Reviewerly/exHarmony huggingface dataset:
Handwritten Devanagari Character Classification Using CNN

Deep Convolutional Neural Network (CNN) has been effectively outlined acknowledgement of the written by hand character in Devanagari content. Convolutional Neural Networks build up a framework that can be set up to perceive the morphology of pictures and perform class estimate and concentrate the property of pictures. Written by hand character acknowledgement is a huge undertaking as it tries to perceive the right class for client autonomous manually written digits. Preparing of the model is finished utilizing MNIST dataset This challenge is much more difficult with a heavily impressionistic, morphologically complicated, and inherently juxtapositional character consisting of dialect such as Devanagari. A dataset of 73,600 samples were trained and tested in this proposed CNN that are separated to perform both training and testing which provided quite impressive results. 97% exactness was gotten for the test information when the system was tried with a Devanagari character dataset.
Designing engineering onboarding for 60+ nationalities

A large international engineering office in Germany needed to double in size in 12 months. We designed an onboarding programme within 3 months to help it do so efficaciously. We wanted to optimize for: fast iterations in the programme rollout, to keep the 'flywheel spinning' by reducing drag on current staff, rapid acceleration where new hires contributed quickly, and smooth integration where new hires adapted to the engineering, company, and country cultures.
Hybrid Bacterial Foraging Sine Cosine Algorithm for Solving Global Optimization Problems

Abstract This paper proposes a new hybrid algorithm between Bacterial Foraging Algorithm (BFA) and Sine Cosine Algorithm (SCA) called Hybrid Bacterial Foraging Sine Cosine Algorithm (HBFSCA) to solve global optimization problems. The proposed HBFSCA algorithm synergizes the strength of BFA to avoid local optima with the adaptive step-size and highly randomized movement in SCA to achieve higher accuracy compared to its original counterparts. The performances of the proposed algorithm have been investigated on a set of single-objective minimization problems consist of 30 benchmark functions, which include unimodal, multimodal, hybrid, and composite functions. The results obtained from the test functions prove that the proposed algorithm outperforms its original counterparts significantly in terms of accuracy, convergence speed, and local optima avoidance.
An alternative practical public-key cryptosystems based on the Dependent RSA Discrete Logarithm Problems

Abstract In this paper, an alternative public-key cryptosystems (PKCs) are proposed based on the new algebraic problems namely “Dependent RSA Discrete Logarithm Problems” derived from the RSA and Discrete Logarithm (DLog) assumptions together. These PKCs are provably secure for the notions of security: indistinguishable encryptions under chosen-plaintext attacks (IND-CPA), and adaptive chosen-ciphertext attacks (IND-CCA2). Initially, a new algebraic “Computational-Dependent RSA Discrete Logarithm Problem” is presented. Then, its variant named “Decisional-Dependent RSA Discrete Logarithm Problem” is presented. Thereafter, a specific discussion has been done about their hardness and their relations to each other. Also, some arguments are given to validate the cryptographic purpose of these problems. Next, using this decisional variant an efficient PKC: “Dependent RSA Discrete Logarithm” (DRDL) cryptosystem that has indistinguishable encryptions under chosen-plaintext attacks, in the standard model is presented. Further, a PKC variant: DRDL-1 cryptosystem with improved security properties that has indistinguishable encryptions under adaptive chosen-ciphertext attacks using this decisional variant in the random oracle model, with a low computational cost is presented. These new algebraic problems constructed by using the apparent hardness of RSA and Discrete Logarithm (DLog) problems are helpful in combining both efficiency and security. Hence, it becomes more efficient than all the cryptosystems specially designed for the ElGamal cryptosystem to make it indistinguishable encryptions under adaptive chosen-ciphertext attacks.
A Gradient Boosting-Seq2Seq System for Latin POS Tagging and Lemmatization

The paper presents the system used in the EvaLatin shared task to POS tag and lemmatize Latin. It consists of two components. A gradient boosting machine (LightGBM) is used for POS tagging, mainly fed with pre-computed word embeddings of a window of seven contiguous tokens—the token at hand plus the three preceding and following ones—per target feature value. Word embeddings are trained on the texts of the Perseus Digital Library, Patrologia Latina, and Biblioteca Digitale di Testi Tardo Antichi, which together comprise a high number of texts of different genres from the Classical Age to Late Antiquity. Word forms plus the outputted POS labels are used to feed a seq2seq algorithm implemented in Keras to predict lemmas. The final shared-task accuracies measured for Classical Latin texts are in line with state-of-the-art POS taggers (∼0.96) and lemmatizers (∼0.95).
Design of English text-to-speech conversion algorithm based on machine learning

