Miracle-dz/UniFAR-FTUs huggingface dataset:
A Predictive Initialization of Hidden State Parameters in a Hidden Markov Model for Hand Gesture Recognition

A hidden Markov model (HMM) has been widely used as a cost-effective solution for time series based applications such as hand gesture recognition. A large number of parameters in an HMM often result in a degradation of classification accuracy due to overfitting. Furthermore, the accuracy is often affected by the initialization values of the parameters. Left-right banded (LRB) HMM is one of the representative models which improve the classification accuracy by reducing the number of parameters. However, LRB HMM still suffers from false negative misclassification when it is applied for hand gesture recognition. Misclassification in hand gesture recognition arise from similar trajectories of multiple gestures that are represented by hidden states. This paper proposes a new initialization process that reduces not only state transition parameters but also hidden state observation parameters. To this end, the gesture trajectory is predicted and then the parameters are masked out when their probabilities are negligibly small in the predicted trajectory. Experiment results show that the confusion rate is nearly 14% lower than the LRB HMM while maintaining the classification accuracy.
Classification of High Resolution Remote Sensing Images using Deep Learning Techniques

High Resolution Satellite Images are widely used in many applications. Since such images are useful to provide more useful information about the details about the every regions around the world. In this work, transfer learning is used efficiently for the feature extraction from a pretrained Convolutional Neural Network(CNN) model which is used for training in the classification task. Using transfer learning the classification yielded a better accurate results. The experiments are carried out on two high resolution remote sensing satellite images such as UC Merced LandUse and SceneSat Datasets. The pre-trained CNN used here is VGG-16 which is trained on millions of Image-Net Dataset. The proposed method yielded a classification accuracy of 93% in UC Merced LandUse Dataset and in SceneSat Dataset it is about 84%. This proposed method yielded a better precision of 0.93 and 0.86 in UC Merced LandUse Dataset and in SceneSat Dataset respectively.
Adaptive Video Encoding for Time-Constrained Compression and Delivery

Some applications require video content to be encoded and uploaded to a remote destination in a fixed time. This paper proposes a novel approach to address this challenge, based on online adaptation of compression parameters to control both encoding and uploading time. In particular, an algorithm to accurately predict the encoding time and bit-rate resulting from using a given quantisation parameter to encode the next frames in the sequence is proposed. This is used to drive decisions during the encoding process, to maintain the cumulative time needed to encode and transmit the video sequence within the constraints. Experimental evaluation shows that when using the proposed method, time constraints can be met with high accuracy under a variety of different target times and bandwidth conditions.
Trollslayer: Crowdsourcing and Characterization of Abusive Birds in Twitter

As of today abuse is a pressing issue to participants and administrators of Online Social Networks (OSN). Abuse in Twitter can spawn from arguments generated for influencing outcomes of a political election, the use of bots to automatically spread misinformation, and generally speaking, activities that deny, disrupt, degrade or deceive other participants and, or the network. Given the difficulty in finding and accessing a large enough sample of abuse ground truth from the Twitter platform, we built and deployed a custom crawler that we use to judiciously collect a new dataset from the Twitter platform with the aim of characterizing the nature of abusive users, a.k. a abusive “birds”, in the wild. We provide a comprehensive set of features based on users' attributes, as well as social-graph metadata. The former includes metadata about the account itself, while the latter is computed from the social graph among the sender and the receiver of each message. Attribute-based features are useful to characterize user's accounts in OSN, while graph-based features can reveal the dynamics of information dissemination across the network. In particular, we derive the Jaccard index as a key feature to reveal the benign or malicious nature of directed messages in Twitter. To the best of our knowledge, we are the first to propose such a similarity metric to characterize abuse in Twitter.
Intracortical Causal Information Flow of Oscillatory Activity (Effective Connectivity) at the Sleep Onset Transition

We investigated the sleep onset transition in humans from an effective connectivity perspective in a baseline condition (approx. 16 h of wakefulness) and after sleep deprivation (40 h of sustained wakefulness). Using EEG recordings (27 derivations), source localization (LORETA) allowed us to reconstruct the underlying patterns of neuronal activity in various brain regions, e.g., the default mode network (DMN), dorsolateral prefrontal cortex and hippocampus, which were defined as regions of interest (ROI). We applied isolated effective coherence (iCOH) to assess effective connectivity patterns at the sleep onset transition [2 min prior to and 10 min after sleep onset (first occurrence of stage 2)]. ICOH reveals directionality aspects and resolves the spectral characteristics of information flow in a given network of ROIs. We observed an anterior-posterior decoupling of the DMN, and moreover, a prominent role of the posterior cingulate cortex guiding the process of the sleep onset transition, particularly, by transmitting information in the low frequency range (delta and theta bands) to other nodes of DMN (including the hippocampus). In addition, the midcingulate cortex appeared as a major cortical relay station for spindle synchronization (originating from the thalamus; sigma activity). The inclusion of hippocampus indicated that this region might be functionally involved in sigma synchronization observed in the cortex after sleep onset. Furthermore, under conditions of increased homeostatic pressure, we hypothesize that an anterior-posterior decoupling of the DMN occurred at a faster rate compared to baseline overall indicating weakened connectivity strength within the DMN. Finally, we also demonstrated that cortico-cortical spindle synchronization was less effective after sleep deprivation than in baseline, thus, reflecting the reduction of spindles under increased sleep pressure.
Experimental Evaluation of Modern TCP Variants in MEC-enabled Cellular Networks

