Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Examination of Intention to Continue to Use Smart Home Services

With the continuous expansion of sub-ecological problems, it is the question of how to effectively reduce environmental pollution and improve energy efficiency that has become a major challenge. At the same time, the energy consumption of family homes accounts for a large proportion, and whether smart homes can become a part of sustainable development is getting more and more attention. Although there are hundreds of smart home products and many investors on the market, consumers are relatively less accepting of this technology. How to make consumers adopt and continue to use has become a key issue in the development of smart homes. From the perspective of consumers, this study aims first to explore the relevant factors that affect the sustainable use of smart homes, and second empirically analyze the hypothetical model by using structural equation modeling. The empirical analysis results are based on the sample data of 488 Chinese respondents with experience in smart home use. The results showed that the service quality and perceived usefulness of smart home services positively impact users’ degree of satisfaction, and a higher degree of satisfaction, in turn, contributes to users’ formation of habits. Users’ desire to continuously use smart home services is influenced by perceived usefulness, degree of satisfaction, and habit; among them, habit is the key factor affecting users’ willingness. These findings expand our understanding of users’ desire to continuously use smart homes and provide theoretical support for the sustainable development of the smart home industry.
Multifaceted quality assessment of gene repertoire annotation with OMArk

Assessing the quality of protein-coding gene repertoires is critical in an era of increasingly abundant genome sequences for a diversity of species. State-of-the-art genome annotation assessment tools measure the completeness of a gene repertoire, but are blind to other types of errors, such as gene over-prediction or contamination. We developed OMArk, a software relying on fast, alignment-free sequence comparisons between a query proteome and precomputed gene families across the tree of life. OMArk assesses not only the completeness, but also the consistency of the gene repertoire as a whole relative to closely related species. It also reports likely contamination events. We validated OMArk with simulated data, then performed an analysis of the 1805 UniProt Eukaryotic Reference Proteomes, illustrating its usefulness for comparing and prioritizing proteomes based on their quality measures. In particular, we found strong evidence of contamination in 59 proteomes, and identified error propagation in avian gene annotation resulting from the use of a fragmented zebra finch proteome as reference. OMArk is available on GitHub (https://github.com/DessimozLab/OMArk), as a Python package on PyPi, and as an interactive online tool at https://omark.omabrowser. org/.
Non-Randomly Sampled Networks: Biases and Corrections

This paper analyzes statistical issues arising from networks based on non-representative samples of the population. We first characterize the biases in both network statistics and estimates of network effects under non-random sampling theoretically and numerically. Sampled network data systematically bias the properties of observed networks and suffer from non-classical measurement-error problems when applied as regressors. Apart from the sampling rate and the elicitation procedure, these biases depend in a non-trivial way on which subpopulations are missing with higher probability. We propose a methodology, adapting post-stratification weighting approaches to networked contexts, which enables researchers to recover several network-level statistics and reduce the biases in the estimated network effects. The advantages of the proposed methodology are that it can be applied to network data collected via both designed and non-designed sampling procedures, does not require one to assume any network formation model, and is straightforward to implement. We apply our approach to two widely used network data sets and show that accounting for the non-representativeness of the sample dramatically changes the results of regression analysis.
Response Generation For An Open-Ended Conversational Agent

Conversation plays a key role in maintaining humans well-being. It constitutes the most natural way of interacting verbally with each other. Over the past decade, dialogue systems have become omnipresent in our daily lives, assisting our daily schedule and routine. Recently, the emergence of neural network models has shown promising results in solving problems such as scalability and language-independence that conventional dialogue system fail to cope with. In particular, Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges by sampling words sequentially conditioned on previous words. However, these models still lag far behind human capabilities in terms of the conversations that they can perform. Notwithstanding the syntactically well-formed responses generated by Seq2Seq models, they are prone to be generic, dull and off-context such as “ i don’t know” or “ i’m not sure what you’re talking about”. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. We demonstrate that incorporating conversation history and topic information with our novel method improves generated conversational responses. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. Addiii tionally, we propose two novel quantitative metrics for measuring the quality of the generated responses, dubbed Semantic Coherence and Response Echo Index. Our experiments on these quantitative metrics along with human evaluation demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines. In contrast to the widely used OpenSubtitles dataset, we exhibit that Reddit dataset can be considered as a better resource for training future conversational systems. Furthermore, we show that both quantitative metrics agree reasonably with human judgment, making a step towards a good automatic evaluation
Assessment of a multi-measure functional connectivity approach

