Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and advantages of using usability and user experience heuristics : case facebook

User experience became a buzzword in the mid 90’s after it was conceived. It quickly took over the previous position of usability as the leading term to cover the interactions between man and machine. Heuristics, originally created to assess usability in systems were adapted to try and evaluate the experience which is drawn from the interaction with the systems. However, usability and user experience differ from one another with many aspects. While the heuristic evaluation is procedurally similar when evaluating usability or user experience, the fundamental differences between the two challenge the applicability of the method itself. The aim of this thesis is to take a critical look at usability and user experience evaluation using general usability heuristics created by Nielsen and Molich in 1990 and user experience heuristics created by Arhippainen in 2009. This thesis will explore the challenges and advantages that heuristic evaluation of systems usability and user experience encompasses. This is done by reviewing relevant literature and by conducting two case studies for evaluating Facebook’s usability and user experience. This thesis will eventually present that the original advantages of heuristic evaluation of systems usability are mostly applicable to the UX heuristics with minor differences. However some additional challenges concerning the subjective and temporal nature of user experience are discovered when applying the UX heuristics. This thesis will also suggest that using heuristic evaluation to evaluate systems user experience is recommended not alone, if possible, but in conjunction with other methods.
Protecting Data by Improving Quality of Stego Image based on Enhanced Reduced difference Expansion

In this era of internet development, security of information sharing is the main problem faced by human being. Data hiding technique is one of the solutions. However, hiding credential information within a multimedia file such as image reduces its visual quality. Therefore, unauthorized users may suspect the existance of secret data within that image. In the past years, various data hiding algorithms have been developed by researchers to overcome the problem of high distortion of image after data embedding process. Achieving a high quality stego image, however, is still a challenging problem. In this paper, we proposed a new data hiding algorithm based on different expansion. It aims to enhance the quality of stego image for a given payload size. The new algorithm is evaluated on various medical images. Thereafter, the experimental results show that the visual quality is improved; and increasing the embedding capacity leads to more noises. Therefore, a better choice of base point and a reduced difference expansion affect the quality of stego image.
Natural language generation as neural sequence learning and beyond

Natural Language Generation (NLG) is the task of generating natural language (e.g., English sentences) from machine readable input. In the past few years, deep neural networks have received great attention from the natural language processing community due to impressive performance across different tasks. This thesis addresses NLG problems with deep neural networks from two different modeling views. Under the first view, natural language sentences are modelled as sequences of words, which greatly simplifies their representation and allows us to apply classic sequence modelling neural networks (i.e., recurrent neural networks) to various NLG tasks. Under the second view, natural language sentences are modelled as dependency trees, which are more expressive and allow to capture linguistic generalisations leading to neural models which operate on tree structures. Specifically, this thesis develops several novel neural models for natural language generation. Contrary to many existing models which aim to generate a single sentence, we propose a novel hierarchical recurrent neural network architecture to represent and generate multiple sentences. Beyond the hierarchical recurrent structure, we also propose a means to model context dynamically during generation. We apply this model to the task of Chinese poetry generation and show that it outperforms competitive poetry generation systems. Neural based natural language generation models usually work well when there is a lot of training data. When the training data is not sufficient, prior knowledge for the task at hand becomes very important. To this end, we propose a deep reinforcement learning framework to inject prior knowledge into neural based NLG models and apply it to sentence simplification. Experimental results show promising performance using our reinforcement learning framework. Both poetry generation and sentence simplification are tackled with models following the sequence learning view, where sentences are treated as word sequences. In this thesis, we also explore how to generate natural language sentences as tree structures. We propose a neural model, which combines the advantages of syntactic structure and recurrent neural networks. More concretely, our model defines the probability of a sentence by estimating the generation probability of its dependency tree. At each time step, a node is generated based on the representation of the generated subtree. We show experimentally that this model achieves good performance in language modeling and can also generate dependency trees.
Channel Variations in MIMO Wireless Communication Systems: Eigen-Structure Perspectives

