Reviewerly/exHarmony huggingface dataset:
What-If Analysis on the Evaluation of User Interface Usability

While interactive systems have the potential to increase human work performance, those systems are predisposed to usability problems. Different factors might contribute to these problems during the interaction process and as result, the decision-making process might be compromised. This work uses decision support system methods and tools to assist in the analysis of the usability of a university library website, measuring the constructs of effectiveness, efficiency, and learnability. The pilot study involved thirty-five subjects, and after collecting data, a multidimensional view of the data is created and discussed. Later, a What-if analysis is used to investigate the impact of different scenarios on system-use. The work has the potential to assist designers and system administrators at improving their systems.
Identification of Writing on Bulletin Board via Tor

In recent years, awareness of protection of personal information has been increased, and anonymous communication that enables communication with hiding personal information attracts attention. Tor (The Onion Router) realizes anonymous communication most widely, but this anonymization is used for crime. This paper aims to identify users who maliciously write to the Internet bulletin board using Tor. This paper proposes a method of detecting Tor packet of writing on bulletin board. This method captures Tor packets and decides whether the packets is used for writing on bulletin board by using fingerprint. This paper implements the method and experiments to evaluates the method. The result of experiments shows that identification of writing on bulletin websites which use usual CGI programs is not successful at high probability and that modifying CGI programs is useful for identification of writing on bulletin websites. The result also shows that identification of writing on bulletin websites is difficult if users write on bulletin websites and access other websites such as streaming websites.
Differentially Private Tree-Based Contextual Online Learning for Service Big Data Selection in IoT

With the rapidly growing number of connected smart devices deployed and diverse services provided in the Internet of Things (IoT), selecting proper services for users is becoming more and more important. However, challenges exist as a result of the highly heterogeneous environments, characteristics of various kinds of users and the myriad services offered by many service providers, which have promising applications in the IoT era. In the meantime, users' contexts (e.g., location, time, and surroundings) are wildly utilized in the IoT scenario to better satisfy individuals' demands, raising privacy issues among people. To address these problems, we proposed a differentially private tree-based contextual online learning approach for IoT service selection to select suitable services for users. Leveraging on the historical records of services' and users' feedback, our algorithm achieves high prediction accuracy. Besides, instead of considering the services as individual items, we utilize a top-down cover tree structure to select services, which supports increasing large-scale dataset and diverse natural conditions. We theoretically prove that the accumulative regret of our approach has a sublinear bound and our experiment confirms that it can handle big data problems while achieving a balance between privacy-preserving level and service selection accuracy.
A hybrid RSA-DH cipher for Signed Encrypted Messages

Although brute-force attacks on the modulus component of the RSA algorithm are possible, increasing the key size frustrates perpetrators more. However large keys increase CPU and Memory usage. Key handling would also be a challenge. Alternative approaches for strengthening the RSA algorithm are required. In addition, verification of message sources is not a feature of the RSA algorithm. Message senders cannot be tracked. If the RSA algorithm is to be used commercially, sender verification is necessary. The two drawbacks to commercial use of the RSA algorithm are, therefore, possibilities of attacks and lack of sender verification. The DH algorithm allows secure transmission of codes over insecure public channels. Can RSA encrypted messages be “signed” using DH generated codes? Can concatenation of DH codes and RSA encrypted messages detour brute force attacks while, at the same time, allowing message verification? We propose the design of a hybrid RSA-DH algorithm which signed encrypted messages are shared. The hybrid version satisfies most properties of good cryptosystems: confidentiality, integrity, availability, authenticity, and accountability. The results reported (CPU and memory usage) show negligible differences in the relative performance of the hybrid and ordinary versions, even when the hybrid version combines the processes of two algorithms.
Disruption Prediction Approaches Using Machine Learning Tools in Tokamaks

Nuclear fusion is one of the best options to achieve a virtually limitless energy source in the future. However, sustaining burning plasma reactions is very challenging because disruptive events cause the loss of plasma confinement and damages to the tokamak machine. Thus, reproducing a nuclear fusion reaction on Earth represents an actual scientific and technical challenge. Next generation machines must be able to predict, mitigate or avoid the disruptions but the present understanding of the phenomenon has not yet gone so far as to provide a physical model describing their root cause. On the contrary, a large quantity of experimental disruption data exists. For these reason, in the last decades, machine learning techniques have been applied to design alarm systems both for mitigating or to actively avoid approaching disruptions. More recently, the disruption predictor concept has been evolving toward a more complex system, able to detect the proximity of the plasma state to the boundaries of the operational space free from disruptions in order to schedule avoidance strategies rather than mitigation actions. This system should also be able to determine which type of disruption is about to occur in order to efficiently take suitable disruption avoidance actions. This paper reports an overview of machine learning algorithms as tools for disruption prediction and classification at JET, the world's largest operational plasma physics experiment, located at Culham Centre for Fusion Energy in Oxfordshire, UK. Both traditional neural network models for disruption prediction and manifold learning approaches as a tool for plasma operational space mapping and visualization, disruption prediction and classification, are described.
Image Enhancement Filter