Efforts to find differences in brain activity patterns of subjects with neurological and psychiatric disorders that could help in their diagnosis and prognosis have been increasing in recent years and promise to revolutionise clinical practice and our understanding of such illnesses in the future. Resting-state functional magnetic resonance imaging (rsfMRI) data has been increasingly used to evaluate said activity and to characterize the connectivity between distinct brain regions, commonly organized in functional connectivity (FC) matrices. Here, machine learning methods were used to assess the extent to which multiple FC matrices, each determined with a different statistical method, could change classification performance relative to when only one matrix is used, as is common practice. Used statistical methods include correlation, coherence, mutual information, transfer entropy and non-linear correlation, as implemented in the MULAN toolbox. Classification was made using random forests and support vector machine (SVM) classifiers. Besides the previously mentioned objective, this study had three other goals: to individually investigate which of these statistical methods yielded better classification performances, to confirm the importance of the blood-oxygen-level-dependent (BOLD) signal in the frequency range 0.009-0.08 Hz for FC based classifications as well as to assess the impact of feature selection in SVM classifiers. Publicly available rs-fMRI data from the Addiction Connectome Preprocessed Initiative (ACPI) and the ADHD-200 databases was used to perform classification of controls vs subjects with Attention-Deficit/Hyperactivity Disorder (ADHD). Maximum accuracy and macro-averaged f-measure values of 0.744 and 0.677 were respectively achieved in the ACPI dataset and of 0.678 and 0.648 in the ADHD-200 dataset. Results show that combining matrices could significantly improve classification accuracy and macro-averaged f-measure if feature selection is made. Also, the results of this study suggest that mutual information methods might play an important role in FC based classifications, at least when classifying subjects with ADHD.
Rich Scientific Publications: Design Guide Based on Cognitive Framework

There is a growth of interactive multimedia publications in the scientific context as well as a demand to discover the advantages of multimodal user interaction with this type of publication. Along these lines, this work in progress presents a design guide based on the development of a cognitive framework, principles of design and concepts of interactivity to promote the user's easier understanding when trying to complement or reinforce the information through interactive multimedia integration. This guide will be further applied and tested in the construction of a model for rich scientific publications.
Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots.
Social Return on Education in 35 Cities/Districts of Central Java Province

Education has a close relation to the quality of labor. With a good level of education, labor productivity will certainly increase. In addition to reducing poverty, productivity will increase economic growth. But in Jawa Tengah Province the correlation of education with economic growth shows contradictions. In Jawa Tengah, economic growth has increased quite well but the average years of schooling is elementary school graduates. By using Mincer Wage Equation, this paper Is trying to calculate social return on education within macroeconomic growth regression methods. This paper aims to capture relationship between education attainment and economic growth in macroeconomic ways.
Real interest rate and its effect on income inequality

Surekli artan gelir esitsizliginin faktorlerini netlestirmek, adaleti saglamada etkin olmak acisindan onemlidir. Bu amac dogrultusunda yapilan bu calisma, 2003-2013 yillari arasi gelismekte olan 5 ulke ve 2005-2015 yillari arasi gelismis 9 ulkeyi incelemektedir. Panel veri teknikleri kullanilarak yapilan calismada gelismekte olan ulkelerde kisi basi yatirimin fiyat seviyesi gelir dagilimi degiskeninin bagimli degisken oldugu durumda %5 duzeyinde anlamli bir iliskiye sahiptir. Reel faiz orani degiskeni ise gelir dagilimi degiskeni uzerinde anlamli bir etkiye sahip degildir. Gelismis ulkelerde ise beseri sermaye ve reel faiz orani degiskeninin gelir dagilimi uzerinde anlamli bir etkisi vardir. Beseri sermayede meydana gelecek artis gelir dagilimi adaletini daha adaletli hale getirmekte iken reel faiz oraninda meydana gelen artis gelir dagilimi adaletini bozmakta daha adaletsiz kilmaktadir.
Investigating the relationship between managerialist employment relations and employee turnover intention

The purpose of this paper is to investigate the relationship between managerialist employment relations and employee turnover intention in Nigeria. The study context is public hospitals in Nigeria, which have a history of problematic human resource management (HRM) practice, a non-participatory workplace culture, managerialist employment relations and a high employee turnover intention. ,Based on a qualitative, interpretive approach, this paper investigates the process by which Nigerian employment relations practices trigger the employee turnover intention of doctors using 33 semi-structured interviews with key stakeholders in public hospitals. ,This study found that Nigeria’s managerialist employment relations trigger the employee turnover intention of medical doctors. Additionally, it was found that although managerialist employment relations lead to turnover intention, Nigeria’s unique, non-participatory and authoritarian employment relations system exacerbates this situation, forcing doctors to consider leaving their employment. ,Studies on the interface between managerialism and employment relations are still under-researched and underdeveloped. This paper also throws more light on issues associated with managerialist employment relations and human resources practice including stress, burnout and dissatisfaction. Their relationship with doctors’ turnover intention has significant implications for employment policies, engagement processes and HRM in general. The possibility of generalising the findings of this study is constrained by the limited sample size and its qualitative orientation. ,This paper contributes to the dearth of studies emphasising employer–employee relationship quality as a predictor of employee turnover intention and a mediator between managerialist organisational system and turnover intention. The study further contributes to the discourse of employment relations and its concomitant turnover intention from developing countries’ perspective within the medical sector.
Fixed Length Differential Encoding for Real-Time Status Updates

