Miracle-dz/UniFAR-FTUs huggingface dataset:
CHAMELEON: a deep learning meta-architecture for news recommender systems

News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Deep Learning (DL) have achieved a great success in complex domains, such as computer vision, Natural Language Processing (NLP), machine translation, speech recognition, and reinforcement learning. Therefore, it became a mainstream approach in Recommender Systems research only since 2016. The main objective of this research is the investigation, design, implementation and evaluation of a Meta-Architecture for personalized news recommendations using deep neural networks. As information about users' past interactions is scarce in such cold-start scenario, user context and session information are explicitly modeled, as well as past user sessions, when available. Users' past behaviors and item features are both considered in an hybrid session-aware recommendation approach. The recommendation task addressed in this work is next-item prediction for user sessions: "what is the next most likely article a user might read in a session? " This paper presents the research methodology for this Doctoral research, the proposed Meta-Architecture and some preliminary results, as well as the next research challenges.
A Comprehensive Approach for Network Security

The Network security is the important concern in this digitized world. Ensuring security in the network, which has very high number of dynamic nodes using software security or endpoint security, is very tedious, particularly when it comes to distributed residential network and Large Scale networks. The proposed work is to develop a system which can predict, detect and mitigate the attacks in the network level. The proposal is to introduce the improved and flexible security engine in the network that will make the network intelligent and resistant to most of the attacks. The improved security engine is applicable for the distributed residential network as well as the Large Scale network, which makes it flexible. This security engine also has the ability to learn about the attacks in the past and works in the concept of community-based-protection network, which increases the immunity of the security engine on the course of time.
Stacking-induced fluorescence increase reveals allosteric interactions through DNA

Abstract From gene expression to nanotechnology, understanding and controlling DNA requires a detailed knowledge of its higher order structure and dynamics. Here we take advantage of the environment-sensitive photoisomerization of cyanine dyes to probe local and global changes in DNA structure. We report that a covalently attached Cy3 dye undergoes strong enhancement of fluorescence intensity and lifetime when stacked in a nick, gap or overhang region in duplex DNA. This is used to probe hybridization dynamics of a DNA hairpin down to the single-molecule level. We also show that varying the position of a single abasic site up to 20 base pairs away modulates the dye–DNA interaction, indicative of through-backbone allosteric interactions. The phenomenon of stacking-induced fluorescence increase (SIFI) should find widespread use in the study of the structure, dynamics and reactivity of nucleic acids.
Semi-supervised Learning Meets Factorization

Recently, latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this article, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.
Separation of Fatigue Content in sEMG Signals Using High Definition Electrodes

SEMG electrodes are applied in numerous studies on muscle fatigue, utilised to detect the fatigue in various muscle groups analysed by a range of feature extraction methods. Newer research are applying multi-channel electrodes with the aim of increasing the quality of the signal emanating from the studied muscle. This research is comparing the use of 4 and 6 multi-channel electrodes to determine the separation between fatigue and non-fatigue segments of the recorded signals from the biceps brachii. Subjects performed isometric biceps brachii arm curl exercises until fatiguing. The signals were analysed using power spectrum density, and the Davies-Bouldin index calculated the separation between the classes. The results show separation was successful for sEMG signals using both 4 and 6 channel electrodes; however, better separation was achieved for signals with 4-channel electrodes.
Predicted Variables in Programming