Many recent research results have concluded that the multiple-input multipleoutput (MIMO) wireless communication architecture is a promising approach to achieve high bandwidth efficiencies. MIMO wireless channels can be simply defined as a link for which both the transmitting and receiving ends are equipped with multiple antenna elements. This advanced communication technology has the potential to resolve the bottleneck in traffic capacity for future wireless networks. Applying MIMO techniques to mobile communication systems, the problem of channel fading between the transmitters and receivers, which results in received signal strength fluctuations, is inevitable. The time-varying nature of the mobile channel affects various aspects of receiver design. This thesis provides some analytical methodologies to investigate the variation of MIMO eigenmodes. Although the scope is largely focussed on the temporal variation in this thesis, our results are also extended to frequency variation. Accurate analytical approximations for the level crossing rate (LCR) and average fade duration (AFD) of the MIMO eigenmodes in an independent, identically distributed (i.i.d.) flat-fading channel are derived. Furthermore, since several channel metrics (such as the total power gain, eigenvalue spread, capacity and Demmel condition number) are all related to the eigenmodes, we also derive their LCRs and AFDs using a similar approach. The effectiveness of our method lies in the fact that the eigenvalues and corresponding channel
Human Activities Recognition Based on Skeleton Information via Sparse Representation

Human activities recognition is a challenging task due to its complexity of human movements and the variety performed by different subjects for the same action. This paper presents a recognition algorithm by using skeleton information generated from depth maps. Concatenating motion features and temporal constraint feature produces feature vector. Reducing dictionary scale proposes an improved fast classifier based on sparse representation. The developed method is shown to be effective by recognizing different activities on the UTD-MHAD dataset. Comparison results indicate superior performance of our method over some existing methods. Category: Human-Computer Interaction
Quantitative approaches to investigating epithelial morphogenesis

Morphogenesis - the generation of biological shape and form - is fascinating, and its study promises to shed light on a wide range of developmental defects and inform strategies for the artificial growth of organs. Recently, the experimental study of morphogenesis has thrived due to a rise in quantitative methods.The resulting avalanche of quantitative data requires us to rethink the scientific method. We need to design quantitative hypotheses through mathematical models, make quantitative experimental predictions, devise methods for quantitative data analysis, and design methods for quantitative inference using models and data. This thesis aims to enable this transition for the integrative analysis of morphogenesis in epithelia, one of the major tissue types in animals. We begin by conducting the first systematic numerical analysis of a widely used cell-based model of epithelia, the vertex model, and estimate to what extent quantitative model predictions may be influenced by parameter values and implementation details. We then apply this model to a key question in developmental biology by constructing a quantitative theory for tissue size control in the embryonic epidermis of the fruit fly Drosophila, using the model to predict the outcomes of future experiments. Subsequently, we devise a method for estimating mechanical parameters of vertex models from imaging data and quantifying the uncertainty associated with such estimates. Finally, we propose a novel algorithm for robust cell tracking in live-imaging microscopy videos of epithelial tissues that illustrates how graph theoretic concepts may be used to overcome challenges in quantitative data analysis. Together, the contributions in this thesis will enable the quantitative study of epithelia for a wide range of applications.
Twenty years of fuzzy control: experiences gained and lessons learnt

The author examines fuzzy control from its inception to its recent widespread acceptance in industrial use and reflects upon matters such as its earlier dismissal, the reasons for its appeal, the nature of scientific research and engineering innovations. The focus is on rule-based fuzzy controllers. It is argued that the dominant position of analytic control theory prevented fuzzy control from being taken seriously until its increasing application in Japan. Many of the arguments against fuzzy control were framed in the language of that dominant theory and also countered in that language. It is further argued that fuzzy control and allied other techniques such as self-organizing fuzzy control, neural networks, genetic algorithms, and so on, provide an alternate paradigm to the analytic control theory. This paradigm consists of nonanalytic approaches to control and is based on decision-making approaches from artificial intelligence. <<ETX>>
Effective authoring of clinical study reports: A companion guide

‘Why write a clinical study report (CSR)? What are the guidance documents? Can I interpret them? Can I deliver my CSR on time? ’ This article addresses these questions – and others, provides a companion guide to CSR authoring for preregistration drug trials intended for regulatory submission in the EU, provides links to applicable regulatory guidance documents, and offers experience-based insights. Between 2008 and 2013, the authoring timeline for a medium complexity first draft (mean [SD]: 16.9 [8.2]; range: 5–45 working days) and final CSR from the first draft (mean [SD]: 25.7 [21.1]; range: 3–120 working days) varied widely across the industry. Understanding regulatory requirements and utilising project intelligence leads to informed CSR authoring and scheduling, thereby assuring a high-quality, on-time, final CSR.
Penyusunan Dan Pengujian Metrik Operabilitas Untuk Sistem Informasi Akademik Berdasarkan ISO 25010

