Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Forensic Tools & Cloud-Based Machine Learning for Analyzing Crime Data

Digital forensics is a branch of forensic science in which we can recreate past events using forensic tools for a legal measure. Also, the increase in the availability of mobile devices has led to their use in criminal activities. Moreover, the rate at which data is being generated has been on the increase which has led to big data problems. With cloud computing, data can now be stored, processed and analyzed as they are generated. This thesis document consists of three studies related to data analysis. The first study involves analyzing data from an Android smartphone while making a comparison between two forensic tools; Paraben E3: DS and Autopsy. At the end of the study, it was concluded that most of the activities performed on a rooted Android device can be found in its internal memory. In the second study, the Snapchat application was analyzed on a rooted Android device to see how well it handles privacy issues. The result of the study shows that some of the predefined activities performed on the Snapchat application as well as user information can be retrieved using Paraben E3: DS forensic tool. The third study, machine learning services on Microsoft Azure and IBM Watson were used in performing predictive analysis to uncover their performance. At the end of the experiments, the Azure machine learning studio was seen to be more userfriendly and builds models faster compared to the SSPS Modeler in the IBM Watson Studio. This research is important as data needs to be analyzed in order to generate insights that can aid organizations or police departments in making the best decisions when analyzing crime data. INDEX WORDS: Digital forensics, Autopsy, Paraben E3: DS, Android, Smartphone, Big data, Cloud, Machine learning, Azure, IBM DIGITAL FORENSIC TOOLS & CLOUD-BASED MACHINE LEARNING FOR ANALYZING CRIME DATA
Pilot power allocation for OFDM systems

In this paper, the current analysis focuses on the demodulation performance in multipath fading propagation environments, which results from the pilot power variation of pilot symbol, assisted modulation (PSAM) M-PSK in orthogonal frequency division multiplexing (OFDM) systems. The pilot-to-data power ratio (PDR) is optimized analytically. Theoretical and simulation results show that the optimum PDR is effected by the signal-to-noise ratio (SNR), Doppler frequency and interpolation size etc, and provide a suitable tool for system design.
A New Approach using Template Matching for Recognition of Handwritten Odia Text

The objective of Handwritten Optical Character Recognition (HOCR) is automatic reading of optically intellect document text materials to translate human-readable characters to machinereadable codes. In Optical Character Recognition, the text lines in a document must be segmented properly before recognition. English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. This is the motivation behind choosing OCR for Odia language. An odia handwritten paragraph is chosen for processing and recognition. The HOCR system is devised to first segment the whole document into text lines, then to words and then to individual characters. These characters are then used to extract the necessary features and recognize those characters and classify them.
Well-being and -ageing with chronical disease: the BV2 project

The BV2 project aims to propose a monitoring system for wellbeing but also well-aging working on the prevention, detection and monitoring using a System of the Systems (SoS) approach. The project partner already uses the IoT technologies and the BV2 platform will combine the different developed systems. The main originality of the project consist s in the development of a virtual platform by combining the existing systems
Production and pricing decisions in a dual-channel supply chain under remanufacturing subsidy policy and carbon tax policy

Abstract This article studies the optimal production and pricing decisions of two firms in a dual-channel supply chain selling both remanufactured and new products under remanufacturing subsidy policy (RSP) and carbon tax policy (CTP), respectively, and examines which policy is better for the society. The results provide insights for firms to make optimal decisions under RSP and CTP, and for government to select policies. The main results and contributions are summarised as follows. First, production quantity of the remanufactured product increases in subsidy rate of RSP while production quantity of the new product decreases in subsidy rate of RSP. Production quantity of the new product decreases in tax rate of CTP while production quantity of the remanufactured product increases or decreases in tax rate of CTP, depending on the remanufactured product’s emission intensity. Second, RSP can be more effective than CTP in curbing carbon emission. Third, when environmental cost coefficient is relatively low (high), social welfare under RSP is greater (smaller) than that under CTP.
Phonological effects shown by normal adult speakers learning new words: Implications for phonological development

ABSTRACT Phonological processes have been used to describe children’s speech for a number of years, but the causes of most processes remain unexplained. In a study investigating possible sources of processes in children’s speech, Aitchison and Chiat (1981) suggested that many developmental phonological processes may be a result of problems in lexical storage and retrieval. One limitation of their study, however, was that they had no adult control group, whose results might have helped clarify their findings. The present research with adults was similar to Aitchison and Chiat’s investigation with children and was intended to provide additional information about whether children’s typical phonological processes generally involve lexical storage and recall difficulties. Caution is advised in assuming that children’s spontaneous phonological processes are generally a result of lexical storage and recall factors.
Experience Assessment and Design in the Analysis of Gameplay

