Miracle-dz/UniFAR-FTUs huggingface dataset:
Enable Changeability in Manufacturing Systems by Adopting a Life Cycle Perspective

To compete, manufacturing companies need production systems that quickly can respond to changes. To handle change drivers such as volume variations or new product variants, reconfigurability is advocated as a competitive means. This implies an ability to add, remove, and/or rearrange the structure of the production system to be ready for future changes. Still, it is not clear how the production system design process can capture and support the design of reconfigurable production systems. Therefore, the objective of this thesis is to increase the knowledge of how to support the design of reconfigurable production systems.Reconfigurability could be defined by a number of reconfigurability characteristics including convertibility, scalability, automatibility, mobility, modularity, integrability, and diagnosability. In eight case studies, reconfigurability characteristics in production system design were studied in order to investigate reconfigurability needs, knowledge, and practice in manufacturing companies. In three of the case studies reconfigurable production systems were studied to identify the links between change drivers and reconfigurability characteristics. In the remaining five case studies, reconfigurability in the production system design processes was addressed in terms of needs, prerequisites, and consideration.Based on the literature review and the case studies, support for reconfigurable production system design is suggested including two parts. The first part comprises support for analyzing the need for reconfigurability. Based on relevant change drivers the need for reconfigurability must be identified to enable selection of right type and degree of reconfigurability for each specific case of application. A comprehensive view of the reconfigurability characteristics is presented and links between change drivers and reconfigurability characteristics are described. The characteristics are divided into critical characteristics, that lead to a capacity or functionality change of the production system, and supporting characteristics, that reduce system reconfiguration time but do not necessarily lead to a modification of functionality or capacity of the production system. The second part provides support in how to consider reconfigurability in the production system design process. A holistic perspective is crucial to design reconfigurable production systems and therefore constituent parts of a production system are described. According to their character physical, logical, and human reconfiguration must be considered through the whole production system design process.
OPTIMAL DISTRIBUTED GENERATION PLACEMENT AND SIZING TO REDUCE ACTIVE POWER LOSS USING GA AND ACO ALGORITHM

In this paper, Genetic Algorithm (GA) and Ant colony Algorithm (ACO) optimization techniquesare proposed to find optimal sizing and location for distributed generation in electrical networks.The objective function of the work relies upon a linearized model to compute the active powerlosses as a function of power generators. This strategy based on a strong coupling between activepower and power flow taking into consideration the voltage angles. With the end goal to exhibitthe adequacy of the proposed method, the proposed strategy is applied on IEEE 30-bus standardsystems. Different maximum penetration level capacity of DG units with three ranges such as,10%, 20% and 30% of maximum power load and various possible places of DG units amongseveral types of DG (active, reactive or active and reactive power) are considered. Results showthat the optimization tools employing GA and ACO are effective in reducing active power lossesand cost loss by finding the optimal placement and sizing of DG units.
The Spectrum Allocation Technology Based on Hybrid Particle Swarm Optimization in Cognitive Communication

With the increase of the number of communication users, the high frequency (HF) communication resources are scarce and more precious. Aiming at the problem of frequency allocation in cognitive HF communication, this paper transforms it into the problem of maximizing network revenue, and solves the problem by using particle swarm optimization algorithm based on natural selection (NSPSO), which can get better solution in a short time. The experimental results show that the proposed algorithm is better than the conventional particle swarm optimization (PSO) and genetic algorithm (GA) in the effectiveness and speed.
A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.
Resistive Memory‐Based In‐Memory Computing: From Device and Large‐Scale Integration System Perspectives