Transmission Control Protocol (TCP) is widely used to provide connection oriented reliable communication, which avoids a persistent overload of the network by adjusting the sender’s transmission rate. In addition to more efficient usage of available bandwidth, lower end-to-end latency further satisfies diverse use cases and service scenarios in 5G era. To reduce latency, Mobile Edge Computing (MEC) emerges as a promising paradigm that converges core capabilities of communications, computing and caching (3C) at the edge of Radio Access Network (RAN), in close proximity to mobile subscribers. This paper aims at evaluating and comparing the performance of modern TCP variants deployed in a real MEC-enabled cellular network. Experimental results suggest that TCP BBR outperforms the commonly used TCP versions such as TCP Reno and CUBIC under severe network conditions, including higher packet loss rate and longer mean delay.
Plasma-Derived Inflammatory Proteins Predict Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a major concern with high morbidity and mortality worldwide, even with the current knowledge and the advancement in treatment. OSCCs diagnosed at late-stage often require wide-excision with or without neck dissection, radiotherapy, or chemotherapy. When deemed successful, treatment often results in diminished quality of life, impaired function, and disfigurement. Strategies for early detection are urgently needed for patients afflicted with this disease. Inflammatory protein plasma biomarkers have shown to be potential tests for early detection and disease monitoring in several cancers. There has been no study on inflammation-related plasma biomarkers in OSCC. The objectives of the study were to use a multiplex approach to screen plasma-derived biomarkers and to examine the association of measurable proteins with OSCC. A total of 260 plasma samples (210 OSCC and 50 normal controls) were collected to measure for concentration of inflammatory related biomarkers using electrochemiluminescence multiplex assay. After screening of 82 potential biomarkers of the first 160 OSCC, 16 cytokines, chemokines, and growth factors were identified and verified in the second set of samples containing 50 OSCC and 50 normal. After adjustment of age and batch effects, the adjusted differential expression analysis showed that the OSCCs were markedly lower in 14 biomarkers and significantly higher level of interleukin 1 receptor antagonist (IL1Ra). By performing unsupervised clustering analysis, we observed distinctive groups of normal and two subgroups of OSCC. Linear regression of IL2, IL1Ra, and macrophage inhibitory factor (MIF) showed high accuracy in classifying OSCC with sensitivity of 0.96 and specificity of 0.92. In conclusion, this is the first paper to identify potential inflammatory plasma protein biomarkers of patients with OSCC. With further validation, the set of biomarkers can potentially be used to assist in early detection of OSCC when the disease is localized and in more treatable stage.
Probabilistic Voting for Sequence Based Visual Place Recognition

Visual place recognition is the task of recognizing the query image in a set of dataset images. It is a challenging problem in computer vision due to frequent and unpredictable environmental changes. In this paper, a novel approach is proposed for visual place recognition. We consider the problem of visual place recognition as a probabilistic voting problem on coherent image sequences. According to the co-visibility relationship of images in the dataset, each query can be represented by a categorical variable. Therefore, the whole sequence is the distribution of several independent and non-identical categorical variables. Introducing the probabilistic framework not only removes the need for heuristic parameters but also recognizes location efficiently and effectively. Two widely used datasets are used to evaluate the performance of the proposed method. The probabilistic voting algorithm achieves superior performance compared with state-of-the-art methods and satisfies the real-time requirement.
Texture Segmentation using Siamese Network and Hierarchical Region Merging

This paper proposes an texture segmentation algorithm. In the proposed texture segmentation algorithm, the feature vectors at each pixel of an input image are extracted by using the deep neural networks such as the deep convolutional network (CNN) or the Siamese Network. Then they are used as input of the hierarchical region merging. Unlike the semantic segmentation such as fully connected network (FCN) or U-Net which are based on the supervised learning, the proposed algorithm can correctly segment the texture regions whose texture is taken from the other types of the texture. The effectiveness of the proposed texture segmentation algorithm is experimentally confirmed by using the famous texture images taken from book by P. Brodatz.
Credit Scoring Model based on Kernel Density Estimation and Support Vector Machine for Group Feature Selection