We report research on player modeling using psychophysiology and machine learning, conducted through interdisciplinary collaboration between researchers of computer science, psychology, and game design at Aalto University, Helsinki. First, we propose the Play Patterns And eXperience (PPAX) framework to connect three levels of game experience that previously had remained largely unconnected: game design patterns, the interplay of game context with player personality or tendencies, and state-of-the-art measures of experience (both subjective and non-subjective). Second, we describe our methodology for using machine learning to categorize game events to reveal corresponding patterns, culminating in an example experiment. We discuss the relation between automatically detected event clusters and game design patterns, and provide indications on how to incorporate personality profiles of players in the analysis. This novel interdisciplinary collaboration combines basic psychophysiology research with game design patterns and machine learning, and generates new knowledge about the interplay between game experience and design.
Self-synchronizing variable-length codes for image transmission

Variable-length codes are vulnerable to loss of synchronization if they are transmitted through a noisy channel. In order to achieve synchronization regardless of the preceding synchronization slippage, the concept of extended synchronizing codewords is introduced. An iterative algorithm is proposed to design extended synchronizing codewords efficiently. The extended synchronizing codewords are placed in fixed positions of an image to control error propagation due to synchronization slippage. However, the image can still be partially corrupted by channel noises. An error concealment method to further improve the image quality is discussed. <<ETX>>
Application-level Flow Scheduling for Efficient Collective Data Transfers

Collective data transfers among sets of processes over high-bandwidth, low-latency data center networks are anintegral part of Big Data computations (e.g., the data shuffle in MapReduce). In this paper, we use a carefullyarchitected microbenchmark that emulates a data shuffle, to gather network traces and perform detailed analysis. The key result of our analysis is that having more than two competing bi-directional flows per node in the transfer reduces throughput by 10%. What this means is that, even at very low cardinality (3- or 4-node shuffle), only 90%of the possible throughput can be achieved when commodity Ethernet-based switches are employed. TCP contention among multiple flows is the reason for the throughput loss experienced by collective data transfers. Though we identify system parameter configurations that minimize such packet losses, we believe application-layer flow management is necessary to circumvent this network-level problem. Towards this end, we designed and implemented a technique, Max2Flows, that generates and orchestrates a schedule of coordinated data exchange stages. Each stage limits the number of competing flows per node to two or fewer, thus avoiding negative networklevel effects. Experimental results show that, when incorporated into our microbenchmark, Max2Flows can operate at ≈ 99% of the peak throughput on a 1 Gigabit Ethernet network for small shuffles.
СROSS PLATFORM TOOLS FOR MODELING AND RECOGNITION OF THE FINGERSPELLING ALPHABET OF GESTURE LANGUAGE

A solution for the problems of the finger spelling alphabet of gesture language modelling and recognition based on cross-platform technologies is proposed. Modelling and recognition performance can be flexible and adjusted, based on the hardware it operates or based on the availability of an internet connection. The proposed approach tunes the complexity of the 3D hand model based on the CPU type, amount of available memory and internet connection speed. Sign recognition is also performed using cross-platform technologies and the tradeoff in model size and performance can be adjusted. the methods of convolutional neural networks are used as tools for gestures of alphabet recognition. For the gesture recognition experiment, a dataset of 50,000 images was collected, with 50 different hands recorded, with almost 1,000 images per each person. The experimental researches demonstrated the effectiveness of proposed approaches.
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs

Linking entities from different sources is a fundamental task in building open knowledge graphs. Despite much research conducted in related fields, the challenges of linkinglarge-scale heterogeneous entity graphs are far from resolved. Employing two billion-scale academic entity graphs (Microsoft Academic Graph and AMiner) as sources for our study, we propose a unified framework --- LinKG --- to address the problem of building a large-scale linked entity graph. LinKG is coupled with three linking modules, each of which addresses one category of entities. To link word-sequence-based entities (e.g., venues), we present a long short-term memory network-based method for capturing the dependencies. To link large-scale entities (e.g., papers), we leverage locality-sensitive hashing and convolutional neural networks for scalable and precise linking. To link entities with ambiguity (e.g., authors), we propose heterogeneous graph attention networks to model different types of entities. Our extensive experiments and systematical analysis demonstrate that LinKG can achieve linking accuracy with an F1-score of 0.9510, significantly outperforming the state-of-the-art. LinKG has been deployed to Microsoft Academic Search and AMiner to integrate the two large graphs. We have published the linked results---the Open Academic Graph (OAG)\footnote\urlhttps://www.openacademic. ai/oag/ , making it the largest publicly available heterogeneous academic graph to date.
Elements of Decision Support in a Simulated Tactical Operations System (SIMTOS)