In‐memory computing is a computing scheme that integrates data storage and arithmetic computation functions. Resistive random access memory (RRAM) arrays with innovative peripheral circuitry provide the capability of performing vector‐matrix multiplication beyond the basic Boolean logic. With such a memory–computation duality, RRAM‐based in‐memory computing enables an efficient hardware solution for matrix‐multiplication‐dependent neural networks and related applications. Herein, the recent development of RRAM nanoscale devices and the parallel progress on circuit and microarchitecture layers are discussed. Well suited for analog synapse and neuron implementation, RRAM device properties and characteristics are emphasized herein. 3D‐stackable RRAM and on‐chip training are introduced in large‐scale integration. The circuit design and system organization of RRAM‐based in‐memory computing are essential to breaking the von Neumann bottleneck. These outcomes illuminate the way for the large‐scale implementation of ultra‐low‐power and dense neural network accelerators.
Hybrid Beamforming for dual-polarized antenna

Recently, dual-polarized antenna has attracted strong attention in Millimeter wave (mmWave) systems. It provides an additional degree-of-freedom in wireless communication, yielding higher throughput. Nowadays, with the development of antenna technology, dual-polarized large scale antenna arrays can be realized inexpensively. However, several challenges must be addressed when using dual-polarized antennas in practical transmission such as mobile phone rotation and non-ideal polarization isolation. We address these challenges in the frame of dual-polarized hybrid beamformers. In this paper, we analyze the performance of dual-polarized beamforming based on two popular techniques: Beam steering and Orthogonal Matching Pursuit. Three categories of dual-polarized beam steering are analyzed: (1) same ray: the two polarizations are sent on the same ray, (2) different ray - same polarization: the two polarizations are sent on different pathes, and the receiver uses the same polarization as the emitter, (3) different ray - different polarization: the receiver uses the orthogonal polarization w.r. t. the emitter. An algorithm to choose the rays and polarizations to use, taking the mobile rotation into account, is also developed. Moreover, we develop a hybrid beamforming algorithm inspired by Orthogonal Matching Pursuit that approaches the fully digital beamforming data-rate and outperforms beam steering. Its developed version - Orthogonal based Matching Pursuit - is also introduced to reduce the computational complexity and overcome the unavailability of the Angle of Arrival.
Ontology Creation and Population for Natural Language Processing Domain

In this paper, we describe our proposed methodology for constructing an ontology of natural language processing (NLP). We use a semi-automatic method; a combination of rule-based and machine learning techniques; to construct and populate an ontology with bilingual (English-Persian) concept labels (lexicon) and evaluate it manually. This methodology results in a complete ontology in the natural language processing domain with 1333 classes (containing concepts, tools, applications, etc.), 88 object properties, and 2437 annotation assertions for different classes. The built ontology is populated with about 428K NLP related papers and 38K authors, and also about 5M "is Related to" relations between papers and ontology classes and 1M "is Author of" relations between papers and authors. The evaluation results show that the ontology achieved a good result. The instantiation is done to enable applications find experts, publications and institutions (such as universities or research laboratories) related to various topics in NLP field.
Joint end-to-end learning for scale-adaptive person super-resolution and re-identification

Persons captured in real-life scenarios are generally in non-uniform scales. However, most generally acknowledged person re-identification (Re-ID) methods lay emphasis on matching normal-scale high-resolution person images. To address this problem, the ideas of existing image reconstruction techniques are incorporated which are expected contribute to recover accurate appearance information for low-resolution person Re-ID. In specific, this paper proposes a joint deep learning approach for Scale-Adaptive person Super-Resolution and Re-identification (SASR2 ). It is for the first time that scale-adaptive learning is jointly implemented for super-resolution and re-identification without any extra post-processing process. With the super-resolution module, the high-resolution appearance information can be automatically reconstructed from scales of low-resolution person images, bringing a direct beneficial impact on the subsequent Re-ID thanks to the joint learning nature of the proposed approach. It deserves noting that SASR2 is not only simple but also flexible, since it can be adaptable to person Re-ID on both multi-scale LR and normal-scale HR datasets. A large amount of experimental analysis demonstrates that SASR2 achieves competitive performance compared with previous low-resolution Re-ID methods especially on the realistic CAVIAR dataset.
A self‐learning stream classifier for flow‐based botnet detection