We consider the status updates of a physical process over an unreliable channel. In this setting, one may not be able to reliably transmit the current state at all times. Instead, one is interested in the timeliness of the accurately received information. This is a setting for several cyber-physical system applications that require real-time monitoring and control. In this paper, we study periodic data transmission schemes at a single source which exploit the temporal correlation in the source messages. When the source has no feedback, it can periodically send the actual information, interspersed with differential messages. On the availability of receiver’s feedback at the source, it can decide to send either the differential or the actual information at each transmission opportunity. For a fixed length coding, we show that the differential encoding improves the timeliness performance only if the receiver’s feedback is available.
Trust-Based Runtime Verification for Multi-Quad-Rotor Motion Planning With a Human-in-the-Loop

In this paper, we propose a trust-based runtime verification (RV) framework for deploying multiple quad-rotors with a human-in-the-loop (HIL). By bringing together approaches from runtime verification, trust-based decision-making, human-robot interaction (HRI), and hybrid systems, we develop a unified framework that is capable of integrating human cognitive skills with autonomous capabilities of multi-robot systems to improve system performance and maximize the intuitiveness of the human-robot-interaction. On top of the RV framework, we utilize a probabilistic trust inference model as the key component in forming the HRI, designed to maintain the system performance. A violation avoidance controller is designed to account for the unexpected/unmodeled environment behaviors e.g. collision with static/moving obstacles. We also use the automata theoretic approaches to generate motion plans for the quad-rotors working in a partially-known environment by automatic synthesis of controllers enforcing specifications given in temporal logic languages. Finally, we illustrated the effectiveness of this framework as well as its feasibility through a simulated case study.
Face Recognition Using Location Averaging and Intensity’s Position Estimation Techniques for Human Authentication

Face recognition is an effective tool in the biometric human recognition system. In this competitive world, several techniques and systems are emerging to satisfy the needs of the face recognition system’s performance. To obtain the high-performance ratio novel techniques are combined and created a new face recognition system. Spatial domain techniques like Gray averaging technique, Location averaging technique and Intensity’s position estimation technique are united with frequency domain technique like Discrete Cosine Transform. Intensity’s position estimation is a novel feature extraction and classification technique proposed in this work. Three standard face databases are tested using this system. Accuracy and runtime are major parameters used to validate the obtained results. The maximum accuracy rate of about 86% is obtained.
Predicting the outcome of CS:GO games using machine learning

This work analyzes the possibility of predicting the result of a Counter Strike: Global Offensive (CS:GO) match using machine learning. Demo files from 6000 CS:GO games of the top 1000 ranked players in the EU region were downloaded from FACEIT.com and analyzed using an open source library to parse CS:GO demo files. Players from the matches were then clustered, using the kmeans algorithm, based on their style of play. To achieve stable clusters and remove the influence of individual win rate on the clusters, a genetic algorithm was implemented to weight each feature before the clustering. For the final part a neural network was trained to predict the outcome of a CS:GO match by analyzing the combination of players in each team. The results show that it is indeed possible to predict the outcome of CS:GO matches by analyzing the team compositions. The results also show a clear correlation between the number of clusters and the prediction accuracy.
A Method of Confidence Evaluation Based on Support Vector Machines

In present, the analysis of students’ behavior is important to instructors and leaders in universities. In order to improve the value of data in the Oran system of Huaiyin Institute of Technology and evaluate the degree of honesty, a method based on Support Vector Machines algorithm is used to evaluate the confidence of the information that about 18000 students filled in the Oran system. Through the experiment, the method achieves good result and achieves a high efficiency, which satisfies the original goal.
A Novel Framework for Context-aware Outlier Detection in Big Data Streams

Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and analysis over data streams is a necessity, the existing solutions are no longer effective and sufficient. While many existing algorithms and approaches consider the content of the data stream, there are few approaches which consider the context and conditions in which the content has been produced. In this paper, we propose a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier detection rather than using the stream content alone or applying the contextual detection on content anomalies only. The detection algorithm incorporates two approaches; the first, a supervised detection method and the other, an unsupervised, which allows the detection process to adapt to the normal change in the stream behavior over time. The detected outliers are either both content and contextual outliers or contextual outliers only. The proposed contextual detection approach prunes the false positive outliers and detects the true negative outliers at the same time. Moreover, in this framework, the detection engine preserves both outliers and context values in which those outliers were detected to be used in the engine self-training and in outliers modeling in order to enhance the outlier prediction accuracy. Journal of Digital Information Management Subject Categories and Descriptors H.2 [Database Management] H.2. 8 Database Applications];
Fit for purpose? Exploring competence in quality management

Purpose The purpose of this paper is to use competence theory to explore the fit between actual competencies of quality management (QM) practitioner and the perception of QM competence needs in organisations. Design/methodology/approach This paper is based on a cross-case quantitative study design featuring a survey of QM practitioners (n = 249) within eight large Swedish organisations. The research instrument was a questionnaire covering seven themes within QM. The analysis is based on descriptive statistics. Findings The results show that while the perception of formal QM competence may seem sufficient, the evolving nature of QM requires knowledge, skills and attitudes that are also apt for more external and explorative perspectives. There is a bias towards competence for exploitative QM rather than explorative QM. Organisational logics preserving and possibly reinforcing a perceived “competence lag” in organisations are identified and described. Originality/value Few empirical studies within QM explore the competencies required for QM practices. This paper contributes to QM research in providing arguments for adopting the competence theory as a foundation for organising current and future QM work.