Pesatnya perkembangan teknologi informasi dapat menjadi suatu keunggulan bagi organisasi untuk meningkatkan daya saingnya, tak terkecuali pada institusi pendidikan di tingkat perguruan tinggi.Kebutuhan teknologi informasi di perguruan tinggi sangat besar, terutama berkaitan dengan akademik.Sistem informasi akademik yang terkomputerisasi merupakan bagian penting di suatu perguruan tinggi. Hal ini dikarenakan dengan adanya sistem tersebut akan memudahkan dosen dalam memasukkan nilai akademik mahasiswa. Mahasiswa dapat mengakses informasi akademik atau kegiatan administrasi akademik.Sistem informasi akademik, sebagaimana sebuah sistem informasi pada umumnya, memerlukan adanya evaluasi untuk dapat mengetahui kekurangan-kekurangan yang ada untuk selanjutnya ditingkatkan dan dikembangkan.Oleh karena itu dibutuhkan metrik untuk mengukur kualitas dan mengevaluasi sistem informasi akademik.Penelitian ini melakukan penyusunan dan pengujian metrik operabilitas untuk sistem informasi akademik berdasarkan ISO 25010.Hasil yang didapatkan dari penelitian ini dapat menjadi masukan yang memperkaya kajian literatur dan empiris terkait pengukuran kualitas perangkat lunak menggunakan ISO/IEC 25010 karakteristik operabilitas.Data yang dibutuhkan untuk melakukan penyusunan dan pengujian metrik operabilitas untuk sistem informasi akademik berdasarkan ISO 25010 diperoleh dari studi liteatur, wawancara serta penyebaran kuesioner kepada responden pakar, dosen dan mahasiswa.Hasil dari penelitian ini dapat digunakan untuk mengevaluasi sistem informasi akademik berdasarkan ISO 25010 karakteristik operabilitas. Dari hasil penelitian, maka didapatkan hasil bahwa model yang diusulkan lebih akurat dibandingkan model lama, karena adanya penambahan dua subkarakteristik pada ISO 25010 karakteristik operabilitas yaitu helpfullness dan technical accessibility, yang mampu menaikkan akurasi model baru. Hasil pengukuran dari penelitian ini sudah memenuhi standar pengukuran kualitas. Standar kualitas untuk suatu aplikasi web adalah 70.
Efficient 3D Human Motion Perception System with Un-supervision, Randomization and Discrimination (Ein effizientes Wahrnehmungssystem menschlicher 3D-Bewegung durch die Nutzung von Nicht-Überwachung, Diskriminierung und Randomisierung)

Building a 3D human motion perception system in an efficient way remains non-trivial. This work presents two frameworks for the development of 3D human motion perception systems: autonomously feature learning with un-supervision and efficient system developing with randomization and discrimination. The major contribution of this thesis is the presentation of three advanced machine learning techniques, Un-supervision, discrimination and randomization , to guarantee the efficiency when developing 3D human motion perception system. Two applications of 3D human action and gesture recognition systems are developed to demonstrate the practicability of the approach.
Comparison of neural network architectures for sentiment analysis of Russian tweets

The paper presents evaluation of three neural network based approaches to Twitter sentiment analysis task performed at SentiRuEval-2016. The task focuses on sentiment classification of tweets about banks and telecommunication companies. Our team submitted three solutions which are based on different supervised classifiers: Gated Recurrent Unit neural network (GRU), convolutional neural network (CNN), and SVM classifier with domain adaptation combined with previous two classifiers. We used vector representations of words obtained with word2vec model as features for classifiers. These classifiers were trained on labeled data provided by organizers of the evaluation. Additionally, we collected several million posts and comments from social networks for training word2vec model. According to evaluation results, GRU-based solution shows the best macro-averaged F1-score for both domains (banks and telecommunication companies) and also has the best micro-averaged F1-score for banks domain among all solutions submitted to SentiRuEval.
Continuous Controls Monitoring: A Case Study

