Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Emotions Assessment Using Heart Rate Variability Analysis and 2D Regression Models of Emotions

Objective assessment of emotions is a challenging task. In this research, a new approach for detection and quantification of four emotions (pleasure, sadness, anger, and joy) is introduced based on the 2D regression models of emotions. ECG signal of 70 healthy female volunteers was recorded while the participants were stimulated by four different colors for five minutes. Induced arousal and valence levels were identified using the Self-Assessment Manikin test. Based on extracted features from Poincare Plot of heart rate series, 1- the label of emotion is determined based on 2-D emotion models using a rule- based approach, and 2- the strength of emotion is calculated in percentages using proposed mathematical formula. The results confirm the findings of previous studies that showed that each color is related to one emotion. The mean square error for estimation of arousal and valence levels were 0.0393 and 0.0536, respectively. In emotion classification, sensitivity of 96.78%, specificity of 99.4%, and the accuracy of98.75% were achieved. The proposed method for measuring emotions can be of great help to psychiatrists to identify mental disorders. Using this method, one can measure and express the strength of emotions individually and has a comparative criterion for emotions in different individuals.
Objective Sleep Quality as a Predictor of Mild Cognitive Impairment in Seniors Living Alone

Singapore has the fastest ageing population in the Asia Pacific region, with an estimated 82,000 seniors living with dementia. These figures are projected to increase to more than 130,000 by 2030. The challenge is to identify more community dwelling seniors with Mild Cognitive Impairment (MCI), a prodromal state, as it provides an opportunity for evidence-based early intervention to delay the onset of dementia.In this paper, we explore the use of Internet of Things (IoT) systems in detecting MCI symptoms in seniors who are living alone, and accurately grouping them into MCI positive and negative subjects. We present feature extraction methods and findings from real data captured via selected sensors installed in the homes of 49 seniors for up to two months. Performance evaluation shows that the sleep state variability, as measured through bed sensors, yields a recall of over 70% in predicting MCI in these community dwelling seniors.Index Terms - Internet of Things (IoT), senior monitoring, mild cognitive impairment, early detection, eldercare, dementia
VoltJockey: Breaking SGX by Software-Controlled Voltage-Induced Hardware Faults

Intel software-guard extensions (SGX) allows applications to run in a trusted space (enclave), which provides a highly secure primitive for the running codes and data. Most state-of-the-art attacks on SGX either exploit software vulnerabilities in the enclave or utilize side channels. In this study, we propose the first fault injection attack to break SGX by using voltage-induced hardware faults. This attack is completely controlled by software. It does not require the availability of any software vulnerability. Our proposed attack targets multi-core processors where dynamic voltage and frequency scaling (DVFS) is equipped, which covers most of the commercial processors including those by Intel and ARM. We follow the basic principle for frequency and voltage based fault injection attacks which adjust the frequency or voltage levels deliberately to create hardware faults. We have demonstrated that the software-controlled voltage glitches are effective to fault ARM processors in the previous work. However, to the best of our knowledge, this is the first realization of such attacks on SGX. More specifically, we develop a kernel module to schedule frequency and voltage for Intel processors through their model-specific registers (MSR). We firstly utilize the module to furnish the processor a transient low voltage with controlled timing to inject a temporal fault into the target location of the program running on SGX. Then we perform differential fault analysis on the outputs before and after the injection of faults. This allows us to obtain the SGX-protected confidential data such as encryption keys, with which we can access program and data in the enclave or run any application in the enclave. For demonstration, we successfully deploy the proposed attack to extract the key of an AES executed in the SGX enclave.
Cluster-based Computation of Exact Betweenness Centrality in Large Undirected Graphs

Nowadays a large amount of data is originated by complex systems, such as social networks, transportation systems, computer and service networks. These systems can be effectively modeled through graphs and studied by exploiting graph metrics, such as Betweenness Centrality (BC), a popular metric to analyze node centrality. In spite of its great potential, this metric requires long computation time, especially for large graphs. In this paper, we present a novel very fast algorithm to compute exact BC of undirected, scale-free graphs. The algorithm is based on clustering and exploits structural properties of graphs to find classes of equivalent nodes. By selecting one representative node for each class, we are able to calculate BC by significantly reducing the number of single-source shortest path explorations adopted by the Brandes’ algorithm. The experimental evaluation of both sequential and map-reduce parallel versions reveals that our solution largely outperforms Brandes and recent heuristics, especially for large graphs while preserving good scalability.
Seasonality-Adjusted Conceptual-Relevancy-Aware Recommender System in Online Groceries

