Reviewerly/exHarmony huggingface dataset:
Exploring Image Selection for Self-Testing in Neural Network Accelerators

Hardware accelerators are essential to the accommodation of computation and memory-intensive neural network (NN) applications on resource-constrained edge devices. While hardware accelerators facilitate fast and energy-efficient convolution operations, their accuracy is threatened by various types of faults in their on-chip and off-chip memories, where millions of NN weights are held. To achieve fast and in-time fault detection, a self-test process that periodically runs a small set of test images in the accelerator can be adopted. This paper focuses on developing and comparing multiple numerical score based test image selection strategies. Various image selection criteria are studied, including output probability distribution, gradient sensitivity, and neuron coverage. Experimental studies show that images selected based on the output probability distribution offers high fault detection accuracy over a wide range of fault rates as well as low computation complexity. The small set of test images allows for real-time monitoring of the healthiness of DNN accelerators as well as the subsequent recovery and self-healing process.
GDPR: A Bibliometric Analysis

EU adapted “General Data Protection Regulation” and serves as privacy law and of human rights law, currently the GDPR data security law is enforced in the EU as from 2018. As the name itself suggest, we all know it is a very complex law of 88 pages long including 173 recitals and 99 articles. To comply with the recent law, organizations have equipped required arsenal. In this article, a detailed bibliometric analysis is conducted, different quantitative metrics are pulled out such as most productive and influential authors, most cited work, work languages, top involved countries and popular keywords. This paper has summarized all the papers that have been published on this particular topic in last few years since till July, 2020 and gives an overall background of various work and application in this field.
Physical acoustics homework problems written by students: Undisciplined, irreverent, and original

“Study hard what interests you the most in the most undisciplined, irreverent and original manner possible” [Perfectly Reasonable Deviations from the Beaten Track, Richard P. Feynman, Basic Books, 2005]. Writing original homework problems is a powerful way students of physical acoustics can practice Feynman’s advice. Three problems that involve a broad range of concepts covered in introductory graduate-level physical acoustics courses illustrate how the student-author unleashed his creativity in an undisciplined manner, injected his problems with an irreverent sense of humor, and derived a great sense of originality and ownership over physical acoustics. The problems synthesize David T. Blackstock’s problems 1D-2, 1E-3, 1G-1, 1G-3, 7-6, 10-10, and 10-11 [Fundamentals of Physical Acoustics, David T. Blackstock, Wiley, 2000], addressing concepts including acoustic intensity, impedance, horns, enclosures, and radiation. [CAG was supported by the ARL:UT Chester M. McKinney Graduate Fellowship in Acoustics.]
On the structural security of a McEliece-type cryptosystem based on the sum of tensor products of binary Reed - Muller codes

The current task of cryptography is the development of cryptosystems resistant to attacks using quantum computing. One of the promising encryption schemes is the McEliece system based on Goppa codes. However, this system has a number of disadvantages due to the structure of Goppa codes, which makes it relevant to search for other codes for the McEliece scheme. Important requirements for these codes are the presence of a fast decoder and ensuring the resistance of the corresponding cryptosystem to known attacks, including attacks with the Schur - Hadamard product. Many attempts to replace Goppa codes have failed because the corresponding cryptosystems have proven to be unstable against structural attacks. In this paper, it is proposed to use the D-construction (D-code) on binary Reed - Muller codes in the McEliece cryptosystem. This construction is a sum of a special kind of tensor products of binary Reed - Muller codes. There is a fast decoding algorithm for it. To analyze the security of the McEliece scheme on D-codes, we have constructed a structural attack that uses the Schur - Hadamard product of a D-code. To select the parameters that ensure the resistance of the cryptosystem to the constructed attack, we investigate the decomposition of the degree of the D-code into the direct sum of Reed - Muller codes and conclude about the set of strong keys of the cryptosystem.
Students’ Responses toward the Implementation of PowToon in EFL Classroom at a Remote Area

The development of technology in this contemporary world was undoubtedly able to support the teaching-learning process. Meanwhile, various types of applications are being used as media. For instance, PowToon. PowToon is categorized as an audiovisual presentation that provides alternative animation, picture, sound, voice, and music. PowToon videos have been used all over the world. However, this would be different for students in remote areas who are hardly taught using technology. This study revealed students' responses in a small area towards implementing PowToon in the EFL Classroom. Regarding this research, the case study was used as a research design. In collecting the data, both semi-structured interviews and close-ended questionnaires were applied. The result showed students' responses on implementing PowToon in the EFL classroom were: First, PowToon could assist students in learning English. Second, PowToon was an exciting application or media in learning, and the last PowToon increased students' creativity and motivation in learning English as Foreign Language.
Innovative Development of Modern Higher Education Concept

