Reviewerly/exHarmony huggingface dataset:
Computer Aided System (CAS) Of Lymphoblast Classification For Acute Lymphoblastic Leukemia (ALL) Detection Using Various Pre-Trained Models

Computer Aided System (CAS) is an automated, fast and accurate approach for detection and classification purposes. It is used to help experts or medical practitioner as a second opinion to analyze the blood smear image. It is done manually by some practitioners but it is time consuming and creates confusion as different pathologists give different observations and results as it is highly dependent on the experts' skills. Other than that, it is also challenging to analyze it manually as there are thousands of images. Some researchers used CAS by applying the machine learning to classify the data. However, significant features must be known before proceeding with classification process. In this paper, Convolutional Neural Network (CNN) is applied to classify the WBC types to identify Acute Lymphoblastic Leukemia (ALL). It is a better approach as no complex features need to be designed and it is a fast response program. Pre-trained models of deep learning which are AlexNet, GoogleNet and VGG-16 are compared to each other to find the model that can classify better. There are 260 images in IDB-2 database and 242 images in LISC database. Five types of WBC are classified for LISC database while for IDB-2 database, Lymphoblast and Non-Lymphoblast is classified specifically. As a result, for both database, AlexNet achieve the best result in terms of the training and testing accuracy for each class. Training accuracy for IDB-2 is 96.15% while testing accuracy for Lymphoblast and Non-Lymphoblast is 97.74% and 95.29% respectively. Training accuracy by AlexNet for LISC is 80.82% and testing accuracy is the highest for each class except Monocyte. Overall, AlexNet works better than the other two models for classification for both databases.
ExperienceThinking: Constrained hyperparameter optimization based on knowledge and pruning

Machine learning models are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize hyperparameter settings according to known evaluation information, so as to effectively promote the performance of the machine learning models within the limited and small budget. Motivated by this, in this paper, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations. ExperienceThinking designs two novel approaches, which make full use of the known evaluation information to intelligently infer optimal configurations from two aspects: search space pruning and knowledge utilization respectively. Two approaches suit for two different kinds of constrained hyperparameter optimization problems, they complement with each other and their combination increases the generality and effectiveness of the ExperienceThinking. To demonstrate the benefit of ExperienceThinking, we conduct extensive experiments using various constrained hyperparameter optimization problems, and compare it with classic hyperparameter optimization algorithms. The experimental results present that our proposed algorithm provides superior results and the design of our proposed algorithm is reasonable.
Digital Forensic Acquisition and Analysis of Discord Applications

Digital forensic analyses are being applied to a variety of domains as the scope and potential of digital evidence available is vast. The importance of forensic analyses of web-based devices and tools is increasing, coinciding with the rise in online criminal activity. Discord - an application that allows text, image, video, and audio communication using VoIP - has become increasingly popular and is consequently subject to increased use by cybercriminals. While researching Discord servers and forensic artefacts, it is apparent that there is limited literature and experimentation in this domain. This paper presents our research into digital forensic analyses of Discord client-side artefacts and presents DiscFor, a novel tool designed for the extraction, analysis, and presentation of Discord data in a forensically sound manner. DiscFor creates a safe copy of said data, presenting the current cache state and converting data files into a readable format.
LibComp: an IntelliJ plugin for comparing Java libraries

Software developers heavily rely on third-party libraries to accomplish their programming tasks. Since many libraries offer similar functionality, it can be difficult and tedious for developers differentiate similar libraries in order to select the most suitable one. In our previous work, we proposed the idea of metric-based library comparisons that allow developers to compare various aspects of libraries within the same domain, empowering them with information to aid with their decision. In this paper we present an IntelliJ plugin, LibComp, that provides this library metric-based comparison technique right within the developer’s IDE. As soon as a developer adds a library dependency that LibComp has information about, LibComp will highlight this dependency to let the developer know that there are alternatives available. Once the user triggers the comparison for that library, they can view various metrics about the library and its alternatives and decide if they want to use one of the alternatives. In the process, LibComp also records the number of times the developer invokes the tool and any completed replacements. Such feedback, if optionally sent to us by the developer, provides us valuable insights into developers’replacement decisions as well as information on how we can improve the tool. A video demonstrating the usage of LibComp can be found at https://youtu.be/YtEEdJan77A
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax

