Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Optimization of a BCI-Driven Telepresence Robot Through Programming by Demonstration

Improving the life quality of people with severe motor paralysis has a significant impact on restoring their functional independence to perform activities of daily living (ADL). Telepresence is a subfield of the robotic-assisted route, where human plays the role of an operator, sending high-level instructions to an assistive robot while receiving sensory feedback. However, for severely motor-impaired people, conventional interaction modalities may not be suitable due to their complete paralysis. Thus, designing alternative ways of interaction such as Brain-Computer Interfaces (BCI) is essential for a telepresence capability. We propose a novel framework that integrates a BCI system and a humanoid robot to develop a brain-controlled telepresence system with multimodal control features. In particular, the low-level control is executed by Programming by Demonstration (PbD) models, and the higher-level cognitive commands are produced by a BCI system to perform vital ADLs. The presented system is based on real-time decoding of attention-modulated neural responses elicited in the brain electroencephalographic signals and generating multiple control commands. As a result, the system allows a user to interact with a humanoid robot while receiving auditory and visual feedback from the robot’s sensors. We validated our system across ten subjects in a realistic scenario. The experimental results show the feasibility of the approach in the design of a telepresence robot with high BCI decoding performances.
Intracranial aneurysm detection and morphologic analysis based on three-dimensional angiographic images

Intracranial aneurysms affect approximately 3.2 % of the general population. It is estimated that 20-50 % do rupture during a person's lifetime. Clinical meta-analyses suggest that larger aneurysms should be treated by neurosurgery as soon as possible, while for small aneurysms the risk of complications during treatment is higher than the risk of rupture. Therefore, early detection and longitudinal monitoring are very important to determine the best treatment option. With the aim to enable early detection and follow-up imaging based monitoring of the aneurysms as a novel rupture risk mitigation paradigm, this thesis is concerned with the development and validation of two computational methods: (1) intracranial aneurysm detection from clinical grade angiographic images using a novel projection-detection-reconstruction approach and (2) intracranial aneurysm growth detection and morphology change characterization from baseline and follow-up imaging using a novel shape deformation algorithm. The detection of intracranial aneurysms was performed on 30 3D DSA images, where following a 3D to 2D digitally reconstructed radiography a convolutional neural network with a U-net architecture was applied for aneurysm detection. Next, the reconstruction step restored the detection probabilities in the original 3D image space thus allowing to highlight potential aneurysms and perform the detection. The approach succeeded in detecting 82 % of all aneurysms on training set and 75 % of all aneurysms on the validation set. The obtained results show that, despite the small number and variety of training datasets, deep learning techniques may be used to address the problem of aneurysm detection. Second set of methods involved longitudinal aneurysm shape analysis, for which we have developed an iterative mesh to mesh deformation (IMTMD) algorithm so as to compensate all the morphologic differences between the baseline and follow-up aneurysm shapes. By analyzing the obtained deformations we have proposed novel morphologic features such as median path length, differential volume and surface area and integral of cumulative deformation distances (MPL, dV, dSA and ICDD). The ability to detect a growing aneurysm was evaluated and compared to clinical-standard cross-sectional measures. It was established that the proposed features based on longitudinal imaging and analysis using the IMTMD algorithm achieved better area under the receiver operating characteristic curve (AUC-ROC) and p-value according to Mann-Whitney U-test than currently established cross-sectional features. The proposed computational tool seems to readily address the posed clinical needs and was designed such that it can be easily adopted in the current health care workflow.
Estimating the Pass-On Effect in Antitrust Damage Cases: Relative Strengths and Weaknesses of the ‘Comparator’ Method vs. the ‘Pass-On Rate’ Method

In this paper we compare the assumptions and information requirements of two common methods to pass-on effect estimation for the calculation of damages in cartel or abuse of dominance cases: the comparator and the pass-on rate methods. We note that the comparator method is feasible in some situations in which the pass-on rate method is not. Moreover, the comparator method may require fewer assumptions on the true (but unobservable) price mechanism. Where both methods are feasible, however, the pass-on rate method arguably requires less data and likely fewer control variables. Therefore, depending on the specific circumstances in a given case and the availability of data, each method has different strengths and weaknesses. A general preference for the comparator method as stated in the EU draft guidelines on pass-on estimation is thus not justified. To the contrary, in some arguably standard situations the lower data and control variable requirements of the pass-on rate method may imply that it is preferable to the comparator method.
Power Allocation for Energy Efficient Optimization of Distributed MIMO System With Beamforming