A credit scoring model (CSM) is a tool that is typically used in the decision-making process of accepting or rejecting a loan. The selection of an appropriate feature subset is crucial for the credit scoring model. In this paper, we propose a novel framework to improve the performance of this task. First, the kernel density estimation (KDE) is used to construct feature groups in order to combine similar features and reduce wasteful computational workload. Second, the correlation among features is not only simply similar, but also other meaningful relations, such as part-of, has-a etc. Therefore, we calculated the corresponding group scores for each feature group, and then obtained the corresponding radar map according to the group score. The purpose is to help improve the quality of the final selected feature subset and to get the specific semantics of each feature group. Finally, each feature group is selected as a separate entity for feature selection to obtain the optimal feature subset. All features are treated as one-dimensional vectors. The support vector machine (SVM) algorithm is used for training and prediction, and corresponding calculations are performed to obtain a total credit score. Extensive experiments on the UCI benchmark database show the advantages and effectiveness of our proposed algorithm.
Empirical Evidences to Validate the Performance of Self-Switching Base Vector Based Mutation of Differential Evolution Algorithm

There exist many tools in Computer Science to solve the optimization problems around us. One such tool is the set of algorithms known as Evolutionary Algorithms $(\boldsymbol{E}\boldsymbol{A}\boldsymbol{s})$ which is under the Evolutionary Computing $(\boldsymbol{E}\boldsymbol{C})$ field. The Differential Evolution (DE) algorithm in the set of $\boldsymbol{E}\boldsymbol{A}\boldsymbol{s}$ is known for its unique mutation scheme. There are many research works in the literature to further study and modify this scheme to propose new mutation schemes. This paper presents detailed and extensive empirical evidences for the Self-Switching Base Vector Selection base mutation scheme (termed as $\boldsymbol{D}\boldsymbol{E}/\boldsymbol{randorbest}/1)$ found in the literature. The results for our validation are obtained by running the $\boldsymbol{D}\boldsymbol{E}$ algorithm for all possible values of its control parameters (Mutation Step Size $(\boldsymbol{F})$ and Crossover Rate $(\boldsymbol{C_{r}})$) on a Benchmarking-Function suite. The obtained results are compared by the performance metrics: Average Solution Accuracy $(\boldsymbol{A}\boldsymbol{S}\boldsymbol{A})$ and Success Rate (SR).
NOMA/CA: NOMA-based Random Access with Pattern Detection and Collision Avoidance

Non-orthogonal multiple access (NOMA) is gaining popularity in 5G communication technologies, which meets the requirements of the massive connectivity and the high data rate. Specifically, code domain NOMA (CD-NOMA) allows multiple users to share the same time-frequency resources with different patterns or codebooks for transmission. However, the conventional CD-NOMA needs to allocate a specific code pattern for each user before it can access the channel. In this paper, we propose a novel random access protocol based on CD-NOMA in which no patterns will be pre-allocated and all the users make fair contention to all the sparse patterns, as does in CSMA/CA (Carrier Sensing Multiple Access with Collision Avoidance), a well-known random access scheme used in WLANs. In the so-called NOMA/CA protocol, multiple users access the channel non-orthogonally with different patterns by code domain multiplexing, and avoid further collisions based on proper pattern detection and backoff procedures. The simulation results show that the uplink saturated throughput can be greatly improved and more channel connections can be supported as in conventional NOMA.
Implementation and Analysis of Data Security in a Real Time IoT Based Healthcare Application

Recently IoT based healthcare applications are gaining more attention due to its advantages like decreased costs, improved disease management, and enhanced management of drugs and remote monitoring. On the other side, it is becoming a lucrative target for the adversaries due to its valuable knowledge and business opportunities. However, healthcare data is very sensitive for its owners and thus it is important to ensure the security of the data. Misuse of this data may cause critical consequences and might result in not only material losses but also endanger the health and lives of people whose information was stolen. This paper aims to analyze the impact of data security methods in an IoT based healthcare application. The analysis is focused towards the computational overhead of the methods and its vulnerability to attacks.
An Effective Deep Learning Based Scheme for Network Intrusion Detection

Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks.
Unsupervised Video Highlight Extraction via Query-related Deep Transfer

