Reviewerly/exHarmony huggingface dataset:
Accelerating NNEF Framework on OpenCL Devices Using clDNN

In recent years, many neural network frameworks have been proposed, such as TensorFlow, Caffe, Keras, and PyTorch. These frameworks have their file formats to store trained parameters, but these file formats are not compatible with each other. Therefore, Khronos has proposed a Neural Network Exchange Format (NNEF) that allows engineers to convert a pre-trained model across different frameworks. However, the NNEF is a file format of intermediate representation (IR). Hence, an efficient solution to execute the inference of the AI model through the NNEF on different devices deserves further research. In this paper, We propose a translator to convert the NNEF to clDNN. The clDNN is an open-source inference SDK, which is provided high-performance computation APIs on Intel hardware platforms. In our preliminary experiment results, the translator can speed up six times compared to the C implementation for the execution time of MobileNet_v1.
Face Forgery Detection Based on Deep Learning

In this paper, we propose two deep learning approaches for face forgery detection. The first approach uses neural networks to detect fake faces in individual images. Three methods of this approach are studied. The second approach uses long short-term memory recurrent neural networks to detect fake videos by checking inconsistencies between successive frames. The experiments show that accuracy of forgery detecting is around 90%.
Brain Storm Optimization based Association Rule Mining Model for Intelligent Phishing URLs Websites Detection

Phishing is an online unlawful act which takes place when a malicious webpage impersonates as genuine webpage for acquiring confidential details about the user. The phishing attack maintains to acquire a crucial risk factor for web user and annoying threat in the domain of electronic commerce. This study proposes a brain storm optimization (BSO) based association rule mining (ARM) model called BSOARM model to detect of genuine and phishing URLs. Here, BSO algorithm is applied to optimize the rules generated by ARM. The rule attained is deduced to highlight the features which are further common in phishing URLs.To performance of the BSO-ARM model has been tested using a Phishing Dataset. The projected BSO-ARM model has optimized the number of generated rules as 45 and attained maximum accuracy of 86.35%, precision of 81.60%, recall of 86.81% and F-score of 84.13% respectively. These values ensured that the BSO-ARM model has offered better outcomes over the compared methods.
A New Technique of Image Enhancement by Intensity Transformation

This paper is devoted to solving the problem of developing new effective technologies, which focused on fully-automated real-time image processing in mobile applications. The purpose of this work was to increase the efficiency of parameter-free image intensity transformation. A new technique for transforming the image intensity was proposed based on the analysis of the joint probability density of brightness and the distribution of the brightness increment of the image objects. The proposed technique is devoid of the main disadvantages and has several significant advantages compared to the traditional approach to the equalization of the histogram. The proposed technique improves the efficiency of increasing the overall image contrast and provides a more even distribution for the contrast in an image without the appearance of undesirable artifacts and can be recommended as an alternative to the traditional equalization of the image histogram. Our proposed technique is simple to implement, has a comparatively low level of computational costs, and designed for use in real-time mobile applications.
De verleidingen en gevaren van GrETEL*

Corpora are a useful and important source of evidence for linguistic research, but they are not the only kind of evidence, do not have any special status as evidence, and have their limitations. Recent very user-friendly applications such as GrETEL make it very easy to search in large and richly annotated corpora on the basis of an example sentence and without knowledge of a query language or the exact nature of the linguistic annotations. It is therefore very tempting to use these applications intensively. That is fine, but also dangerous in ways, because in many cases, in order to interpret the results correctly, the researcher must really be aware of the precise nature of the linguistic annotations and of the way in which the user-friendly interface generates a query on the basis of an example sentence. I will illustrate this with several examples. I also sketch some methods for avoiding or mitigating the dangers and argue that the applications should support these methods also in as user-friendly a manner as possible.
Blockchain Smart Contracts and Empathy Trade-Off

Contracts have emerged as an appropriate expanse for the application of Blockchain to eliminate human mediation perceived to be mired by weaknesses. Smart contracts date back to the 1990s, but the proposed Blockchain technology makes it a great force economically. Beyond the transactional processing qualities of blockchain, industries envisage the technology to resolve divergent human-related complications with traditional contracts. Per literature, smart contracts offer superior economic value with respect to legality, formation, deployment, execution, and cost. These qualities of smart contract ensure performance and eliminate risk. Criticised on the inhumane aspect of the technology in terms of contract amendments and the current influx of foreign-based blockchain companies in Africa limiting indigenous design considerations, the application of smart contracts in continent could be hindered by contract renegotiations strongly embedded in cultural values of empathy. Nonetheless, a trade-off would resolve the contractual bottlenecks in Africa.
Novice Teachers Entering the Profession: Problems and Needs Analysis

