Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating a Breast Cancer Gene Expression Data Using a Novel Clustering Approach

Historically, breast cancer has been perceived as a disease with varying histological and clinical features. Breast cancer tumor classification is important in disease prognosis and prediction because different breast tumors respond differently to different treatments and have different survival rates. Gene expression profiling studies have increasingly been motivated in the past decades to develop a good classification of breast cancer in molecular subtypes, which can improve the standard clinical assessments by providing extra prognostic information. In this research, one of the most comprehensive breast cancer gene expression datasets is analyzed by applying a novel clustering approach to predict the breast cancer subtypes. The novel unsupervised clustering approach initially model the gene expression data as a network and employ a community detection method to identify network clusters. This method utilizes an efficient problem specific metaheuristic algorithm to optimize the modularity value and identify clusters of breast cancer samples with similar characteristics that presents different subtypes of breast cancer. To assess the significant of the newly defined breast cancer subtypes, we compared our findings with three breast cancer subtyping methods.
Development of a Quadruple Bottom Line-based Composite Sustainability Index to Measure Sustainable Performance

Manufacturing companies worldwide are faced with the critical issue of sustainable development. The aim of this paper is to develop a quadruple bottom line-based composite sustainability index which can be used by manufacturing firms in measuring their sustainable performance. The proposed index simultaneously considers a firm's economic performance, social performance, environmental performance, and corporate governance. Field study results indicated that an index having a value from 0 to 1 can indeed be computed based on several sustainability indicators expressed in varying units. The index can be employed for performance evaluation to monitor the sustainable performance of a manufacturing company through the years. It can also be utilized for benchmarking to compare sustainable performance with other firms.
Calculation and Allocation of Complexity Costs Using Process Data Mining

Over the last years, manufacturing companies extended their product portfolio and derived numerous variants of their products. To make strategic decisions regarding the product portfolio, it is essential to know the costs for each product and each product variant. The exact allocation of the indirect costs in a company to the products that cause them is thereby often not possible. Existing methods, such as activity-based costing or process costing, aim for a cause-related allocation of costs, particularly indirect and overhead costs, to products. Because required data for the cost model has to be collected manually, existing approaches are extremely time consuming, costly and do not represent the current cost status. The increasing digitization and use of business information systems in companies provide new capabilities to get cost-relevant data faster and increase the timeliness of the cost calculation. In this paper, a method to calculate and allocate the complexity costs to related products is provided. The method makes use of data in different information systems which is analyzed and structured by process data mining. Thus, the effort for the calculation of complexity costs should be decreased and the transparency and timeliness should be increased.
Expansion of Models for Heart Rate Variability beyond the Autonomic Nervous System

According to the World Health Organisation, diseases of the cardiovascular system (CVS) are currently the main cause of death all over the world. Therefore, their understanding, prediction, and prevention with the help of non-invasive, cost effective methods is of great interest. Analysis of the heart rate and its change over time can give valuable insight into the health status of a patient, and is easily derived from electrocardiogram (ECG) data. Reduced heart rate variability (HRV) is associated to an increased probability of dying after myocardial infarctions and indicates inflammatory processes. It is symptomatic of mental disorders such as depression and burn-out. Different approaches in modeling and simulation of HRV can provide new insight into the nonlinear interplay of cardiovascular regulation. In this work, three models for HRV are implemented and compared. They include the firing rate of the baroreceptors, respiration, activity of the sympathetic and parasympathetic nervous system, stroke volume, cardiac noradrenaline and acetylcholine concentration, as well as a windkessel model including peripheral resistance and arterial compliance. First, an existing model for HRV based on respiration and baroreflex activity was implemented and analyzed. A second model was created through adaption of the first model. Based on a model for the autonomic response to orthostatic stress, a third model was implemented as well. All models were realized in Simulink 2017b, and their validation is performed based on 60 five-minute ECG recordings from 30 subjects. The simulation results are compared to subject data based on the standards of HRV measurement by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Each of the three modeling approaches showed specific advantages, disadvantages, and possibilities for further improvement. The results provide basis for extension of HRV models, paving the way for the future usage of model prediction in the field of cardiovascular diseases.
Reduction of Nonconformities in Galvanized Process Using Model Based on Lean Manufacturing Tools

From our analysis of the situation within a manufacturing company in the metal-mechanical sector in Lima, Peru, we found problems due to the noncompliant products index. They originated mostly during the galvanizing process, for which analysis of the current situation was initially developed. To reduce nonconforming products, we designed and implemented a proposal using lean manufacturing tools. Then we validated the attainment of positive results compared to the initial situation. For example, we achieved a 6% reduction of nonconforming products, a 10% decrease in claims, a 6% reduction of reprocessing, and a 0.8% lower percentage of elimination.
Prognostic Study of CNC Machine Component Using a Systematic Method

