Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Makeup Detection Using HSV Color Space and Texture Analysis

Facial Makeup Detection Using HSV Color Space and Texture Analysis In recent decades, 2D and 3D face analyses in digital systems have become increasingly important because of their vast applications in security systems or any digital systems that interact with humans. In fact the human face expresses many of the individual’s characteristics such as gender, ethnicity, emotion, age, beauty and health. Makeup is one of the common techniques used by people to alter the appearance of their faces. Analyzing face beauty by computer is essential to aestheticians and computer scientists. The objective of this research is to detect makeup on images of human faces by image processing and pattern recognition techniques. Detecting changes of face, caused by cosmetics such as eye-shadow, lipstick and liquid foundation, are the targets of this study. Having a proper facial database that consists of the information related to makeup is necessary. Collecting the first facial makeup database was a valuable achievement for this research. This database consists of almost 1290 frontal pictures from 21 individuals before and after makeup. Along with the images, meta data such as ethnicity, country of origin, smoking habits, drinking habits, age, and job is provided. The uniqueness of this database stems from, first being the only database that has images of women both before and after makeup, and second because of having light-source from different angles as well as its meta data collected during the process. Selecting the best features that lead to the best classification result is a challenging issue, since any variation in the head pose, lighting conditions and face orientation can add complexity to a proper evaluation of whether any makeup has been applied or not. In addition, the similarity of cosmetic’s color to the skin color adds another level of difficulty. In this effort, by choosing the best possible features, related to edge information, color specification and texture characteristics this problem was addressed. Because hue and saturation and intensity can be studied separately in HSV (Hue, Saturation, and Value) color space, it is selected for this application. The proposed technique is tested on 120 selected images from our new database. A supervised learning model called SVM (Support Vector Machine) classifier is used and the accuracy obtained is 90.62% for eye-shadow detection, 93.33% for lip-stick and 52.5% for liquid foundation detection respectively. A main highlight of this technique is to specify where makeup has been applied on the face, which can be used to identify the proper makeup style for the individual. This application will be a great improvement in the aesthetic field, through which aestheticians can facilitate their work by identifying the type of makeup appropriate for each person and giving the proper suggestions to the person involved by reducing the number of trials.
COMPARATIVE ANALYSIS OF NATIONAL OPEN DATA PORTALS OR WHETHER YOUR PORTAL IS READY TO BRING BENEFITS FROM OPEN DATA

This paper focuses on the analysis of usability of the national open data portals. Open [government] data are considered as one of the most influenceable tool for preventing and reducing corruption and reaching innovative solutions that create added value for society. Thus, it is important to ensure that they are provided in a form that are useful and suitable for the original purpose of the open data. Critical voices and many discussions on whether open government data and national open data portals are of sufficient quality appear more frequently. Therefore, this study deals with this topic and aims to find the main challenges that can negatively impact users’ experience through an analysis of usability of 42 open data portals by applying a unified methodology on them allowing their comparative analysis to be carried out. This study highlights the weakest aspects for 42 national open data portals, pointing on both, the most common weakest points, and individual. The analysis carried out also identifies portals that can be considered as leaders and as an example for the less successful open data portals.
Principles of motor adaptation when walking with a powered exoskeleton

A robotic ankle-foot orthosis (AFO) that provides powered assistance could adjust to varying gait dynamics much better than a rigid AFO. To provide insight into how humans would adapt to a powered AFO, we studied the response of neurologically intact subjects walking with an active dorsiflexion assist orthosis proportionally controlled by tibialis anterior electromyography (EMG). We examined the two mechanical functions of ankle dorsiflexors in gait (power absorption at heel strike and power generation at toe-off) by recruiting two groups of healthy subjects: Group One, called Continuous Control, (n=5) had dorsiflexion assistance both at the initial heel contact and during swing; Group Two, called Swing Control, (n=5) had the assistance only during swing. We hypothesized both groups of subjects would reduce tibialis anterior EMG amplitude with practice walking with the powered dorsiflexion assist. Ten healthy subjects were fitted with custom-made orthoses that included an artificial pneumatic muscle providing dorsiflexor torque. We collected lower body kinematics, EMG, and artificial muscle force while subjects walked on a treadmill 13 for two 30-minute training sessions. We found that subjects walked with increased ankle dorsiflexion by 9 degrees but showed different adaptation responses of the two tibialis anterior EMG bursts. The first EMG burst around heel strike had ~28% lower amplitudes (p<0.05) but the second EMG burst during swing had similar amplitudes. These results provide baseline data of EMG controlled dorsiflexion assist in neurologically intact humans that can be used to guide future studies on neurologically impaired individuals.
Industrial applications of pulsed-laser espi vibration analysis

