Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to Gait Analysis from Human-Rollator Interaction: The i-Walker

Falls are one of the main causes of injury and injury-related deaths in the older demographic sector. During the last decade, several solutions have been proposed for fall detection or prevention, but also to assist old adults in their mobility. The main concern is to provide means to improve their quality of life, allowing them to live in community as long as possible, but also to help maintaining public health systems as sustainable as possible. In this paper, we compare two different methodologies that aim to define walking patterns in 42 adults aged 22 to 94 years that participated in a pilot study. Two methodologies are compared: one focused on the whole exercise performance, while the other tries to identify strides and categorize them. Both proposals use a technique based on the bag-of-words approach.
Discrete model-based clustering with overlapping subsets of attributes

Traditional model-based clustering methods assume that data instances can be grouped in a single “best” way. This is often untrue for complex data, where several meaningful sets of clusters may exist, each of them associated to a unique subset of data attributes. Current literature has approached this problem with models that consider disjoint subsets of attributes to define distinct clustering solutions. Each solution being represented by a cluster variable. However, restricting attributes to a single cluster variable diminishes the expressiveness and quality of these models. For this reason, we propose a novel kind of models that allows cluster variables to have overlapping subsets of attributes between them. In order to learn these models, we propose to combine a searchbased method with an attribute clustering procedure. Experimental results with both synthetic and real-world data show the utility of our approach and its competitiveness with the state-of-the-art.
Family of Constrained Codes for Archival DNA Data Storage

DNA-based data storage systems have evolved as a solution to accommodate data explosion. In this letter, some properties of DNA codewords that are essential for an archival DNA storage are considered for the design of codes. Constraint-based DNA codes, which avoid runs of nucleotides, have fixed GC-weight, and a specific minimum distance is presented. An altruistic algorithm that enumerates DNA codewords with the above constraints is provided. A theoretical bound on such DNA codewords is obtained. This bound is tight when there is no minimum distance constraint.
A cellular Automata approach for noisy images edge detection under null boundary conditions

Cellular automata (CA) refer to a simple and conventional method which performs parallel processing, thereby exhibiting better performance than serial processing in certain contexts, particularly in terms of reduced time complexity. Edge detection is widely used in image processing and numerous methods have been proposed for this purpose. However, most of existing methods are serial techniques and fail to take into consideration noise content of the image. In this paper, an edge detection technique was proposed for noisy images based on a four-neighborhood under Null boundary cellular automata (FNNBCA) for noise elimination and a two-dimensional twenty-five neighborhoods under Null Boundary cellular automata (TFNNBCA) for edge detection. This method considers linear CA rules under null boundary conditions only. Efficiency of the proposed method was further compared to those of existing methods, indicating much promising performance of the proposed method for binary images, so that all edges were well detected even on complex images. Finally, results of implementing the method in MATLAB are presented.
KeyChain: Blockchain-Based Key Distribution

End-to-End Encryption (E2EE) is widely used in messaging services to protect the security of messages, and most E2EE systems are implemented by asymmetric encryption. However, most mechanisms of public key distribution rely on centralized servers, which may cause leakage and tampering of messages if centralized servers are compromised. In this paper, we propose a decentralized key distribution protocol based on blockchain to solve this problem, and we call it KeyChain. Blockchain can build transparent systems without centralized servers and inherently resist tampering with high Byzantine fault tolerance, therefore it is employed in KeyChain to store public-key-to-id bindings. In addition, in KeyChain, every modification of the public key will be added to the blockchain, so that tampering behaviors can be traced; for the purpose of implementing Byzantine fault tolerance by a low computation cost, KeyChain employs Delegated Proof of Stake (DPOS) as the consensus mechanism to avoid waste of computing resources. Our experiments results show that KeyChain can distribute public keys within 1.3 seconds and only cost a client 0.262MB hard disk space in 24 hours.
A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets

MAP inference over discrete Markov networks with large label sets is often applied, e.g., in localizing multiple key points in the image domain. Often, approximate or domain specific methods are used to make the problem feasible. An alternative method is to preselect a limited (much smaller) set of suitable labels, which bears the risk to exclude the correct solution. To solve the latter problem, we propose a two-step approach: First, the reduced label sets are extended by a novel “refine” label, which — when chosen during inference — marks nodes where the label set is insufficient. The energies for this additional label are learned in conjunction with the network’s potential weights. Second, for all nodes marked with the “refine” label, additional local inference steps over the full label set are performed. This greedy refinement becomes feasible by extracting small subgraphs around the marked nodes and fixing all other nodes. We thoroughly evaluate and analyze our approach by solving the problem of localizing and identifying 16 posterior ribs in 2D chest radiographs.
Anatomical variations and dimensions of arteries in the anterior part of the circle of Willis.