Two elements of decision support were integrated into the Army Research Institute's Simulated Tactical Operations System (SIMTOS). The estimate of the situation element provided the tactical decision maker with relevant information in a format complementary to his decision style. A resource allocation element enabled the tactical decision maker to survey his tactical response alternatives and to rapidly implement a chosen alternative. A preliminary investigation (N=10) indicates that tactical decision makers who used the decision support complex performed more effectively than those who did not use the complex.
Photoluminescence spectra of high temperature vacuum annealed porous silicon

The photoluminescence (PL) spectra of porous silicon layers annealed for 15 to 180 sec in vacuum at temperatures above 800/spl deg/C are presented. The experimental spectra consist of two main PL bands at 1.8 eV and 2.2 eV. The 2.2 eV band is independent of the annealing time and origins from silicon nanocrystallites. The 1.8 eV band decreases with increasing annealing time following second-order kinetics, typical for silicon dihydride decomposition.
Pavement Distress Classification in 3D Pavement Measurements using Convolutional Neural Networks

Modern roads provide higher levels of mobility than ever before, and the number of vehicles on those roads has been increasing since the beginning of the 20th century. This development has led to automated road surface scanning and processing systems for the detection of damage. Recently, systems have been developed for the detection of cracks in 3D pavement surface measurements. These systems, however, often detect patterns that are not indicative of structural pavement damage. To improve the time-to-maintenance prediction for highway agencies, the number of these false-positive detections should be minimized. In this work, we, therefore, propose a convolutional neural network approach to improve the classification of pavement cracks. The proposed method has been tested on real-world pavement scans, captured by the Dutch Highway Agency (Rijkswaterstaat) and The Netherlands Organisation for Applied Scientific Research (TNO). Using this data, we show that the proposed method is a useful addition to the present maintenance pipeline, aiding the current manual labelling approach.
Security Model for Pervasive Multimedia Environment

With the rapidity of the development on electronic technology, various mobile devices are produced to make human life more convenient. The user is always in constant search of middle with ease of deployment. Therefore, the development of infrastructure and application with ubiquitous nature gets a growing keen interest. Recently, the number of pervasive network services is expanding into ubiquitous computing environment. To get desired services, user presents personal details about ¬his identity, location and private information. The information transmitted and the services provided in pervasive computing environments (PCEs) are exposed to eavesdropping and various attacks. Therefore, the need to protect this environment from illegal accesses has become extremely urgent. In this paper, we propose an anonymous authentication and access control scheme to secure the interaction between mobile users and services in PCEs. The proposed scheme integrates a biometric authentication in PKI model. The proposed authentication aims to secure access remote in PCE for guaranteeing reliability and availability. Our authentication concept can offer pervasive network service users convenience and security.
An automated approach for the recognition of intended messages in grouped bar charts

Information graphics (bar charts, line graphs, grouped bar charts, etc) often appear in popular media such as newspapers and magazines. In most cases, the information graphic is intended to convey a high‐level message. This message plays a role in facilitating the discourse purpose of the document but is seldom repeated in the document's text, headlines, or captions. We present a methodology and an implemented system for recognizing the intended message of a grouped bar chart. The recognition system relies on the following components: (1) a linguistic classifier that processes text in the graphic and predicts the most linguistically salient entity from those that are mentioned in text, (2) a cognitive model that estimates the relative perceptual effort required for an individual to recognize some high‐level message in a graph, and (3) a Bayesian network that captures the probabilistic relationship between the high‐level intended message of a graphic and its communicative signals. This research contributes to three applications: accessibility of information graphics for sight‐impaired individuals, retrieval of information graphics from a digital library, and summarization of multimodal documents.
Bayesian regularized artificial neural networks for the estimation of the probability of default

Artificial neural networks (ANNs) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANNs are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANNs and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.
Battle against for Phishing Based on Captcha Security