At this stage, our society has entered a new development period and ushered in new development goals.Under this important social background, the concept of higher education has made new development and breakthrough, showing a new era value and development direction, which is very different from the traditional educational concept.Therefore, the major colleges and universities in China have also made positive adjustments, actively studied the new national policies and new development directions, scientifically integrated the development characteristics of the new period with the school's education and teaching, and realized the comprehensive innovation of the educational concept on the basis of following the people-oriented teaching concept.This paper comprehensively discusses the innovative development of higher education ideas from the perspective of value, development, time and space, talents and school running.
Unsupervised Image Semantic Segmentation Through Superpixels and Graph Neural Networks

Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harnesses recent advances in unsupervised learning using a combination of Mutual Information Maximization (MIM), Neural Superpixel Segmentation and Graph Neural Networks (GNNs) in an end-to-end manner, an approach that has not been explored yet. We take advantage of the compact representation of superpixels and combine it with GNNs in order to learn strong and semantically meaningful representations of images. Specifically, we show that our GNN based approach allows to model interactions between distant pixels in the image and serves as a strong prior to existing CNNs for an improved accuracy. Our experiments reveal both the qualitative and quantitative advantages of our approach compared to current state-of-the-art methods over four popular datasets.
Lighting Enhancement Using Self-attention Guided HDR Reconstruction

Computational photography has become an increasingly popular technique for capturing images in high contrast scenes. Current imaging systems solve this problem by capturing a set of images with different exposure settings and then reconstructing a final image. However, this approach cannot solve the problem of revealing or predicting details in already-captured images. Convolutional neural networks (CNNs) can address this problem to some extent, but existing single image lighting enhancement methods based on deep learning suffer from CNNs’ limited receptive field and thus cannot yield the optimal results. To overcome this problem, we propose a self-attention based learning strategy inspired by high dynamic range (HDR) reconstruction process to reconstruct a properly exposed image from a single input image. Specifically, we leverage the self-attention mechanism to model the interdependencies between different locations and help reduce the local color artifacts during reconstruction. Furthermore, we adapt the idea of a generative adversarial network (GAN) and design a custom HDR loss function to achieve better image quality. We compare our method with several other recent image enhancement methods using several full-reference and non-reference image quality assessment methods. Experimental results show that our approach can produce images with better details in both over-exposed and under-exposed areas, and thereby outperform existing methods.
Deliberation Selector for Knowledge-Grounded Conversation Generation

The integration of external knowledge is an important aspect for developing multi-turn conversation generation. However, most existing knowledge-background conversation generation models ignore the characteristics of the human brain to select knowledge, which often results in the generation of inappropriate responses. To tackle this issue, we propose a novel Deliberation Knowledge Selector (DKS) for neural conversation generation. It comprises two-pass attention knowledge selector to select appropriate knowledge and generates high-quality responses in the multi-turn conversation, further improving the performance of utterance generation. In addition, the DKS model achieves competitive results in simulating the human cognitive process for knowledge selection. Experimental studies on Wizard of Wikipedia and Holl-E show that the DKS delivers better predictive performance than several state-of-the-art methods in knowledge selection.
Improving detection of manipulated passport photos - Training course for border control inspectors to detect morphed facial passport photos - Part II: Training course materials

In recent years, ID controllers have observed an increase in the use of fraudulently obtained ID documents [1].This often involves deception during the application process to get a genuine document with a manipulated passport photo.One of the methods used by fraudsters is the presentation of a morphed facial image.Face morphing is used to assign multiple identities to a biometric passport photo.It is possible to modify the photo so that two or more persons, usually the known applicant and one or more unknown companions, can use the passport to pass through a border control [2].In this way, persons prohibited from crossing a border can cross it unnoticed using a face morphing attack and thus acquire a different identity.The face morphing attack aims to weaken the application for an identity card and issue a genuine identity document with a morphed facial image.A survey among experts at the Security Printers Conference revealed that a relevant number of at least 1,000 passports with morphed facial images had been detected in the last five years in Germany alone [1].Furthermore, there are indications of a high number of unreported cases.This high presumed number of unreported cases can also be explained by the lack of morphed photographs' detection capabilities.Such identity cards would be recognized if the controllers could recognize the morphed facial images.Various studies have shown that the human eye has a minimal ability to recognize morphed faces as such [2], [3], [4], [5], [6].This work consists of two parts.Both parts are based on the complete development of a training course for passport control officers to detect morphed facial images.Part one contains the conception and the first test trials of how the training course has to be structured to achieve the desired goals and thus improve the detection of morphed facial images for passport inspectors [7].The second part of this thesis includes the training course and the evaluation of its effectiveness.
Comparison of K-Medoids Algorithm with K-Means on Number of Student Dropped Out