BACKGROUND The aim of this study was to investigate different anatomic variations and dimensions of anterior part of the circle of Willis (CW) and their prevalence on Kosovo's population. MATERIALS AND METHODS This is an observational descriptive and retrospective study performed at the University Clinical Centre, Clinic of Radiology. The three-dimensional time-of-flight technique was used for magnetic resonance angio- graphy imaging to evaluate the anatomy of the CW in 513 adults without clinical manifestations for cerebrovascular disease. The diameters of arteries of the CW were measured and variations were recorded. RESULTS The complete anterior part of CW was found in 64.3% of cases, more evident in female at about 66% than male 62.2%. Morphologic variations of the ACA1 are agenesis or hypoplasia in 5.65%, fusion of the anterior cerebral arteries (ACAs) on a short distance in 6.5% and fusion of the ACA on a long distance in 2.5%, median ACA is presented in 11 examined patients or 2.1%. Normal anterior communicating artery (ACoA) was seen in 68.2%, hypoplasia or absence 15.66%, double ACoA in 0.6% and fenestrations in 3.89%. The mean calibrations of the vessels were measured as 2.04 mm in right A1, 2.06 mm in left A1 and 1.16 mm of ACoA. While, 14.1 mm length of right A1, 13.87 mm of left A1 and 2.99 mm of ACoA. CONCLUSIONS Knowledge of the variations and diameter of the anterior part of the CW has a great importance in determination of anatomical variations and diameter in general populations, also, in interventional radiology for various endovascular interventions as well as during anatomy lessons.
Optimization of Robot Tasks by Intelligent Objects Using RFID Technology

The paper investigates useful applications of RFID (Radio Frequency Identification) technology in combination with industrial robots, because these automation devices are very important in the context of the term Industry 4.0. In industrial automation, RFID transponders can be placed on tools and workpieces. Besides, the identification of objects, it is also possible to store information locally on the object. Such an object may be regarded as an intelligent object, because its supports the decentralization of decisions. The paper makes proposals about what kind of data can be stored in workpieces and how this information can be used to optimize the robot task. Room for improvement is found with respect to, e.g., the path accuracy of the robot motion, force/torque control and collision avoidance. Selected algorithms relating to the proposals are verified successfully by practical experiments.
Balance the Loss: Improving Deep Hash via Loss Weighting and Semantic Preserving

Learning to hash is widely used in approximate nearest-neighbor (ANN) search. However, traditional hash learning methods, which split the hashing into two parts: feature extraction and hash function learning, usually result in a low retrieval accuracy. Although existing deep learning based hashing methods can improve hashing quality by coupling feature learning and hash encoding, they are always affected by the positive-negative sample imbalance problem. It often deteriorates the performance of the generated hash code. In this paper, we propose an end-to-end deep hashing framework, in which a weighted pairwise loss function is employed to alleviate sample imbalance problem. The loss generated by the positive pairs and negative pairs are given different weights automatically. Moreover, we integrate a classification network into the hashing framework, which can preserve the semantic information by making sure the generated hash codes are also optimal for classification. Comparison experiments are conducted on two benchmark datasets to demonstrate the performance of our proposed approach.
Magnet Thermal and Magnetization State Monitoring in PMSMs Based on Magnet Resistivity Estimation

Thermal and magnetization state monitoring of permanent magnet synchronous machines (PMSMs) is important for control and reliability issues. Magnet resistance can be used for this purpose as it directly depends on PM temperature and magnetization state. Injection of a high frequency (HF) signal in the stator via inverter has been demonstrated to be a viable option for magnet resistance estimation as it does not interfere with the normal operation of the drive and can be implemented therefore in real time. Furthermore, this method can be applied to both interior PMSMs (IPMSMs) and surface PMSMs (SPMSMs). This paper reviews the use of magnet resistance for thermal and magnetization state monitoring of PMSMs, with special focus on the expected accuracy and sensitivity to operating condition of the machine. ’
Agreement of body adiposity index (BAI) and paediatric body adiposity index (BAIp) in determining body fat in Brazilian children and adolescents