First, our browser extension provides a trusted window in the browser dedicated to username and password entry. We use a photographic image to create a trusted path between the user and this window to prevent spoofing of the window and of the text entry fields. Second, our scheme allows the remote server to generate a unique abstract image for each user and each transaction. This image creates a "skin" that automatically customizes the browser window or the user interface elements in the content of a remote web page. Our extension allows the user's browser to independently compute the image that it expects to receive from the server. To authenticate content from the server, the user can visually verify that the images match. We contrast our work with existing anti-phishing proposals. In contrast to other proposals, our scheme places a very low burden on the user in terms of effort, memory and time. Phishing is a model problem for illustrating usability concerns of privacy and security because both system designers and attackers battle using user interface to guide (or misguide) users. We propose a new scheme, Dynamic Security Skins, that allows a remote web server to prove its identity in a way that is easy for a human user to verify and hard for an attacker to spoof. We describe the design of an extension to the Mozilla Firefox browser that implements this scheme. We present two novel interaction techniques to prevent spoofing. To authenticate himself, the user has to recognize only one image and remember one low entropy password, no matter how many servers he wishes to interact with. To authenticate content from an authenticated server, the user only needs to perform one visual matching operation to compare two images. Furthermore, it places a high burden of effort on an attacker to spoof customized security indicators.
Out-ward monitoring of neurorehabilitation in subacute stroke

Validated clinical scales of functional dexterity of stroke patients provide only sparse and controlled observations of the neurorehabilitation process. Continuous naturalistic monitoring the progress during the rehabilitation therapy of out-patients remains unsatisfactorily resolved. We propose a new indicator of functional performance capitalizing on smart devices and the cloud services. The new index of limb usage balance (ILUB) derived from accelerometry data, provides an objective scalar that it is easy to estimate and interpret, and adequate for continuous uncontrolled monitoring. Three studies for the verification and validation of the index and accompanying platform are presented. We show that the index is unbiased to handedness dominance and provide preliminary evidence that it is appropriate for patients even during the subacute stage. The new index can complement clinical scales to observe the patient progress. CCSConcepts • Social and professional topics→Medical technologies; •Human-centered computing→Ubiquitous and mobile computing; Ubiquitous and mobile computing systems and tools; •Applied computing→ Health care information systems.
Probo, an Intelligent Huggable Robot for HRI Studies with Children

Nowadays, robots are mostly known for their work in factories in industries such as automotive, electrical and electronics, chemical, rubber and plastics and many others. Robots are used for a wide variety of tasks like handling of materials and processes, welding and soldering, etc. The next generation of robots will be used in close collaboration with people in a wide spectrum of applications. For example, service robots will help elderly and assist disabled people. Household robots will be used in our homes and offices. Our children will play with entertainment robots. Medical robots assist in surgery and robotic prostheses replace limbs for amputees. Orthoses and exoskeletons can facilitate the rehabilitation process to regain mobility or manipulation skills. They also enlarge human strengths to carry heavy objects, for instance, nurses lifting a patient in and out of a bed. In lots of applications human and robots will work in a more close collaboration. For instance, NASA is developing a Robonaut to work together with astronauts in space. Statistics from the International Federation of Robotics (IFR) show worldwide an increasing request of innovative robots, especially in non-automotive sectors. A growth can be noticed in both traditional and new markets, ranging from the industrial field to the service robotics, both for professional use and for domestic applications. However, European society has a different relationship towards robots than the Japanese or US society. The Japanese are more accepting of technological change. For instance, robots have always been a source of comics and amusement in Japan, making it easier to introduce robots into a personal environment. The Japanese Robot Association (JARA) predicts that the personal robot industry will be worth more than $50 billion dollars a year worldwide by 2025, compared with about $5 billion today. The technology of current industrial robots is insufficient to respond to this issue. This means that human-robot interaction using social robots must be studied in the different regions all over the world to address the different needs. Reasons why personal robotics is emerging now are the fact that actuators and sensors can be made very small and cheap, and the required computational power for computing real time all the software components is still increasing. And, last but not least, the markets for such robots are coming. Especially the aging population in Japan, Europe and United States faces a number of daunting societal problems. Also its dwindling work force and the increased cost of care demand out-of-the-box thinking. Companion and service robots are one part of the solution. However, the shift from industrial robots towards these service, personal and domestic robots leads to specific design criteria. For instance, an industrial robots can carry heavy