Conceptual relevancy – defined as how well a pair of products are related to each other – plays a significant role in online-grocery shopping behavior. When a customer goes grocery shopping, they first come up with a list of ingredients for recipes they may be interested in. A typical shopping list contains categories of products, many of them conceptually relevant to each other. However, the said user may be flexible on which exact product to purchase. For instance, a user may put ‘milk,’ and ‘cheese’ in their shopping list, but these may not refer to specific products such as ‘Great value 2% milk,’ or ‘Kraft Singles American Slices. ’ Modern recommender systems, however, focus much more on how to identify specific items to recommend to customers, rather than the categories that may be relevant. Such an approach may lead the system to occasionally recommend outlier, or noise, ultimately violating conceptual relevancy. Moreover, many recommender systems ignore seasonal components; they assume that customers’ shopping behavior is independent of the time of the year. However, conceptual relevancy between two products shifts over time. For instance, if a user is shopping for groceries in the middle of the winter, recommending particular products (say, barbecue-related products) may not be the best strategy even if the contextual (user’s past interests, or item that the user is currently viewing) may suggest otherwise. In this paper, we introduce a novel strategy to enforce conceptual relevancy in online-grocery domain. Furthermore, recognizing that conceptual relevancy is heavily influenced by the time of the year, we propose a Bayesian-based seasonality algorithm to capture the drift in conceptual relevancy over time without having to re-train the whole model. The algorithm can be based on any of the popular approaches in recommender systems – either based on matrix factorization or that on neural networks. Through our experiments, we show that our seasonality framework can capture drifts in conceptual relevancy.
Classifying HCM Subtypes via Left Ventricular Two-Dimensional Curvature and Dynamic Time Wrapping

Identification of Hypertrophic cardiomyopathy (HCM) morphological subtypes has the same significance as diagnosis HCM due to the close relation of subtypes and genetic mutations. This study aims to automatically classify HCM cases into subtypes via two-dimensional endocardial curvature and dynamic time wrapping (DTW). A total of 19 HCM patients underwent cardiac magnetic resonance (CMR) scans. Endocardial boundaries were delineated on long axis images. Endocardial curvatures were computed. Dynamic time wrapping and its variant soft-DTW were used to quantify the dissimilarity between a pair of endocardial curvature series. The dissimilarity was utlized in classifiers. The classifier using DTW has a 0.84 in-sample accuracy and 0.58 out-of-sample accuracy. Soft-DTW has a 0.79 in-sample accuracy and 0.68 out-of-sample accuracy. We envision the use of this method in current clinical practice to improve the subtype identification efficiency and effectiveness.
Stock Index Forecasting by Hidden Markov Models with Trends Recognition

Stock index forecast is a complicated problem since the financial market is influenced by various underlying factors and some of them are unobservable. Hidden Markov Model (HMM) is an effective probabilistic graphic model which can models the hidden states of the observed sequence. So the underlying patterns of the stock movements can be learnt by HMM models. In this paper, HMM is employed to forecast the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Composite Index, which are the two representative stock indices in China. The proposed method is empirically tested on the target data sets and compared with other models based on HMM using Mean Absolute Percentage Error (MAPE). Results show that the proposed HMM model achieves good forecasting performance, and at the same time reveals meaningful hidden states of stock market
Detection of First-Degree Atrioventricular Block on Variable-Length Electrocardiogram via a Multimodal Deep Learning Method

Automatic detection of first-degree atrioventricular block (I-AVB) from electrocardiogram (ECG) is of great importance in prevention of more severe cardiac diseases. I-AVB is characterized by a prolonged PR interval. However, due to various artifacts and diversity of ECG morphology, existing ECG delineation algorithms is unable to provide robust measurement of the PR interval. Deep neural network is good at extracting high-level feature from ECG waveform, but merely using waveform as input of neural network may aggravate overfitting when lack of I-AVB records. In this paper, we propose a multimodal-input deep learning method to effectively detect I-AVB from 12-lead ECG records. We utilize ECG waveform and delineation result as the multimodal input of neural network. Our neural network, mainly composed of convolutional neural network and Long Short-term Memory, is well designed to adapt to variable-length ECG. Our method is evaluated on dataset of CPSC2018, and outperforms the baseline methods in F1 score.
Secure Fog-Based E-Learning Scheme

