A bi-objective optimization approach for selection of passive energy alternatives in retrofit projects under cost uncertainty
یک روش بهینه سازی دو هدفه برای انتخاب گزینه های انرژی منفعل در پروژه های مقاوم سازی تحت عدم اطمینان هزینه-2020
Improving energy performance of buildings is of particular importance in new construction and existing buildings. Building refurbishment is considered a practical pathway towards energy efficiency as the replacement of older buildings is at a slow pace. There are various ways of incorporating energy conservation measures in buildings through refurbishment projects. As such, we have to choose among various passive or active measures. In this study, we develop an integrated assessment model to direct energy management decisions in retrofit projects. Our focus will be on alternative passive measures that can be included in refurbishment projects to reduce overall energy consumption in buildings. We identify the relative priority of these alternatives with respect to their non- monetary (qualitative) benefits and issues using an analytic network process. Then, the above priorities will form a utility function that will be optimized along with the energy demand and retrofit costs using a multi-objective optimization model. We also explore various approaches to formulate the uncertainties that may arise in cost estimations and incorporate them into the optimization model. The applicability and authenticity of the proposed model is demonstrated through an illustrative case study application. The results reveal that the choice of the optimization approach for a retrofit project shall be done with respect to the extent of variations (uncertainties) in expected utilities (benefits) and costs for the alternative passive technologies.
Keywords: Construction technologies | assive energy measures | Building retrofit | Multi-Objective Optimization | Cost uncertainty | Fuzzy set theory
Using retracted journal articles in psychology to understand research misconduct in the social sciences: What is to be done?
استفاده از مقالات مجله جمع شده در روانشناسی برای درک سوء رفتار پژوهشی در علوم اجتماعی: چه کاری باید انجام شود؟-2020
This paper explores the nature and impact of research misconduct in psychology by analyzing 160 articles that were retracted from prominent scholarly journals between 1998 and 2017. We compare findings with recent studies of retracted papers in economics, and business and management, to profile practices that are likely to be problematic in cognate social science disciplines. In psychology, the principal reason for retraction was data fabrication. Retractions took longer to make, and generally were from higher ranked and more prestigious journals, than in the two cognate disciplines. We recommend that journal editors should be more forthcoming in the reasons they provide for article retractions. We also recommend that the discipline of psychology gives a greater priority to the publication of replication studies; initiates a debate about how to respond to failed re- plications; adopts a more critical attitude to the importance of attaining statistical significance; discourages p- hacking and Hypothesizing After Results are Known (HARKing); assesses the long-term effects of pre-registering research; and supports stronger procedures to attest to the authenticity of data in research papers. Our con- tribution locates these issues in the context of a growing crisis of confidence in the value of social science research. We also challenge individual researchers to reassert the primacy of disinterested academic inquiry above pressures that can lead to an erosion of scholarly integrity.
Keywords: Misconduct | Psychology | Research | Replication | Retractions
Leveraging crowdsourcing methods to collect qualitative data in addiction science: Narratives of non-medical prescription opioid, heroin, and fentanyl use
استفاده بی نظیر از روش های تأمین منابع انبوه برای جمع آوری داده های کیفی در علوم اعتیاد: روایات استفاده از داروهای افیونی ، هروئین و فنتانیل با تجویز غیر پزشکی-2020
Background: Online crowdsourcing methods have proved useful for studies of diverse designs in the behavioral and addiction sciences. The remote and online setting of crowdsourcing research may provide easier access to unique participant populations and improved comfort for these participants in sharing sensitive health or behavioral information. To date, few studies have evaluated the use of qualitative research methods on crowdsourcing platforms and even fewer have evaluated the quality of data gathered. The purpose of the present analysis was to document the feasibility and validity of using crowdsourcing techniques for collecting qualitative data among people who use drugs. Methods: Participants (N=60) with a history of non-medical prescription opioid use with transition to heroin or fentanyl use were recruited using Amazon Mechanical Turk (mTurk). A battery of qualitative questions was included indexing beliefs and behaviors surrounding opioid use, transition pathways to heroin and/or fentanyl use, and drug-related contacts with structural institutions (e.g., health care, criminal justice). Results: Qualitative data recruitment was feasible as evidenced by the rapid sampling of a relatively large number of participants from diverse geographic regions. Computerized text analysis indicated high ratings of authenticity for the provided narratives. These authenticity percentiles were higher than the average of general normative writing samples as well as than those collected in experimental settings. Conclusions: These findings support the feasibility and quality of qualitative data collected in online settings, broadly, and crowdsourced settings, specifically. Future work among people who use drugs may leverage crowdsourcing methods and the access to hard-to-sample populations to complement existing studies in the human laboratory and clinic as well as those using other digital technology methods.
