Reviewerly/exHarmony huggingface dataset:
Automation of Deep Learning - Theory and Practice

The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of methods to automate deep learning. The choice of network architecture has proven critical, and many improvements in deep learning are due to new structuring of it. However, deep learning techniques are computationally intensive and their use requires a high level of domain knowledge. Even a partial automation of this process therefore helps to make deep learning more accessible for everyone. In this tutorial we present a uniform formalism that enables different methods to be categorized and compare the different approaches in terms of their performance. We achieve this through a comprehensive discussion of the commonly used architecture search spaces and architecture optimization algorithms based on reinforcement learning and evolutionary algorithms as well as approaches that include surrogate and one-shot models. In addition, we discuss approaches to accelerate the search for neural architectures based on early termination and transfer learning and address the new research directions, which include constrained and multi-objective architecture search as well as the automated search for data augmentation, optimizers, and activation functions.
Digital Imaging Processing and Reconstruction for General Applications

Because the internet broadcasting, most photos or video streams need copyright protection or forgery detection. Nowadays, the metadata tries to embed and preserve copyright data and general information. The ownership metadata should never be removed, for an efficient protection, but practically it could be relatively easy extracted. This research proposes, based on a comparison between metadata and watermarking methods, a watermarking framework that inserts visual and hide information into digital images, instead of metadata protection. The visible watermark inserted into the host image could be removable or not, based on the bijective or non-bijective embedding watermark function. A removable watermark allows the far end receiver to eliminate the visible watermark, whether it uses a framework that uses the invers embedding function. In this way, only the controlled receivers could profit by the clean photos/video stream. More of this, the hidden watermark could embed typical information that identify the owner.
A knowledge-based reasoning model for crime reconstruction and investigation

Artificial intelligence has been successfully applied in many areas including forensic sciences. Perhaps all forensic works can be regarded as helping reconstruct crimes, i.e. clarify and sequence the events that took place in the commission of a crime through evidence. However, there are few researches on the crime reconstruction using artificial intelligence methods. In this paper, we present a model based on Bayesian networks to help solve crimes. The model, which is termed ‘case-type based model’, is based on the knowledge of a type of crimes. We use Bayesian networks to represent the knowledge and conduct the uncertainty reasoning. We propose a growth algorithm of Bayesian networks to adapt the model to different cases. The model was tested through a real case, and the results indicate that the model can provide effective investigation suggestions and achieve the crime reconstruction.
Analysis of CT images for detection of Colorectal Cancers using Hybrid Artificial Neural Networks and Fire Fly Algorithm

The longest part of the large intestine or colon is called as rectum. The uncontrolled division of cells in the colon forming a polyp at initial stage is called as colorectal cancer. Initially the polyp formed may be benign and later it may turn out to be malignant. The advancements in the field of medicine has made the screening techniques to be an effective tool in identification of the colon cancer. Early detection of the colon cancer may lead to complete cure of the disease normally, but it is very difficult to diagnose at the start. It is because, it does not show any symptoms at initial stages. Colonoscopy, a type of computed tomography (CT) is usually recommended for the patients to detect the colon cancer and it is usually a painful test conduct to identify the disease. To relieve the patients from the suffering, image processing algorithms like Weighted Adaptive Scalable Invariant Transform (WASIT) is used for extraction of features like location, orientation and scale are used as inputs to train the Artificial Neural Network (ANN) using Back Propagation Algorithm (BPA). The optimal set of weights are obtained by adjusting the weights for BPA hybrid with Genetic Algorithm (GA) and Fire Fly Algorithm (FFA). The Likelihood Ratio (LR+) is found to optimal for the BPA tuned with FFA and is inferred to be in the range of 1.5 to 4 which varies with a deviation of ±0.4% from the nominal value.
The Effect of First Impressions of an E-Commerce Chatbot's Personality and Abilities on Expectations for the User Experience

Research shows that a user is less likely to use a chatbot again if there is a failure in the interaction. Hence, first impressions with chatbots could help retain more users and loyal customers for the business. A chatbot's personality, like other anthropomorphic qualities, can give users a sense of the chatbot's abilities. Therefore, we hypothesize that a combination of personality cues and clear information about a chatbot's abilities will help to maximize user experience and increase the number of repeat users of the chatbot. A chatbot's first greeting is important because it is what the user sees before deciding whether to continue using the chatbot. It is also the basis for a user's initial assessment of what using the chatbot will be like and can thus affect the size of the expectation gap. This research could help inform and guide the design of e-commerce chatbots.
Further study on constructing bent functions outside the completed Maiorana–McFarland class