This paper presents three energy-efficient power allocation (PA) schemes for the downlink of a distributed multiple-input multiple-output (D-MIMO) system with beamforming at distributed antenna ports and antenna selection (AS) at a mobile station over composite Rayleigh channels. Subject to maximum power constraints, a constrained maximization problem on energy efficiency (EE) is formulated. Based on theoretical analysis, the optimal beamforming is shown to be aligned with the channel direction that renders the joint design problem to be a PA optimization one. It is shown that the optimization problem is strictly pseudo-concave with a unique maximum. Thus, an optimal PA scheme based on a gradient projection method is proposed to obtain the global solution. To speed up the gradient projection method, a closed-form initialization is derived. Furthermore, a low-complexity optimal PA schemes based on the Karush–Kuhn–Tucker conditions is proposed. This optimal scheme has a closed-form PA expression for a certain condition or else computes the PA by the Lambert function or the bisection method, and gives the same EE performance as that of the optimal gradient-based scheme. The performances of these two optimal schemes are optimal and verified to be valid in contrast to the inaccurate optimal scheme in the literature. For large maximum power, the closed-form initial PA will become the optimal solution and can be considered as a suboptimal solution with no iterative computation. Making use of the tight upper and lower bounds of the Lambert function, a very low-complexity suboptimal PA scheme is developed. Simulation results show that the proposed suboptimal scheme can achieve nearly optimal performance. Maximum EE and maximum channel power, which are optimal and suboptimal criteria, are adopted for antenna selection. The simple suboptimal criterion can provide EE performance very close to that of the optimal one. The D-MIMO with AS is shown to give considerable EE gain over the single receive antenna system. Moreover, the proposed three PA schemes can be used for multiuser diversity D-MIMO systems to achieve superior EE performance.
Steered Mixture-of-Experts for Light Field Images and Video: Representation and Coding

Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution.
A Preliminary Proposal Towards Unambiguous Definitions for Modular Interfaces and Interactions

Modularity is acknowledged to provide benefits across the whole product lifecycle. Accordingly, many literature contributions can be found about modularization methods, metrics and definitions. In particular, recent studies focused on the development of heuristic principles for exploiting modularity early in the design process. However, to design modules it is necessary to define their mutual interactions, the related interfaces and their production strategies. Concerning interfaces and interactions, this paper highlights that current definitions are often ambiguous and overlapping each other. Therefore, extracting univocal information about interfaces and interactions of existent modular products could be difficult. This could hinder the identification of comprehensive heuristic design guidelines, about how to design modules from a structural point of view. This paper proposes a new set of interface and interaction definitions, which allows to overcome the flaw observed for current ones. The proposed set and the classical one have been applied on 110 products identified on the web, showing that the new definitions allow to extract more reliable information.
Signal Processing Techniques Intended for Peculiar Star Detection in APOGEE Survey

Like other disciplines, Astronomy faces the era of Big Data, where the analyses and discovery of specific objects is a significant and non-trivial matter. The APOGEE survey and Gaia mission are good examples of how these kind of projects have increased the amount of data to be managed. In this context, we have developed an algorithm to search for specific features in the APOGEE database. The main purpose is to seek spectral lines both in absorption or emission, in the whole APOGEE database, in order to find chemically-peculiar stars. We propose an algorithm which has been validated using cerium lines and we have applied it to the search for other chemical compounds.
Morphological brain images acquired with a tilting MRI scanner: feasibility and quality evaluations

Poster: "ECR 2018 / C-1387 / Morphological brain images acquired with a tilting MRI scanner: feasibility and quality evaluations" by: "M. Lagana1, S. Carrara1, M. Olmi2, M. Cresti2, L. Forzoni3, P. Cecconi4; 1Milano/IT, 2Genova/IT, 3Firenze/IT, 4Como (CO)/IT"
Switching attacks on smart grid using non-linear sliding surface

A cyber-intruder uses the local knowledge of grid in order to develop and apply a coordinated switching sequence to attack and destabilise an otherwise healthy smart grid system. In this study, the attacker utilises the swing equation to discover the stable boundary limit of the target generator and applies non-linear sliding surface (NLSS) technique to compute switching sequences and perpetrate attacks through gained access over a target circuit breaker for the disruption of normal operation. The targeted generator in the Western Electricity Coordinating Council 3 machine, 9-bus system, and New England 10-generator, 39-bus system are attacked using a line switching event. An NLSS-based load switching triggered an attack is also executed in a single machine infinite bus system to compare against the linear sliding surface-based attack performance. The valour of an attack lies in its swift and fast action. Both these aspects are highlights of this work as the application of NLSS reduces chattering and achieves much faster spiralling out to instability.
Event-Participant and Incremental Planning over Event-Based Social Networks

In recent years, online Event Based Social Network (EBSN) platforms have become increasingly popular. One typical task of EBSN platforms is to help users make suitable and personalized plans for participating in different interesting social events. Existing techniques either ignore the minimum-participant requirement constraint for each event, which is crucially needed for some events to be held successfully, or assume that events would not change once announced. In this paper, we address the above inadequacies of existing EBSN techniques. We formally define the Global Event Planning with Constraints (GEPC) problem, and its incremental variant. Since these problems are NP-hard, and provide approximate solutions. Finally, we verify the effectiveness and efficiency of our proposed algorithms through extensive experiments over real and synthetic datasets.
New Experimental Results for AES-CCMP Acceleration on Cyclone-II FPGA

