Miracle-dz/UniFAR-FTUs huggingface dataset:
Capitalizing on the (false) consensus effect: Two tractable methods to elicit private information

We propose and experimentally test two tractable methods to incentivize the elicitation of private information: Benchmark and Coordination. Both mechanisms capitalize on the false consensus effect, a well-documented phenomenon that follows Bayesian reasoning. That is, individuals use their own type when predicting the type of others. Since it is not feasible to incentivize the elicitation of private information using facts, when these are not verifiable, we incentivize the respondent to reveal her perceptions about others and use that statement to predict the subject´s private information. The stronger the relationship between a subject's type and her perception about the type of others, the more effective the mechanisms are in revealing the subject´s privately held information. In an experiment, we apply the mechanisms to reveal beliefs about probabilities. On the aggregate level, both mechanisms accurately reveal mean first-order beliefs of the population. On the subject level, the modal difference between probabilities elicited in either mechanism and actual first-order beliefs is zero. The results indicate that subjects strongly anchor their statements in Benchmark and Coordination on their private information.
A novel conditional anonymity scheme for vehicular communication networks

Vehicular communication networks are traffic applications of the Internet of Things consisting of vehicle nodes, roadside units, service providers, and other components. To protect the vehicle privacy, some vehicular communication networks adopt anonymous schemes. However, in these anonymous schemes, anonymity abuse is inevitable. In order to protect vehicular privacy and prevent anonymity abuse, some conditional anonymous authentication schemes based on cryptography have been proposed. However, most of these methods have complex computation and large communication overhead, which makes them difficult to meet the actual requirement of high‐speed traffic in vehicular communication networks. In this paper, a novel conditional anonymity scheme called VKPCA (vehicular communication network based on kernel principal component analysis [KPCA]) is proposed, which can protect vehicle privacy while avoid anonymity abuse. The simulation results show that this scheme has lower computational complexity and communication overhead than the conventional anonymous authentication scheme.
A Survey on Disease Detection in Plant using Image Processing Techniques

: We are familiar with this fact that there are major production and economic losses due to the disease in plants; a disease also degrades the quality of Agricultural Products as well as the quantity of production. Now a day's farmers are facing many problems, they are always confused to use the different type of disease detection techniques. The paper shows various steps of image processing which covers Image acquisition followed by pre-processing then segmentation along with feature extraction and then classification for identification and discovering various plant diseases in the leaf’s of the plants.
A Proposal for Making Eiel Type-safe

AbstractStatically type-correct Eiﬀel programs may produce run-time er-rors because (1) attributes may be redeclared during inheritance, in-validating assignments in the superclass, (2) a formal method argu-ment type may be restricted in violation of the contravariance of func-tion types, and (3) two applications of a generic class are assumed toconform if the actual arguments conform. The third problem is solvedby case analysis on the variance of generic parameters. Declarationby association provides a solution to the ﬁrst two problems, but it suf-fers from additional diﬃculties. Type attributes, or generic parameterswith default values, are suggested as a replacement for most cases ofdeclaration by association. The special association type used to ex-press type recursion cannot be explained using type attributes, and itappears to be a truly novel construct for typing object-oriented pro-grams. One consequence of this construct is that Eiﬀel’s conformancehierarchy is a proper subset of its inheritance hierarchy.
Tuned sustainable anodic coatings for reduced ice adhesion

Aluminum alloys are widely used materials in the aircraft industry due to their high specific strength and durability. The natural corrosion resistance of aluminum can be improved through an electrochemical anodizing process. Due to recent restrictions in the use of chromic acid with toxic hexavalent chromium as electrolyte, the industry has shifted towards the use of the functional comparable tartaric sulfuric acid (TSA). TSA anodizing provides a porous alumina layer with good corrosion resistance, yet there is a desire to tune the process to fit other purposes. For instance, ice accretion to aircraft surfaces implies a safety risk and reduced energy efficiency. Due to insufficient active anti-icing systems, aircraft manufacturers are in the search for passive anti-acing materials. The ice adhesion properties of a material are thought to be affected by wettability. In turn, the wettability is affected by the morphology of the alumina influenced by the anodizing conditions. Herein, the effects of the anodizing voltage, electrolyte temperature and anodizing time on the morphology and wettability of TSA-anodized aluminum alloy 2024-T3 were studied by scanning electron microscopy (SEM) and contact angle (CA) measurements. The morphology in relation to wettability and ice adhesion strength as well as the use of posttreatments such as hydrothermal sealing and silanization was investigated. SEM images show a clear influence by the anodizing conditions on the porosity, interpore distance and pore diameter of the porous alumina. The morphology has influence on the wettability although the relationship needs further investigation. A superhydrophobic surface obtained by silanization of a surface anodized at high voltage characterized by a rod-like morphology has potential as a passive anti-icing surface. Future workmay include additional polishing pretreatments, testing of additional parameters, investigating the CA hysteresis and roll-off angle as well as measuring the adhesion strength of high-impact ice. By tuning the morphology of sustainable anodic coatings, the research area is one step closer to implementing passive anti-icing materials in aircrafts.
The adoption of NFC-based mobile payment services: an empirical analysis of Apple Pay in China

