Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of imbalanced datasets in the induction of Full Bayesian Classifiers

in English, 250 words or less): Machine Learning algorithms have received a growing interests in the last years with the increase in computing power and the availability of new data sources coming from digital services and a growing number of connected devices. Concerning the task of classification, one of the problems the field faces is how to deal with datasets where most of the interesting information (examples of the class or classes of interest that have to be classified) is obscured by a greater number of uninteresting examples: this is called the class imbalance problem. Much effort has been put solving this problem, either by fixing the problem modifying the dataset, modifying the particular algorithm used for the learning, or assembling different algorithms to provide imbalance-invariant assemblies. Bayesian Classifiers are probabilistic graphical models that can be used to efficiently capture the structure of the joint probability distribution that generated a dataset. This work will analyze the impact that imbalanced datasets have in the induction of Bayesian Network models, and try to offer a way to tackle the problem directly in the algorithm, without resorting to modifying the dataset or combining algorithms.
Multimodal Media Content Classification using Keyword Weighting for Recommendation

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user’s favorite the keyword as well as the genre.
Illegal Audio Copy Detection using Fundamental Frequency Map

In this paper, we present a new audio identification system which is robust to various attacks. The types of attacks employed are modification such as changes of tempo, pitch and speed and noise addition. We propose a two-dimensional representation for the audio signal called FFMAP. This consists of pitch components and frame components. We also employ Pearson’s correlation score to calculate similarity between original audio data and query. Experimental results show that the proposed algorithm has a high performance.
DAISY: A Detection and Mitigation System Against Denial-of-Service Attacks in Software-Defined Networks

Software-defined networking (SDN) is becoming more and more popular due to its key features like scalability, flexibility, and monitoring. SDN simplifies the network management and enables innovations in the network architecture and protocols. However, this flexible architecture also makes it vulnerable to different types of attacks, such as flooding, spoofing, and denial-of-service (DoS). Among these attacks, the DoS attack has the most severe impact as it can overwhelm the major components of SDN to degrade its performance. In this paper, we propose a simple and lightweight detection and mitigation system (DAISY) to secure SDN from DoS attacks by blocking malicious traffic from the attacker, after analyzing the collected statistics. The proposed system specifically blocks malicious traffic rather than blocking the whole port or a host. In addition, it unblocks a port or a host when there is no more malicious traffic originating from it. We evaluate our proposed system by comparing it with other approaches, and the simulation results show increased performance of SDN with DAISY in terms of CPU usage, response time, control channel bandwidth, packet delivery ratio, and flow requests sent to the controller.
Construction and Performance Analysis of Image Steganography-Based Botnet in KakaoTalk Openchat

Once a botnet is constructed over the network, a bot master and bots start communicating by periodically exchanging messages, which is known as botnet C&C communication, in order to send botnet commands to bots, collect critical information stored in bots, upgrade software functions of malwares installed in bots, and so on. For this reason, most existing botnet detection techniques focus on monitoring and capturing suspicious communications between the bot master and bots. Meanwhile, botnets continue to evolve to hide their C&C communication. Recently, a novel type of botnet using image steganography techniques and SNS (Social Network Service) platforms, which is known as image steganography-based botnet or stegobotnet, has emerged to make its C&C communications undetectable by existing botnet detection systems. In stegobotnets, image files used in SNSs carry messages (between the bot master and bots) which are hidden in them by using image steganography techniques. In this paper, we first investigate whether major SNS platforms such as KakaoTalk, Facebook, and Twitter can be suitable for constructing image steganography-based botnets. Next, we construct a part of stegobotnet based on KakaoTalk, and conduct extensive experiments including digital forensic analysis (1) to validate stegobotnet C&C communication can be successful in KakaoTalk and (2) to examine its performance in terms of C&C communication reliability.
Two-Stage Isolated Bidirectional DC–AC Converters With Three-Port Converters and Two DC Buses