Deep neural networks have achieved remarkable successes in learning feature representations for visual classification. However, deep features learned by the softmax cross-entropy loss generally show excessive intra-class variations. We argue that, because the traditional softmax losses aim to optimize only the relative differences between intra-class and inter-class distances (logits), it cannot obtain representative class prototypes (class weights/centers) to regularize intra-class distances, even when the training is converged. Previous efforts mitigate this problem by introducing auxiliary regularization losses. But these modified losses mainly focus on optimizing intra-class compactness, while ignoring keeping reasonable relations between different class prototypes. These lead to weak models and eventually limit their performance. To address this problem, this paper introduces a novel Radial Basis Function (RBF) distances to replace the commonly used inner products in the softmax loss function, such that it can adaptively assign losses to regularize the intra-class and inter-class distances by reshaping the relative differences, and thus creating more representative prototypes of classes to improve optimization. The proposed RBF-Softmax loss function not only effectively reduces intra-class distances, stabilizes the training behavior, and reserves ideal relations between prototypes, but also significantly improves the testing performance. Experiments on visual recognition benchmarks including MNIST, CIFAR-10/100, and ImageNet demonstrate that the proposed RBF-Softmax achieves better results than cross-entropy and other state-of-the-art classification losses. The code is at https://github.com/2han9x1a0release/RBF-Softmax .
Motivational Effects of Immersive Media on Adolescents’ Engagement in Cantonese Opera

Recent advancements in communication and computer technologyTechnology have made available and affordable the immersive technologyTechnology necessary for broader applications beyond industrial use. Facing growing competition from the emergence of new mediaMedia as an attractive option for youngsters' leisure time and cultural enjoyment, traditional arts such as Cantonese operaCantonese opera have undergone important transformations in a bid to survive the rapidly changing sociocultural landscape. With a dearth of research on the stimulating effects of new mediaMedia, this chapter presents the findings of a project that examined the motivational effects of immersive mediaMedia on adolescents' engagement in Cantonese operaCantonese opera. Thirty-two undergraduate students from Hong Kong were invited to participate in a study that required them to watch a Cantonese operaCantonese opera theatre performance using both conventional and immersive mediaMedia; to that end, a 360-degree Cantonese operaCantonese opera theatre performance was produced and viewed using a virtual realityVirtual reality (VR) headset, after which the participants' viewing experiences and perceived motivational effects were compared with a conventional 2-D screen display via a questionnaire survey. Findings revealed that the immersive mediaMedia was found to be more interactive and motivational than the conventional approach, revealing the possibility of better engaging the younger generation in the appreciation and learning of this particular genre.
Story-driven Video Editing

This paper proposes a novel multimedia task: story-driven video editing. Given a story paragraph, this task aims to retrieve related video segments from a gallery of collected video segments and compose them into a video sequence by the storyline order. Our proposed baseline solution consists of three modules: a retrieval module, which returns lists of candidate segments for all query sentences in the story paragraph using an object-aware sentence-segment matching method; a sequence candidate proposal module, which aggregates the retrieved segment sets into a sequence proposal by the submodular optimization method; a sorting module, which arranges the candidates according to the storyline of the paragraph using the Sinkhorn network. We build a benchmark for this task including a reorganized version of the ActivityNet Captions dataset, a well-defined quantitative metric called Evaluation of Segment-to-Sequence Matching (ESSM) for measuring the difference between the generated video segment sequence and the ground truth. Quantitative results of the proposed baseline solution are reported. We hope this new task and benchmark will bring broad research attention and push forward a lot of novel online short video editing applications.
Performance evaluation of non-linear techniques UMAP and t-SNE for data in higher dimensional topological space

Dimension reduction is the vital area in data science & analytics for visualization, and significant pre-processing step for artificial intelligence and machine learning based analysis. For 3D visualization and data analytics of higher dimensional data, it is mandatory to reduce it into lower dimensional subspace. Higher dimensional data existence is everywhere in all type of sectors like Telecom, healthcare infrastructure, Finance, Banking, Transport, eCommerce etc. Applying regression analysis directly on higher dimensional data in machine learning or AI based analytics not recommended. Generally, before analysis, such data is reduced to lower dimensional topological subspace, maintaining the essence of original data. In this paper, a performance comparison of two competitive projection-based non-linear dimension reduction techniques - UMAP and t-SNE with a combination of PCA as a linear based method is analyzed with telecom gateway data. Apart from this, both non-linear techniques are compared based on 3D visualization of handwritten digits images.
On the Complexity of the Smallest Grammar Problem over Fixed Alphabets