In the mid-sixties, Rothaus introduced the notion of bent function and later presented a secondary construction of bent functions (building new bent functions from already defined ones), called Rothaus’ construction. In Zhang et al. 2017 (‘Constructing bent functions outside the Maiorana–Mcfarland class using a general form of Rothaus,’ IEEE Transactions on Information Theory, 2017, vol. 63, no. 8, pp. 5336–5349.’) provided two constructions of bent functions using a general form of Rothaus and showed that the obtained classes lie outside the completed Maiorana–McFarland ( ) class. In this study, the authors propose two similar methods for constructing bent functions outside the completed class but with significantly simplified sufficient conditions compared to those in Zhang et al. 2017. These simplified conditions do not induce any serious restrictions on the choice of permutations used in the construction apart from a simple requirement on their algebraic degree and the request that the component functions of one permutation do not admit linear structures. This enables us to generate a huge class of bent functions lying outside the completed class. Even more importantly, they prove that the new classes of bent functions are affine inequivalent to the bent functions in Zhang et al. 2017.
Mapping of Real World Problems to Nature Inspired Algorithm using Goal based Classification and TRIZ

Abstract The technologies and algorithms are growing at an exponential rate and solving technically challenging and complex problems which seemed impossible task. However, the trending methods and approaches are facing multiple challenges on various fronts of data, algorithms, software, computational complexities, and energy efficiencies. Nature also faces similar challenges, and it has found solutions. Nature has solved those challenges and formulation of solutions is available as Nature Inspired Algorithms (NIA) based on the study of nature. A novel method based on TRIZ to map the real-world problems to nature problems is proposed here.TRIZ is Theory of inventive problem solving. Using the proposed framework, best Nature Inspired Algorithm can be identified to solve the real-world problems. For this framework to work, a novel classification of Nature Inspired Algorithm based on the end goal that nature is trying to achieve is devised. The application of the this framework will also be discussed.
Motorised Wheelchair with Multicontrol System

A groups of people who suffering from paralysis has been increasing every year. Thus, this groups needs other people help to move because they loss of self-mobility caused by paralyzed body. This paper develops a motorised wheelchair to assists the disable people to gain confident level to reach their destination. The motorised wheelchair consists of few interesting features such as allow the user to choose whether to control the wheelchair by using eye movement or by using mobile application. This motorised wheelchair also consists of emergencies call and text if emergency's situation happen to alert user guardian about the situation.
Structure Learning of H-Colorings

We study the following structure learning problem for H -colorings. For a fixed (and known) constraint graph H with q colors, given access to uniformly random H -colorings of an unknown graph G=(V,E) , how many samples are required to learn the edges of G ? We give a characterization of the constraint graphs H for which the problem is identifiable for every G and show that there are identifiable constraint graphs for which one cannot hope to learn every graph G efficiently. We provide refined results for the case of proper vertex q -colorings of graphs of maximum degree d . In particular, we prove that in the tree uniqueness region (i.e., when q≤ d ), the problem is identifiable and we can learn G in poly( d,q )× O(n 2 log n ) time. In the tree non-uniqueness region (i.e., when q≤ d), we show that the problem is not identifiable and thus G cannot be learned. Moreover, when q ≤ d - √d + Θ (1), we establish that even learning an equivalent graph (any graph with the same set of H -colorings) is computationally hard—sample complexity is exponential in n in the worst case. We further explore the connection between the efficiency/hardness of the structure learning problem and the uniqueness/non-uniqueness phase transition for general H -colorings and prove that under a well-known uniqueness condition in statistical physics, we can learn G in poly( d,q )× O(n 2 log n ) time.
Continuous ODE-defined Image Features for Adaptive Retrieval