Abstract Objective To evaluate agreement of the body adiposity index (BAI) and paediatric body adiposity index (BAIp) in estimating body fat compared with dual-energy X-ray absorptiometry (DXA) and to propose cut-off points for these indices to classify excess adiposity in Brazilian children and adolescents. Design Cross-sectional study. Measures of weight, height, hip circumference, BMI and body fat percentage (%BF) assessed by DXA were taken, and BAI and BAIp were calculated. The Bland–Altman plot was used to estimate agreement between the methods, and the receiver-operating characteristic curve to determine the cut-off points for BAI and BAIp per age and sex in comparison with DXA. Setting Viçosa, Minas Gerais, Brazil. Subjects Children and adolescents aged 8–19 years (n 1049). Results Of the children and adolescents, 52·4 % were girls. BAI and BAIp had satisfactory performance by the receiver-operating characteristic curve, except for the 18–19 years age group, whose BAIp had better predictive capacity than BAI. The agreement analysis showed that BAI overestimated %BF by 2·64 %, on average, using DXA; while BAIp underestimated %BF by 3·37 %. Conclusions BAI and BAIp showed low agreement with the body fat obtained by DXA, requiring caution when interpreting body composition data in children and adolescents.
Modeling and Control of a Nine-Phase Induction Machine With Open Phases

Multiphase machines have several advantages when compared to three-phase machines. Reliability is one of the most important of those advantages, since the multiphase machine can operate with one or more phases open-circuited. However, the traditional models and control of a balanced induction machine can no longer be used under such open-phase conditions. This paper addresses important issues in the modeling and control of nine-phase induction machines operating under open-phase faults. The development of the proposed model is presented in detail, including an algorithm to guarantee the proper representation of isolated neutral motors. Simulation results illustrate the postfault behavior of the nine-phase motor due to an open phase, including the elimi-nation of torque ripple by applying the proper control methods. Experimental results from a laboratory test setup illustrating the operation of a nine-phase induction motor under one open-phase fault are presented and used to validate the proposed model.
Rainbow: Preventing Mobile-Camera-based Piracy in the Physical World

Since the mobile camera is often small in size and easy to conceal, existing anti-piracy solutions are inefficient in defeating mobile-camera-based piracy, thus it remains a serious threat to copyright of Intellectual property. This paper presents Rainbow, a low-cost lighting system which prevents mobile-camera-based piracy attacks on intellectual property in the physical world, e.g., art paintings. Through embedding invisible illuminance flickers and chromatic changes into the light, our system can significantly degrade the imaging quality of a camera while maintaining a good visual experience for human eyes. Extensive objective evaluations under different scenarios demonstrate that Rainbow is robust to different confounding factors and can effectively defeat piracy attacks on various mobile devices. Subjective tests on volunteers further evidence that our system can not only significantly pollute pirated photos but also be able to provide good lighting conditions.
A deep learning model generation method for code reuse and automatic machine learning

In recent years, deep neural networks are active in numerous applications such as predictive, advertising and healthcare applications using of image, voice and text recognitions. However, deep neural networks are useful methods but usually require a proper modeling to construct a deep neural network method in any application. Designing a model is a tedious task to be realized in the network, which opens an issue to design an effective software tool for modeling deep neural networks. To get an excellent model for deep neural networks, the developers should have sufficient understanding and experience for deep neural network methods. The developers also require coding skills with the deep learning frameworks and knowledge for the computing resources. This paper presents a software tool based on a Graphical User Interface (GUI) to develop deep neural network models, which train the models with external computing resources and automate the hyper-parameter tuning.
Glia as architects of central nervous system formation and function

Glia constitute roughly half of the cells of the central nervous system (CNS) but were long-considered to be static bystanders to its formation and function. Here we provide an overview of how the diverse and dynamic functions of glial cells orchestrate essentially all aspects of nervous system formation and function. Radial glia, astrocytes, oligodendrocyte progenitor cells, oligodendrocytes, and microglia each influence nervous system development, from neuronal birth, migration, axon specification, and growth through circuit assembly and synaptogenesis. As neural circuits mature, distinct glia fulfill key roles in synaptic communication, plasticity, homeostasis, and network-level activity through dynamic monitoring and alteration of CNS structure and function. Continued elucidation of glial cell biology, and the dynamic interactions of neurons and glia, will enrich our understanding of nervous system formation, health, and function.
An asynchronous and reconfigurable CNN accelerator

In this paper, we introduce a novel asynchronous and reconfigurable convolutional neural network(CNN) accelerator including a 5x5 computation array of processing elements(PE) with a dynamically reconfigurable architecture and a pooling unit(PU). With this architecture, the data path, calculation method, the pooling way and pooling size can be changed according to the configurable information for different CNN models. In this accelerator, the global clock is replaced by the local pulse signals from Click elements. And an asynchronous pipeline formed by Click elements in series enables the circuits to work in pipeline mode without any sacrifice of speed because of the self-timed property of asynchronous circuits. A 5x5 registers array together with the computation array is fully connected by an asynchronous Mesh network, which yields a 60% decrease of the access to off-chip memory by reusing the input data. A CNN model, LeNet-5 is simulated with the FPGA of Xilinx VC707. Compared with previous synchronous work, the unit performance achieves a 15.6% increase in speed.
Interactive Multiple-User Location-Based Keyword Queries on Road Networks