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.
Altitude-Adaptive and Cost-Effective Object Recognition in an Integrated Smartphone and UAV System

Human Search and Rescue (SAR) tasks are mission-critical and take place in the wild, and thus solutions require timely and accurate human detection on a highly portable platform. This paper proposes a novel lightweight and practical SAR system that meets those demanding requirements by running optimised machine learning in a smartphone, interoperable with Unmanned Aerial Vehicles (UAV) that provides live video feed. In particular, the proposed approach significantly extends a standard machine learning algorithm to achieve adaptive object recognition in response to changing altitudes to accelerate the speed of finding missing people and eliminate redundant computing. Our approach achieved 91.02% of accuracy and real-time speed on a smartphone that hosts the machine learning platform and the new algorithm. This proposed system is highly portable, cost-effective, fast with high accuracy suitable for UAV applications.
Improving evolutionary constrained clustering using Active Learning

The high cost of labeling data for analysis increased the interest in semi-supervised learning, especially constrained clustering, which usually involves reduced cost. At the same time, Active Learning (AL) aims to minimize the cost of creating labeled datasets by trying to identify which unlabeled data are more relevant for using during the learning process, considering which labels are already available. This paper proposes four AL strategies to an evolutionary constrained clustering algorithm (FIECE-EM) based on Gaussian Mixture Models (GMM), with corresponding theoretical asymptotic analyses. These strategies use information from many different sources, such as the partition, the population, and even specific aspects of the algorithm. We perform empirical evaluation on 14 well-known datasets, as a way to measure the impacts of each strategy both in relation to accuracy and labeling cost. The results were compared with baseline supervised algorithms as well as COBRAS, a state-of-the-art Active Learning algorithm for constrained clustering. Three of the proposed strategies obtained significantly better results than COBRAS in our empirical evaluation. Thus, the combination of FIECE-EM with these strategies can be considered viable alternatives for AL in a constrained clustering setting.
3D object reassembly using region-pair-relation and balanced cluster tree

Object reassembly is a key technology in scenarios such as surgical planning and broken object restoration. Based on previous research, this work intends to explore the general tasks of 3D object reassembly, including conventional object reconstruction and bone fracture reduction. We introduce an efficient and robust region-pair-relation descriptor, which incorporates strong geometric constraints and remains invariant to rotation and translation. We segment the fractured objects using balanced cluster tree, and develop a coarse-to-fine method for object reassembly. The matching quality of potential region contact pairs at different depths is estimated recursively from the root of the tree. Once the best contact pairs are determined, the least squares method is implemented to obtain the matching results. In addition, we also provide a semi-interactive manipulation to deal with the complex objects. For most types of broken objects, our approach can generate high accuracy matching results within 10 s, with the cluster tree depth equals to 11. It allows the automatic reassembly of different-sized fragments. For bone fracture blocks with cancellous structures, a semi-interactive operation is integrated so that the precise matching can also be achieved in 30 s. The proposed framework can be expanded to various object reassembly tasks in either automated or semi-automated manner, including the fracture reduction problem which used to be an intensive manual process. Therefore, our work shows significant advantages in medical applications.
Developing Safe Smart Contracts

Blockchain is a shared, distributed ledger on which transactions are digitally recorded and linked together. Smart Contracts are programs running on Blockchain and are used to perform transactions in a distributed environment without need for any trusted third party. Since smart contracts are used to transfer assets between contractual parties, their safety and security are crucial and badly written and insecure contracts may result in catastrophe. Actor-based programming is known to solve several problems in building distributed software systems. Moreover, formal verification is a solid technique for developing dependable systems. In this paper, we show how the actor model can be used for modeling, analysis and synthesis of smart contracts. We propose Smart Rebeca as an extension of the actor-based language Rebeca, and use the model checking toolset Afra for verification of smart contracts. We implement a synthesizer to synthesize Solidity programs that run on the Ethereum platform from Smart Rebeca models. We examine the challenges and opportunities of our approach in modeling, formal verification, and synthesis of smart contracts using actors.
A Survey of Blockchain Based Government Infrastructure Information