Keywords: Crowdsourcing | Heroin | Mechanical Turk | Opioid | Qualitative | Mixed method
AI Powered THz VLSI Testing Technology
فناوری تست THz VLSI با قدرت هوش مصنوعی-2020
Abstract—Increasing complexity of digital and mixed-signal systems makes establishing the authenticity of a chip to be a challenging problem. We present a new terahertz testing technique for non-destructive identification of genuine integrated circuits, in package, in-situ and either with no or under bias, by measuring their response to scanning terahertz and sub-terahertz radiation at the circuit pins. This novel, patent pending non-invasive nondestructive technology when merged with Artificial Intelligence (AI) engine will evolve and self-improve with each test cycle. By establishing and AI processing of the THz scanning signatures of reliable devices and circuits and comparing this signatures with devices under test using AI, this technology could be also used for reliability and lifetime prediction.
Keywords: Terahertz | hardware cybersecurity | reliability | authentication | artificial intelligence
Digital Evidence Certainty Descriptors (DECDs)
توصیف کنندگان قطعیت شواهد دیجیتال (DECD)-2020
Whilst many other traditional forensic science disciplines are encouraged to describe the weight of their evidence in some form of quantifiable measurement/expression, this is rarely done in digital forensics. There are calls to rectify this situation, suggesting that the field should begin to develop more robust, scientific methods for evaluating the digital evidence presented by its practitioners. Whilst such a recommendation carries a number of potential benefits, caution must be exercised as at present there are no available satisfactory methods for achieving this. This work suggests that attaining such methods may not actually be possible due to the intricacies of digital data and the difficulties involved with the finegrained interpretation of events. As a result it is argued that attempts to quantify any uncertainty should be abandoned in favour of methods which reliably describe when uncertainty exists and in what capacity. Here, the Digital Evidence Certainty Descriptors (DECDs) framework is offered as a method for conveying when uncertainty exists in a set of digital findings. The DECDs framework is discussed and applied to working examples to demonstrate the difficulties involved with determining the authenticity of a given hypothesis regarding digital evidence.
Keywords: Digital forensics | Digital evidence | Likelihood | Uncertainty | Criminal investigation
The evolution of founder identity as an authenticity work process
تکامل هویت بنیانگذار به عنوان یک فرایند کار اصالت-2020
Research has shown founders identities have a significant impact on their ventures. Yet, the process through which founder identity evolves and takes shape remains relatively unexplained. This paper explores the evolution of founder identity through a qualitative study of first-time sustainable entrepreneurs, and their stakeholders, over a three years period. Our analysis revealed the importance of personal identity, the aspect of the self that defines a person as a unique individual based largely on values and beliefs. We found that first-time founders sought to align their personal identity with their evolving founder identity over time. Based on these findings we theorize a process model of founder authenticity work, defined as the activities founders engage in to feel and seem authentic while engaged in entrepreneurial action. This study thus details the significance of personal identity as a guidepost for founder identity evolution, complementing extant founder identity studies focused on role and social identities. In addition, our analysis enriches the current conceptualization of authenticity in entrepreneurship research by linking it to validation of personal identity and highlighting its negotiated nature in the evolution of authentic founder identities.
Keywords: Founder identity | Personal identity | Authenticity | Sustainable entrepreneurship | Qualitative methods
Authentication and integrity of smartphone videos through multimedia container structure analysis
احراز هویت و یکپارچگی فیلم های تلفن های هوشمند از طریق تجزیه و تحلیل ساختار چند رسانه ای-2020
Nowadays, mobile devices have become the natural substitute for the digital camera, as they capture everyday situations easily and quickly, encouraging users to express themselves through images and videos. These videos can be shared across different platforms exposing them to any kind of intentional manipulation by criminals who are aware of the weaknesses of forensic techniques to accuse an innocent person or exonerate a guilty person in a judicial process. Commonly, manufacturers do not comply 100% with the specifications of the standards for the creation of videos. Also, videos shared on social networks, and instant messaging applications go through filtering and compression processes to reduce their size, facilitate their transfer, and optimize storage on their platforms. The omission of specifications and results of transformations carried out by the platforms embed a features pattern in the multimedia container of the videos. These patterns make it possible to distinguish the brand of the device that generated the video, social network, and instant messaging application that was used for the transfer. Research in recent years has focused on the analysis of AVI containers and tiny video datasets. This work presents a novel technique to detect possible attacks against MP4, MOV, and 3GP format videos that affect their integrity and authenticity. The method is based on the analysis of the structure of video containers generated by mobile devices and their behavior when shared through social networks, instant messaging applications, or manipulated by editing programs. The objectives of the proposal are to verify the integrity of videos, identify the source of acquisition and distinguish between original and manipulated videos.