The paper is centered on the problems faced by novice foreign language teachers during the process of professionalization. Previous studies in this field demonstrate that despite different theoretical approaches to this issue, the researchers in our country and abroad tend to view the problems as internal (personal) and external (the ones caused by the institution, students and curricula). Our research was carried out in the form of questionnaire and semi-structured interview among young Engineering lyceum and private language school teachers with teaching experience of 1–3 years. The results suggest that teachers’ problems fall into two broad categories: psychological and methodological. In addition to outlining the major difficulties of entering into the profession, we aimed at finding out the practices which can facilitate successful transition from novice teachers to professionals. The teachers expressed the opinion that supervision of a senior colleague or mentor during the period of induction might solicitously help. The findings led to the conclusion that professional communication plays crucial role not only in developing practical teaching skills but also in retaining novice teachers in the profession.
Hacking the Foodscape: Digital Communication in the Co-design of Sustainable and Inclusive Food Environments

Supplying healthy, eco-friendly, and affordable food is becoming a priority of governments all over the world as the unsustainability of the industrial food system is realised. At the same time planners, technology companies, and municipal authorities have driven the concept of the “smart” and “resilient” city which has digital communications infrastructure embedded across all functions to ensure sustainable economic development, high standards of living for residents, and efficient resource management. In relation to food, much of this infrastructure to date has focused on the enhancement of procurement strategies and consumption rather than engaging eaters in the co-design of local food environments or “foodscapes”—the places we buy, prepare, talk, share, or glean meaning from food. This chapter explores the potential for digital technologies to enable the participation of all residents, including the food insecure, in co-designing more sustainable and inclusive foodscapes for their communities and therefore be part of broader systems transformation.
Implementation of Minimally Shared Blockchains using Big Data Applications

Due to the scalability factor associated with a blockchain, it is inevitable that it will grow with time. Therefore, one of the primary concerns associated with a blockchain is its growing size as more blocks are introduced into the chain. When a full node or a mining node joins a blockchain network, it requires downloading all the existing information available in the distributed chain before it can add new blocks. Thus, as time progresses and more blocks are appended to the chain, a large amount of information needs to be replicated in its local machine. We introduce an approach to keep a minimally shared blockchain in the network which keeps the size of the distributed ledger limited and, thus, new miners can participate in the mining process at a faster pace.
Accurate Line-Based Relative Pose Estimation With Camera Matrices

While most monocular structure-from-motion frameworks rely on sparse keypoints, it has long been acknowledged that lines represent an alternative, higher-order feature with high accuracy, repeatability, and abundant availability in man-made environments. Its exclusive use, however, is severely complicated by its inability to resolve the common bootstrapping scenario of two-view geometry. Even with stereo cameras, a one-dimensional disparity space, as well as ill-posed triangulations of horizontal lines make the realization of purely line-based tracking pipelines difficult. The present paper successfully leverages the redundancy in camera matrices to alleviate this shortcoming. We present a novel stereo trifocal tensor solver and extend it to the case of two camera matrix view-points. Our experiments demonstrate superior behavior with respect to both 2D-2D and 3D-3D alternatives. We furthermore outline the camera matrix's ability to continuously and robustly bootstrap visual motion estimation pipelines via integration into a robust, purely line-based visual odometry pipeline. The result leads to state-of-the-art tracking accuracy comparable to what is achieved by point-based stereo or even dense depth camera alternatives.
Blockchain-Powered Real Estate System

The Indian real estate industry has played a significant role in the economic development of the country. It is continually showing lush and future growth. However, due to traditional practices that are related to the following industry have given rise to many significant issues. Some of the issues pertaining to the Indian real estate industry are bureaucracy, mistakes in the public record, lack of transparency, fraud, fees, amount of time, and the third parties that are involved in closing on a property. Blockchain technology has the potential to solve these problems as it provides immutable storage, traceability as a default feature, and greater transparency through a distributed ledger. In this paper, we review some of the existing systems related to the use of blockchain in real estate and propose an alternate blockchain-based real estate system for the Indian real estate industry. In the proposed system, blockchain technology serves as a reliable storage model for property-related information and can be used to perform operations such as property transactions like purchase, sale, renting, leasing, and management transactions through the use of smart contracts. The goal is to eliminate the flaws of the traditional system and ease, evolve, and secure the experience of the two parties performing various trading on the land and real estate.
Rapid Aircraft Classification in Satellite Imagery using Fully Convolutional Residual Network