A novel two-stage structure with two dc buses is proposed for isolated bidirectional dc–ac power conversion. A variable dc-link voltage plus a constant dc-link voltage operation is adopted for the proposed two-stage structure. One of the dc-link voltages is regulated adaptively according to the variation of dc-side voltage, so that the dc–dc power stage can always work at its optimized point. New families of three-port (TP) bidirectional dc–dc converters and TP bidirectional inverter/rectifier (TP-BIR) are proposed to implement the proposed two-stage dc–ac power conversion structure. A bidirectional dc–ac converter composed of a TP-CLLC resonant converter and a T-type TP-BIR is taken as an example to verify the effectiveness of the proposed structure. Operation principles of both the TP-CLLC resonant converter and the T-type TP-BIR are analyzed in detail. Modulation and control strategies are proposed for the TP-BIR to achieve voltage regulation and power distribution between the two dc buses. A 1.5-kW prototype is built and tested to verify the feasibility and advantages of the proposed solution.
PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows

Motivation: Hand amputations can dramatically affect the quality of life of a person. Researchers are developing surface electromyography and machine learning solutions to control dexterous and robotic prosthetic hands, however long computational times can slow down this process. Objective: This paper aims at creating a fast signal feature extraction algorithm that can extract widely used features and allow researchers to easily add new ones. Methods: PaWFE (Parallel Window Feature Extractor) extracts the signal features from several time windows in parallel. The MATLAB code is publicly available and supports several time domain and frequency features. The code was tested and benchmarked using 1,2,4,8,16,32, and 48 threads on a server with four Xeon E7- 4820 and 128 GB RAM using the first 5 datasets of the Ninapro database, that are recorded with different acquisition setups. Results: The parallel time window analysis approach allows to reduce the computational time up to 20 times when using 32 cores, showing a very good scalability. Signal features can be extracted in few seconds from an entire data acquisition and in <100 ms from a single time window, easily reducing of up to over 15 times the feature extraction procedure in comparison to traditional approaches. The code allows users to easily add new signal feature extraction scripts, that can be added to the code and on the Ninapro website upon request. Significance: The code allows researchers in machine learning and biosignals data analysis to easily and quickly test modern machine learning approaches on big datasets and it can be used as a resource for real time data analysis too.
Locally differentially private continuous location sharing with randomized response

With the growing popularity of fifth-generation-enabled Internet of Things devices with localization capabilities, as well as on-building fifth-generation mobile network, location privacy has been giving rise to more frequent and extensive privacy concerns. To continuously enjoy services of location-based applications, one needs to share his or her location information to the corresponding service providers. However, these continuously shared location information will give rise to significant privacy issues due to the temporal correlation between locations. In order to solve this, we consider applying practical local differential privacy to private continuous location sharing. First, we introduce a novel definition of ( ε , δ ) -local differential privacy to capture the temporal correlations between locations. Second, we present a generalized randomized response mechanism to achieve ( ε , δ ) -local differential privacy for location privacy preservation, which obtains the upper bound of error, and serve it as the basic building block to design a unified private continuous location sharing framework with an untrusted server. Finally, we conduct experiments on the real-world Geolife dataset to evaluate our framework. The results show that generalized randomized response significantly outperforms planar isotropic mechanism in the context of utility.
Social-Enhanced Attentive Group Recommendation

With the proliferation of social networks, group activities have become an essential ingredient of our daily life. A growing number of users share their group activities online and invite their friends to join in. This imposes the need of an in-depth study on the group recommendation task, i.e., recommending items to a group of users. Despite its value and significance, group recommendation remains an unsolved problem due to 1) the weights of group members are crucial to the recommendation performance but are rarely learnt from data; 2) social followee information is beneficial to understand users’ preferences but is rarely considered; and 3) user-item interactions are helpful to reinforce the performance of group recommendation but are seldom investigated. Toward this end, we devise neural network-based solutions by utilizing the recent developments of attention network and neural collaborative filtering (NCF). First of all, we adopt an attention network to form the representation of a group by aggregating the group members’ embeddings, which allows the attention weights of group members to be dynamically learnt from data. Second, the social followee information is incorporated via another attention network to enhance the representation of individual user, which is helpful to capture users’ personal preferences. Third, considering that many online group systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, the recommendation for groups and users can be mutually reinforced. Extensive experiments on the scope of both macro-level performance comparison and micro-level analyses justify the effectiveness and rationality of our proposed approaches.
Strategic Location and Dispatch Management of Assets in a Military Medical Evacuation Enterprise