The prognostic study of CNC machine component plays a key role in increasing the reliability and safety of manufacturing systems. A systematic prognostic method based on multi-sensor information for CNC machine component is proposed in this paper, where the data characteristics and system properties are all considered. The proposed method includes data pre-processing based on a noise-assisted multivariate empirical mode decomposition method, a two-stage feature selection strategy and remaining useful life prediction based on the trajectory similarity method. An experimental case study is given to evaluate the proposed method. The results show that the systematic prognostic method could be applied in the industrial field.
A Review of Metrics, Algorithms and Methodologies for Network Reliability

Networks have become an indispensible part of the modern society. The numerous engineering realms, mainly computers and communication, transportation, electric transmission, and oil and gas distribution are based on interconnection of complex networks. However, the basic theories of such networks are similar to each other. This article provides a holistic view of the complex network analysis. The metrics of network connectivity are explained in this paper. The practicalities of each metric for network analysis as well as their limitations are presented in a condensed manner. The algorithms and methodologies that have evolved over time along with their scope, advantages, and limitations are presented in this paper. This article may prove to be beneficial for practitioners and researchers associated with network reliability evaluation.
Engineering Meaningful Computing Education: Programming Learning Experience Model

An e-learning system provides a platform for communication between educators and students. It facilitates the sharing of information and learning content in a form which can be easily accessed by the users. The scope of the research revolves around the idea that an automated assessment in an e-learning platform would be able to help programming learners understand defects in their program and independently learn how to program. This research would investigate the effects on college or university students. This research analyzes the effectiveness of various ways to learn a programming language. It is believed that automated assessment tools would be able to monitor and improve students' programming skills over time and reduce the effort for both instructors and students.
Future Distribution Generation in an Intelligent Smart Energy Network

The smart city policymakers are incorporating environmental sustainability ICT that contribute to making future cities more sustainable and intelligent future energy networking. Since technological innovation is moving in several directions (smart construction, smart mobility, e- health, e-government, etc.), cities should embrace policy, technology, awareness and retrofitting. Smart city energy disruption solution and evolution of the fourth industrial revolution (4IR) allows organization to be self-sufficient in terms of managing respective supply and demand. The internet of things (IoT) is a key enabler for smart cities where, together with communication and network devices, sensing devices and actuators are major components. The evolution supports smart network towards smarter technology that improve the quality of life. The key role in the ICT support framework is played by local administration and government and they can carry out control and supervision of the organization at various levels. Smart city is a centralized system; therefore, not only should citizens be able to “actively participate in demand side management”, but they should also be major providers of feedback, participate in the design approach and be the recipients of personalized services.
Overview of Computer Vision Applications and Risks in the Global South

We present a study of recent advances in computer vision (CV) research for the Global South to identify the main uses of modern CV and its most significant ethical risks in the region. We review 55 research papers and analyze them along three principal dimensions: where the technology was designed, the addressed problems and the potential ethical risks arising following deployment. Results suggest: 1) CV is most used in policy planning and surveillance applications, 2) privacy violations and spoofing are the two most likely risks to arise from modern CV systems, and 3) researchers from the Global North differ from researchers from the Global South in their uses of CV to solve problems in the Global South. Results of our risk analysis also differ from previous work on CV-risk perception in the West, suggesting locality to be a critical component of each risk’s importance. CCS CONCEPTS • General and reference → Surveys and overviews; • Computingmethodologies→Computer vision; Knowledge representation and reasoning; Distributed artificial intelligence; • Social and professional topics → Codes of ethics; • Security and privacy → Security services;
The Role of Mental Imagery in Osteopathic Palpation: a qualitative study

Background: Palpation constitutes a central element of osteopathic diagnosis and therapy. It is a complex tactile performance and as such also depends on cognitive factors. References by experienced osteopaths (EOs) suggest that the ability to visualise anatomical structures is essential during palpation. Scientific studies in this field can hardly be found. This study aimed to systematically determine what role mental imagery (MI) plays for osteopaths and especially what the benefits are when it is used spontaneously and deliberately. Material and Methods: The present study is a qualitative study. Nine interviews with EOs were carried out with the help of an interview guideline and analysed using a qualitative analysis of content using the software MAXQDA. Results: All EOs regarded MI as a tool as well as an interfering factor for palpation. Its supporting role was associated with the identification, recognition and comparing of anatomical conditions, with directing attention, with navigation and orientation. The limiting character showed up in distraction and deception by mental images. The results also revealed the situational use of MI as well as different mental images and influencing factors (e.g. visualisation training). Discussion: The study provides an insight into the application knowledge of EOs and shows some aspects of palpation, which have been subject to little research so far. Being aware of and understanding the role of MI as a top-down process in tactile-haptic perception should change the focus in palpation. The findings can be incorporated in daily practice as well as in the training system. To regard MI as a possible bias can lead to reassessment and realignment of reliability studies. This study offers a basis for other studies to work from to explore the nature of MI in the tactile-haptic perception of osteopaths. Keywords: mental imagery, palpation, osteopathy
The Private and External Costs of Germany&Apos;S Nuclear Phase-Out

