Miracle-dz/UniFAR-FTUs huggingface dataset:
Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.
Apex: a High-Performance Hierarchical Distributed Ledger

Designing a secure permission distributed ledger that has the same performance a centralized counterpart, such as Visa, is a challenging task. The majority of available distributed ledgers are unable to scale to that degree. To increase the number of transactions per second, we can either compromise on security or partition the Blockchain.Here, we present Apex, a novel hierarchical distributed ledger that increases the overall performance by 1) partitioning groups of nodes into smaller concurrent distributed ledgers 2) limiting how transactions can flow to the system while guaranteeing the security and 3) combining blocks of each partition into a separate aggregated distributed ledger. Apex is designed for use-cases that require public and transparent accounting such as intellectual property rights in the music industry. An evaluation of our experimental prototype shows that Apex’s throughput scales linearly with the number of active nodes while transaction confirmation is less than two seconds. By applying these tree constructs we achieve almost linear speedup in terms of transactions per second simultaneously lowering latency and verification computation-cost per node.
Minimal Rational Interpolation and its Application in Fast Broadband Simulation

Broad bandwidth simulation in frequency domain benefits from fast frequency sweep, and frequency-domain electromagnetic solvers are usually combined with asymptotic wave evaluation or interpolation techniques. In this work, we introduce the minimal rational interpolation from the perspective of linear algebra and utilize it in fast broadband simulation. An efficient approach is formed by combining adaptive frequency sampling and rational interpolating. Approximate models of the quantities of interests are updated iteratively until the the broadband spectral dynamics are captured. Thanks to the minimal rational interpolation, the approach is fully automatic and does not require pre-estimates of the orders of interpolating functions in each step of the iterations. The obtained functions always have low complexities, hence no further model order reduction is needed. Broadband simulations on admittance of a multi-port interconnects and backward scattering of a cube are given as numerical examples. Results show that the approach can significantly improve the computational efficiency.
Low Complexity Detection for an AF Relay Assisted MIMO QSM System

This paper presents a low complexity near maximum likelihood (Near-ML) detection algorithm for an amplify and forward (AF) relay assisted multiple input-multiple output (MIMO) quadrature spatial modulation (QSM) transmission system. The proposed low complexity detection algorithm is based on a tree search and a spherical detection strategy. First, we analyze the performance of the relay assisted AF-MIMO-QSM system for a different number of relays and compare the bit error rate (BER) performance of the proposed scheme with the conventional relay assisted AF-MIMO system considering the optimal ML criterion. Then, we compare the performance and complexity of the proposed algorithm with the optimal ML detector for the AF-MIMO-QSM system. Results show that the system’s BER performance can be significantly improved as the number of relays increases. The AF-MIMO-QSM system has up to 8 dB gain in BER performance compared with the conventional AF-MIMO system for the analyzed cases. Mainly, the proposed low complexity detection algorithm performs very near to the optimal with the advantage of a reduction in complexity of 45% and 61% for a spectral efficiency of 4 bits per channel use (bpcu) and 6 bpcu respectively.
Research on Knowledge Representation of the National Cultural Resources based on $\mathrm{CTL}-\mathrm{DDL}_{\mathrm{ALC}}$

Description logic is a formal language of knowledge representation. Among the knowledge of national cultural resources, most knowledge are dynamic. Through the analysis ALC, ALC lacks the ability to represent dynamic cultural resources. To solve this problem, this paper adds action operator and temporal operator based on ALC, which can be extended in order to describe the national cultural resources with knowledge changes, and puts forward the knowledge representation based on national cultural resources of $\mathbf{CTL-DDL}_{\mathbf{ALC}}$. The knowledge and rules needed in the reasoning process of national cultural resources should be unified and standardized, in order to ensure the expression and reasoning research of national cultural resources knowledge.
High-Gain Wideband Metasurface Antenna With Low Profile

In this paper, a novel compact wideband and high-gain antenna based on metasurface is proposed. The antenna is an octagon with a side length of 32.3 mm and a height of 5.6 mm. The metasurface consists of two stacking layers, and both layers contain a $4\times 4$ copper patch array. The antenna is excited by an aperture coupled structure made of an anomalous microstrip line and a narrow slot etched in the ground plane. Genetic algorithm (GA) is adopted to optimize all the parameters and obtain the valid performance of the metasurface antenna. Moreover, the antenna is modified by introducing nonuniform patch. Modification leads to varying degrees gain enhancement, and the peak is approximately 1.5 dB at 6.1GHz. The impedance bandwidth of the proposed antenna is 54.9%, ranging from 4.48–7.87 GHz. A prototype of the antenna is fabricated, and the measured results verify the design.
Secured Electronic Voting System Using the Concepts of Blockchain