ABSTRACT: This case investigates the benefits and challenges that come with implementing continuous controls monitoring (CCM). CCM has been considered by the audit and information systems professions for years. However, a primary problem entails how to effectively integrate this methodology within large technological infrastructures. Because of increasing interest in controls monitoring, professional publications have generated information regarding the potential benefits of CCM, thereby creating awareness and addressing practical implementation issues along the way. We analyze the CCM program used by Premier Plasma Center, a global biotherapeutic and biotechnology company. This case is applicable to auditing, accounting information systems, and other business curricula that examine company controls and their monitoring activities. Data Availability: For data availability, please email the corresponding author.
A synthesis technique of time-domain interconnect models by MIMO type of selective orthogonal least-square method

This paper describes an efficient synthesis technique of time-domain models for interconnects characterized by sampled data. In this method, poles are derived from the sampled data in the frequency domain. Next, the dominant poles and the residues are obtained efficiently by multiinput-multioutput type of selective orthogonal least-square method. Furthermore, time-domain models are derived from the frequency-domain models. Finally, the accuracy and efficiency of the proposed method are substantiated by transient simulation of example circuits using this model.
Information capacity and power control in single-cell multiuser communications

We consider a power control scheme for maximizing the information capacity of the uplink in single-cell multiuser communications with frequency-flat fading, under the assumption that the users attenuations are measured perfectly. Its main characteristics are that only one user transmits over the entire bandwidth at any particular time instant and that the users are allocated more power when their channels are good, and less when they are bad. Moreover, these features are independent of the statistics of the fading. Numerical results are presented for the case of single-path Rayleigh fading. We show that an increase in capacity over a perfectly-power controlled (Gaussian) channel can be achieved, especially if the number of users is large. By examining the bit error-rate with antipodal signalling, we show the inherent diversity in multiuser communications over fading channels.
Characterization of extrusion formation during high temperature anneal

Stress relaxation in aluminum-copper (Al-Cu) films at elevated temperatures can result in problematic extrusion formation. Extrusions in Al-Cu lines can potentially grow towards neighboring lines and short devices, resulting in reliability and product yield concerns. Improved understanding of the driving mechanisms behind extrusion formation is necessary to quantify the contribution of different variables for better process window control. Scanning electron microscopy was used to view the extrusions that form along the sides of metal lines before oxide deposition. A kinetics study was performed on various metal stacks and metal line dimensions in addition to a comparison study of standard anneal and rapid thermal anneal processes. The results of these studies were analyzed and compared with a model assuming diffusion based growth as well as with a finite element model.
Remote laboratory experiments in electrical engineering education

A remote or online laboratory is a laboratory where one can access experiments and instruments or other equipment from outside over the Internet. Laboratories for undergraduate education or vocational training in basic electrical engineering are easy to control remotely. One cannot see or hear the electrical current, so there is no need for sound or video transmission. Computer-based instruments do not have any control buttons or displays on the front panel. They have virtual front panels on the host computer only and those panels can be moved to a remote computer screen. However, the manual forming of circuits and connecting of test probes cannot be transferred. These actions must be performed in another way in a remote laboratory. Remotely controllable switch matrices must be used. In the remote laboratory at BTH (Blekinge Institute of Technology) a client/server architecture is used. The student makes all the settings wanted on the client computer and then sends them to a lab server. The server makes measurements requested and returns the data obtained. The whole procedure takes only a second or two. A number of clients can access the experiments simultaneously. The laboratory is used in ordinary courses for on-campus students. They access the laboratory from a computer hall or from elsewhere outside the university. Due to the low number of bytes transferred, a 56 kbit modem is sufficient.
International Journal of Affective Engineering／Instructions for Authors

Past studies have reported that language-specific color focality has substantial influence on short-term memory (STM) performance of colors of the speakers of the language, which we call the “focality effect. ” This study attempts to clarify the continuous patterns of this effect, that is, the manner in which correct recognition possibilities and misrecognition error distances of colors, which are two aspects of the STM performance of colors, change in a gradual fashion along the continuum of color focality. Our experiment, which tests the Japanese language, finds that a U-shaped relationship exists between the focality and the possibility of correct recognition, and that the misrecognition error distance increases as the focality decreases. We speculate that the subjects’ frequent and conscious employment of the memorization strategy of coding colors using linguistic color categories is one important cause of the detected effect patterns.
On model parametrization and model structure selection for identification of MIMO-systems

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