Reviewerly/exHarmony huggingface dataset:
Development of an Ensemble Multi-stage Machine for Prediction of Breast Cancer Survivability

Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. ‎In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the final decision and can be ignored from the feature set‎. ‎Therefore‎, ‎developing a machine for prediction of survivability‎, ‎which ignores these features for simple cases and yields an acceptable prediction accuracy‎, ‎has turned into a challenge for researchers‎. ‎In this paper‎, ‎we have developed an ensemble multi-stage machine for survivability prediction which ignores difficult features for simple cases‎. ‎The machine employs three basic learners‎, ‎namely multilayer perceptron (MLP), ‎ support vector machine (SVM), and decision tree (DT)‎, ‎in the first stage to predict survivability using simple features‎. ‎If the learners agree on the output‎, ‎the machine makes the final decision in the first stage‎. Otherwise, ‎for difficult cases where the output of learners is different‎, ‎the machine makes decision in the second stage using SVM over all features‎. The developed model was evaluated using the Surveillance, Epidemiology, and End Results (SEER) database. The experimental results revealed that ‎the developed machine obtains considerable accuracy while it ignores difficult features for most of the input samples‎‎.
3D Printing Process of Making a Smartphone Holder

Smartphones have become a vital gadget that cannot be separated from human daily life. Various features provided on smartphones can attract attention, especially for children. Children today are very familiar with the smartphone. However, when they are using the smartphone, they often cannot manage the distance of eyes and the smartphone. Therefore, a smartphone holder that can control the distance can be a solution to this problem. The smartphone is designed with the flexibility to arrange the length of the stand depending on user needs. This paper presents the process of making the smartphone holder using a 3D printer. Through product development phase like design creation, digital simulation, computer aided design-computer aided manufacturing (CAD-CAM), and the production process of the holder with 3D printer, the smartphone holder is produced.
Object Tracking Based on Saliency and Adaptive Background Constraint

A target tracking algorithm with significant target indication and adaptive background constraint is proposed to solve the problem of tracking target lost caused by the disturbance of environment and the posturechange. Based on the particle filter tracking framework, the method weights the pixel features of the target region and the extended target region respectively, and constructs the target's indicative model. The background region is adaptively selected to constrain the tracking process according to the significance of the background region. The algorithm reduces the error in target matching and improves the tracking accuracy when the target is occluded or the attitude changes. Experimental results show that the algorithm has strong tracking robustness and high tracking accuracy.
Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

Image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. Present research work mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. It focuses on the study of image segmentation methods and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region and summarizes their advantages and disadvantages. Then, the segmentation method based on the active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model are improved and simulated. Furthermore, from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models computational efficiency of Segmentation method is increased by 9.2 times, 2.64 times, and 1.44 times, respectively.
Practical CCA-Secure Functional Encryptions for Deterministic Functions

Functional encryption (FE) can implement fine-grained control to encrypted plaintext via permitting users to compute only some specified functions on the encrypted plaintext using private keys with respect to those functions. Recently, many FEs were put forward; nonetheless, most of them cannot resist chosen-ciphertext attacks (CCAs), especially for those in the secret-key settings. This changed with the work, i.e., a generic transformation of public-key functional encryption (PK-FE) from chosen-plaintext (CPA) to chosen-ciphertext (CCA), where the underlying schemes are required to have some special properties such as restricted delegation or verifiability features. However, examples for such underlying schemes with these features have not been found so far. Later, a CCA-secure functional encryption from projective hash functions was proposed, but their scheme only applies to inner product functions. To construct such a scheme, some nontrivial techniques will be needed. Our key contribution in this work is to propose CCA-secure functional encryptions in the PKE and SK environment, respectively. In the existing generic transformation from (adaptively) simulation-based CPA- (SIM-CPA-) secure ones for deterministic functions to (adaptively) simulation-based CCA- (SIM-CCA-) secure ones for randomized functions, whether the schemes were directly applied to CCA settings for deterministic functions is not implied. We give an affirmative answer and derive a SIM-CCA-secure scheme for deterministic functions by making some modifications on it. Again, based on this derived scheme, we also propose an (adaptively) indistinguishable CCA- (IND-CCA-) secure SK-FE for deterministic functions. The final results show that our scheme can be instantiated under both nonstandard assumptions (e.g., hard problems on multilinear maps and indistinguishability obfuscation (IO)) and under standard assumptions (e.g., DDH, RSA, LWE, and LPN).
Deep Neural Network-Based Human Emotion Recognition by Computer Vision