Summary This paper presents a possible solution for accelerating IEEE 802.11i. First, it offloads the process of AES-CCMP encryption from the master CPU onto a co-processor, which frees the master CPU resources for other uses. Second, its implementation on FPGA offers the possibility of using many threads to run the AES-CCMP encryption. Different optimizations have been applied on the hardware architecture of AES and on the basic unit of AES-CCMP, in order to satisfy different constraints in terms of latency, area occupation and speed. Performance measurement of the hardware solution is compared to AES software implemented on a NIOS II processor. A strong focus is devoted to the achievement of high throughput, which is required to support security requirements for current and future high bandwidth applications.
Reinforcement Learning in Financial Markets

Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and no bid or ask spread issues. Transaction costs had significant impacts on the profitability of the reinforcement learning algorithms compared with the baseline algorithms tested. Despite showing statistically significant profitability when reinforcement learning was used in comparison with baseline models in many studies, some showed no meaningful level of profitability, in particular with large changes in the price pattern between the system training and testing data. Furthermore, few performance comparisons between reinforcement learning and other sophisticated machine/deep learning models were provided. The impact of transaction costs, including the bid/ask spread on profitability has also been assessed. In conclusion, reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.
Compositional Learning of Human Activities With a Self-Organizing Neural Architecture

An important step for assistive systems and robot companions operating in human environments is to learn the compositionality of human activities, i.e., recognize both activities and their comprising actions. Most existing approaches address action and activity recognition as separate tasks, i.e., actions need to be inferred before the activity labels, and are thus highly sensitive to the correct temporal segmentation of the activity sequences. In this paper, we present a novel learning approach that jointly learns human activities on two levels of semantic and temporal complexity: (1) transitive actions such as reaching and opening, e.g., a cereal box, and (2) high-level activities such as having breakfast. Our model consists of a hierarchy of GWR networks which process and learn inherent spatiotemporal dependencies of multiple visual cues extracted from the human body skeletal representation and the interaction with objects. The neural architecture learns and semantically segments input RGB-D sequences of high-level activities into their composing actions, without supervision. We investigate the performance of our architecture with a set of experiments on a publicly available benchmark dataset. The experimental results show that our approach outperforms the state of the art with respect to the classification of the high-level activities. Additionally, we introduce a novel top-down modulation mechanism to the architecture which uses the actions and activity labels as constraints during the learning phase. In our experiments, we show how this mechanism can be used to control the network's neural growth without decreasing the overall performance.
Information Technology Work Value Differences

ABSTRACT This study investigates reasons behind high employee turnover of IT professionals through the value placed on intrinsic, extrinsic, leisure, and social rewards. It addresses two questions; what are the most preferred work values by IT professionals, and how do they differ from other professions. Data was collected via a survey using subjects from Amazon Mechanical Turk. A sample of 380 respondents was used. Key findings reveal that intrinsic rewards were the most important work values for IT professionals followed by leisure, extrinsic, and social rewards. Furthermore, when compared to the preferences of non-IT professionals, the levels of preferences for intrinsic rewards were statistically significantly higher for IT professionals than all other professions. This study provides valuable insight regarding key motivating factors of IT professionals and offers practical implications for practitioners relating to job characteristics that are most likely to reduce IT turnover and rethink their rewards systems.
A Text Mining Based Map of Engineering Design: Topics and their Trajectories Over Time

The Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.
Battery Life Estimation for Embedded System

A battery system consist many battery cells with different characteristics to achieve reliable, efficient and extended utilization of battery. The battery management system provides individual level of battery. This level of battery provide information about state of charge, state of health of the battery. The current battery technology provide module level with voltage sensor. The object of this project to determine and the display the supply frequency. This can be done by using c8051f996DK. This designed circuit consist liquid crystal display(LCD),Zero crossing detector(ZCD), transformer, variable power supply unit . The supply power is sinusoidal in nature given to the step down transformer. This output of the transformer are also in sinusoidal in nature given to the zero crossing detector. When signal cross the zero then zero crossing detector send high signal to microcontroller. Based on the source code given to the 8051 microcontroller. It detect the number of zeros crosses and convert into frequency and display on the LCD. Keywords-Battery life, C8051f996 TB, SOH/ SOC Battery.
SocialRec: A Context-Aware Recommendation Framework With Explicit Sentiment Analysis

In recent years, recommendation systems have seen significant evolution in the field of knowledge engineering. Usually, the recommendation systems match users’ preferences based on the star ratings provided by the users for various products. However, simply relying on users’ ratings about an item can produce biased opinions, as a user’s textual feedback may differ from the item rating provided by the user. In this paper, we propose SocialRec, a hybrid context-aware recommendation framework that utilizes a rating inference approach to incorporate users’ textual reviews into traditional collaborative filtering methods for personalized recommendations of various items. We apply text-mining algorithms on a large-scale user-item feedback dataset to compute the sentiment scores. We propose a greedy heuristic to produce ranking of items based on users’ social similarities and matching preferences. To address challenges resulting from cold start and data sparseness, SocialRec introduces pre-computation models based on Hub-Average (HA) inference. Rigorous evaluations of SocialRec (on large-scale datasets) demonstrate high accuracy, especially in comparison with previous related frameworks.