Advancement in high-performance computing technology has paved way for development of Deep Learning algorithms for computer vision to provide unprecedented performance both in terms of accuracy and speed. Image recognition, a subfield of computer vision, is one of the key application areas in which deep learning based Convolutional Neural Networks (CNN have achieved ground-breaking performance). Majority of the algorithms of object classification in CNN are focused on street view imagery that is of high resolution and have small size. The problem with satellite imagery is that it has objects in small size and images are especially in large size. There are two main objectives of this research: time reduction of processing large dimensions satellite images, and achieving acceptable accuracy of classifying small aircrafts. For this purpose, ResNet-50 has been modified such that it can process high-resolution satellite imagery of large dimension in one go instead of processing it in small patches sequentially, without affecting the accuracy of object classification. ResNet-50 with sliding-window scanning technique and the proposed model trained on satellite imagery are compared. The proposed method reduces the processing time by 99.9% by keeping the accuracy at the same level.
Implementation of AES Using Composite Field Arithmetic for IoT Applications

The presented work carries out a Very Large Scale Integration (VLSI) implementation of the Advanced Encryption Standard (AES) symmetric cipher to investigate for its best-suited architecture for IoT applications. Standard architectures, such as, rolling, unrolling and combinational were examined. S-box, which forms the core of AES was designed using composite field arithmetic and an optimized form was used in each architecture design to improve hardware efficiency. The design, verification and RTL synthesis of the algorithm was done using Xilinx Vivado 2018.3 simulator. Stringent area and power requirements being the prior criteria for IoT devices, the rolled architecture turned out to be the favorite candidate upon analysis of the result.
An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive Physical Layer (PHY) design for beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE provides a new PHY paradigm with a pure data-driven and end-to-end learning based solution. However, significant challenges are to be overcome before this approach becomes a serious contender for practical beyond-5G systems. One of such challenges is the robustness of AE under interference channels. In this paper, we first evaluate the performance and robustness of an AE in the presence of an interference channel. Our results show that AE performs well under weak and moderate interference condition, while its performance degrades substantially under strong and very strong interference condition. We further propose a novel online adaptive deep learning (ADL) algorithm to tackle the performance issue of AE under strong and very strong interference, where level of interference can be predicted in real time for the decoding process. The performance of the proposed algorithm for different interference scenarios is studied and compared to the existing system using a conventional DL-assist AE through an offline learning method. Our results demonstrate the robustness of the proposed ADL-assist AE over the entire range of interference levels, while existing AE fail to perform in the presence of strong and very strong interference. The work proposed in this paper is an important step towards enabling AE for practical 5G and beyond communication systems with dynamic and heterogeneous interference.
Implementation of an Image Compression Algorithm Based on Low Complexity Integer Approximate Discrete Tchebichef Transform

The discrete cosine transform (DCT) is used widely in the area of image compression. Recently, the Discrete Tchebichef Transform (DTT) is reported to be superior to DCT in terms of coding performance. In this paper, DTT is presented first. Then a scheme of deriving the integer approximate DTT is introduced. The integer approximate DTT is used to compress a gray-scale image. Since the resulting integer approximate DTT matrix is not normal, we need to multiply a diagonal matrix to its left. When we use the integer matrix to perform the transform, the diagonal matrix have to be merged into the quantization process. We derive the equivalent quantization matrix and the inverse quantization matrix. Compared with JPEG and exact DTT, the integer approximated DTT shows a bit lower reconstructed image quality but with extreme low complexity.
Swarm intelligence and fuzzy sets for bed exit detection of elderly

Falls in the elderly are a public health problem because this population tends to have a longer recovery time and consequently longer hospital beds. Studies show that 84% of falls in hospital rooms occur near the bed, that led to strategies to prevent falls in the elderly population have been studi ed. In this context, this paper presents a schema for the detection and emission of bed exit alerts in the elderly. This schema uses signals derived from RFID sensors processed by a model based on Intelligent Swarm and Fuzzy Sets. The main contribution of this study is the use of a Membership Windows that reduces the effects of missclassification of other strategies. The proposed work evaluated a data set containing 14 elderly aged between 66 and 86 years divided into two rooms. The results show that the presented approach improves the precision and recall in environments with greater uncertainty of classification.
Application of Bayesian Tobit Regression to Global Radiation