Over the past years and in recent times, a lot of research is being focused on intensifying the human–machine interaction because there is a great need of communication channel between machines and humans to share a variety of tasks. The communication between humans and machines can be verbal or non-verbal. Even though verbal communication provides complete understanding of the communication, considering the non-verbal communication can sometimes help in better understanding the correctness of the message. So, artificial intelligent systems with visual perception helps in better understanding their environment to provide a smoother and natural interaction with humans. As human face is extremely expressive, facial expression plays a pivotal role in non-verbal communications. This paper presents a deep learning-based system for computer vision, i.e., a system for automatically recognizing human emotion by analyzing the facial expressions of humans using convolutional neural networks. Experiments were carried out on standard facial expression recognition dataset taken from Kaggle challenges repository, and graphical processing unit is also used to reduce the training time of the system. The results revealed that the accuracy of CNN model used can achieve the state-of-the-art recognition rate.
Pengembangan Bahan Ajar Sistem Peredaran Darah Manusia Berbasis Electronic Book Terintegrasi dengan Virtual Labs untuk Siswa Kelas XI IPA SMA

Abstract. Teaching materials for electronic blood circulation systems based on electronic books were developed based on the lack of learning resources and media available in schools. This product development will make students learn independently and use learning resources that can be used anytime and anywhere. This study aims to produce products in the form of teaching materials for electronic bookbased circulatory systems in which there are media in the form of text, images, videos, virtual labs that are done through e-learning media (bioedukasi.com) and are valid. Electronic book-based teaching material developed using the Kvisoft application so that the display of teaching materials is like a printed book in general. The fundamental differences in teaching materials developed have a variety of media in the form of videos that can be played directly in teaching materials. The development research model conducted refers to the four D (4D) model initiated by Thiagarajan. Model four D (4D) consists of the defining stage (define), the design phase (design), the stage of development (develop) and the stage of deployment (disseminate). Based on the research results obtained that the product in the form of teaching materials based on electronic books and e-learning media (bioedukasi.com) is valid. The valid value of the teaching material obtained is 4.36 while the valid value of e-learning is 4.43. The two products produced are valid so it is feasible to use. Keywords: teaching materials, electronic books, virtual Labs, elearning
Public policy in a blockchain era

This chapter provides a framework to think about public policy choices in response to blockchain technology. It first introduces the idea of ‘crypto-friendly’ public policy as a set of policy dispositions around regulatory clarity and consistency and a permissionless innovation attitude to the risks posed by new technologies. The chapter explores the complex balance between regulatory certainty and the rapid need for regulatory change in response to new institutional innovation. It then presents these policy change dilemmas in an international context. Blockchains are available wherever the internet is available, and blockchain firms can locate themselves in the most crypto-friendly jurisdiction.
Sentiment Analysis on Weibo Platform for Stock Prediction

Recent years has witnessed a growing trend on the stock prediction through sentiment analysis of social media. This paper proposes a stock-predicting method based on the sentiment analysis of Weibo, utilizing sentiment orientation exhibited in texts posted on Weibo and analysis on the historical data of stock market to forecast the price movements in stock market. To achieve the goal, three steps are strictly followed, corresponding to three parts of the paper: data pre-processing (DP), sentiment analysis (SA) and stock prediction (SP). In the data pre-processing phase, the construction of a financial dictionary is elaborated, and then, an improved LDA model is shown to classify Weibo text in accordance with Industrial Classification Benchmark (ICB) and to match them with corresponding topics. Then, the sentiment analysis part demonstrates that how the accuracy of sentiment analysis is enhanced through the newly designed method by incorporating the interaction between words to the rule set to quantify sentiment value. In the last part, the stock prediction is accomplished by using Multivariate SVM incorporating Weibo sentiment. The time range of collected Weibo data is 12 months.
SAFC: Scheduling and Allocation Framework for Containers in a Cloud Environment