Electronic Voting Machines (EVMs) are replacing paper ballots because of (i) the errors involved in the manual counting process and (ii) the large time period required to count the votes. Even though these digital recording electronic systems are advancements, they are prone to tampering and electoral frauds. The suspected vulnerabilities in EVMs are (i) the possibilities of tampering with the EVM’s memory chip or switching it with a fake one, (ii) their simplicity, which enables them to be tampered without requiring much skill and (iii) the chances of double voting. This paper attempts to solve the above problems by storing the vote data in a decentralized network and securing the data using a security mechanism derived from the blockchain framework (which is currently used for securing cryptocurrencies). The vote data is shared among all the devices in the network and peer to peer verification is done to verify the authenticity of the vote data. In order to successfully tamper with the system, the data stored in all the nodes must be changed. This makes the proposed system more efficient and reliable. In this paper, we propose an algorithm, which is based on the concepts of blockchain, to secure the votes stored in an EVM. A fingerprint authentication system is also added as an extra layer of security to prevent double voting. The proposed EVM will be tamperproof, and any attempt to manipulate the registered votes, will be detected and the tampered data will be replaced with the correct data.
Development of Myoelectric Robotic Hand using 3D Printer

This paper presents a low-cost myoelectric robotic hand which was developed by a 3D printer. The purpose of the developed system is to be used as a low-cost prosthesis. It is controlled by electromyogram (EMG) signals from flexor and extensor muscles in the lower arm. Each part of the robotic hand is created using the 3D printer. Arduino MEGA microcontroller is used as the main controller. Experimental results show that the robotic hand can perform the objects grasping operations according to EMG signals from muscles without dropping the objects in the experiments.
Modified RAP-WOTA for Preventing Man in the Middle and Replay Attacks

RFID is a technology that utilizes radio waves to communicate and transmit data. The components involved in this communication process are tag, reader, and server. These three components must verify each other to ensure the authenticity of the components. Rajaguru K. and R.C Hansdah proposed an authentication protocol called RFID Authentication Encryption Protocol Without Tag Anonymity (RAP-WOTA). The protocol is claimed to fulfill key confidentiality, message confidentiality, message authenticity, tag anonymity, de-synchronization, mutual authentication, forward secrecy, and resistant to Man-in The-Middle (MTM) and replay attacks. Although it was claimed that RAP WOTA is secure against MTM and replay attacks, an analysis of RAP-WOTA using Scyther Tool shows that RAP-WOTA is vulnerable to MTM and replay attacks. In this paper we proposed modified RAP-WOTA that prevent MTM and replay attacks by adding some nonces or timestamps. Analyzing using Scyther Tool showed that this modification is secure against MTM and replay attacks.
Self-Adjustable Active Sequence to Reduce Latency in Duty Cycle WSNs

The wireless sensor networks (WSNs) have a great application prospect, which is composed of much simple hardware and small-size sensor nodes to realize the perception of the surrounding environment. It is often widely used that nodes work in a duty cycle mechanism of periodic sleep and awakening in WSNs. However, this mechanism also increases latency and the routing table length whereas saving energy. In this paper, the Self-adjustable Active Sequence (SAC) scheme is proposed to solve the above problems. It enables optimization of energy, latency and routing tables. The main innovations are as follows. Firstly, the SAC scheme divides the continuous active slots into multiple shorter slots to reduce the latency. Second, SAC scheme adds more active slots by using the remaining energy. Third, decreasing the number of the forwarding nodes to reduce the routing table length. In this case, the optimization of delay only brings small gains, so we should focus on the improvement of routing. It has a better performance in reducing the delay and reducing the length of the routing table through both theoretical analysis and experimental results. If and only if the continuous active slots are divided into several segments, the delay is reduced by 33%. And the number of active slots by using the far sink region energy further reduce the delay by 29% in general. What’s more, when reducing the size of the forwarding nodes set, the routing table length is further reduced by 29%.
DAGMaR: A DAG-based Robust Road Membership Estimation Framework for Scenario Mining

For the identification of scenarios in real-world testdrive data (scenario mining) knowledge about the road, the vehicle drives on, is required because the set of possible scenarios varies according to the road type. Since these scenarios are needed for the scenario-based validation of automated driving functions, a robust approach for road membership estimation is vital.For that purpose, the vehicle location is estimated using a particle filter and mapped on a digital map providing the required road information. But, since particle filter-based solutions lack precision in ambiguous situations, reliable road membership estimation is not possible.In this work, a map matching framework is proposed to provide high accuracy and complete trajectories of vehicle poses by utilizing particle filter for vehicle localization and a directed acyclic graph of mapped roads (DAGMaR). The presented approach is evaluated with an inner-city, a mixed and a motorway drive showing promising results.
Neural Nets to Detect Abnormal Traffic in Communication Networks

The paper describes using a multilayer perceptron to detect abnormal traffic in communication networks; the perceptron is based on Adam, a first-order algorithm for gradient optimization of stochastic objective functions. The traffic it detects pertains to four attack types: Probe, DoS, R2L, U2R. The applied ANN model shows 92% cross-validation accuracy on NSL-KDD dataset. Assumingly, an optimal algorithm for weight optimization and attribute selection will help improve the accuracy.
Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system’s performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.
Soluble limit and criticality of fermions in 
Z2
 gauge theories