With the growing trend of mobile services prevailing in every sphere of our daily life, near field communication (NFC) is at the forefront of transforming the existing payment experience through providing a unified, secure transaction channel which can serve as an alternative to smart cards. Taking the opportunity offered by the recent launch of Apple Pay in China, this research aims to investigate the determinants of the behavioural intention to adopt NFC-enabled mobile payment services. The proposed model extends the technology acceptance model (TAM) with consumer trust, service quality dimensions and prior experience of users. The model is tested using survey collected from 166 iPhone users in China. Perceived usefulness and trust are identified as significant drivers of behavioural intention to use Apple Pay. Among all the service quality dimensions, reliability is identified the most important factor affecting behavioural intention through perceived usefulness, which reflects consumers' emphasis for stable, error-free transaction experience. Structural assurances, calculative-based trust and confidentiality are also shown to have indirect positive influence on behavioural intention through paths on trust. Finally, the mediation effects of perceived usefulness and perceived ease of use are analysed.
Retrieval of Disease-Treatment Information from Online Forums Using NLP and Convoluted Neural Network Pipeline

—This paper describes how the authors have applied Convoluted Neural Network to classify messages extracted from online healthcare forums as solutions or non-solutions for the disease Psoriasis. In this work, the comments gathered from online healthcare forums regarding the disease Psoriasis are classified as either giving information about the treatments that have worked and treatments that have not worked or comments that pose more questions. To do this a combination of NLP and Convoluted Neural Network is used
A lightweight IoT‐based security framework for inventory automation using wireless sensor network

Internet of things (IoT) has evolved as an innovation of next generation in this world of smart devices. IoT tends to provide services for data collection, data management, and data and device security required for application development. Things or devices in IoT communicate and compute to make our lives comfortable and safe. In inventory automation, real‐time check on items, their information management, and status management, monitoring can be carried out using IoT. The huge amount of data that flows among the devices in the network demands for a security framework that ensures authentication, authorization, integrity, and confidentiality of data. The existing security solutions like SIMON or SPECK offer lightweight security solutions but are vulnerable to differential attack because of their simplicity. Moreover, existing solutions do not offer inbuilt authentication. Therefore, this research work contributes a secure and lightweight IoT‐based framework using wireless sensor network (WSN) as a technology. The existing security solutions SPECK and SIMON are compared with the proposed security approach using COOJA simulator. The results show that proposed approach outstands others by 2% reduction in number of CPU cycles, 10% less execution time, 4% less memory requirements of security approach, and with minimum 10% more security impact.
Research in Computer Science and Software Engineering A Review on Automatic Diagnosis of Skin Lesion Based on the ABCD Rule & Thresholding Method

—Human Cancer is one of the most dangerous disease which is mainly caused by genetic instability of multiple molecular alterations. Among many forms of human cancer, skin cancer is the most common one. Basically, there are two types of skin cancer named as malignant melanoma and non-melanoma which can be more dangerous if not treated earlier. Therefore, early finding of skin cancer can reduce mortality and morbidity of patients. To identify skin cancer at an early stage we will study and analyze them through various techniques named as segmentation and feature extraction. Both of these images are used to analyze different digital images appropriately. Based on the experiment, a result will be computed.
On improving cybersecurity through memory isolation using systems management mode