Abstract In the smallest grammar problem, we are given a word w and we want to compute a preferably small context-free grammar G for the singleton language { w } (where the size of a grammar is the sum of the sizes of its rules, and the size of a rule is measured by the length of its right side). It is known that, for unbounded alphabets, the decision variant of this problem is N P -hard and the optimisation variant does not allow a polynomial-time approximation scheme, unless P = N P . We settle the long-standing open problem whether these hardness results also hold for the more realistic case of a constant-size alphabet. More precisely, it is shown that the smallest grammar problem remains N P -complete (and its optimisation version is A P X -hard), even if the alphabet is fixed and has size of at least 17. The corresponding reduction is robust in the sense that it also works for an alternative size-measure of grammars that is commonly used in the literature (i. e., a size measure also taking the number of rules into account), and it also allows to conclude that even computing the number of rules required by a smallest grammar is a hard problem. On the other hand, if the number of nonterminals (or, equivalently, the number of rules) is bounded by a constant, then the smallest grammar problem can be solved in polynomial time, which is shown by encoding it as a problem on graphs with interval structure. However, treating the number of rules as a parameter (in terms of parameterised complexity) yields W [1]-hardness. Furthermore, we present an $\mathcal {O}(3^{\mid {w}\mid })$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>∣</mml:mo> <mml:mi>w</mml:mi> <mml:mo>∣</mml:mo> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:math> exact exponential-time algorithm, based on dynamic programming. These three main questions are also investigated for 1-level grammars, i. e., grammars for which only the start rule contains nonterminals on the right side; thus, investigating the impact of the “hierarchical depth” of grammars on the complexity of the smallest grammar problem. In this regard, we obtain for 1-level grammars similar, but slightly stronger results.
Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding

Information extraction from documents such as receipts or invoices is a fundamental and crucial step for office automation. Many approaches focus on extracting entities and relationships from plain texts, however, when it comes to document images, such demand becomes quite challenging since visual and layout information are also of great significance to help tackle this problem. In this work, we propose the attention-based graph neural network to combine textual and visual information from document images. Moreover, the global node is introduced in our graph construction algorithm which is used as a virtual hub to collect the information from all the nodes and edges to help improve the performance. Extensive experiments on real-world datasets show that our method outperforms baseline methods by significant margins.
A True AR Authoring Tool for Interactive Virtual Museums

In this work, a new and innovative way of spatial computing that appeared recently in the bibliography called True Augmented Reality (AR), is employed in cultural heritage preservation. This innovation could be adapted by the Virtual Museums of the future to enhance the quality of experience. It emphasises, the fact that a visitor will not be able to tell, at a first glance, if the artefact that he/she is looking at is real or not and it is expected to draw the visitors' interest. True AR is not limited to artefacts but extends even to buildings or life-sized character simulations of statues. It provides the best visual quality possible so that the users will not be able to tell the real objects from the augmented ones. Such applications can be beneficial for future museums, as with True AR, 3D models of various exhibits, monuments, statues, characters and buildings can be reconstructed and presented to the visitors in a realistic and innovative way. We also propose our Virtual Reality Sample application, a True AR playground featuring basic components and tools for generating interactive Virtual Museum applications, alongside a 3D reconstructed character (the priest of Asinou church) facilitating the storyteller of the augmented experience.
A literature review on the usage of Technology Acceptance Model for analysing a virtual reality's cycling sport applications with enhanced realism fidelity