The present paper reviews the critical elements of a vibration testing system based on pulsed-laser holographic ESPI measurements. Next to the optical parts, the integration with the modal testing and analysis procedure is reviewed. Specific extensions have been added in view of the industrial application of the approach. These include for example the integration of geometry and response information, the integration of multiple views into one single model, the integration with FE model data and the prior identification of the critical panels and critical modes. A global procedure was hence established. The approach has been applied to several industrial case studies, including car panels, the firewall of a monovolume car, a full vehicle, panels of a light truck and a household product.
Cooperative Networks with Channel Uncertainty

In this thesis, cooperative networks are studied under the assumption that the source is uncertain about the channel in operation. In the first chapter, cooperative strategies are developed for simultaneous relay channels (SRC) which consist of a set of two single relay channels out of which the channel in operation is chosen. This is equivalent to the broadcast relay channel (BRC). Bounds on the capacity region of the general BRC with two helper relays are derived. Capacity results are obtained for specific cases of semi-degraded and degraded Gaussian simultaneous relay channels. In the second chapter, the composite relay channel is considered where the channel is randomly drawn from a set of conditional distributions according to a given distribution. The transmission rate is fixed regardless of the current channel and the asymptotic error probability (EP) is characterized. A novel selective coding strategy (SCS) is introduced which enables relays to select –based on their channel measurement– the best coding scheme between Compress-and-Forward (CF) and Decode-and-Forward (DF). Generalized Noisy Network Coding theorems are shown for the case of unicast general networks where the relays use either DF or CF scheme. In the third chapter, the asymptotic behavior of EP is studied for composite multiterminal networks. The asymptotic spectrum of EP is introduced as a novel performance measure for composite networks. It is shown that every code with rate outside cut-set bound, yields EP equal to one and for the networks satisfying strong converse condition, the asymptotic spectrum of EP coincides with the outage probability.
PROTECTION AND REPAIRS OF STEEL SHEET PILES IN TIDAL ZONE

Corrosion damage to steel sheet pile wharves is a major item of maintenance activities in the Mediterranean and Red Sea ports of Israel. During mid-90s, the problem of underwater corrosion was substantially mitigated by installation of cathodic protection. However, recent inspections indicated the need to protect sheet piles in the tidal zone as well. The paper describes the types of corrosion damage above MHW, and the methods of repairs and corrosion protection in this zone that are applied in contemporary international practice. The methods of tidal zone protection that were used in the Israeli ports included epoxy coating in caissons, jacketing with steel fiber concrete and cast-in-place capping beam extensions using high - plasticity polymer fiber reinforced concrete. The analysis of these methods showed that the most important factor is the effect of applied repair technology on wharf operations. The operational downtime and the durability assessment indicated that the polymer fiber concrete extension is a preferred alternative. The main message of the paper is that repairs of sheet piles in the tidal zone should be designed as an integral component of a larger system of corrosion protection of bulkheads. The guidelines on how to achieve this goal are provided.
7-Dehydrocholesterol is an endogenous suppressor of ferroptosis.

Ferroptosis is a form of cell death that has received considerable attention not only as a means to eradicate defined tumour entities but also because it provides unforeseen insights into the metabolic adaptation that tumours exploit to counteract phospholipid oxidation1,2. Here, we identify proferroptotic activity of 7-dehydrocholesterol reductase (DHCR7) and an unexpected prosurvival function of its substrate, 7-dehydrocholesterol (7-DHC). Although previous studies suggested that high concentrations of 7-DHC are cytotoxic to developing neurons by favouring lipid peroxidation3, we now show that 7-DHC accumulation confers a robust prosurvival function in cancer cells. Because of its far superior reactivity towards peroxyl radicals, 7-DHC effectively shields (phospho)lipids from autoxidation and subsequent fragmentation. We provide validation in neuroblastoma and Burkitt's lymphoma xenografts where we demonstrate that the accumulation of 7-DHC is capable of inducing a shift towards a ferroptosis-resistant state in these tumours ultimately resulting in a more aggressive phenotype. Conclusively, our findings provide compelling evidence of a yet-unrecognized antiferroptotic activity of 7-DHC as a cell-intrinsic mechanism that could be exploited by cancer cells to escape ferroptosis.
MODELING AND CONTROL OF A DUAL-SOLENOID ACTUATOR FOR UNSTABLE VALVE

Unstable valve is a new concept that is concerned with implementing fast responsive and cost effective single stage direct acting electrohydraulic valves. The main issue is that in high o w rate and high bandwidth operations, the force and power required of the solenoid actuator for stroking the spool become prohibitive. Our approach is to utilize o w force induced instability to enhance the spool agility, so that the demand on the solenoids can be alleviated. However, the open loop instability and the nonlinear nature of the valve make the controller design more challenging. In this paper, a sliding mode controller is designed to stabilize the system and to achieve the displacement trajectory tracking. A case study validates the controller. The simulation also shows that this control scheme provides unstable valves with a benet of saving the electrical energy.
Smart Parking Guidance System Using 360o Camera and Haar-Cascade Classifier on IoT System