Digital image processing begins with the acquisition of image which is a start point for further analysis. Whenever an imaging camera takes a picture of some object, more often than not, that particular picture is unusable for the intended purposes directly. Noise in an image is a common phenomenon and is affecting the quality of the image hugely as there is significant amount of deviation in the concentration of pixels of the image. Disturbances and variation in image intensity largely contribute to noising in these images. Image filters are very useful in emphasizing edges or the boundaries between objects or parts of objects in images. Filters provide greater support in the visual interpretation of images. This paper reviews types of noises present in images. It also discusses ways for reducing the quantity of noise and thereby increasing the quality of an image. This paper discusses various filtering techniques and finally how to jointly remove Gaussian and salt-and-pepper noise.
TRANSFORMANTS BIT REPRESENTATION ENCODING WITHIN VIDEO BIT RATE CONTROL

The conceptual basements of constructing an effective encoding method within the bit rate control module of video traffic in the video data processing system at the source level are considered. The essence of using the proposed method in the course of the video stream bit rate controlling disclosed, namely, the principles of constructing the fragment of the frame code representation and approaches for determining the structural units of the individual video frame within which the control is performed. The method focuses on processing the bit representation of the DCT transformants, and at this processing stage transformant was considered as a structural component of the video stream frame at which the encoding is performed. At the same time, to ensure the video traffic bit rate controlling flexibility, decomposition is performed with respect to each of the transformants to the level of the plurality of bit planes. It is argued that the proposed approach is potentially capable to reducing the video stream bit rate in the worst conditions, that is, when component coding is performed. In addition, this principle of video stream fragmen code representation forming allows to control the level of error that can be made in the bit rate control process. However, in conditions where the bit representation of the transformant is encoded, the method is able to provide higher compression rates as a result of the fact that the values ​​of the detection probability of binary series lengths and the values ​​of detected lengths within the bit plane will be greater than in the case of component coding. This is explained by the structural features of the distribution of binary elements within each of the bit planes, which together form the transformer DCT. In particular, high-frequency transformer regions are most often formed by chains of zero elements. The solutions proposed in the development of the encoding method are able to provide sufficient flexibility to control the bit rate of the video stream, as well as the ability to quickly change the bit rate in a wide range of values.
Implementation of Intelligent Embryonic Green House System

In this paper, we try to develop a transparent green house system whose controllable ray, temperature and humidity modulation. This development will offer new step for the agriculture in our country.
A Link Growing Method for Clustering

Cluster analysis plays an important role in machine learning. In cluster analysis, there still exist many classical problems, like how to determine the number of clusters and how to find arbitrary-shaped clusters. We focus on these problems in this paper. We propose a link growing method by using the fact that the similarity within a cluster is usually high than the similarity in other clusters. The method first constructs multiple links, then merge links into clusters, finally a dataset is divided into multiple clusters. Experiments demonstrate the effectiveness of the proposed algorithm. The proposed algorithm has good performance on non-spherical datasets.
An Experience in Distance Robotics Education through an Extension Course

We deal in this article with the experience experienced with the elaboration of a fully EAD training course in robotics. We present the methodological proposal adopted using open source tools with the objective of reaching students and teachers of the public basic education network of a city in the state of Minas Gerais.
Selective Ensemble Modeling Method Based on Random Vector Functional Link Network and Game Theory

Ensemble learning can deal well with the concept drift problem in industrial process data. However, the accuracy and diversity of base learners have a great impact on the generalization performance of the ensemble model. In order to further improve the ability of ensemble model to cope with concept drift, this paper proposes a selective ensemble modeling method based on Random Vector Functional Link network (RVFL) and game theory. RVFL network is used as base learner, the accuracy of the base learner and the contribution rate of the base learner to the diversity of the ensemble model are regarded as the two sides of the game. Game theory is used to solve the optimal selection scheme for the accuracy and diversity of the ensemble model. The rationality and effectiveness of the proposed algorithm are verified by using open data sets and real industrial data.
Incepting on Language Structures with Phonological and Corpus Analysis Using Multilingual Computing