Education is an important part of human life in a nation. The higher level of education a person, the better quality of life that person. The existence of 12-year education program is expected to improve the quality of life the community. In practice, various problems appear as indicators of students dropping out of school. For this reason, the aim of this research is to group students who are vulnerable to schools. This study using two methods which are the K-Medoids and K-Means algorithm. This research also compared the output of both of them. The dataset is taken from Jakarta Open Data. The software used is KNIME. The results of the evaluation of 36 data stated that the best algorithm for grouping the number of dropout students at vocational schools in DKI Jakarta was the K-Means Algorithm with smaller results compared to the K-Medoids Algorithm.
A blockchain-based efficient data storage and sharing scheme for renewable energy trading in smart grid

The popularity of renewable energy is increasing due to its cost effectiveness. However, not everyone can generate and fulfill their energy demand, so energy trading is necessary. Current solutions are centralised and charged at a high fee for energy trading as they have a monopoly in the market. Energy trading requires the storage, verification, and sharing of data related to the trade while keeping records tamper-proof. Traditional database solutions are centralised and susceptible to data tempering. In our proposed scheme, we aim to solve those problems with the help of blockchain technology by storing data on blockchain and verifying transactions with the PoA consensus algorithm for faster processing. We tested our scheme against the Ethereum network and found that our scheme has a significant improvement in cost and processing. In the future, with the help of machine learning, pricing for each transaction can be optimised.
Measurement and Analysis of Electrical Field around Secondary Equipment of Electronic Transformer in Equivalent GIS Due to Switch Operation

The radiated E-field around secondary equipment of electronic current transformer and electronic voltage transformer are measured using the developed 3-D E-field measurement system under switch on operation of an equivalent 330 kV GIS system. Based on the electrically small antenna and fiber-optic transmission technology, the designed measurement system has a wide response frequency band from 10 kHz to 400 MHz. The time-domain and frequency-domain of measured signals are analyzed respectively. Under the switch on operation with charge voltage 200 kV and load 200 pF load, the maximum E-field can reach 18.67 kV/m and the frequency domain component are mainly over megahertz.
Pattern Language for Designing Distributed AI Systems

Design of Artificial Intelligence (AI) and Machine Learning (ML) applications, hereafter referred to as AI systems, is often based on a typical ML pipeline. One of the reasons for choosing this approach is its simplicity and modularity. While simple, such an approach tends to be rigid with respect to changing needs, technologies, devices, and algorithms. Recent research on design patterns for ML has introduced best practices for engineering AI systems. We examine a set of these patterns, or a pattern language, where individually selected patterns can build on each other to offer a complete design solution for a distributed AI system. We demonstrate the use of this pattern language to design an AI system for emotion classification of social media content. The result is an AI system that is not only easy to change and reuse in a similar context, for instance emotion classification of image data, but one whose architecture has better performance, usability, maintainability, security, and reliability.
Prediction of seawater salinity using truncated spline regression method

Salinity is the level of dissolved salt in water, which is one of the factors that affect salt production, the higher the salinity dissolved in seawater, the better the resulting salt production. The factors that affect seawater salinity include air humidity, wind speed and seawater temperature. In this study, the Spline Truncated Regression method was applied to predict seawater salinity based on the variables that influence it. Data collection was obtained using satellite images obtained from Landsat 8. The data was taken within 1 year from January to December 2019. From the analysis results, it was found that the linear spline model with 1 knot point is the best model with a minimum GCV value.0.3648021178, with an R-Sq of 0.580443194, the MSE value is 0.136237833, and the MAPE is 0.98349512. Based on the MAPE value, the prediction model is said to be very accurate forecasting.
On the Application of Agile Project Management Techniques, V-Model and Recent Software Tools in Postgraduate Theses Supervision