The emergence of user-operated media motivates the explosive growth of online videos. Browsing these large amounts of videos is time-consuming and tedious, which makes finding the moments of user major or special preference (i.e. highlights extraction) becomes an urgent problem. Moreover, the user subjectivity over a video makes no fixed extraction meets all user preferences. This paper addresses these problems by posing a query-related highlight extraction framework which optimizes selected frames to both semantically query-related and visually representative of the entire video. Under this framework, relevance between the query text and the video frames is first computed on a visual-semantic feature embedding space induced by a convolutional neural network (Query-Inception network). Then we enforce the diversity on the video frames with the determinantal point process (DPP), a recently introduced probabilistic model for diverse subset selection. The experimental results show that our query-related highlight extraction method is particularly useful for news videos content fetching, e.g. showing the abstraction of the entire video while playing focus on the parts that matches the user queries.
A Numerical Approach of Fornberg-Whitham Equation Using Residual Power Series Method

In this paper, a modern and approach method called residual power series technique has been applied to find numerical solution for an important equation in optical communication systems and optical fibers called Fornberg-Whitham equation. In addition to that, the technique has been used in regard of the effectiveness of time. The present results have been used to compare it with the exact solution and have been shown graphically to demonstrate the effectiveness and suitability of RPS method. Furthermore, the steadiness and the action of time of the Fornberg-Whitham equation and the modified Fornberg-Whitham equation have been discussed and concluded that the RPSM method is an influential and beneficial one to solve strong nonlinear partial differential equations.
Fully Automatic Segmentation of the Left Ventricle Using Multi-Scale Fusion Learning

Segmentation of the left ventricle (LV) is essential for quantitative calculation of clinical indices for analyzing the cardiac contractile function. However, it is challenging to automatically segment small-contour cardiac magnetic resonance (CMR) images for traditional convolutional neural networks (ConvNets) because of their low robustness to scale variation. In this paper, we propose a multi-scale fusion learning method to advance the performance of ConvNets for the LV segmentation. To realize our multi-scale fusion learning, single-scale input and multi-scale output (SIMO) networks are firstly trained to construct a SIMO-based multi-scale fusion network (SIMO-based MSF_Net). The trained SIMO networks produce different-scale coarse prediction results which are then fused into another multi-scale network. Finally, the coarse results are progressively refined to yield finer segmentation results. Our multi-scale fusion learning is evaluated on MICCAI 2009 challenging database for the LV segmentation. Experimental results demonstrate the robustness of our SIMO-based MSF_Net for the segmentation of challenging CMR images and the metric of “Good contours” achieves 98.35% on the testing set, which is greatly improved compared with the state-of-the-art methods.
A Survey on Motion Models Used for Object Detection in Videos

Object detection in videos is widely used in entertainment, robotics, surveillance etc. A major challenge in effective object detection is observed when there is occlusion, bad illumination or cluttered background. Besides, cameras do not provide any mechanism for detecting moving objects after capture. Researchers have proposed different methods for object detection in video frames, ranging from traditional to deep learning approaches. However, using the right method in the right situation for efficient and accurate detection is a concern. Even though deep learning has shown high accuracy in detection, the training and testing time and cost is a concern. In our research, we explore the best motion models for tracking objects in a video sequence. The characteristics of each model was analyzed and the results were compared using different types of videos. Our observations determine that depending on the quality of input video, traditional approaches show high accuracy in detection, comparable to state-of-the art methods.
Modeling the Influence of Grains and Material Interfaces on Electromigration

We present an efficient approach to properly treat grain boundaries and material interfaces when modeling electromigration in copper nano-interconnects. Our approach uses several spatial material parameters to identify the locations of the grain boundaries and material interfaces during simulation, thereby not requiring the definition of multiple materials or complex meshes and geometrical interfaces. Using this method even very coarse meshes, with a grid spacing twice the size of the thinnest element (the grain boundary thickness), were able to reasonably reproduce the vacancy concentration of thin copper interconnects, including the microstructure. However, using a grid spacing greater than one half the grain boundary thickness resulted in underestimates of the induced stress.
Improving Real-Time Pedestrian Detection Using Adaptive Confidence Thresholding and Inter-Frame Correlation

The pedestrian detection algorithms form a key component in the multiple pedestrian tracking (MPT) systems. Despite efforts to detect a pedestrian accurately, it is still a challenging task. We propose a novel and efficient online method to improve the performance of the multiple person/pedestrian detector by introducing novel post-processing steps. These steps use an adaptive approach to determine both area and confidence score constraints for the output of any given multiple pedestrian detector. In this paper, we focus on pedestrian detection in video surveillance applications that require an automated, accurate and precise pedestrian detection algorithm. We demonstrate that the new steps make the multiple pedestrian detector more accurate, precise and tolerant to false positive detections. This is illustrated by evaluating the performance of the proposed method in test video sequences taken from the Pedestrian Detection Challenge, Multiple Object Tracking Benchmark (MOT Challenge 2017).