In this paper it is tried to incept into the structures of different languages around the world. The languages are selected on the basis of the four directions of this part of the world. Initially, the structures of the languages are explained. A model for machine translation is proposed in which the audio of a language is taken as input and subsequently convert the data into text and finally in binary to process the language. During the process, the model finds the phonetic distances and phonological distance and then blended both. The corpus generation is also made the model more useful for the further language computing. There are three languages such as Sanskrit Language (SL), English Language (EL) and the Russian Language (RL) considered for the analysis.
Kernel-Based Fuzzy Clustering for Sinusitis Dataset

Sinusitis is a condition resulting from inflammation of sinus walls. In handling the disease, machine learning method is often used to find more precise and accurate treatment plan for patients. For instance, fuzzy clustering is widely used for pattern recognition and data mining. Due to uncertainty and ambiguity, this method is used to overcome the non-linearity of medical dataset. In this study, fuzzy clustering was provided with kernel methods. We used some Kernel methods such as, Kernelized Fuzzy c-Means (KFCM), Kernelized Possibilistic c-Means (KPCM), Kernelized Fuzzy Possibilistic c-Means (KFPCM), and Kernelized Possibilistic Fuzzy c-Means (KPFCM) for clustering sinusitis dataset. The dataset was retrieved from Cipto Mangunkusumo Hospital Jakarta, Indonesia, which contains 4 features and 200 instances of this condition. These level of accuracy and model performance are used to compare these approaches. The result showed that KFCM has the highest accuracy for categorizing sinusitis dataset with accuracy of 96.97% and running time of 0.01 s.
Defending against ROP Attacks with Nearly Zero Overhead

Return-Oriented Programming (ROP) is a sophisticated exploitation technique that is able to drive target applications to perform arbitrary unintended operations by constructing a gadget chain reusing existing small code sequences (gadgets) collected across the entire code space. In this paper, we propose to address ROP attacks from a different angle-shrinking available code space at runtime. We present ROPStarvation , a generic and transparent ROP countermeasure that defend against all types of ROP attacks with almost zero run-time overhead. ROPStarvation does not aim to completely stop ROP attacks, instead it attempts to significantly increase the bar by decreasing the possibility of launching a successful ROP exploit in reality. Moreover, shrinking available code space at runtime is lightweight that makes ROPStarvation practical for being deployed with high performance requirement. Results show that ROPStarvation successfully reduces the code space of target applications by 85%. With the reduced code segments, ROPStarvation decreases the probability of building a valid ROP gadget chain by 100% and 83% respectively, with the assumptions that whether the adversary knows the vulnerable applications are protected by ROPStarvation . Evaluations on the SPEC CPU2006 benchmark show that ROPStarvation introduces nearly zero (0.2% on average) run-time performance overhead.
Dissemination and Development of Sino-japanese Literary Communication by Korean Envoys in Edo Period

Edo era is a special period of time in the history. It is between the end of Ming Dynasty and the beginning of Qing Dynasty. At that time, non-government trade is the main method of Chi-Japan communication because of the Seclusion Policy. However, Chinese government had little contact with Japan. On the contrary, Korea, who was the vassal state of Qing, began to send envoys to China as well as Japan. In this way, Korean envoys played a very important role in the literary communication between China and Japan. Korea, who was called “The Little China” at that time, had a deep relationship with China. As a result, Korea had much more knowledge about Sinology than Japan. Therefore, Korean scholars looked down on Japanese Sinology scholars. Nevertheless, in 1764, Korean envoys began to pay attention to Sorai’s literature and appreciate Japanese Sinology because of the great development of Sorai’s literature in Japan. This article will begin with the historical relationship among China, Korea and Japan, and research the reason why Korean scholars changed its attitude towards Japanese Sinology.
An Assistant Service for Customers in QR-payment with the Merchant Presented Mode

A QR payment assistant service in Merchant presented mode is proposed. The service could improve consumer's payment convenience and help the big data analysis of the payment data and goods supply chain. In this paper, we show the prototype design and implementation of the ordering system. The front-end Ajax helps customers to select the desired category/menu from the main menu, and the total price is calculated automatically.
Tibetan-Chinese cross-lingual word embeddings based on MUSE

Abstract The idea of word embedding is based on the semantic distribution hypothesis of linguist Harris (1954), who believes that words with the same semantics are distributed in similar contexts. The learning of word embedding is a crucial technology in natural language processing. In recent years, cross-language word vectors have received more and more attention. Cross-language Word vectors can transfer knowledge between different languages. Most importantly, this transfer can occur between rich-resource and low-resource languages. This paper uses the Tibetan-Chinese Wikipedia corpus to train monolingual word vectors. Based on the Tibetan-Chinese bilingual translations, we use the supervised method in the MUSE library to train the Tibetan-Chinese bilingual cross-language word embeddings. In the experiment, we evaluate the result of word representation on the standard lexical semantic evaluation task. The results show that the method has a certain improvement in the semantic representation of the word embedding.
Bi-directional Generation between Attributes and Images