Nowadays, smart parking guidance system is a crucial research for people’s convenience. The main objective of this research is to develop and analyze on a smart parking guidance system where current available system was compared to this new proposed system. Limited parking space has become serious issue since the number of Malaysia’s populations who are using car keep increasing. Some of the big companies, shopping malls and other public facilities already deployed a smart parking system on their building. However, there are still a lot of buildings that do not own it because the system required a lot of investment, where the huge parking areas need higher cost to install sensors on each parking lot available. The proposed smart parking guidance system in this research was depending on a 360° camera that was modified on raspberry pi camera module and 360o lens and Haar-Cascade classifier. The image and video processing was by Open CV and python program to detect the available parking space and cloud firebase was used to update data where users can access the parking space availability by android mobile phone specifically at a closed parking space. A single 360°camera was replaced several sensors and camera which was implemented on traditional smart parking system. An analysis was done on the performance of the system where it can detect the parking availability with 99.74% accuracy and which is far better than conventional system including reliability and cost for the parking space guidance system.
Design of a route guidance system with shortest driving time based on genetic algorithm

Nowadays, with the advancement of the technology on mobile devices, route guidance systems that assist drivers on the traffic have become widespread in daily life. For an accurate routing, a route guidance system should consider the effectual factors of traffic flow such as density and allowable velocity limits of the roads. With the increase of effectual factors and amount of nodes in road network, the computational cost increases. It is not proper to find exact optimal solution in real time for the road networks with excessive number of nodes using some well known deterministic methods such as Dijkstra's algorithm on navigation systems using mobile devices with limited processing speed and memory capacity. This paper proposes a route guidance system and a Genetic Algorithm (GA) approach applied on this routing system to find the shortest driving time. Excluding classical methods, a gene search method of chromosomes named "first-matched-genes" on crossover operation had been introduced. The efficiency of the genetic algorithm was tested by applying on the networks with different sizes and a mobile application on the traffic network of Ankara was presented.
Deriving pilots’ knowledge structures for weather information: an evaluation of elicitation techniques

Systems that support or require human interaction are generally easier to learn, use, and remember when their organization is consistent with the user’s knowledge and experiences (Norman, 1983; Roske-Hofstrand & Paap, 1986). Thus, in order for interface designers to truly design for the user, they must first have a way of deriving a representation of what the user knows about the domain of interest. The current study evaluated three techniques for eliciting knowledge structures for how General Aviation pilots think about weather information. Weather was chosen because of its varying implications for pilots of different levels of experience. Two elicitation techniques (Relationship Judgment and Card Sort) asked pilots to explicitly consider the relationship between 15 weather-related information concepts. The third technique, Prime Recognition Task, used response times and priming to implicitly reflect the strength of relationship between concepts in semantic memory. Techniques were evaluated in terms of pilot performance, conceptual structure validity, and required resources for employment. Validity was assessed in terms of the extent to which each technique identified differences in organization of weather information among pilots of different experience levels. Multidimensional scaling was used to transform proximity data collected by each technique into conceptual structures representing the relationship between concepts. Results indicated that Card Sort was the technique that most consistently tapped into knowledge structure affected by experience. Only conceptual structures based on Card Sort data were able to be used to both discriminate between pilots of different experience levels and accurately classify experienced pilots as “experienced”. Additionally, Card Sort was the most efficient and effective technique to employ in terms of preparation time, time on task, flexibility, and face validity. The Card Sort provided opportunities for deliberation, revision, and visual feedback that allowed the pilots to engage in a deeper level of processing at which experience may play a stronger role. Relationship Judgment and Prime Recognition Task characteristics (e.g., time pressure, independent judgments) may have motivated pilots to rely on a more shallow or text-based level of processing (i.e., general semantic meaning) that is less affected by experience. Implications for menu structure design and assessment are discussed. DERIVING PILOTS’ KNOWLEDGE STRUCTURES FOR WEATHER INFORMATION: AN EVALUATION OF ELICITATION TECHNIQUES
Deep learning-based detection of tax frauds: an application to property acquisition tax

PurposeSampling taxpayers for audits has always been a major concern for policymakers of tax administration. The purpose of this study is to propose a systematic method to select a small number of taxpayers with a high probability of tax fraud.Design/methodology/approachAn efficient sampling method for taxpayers for an audit is investigated in the context of a property acquisition tax. An autoencoder, a popular unsupervised learning algorithm, is applied to 2,228 tax returns, and reconstruction errors are calculated to determine the probability of tax deficiencies for each return. The reasonableness of the estimated reconstruction errors is verified using the Apriori algorithm, a well-known marketing tool for identifying patterns in purchased item sets.FindingsThe sorted reconstruction scores are reasonably consistent with actual fraudulent/non-fraudulent cases, indicating that the reconstruction errors can be utilized to select suspected taxpayers for an audit in a cost-effective manner.Originality/valueThe proposed deep learning-based approach is expected to be applied in a real-world tax administration, promoting voluntary compliance of taxpayers, and reinforcing the self-assessing acquisition tax system.
The Norrköping Way : A Knowledge-based Strategy for Renewing a Declining Industrial City