In the last years, content-based image retrieval largely benefited from representation extracted from deeper and more complex convolutional neural networks, which became more effective but also more computationally demanding. Despite existing hardware acceleration, query processing times may be easily saturated by deep feature extraction in high-throughput or real-time embedded scenarios, and usually, a trade-off between efficiency and effectiveness has to be accepted. In this work, we experiment with the recently proposed continuous neural networks defined by parametric ordinary differential equations, dubbed ODE-Nets, for adaptive extraction of image representations. Given the continuous evolution of the network hidden state, we propose to approximate the exact feature extraction by taking a previous "near-in-time" hidden state as features with a reduced computational cost. To understand the potential and the limits of this approach, we also evaluate an ODE-only architecture in which we minimize the number of classical layers in order to delegate most of the representation learning process --- and thus the feature extraction process --- to the continuous part of the model. Preliminary experiments on standard benchmarks show that we are able to dynamically control the trade-off between efficiency and effectiveness of feature extraction at inference-time by controlling the evolution of the continuous hidden state. Although ODE-only networks provide the best fine-grained control on the effectiveness-efficiency trade-off, we observed that mixed architectures perform better or comparably to standard residual nets in both the image classification and retrieval setups while using fewer parameters and retaining the controllability of the trade-off.
A Comparison of Interactive Multiple Modeling Algorithms for Maneuvering targets tracking

Interacting Multiple Model (IMM) filter is a popular method for estimating systems, whose model changes according to a finite-state, discrete-time Markov chain. In this paper, four improved IMM algorithms (IMM-EKF, IMM-CKF, IMM-UKF and IMM-CIF) are compared for nonlinear maneuvering target tracking problem. IMM disposes all the models simultaneously through Markov Chain, which can enhance the quick response of the filter. The designing of the IMM model filters is analyzed in this work, and we compare the performance of the four IMM-Improved methods for the coordinated turn model and bearing only tracking of a maneuvering target separately. We analyze the differences in the performance of the four IMM- filters using simulated data.
Large-scale Distributive Matrix Collaborative Filtering for Recommender System

In the modern world, people face an explosion of information and difficulty finding the right choice for their interests. Nowadays, people prefer online shopping for their needs. Recently, the recommender system has become one of the key technology for the online purchasing system. The collaborative filtering technique has been extensively applied for the Recommender Systems. However, collaborative filtering is suffering from data sparsity, cold start problems, and inaccuracy problems. To overcome these problems, we propose a novel approach of the Matrix Distributive collaborative filtering with ensemble integration. The experimental results illustrate the increase in performance against the existing methods.
Observation and heuristics evaluation of student web-based application of SIPADU-STIS

Abstract Politeknik Statistika STIS has an integrated information system called SIPADU-STIS. One part of the system is a web-based application for students. Students use this system to support learning daily activities like check lecture schedule, grade, etc. This system was built by personnel and students. However, this new system was tested only by the black box method and tested to users with the System Usability Scale (SUS) questionnaire. The system has not been tested in terms of interface and interaction with users. Whereas the well-designed interface and user interaction can improve the quality of service to users (students). Therefore, researchers want to test the interface and interaction with users of the system. We conduct an observation of the application and evaluation of heuristics. The heuristic evaluation used is based on 10 principles by Nielsen. Respondents are chosen by students using convenience sampling. Based on the data obtained, all aspects still have shortcomings or problems, even though only 16.7% stated it. There is only one aspect that has a cosmetic problem that is the aspect of match between the system and the real world. Six aspects have minor usability problems, those are visibility of system status/feedback, consistency and standards, error prevention, recognition rather than recall, aesthetic and minimalist design, and help and documentation. There is one aspect that has a major usability problem, help user recognize, dialogue, and recovers from errors. Two of the last aspects that must be redesigned, are the aspect of user control and freedom and aspect of flexibility and efficiency of Use.
Consumer Behavior Analysis in the Offline Retail Stores based on convolutional neural network

Abstract Pedestrian attributes recognizing (PAR) is an important task in computer vision area due to it plays an important role in video surveillance. On the other hand, pedestrian visual attributes are treated as middle-level semantic features which can provide calibration information for high- level human related visual tasks in order to improve the discriminative ability of the models, such as pedestrian detection, people tracking, person re-identification, action recognition and scene understanding. However, the most study of PAR is based on single person. In this work, I implements a multiple pedestrian attributes recognition model based on the offline retail scenes. This model combines object detection and multitask classification techniques and can be trained end-to-end directly for back propagation. This paper also demonstrates the performance of the final model through a series of experiment.
Privacy Preserving Data Aggregation in Fog Computing using Homomorphic Encryption: An Analysis

In recent days the attention of the researchers has been grabbed by the advent of fog computing which is found to be a conservatory of cloud computing. The fog computing is found to be more advantageous and it solves mighty issues of the cloud namely higher delay and also no proper mobility awareness and location related awareness are found in the cloud environment. The IoT devices are connected to the fog nodes which support the cloud services to accumulate and process a component of data. The presence of Fog nodes not only reduces the demands of processing data, but it had improved the quality of service in real time scenarios. Nevertheless the fog node endures from challenges of false data injection, privacy violation in IoT devices and violating integrity of data. This paper is going to address the key issues related to homomorphic encryption algorithms which is used by various researchers for providing data integrity and authenticity of the devices with their merits and demerits.
Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation

