2,4.
Sketch-based Image Retrieval: Effectivity Notion of Recent Approaches

Retrieving image on the basis of the using sketch as the query image is termed as Sketch Based Image Retrieval (SBIR) which is increasing its pace of adoption motivated from nearly similar form of free hand drawing identification in the modern tablet, mobile devices, as other interactive computing devices. There are various work carried out in order to improve the process of SBIR especially from accuracy viewpoint. However, there are no significant discussions being carried out over the process effectiveness of recently implemented work towards this direction. This manuscript contributes towards studying the effectiveness of the recently implemented work in SBIR and discuss about the research trend and highlights about the open end research issues in this field. The study contributes towards framing up better decision process before implementing SBIR to the readers by highlighting the limitations as well as research trends towards SBIR.
Keynote: A Cross-Domain Machine Learning Framework for Pervasive Sensing

Recognizing human activity and behavior is a well-known research problem. State-of-the-art machine learning approaches, a variety of smart home sensor systems, and Internet-of-Things (IoT) devices are in place to help aid activity discovery, recognition, and inference processes. However, the current approaches work with single user, a predefined set of devices and in a specific smart environment. Scaling, adapting, and crossing the boundaries beyond those presumed settings in presence of limited or unlabeled datasets posit challenging research avenues to investigate. In this talk, I will first articulate the underpinning challenges across heterogenous IoT devices, different users and environments. I will discuss our work on addressing those challenges using deep learning-based domain adaptation and transfer learning-based techniques, and present experimental results using real datasets collected from different IoT devices such as smartphones, smartwatches, and diverse users. I will conclude this talk with our ongoing work in this area and future research directions.