This dissertation considers the importance of optimizing deployed military medical evacuation (MEDEVAC) systems and utilizes operations research techniques to develop models that allow military medical planners to analyze different strategies regarding the management of MEDEVAC assets in a deployed environment. For optimization models relating to selected subproblems of the MEDEVAC enterprise, the work herein leverages integer programming, multi-objective optimization, Markov decision processes (MDPs), approximate dynamic programming (ADP), and machine learning, as appropriate, to identify relevant insights for aerial MEDEVAC operations. This research is conducted in the form of three related research components: the first component develops models for optimal MEDEVAC location within an enterprise of assets, whereas the second and third components seek optimal MEDEVAC dispatching policies for a set of established assets via a sequence of models of increasing operational complexity and corresponding solution methods. Determining where to locate mobile aeromedical staging facilities (MASFs) as well as identifying how many aeromedical helicopters to allocate to each MASF, commonly referred to as the MEDEVAC location-allocation problem, is vital to the success of a deployed MEDEVAC system. An integer mathematical programming formulation is developed to determine the location and allocation of MEDEVAC assets over the phases of a military deployment to support operations ranging from peacekeeping through combat to post-combat resolution. The model seeks to address the multi-objective problem of maximizing the total expected coverage of demand as a measure of solution effectiveness, minimizing the maximum number of located MASFs in any deployment phase as a measure of solution efficiency, and minimizing
Confidence-Based Hybrid Tracking to Overcome Visual Tracking Failures in Calibration-Less Vision-Guided Micromanipulation

This article proposes a confidence-based approach for combining two visual tracking techniques to minimize the influence of unforeseen visual tracking failures to achieve uninterrupted vision-based control. Despite research efforts in vision-guided micromanipulation, existing systems are not designed to overcome visual tracking failures, such as inconsistent illumination condition, regional occlusion, unknown structures, and nonhomogenous background scene. There remains a gap in expanding current procedures beyond the laboratory environment for practical deployment of vision-guided micromanipulation system. A hybrid tracking method, which combines motion-cue feature detection and score-based template matching, is incorporated in an uncalibrated vision-guided workflow capable of self-initializing and recovery during the micromanipulation. Weighted average, based on the respective confidence indices of the motion-cue feature localization and template-based trackers, is inferred from the statistical accuracy of feature locations and the similarity score-based template matches. Results suggest improvement of the tracking performance using hybrid tracking under the conditions. The mean errors of hybrid tracking are maintained at subpixel level under adverse experimental conditions while the original template matching approach has mean errors of 1.53, 1.73, and 2.08 pixels. The method is also demonstrated to be robust in the nonhomogeneous scene with an array of plant cells. By proposing a self-contained fusion method that overcomes unforeseen visual tracking failures using pure vision approach, we demonstrated the robustness in our developed low-cost micromanipulation platform. Note to Practitioners—Cell manipulation is traditionally done in highly specialized facilities and controlled environment. Existing vision-based methods do not readily fulfill the need for the unique requirements in cell manipulation including prospective plant cell-related applications. There is a need for robust visual tracking to overcome visual tracking failure during the automated vision-guided micromanipulation. To address the gap in maintaining continuous tracking for vision-guided micromanipulation under unforeseen visual tracking failures, we proposed a purely visual data-driven hybrid tracking approach. Our proposed confidence-based approach combines two tracking techniques to minimize the influence of scene uncertainties, hence, achieving uninterrupted vision-based control. Because of its readily deployable design, the method can be generalized for a wide range of vision-guided micromanipulation applications. This method has the potential to significantly expand the capability of cell manipulation technology to even include prospective applications associated with plant cells, which are yet to be explored.
PENERAPAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LAPTOP

Proses pemilihan sebuah laptop harus didasarkan pada kemampuan dan kebutuhan pembeli. Ketika pembeli dihadapkan pada banyak pilihan merk laptop dan berbagai sepesifikasinya, Dari hal tersebut dibuatlah sebuah sistem pendukung keputusan yang ditujukan untuk membantu pembeli memilih laptop yang sesuai dengan kriteria Style,Reability dan Price,serta mengambil alternatif Samsung,HP dan Lenovo. Penelitian ini memanfaatkan metode Analitical Hierarchy Process (AHP). Didapatkan hasil perengkingan Samsung,HP ( Hewlett-Packard ) dan Lenovo.
Physical-Virtual Patient Simulators: Bringing Tangible Humanity to Simulated Patients

