Miracle-dz/UniFAR-FTUs huggingface dataset:
Path-Specific Counterfactual Fairness

We consider the problem of learning fair decision systems from data in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a counterfactual approach to disregard effects along unfair pathways that does not incur in the same loss of individual-specific information as previous approaches. Our method corrects observations adversely affected by the sensitive attribute, and uses these to form a decision. We leverage recent developments in deep learning and approximate inference to develop a VAE-type method that is widely applicable to complex nonlinear models.
A TOOL FOR PRACTICING ABOUT CLASSIC CRYPTOGRAPHIC ALGORITHMS INTEGRATED IN VIRTUAL LEARNING ENVIRONMENTS

Classic cryptographic techniques and algorithms are typically taught in all basic courses of cybersecurity because they are the foundations of modern cryptographic techniques used to implement symmetric encryption or hashing algorithms. The correct comprehension of the operation of these algorithms can be facilitated if students get familiar with classical encryption algorithms. This paper shows a tool that has been specially conceived for practicing with classic cryptographic algorithms. The main functionalities of this tool rely on its ability to graphically illustrate the operation of the algorithms in a step-by-step way accompanied with detailed explanations about the basis of the algorithms are offered. The proposed tool also evaluates the most relevant metrics related to the performance of the classical algorithms such as consumed time, entropy, strength or weakness, among others. Furthermore, a set of predefined illustrative examples are included in order to allow the student compare several configurations taking into account these metrics. These examples could be used to discuss the robustness of each algorithm against cryptanalytic attacks, like those based on language statistics. Nevertheless, the tool also allows teachers or students to define new examples in order to study the internal details of each algorithm. The tool has been deployed as a web-based system and integrated in a virtual learning environment in order to enhance the usefulness and effectivity when assisting the teaching-learning process.
Intelligent Age Estimation From Facial Images Using Machine Learning Techniques

Lately, a growing interest has been emerging in age estimation from face images because of the wide range of potential implementations in law enforcement, security control, and human computer interactions. Nevertheless, in spite of the advances in age estimation, it is still a challenging issue. This is due to the fact that face aging process is not only set by distinct elements, such as genetic factors, but by extrinsic factors, such as lifestyle, expressions, and environment as well. This paper applied machine learning technique to intelligent age estimation from facial images using J48 classifier on FG_NET dataset. The proposed work consists of three phases; the first phase is image preprocessing which include five stages: gray scale image, noise removable, face detection, image size normalization and clipping process. The second phase is a data mining process which includes three stages: feature extraction, feature selection and classification using j48 classifier. The third phase includes two stages, estimation and evaluation. FG-NET dataset is used which is divided into three classes; first class represents (3-7), (26-30) ages and this class represents the ages from 3 to 7 years and from 26 to 30 years because this class have four attributes from any one of this images, second class represents (8-25) ages and this class represents the ages from 8 to 25 years because this class have five attributes from any one of this images, last class represents (31-50) ages and have nine attributes from any one of this images . The Experimental results illustrate that the proposed system can give results with high precision and low time complexity. The practical evaluation of the proposed system gives accuracy up to 89.13 % with time taken of 0.023.
Assessment and Verification of SLS Block 1-B Exploration Upper Stage and Stage Disposal Performance

Delta-v allocation to correct for insertion errors caused by state uncertainty is one of the key performance requirements imposed on the SLS Navigation System. Additionally, SLS mission requirements include the need for the Exploration Upper Stage (EUS) to be disposed of successfully. To assess these requirements, the SLS navigation team has developed and implemented a series of analysis methods. Here the authors detail the Delta-Delta-V approach to assessing delta-v allocation as well as the EUS disposal optimization approach.
Analysis of bistable perception based on MEG data

In the present research we studied the cognitive processes, associated with the perception of ambiguous images using the multichannel MEG recordings. Using the wavelet transformation, we considered the dynamics of the neural network of brain in different frequency bands, including high (up to 100 Hz) frequency gamma-waves. Along with the time-frequency analysis of single MEG traces, the interactions between remote brain regions, associated with the perception, were also taken into consideration. As the result, the new features of bistable visual perception were observed and the effect of image ambiguity was analyzed.
Analysis of straw row in the image to control the trajectory of the agricultural combine harvester

The paper proposes a solution to the automatic operation of the combine harvester along the straw rows by means of the images from the camera, installed in the cab of the harvester. The U-Net is used to recognize straw rows in the image. The edges of the row are approximated in the segmented image by the curved lines and further converted into the harvester coordinate system for the automatic operating system. The “new” network architecture and approaches to the row approximation has improved the quality of the recognition task and the processing speed of the frames up to 96% and 7.5 fps, respectively. Keywords: Grain harvester,
Power Efficient Physiological Response Prediction for Wearable Health Monitoring Platforms.