Keywords: Forensic analysis | Metadata | Mobile device camera | Multimedia container structure | Social network video analysis | Video analysis | Video authenticity | Video integrity
A machine learning forensics technique to detect post-processing in digital videos
یک روش پزشکی قانونی برای یادگیری ماشین برای تشخیص پس از پردازش در فیلم های دیجیتال-2020
Technology has brought great benefits to human beings and has served to improve the quality of life and carry out great discoveries. However, its use can also involve many risks. Examples include mobile devices, digital cameras and video surveillance cameras, which offer excellent performance and generate a large number of images and video. These files are generally shared on social platforms and are exposed to any manipulation, compromising their authenticity and integrity. In a legal process, a manipulated video can provide the necessary elements to accuse an innocent person of a crime or to exempt a guilty person from criminal acts. Therefore, it is essential to create robust forensic methods, which will strengthen the justice administration systems and thus make fair decisions. This paper presents a novel forensic technique to detect the post-processing of digital videos with MP4, MOV and 3GP formats. Concretely, detect the social platform and editing program used to execute possible manipulation attacks. The proposed method is focused on supervised machine learning techniques. To achieve our goal, we take advantage that the social platforms and editing programs, execute filtering and compression processes on the videos when they are shared or manipulated. The result of these transformations leaves a characteristic pattern in the videos that allow us to detect the social platform or editing program efficiently. Three phases are involved in the method: 1) Dataset preparation; 2) data features extraction; 3) Supervised model creation. To evaluate the scalability of the technique in real scenarios, we used a robust, heterogeneous and far superior dataset than that used in the literature.
Keywords: Editing programs detection | Machine learning processing | Multimedia container structure | Social networks detection | Video forensics | Video post-processing detection
Do international marketing simulations provide an authentic assessment of learning? A student perspective
آیا شبیه سازی بازاریابی بین المللی ارزیابی معتبر از یادگیری ارائه می دهد؟ دیدگاه دانشجویی-2020
This paper seeks to determine whether international marketing simulations provide an authentic assessment of learning. The principles of authentic assessment dictate that assigned learning activities be aligned with the attitudes, skills and knowledge that students will be required to demonstrate in the real world. Research on the application of authentic assessment principles in management education is limited and most of the works that have examined the issue have done so from the perspective of the educator, not the student. A content analysis was undertaken of 122 final reports submitted by teams participating in an online international marketing simulation. The results demonstrate that the simulation provided students with opportunities for reflection and the development of an understanding of the real world of international marketing, with all its complexities and challenges. The simulation also allowed students to receive feedback, correct mistakes and gain an appreciation of the varied activities that contribute to the achievement of an overall objective. Students did not, however, appreciate the transferability of skills acquired in the simulation to other domains of knowledge. Similarly, the final reports did not reflect a significant appreciation of the communication and collaboration benefits that simulations should provide. Further, provision of instructional support was shown to have no impact on students perceptions of the simulations authenticity.
Keywords: Authentic assessment | International marketing simulation | Business simulation | Learning | Marketing education | Scaffolding | International marketing | Management education
The impact of destination brand authenticity and destination brand selfcongruence on tourist loyalty: The mediating role of destination brand engagement
تأثیر اصالت برند مقصد و تطابق نام تجاری مقصد بر وفاداری توریست ها: نقش واسطهای تعامل برند مقصد-2020
Brand engagement has become a major topic in brand management, but its application to the tourism industry remains limited. This study proposes an integrated framework for destination brand engagement, with two key drivers (destination brand authenticity and destination brand self-congruence) developed from both destinationled and tourist-centered perspectives and their associated outcomes: revisit intention and recommendation intention. Additionally, the mediating effect of destination brand engagement was examined. The findings indicate that destination brand authenticity and destination brand self-congruence positively influence destination brand engagement, revisit intention, and recommendation intention and that destination brand engagement mediates this relationship. Destination brand authenticity directly influences recommendation intention but indirectly influences revisit intention. Destination brand self-congruence has a direct effect on revisit intention but an indirect effect on recommendation intention. The findings could help destination marketing organizations recognize the importance of destination brand engagement and improve brand performance in destination brand management.
Keywords: Authenticity | Engagement | Self-congruence | Revisit intention | Recommendation intention