In lieu of real patients, healthcare educators frequently use simulated patients. Simulated patients can be realized in physical form, such as mannequins and trained human actors, or virtual form, such as via computer graphics presented on two-dimensional screens or head-mounted displays. Each of these alone has its strengths and weaknesses. I introduce a new class of physical-virtual patient (PVP) simulators that combine strengths of both forms by combining the flexibility and richness of virtual patients with tangible characteristics of a human-shaped physical form that can also exhibit a range of multi-sensory cues, including visual cues (e.g., capillary refill and facial expressions), auditory cues (e.g., verbal responses and heart sounds), and tactile cues (e.g., localized temperature and pulse). This novel combination of integrated capabilities can improve patient simulation outcomes. In my Ph.D. work I focus on three primary areas of related research. First, I describe the realization of the technology for PVPs and results from two user-studies to evaluate the importance of dynamic visuals and human-shaped physical form in terms of perception, behavior, cognition, emotions, and learning. Second, I present a general method to numerically evaluate the compatibility of any simulator-scenario pair in terms of importance and fidelity of cues. This method has the potential to make logistical, economic, and educational impacts on the choices of utilizing existing simulators. Finally, I describe a method for increasing human perception of simulated humans by exposing participants to the simulated human taking part in a short, engaging conversation prior to the simulation.
Feature extraction of the brain tumours with the help of MRI, based on symmetry and partitioning

Computer-aided diagnostic (CAD) studies are used for scientific observations for explanation since very long time, but they are extraordinarily powerful to perform completely machine-driven algorithmic analyses for brain magnetic resonance imaging lesions. Structural and purposeful imbalance within the human brain could be reviewed. This imbalance analysis of the brain has terrific importance in an image analysis. In the present work, the imbalance between the two hemispheres is considered as the base for the detection of the tumour. We have segmented the brain into the two halves using thresholding technique, followed by statistical feature extraction for the double authentication of the existence of tumour which proves to be the better approach. The approach also takes into consideration corrections needed for the tilt observed while capturing the MRI.
AI-assisted PHY technologies for 6G and beyond wireless networks

Machine Learning (ML) and Artificial Intelligence(AI) have become alternative approaches in wireless networksbeside conventional approaches such as model based solutionconcepts. Whereas traditional design concepts include the mod-elling of the behaviour of the underlying processes, AI basedapproaches allow to design network functions by learning frominput data which is supposed to get mapped to specific outputs(training). Additionally, new input/output relations can be learntduring the deployement phase of the function (online learning)and make AI based solutions flexible, in order to react to newsituations. Especially, new introduced use cases such as UltraReliable Low Latency Communication (URLLC) and MassiveMachine Type Communications (MMTC) in 5G make this ap-proach necessary, as the network complexity is further enhancedcompared to networks mainly designed for human driven traffic(4G, 5G xMBB). The focus of this paper is to illustrate exemplaryapplications of AI techniques at the Physical Layer (PHY) offuture wireless systems and therfore they can be seen as candidatetechnologies for e.g. 6G systems.
Big Data Issues in Smart Grids: A Survey

The smart power systems are based upon information and communication technologies, which lead to a deluge of data originating from various sources. To address these challenges concerning accumulated voluminous data, big data analysis in smart power systems is inevitable. This article comprehensively surveys the literature related to the big data issues in smart power systems. The background and motivation of the big data paradigm in smart power systems are first provided, and then the major issues related to the architectures, the key technologies, and standardizations of big data analytics in smart power systems are analyzed. Also, the potential applications of big data in smart power systems based upon the state-of-the-art research are highlighted. Finally, the future issues and challenges of the big data issues in modern power systems are discussed.
Audio–Visual Particle Flow SMC-PHD Filtering for Multi-Speaker Tracking

Sequential Monte Carlo probability hypothesis density (SMC-PHD) filtering is a popular method used recently for audio-visual (AV) multi-speaker tracking. However, due to the weight degeneracy problem, the posterior distribution can be represented poorly by the estimated probability, when only a few particles are present around the peak of the likelihood density function. To address this issue, we propose a new framework where particle flow (PF) is used to migrate particles smoothly from the prior to the posterior probability density. We consider both zero and non-zero diffusion particle flows (ZPF/NPF), and developed two new algorithms, AV-ZPF-SMC-PHD and AV-NPF-SMC-PHD, where the speaker states from the previous frames are also considered for particle relocation. The proposed algorithms are compared systematically with several baseline tracking methods using the AV16.3, AVDIAR and CLEAR datasets, and are shown to offer improved tracking accuracy and average effective sample size (ESS).