Many countries have phased out nuclear electricity production in response to concerns about nuclear waste and the risk of nuclear accidents. This paper examines the impact of the shutdown of roughly half of the nuclear production capacity in Germany after the Fukushima accident in 2011. We use hourly data on power plant operations and a novel machine learning framework to estimate how plants would have operated differently if the phase-out had not occurred. We find that the lost nuclear electricity production due to the phase-out was replaced primarily by coal-fired production and net electricity imports. The social cost of this shift from nuclear to coal is approximately 12 billion dollars per year. Over 70% of this cost comes from the increased mortality risk associated with exposure to the local air pollution emitted when burning fossil fuels. Even the largest estimates of the reduction in the costs associated with nuclear accident risk and waste disposal due to the phase-out are far smaller than 12 billion dollars.
Analysis of HIV high-risk population characteristics with Baidu Tieba data

The textual content and temporal pattern of online activities for users gathered in the “Fear of HIV Bar” of Baidu Tieba were analyzed. LDA topic model was used to analyze the main differences between topics discussed among HIV-infected people and non-HIV-infected people. A machine learning method based on key words was used to distinguish the sexual orientation of users who start a discussion in “Fear of HIV Bar”, and calculate the epidemic rate of HIV among groups with different sexual orientations. The techniques used in this paper can be supplemented as an important tool for high-risk populations research. In addition, this paper can be applied to assess the epidemic of HIV in populations with different sexual orientations by using machine learning technique to intelligently classify the sexual orientation of a user, which is of great significance for the public health agencies.
Codon 249 P53 Gene Mutation Among Hepatocellular Carcinoma Patients in Western Kenya

Background: Alteration(s) on p53 gene adversely affects their regulatory functions resulting to cancers. To our knowledge, no previous studies have analyzed mutation in codon 249 of p53 gene in hepatocellular carcinoma (HCC) patients in Kenyan population. In our study, we investigated for these mutations in HCC patients presenting with stage one cancer in the Kenyan population. Methods: A total of forty-six (46) stage one HCC patients and ten (10) controls were prospectively recruited from Moi Teaching and Referral Hospital (MTRH), Kenya. DNA was extracted and purified from 200 uL aliquots of plasma and PCR amplified then sequenced using p53 exon 7 forward and reverse primers. Codon 249 p53 gene mutation detection and analysis were done using Molecular Evolutionary Genetics Analysis v.6. 0, Genetyx v.9. 0 and Integrative Genome Viewer v. 2.4. 4. The male to female ratio for both patients and controls was 1:1. The age range for HCC patients was from 25 to 67 years with a median of 42 years, and from 24 to 64 years with a median of 41 years for the controls. Results: Guanine (G) to thymine (T) transversion in the third base of codon 249 of p53 gene was detected in 8 of the 46 HCC patients (17.39%) and 1 of the 10 controls (10%). There was no significant difference across gender among HCC subjects with and without mutation (p=0.4549) at 5% level of significance. There was a striking picture of significant existence of such mutation in a much older population in both the HCC patients (p=<.0001) and the controls (p=0.0003) at 5% level of significance, suggesting that being in the old age is more susceptible to such mutation. There was no significant statistical association of codon 249 mutation with HCC (p=0.6821) at 5% level of significance. Conclusion: There is no association between p53 exon 7 codon 249 mutations and hepatocellular carcinoma from patients presenting with stage I cancer. There is need for supplemental cross-sectional large sample sizes studies of HCC patients presenting with different stages to observe pattern across stages.
OptFlow: A Flow-based Abstraction for Programmable Topologies