Nowadays, several universities and institutions make profit from the information technologies to enhance and develop their educational strategies and attract more learners. Therefore, distance learning (e-learning) and learning-on-the-go are technologies adopted by universities and service providers to afford more flexible education system. In fact, e-learning is gaining popularity worldwide and the number of learners enrolled in on-line courses is growing. This trend is explained mainly by the opportunities provided by Cloud Computing. In the cloud based educational context, the security factor in sharing the educational content is important and poses several security challenges, such as fine-grained access control and security preservation of content learning. Moreover, there is emergence of the new concept of User-Fog-Cloud architecture to bring closer the services to the client. In this paper, a new fog computing e-learning scheme is provided. Specifically, the proposed solution extends learning content from the cloud to the edge of the network. It can improve the efficiency of learning data analysis, reduces the encryption burden in terms of computation cost on user’s devices by offloading part of encryption cost to fog servers and provides fine grained access control to learning content by encrypting the course and the exam with different cryptographic techniques like IBBE and CP-ABE. Further, we present a profile matching mechanism that helps teachers to find colleagues within their vicinity in an efficient and secure way. Security analysis shows that our scheme can achieve data confidentiality, fine-grained access control, collusion resistance and unforgeability. Performance evaluations demonstrate the efficiency of our solution, especially in terms of encryption computation costs.
Efficient and Robust Syslog Parsing for Network Devices in Datacenter Networks

Syslog parsing is of vital importance for the detection, diagnosis and prediction of network device failures in a datacenter. A common approach to syslog parsing is to extract templates from historical syslogs, after which syslogs are matched to these templates. To address the problems in the existing syslog parsing techniques, we propose a novel framework, Craftsman, which identifies frequent combinations of (syslog) words and then applies them as templates. Craftsman empirically extracts templates accurately, is extremely efficient in template matching, and naturally supports incremental learning. To compare the performance of Craftsman and three other template learning techniques designed for network devices, we experiment them on two-years’ worth of syslogs collected from network devices deployed across 10+ datacenters of a tier-one service provider. The experiments demonstrate that Craftsman achieves a close-to-one accuracy (as measured by rand index), and improves the computational efficiency by 6.88 to 10.25 times in template matching, and by 730 to 6847 times in syslog parsing. It also improves the accuracy (as measured by F1 measure) of failure prediction by 13.07% to 188%. In addition, we demonstrate Craftsman’s superior generality by comparing it with three widely-applied log parsing methods over five large log datasets collected from servers, distributed systems and applications.
Routing Protocol Using Virtual Nodes for Traffic Information Collection in Information Centric Vehicular Ad-hoc Networks

Dynamic Route Guidance (DRG) systems determine the traveling routes of vehicles and solve traffic problems in urban areas. Ad-hoc network composed of vehicles, called VANET is being conducted and VANET-based DRG systems are proposed. In VANET-based DRG systems, a dynamic topology on VANET is one of the biggest challenges. Packet loss prevents decision of proper traveling routes. Therefore, this paper adapts the ICN concept to VANET, called IC-VANET. In IC-VANET, each vehicle acquires traffic information by multi-hop request / response type communication, and it caches contents. Then, we propose a traffic information collection and routing protocols using Virtual Nodes (VNRP) where packets are relayed via Virtual Nodes preferentially for improving data acquisition rate and communication overhead. Virtual Node (VN) is a logical node that is maintained at a specific area considering the vehicle density by inter-vehicle communication with surrounding vehicles, and it collects traffic information. We compared the proposal method with the conventional VANET method by simulation and evaluated the performance.
Multifunctional Remotely Actuated 3-DOF Supernumerary Robotic Arm Based on Magnetorheological Clutches and Hydrostatic Transmission Lines

Supernumerary robotic limbs (SRL) are wearable extra limbs intended to help humans perform physical tasks beyond conventional capabilities in domestic and industrial applications. However, unique design challenges are associated with SRLs as they are mounted on the human body. SRLs must 1) be lightweight to avoid burdening the user, 2) be fast enough to compensate for human unpredictable motions, 3) be strong enough to accomplish a multitude of tasks, 4) have high force-bandwidth and good backdrivability to control interaction forces between the user and the environment. This letter studies the potential of a remotely actuated 3-DOF supernumerary robotic arm equipped with a 3 fingers soft gripper, both powered by magnetorheological (MR) clutches and hydrostatic transmission lines. The tethered configuration allows the power-unit to be located on the ground, which minimizes the remote mass (4.2 kg) on the user. MR clutches minimize the actuation inertia in order to provide fast dynamics and good backdrivability. An experimental open-loop force-bandwidth of 18 Hz is obtained at each joint and the maximal speed reached by the device end-effector is 3.4 m/s, which is sufficient for compensating human motions. In addition, the two first joints provide 35 Nm and the third joint provides 29 Nm, which is strong enough to hold manual industrial tools. Finally, the SRL is put in real practical situations, as fruit and vegetables picking, painting, tools holding and badminton playing. The capability of the proposed MR-Hydrostatic SRL to perform successfully various tasks with high speed and smoothness suggests a strong potential of SRLs to become future commonly used devices.
A Review of Augmented Reality in Robotic-Assisted Surgery