Currently, the intrinsic nature of cloud computing offers new opportunities for scheduling unit of works as containers. In this paper we present a new Scheduling and Allocation Framework for Containers (SAFC) based on an economic model. The novelty of our framework is to gives the possibility to specify ranges on the resources demand for containers instead of specifying a fixed amount of resource. Thus, the goal and the new developments in this paper are to propose a general framework which dynamically schedules containers and decides, on the fly, the number of resources that must be allocated to optimize the use of cloud resources and to maximize the number of containers executed. The key idea of the paper is in relaxing strict constraints on the requested number of resources and on letting the system to adjust the amount of resources according to the execution context, such as the peaks of activities. The SAFC framework is evaluated using the Linux Containers (LXC) inside the Grid’5000 testbed.
A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud

The requests of the companies for the development and deployment of their business-applications in Cloud become more complex so that, sometimes, a one single service cannot carry out the target task on its own. Hence, a user-request is provided as a composite service. On another note, the number of available services is significantly increasing. Therefore, the authors would need to find the optimal cloud service-compositions that satisfy the quality of service values as well as user requirements. The methods proposed in literature for composing cloud services do not consider the composition and deployment constraints of candidate cloud services. This paper presents a novel optimization-based approach for building business-application in Cloud. The proposed approach combines the particle swarm optimization algorithm with some principles of ant colony optimization algorithm to deal with multiple QoS parameters, but also to satisfy the composition and deployment constraints of cloud services. The experimental results show the efficiency of the method for all tests instances.
The Four Arithmetic Operations for Handwritten Digit Recognition Based On Convolutional Neural Network

The deep learning networks has become an active area in the realm of machine learning in recent years and some relevant models such as convolutional neural network (CNN) models have a good performance in handwritten character recognition. The MNIST is a typical open source data set, which only has ten kinds of handwritten digital characters from 0 to 9. Furthermore, the majority of the open source data sets in the current literature exclude arithmetic operators of "+" , "-" , "×" , "÷" , "=". In order to address this issue, firstly, we established a data sets of 22,200 characters, which includes fifteen categories. Specially, for each category, 1,200 characters is used for training and 280 characters is used for testing respectively. Secondly, we proposed a deep learning networks algorithm for characters recognition based on CNN. Thirdly, as an application, we further implemented four arithmetic operation using the recognized characters. The experimental results demonstrate that the proposed deep learning networks algorithm has shown strong ability for feature extraction and classification with the average accuracy of 97.42 %. More meaningfully, the four arithmetic operation in the present work has strong robustness and the potential applied value.
Understanding What Software Engineers Are Working on

Understanding what a software engineer (a developer, an incident responder, a production engineer, etc.) is working on is a challenging problem -- especially when considering the more complex software engineering workflows in software-intensive organizations: i) engineers rely on a multitude (perhaps hundreds) of loosely integrated tools; ii) engineers engage in concurrent and relatively long running workflows; ii) infrastructure (such as logging) is not fully aware of work items; iv) engineering processes (e.g., for incident response) are not explicitly modeled. In this paper, we explain the corresponding 'work-item prediction challenge' on the grounds of representative scenarios, report on related efforts at Facebook, discuss some lessons learned, and review related work to call to arms to leverage, advance, and combine techniques from program comprehension, mining software repositories, process mining, and machine learning.
Semantic strategies in ubiquitous music: Deploying the sound sphere ecology in transitional settings

We report the results of a study involving twenty subjects doing musical activities in transitional settings, supported by an ecology of tools based on the metaphor for creative action Sound Sphere. The Sound Sphere Ecology (SFS) is a set of web-based tools, loosely organized around audio mixing and processing tasks. It employs verbal strategies for knowledge transfer to provide support for lay participants and specialists. To understand how the stakeholders influence and are influenced by this design strategy, we carried out a series of experiments involving assessments of the participants' behaviours and of the sonic products during various creative musical tasks with SFS. The overall results were positive, indicating that the proposed metaphor provides effective support for casual interaction, highlighting the participants' level of engagement. As a downside, the assessments pointed to ease of use as the lowest and less consistent item among the rated creative factors. We discuss the implications of these results and propose various design enhancements to enable the usage of a larger pool of resources. Considering the heterogeneous profiles of casual stakeholders, methodological refinements are also proposed to assess the knowledge gained by the participants during the exploratory activities, while augmenting their ability to share knowledge. This is one of the first studies on creativity-action metaphors for casual interaction.
A super-resolution reconstruction algorithm based on feature fusion