MOHAMMADZADEH, FARROKH FABIAN. Power Efficient Physiological Response Prediction for Wearable Health Monitoring Platforms. (Under the direction of Dr. Edgar Lobaton and Dr. Chang S. Nam.) The main objective of this dissertation is the development of a power-efficient framework for prediction of physiological responses such as breathing rate in a wearable health monitoring platform when the user is performing various activities. In developing the prediction model, we established that using all available features for prediction may result in fast battery depletion.We specifically aimed our attention at feature selection to optimize power consumption for prediction of breathing rate. We performed dynamic feature selection by identifying the activity state of the subject using features extracted from a single accelerometer.In each activity state, a group lasso regression model was used to predict breathing rate in a power-efficient way by selecting most important features. Moreover, we evaluated different algorithms such as a nonlinear support vector machine (SVM) regression model for prediction of future physiological response and discovered that physiological responses are highly correlated to past measurements. We also concluded that environmental factors can help prediction of physiological responses under real-world conditions. The physiological responses we aimed to predict depend on the activity/motion-behavior of the subject as well as environmental factors. Accordingly, we first focused on the characterization of different activities, then on the exploratory analysis of physiological response prediction, and finally an energy efficient and activity-aware physiological response framework is introduced. © Copyright 2018 by Farrokh Fabian Mohammadzadeh
Sub-threshold resonance organizes activity and optimizes learning in neural networks

Network oscillations across and within brain areas are critical for learning and performance in memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effects on neuronal populations’ spiking activity and information encoding is less known. Here, we use computational modeling and in vivo recording to demonstrate that a shift in sub-threshold resonance can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input-current dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further “learning” (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of sub-threshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that sub-threshold resonance provides a plausible network-level mechanism to accurately encode and retrieve information without over-strengthening connections between neurons.
Understanding the Customer: Examining the Perceived Value of a New Product in a B2B Context

“Value” is one of the most overused and misused concepts in management literature and, even though research shows that it is critical to understand your company’s value offering to stay competitive, few suppliers actually know what value they create and how it is perceived by their customers. This paper extends the knowledge about customer perceived value in B2B markets by studying the value construct’s characteristics, elements and underlying drivers in a case study of a newly developed consumer cartonboard in the pulp and paper industry. Through numerous interviews with the cartonboard supplier’s employees and customers, data is collected and analyzed based on research originating from the Consumption-values theory. The findings present five characteristics of the construct customer perceived value in B2B markets and identify value drivers within seven value elements. Five of which presented in the literature: (1) functional value quality of product, (2) functional value quality of service, (3) functional value sacrifices, (4) social value, and (5) emotional value, as well as two value elements suggested in this paper: (6) innovation value and (7) environmental value.
TAME: An Efficient Task Allocation Algorithm for Integrated Mobile Gaming

We consider an integrated mobile gaming platform, in which the mobile device (e.g., smartphone) of a player can offload some game tasks toward a server as well as some neighboring mobile devices. The advantages of such a platform are manyfold: it can lead to an improved game experience, to a better use of energy resources, and, while offloading tasks to other mobile users, to the exploitation of the unused computing and storage resources of the mobile equipment, thus reducing the bandwidth and computing costs of the overall system. In this context, we formulate the problem of offloading the game computational tasks as an optimization problem that minimizes the maximum energy consumption across a set of mobile devices, under the constraints of a maximum response time and a limited availability of computation, communication, and storage resources. In light of the problem complexity, we then propose a heuristic, called Task Allocation with Minimal Energy cost (TAME), which is shown to closely approximate the optimal solution in all scenarios we considered. TAME also outperforms state-of-the-art algorithms under both synthetic and real scenarios, which have been devised based on a realistic and detailed energy consumption model for computation and communication resources. Our results, although tailored to mobile gaming, could be extended to other applications, where it may be beneficial to offload computational and storage tasks through device-to-device communications, as enabled by Wi-Fi, Bluetooth, or the upcoming 5G technology.
Synthesis of Reliable Digital Microfluidic Biochips using Monte Carlo Simulation

