MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images

This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. To this end, we employed the popular knowledge distillation (KD) method and identified two major shortcomings with its use: 1) a fine-grained grid search is needed for tuning the temperature hyperparameter and 2) to find the optimal size-accuracy balance, one needs to search for the final network size (or the compression rate). On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size. In addition, we hypothesized that translation invariance achieved using max-pooling layers would not be useful for the FER problem as the expressions are sensitive to small, pixel-wise changes around the eye and the mouth. However, we have found an intriguing improvement on generalization when max-pooling is used. We conducted experiments on two widely-used FER datasets, CK+ and Oulu-CASIA. Our smallest model (MicroExpNet), obtained using knowledge distillation, is less than 1MB in size and works at 1851 frames per second on an Intel i7 CPU. Despite being less accurate than the state-of-the-art, MicroExpNet still provides significant insights for designing a microarchitecture for the FER problem.Comment: International Conference on Image Processing Theory, Tools and Applications (IPTA) 2019 camera ready version. Codes are available at: https://github.com/cuguilke/microexpne

DİJİTAL OLGUNLUK İNDEKSİ: ORGANİZASYONLARIN DİJİTAL DÖNÜŞÜM YOLCULUĞUNDA VERİMLİLİĞİ ARTIRMAK İÇİN BİR KANTİTATİF YÖNTEM

Amaç: Literatürde dijital dönüşüm için geliştirilmiş olgunluk modellerinin incelenmesi ve yeni bir indeks yapısının temellerinin oluşturulması hedeflenmektedir. Böylece, işletmelerin mevcut dijital dönüşüm düzeylerini analiz edebilmesi, diğer işletmelerle objektif bir şekilde kıyas yapabilmesi, etkin bir şekilde dijital dönüşümü yönetebilmesi ve verimliliğini artırması beklenmektedir. Yöntem: Sistematik literatür taraması sonucunda tespit edilen 23 Olgunluk Modeli kapsam, amaca uygunluk, boyutların tamlığı gibi bir dizi kritere göre karşılaştırılarak analiz edilmiştir. Belirlenen eksiklikleri gidermek amacıyla işletmelerin dijital dönüşüm yeteneklerini kantitatif olarak değerlendiren yeni bir dijital olgunluk indeksi önerilmiştir. Bulgular: Analiz edilen olgunluk modellerinden hiçbiri beklenen kriterleri tam olarak karşılamadığından iyileştirilmeleri gerekmektedir. Bu çalışmada, ‘Strateji, Bilgi Teknolojileri, İnsan, Veri ve Süreçler’ boyutlarının değerlendirilmesinden oluşan bütünsel bir yaklaşım sunulmuştur. Özgünlük: İşletmeler dijital dönüşüm yolculuğunda kaynaklarını en verimli şekilde kullanarak, nereden başlanması ve nelerin yapılması gerektiğiyle ilgili bir yol haritası eksikliği yaşamaktadır. Bu çalışma, yatırım geri dönüş oranı en yüksek dijital dönüşüm projelerinin takvimini içeren bir rehberlik sunarak bu alandaki literatüre katkı sağlayacaktır

Dinamik ağ budama yöntemiyle geliştirilmiş bilgi damıtma.

Deploying convolutional neural networks to mobile or embedded devices is often prohibited by limited memory and computational resources. This is particularly problematic for the most successful networks, which tend to be very large and require long inference times. In the past, many alternative approaches have been developed for compressing neural networks based on pruning, regularization, quantization or distillation. In this thesis, we propose the Knowledge Distillation with Dynamic Pruning (KDDP), which trains a dynamically pruned compact student network under the guidance of a large teacher network. In KDDP, we train the student network with supervision from the teacher network, while applying L_1 regularization on the neuron activations in a fully-connected layer. Subsequently, we prune inactive neurons. Our method automatically determines the final size of the student model. We evaluate the compression rate and accuracy of the resulting networks on image classification datasets, and compare them to results obtained by Knowledge Distillation (KD). Compared to KD, our method produces better accuracy and more compact models.Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering

Development of an Assessment Model for Industry 4.0: Industry 4.0-MM

The application of new technologies in the manufacturing environment is ushering a new era referred to as the 4th industrial revolution, and this digital transformation appeals to companies due to various competitive advantages it provides. Accordingly, there is a fundamental need for assisting companies in the transition to Industry 4.0 technologies/practices, and guiding them for improving their capabilities in a standardized, objective, and repeatable way. Maturity Models (MMs) aim to assist organizations by providing comprehensive guidance. Therefore, the literature is reviewed systematically with the aim of identifying existing studies related to MMs proposed in the context of Industry 4.0. Seven identified MMs are analyzed by comparing their characteristics of scope, purpose, completeness, clearness, and objectivity. It is concluded that none of them satisfies all expected criteria. In order to satisfy the need for a structured Industry 4.0 assessment/maturity model, SPICE-based Industry 4.0-MM is proposed in this study. Industry 4.0-MM has a holistic approach consisting of the assessment of process transformation, application management, data governance, asset management, and organizational alignment areas. The aim is to create a common base for performing an assessment of the establishment of Industry 4.0 technologies, and to guide companies towards achieving a higher maturity stage in order to maximize the economic benefits of Industry 4.0. Hence, Industry 4.0-MM provides standardization in continuous benchmarking and improvement of businesses in the manufacturing industry