The usage of Virtual Reality (VR) technology in VR sport to train athletes is becoming more popular. Previous researches on VR sport shows promising results where the acceptance of VR technology among athletes are higher. It is also found that VR sport has created a positive improvement to athletes training performance. In many research related to VR sport, it is discovered that the acceptance of VR technology in sport is often validated through the use of Technology Acceptance Model (TAM). TAM has evolved where a new model called Unified Theory of Acceptance and Use of Technology (UTAUT) is introduced. One of the elements currently being evaluated is visual realism. Visual realism has elements that can be divided into two groups: basic and advance. Basic elements are visual, audio and interaction. Meanwhile advance elements are interactive platform, altitude-effect, temperature level, and wind-effect. These elements influence the fidelity of realism when athletes undergoes their VR training exercise to enhance athlete's performance. The aim of this paper is to identify the usage of TAM for validating users' acceptance towards VR-base exercise. In addition this paper also focuses on identifying the effective elements that influence athletes' acceptance on VR technology targeted to sport cycling.
Deep Multi-View Learning to Rank

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We present a generic framework for multi-view subspace learning to rank (MvSL2R), and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. Moreover, we introduce an end-to-end solution to learning towards both the joint ranking objective and the individual rankings. The proposed solution enhances the joint ranking with minimum view-specific ranking loss, so that it can achieve the maximum global view agreements in a single optimization process. The proposed method is evaluated on three different ranking problems, i.e. university ranking, multi-view lingual text ranking and image data ranking, providing superior results compared to related methods.
Extreme Learning Machine with Evolutionary Parameter Tuning Applied to Forecast the Daily Natural Flow at Cahora Bassa Dam, Mozambique

This paper proposes a hybrid approach combining an Extreme Learning Machine and a Genetic Algorithm to predict the short-term streamflow at the Cahora Bassa dam, the largest hydroelectric power plant in southern Africa. To predict the streamflows seven days ahead, the model uses as input the past river flows, information from humidity, rainfall, and evaporation measures from the lake upstream of the dam. The choice of the Extreme Learning Machine’s internal parameters, crucial for excellent model performance, is performed by a Genetic Algorithm. A set of five metrics was used to assess the performance of the hybrid approach. The computational experiments show the proposed approach outperforms other machine learning methods such as ElasticNet linear model, Support Vector Machines, and Gradient Boosting. However, the ELM prediction model overestimates higher flows. The approach arises as a practical tool to predict the streams which have the potential to help the dam operations balancing the needs of energy production and the safety of the population living downstream of the dam.
Verifiable Quantum Secret Sharing Based on a Single Qudit

To detect frauds from some internal participants or external attackers, some verifiable threshold quantum secret sharing schemes have been proposed. In this paper, we present a new verifiable threshold structure based on a single qubit using bivariate polynomial. First, Alice chooses an asymmetric bivariate polynomial and sends a pair of values from this polynomial to each participant. Then Alice and participants implement in sequence unitary transformation on the $d$-dimensional quantum state based on unbiased bases, where those unitary transformations are contacted by this polynomial. Finally, security analysis shows that the proposed scheme can detect the fraud from external and internal attacks compared with the exiting schemes and is comparable to the recent schemes.
WITHDRAWN: Network piano teaching platform based on FPGA and machine learning

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
HISyn: human learning-inspired natural language programming

Natural Language (NL) programming automatically synthesizes code based on inputs expressed in natural language. It has recently received lots of growing interest. Recent solutions however all require many labeled training examples for their data-driven nature. This paper proposes an NLU-driven approach, a new approach inspired by how humans learn programming. It centers around Natural Language Understanding and draws on a novel graph-based mapping algorithm, foregoing the need of large numbers of labeled examples. The resulting NL programming framework, HISyn, using no training examples, gives synthesis accuracy comparable to those by data-driven methods trained on hundreds of training numbers. HISyn meanwhile demonstrates advantages in interpretability, error diagnosis support, and cross-domain extensibility.
Edge Computing for Video Analytics in the Internet of Vehicles with Blockchain

In intelligent transportation systems (ITS), video analytics is a potential technology to enhance the safety of the Internet of Vehicles (IoV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for IoV. Furthermore, due to the unstable network connection, the video data are not always reliable, which makes data sharing lack of security and scalability in IoV. In this paper, for video analytics applications, the multi-access edge computing (MEC) and blockchain technologies are integrated into IoV to optimize the transaction throughput as well as reducing the latency of the MEC system. Furthermore, the joint optimization problem is formulated as a Markov decision process (MDP), and the asynchronous advantage actor-critic (A3C) algorithm is adopted to solve this problem. Simulation results show that the proposed approach can fast converge and signifcantly improve the performance of blockchain-enabled IoV with MEC.