Botnets have been recently recognized as one of the most formidable threats on the Internet. Different approaches have been designed to detect these types of attacks. However, as botnets evolve their behavior to mislead the signature‐based detection systems, learning‐based methods may be deployed to provide a generalization capacity in identifying unknown botnets. Developing an adaptable botnet detection system, which incrementally evolves with the incoming flow stream, remains as a challenge. In this paper, a self‐learning botnet detection system is proposed, which uses an adaptable classification model. The system uses an ensemble classifier and, in order to enhance its generalization capacity, updates its model continuously on receiving new unlabeled traffic flows. The system is evaluated with a comprehensive data set, which contains a wide variety of botnets. The experiments demonstrate that the proposed system can successfully adapt in a dynamic environment where new botnet types are observed during the system operation. We also compare the system performance with other methods.
Workstation design and postural stress part 2: A case study

An evaluation of the musculoskeletal problems associated with seated workstations was undertaken for employees of the Human Sciences Research Council. A sample of 37 workers was studied for 14 days to determine which symptoms were due to chronic disorders and which were linked to postural stress or constrained posture as a result of workstation and task design. Techniques used included two subjective comfort ratings, clinical examinations by physiotherapists, anthropometric and workstation measurement, and video recordings of subjects’ posture over time. Subjects were selected from four occupations characterised by different levels of constraint in their work posture; data-entry typists, typists, programmers and researchers. Examinations revealed that 68% of the presenting symptoms were not related to any known previous trauma or pathology. Of these problems 86% were reported to be occupationally aggravated or related. The findings confirmed the hypothesis that postural constraint is accompanied by an increased likelihood of developing chronic musculoskeletal disorders. * The concepts of postural stress and postural constraint and their relationship to the development of muscle strain are discussed in Part 1: Background to occupational syndromes.
Are two pairs of eyes better than one? A comparison of concurrent think-aloud and co-participation methods in usability testing

This paper presents the results of a study that aimed to compare the traditional concurrent think-aloud protocol with the co-participation method to determine the benefit of adding an additional participant to the testing session. The two methods were compared through an evaluation of a library website, and their relative validity and utility were measured using four points of comparison: overall task performance, test participants’ experiences, quantity and quality of problems discovered, and the cost of employing each method. The results of the study show significant differences between the performances of the two types of testing methods. The co-participation method was evaluated more positively by users and led to the detection of a greater number of minor usability problems. This method, however, was found to require a greater investment of time and effort on the part of the evaluator in comparison to the classical method. This study found no difference between the methods in terms of task performance.
Learning-Aided Computation Offloading for Trusted Collaborative Mobile Edge Computing

Cooperative offloading in mobile edge computing enables resource-constrained edge clouds to help each other with computation-intensive tasks. However, the power of such offloading could not be fully unleashed, unless trust risks in collaboration are properly managed. As tasks are outsourced and processed at the network edge, completion latency usually presents high variability that can harm the offered service levels. By jointly considering these two challenges, we propose OLCD, an Online Learning-aided Cooperative offloaDing mechanism under the scenario where computation offloading is organized based on accumulated social trust. Under co-provisioning of computation, transmission, and trust services, trust propagation is performed along the multi-hop offloading path such that tasks are allowed to be fulfilled by powerful edge clouds. We harness Lyapunov optimization to exploit the spatial-temporal optimality of long-term system cost minimization problem. By gap-preserving transformation, we decouple the series of bidirectional offloading problems so that it suffices to solve a separate decision problem for each edge cloud. The optimal offloading control can not materialize without complete latency knowledge. To adapt to latency variability, we resort to the delayed online learning technique to facilitate completion latency prediction under long-duration processing, which is fed as input to queued-based offloading control policy. Such predictive control is specially designed to minimize the loss due to prediction errors over time. We theoretically prove that OLCD guarantees close-to-optimal system performance even with inaccurate prediction, but its robustness is achieved at the expense of decreased stability. Trace-driven simulations demonstrate the efficiency of OLCD as well as its superiorities over prior related work.
Evaluating and Improving Computational Models for Physical Property Predictions