We present Predicted Variables (PVars), an approach to making machine learning (ML) a first class citizen in programming languages. There is a growing divide in approaches to building systems: using human experts (e.g. programming) on the one hand, and using behavior learned from data (e.g. ML) on the other hand. PVars aim to make ML in programming as easy as `if' statements and with that hybridize ML with programming. We leverage the existing concept of variables and create a new type, a predicted variable. PVars are akin to native variables with one important distinction: PVars determine their value using ML when evaluated. We describe PVars and their interface, how they can be used in programming, and demonstrate the feasibility of our approach on three algorithmic problems: binary search, Quicksort, and caches. We show experimentally that PVars are able to improve over the commonly used heuristics and lead to a better performance than the original algorithms. As opposed to previous work applying ML to algorithmic problems, PVars have the advantage that they can be used within the existing frameworks and do not require the existing domain knowledge to be replaced. PVars allow for a seamless integration of ML into existing systems and algorithms. Our PVars implementation currently relies on standard Reinforcement Learning (RL) methods. To learn faster, PVars use the heuristic function, which they are replacing, as an initial function. We show that PVars quickly pick up the behavior of the initial function and then improve performance beyond that without ever performing substantially worse -- allowing for a safe deployment in critical applications.
GeoFPE: Format Preserving Encryption of Geospatial Data for the Internet of Things

The rise Internet of Things (IoT) over the last years has allowed more and more devices to be connected and interact between themselves. Many of these devices, such as vehicles have to deal with geographic information, often considered as sensitive by many car manufacturers, like the BMW Group. For securely storing this geographic information, usually block ciphers are used. Unfortunately, they imply the disadvantage of breaking existing formats and compatibilities. To address this problem, we propose a new algorithm for storing geographic spatial information in IoT devices and the Cloud securely while preserving the existing format.
Time Performance Analysis of Multi-CPU and Multi-GPU in Big Data Clustering Computation

Big data is a hot topic that is regularly discussed in the computer science field for the past year. Big data provides numerous benefits for the development of technologies, such as business intelligence and deep learning. Processing big data requires specialized tools and environment, ranging from a commodity-clustered workstation to high performance computing server, especially in big data clustering where unsupervised learning takes place. In this paper, we conduct time analysis of commodity-clustered workstation equipped with Spark as a baseline for multi-CPU big data clustering and TensorFlow installed in a high-performance computing workstation as a baseline for multi-GPU big data clustering. Based on the analysis, it shows that TensorFlow performs have around 5 to 12 times faster computation time than Spark.
Distributed Location Algorithms for Flexible BBU Hotel Placement in C-RAN

Cloud Radio Access Networks (C-RAN) are characterized by huge introduction of Network Function Virtualization (NFV) supported by software-based control of relevant functionalities within the Software Defined Network (SDN) context. Distributed location algorithms, as a practical approach to optimally locate virtual functionalities in C-RAN, are described and compared to conventional optimization techniques, with application to BBU hotel placement and extensions to dynamic evolution of C-RAN configuration. Near optimal solutions and incremental adaptability to topology changes are achieved.
Combining Features and Semantics for Low-level Computer Vision

Visual perception of depth and motion plays a significant role in understanding and navigating the environment. Reconstructing outdoor scenes in 3D and estimating the motion from video cameras are of utmost importance for applications like autonomous driving. The corresponding problems in computer vision have witnessed tremendous progress over the last decades, yet some aspects still remain challenging today. Striking examples are reflecting and textureless surfaces or large motions which cannot be easily recovered using traditional local methods. Further challenges include occlusions, large distortions and difficult lighting conditions. In this thesis, we propose to overcome these challenges by modeling non-local interactions leveraging semantics and contextual information. Firstly, for binocular stereo estimation, we propose to regularize over larger areas on the image using object-category specific disparity proposals which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The disparity proposals encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class ’car’ into a superpixelbased graphical model and demonstrate its benefits especially in reflective regions. Secondly, for 3D reconstruction, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. Evaluations with respect to LIDAR ground-truth on a novel challenging suburban dataset show the advantages of modeling structural dependencies between objects. Finally, motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network’s output forms the data term for discrete MAP inference in a pairwise Markov random field. Extensive evaluations reveal the importance of context for feature matching.
Evaluating Prediction Error for Anomaly Detection by Exploiting Matrix Factorization in Rating Systems