In this work, we perform a wide-ranging evaluation of Convolutional Neural Networks (CNNs) as feature extractors for matching visual features under large changes in appearance, perspective, and visual scale. Our evaluation covers 82 different layers from twelve different CNN architectures belonging to four families: AlexNets, VGG Nets, ResNets, and DenseNets. To our knowledge, this is the most comprehensive analysis of its kind in the literature. We find that the intermediate layers of DenseNets serve as the best feature extractors overall, providing the best overall trade-off of robustness to feature size. Moreover, we find that for each network, the later-intermediate layers provide the best performance, regardless of the total number of layers in the network.
Visual-based Items Recommendation Using Deep Neural Network

Online shopping websites throughout the world are increasing at an immense pace. Most of these websites are focused on search engines that tend to be based mainly on the information base and utilize matching keywords to find related products. However, consumers prefer a simple and secure digital set-up to search the related products. To address these problems, we propose a new solution to browse the items in an online buying system using a visual-based technique employing a deep learning approach. A consumer can supply or choose a visual, and related visual-based items will be presented to the consumer. The proposed approach encompasses two key steps: the first step is used for the classification and feature extraction. In the second step, the proposed recommendation system obtains similar matched related items. In the first step, the evaluation of the proposed model produces an 85.5% accurate model, thereby signifying its effectiveness for the items' recommendation in related applications.
TAC: A unified trust anchor framework based on consortium blockchain

Abstract In the current network, there are still many security issues, such as identity fraud and address spoofing. In traditional Public Key Infrastructure(PKI), most trust models adopt a centralized trust model with a single-point trust anchor. The existing PKI system lacks a unified authentication framework and is low efficiency. Aiming at the problems of how to store public keys efficiently and how to make the network secure and trustworthy, this paper proposes a new trust anchor framework based on consortium blockchain, named Trust Anchor Chain( TAC ). This study uses the form of consortium to manage TAC , proposes a hierarchical naming ID, named TAC ID to uniquely identify the user in TAC , and proposes a new data structure, named Struct Radix Trie, to store public key information in TAC to improve efficiency. Section 4, TAC has superiority through the comparison between local experiments and remote experiments. In TAC , users can access each other in the TAC , get the public key, and ensure the correctness of the identity. All users work together to maintain TAC to achieve equal network interconnection, security and credibility.
A survey on automatically constructed universal knowledge bases

A universal knowledge base can be defined as a domain-independent ontology containing instances. Ontologies define the concepts and relations among these concepts and are used to represent a domain of interest. These universal knowledge bases are the elementary units for automated reasoning on the Semantic Web. The Semantic Web is an extension of the World Wide Web which facilitates software agents to share content beyond the limitations of applications and websites. This survey focuses on the most prominent automatically constructed universal knowledge bases including KnowItAll, DBpedia, YAGO, NELL, Probase, BabelNet and Knowledge Vault. We take a closer look at how these knowledge bases are built, in particular at the information extraction and taxonomy generation process and investigate how they are used in practical applications. Due to quality concerns, the most successful and widely employed knowledge bases are manually constructed to maintain high quality, but they suffer from low coverage, high assembly and quality assurance cost. On the contrary, automatic approaches for building knowledge bases try to overcome these drawbacks. Although it is strenuous to achieve the same level of quality as for manual knowledge bases, we found that the surveyed automatically constructed knowledge bases have shown promising results and are useful for many real-world applications.
When Software Engineering meets Cybersecurity at the classroom

Software Engineering is a foundation on modern computer systems development. This discipline is offered by the large branch of related university degrees these days around the world, among with other computer science courses. As we all know, cybersecurity problems are mostly related with software, for example bad implementations, poor quality achieved, lack of testing. Overall the bigger problem is lack of security planification since the inception and designing of the project. Cybersecurity had gained a lot of attention in recent years. From everyday desktop software been exploited, such as browser exploits and Windows RDP protocol exploits, to abusing Internet of Things to create massive botnets capable to disrupt the Internet service, the case of Mirai in 2017 as an example. We believe that if security played a basic role from the inception of the software project, much of this incidents could be prevented. So, we are proposing to introduce cybersecurity basics in the software engineering courses to raise the awareness of this problems to future professional software engineers.