The rapid adoption of Reconfigurable Optical Add-Drop Multiplexers (ROADMs) is setting the stage for the dynamic reconfiguration of the network topology in optical backbones. The conventional approach to enable programmability in the physical layer requires solving a cross-layer optimization formulation that captures the interplay between the IP and optical layers. However, as the network scales, the complexity and run time of cross-layer optimization formulations grow prohibitively, resulting in heuristic-based solutions that sacrifice optimality for scalability. We propose a flow-based graph abstraction, called OptFlow, that is able to find the optimal allocation faster than a cross-layer optimization formulation. The key idea in OptFlow is that topology programmability is abstracted by "network flows," enabling service providers to use multi-commodity flow formulations, such as conventional Traffic Engineering, to solve a cross-layer optimization. OptFlow augments the physical graph and uses it as input to the unmodified flow-based Traffic Engineering algorithm, capturing a variety of IP-layer optimization goals such as max throughput, min hop count, and max-min fairness. Due to its flow-based nature, OptFlow inherently provides an abstraction for consistent network updates. To benchmark our key assumptions in OptFlow, we build a small testbed prototype consisting of four ROADMs. To evaluate the optimality and run time of large networks, we simulate five WAN topologies with up to 100 nodes and 390 links. Our results show that OptFlow matches the throughput performance of an optimal cross-layer formulation but has faster computation times. The run time speed-up of OptFlow increases as the network scales, with up to 8x faster execution times in our simulations.
The TV is Smart and Full of Trackers: Measuring Smart TV Advertising and Tracking

Abstract In this paper, we present a large-scale measurement study of the smart TV advertising and tracking ecosystem. First, we illuminate the network behavior of smart TVs as used in the wild by analyzing network traffic collected from residential gateways. We find that smart TVs connect to well-known and platform-specific advertising and tracking services (ATSes). Second, we design and implement software tools that systematically explore and collect traffic from the top-1000 apps on two popular smart TV platforms, Roku and Amazon Fire TV. We discover that a subset of apps communicate with a large number of ATSes, and that some ATS organizations only appear on certain platforms, showing a possible segmentation of the smart TV ATS ecosystem across platforms. Third, we evaluate the (in)effectiveness of DNS-based blocklists in preventing smart TVs from accessing ATSes. We highlight that even smart TV-specific blocklists suffer from missed ads and incur functionality breakage. Finally, we examine our Roku and Fire TV datasets for exposure of personally identifiable information (PII) and find that hundreds of apps exfiltrate PII to third parties and platform domains. We also find evidence that some apps send the advertising ID alongside static PII values, effectively eliminating the user’s ability to opt out of ad personalization.
Location-Aware Distributed Clustering with Eliminating GPS in Vehicular Ad-hoc Networks

Vehicular ad-hoc network (VANET) is a type of mobile network which is used for establishing connection between vehicles (M2M) and also between vehicles and nearby stationary equipment which are often road-side equipment. The main target of VANET is to provide security and convenience for the passengers. In order to achieve this goal, a special electronic device called OBU (On-Board Unit) is embedded in each vehicle which makes the connection between vehicles and between the vehicles and the road-side equipment possible. In this paper, the LocationAware Clustering in Vehicular Ad-hoc Networks (LAC-VANET) is proposed. We try to achieve the main and major goal in VANET networks, i.e. fast propagation of security and urgent messages in ITS systems, using clustering and selecting the best cluster head based on Fuzzy logic such that the cluster head can transfer important information such as the obstacles and accidents detected on the road with a suitable speed and without creating a large traffic load in the vehicle network in order to notify other vehicles and prevent the danger and vehicle accidents. Moreover, LAC-VANET method is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the VANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), end to end delay, and packet loss rate. KeywordsVehicular ad-hoc network (VANET); M2M; Fuzzy logic; ITS; LAC-VANET
Response to book review of Verb Derivation in Modern Greek: Alternation Classes, Conceptual Structures, Semantic Fields in JGL7

I would hereby like to reply to the review made by Marina Tzakosta in the Journal of Greek Linguistics 7 (Tzakosta 2007, hereafter also referred to as T07). 1 I will discuss the most important points, leaving aside formal issues, such as transcription problems, etc. In Charitonidis (2005, hereafter also referred to as C05), the term ‘multi-mapping’ (19f.) is introduced. By this, I mainly mean cases of voice switch such as skórpisan (active) and skorpístikan (non-active/passive) in (1), where both verb forms denote the same auto event,2 as occurs in (1).
IEOR8100: Economics, AI, and Optimization Lecture Note 2: Intro to Game Theory and Regret

We will use the shorthand s−i to denote the subset of a strategy vector s that does not include player i’s strategy. As a first solution concept we will consider dominant-strategy equilibrium (DSE). In DSE, we seek a strategy vector s ∈ S such that each si is a best response no matter what s−i is. A classic example is the prisoner’s dilemma: two prisoners are on trial for a crime. If neither confesses (stay silent) to the crime then they will each get 1 year in prison. If one person confesses and the other does not, then the confessor gets no time, but their co-conspirator gets 9 years. If both confess then they both get 6 years.