This study investigated global radiation with respect to some independent variables such as relative humidity, air temperature, net radiation and soil heat flux, wind direction and wind speed including standard-in-wind direction. Bayesian Tobit regression model was used with informative prior to determine which of these independent variables correlate with global radiation. The data used for this research was obtained from the National Meteorological Institute, Lagos. The data contains over six thousand observations with the dependent variable consisted of over three thousand zeros which was left censored. The coefficients were estimated by the MLE method. The result showed that all the variables were positively skewed except relative humidity that was left skewed. Wind speed and soil influx strongly associated with global radiation while relative humidity helps in reducing global radiation.
Secure Data Hiding Technique Using Batch Video Steganography

Steganography is an evolving technology that has become a significant contributor to the field of data protection and security. The art of masking the secret information and denying its existence in such way no one can detect, by covering confidential information inside an innocent cover, no one can know what is hidden in the carrier except the concerned sender and receiver. Due to the rapid innovation of software programming on the Internet, there has been increasing attention in ways of hiding information in other information. Many techniques are available to prevent unauthorized users from accessing information without owner approval. Steganography is the method to provide a secure communication atmosphere with minimum doubt of the human imperceptibility. Specialty the video steganography because of the huge amount of the frames in each second in the video where we can utilize the frames to replace the secret information within, batch video steganography system (BVS) were used in this work to enhance the security and to avoid any unauthorized access, when split the secret payloads into a batch of fragments where each fragment is stored in a different video cover. Without any increase or decrease that may occur on the video file that containing the confidential data.
Which Software Faults Are Tests Not Detecting?

Context: Software testing plays an important role in assuring the reliability of systems. Assessing the efficacy of testing remains challenging with few established test effectiveness metrics. Those metrics that have been used (e.g. coverage and mutation analysis) have been criticised for insufficiently differentiating between the faults detected by tests. Objective: We investigate how effective tests are at detecting different types of faults and whether some types of fault evade tests more than others. Our aim is to suggest to developers specific ways in which their tests need to be improved to increase fault detection. Method: We investigate seven fault types and analyse how often each goes undetected in 10 open source systems. We statistically look for any relationship between the test set and faults. Results: Our results suggest that the fault detection rates of unit tests are relatively low, typically finding only about a half of all faults. In addition, conditional boundary and method call removals are less well detected by tests than other fault types. Conclusions: We conclude that the testing of these open source systems needs to be improved across the board. In addition, despite boundary cases being long known to attract faults, tests covering boundaries need particular improvement. Overall, we recommend that developers do not rely only on code coverage and mutation score to measure the effectiveness of their tests.
Measuring and Controlling Text Generation by Semantic Search

Our motivation in this work is to measure patent text generation by semantic search, particularly by textual similarity in high dimensional space for neural network models. The objective is to control patent text generation by semantic search. Conceptually it is an attempt to integrate two subfields in NLP: text generation and semantic search. In our previous milestone of the PatentTransformer project, a prototype based on GPT-2 is capable of generating fluent patent title, abstract, independent claim, and dependent claim. However, beneath the surface form, the quality issue in the generated patent text was less explored. How to control text generation is also a hard problem in NLP field. We would like to address these issues in this work and experiment with different approaches. On the measurement side, this work will address the quality measurement issue from the perspective of textual similarity. Based on that, the approaches we propose include two embedding spaces, span-based textual similarity, and language model for patent claim spans. One the control side, we propose a knob-turning approach for controlling text generation based on measuring a range of textual similarity. In this way, we can search for a Goldilocks zone in which the similarity of generated patent text is close to but not too far from prior patents. We hypothesize that patent novelty may exist in such a zone.
Cost Sensitive Evaluation of Instance Hardness in Machine Learning

Measuring hardness of individual instances in machine learning contributes to a deeper analysis of learning performance. This work proposes instance hardness measures for binary classification in cost-sensitive scenarios. Here cost curves are generated for each instance, defined as the loss observed for a pool of learning models for that instance along the range of cost proportions. Instance hardness is defined as the area under the cost curves and can be seen as an expected loss of difficulty along cost proportions. Different cost curves were proposed by considering common decision threshold choice methods in literature, thus providing alternative views of instance hardness.