This thesis describes research into security mechanisms for protecting sensitive areas of memory from tampering or intrusion using the facilities of Systems Management Mode. The essence and challenge of modern computer security is to isolate or contain data and applications in a variety of ways, while still allowing sharing where desirable. If Alice and Bob share a computer, Alice should not be able to access Bob’s passwords or other data; Alice’s web browser should not be able to be tricked into sending email, and viewing a social networking web page in that browser should not allow that page to interact with her online banking service. The aim of this work is to explore techniques for such isolation and how they can be used usefully on standard PCs. This work focuses on the creation of a small dedicated area to perform cryptographic operations, isolated from the rest of the system. This is a sufficiently useful facility that many modern devices such as smartphones incorporate dedicated hardware for this purpose, but other approaches have advantages which are discussed. As a case study, this research included the creation of a secure web server whose encryption key is protected using this approach such that even an intruder with full Administrator level access cannot extract the key. A proof of concept backdoor which captures and exfiltrates encryption keys using a modified processor was also demonstrated.
Effects of discharge teaching and counselling on anxiety and depression level of CABG patients Taburculuk eğitimi ve danişmanlik hizmetinin KABG hastalarinin anksiyete ve depresyon düzeylerine etkisi

Background:­In this study we evaluate the effect of discharge teaching and counselling on anxiety and depression levels of patients undergoing coronary artery bypass graft surgery. Methods: Hundred and nine patients were included in this quasi-experimental survey study and divided into two groups as; the intervention group (n=57) and the control group (n=52). Patients in the intervention group were given planned discharge teaching and counselling by the research nurse beginning from hospitalization while the patients in the control group did not receive planned discharge teaching and counselling other than the routine clinical procedures. The patient data were collected using the “Personal Information Form” and the Hospital Anxiety and Depression Scale. The anxiety and depression levels of the patients in the control and intervention groups were measured on the day of their admission to the hospital, on the day of discharge and one week and one month after discharge using the Hospital Anxiety and Depression Scale. Results:­It was found that the mean anxiety and depression scores of the patients in the intervention group were lower than in the control group at the time of discharge and one week and one month after discharge. Conclusion:­ Discharge training and counselling service given to patients in the intervention group had a positive impact on alleviating the anxiety and depression they had. Therefore, the institutions may be recommended to support multidisciplinary patient training and counselling activities using the methods decribed in this study.
An algorithmic-based software change effort prediction model using change impact analysis for software development

Software changes are inevitable due to the dynamic nature of the software development project itself. Some software development projects practice their own customised methodology but mostly adopt two kinds of methodologies; Traditional and Agile. Traditional methodology emphasizes on detailed planning, comprehensive documentation and extensive design that resulted a low rate of changes acceptance. In contrast, Agile methodology gives high priority on accepting changes at any point of time throughout the development process as compared to the Traditional methodology. Among the primary factor that has direct impact on the effectiveness of the change acceptance decision is the accuracy of the change effort prediction. There are two current models that have been widely used to estimate change effort which are algorithmic and non-algorithmic models. The algorithmic model is known for its formal and structural way of estimation and best suited for Traditional methodology. While non-algorithmic model is widely adopted for Agile methodology of software projects due to its easiness and requiring less work in term of effort predictability. The main issue is that none of the existing change effort prediction models is proven to suits for both, Traditional and Agile methodology. Additionally, there is as yet no clear evidence of the most accurate change effort prediction model for software development phase. One of the method to overcome these challenges is the inclusion of change impact analysis in the estimation process. The aim of the research is to overcome the challenges of change effort prediction for software development phase: inconsistent states of software artifacts, repeatability using algorithmic approach and applicability for both Traditional and Agile methodologies. This research proposed an algorithmic change effort prediction model that used change impact analysis method to improve the accuracy of the effort estimation. The proposed model used a current selected change impact analysis method for software development phase which is the SDP-CIAF (Software Development Phase-Change Impact Analysis Framework). A software prototype was also developed to support the implementation of the model. The proposed model was evaluated through an extensive experimental validation using case scenarios of six real Traditional and Agile methodologies software projects. A comparative study was also conducted for further validation and verification of the proposed model. The analysis result showed an accuracy improvement of 13.44% average mean difference for change effort prediction over the current selected change effort prediction model. The evaluation results also confirmed the applicability for both Traditional and Agile methodologies.
A Novel Approach of Ranking Web Documents by Using ELO-DCG Method

Figuring out how to rank emerges in numerous information mining applications, going from web crawler, internet publicizing to suggestion framework. In figuring out how to rank, the execution of a positioning model is emphatically influenced by the quantity of named cases in the preparation set; then again, acquiring marked illustrations for preparing information is exceptionally costly and tedious. This exhibits an incredible requirement for the dynamic learning ways to deal with select most educational cases for positioning adapting; notwithstanding, in the writing there is still extremely restricted work to address dynamic learning for positioning.proposed a general dynamic learning system, expected misfortune advancement (ELO), for positioning. The ELO system is material to an extensive variety of positioning capacities. Under this structure, we infer a novel calculation, expected marked down aggregate addition (DCG) misfortune streamlining (ELO-DCG), to choose most useful cases. At that point, we research both question and report level dynamic learning for raking and propose a twostage ELO-DCG calculation which join both inquiry and archive choice into dynamic learning. Besides, demonstrate that it is adaptable for the calculation to manage the skewed evaluation appropriation issue with the adjustment of the misfortune capacity. Broad trials on certifiable web seek information sets have shown awesome potential and adequacy of the proposed structure and calculations.
Improving Invisible Food Texture Detection by using Adaptive Extremal Region Detector in Food Recognition