As the use of smart mobile devices increases, users can connect and coordinate with local people or events that match their interests through geosocial networking applications. Such applications require techniques that support multiple-user location-based keyword (MULK) query processing, which takes a set of geo-locations and a set of keywords as arguments and returns a list of relevant POIs that match the arguments. In geosocial networking, processing this query involves two issues: mining users’ individual preferences and indexing road networks. In this paper, we investigate how to mine users’ individual preferences by enhancing MULK query processing with interactions, resulting in interactive multiple-user location-based keyword query. Furthermore, we propose a hierarchical road network index structure to support efficient query processing. Finally, we conduct a set of experiments that demonstrate the effectiveness and efficiency of our solutions under various parameter settings.
Dry Electrode-Based Fully Isolated EEG/fNIRS Hybrid Brain-Monitoring System

A portable hybrid brain monitoring system is proposed to perform simultaneous 16-channel electroencephalogram (EEG) and 8-channel functional near-infrared spectroscopy (fNIRS) measurements. Architecture-optimized analog frontend integrated circuits (Texas Instruments ADS1299 and ADS8688A) were used to simultaneously achieve 24-bit EEG resolution and reliable latency-less (<0.85 μs) bio-optical measurements. Suppression of the noise and crosstalk generated by the digital circuit components and flashing NIR light sources was maximized through linear regulator-based fully isolated circuit design. Gel-less EEG measurements were enabled by using spring-loaded dry electrodes. Several evaluations were carried out by conducting an EEG phantom test and an arterial occlusion experiment. An alpha rhythm detection test (eye-closing task) and a mental arithmetic experiment (cumulative subtraction task) were conducted to determine whether the system is applicable to human subject studies. The evaluation results show that the proposed system is sufficiently capable of detecting microvoltage EEG signals and hemodynamic responses. The results of the studies on human subjects enabled us to verify that the proposed system is able to detect task-related EEG spectral features such as eye-closed event-related synchronization and mental-arithmetic event-related desynchronization in the alpha and beta rhythm ranges. An analysis of the fNIRS measurements with an arithmetic operation task also revealed a decreasing trend in oxyhemoglobin concentration.
Load Balancing Across Microservices

With the advent of cloud container technology, enterprises develop applications through microservices, breaking monolithic software into a suite of small services whose instances run independently in containers. User requests are served by a series of microservices forming a chain, and the chains often share microservices. Existing load balancing strategies either incur significant networking overhead or ignore the competition for shared microservices across chains. Furthermore, typical load balancing solutions leverage a hybrid technique by combining HTTP with message queue to support microservice communications, bringing additional operational complexity. To address these challenges, we propose a chain-oriented load balancing algorithm (COLBA) based solely on message queues, which balances load based on microservice requirements of chains to minimize response time. We model the load balancing problem as a non-cooperative game, and leverage Nash bargaining to coordinate microservice allocation across chains. Employing convex optimization with rounding, we efficiently solve the problem that is proven NP-hard. Extensive trace-driven simulations demonstrate that COLBA reduces the overall average response time at least by 13% compared with existing load balancing strategies.
Multilayered Risk analysis of Mobile systems and Apps

Mobile systems and applications face a number of vulnerabilities that can lead to a breach of confidentiality of information. Users these days rely more on mobile systems and various applications for their day to day activities. Different applications can pose different risks to the security of mobile systems and can sometimes become the cause of other vulnerabilities as well. This paper presents and reviews risk analysis done at various levels in mobile systems, namely, the static analysis layer, dynamic analysis layer and the behavioral analysis layer. Risk can propagate through these layers and various techniques and approaches have been proposed to evaluate such potential risks. These strategies can help improve the security of mobile applications as well as the entire mobile applications.
How Accurate is Your Gaussian/Gamma Approximation?

Gaussian and Gamma approximations appear very frequently in communications theory applications. However, in most of the applications, numerical methods are used to illustrate the accuracy of such approximations as there are no simple general purpose analytical tools available to provide a more rigorous assessment of the error. We present a simple and accurate tool to measure the accuracy of Gaussian and Gamma approximations. Specifically, we consider the first order correction terms of the approximations and derive their upper bounds. Our tool provides an accurate approximation of the error term based on the third and the fourth moments of the random variable. To demonstrate the usefulness of our analysis we consider two topical applications based on mmWave channel modeling and distributed massive multiple-input-multiple-output systems. In both cases, the proposed technique provides valuable insights into the accuracy of the commonly used approximations.