Microfluidic-based biochips are replacing the co nventional biochemical analyzers, and are able to integrate on-chip all the necessary functions for bioche mical analysis using microfluidics. The “digital microfluidic” biochips are based on the manipulation of liquid s not as a continuous flow, but as discrete droplets (hence the term “digital”), and thus are highly reconfigurab le and scalable. We model a biochemical application using an abstract model consisting of a sequencing graph. Th e digital biochip is modeled as a two-dimensional array of cells, where each cell can hold a droplet. In this pap er we propose an integer linear programming (ILP) synthesis methodology that, starting from a biochemical ap p ication and a given biochip, determines the allocation, placement, resource binding, and scheduling of the op erations in the application. Our goal is to find that particular implementation of an application onto a biochip , which has the highest probability to be reconfigured successfully in case of multiple faulty cells. We propose a f ault model for biochips, and use Monte Carlo simulation to evaluate the probability of successful reconfigu ration of each implementation in case of faults. The proposed methodology has been evaluated using a real-life e xample.
Computer assisted total knee arthroplasty using patient specific templates

Current techniques used for total knee arthroplasty (TKA) rely on conventional instrumentation (CI) systems that violate the intramedullary canals. The accuracy of these systems is questionable, and set up and assembly of their numerous pieces is time consuming. Navigation techniques are more accurate, but their broad application is limited by cost and complexity. The aim of this study was to prove a new concept of computer assisted preoperative planning to provide patient-specific templates (PST) that can replace conventional instruments. Computed tomography based planning was used to design two virtual templates. Using rapid prototyping technology, virtual templates were transferred into physical templates (cutting blocks) with surfaces that matched the distal femur and proximal tibia. Forty five TKA procedures were performed on 16 cadaveric and 29 plastic knees using the PST technique. Six out of 29 TKA procedures were included in a comparative trial against 6 procedures performed using CI systems. Computer assisted analysis of 6 random postoperative CT scans was performed to evaluate the accuracy of this technique. A reliability test was performed, in which five observers positioned the templates on a plastic knee model and a navigation system was used to measure alignment and the level of bone cutting for the planned tibial and femoral cuts. Each observer repeated the test 5 times. Errors in placement of the templates as well as intraobserver and interobserver variations were measured. The study showed that it was possible to perform all 45 TKA procedures without CI systems. There was no need for intramedullary perforation, tracking or registration. The mean time for bone cutting was 9 minutes (15 minutes for CI systems), when the surgeon had an assistant and 11 minutes (30 minutes for CI systems), when the surgeon was unassisted. Postoperative CT scans showed mean errors of 1.70 and 0.8 mm (maximum 2.30 and 1.2 mm) for alignment and bone resection respectively. The reliability test had a mean alignment error of 0.670(maximum 2.50). The mean error for bone resection was 0.32 mm (maximum 1 mm). The positioning of the templates was reliable, as there was no significant intraobserver and interobserver variation. This study proved the concept of patient-specific templating for TKA. It also showed a satisfactory level of accuracy and reliability of this technique. In conclusion, the PST technique has several advantages over conventional instrumentation and it is a simple alternative to navigation and robotic techniques for TKA. Further clinical validation is required before recommending this technique for new users.
Design and Analysis of 4x4 Vedic Multiplier using Carry Save and Vertical Adder

In digital signal processing multiplication is one of the key operations between two sequences. Multiplication is used in convolution of two signals used especially for filter design operation. So it is necessary that multiplication should be performed efficiently with less area and delay. Vedic mathematics is one of the method by which we can reduced area and delay. In this paper we have design 4x4 Vedic multiplier by using the concept of Vedic mathematics; we have used Urdhva Tiryagbhyam which is one of the 16 sutras which are used in Vedic mathematics. We have design Vedic multipliers using vertical adder and carry save adder in Xilinx 14.7 software using VHDL and stimulation result are obtain and there area and delay are computed.
Exploring a Two-Solver Architecture for Clause Learning CSP Solvers