Blockchain inherently involves a growing list of records, called blocks, each block is encrypted and distributed across the network. This attribute of Blockchain provides added security to the transmitted data, which has been extensively exploited in the finance sector. Blockchain is a recent technology that can revolutionize conventional business practices. There are multiple advantages of using Blockchain technology on an international scale, it can reduce departmentalism, increase the efficiency of administrative processes and enhance the level of trust in public recordkeeping and provide significant value to citizens. Based on state-of-the-art developments, there is a dire need to successfully demonstrate that Blockchain is a useful tool to governments all across the planet. This paper provides a comprehensive survey that offers details of the various solutions that countries across the globe have adopted for effective use of Blockchain Technology in strengthening secure information flow between nations.
A High-Throughput Tumor Location System with Deep Learning for Colorectal Cancer Histopathology Image

Colorectal cancer is one of the major causes of morbidity and mortality worldwide, however, when discovered at an early stage, it is highly treatable. As the number of specimens increases every year, there has been a boost in the diagnostic workload on pathologists in recent years. In parallel to the development of digital pathology, deep learning has demonstrated its strong capability in feature extraction and interpretation in a variety of medical applications. In this paper, we propose a high-throughput whole-slide image (WSI) analysis system to localize tumor regions accurately with a patch-based convolutional neural network (CNN). We employ Monte Carlo adaptive sampling for a fast detection of tumors at slide level and a conditional random field (CRF) model to integrate spatial correlation for better classification accuracy. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) for performance evaluation. Compared with the regular WSI analysis, the experimental benchmark shows an obvious decrease in processing time while a noticeable improvement in classification accuracy.
Named Entity Recognition for the Horticultural Domain

Abstract Named Entity Recognition (NER) is one of the fundamental tasks in natural language processing and knowledge engineering, as well as a prerequisite step of many downstream applications. Horticulture, a major branch of agricultural science, means the cultivation, processing, and sale of fruit, nuts, vegetables, and ornamental plants as well as numerous additional services. NER for the horticulture domain means to find key biological traits and state-of-art experimental methods for the horticulturists, new cultivation methods and useful tools for farmers, as well as other important information for planners and policy makers to trigger decision-making procedures. In this paper we designed an NER tagging-set of 7 fine-grained types, and since there is no publicly shared annotated corpus available in horticulture domain, we constructed training and testing corpora manually. Thus, we realized Horticulture NER in scientific literature abstracts with CRF method. Results showed that our system’s accuracy and precision were satisfactory but still have room for improvement.
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding

Heterogeneous information networks(HINs) become popular in recent years for its strong capability of modelling objects with abundant information using explicit network structure.Network embedding has been proved as an effective method to convert information networks into lower-dimensional space, whereas the core information can be well preserved.However, traditional network embedding algorithms are sub-optimal in capturing rich while potentially incompatible semantics provided by HINs.To address this issue, a novel meta-path-based HIN representation learning framework named mSHINE is designed to simultaneously learn multiple node representations for different meta-paths.More specifically, one representation learning module inspired by the RNN structure is developed and multiple node representations can be learned simultaneously, where each representation is associated with one respective meta-path.By measuring the relevance between nodes with the designed objective function, the learned module can be applied in downstream link prediction tasks.A set of criteria for selecting initial meta-paths is proposed as the other module in mSHINE which is important to reduce the optimal meta-path selection cost when no prior knowledge of suitable meta-paths is available.To corroborate the effectiveness of mSHINE, extensive experimental studies including node classification and link prediction are conducted on five real-world datasets.The results demonstrate that mSHINE outperforms other state-of-the-art HIN embedding methods.
A Bayesian semiparametric Gaussian copula approach to a multivariate normality test

Semiparametric copulas are useful tools for modeling a multivariate distribution whose dependence structure is induced by a known copula and whose marginal distributions are estimated. In this paper, a Bayesian semiparametric copula approach is used to model the underlying multivariate distribution Ftrue. First, the Dirichlet process is constructed on the unknown marginal distributions of Ftrue. Then a Gaussian copula model is utilized to capture the dependence structure of Ftrue. As a result, a Bayesian multivariate normality test is developed by combining the relative belief ratio and the Energy distance. Various interesting theoretical results of the approach are derived. Several examples that cover the high dimensional case are discussed to illustrate the approach.
Inversion Framework: Reasoning about Inversion by Conditional Term Rewriting Systems