Augmented reality (AR) and robotic-assisted surgery (RAS) are both rapidly evolving technologies in recent years. RAS systems, such as the da Vinci Surgical System, aim to improve surgical precision and dexterity, as well as access to minimally-invasive procedures, while AR provides an advanced interface to enhance user perception. Combining the features of both, AR-integrated RAS has become an appealing concept with increased interest among the academic community. In this paper, we review the existing literature about AR-integrated RAS. We discuss the hardware components, application paradigms and clinical relevance of the literature. The concept of AR-integrated RAS has been shown to be feasible for various procedures. Encouraging preliminary results include reduced sight diversion and improved situation awareness. Special techniques, e.g., activation-on-demand, are taken into consideration to address visual clutter of the AR interface and ensure that the system is fail-safe. Although AR-integrated RAS is not yet mature, we believe that if the current trend of development continues, it will soon demonstrate its clinical value.
Investigation of the Effects of Switching Technique on the Performance of Four Switch Buck-Boost Bidirectional DC/DC Converters

In general, a DC/DC converter provides energy transfer from a source to a load in one direction. Today, bidirectional power transfer is needed in many applications that include battery based energy storage. Serving a compact solution for this problem is crucial in order to reduce the total size of the system where the physical size is a critical subject. Various types of bidirectional DC/DC converters are implemented for this purpose in literature. Highlighting the reasons, one of them is selected, proposed and simulated for a special double battery suited system which has a rated power of 1 kW. In order to compare efficiency performance of the switching strategy design for this topology; proposed in [12], with conventioal one throughout the whole operating points, simulations are realized at various operating conditions. Since proposed method is a software based solution, there will be no need for extra circuit components. With the results of this study, it is seen that the selected topology and applied modulation technique are suitable for bidirectional power flow and the power density can be increased with a software-only solution for this kind of applications.
An Efficient Approach based on Image Pixel and Semantic Features Towards Video Retrieval

With the development of image vision technology, video data emerges in large numbers. Even though varieties of methods have achieved excellent performance, how to quickly and accurately retrieve the video needed in a large number of videos is still of vital importance but challenging. In this paper we propose a video retrieval method that combines keyframe of pixels and semantic features. We extract the pixels and semantic features of input image during retrieval, and obtain the shot candidate set by matching the pixel features. Besides we sort the candidate set semantic features of the centralized keyframe. Finally we return the video and the shots position. On this basis, an image-based video retrieval tool was designed and implemented. The effectiveness of the tool were verified in related datasets and practical applications.
MC2:Unsupervised Multiple Social Network Alignment

Social network alignment, identifying social accounts of the same individual across different social networks, shows fundamental importance across a wide spectrum of applications. Individuals more often than not join in multiple social networks and it is in fact intractable or even impossible to acquiring supervision for guiding the alignment. However, to the best of our knowledge, none of existing methods can align multiple social networks without supervision. In this paper, we propose to study the problem of unsupervised multiple social network alignment. To address this problem, we propose a novel unsupervised model of Matrix factorization with diagonal Cone under orthogonal Constraint, referred to as MC2. Its core idea is to embed and align multiple social networks in the common subspace via an unsupervised approach. Specifically, in MC2 model, we first design a matrix optimization to infer the common subspace from different social networks. To address the nonconvex optimization, we then design an efficient alternating algorithm by leveraging its inherent functional property. Through extensive experiments on real-world datasets, we demonstrate that the proposed MC2 model significantly outperforms the state-of-the-art methods.
Comparing Semantic and Nutrient Value Similarities of Recipes

Although food and nutrition have been studied for centuries, modern nutritional science is surprisingly young. Human knowledge about food and nutrition has evolved drastically with time, and it has especially expanded the last few decades, with information and data being mass produced and available everywhere and in any form it is really easy to get overwhelmed and confused when it comes to what is right and what is wrong. Macronutrient assessment is a crucial task for individuals suffering from various diseases, and also very relevant for professional athletes, and nowadays it is becoming part of everyday life for many people, because of health or fitness reasons. Assessing the nutritional components of food is very challenging and requires reliant source of data. In this paper, we introduce an idea of finding similar recipes with regard to their macronutrient values based on learned recipe vector representation. On a scientifically proven dataset of recipe data containing description and macronutrient values we introduce word and paragraph embeddings, learn concept representations for the textual descriptions, proceed with calculating similarity between the embeddings, and then compare with the similarity between nutrient values. The results show a strong correlation between these two similarities. This study is a promising beginning for continuing in this direction-introducing multi-label classification or regression for predicting nutrient content of food.