Bulut Tabanlı Kurumsal Bilgi Sistemlerinin Benimsenmesini Etkileyen Faktörlerin Değerlendirilmesi

Bulut bilişimin, sağladığı düşük maliyet, daha fazla esneklik ve en uygun kaynak yönetimi sayesinde rekabet edebilirliği arttırdığı bilinmektedir. Buna bağlı olarak, müşteri memnuniyeti ve iş mükemmelliğini arttırıp maliyetlerini azaltmak gibi sağladığı faydalar nedeniyle, bulut tabanlı Kurumsal Bilgi Sistemleri (KBS) kullanımı hızla yaygınlaşmaktadır. Gördüğü büyük ilgiye rağmen, literatürde bulut tabanlı KBS ile ilgili az sayıda araştırma mevcuttur. Dolayısıyla, bu çalışmada, bulut tabanlı KBS’nin Teknoloji Organizasyon ve Çevre (TOÇ) modeli temelinde, benimsenmesini ve kullanımını etkileyen faktörlerin incelenmesi hedeflenmiştir. Böylece, bu sistemlerin benimsenmesini ve kullanımını arttırabilmek için bulut sağlayıcılara ürünlerinin tasarımları konusunda yol gösterici bilgiler sağlanabilecektir. Bu faktörlerin önem sıralarını belirlemek için Analitik Hiyerarşi Süreci (AHS) yöntemi kullanılmıştır. Akademisyenler ve uygulayıcılar olarak iki ayrı gruptan veri toplanarak, elde edilen sonuçlar karşılaştırılıp tartışılmıştır. Çalışma sonucunda, bulut tabanlı KBS’lerin benimsenmesini ve kullanımını etkileyen en önemli faktörler: güvenlik ve gizlilik, iş faaliyetlerinin bulut tabanlı KBS’ne uygunluğu, üst yönetim desteği, güven, ve organizasyonun Bilişim Teknolojileri kaynakları olarak belirlenmiştir

Endüstri 4.0 için olgunluk modeli: sistematik literatür taraması ve model önerisi

Recently, Industry 4.0 has received a great deal of interest from both enterprises and practitioners. With this revolution, competitive market conditions stimulate enterprises to restructure their business operations through digital transformation and to improve business accordingly. This creates a need for a guideline regarding how to pursue this transformation. Maturity Models (MMs) are standard structures that are employed to determine opportunities for improvement by assessing the current situation. In this study, a systematic literature review of existing maturity models in the context of Industry 4.0 is conducted, and seven existing MMs are analyzed by a set of criteria in terms of the scope of study, suitability for purpose, completeness of dimensions, objectivity, and level of granularity. Consequently it is concluded that none of them satisfies the expected criteria. To improve this situtation, and to provide a guideline that can assess the capabilities of enterprises in the context of Industry 4.0 with a set of standard, consistent, and repeatable metrics, the main structure of the proposed model, Endüstri 4.0-OM, is presented

Cloud-Based Enterprise Information Systems

Cloud computing is growing at a very fast pace. Enterprise information systems (EISs) such as ERP, SCM, and CRM are used in organizations in order to increase customer satisfaction, operational excellence, and to decrease operational costs. Looking at the widespread literature available on both EIS and Cloud Computing, few researchers have examined the integration of both systems. While this area has not been fully investigated in the academia due to limited available literature, it has attracted significant interest from general practitioners. Accordingly, the Cloud-EIS can be considered as an important research problem. In this study, we attempt to investigate the factors influencing the usage and adoption of Cloud-EISs by considering Technology-Organization- Environment (TOE) framework as the basis to give directions to cloud service providers on how to design their products in order to increase adoption and usage. Analytic Hierarchy Process (AHP) is used in order to rank the determined factors. The results show that the most significant factors influencing the usage and adoption of Cloud-EISs are security & privacy, business activity-cloud EIS fitness, top management support, trust, and organization's IT resource

ClouDSS: A decision support system for cloud service selection

Cloud computing brings in significant technical advantages and enables companies, especially small and medium size enterprises (SMEs), to eliminate up-front capital expenditures. This is due to the various benefits it provides, such as pay-as-you-go service model, flexibility of services, and on-demand accessibility. The proliferation of cloud services leads to their wide spread use and calls for comprehensive evaluation approaches in order to be able to choose the most suitable alternatives. To this end, existing studies in the literature generally provide solutions incorporating a single method for making such decisions. Therefore, this study proposes a more comprehensive solution in the form of a decision support system named as ClouDSS which employs various Multi-Criteria Decision Making (MCDM) methods with the aim of optimizing cloud service selection decisions. ClouDSS has a default decision model, which can be customized according to enterprise-specific requirements, for evaluating the suitability of cloud services with respect to business needs. After presenting the main components of ClouDSS, the employed cloud service selection process is described in order to highlight the associated tasks, including both objective and subjective evaluation approaches. Furthermore, the applicability of the proposed system is demonstrated through a case study