We introduce a language-independent framework for reasoning about program inverters by conditional term rewriting systems. These systems can model the three fundamental forms of inversion, i.e., full, partial and semi-inversion, in declarative languages.
Designing a Neural Network Primitive for Conditional Structural Transformations

Among the problems of neural network design the challenge of explicit representing conditional structural manipulations on a sub-symbolic level plays a critical role. In response to that challenge the article proposes a computationally adequate method for design of a neural network capable of performing an important group of symbolic operations on a sub-symbolic level without initial learning: extraction of elements of a given structure, conditional branching and construction of a new structure. The neural network primitive infers on distributed representations of symbolic structures and represents a proof of concept for the viability of implementation of symbolic rules in a neural pipeline for various tasks like language analysis or aggregation of linguistic assessments during the decision making process. The proposed method was practically implemented and evaluated within the Keras framework. The network designed was tested for a particular case of transforming active-passive sentences represented in parsed grammatical structures.
Negative Comments Multi-Label Classification

It is a known fact that on the daily basis significant amount of information is produced due to large number of people being connected to social networking sites. Online interaction is now included in our lifestyle whether it is through a tweet, a message or through commenting on each other posts on different platforms. Online interaction contributes a significant part to our society but also contains several dangerous results. This paper mainly focuses on the cons of social interaction and a way through which it can be decreased. Aggression and related activities such as trolling peoples, harassing online involves hate comments in various forms. There are numerous such cases coming in present time and sites respond by closing down their remark areas. After the introduction of Machine Learning and having data in massive amount now its quite logical to build a tool which can tackle this situation. While there are algorithmic solution for these problem but they are slow and expensive which make us curious about new approaches and frameworks. Four models are used for the purpose of classifying the comments, however the model which performed better than other three is Bi-LSTM + Bi-GRU model with accuracy of 97.89.
Cryptanalysis of “Certificateless remote data integrity checking using lattices in cloud storage”

C. Sasikala et al.'s proposed a certificateless RDIC protocol to audit the outsourced data without the requirement of PKI infrastructure and against quantum computer attacks. They argue the RDIC is correct and possesses desired security properties. However, through cryptoanalysis technique we find that their scheme is neither correct nor secure against two common attacks, that is, signature forgery attack from the malicious cloud service provider (CSP) and public key replacement attack from the curious private key generator (PKG) and other malicious users. Furthermore, we remark that their RDIC is not a rigorous certificateless protocol.
JomDesignLab: Bringing Artwork Design Nearer

This paper explores the knowledge of how PrintingLab.my could improve their customer experience with the options of hiring a designer and enable client to make own name card design through a self-developed web-based software called DesignLab. The main purpose of this study is to understand the client's need, understand the pain points of small and medium enterprise to the need of printing and based on this study to develop a prototype product. Based on the data and requirements analysis, a web-based hiring designer and name card design software has been developed. It could analyse the user preference and suggests the suitable designer to their name card design need with the help of machine learning. There is second option where the client could design their own name card design. However, the software currently is still on testing phase, it still needs to undergo trials and testing before commercialise.
Designing for Experiences in Blended Reality Environments for People with Dementia

Blended Reality environments have the potential to provide scalable solutions that are affordable, adaptable and easily deployable to support people with dementia. Use of these technologies is associated with experience of presence which is an experience with technologically mediated perceptions that generates a feeling of being there and the illusion of non-mediation. Our study examines what constitutes an experience of presence for people with dementia when they interact with MRTs.An observational study with ten participants (MoCA = 18 to 23, Age = 63 to 88 years) played a game of Tangram on Osmo. Six of these participants also played Young Conker on HoloLens. The experiences of the participants in the digital space, the physical space, and their attention crossover between the two spaces were coded in Noldus Observer XT 14.1.The study found four main themes that have an impact on the experience of presence in PwD – correspondences, effortless access to physical and digital content, awareness of reality and emergence. Correspondences between physical and digital spaces require PwD to have constant information about the state and nature of physical and digital content. The transitions between physical and digital should be seamless. PwD demonstrated positive experiences with Osmo, an augmented Virtuality technology while their experience with HoloLens, augmented reality technology was negative. The factors impacting experience of presence were prominent in Osmo while they were mostly absent in HoloLens throughout the game play. The outcomes of this study have resulted in a set of recommendations and guidelines for designers to design correspondences for experience of presence. We are currently working on developing prototypes using these guidelines for evaluations with PwD.