Author(s): Bannan, Caitlin Colleen | Advisor(s): Mobley, David L | Abstract: Simulations allow us to predict free energies and physical properties of molecules in advance of their synthesis saving time and resources. My research focuses on how to automatically evaluate and improve these predictions. I begin by describing my work to test the accuracy of free energy calculations by computing various partition coefficients. Then, I report on applications of the resulting procedure in the SAMPL5 blind challenge for 53 small drug-like molecules. Comparing computed and experimental values highlighted three areas which still need improvement: conformational sampling, protonation assignment, and force field accuracy. These results motivated my next project, designing a Gaussian process model for pKa prediction based on computed properties of small molecules. I tested this model in the SAMPL6 blind challenge on pKa prediction where it performed competitively with many established methods. My partition coefficient results also highlighted the limitations of current force fields -- used to calculate potential energy of a system based on atomic coordinates. To address these concerns, I joined the the Open Force Field Initiative, a collaboration working to automate force field parametrization. The culmination of my Ph.D. focuses on generating chemical perception -- the way a force field assigns parameters to a molecule -- without the historically required human intuition. Improved force fields will result in more accurate predictive models and a better understanding of a wide variety of fields including computer-aided drug design, biomaterials, and polymer chemistry.
BTG-RKASE: Privacy Preserving Revocable Key Aggregate Searchable Encryption with Fine-grained Multi-delegation & Break-The-Glass Access Control

Delegation is the technique of sharing the available rights from the delegator to the delegatee for the purpose data sharing. The Key Aggregate Searchable Encryption (KASE) scheme supports delegation of search rights for any set of ciphertexts using a key of constant-size. However, three critical issues still need to be considered. Firstly, the existing KASE schemes only discuss delegation of rights from the data owner to other user. However, if a subject receiving a delegation cannot perform time-critical task on the shared data, it becomes necessary for the delegatee to further delegate their received rights to another user. Secondly, the existing delegation mechanisms tend to rely on manual processes initiated by end-users. If no authorized user exists to perform (or to delegate) a time-critical task, in such exceptional case, we require mechanism that flexibly handles emergency situations by breaking or by controlled overriding of the standard access permissions. Thirdly, the access of user in the system changes dynamically and it requires KASE to support user revocation securely while not affecting the legitimate users’ access to the shared files. To address all of the above issues, we propose Revocable KASE with Break-The-Glass access control (BTG-RKASE) to provide (i)fine-grained multi-delegation of available rights from the delegatee to another user,(ii)break-the-glass access mechanism when no authorized user exists to perform (or to delegate) a time-critical task,(iii)revocation of delegated rights (even in case of multi-delegation). The security and empirical analysis shows that BTG-RKASE performs better than the existing KASE schemes.
Green location and routing problems with conventional vehicles and drones

GREEN LOCATION AND ROUTING PROBLEMS WITH CONVENTIONAL VEHICLES AND DRONES Okan Dükkancı Ph.D. in Industrial Engineering Advisor: Bahar Yetiş Co-Advisor: Tolga Bektaş May 2019 Green Location and Routing Problems extend the network design problems that consider location and routing decisions by explicitly accounting environmental impacts such as CO2 emissions caused by fuel or energy consumption of delivery vehicles. These environmental impacts estimated by fuel or energy consumption models are affected by several factors including payload and speed of delivery vehicles. We present four new green location and routing problems where we consider these factors while calculating the environmental impacts. We first introduce the Green Location-Routing Problem, in which vehicle payload and speed decisions are incorporated to a location-routing problem and the fuel consumption of trucks is estimated and minimized. Second, we study the Green Hub Location Problem, where we minimize the fuel consumption by optimizing truck payload and speed decisions on a hub network. Third, we present a freight transportation problem called the Drone Delivery Problem, where the integration of trucks and drones is used to make deliveries. Drone speed is considered as a decision of the problem in order to minimize energy consumption of drones while not exceeding the drone range. Fourth, we study an extension of the Drone Delivery Problem, called the Stochastic Drone Delivery Problem, where uncertainty of wind speed and its impact on the drone speed are considered.