A rating system provides rating data about products or services, which is a key feature of e-commerce websites such as Amazon, TripAdvisor, and so on. In reality, rating systems generally suffer from threats of profile injection attacks or anomalous ratings due to the integration of collaborative recommendation techniques. To reduce these risks, a number of detection methods have been developed for defending such potential threats. However, they either directly calculate similarity between users or items to recognize attack profiles and genuine profiles, or utilize supervised learning methods by extracting features from user profiles for anomaly detection. In this paper, we propose a stepwise detection method to spot anomalous ratings or attacks, which bypasses the hard problems of similarity calculation and feature extraction. First, a part of samples are randomly selected from original user profiles for constructing a sub-matrix (rating matrix). A fast max-margin matrix factorization is then employed to make rating prediction. After that, suspected items can be captured by comprehensively analyzing both the distributions of mean prediction errors of items and users. Finally, anomalous ratings and potential attackers can be directly returned. Extensive experiments on MovieLens-100K data set demonstrate the effectiveness of the proposed approach compared with benchmarked methods. It is noteworthy that suspected items on a large-scale real-world data set, Amazon data, are detected by the proposed method and further analyzed from diverse perspectives, including rating distribution, rating intention, and time series analysis.
Clinical commissioning of a new patient positioning system, SyncTraX FX4, for intracranial stereotactic radiotherapy

Abstract Background & Aims A new real‐time tracking radiotherapy (RTRT) system, the SyncTraX FX4 (Shimadzu, Kyoto, Japan), consisting of four X‐ray tubes and four ceiling‐mounted flat panel detectors (FPDs) combined with a linear accelerator, was installed at Uonuma Kikan Hospital (Niigata, Japan) for the first time worldwide. In addition to RTRT, the SyncTraX FX4 system enables bony structure‐based patient verification. Here we provide the first report of this system's clinical commissioning for intracranial stereotactic radiotherapy (SRT). Materials & Methods A total of five tests were performed for the commissioning: evaluations of (1) the system's image quality; (2) the imaging and treatment coordinate coincidence; and (3) the localization accuracy of cone‐beam computed tomography (CBCT) and SyncTraX FX4; (4) the measurement of air kerma; (5) an end‐to‐end test. Results & Discussion The tests revealed the following. (1) All image quality evaluation items satisfied each acceptable criterion in all FPDs. (2) The maximum offsets among the centers were ≤0.40 mm in all combinations of the FPD and X‐ray tubes (preset). (3) The isocenter localization discrepancies between CBCT and preset #3 in the SyncTraX FX4 system were 0.29 ± 0.084 mm for anterior‐posterior, −0.19 ± 0.13 mm for superior‐inferior, 0.076 ± 0.11 mm for left‐right, −0.11 ± 0.066° for rotation, −0.14 ± 0.064° for pitch, and 0.072±0.058° for roll direction. the Pearson's product‐moment correlation coefficient between the two systems was >0.98 in all directions. (4) The mean air kerma value for preset #3 was 0.11 ± 0.0002 mGy in predefined settings (80 kV, 200 mA, 50 msec). (5) For 16 combinations of gantry and couch angles, median offset value in all presets was 0.31 mm (range 0.14–0.57 mm). Conclusion Our results demonstrate a competent performance of the SyncTraX FX4 system in terms of the localization accuracy for intracranial SRT.
IRS: Incentive Based Routing Strategy for Socially Aware Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are special class of networks which can work in the challenged environment where conventional networks fail to work. The main reason for the failure of conventional or traditional network is absence of end-to-end path. If end-to-end path does not exist traditional network can not work, but in modern day to day life computer network and internet is an essential part of our life. Thus, DTNs is the most probable solution for challenged area where traditional networks can not provide services. In real life everyone carries smart phones and handheld devices, these devices can play important role in relaying data for DTNs i.e, social DTNs. Socially aware (DTNs) are human-driven DTNs in which nodes act similar to human behavior. They can act selfishly to save their resources and deny to relay packets. For the solution of this problem we have proposed IRS, incentive based routing strategy. In this approach intermediate nodes can participate and earn incentives for sacrificing their selfishness.
Fast and Accurate Load Balancing for Geo-Distributed Storage Systems