This paper investigates the problem of generating images from visual attributes and vice versa. Given the prevailing research of image recognition, the bi-directional generation between attributes and images is rarely explored due to the challenges of learning a good bidirectionally generative model and the different structure of these two modalities. To address this problem, the bidirectional generative model (BGM) which based on a variant of variational auto-encoders (VAEs) is proposed in this paper. The attributes in BGM are represented by attribute functions. The attribute functions directly ground the meaning of attributes in visual representations. They also allow the BGM to generate images and attributes bi-directionally. The BGM is applied to 3D chairs dataset to verify its validity. The BGM achieves 85.2% and 81.7% accuracy in attribute inference and image reconstruction tasks, respectively. The experimental results demonstrate the efficiency of the BGM.
Product Backlog and Requirements Engineering for Enterprise Application Development

The combination of software engineering and agile development process can bring great benefits to the development and maintenance of enterprise applications. This chapter introduces the engineering of requirements in an agile Scrum development process. In Scrum, requirements are usually defined as user stories in a product backlog. Since many enterprise applications do not have intensive user actions, stories cannot be linked to users. Topics such as user and enabler stories, non-functional requirements, considerations of architecture and business components, business workflow, and breakdown of user stories are addressed. The requirements engineering of PBIs is illustrated by practical examples. Using UML use cases and collaboration models is recommended to transform the stories in the product backlog into formal requirements specifications. The proposed approach can be part of the agile development framework for flexible software products that are easy to customize and maintain.
iABR: An Intelligent Joint Adaptive Bitrate Selection and Communication Resource Allocation in F-RAN

Benefiting from the caching and transcoding capacity, the fog radio access network (F-RAN) has exhibited the promising potential to promote the performance of the adaptive bitrate (ABR) at the fog server. Despite extensive research on ABR algorithms, the joint optimization of bandwidth resource allocation and bitrate selection is a significant and interesting topic to maximize the QoE for all users in F-RAN. However, the joint optimization problem is intractable to be solved by traditional methods according to complex network dynamics and large user preference variances. In this paper, we propose an intelligent adaptive bitrate algorithm (iABR) based on a hierarchical actor-critic (HAC) agent at the F-RAN server to maximize the overall QoE of multiple users. The proposed iABR can perceive user preference and dynamics of wireless communication environment to automatically adjust the bandwidth resource allocation and bitrate selection policy. Simulation results illustrate that the proposed iABR exhibits more satisfying performance on overall QoE for multiple users compared with state-of-art reinforcement learning based ABR algorithm when adopting average bandwidth allocation and greedy bandwidth allocation.
Face Recognition Using Transfer Learning on UFI Dataset

Face Recognition is one of the most popular ways of biometric verification that are being applied worldwide, because of the convenience it offers. This encompasses large scale applications like corporate attendance systems to smaller ones such as unlocking hand-held devices and other various such kinds of applications. With the evolution of deep learning, face recognition systems have become increasingly accurate. One of the major reasons deep learning has become so popular for these types of tasks is because it does not require hand-crafted features. However, a major disadvantage of creating a deep learning model to recognize faces from scratch is the primary requirement of a huge amount of data. To counter this particular problem, another extremely popular technique called transfer learning is used to make the training process faster and with less data. In the current work, a novel real-world benchmark dataset is taken and the benchmark accuracy on it is increased by a large margin. The model in the current approach uses the concept of Siamese networks where triplets are generated for training. The triplets consist of an anchor image, a positive image of the same person and a negative image of a different person. This approach is particularly useful in this case because the amount of available data is less, along with the problem of class imbalance. The performance of the various models are compared with the previous results obtained using various features and classifiers and against one another.
Wavelet Analysis and Decomposition Into Color Spaces in Researching of Human Fluorescently Labeled Images Tissues

The image of human fluorescently labeled tissues is taken as short view of the subject. These images are in RGB format. We must highlight areas of interest. In this case we use the ideology of wavelets. It allows us to find a lot of points of the brightness change in the original image. We get additional information, as well as decompose the input image into separate color spaces. This allows us to specify the areas of interest for the original image. The advantage of the original image decomposition into separate color spaces is clearly shown in comparison with the image in the RGB format. The results are received by using the real images.