Super-resolution reconstruction is a technique for recovering corresponding high-resolution images from low-resolution images. Aiming at the problem of insufficient learning ability of the generative network in the SRGAN, a super-resolution reconstruction algorithm of generative adversarial network based on feature fusion is proposed. Recursive residual networks and prior knowledge are used to extract edge and texture features, while densely connected networks are used to fully utilize the features. Aiming at the problem that the input of the discriminative network of the SRGAN has a high computational complexity for the entire image, a method for discriminant residual discrimination is proposed. Two difference maps are used as input images of the discriminative network, and two discriminative methods are used for training. The test data set was used to verify the performance of the algorithm. The subjective index was significantly better than the original algorithm. The objective index PSNR increased by 0.77db, and SSIM increased by 2.8%.
Numerical computing on the web: benchmarking for the future

Recent advances in execution environments for JavaScript and WebAssembly that run on a broad range of devices, from workstations and mobile phones to IoT devices, provide new opportunities for portable and web-based numerical computing. Indeed, numerous numerical libraries and applications are emerging on the web, including Tensorflow.js, JSMapReduce, and the NLG Protein Viewer. This paper evaluates the current performance of numerical computing on the web, including both JavaScript and WebAssembly, over a wide range of devices from workstations to IoT devices. We developed a new benchmarking approach, which allowed us to perform centralized benchmarking, including benchmarking on mobile and IoT devices. Using this approach we performed four performance studies using the Ostrich benchmark suite, a collection of numerical programs representing the numerical dwarf categories identified by Colella. We studied the performance evolution of JavaScript, the relative performance of WebAssembly, the performance of server-side Node.js, and a comprehensive performance showdown for a wide range of devices.
A Low Quantum Cost Implementation of Reversible Binary-Coded-Decimal Adder

The prediction and forthcoming of a quantum computer into the real-world is the much gained research area over the last decades, which initiated the usefulness and profit of reversible computation because of its potentiality to reduce power consumption in designing arithmetic circuits. In this paper, two design approaches are proposed for the design of a reversible Binary-Coded-Decimal adder. The first approach is implemented and realized from reversible gates proposed by researchers in the technical literature capable of breaking down into primitive quantum gates, whereas the second approach is realized from the existing synthesizable reversible gates only. Parallel implementations of such circuits have been carried out through the proper selection and arrangements of the gates to improve the reversible performance parameters. The proposed design approaches offer a low quantum cost along-with lower delay and hardware complexity for any n-digit addition. Analysis results of proposed design 1 show appreciable improvements over gate count, quantum cost, and delay by at least 9 %, 17 %, and 26 % respectively, whereas, the proposed design 2 show that the results significantly improve the parameters (gate count, quantum cost, and delay) by at least 45 %, 33 %, and 50 % respectively compared to existing counterparts found in the literature.
Developing a Magnification Prototype Based on Head and Eye-Tracking for Persons with Low Vision

Severe visual impairments make it difficult for users to work on a computer. For this reason, there is great demand for new technical aids on the computer to compensate for these limitations. Current magnification software makes it possible to adjust the screen content, but due to the lack of overview and the time-consuming use of the mouse it is sometimes difficult to find the right content. If another physical disability is involved, working on a computer often becomes even more difficult. In this paper, we present the development of an affordable magnification system based on a low-cost eye-tracking device, which can be adjusted to the visual impairment without the need for a mouse or keyboard by using the line of vision derived from eye or head movements. Two studies with experts and potential users showed the usefulness of the system.
Assisting the RDF Annotation of a Digital Humanities Corpus Using Case-Based Reasoning

The Henri Poincaré correspondence is a corpus composed of around 2100 letters which is a rich source of information for historians of science. Semantic Web technologies provide a way to structure and publish data related to this kind of corpus. However, Semantic Web data editing is a process which often requires human intervention and may seem tedious for the user. This article introduces , an editor which aims at facilitating annotation of documents. This tool uses case-based reasoning (cbr) to provide suggestions for the user which are related to the current document annotation process. These suggestions are found and ranked by considering the annotation context related to the resource currently being edited and by looking for similar resources already annotated in the database. Several methods and combinations of methods are presented here, as well as the evaluation associated with each of them.