The increasing density of globally distributed datacenters reduces the network latency between neighboring datacenters and allows replicated services deployed across neighboring locations to share workload when necessary, without violating strict Service Level Objectives (SLOs). We present Kurma, a practical implementation of a fast and accurate load balancer for geo-distributed storage systems. At run-time, Kurma integrates network latency and service time distributions to accurately estimate the rate of SLO violations for requests redirected across geo-distributed datacenters. Using these estimates, Kurma solves a decentralized rate-based performance model enabling fast load balancing (in the order of seconds) while taming global SLO violations. We integrate Kurma with Cassandra, a popular storage system. Using real-world traces along with a geo-distributed deployment across Amazon EC2, we demonstrate Kurma's ability to effectively share load among datacenters while reducing SLO violations by up to a factor of 3 in high load settings or reducing the cost of running the service by up to 17%.
Low Level Big Data Compression

In the last years, some specialized algorithms have been developed to work with categorical information, however the performance of these algorithms has two important factors to consider: the processing technique (algorithm) and the representation of information used. Many of the machine learning algorithms depend on whether the information is stored in memory, local or distributed, prior to processing. Many of the current compression techniques do not achieve an adequate balance between the compression ratio and the decompression speed. In this work we propose a mechanism for storing and processing categorical information by compression at the bit level, the method proposes a compression and decompression by blocks, with which the process of compressed information resembles the process of the original information. The proposed method allows to keep the compressed data in memory, which drastically reduces the memory consumption. The experimental results obtained show a high compression ratio, while the block decompression is very efficient. Both factors contribute to build a system with good performance.
Development of a Mobile App for Fault Detection Assessment based on Information Fusion

Machine faults cause high financial losses for industrial companies. Machine faults stop the process flow, and are associated with idle time and additional expenses due to unexpected corrective maintenance. Depending on the fault, the idle time can span from short time actions to long inactivity periods for the machine. This idle time can risk a customer shipment, subsequently putting at risk the customer trust. A complex interaction between process variables cannot be easily traced using conventional condition monitoring approaches. There is an increasing trend to use machine learning methods to assess condition based maintenance. This paper proposes a mobile App for the fault detection assessment of industrial machinery. A proposal for the design and implementation of the mobile is presented, as well as the methodology followed for the fault detection assessment. The fault detection assessment is adressed using machine learning techniques, specifically a majority voting classifier. This majority voting classifier combines the predictions of three classification methods. The results show that the majority voting approach improves the performance of the fault detection assessment in comparison with the invidual classification methods.
A Mobile Complex Event Processing System for Remote Patient Monitoring

Complex Event Processing (CEP) is widely used for Remote Patient Monitoring (RPM). However, the state-of-the-art techniques use a centralized server to receive health sensor streams and detect complex events. This paper introduces an Internet of Things (IoT) based CEP approach that uses a mobile device on the edge and a remote IoT Hospital Server (IHS) deployed on the cloud. The prototype implementation of the system uses an open-source Siddhi CEP engine on the mobile device and an open-source Web Services Oxygen (WSO2) IoT Server on the IHS. In this architecture, complete complex event detection is performed on the edge and the complex event streams are sent to the hospital server using the Message Queuing Telemetry Transport (MQTT) protocol. The MQTT broker forwards complex event streams to an event listener service running on the hospital server that notifies the hospital staff. Advantages of performing CEP on the edge include circumventing out-of-order delivery of various health sensor streams, avoiding queuing delays at the hospital server and reducing the user cost for data transfer between the mobile device and the hospital server. Also, this technique provides the ability to generate local alarms for the patient even when the mobile network connecting the device to the hospital server is unavailable.