Due to the nature of most postgraduate theses in control engineering and their similarities to industrial and software engineering projects, invoking novel project control techniques could be effective. In recent decades, agile techniques have attracted popularity thanks to their attributes in delivering successful projects. Hence exploiting those methods in education and thesis supervision of engineering topics can facilitate the process. On the other hand, because of the limitations imposed by the CoVid19 pandemic, the integration of well-established online tools in collaborative education is noteworthy. This paper proposes an application of the agile project management method for the supervision of postgraduate students’ theses in the general field of engineering. The study extends a Scrum technique combined with approved systems engineering and team working tools such as Jira Software, Microsoft Teams, and Git version control (Github website). A custom designed V-model to nail an outstanding thesis is presented. The overall blended method is beneficial to provide feedback and self-assessment aid for the students and the supervisors. Employing this technique has shown promising progress in easing the supervision of students whilst helping them to manage their projects.
Comparative analysis of hotel reviews using proposed LSTM based deep learning model with classical machine learning techniques

The purpose of sentiment analysis is to find the positive and negative reviews. Hotel reviews have been analyzed in this paper using some machine learning algorithms and further, the most efficient machine learning technique that is called deep learning method of LSTM has been implemented. Further, the comparison has been done and it is analyzed that how LSTM has overcome the classical machine learning methods as the output and efficiency of deep learning method is better than machine learning algorithms like Naive Bayesian Classifiers, SVM classifier and Decision Trees. The idea is to build a deep learning model using LSTM technique that works on hotel reviews in context of online tourism that gives better results in comparison of previous machine learning techniques. This model will help tourism industry to enhance business by analyzing the hotel reviews of customers. As Sentiment analysis is tremendously used in business field to improve the current products and services by taking the input in the form of opinions of customers regarding these services and hence is the case of hotel reviews analysis using LSTM to get the efficient and clear results that would definitely help the tourism industry to know the opinion of customers with more clarity and efficiently so that industry can provide better services to the customers in future.
Formalizing Anomaly Detection

In the prior chapter, we looked at the sensory tools humans have to perform outlier and anomaly detection, taking the time to understand how they work. Computers have none of these sensory tools, and so we need to use other processes to allow them to detect outliers. Specifically, we need to implement at least one of the three approaches to outlier detection in order to give a computer some process for outlier detection. This chapter will begin to lay out the first of the three approaches to outlier detection that we will cover throughout the book: the statistical approach.
ComLoss: A Novel Loss Towards More Compact Predictions for Pedestrian Detection

CNN-based detectors have achieved great success in pedestrian detection. However, poor localization of bounding boxes leads to high false positive detection errors to a large degree. This paper gives insight into reducing localization errors and better performing bounding box regression. We propose a novel loss named ComLoss to enforce predicted bounding boxes with the same designated target ground truth box to locate more compactly around the target, which minimizes the internal region distances of these predicted boxes to some extent. Evaluations on both CityPersons and Caltech dataset show the effectiveness of our method: (1) ComLoss yields an absolute 1.05% and 0.60% miss rate improvement on these two datasets respectively. (2) By adding ComLoss during training, our baseline detector achieves competitive results compared to state-of-the-arts on both datasets.
Typographic landscape in urban space: a sociolinguistic approach

This paper suggests a sociolinguistic approach to typographic landscape analysis. Typography is discussed as a semiotic resource with meaning-making potential. The paper argues that typographic variation provides dynamic indexical links to social practice. It obtains its ‘social voice’ and becomes an integral part of the social context in which it is perceived as typical and able to generate particular socially loaded meanings. This research is in line with contemporary social semiotics, interactional linguistics, and discourse studies and is based on typographic meaning as a key notion providing the basis for social actors’ ideological ascriptions. Typography and typographic meaning formation are discussed within modern Russian urban space. It is argued that urban area enables addressing agency and interaction aspects of social communication. The city space provides access points for observing, shaping and interpreting meanings in the social context. As cases in point, the paper discusses the typefaces such as Antiqua font used in pre-revolutionary Russia, lettering imitating the font of Soviet newspapers, Handwriting font, and Stencil font and their em­bed­dedness in current socio-cultural practice. The analysis uses advertising, social and com­mercial texts. The findings indicate that typography should be considered as a social meaning which results from indexical connections of a sign and the context it is used in. Semio­tification of space allows observing stronger reflexivity and, therefore, metapragmatic activity of communicants.