Geographical regions as diverse as Silicon Valley, California and Linkoping, Sweden have been the sources of new technology and endogenously created innovations. Scholars and policymakers recognise that specific regions or clusters of businesses have the capability to engage in more innovative activities and new business formation and to experience higher employment growth than others. This dissertation uses qualitative methods to study various aspects of regional development and innovation. It is based on five papers by the author and colleagues with levels of analysis ranging from regional to firms’ first sales in order to capture the dynamics of both the top and bottom levels of regional development. It then uses these papers’ empirical material to address the research questions of (a) how a new scientific knowledge base becomes established and exploited in a spatial context, and (b) how people create and diffuse innovations in a social and spatial context. This dissertation’s main findings are that (a) regional leadership involving the building of alliances with triple-helix actors is crucial for initiating a knowledge-based regional development process, (b) a consensus space is a catalytic mechanism for ensuring the speed and effectiveness of regional development, (c) lowering the barriers for the actors involved boosts participation and the rate of innovation, and (d) users’ perspectives are essential for social, institutional and commercial innovation. This dissertation’s main implications are that knowledge-based regional development’s initial stages require leadership that (a) builds alliances and establish an arena for the triple-helix actors, (b) analyses the regional barriers to the commercialisation of knowledge, and (c) utilises both endogenous and exogenous resources.
A Combinatorial Strategy for Tageting Papillary Thyroid Carcinoma with MEK Inhibitor and SHP2 Inhibitor

Background: Pharmacologic targeting of components of MAPK/ERK pathway in thyroid carcinoma is often limited due to the development of adaptive resistance. However, the detailed mechanism for MEK inhibitor (MEKi) resistance is not fully understood in papillary thyroid carcinoma (PTC). Methods: RNA-seq was performed in MEKi-resistant PTC cell lines (K1, BCPAP, TPC-1 and KTC-1) to investigate the intrinsic mechanism of drug resistance. Colony formation assay, cell viability assay, cell cycle analysis and various murine models, including xenograft model, long-term MEKi-treated model, and transgenic model were conducted to evaluate the treatment effect of combination therapy (SHP099 and selumetinib). Results: Multiple receptor tyrosine kinases (RTKs) signaling pathways as well as Src-homology 2 domain-containing phosphatase 2 (SHP2) were activated in MEKi-resistant cells. Given the physiological role of SHP2 as the downstream of many RTKs, we first found that blockage of SHP2 abrogated MEKi resistance in thyroid cancer. Interestingly, we also found MEKi in combination with SHP2 inhibitor remarkably suppress rebound of MEK/ERK pathway compared to that of MEKi treatment alone, which significantly improved antitumor effects of MEKi. Various murine models confirmed the synergistic suppression on PTC in mice treated with both inhibitors. Conclusion: SHP2 blockade by SHP099 in combination with selumetinib is a promising therapeutic approach for advanced thyroid cancer.
Extraction optimization in food engineering

Introduction to extraction theory, Petros Tzias solid-liquid extraction, Jose Miguel Aguilera supercritical fluid extraction in food engineering, Nurhan Turgut Dunford, Jerry W. King, and Gary R. List extraction systems, Rudolph Eggers and Ph. T.Jaeger optimization methods, Constantina Tzia fats and oils from plant materials, L. Xu and Levente L. Diosady proteins from plant materials, L. Xu and Levente L. Diosady sugars and carbohydrates, Pascal Christodoulou flavour and aroma substances,Youn-Woo Lee and Youn Yong Lee extraction of natural antioxidants, Vassiliki Oreopoulou extraction of alkaloids from natural plants using supercritical fluids, Marleny D.A. Saldana and Rahoma S. Mohamed removal of cholesterol from food products usingsupercritical fluids, Rahoma S. Mohamed, Marleny D.A. Saldana, Alvaro B. de Azevedo and Uiram Kopcak solvent extraction - safety, health and environmental issues, Phillip J. Wakelyn and Peter J. Wan.
Explanatory Distance

When a train operator tells us that our train will be late ‘because of delays’, their attempt at explanation fails because there is insufficient distance between the explanans and the explanandum. In this article, I motivate and defend an account of ‘explanatory distance’, based on the idea that explanations give information about dependence. I show that this account offers useful resources for addressing problem cases, including recent debates about grounding explanation, and the historical case of Molière’s dormitive virtue.