Exploring A Two-Solver Architecture for Clause Learning CSP Solvers Ozan Erdem Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2017 Many real world problems can be encoded as Constraint Satisfaction Problems (CSPs). Constraint satisfaction problems contain variables over nite domains, as well as constraints over these variables. There exists many solvers that can solve these problems e ciently. Once these problems are solved, their solutions correspond to solutions of the real world problem the CSP encodes. Similarly, many problems can be encoded as instances of the Satis ability Problem (SAT). In SAT all the variables are binary propositions, and the only kind of constraint they allow is the disjunction of a set of variables. SAT and CSP are closely related, and they both are NP-complete. These two approaches have their own advantages and disadvantages. SAT solvers perform clause learning, which prevents them from visiting similar subtrees in the search tree. The advantage of CSP solvers is that the CSP instances contain higher level constraints, which allows better reasoning. Recently, methods combining the advantages of SAT and CSP solvers have emerged. In such methods, solvers maintain a propositional representation of the CSP problem, and perform clause learning over this representation. Such solvers are called clause learning CSP solvers . However, the question of how to handle constraints in such solvers is unresolved. For each constraint, a solver must choose whether to encode the constraint entirely in SAT or use a special purpose propagator, and this choice also determines the propagation strength and e ciency. Di erent choices perform better in di erent scenarios, hence hybrid methods which can be adaptive are promising. In this thesis, we propose an alternative architecture for clause learning CSP solvers, which incorporates two solvers of di erent strength. One of the solvers directs the search using fast and weak propagators. Once it gets into a dead end, it produces a con ict clause, and asks the other solver to enhance it using stronger but slower propagators. In this way, the slower propagators are run only whenever they are likely to be needed, instead of each node in the search tree. We instantiate this architecture in di erent ways, and perform experiments to demonstrate the trade-o s between these instantiations.
Development of AHTS-vessel Marine Engine Room Virtual Training Platform and Research on Key Technology

“HYSY681” is selected as the parent ship to develop the AHTS (Anchor Handling Tug Supply) vessel MERVTP (Marine Engine Room Virtual Training Platform). Hybrid simulation design mode is applied to the training platform and the advantage of each applied simulation method is fully made. In order to give consideration to both the instantaneity and accuracy required by 2D-MERSS (2D Marine Engine Room Simulation Software), simulation algorithm and strategy are analyzed and chosen reasonably. To improve the efficiency of development and expansibility, the simulation data is planed from the whole prospect, WPF (Windows Presentation Foundation) technique is applied to the development of 2D-HMI (2D Human-machine Interface) and the refresh mechanism of 2D-HMI is optimized to improve the simulation efficiency. In order to promote the overall training effect, visual simulation technique is applied to this training platform and 3D-MERSS (3D Marine Engine Room Simulation Software) is developed. Optimization techniques including LOD (Level of Detail), structural nodes optimization, Prefab and rendering method based on Shader are adopted to promote the user experience. To further enhance the immersion, 3D-MERSS is also applied to the 3channel stereo projection environment, meanwhile, the relevant techniques including scene segmentation, edge blending and geometric correction are analyzed and researched.
Simulation-Based Packet Delivery Performance Evaluation with Different Parameters in Flying Ad-Hoc Network (FANET) using AODV and OLSR

The article examines node connectivity as one of the most significant quality characteristics of any information and telecommunication network. The article is aimed to determine FANET parameters providing PDR (Packet Delivery Ratio) more than 90% for the specified area. All results were received on the basis of the experimental study. Imitation modeling based on an ns-2 network simulator was applied as a research method. Characteristics of the network based on the simulation model utilizing the AODV and OLSR routing protocols have been evaluated. The authors studied also the connectivity dependence on such network parameters as the nodes number and the transmission range.
Differential Evolution Algorithm in the Construction of Interpretable Classification Models

In this chapter, the application of a differential evolution-based approach to induce oblique decision trees (DTs) is described. This type of decision trees uses a linear combination of attributes to build oblique hyperplanes dividing the instance space. Oblique decision trees are more compact and accurate than the traditional univariate decision trees. On the other hand, as differential evolution (DE) is an efficient evolutionary algo- rithm (EA) designed to solve optimization problems with real-valued parameters, and since finding an optimal hyperplane is a hard computing task, this metaheuristic (MH) is chosen to conduct an intelligent search of a near-optimal solution. Two methods are described in this chapter: one implementing a recursive partitioning strategy to find the most suitable oblique hyperplane of each internal node of a decision tree, and the other conducting a global search of a near-optimal oblique decision tree. A statistical analysis of the experimental results suggests that these methods show better performance as decision tree induction procedures in comparison with other supervised learning approaches.