The advancement of mobile technology with reasonable cost has indulge the mobile phone users to photograph foods and shared in social media. Since that, food recognition has become emerging research area in image processing and machine learning. Food recognition provides an automatic identification of the types of foods from an image. Then, further analysis in food recognition is performed to approximate the calories and nutritional information that can be used for health-care purposes. The interest region-based detector by using Maximally Stable Extremal Region (MSER) may provides distinctive interest points by representing the arbitrary shape of foods through global segmentation especially the food images with strong mixture of ingredients. However, the classification performance on food categories with less diverse texture food images by using MSER are obviously low compared to the other food categories that have more noticeable texture. The texture-less food objects were suffered from small number of extremal regions (ER) detection beside having low image brightness and small resolutions. Therefore, this paper proposed an adaptive interest regions detection by using MSER (aMSER) that provide a mechanism to choose appropriate MSER parameter configuration to increase the density of interest points on the targeted food images. The features are described by using Speeded-up Robust Feature Transform (SURF) and encoded by using Bag of Features (BoF) model. The classification is performed by using Linear Support Vector Machine and yield 84.20% classification rate on UEC100-Food dataset with competitive number of ER and computation cost.
Improving explicit term matching with implicit topic matching for short text conversation

Short Text Conversation (STC) is the task of retrieving comments made to a post and reuse them as appropriate comments to a later post. Large vocabulary variations and very limited length of posts and comments pose great challenges to traditional retrieval techniques of matching terms. To address these problems, we propose a new technique of re‐ranking comments initially retrieved through explicit term matching by using evidence of implicit topic matching gained from topic models of large collections of posts and comments. The key of our technique lies in (1) combining each post with the comments responding to it into a longer pseudo‐document for topic modeling purposes, and (2) carrying out implicit topic matching by comparing the most probable topics contained in the topic composition of a comment and that of the query post. Experiments using the NTCIR‐13 STC Chinese WeiBo collection showed that with properly tuned parameters, our technique can lead to improved retrieval effectiveness. The paper analyzes and discusses the results and outlines future work.
The Interplay between Perceived Business Uncertainty and Organizational Capabilities in Firms’ Response to Climate Change

With climate change emerging as one of the most important issues increasing uncertainty to the business circle, firms have shown different reactions. Why do firms differ in adopting and implementing carbon management practices (CMPs) in response to the global warming issue? This paper attempts to explore this question with particular attention to two factors: external business uncertainty and internal organizational capabilities. In this paper, we investigated whether business uncertainty, organizational learning and lean production capabilities influenced the adoption and implementation of CMPs as well as examined how organizational capabilities’ moderate the relationships between business uncertainty and the level of CMPs. The results of a cross-sectional survey and hierarchical regression analyses indicate that (a) perceived business uncertainty decreases the adoption of CMPs, in particular those conducted within an organization; (b) organizational learning and lean production capabilities strongly facilitate the adoption and implementation of CMPs; and (c) lean production capability positively moderates the impacts of business uncertainty on the adoption of CMPs. This study provides guidance for managers and academics considering how to identify, design, and manage the dimensions of a firm’s practices in response to the global warming issue within organization as well as with other organizations.
IOT STATUS COMMUNICATION FOR HOME AUTOMATION

The vast majority of home automation systems depend on a permanent central controller and this is the source of many serious problems. The controller may be eliminated by using one or more Intermittent Control Devices such as a smartphone, but the responsibilities of the controller must be redistributed. This paper considers the problem of keeping multiple intermittent controllers up-to-date with status information for devices around the home. Four protocols are proposed, taking key lessons from existing network protocols, and then implemented, tested and compared with the expected performance of UPnP. All four protocols outperform UPnP and further analysis shows that these protocols can be implemented in a robust and user-friendly manner. A comparison of the packet efficiency of these protocols demonstrates that the combination of device registration and packet broadcasting makes for the most efficient protocol and should form the basis of devices status communications in home automation systems without a permanent central controller.