Quantum information theory and strongly correlated electron systems share a common theme of macroscopic quantum entanglement. In both topological error correction codes and theories of quantum materials (spin liquid, heavy fermion and high-$T_c$ systems) entanglement is implemented by means of an emergent gauge symmetry. Inspired by these connections, we introduce a simple model for fermions moving in the deconfined phase of a $\mathbb Z_2$ gauge theory, by coupling Kitaev's toric code to mobile fermions. This permits us to exactly solve the ground state of this system and map out its phase diagram. Reversing the sign of the plaquette term in the toric code, permits us to tune the groundstate between an orthogonal metal and an orthogonal semimetal, in which gapless quasiparticles survive despite a gap in the spectrum of original fermions. The small-to-large Fermi surface transition between these two states occurs in a stepwise fashion with multiple intermediate phases. By using a novel diagrammatic technique we are able to explore physics beyond the integrable point, to examine various instabilities of the deconfined phase and to derive the critical theory at the transition between deconfined and confined phases. We outline how the fermionic toric code can be implemented as a quantum circuit thus providing an important link between quantum materials and quantum information theory.
Geometric Obstructions on Gravity

These are notes for a short course and some talks gave at Departament of Mathematics and at Departament of Physics of Federal University of Minas Gerais, based on the author's paper arXiv:1808.09249. Some new information and results are also presented. Unlike the original work, here we try to give a more physical emphasis. In this sense, we present obstructions to realize gravity, modeled by the tetradic Einstein-Hilbert-Palatini (EHP) action functional, in a general geometric setting. In particular, we show that if spacetime has dimension $n\geq4$, then the cosmological constant plays no role in any "concrete geometries" other than Lorentzian. If $n\geq6$, then the entire EHP theory is trivial, meaning that Lorentzian geometry is (essentially) the only "concrete geometry" in which gravity (i.e, the EHP action functional) makes sense. Examples of "concrete geometries" include those locally modeled by group reductions $H\hookrightarrow O(k;A)$ for some $k$ and some algebra $A$, so that Riemannian geometry, Hermitian geometry, K\"ahler geometry and symplectic geometry, as well as Type II geometry, Hitchin's generalized complex geometry and $G_{2}$-geometry are included. We also study EHP theory in "abstract geometries", such as graded geometry (and hence supergeometry), and we show how the obstruction results extend to this context. We construct two theories naturally associated to EHP, which we call the geometric/algebraic dual of EHP, and we analyze the effect of the obstructions in these new theories. Finally, we speculate (and provide evidence for) the existence of a "universal obstruction condition".
Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Over the last few years, the evolution of network and user handsets’ technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and added-value services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.
Integrating Hardware Diversity with Neural Architecture Search for Efficient Convolutional Neural Networks

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, humandesigned search space without considering hardware diversity, we propose HURRICANE 1 that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet show that our algorithm consistently achieves a much lower inference latency with a similar or better accuracy than state-of-the-art NAS methods on three types of hardware. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. For VPU, HURRICANE achieves a 0.53% higher top-1 accuracy than Proxylessmobile with a 1.49× speedup. Even for well-studied mobile CPU, HURRICANE achieves a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% or even 54.7% (with less than 0.5% accuracy loss) compared to Singlepath-Oneshot.
Dark matter with excitable levels

This paper explores the possibility that dark matter particles are objects with a set of excitable levels, as for a string or drum. A change in excitation is associated with the absorption or emission of a photon, and the probability of such a change is dependent on the ambient photon energy density. Particle mass comes entirely from excitations. Such dark matter with excitable levels, or DMWEL, particles have a distribution of masses which freezes out to its present-day form during the early universe. In the particular model considered, consistency with Planck CMB anisotropy observations implies a cautious lower bound on the energy of the first excitable level of 2 to 200 eV for a set of reference parameters. While laboratory detection looks to require a considerable increase in the power of controlled light-sources, it might be possible in the near future to detect or constrain DMWEL further through improved anisotropy observations, closer determination of the CMB's maximum deviation from a blackbody shape, or from gravitational probes of the dark matter power spectrum. For certain parameters near to the boundary of the region allowed by CMB anisotropies, DMWEL dark matter is cold, but with a cut-off halo mass many orders of magnitude greater than that typical for WIMP cold dark matter.
"Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive Jammers in IoT Networks

In this article, we introduce a novel deception strategy inspired by the "Borrowing Arrows with Thatched Boats" strategy, one of the most famous military tactics in history, in order to defeat reactive jamming attacks for low-power IoT networks. Our proposed strategy allows resource-constrained IoT devices to be able to defeat powerful reactive jammers by leveraging their own jamming signals. More specifically, by stimulating the jammer to attack the channel through transmitting fake transmissions, the IoT system can not only undermine the jammer's power, but also harvest energy or utilize jamming signals as a communication means to transmit data through using RF energy harvesting and ambient backscatter techniques, respectively. Furthermore, we develop a low-cost deep reinforcement learning framework that enables the hardware-constrained IoT device to quickly obtain an optimal defense policy without requiring any information about the jammer in advance. Simulation results reveal that our proposed framework can not only be very effective in defeating reactive jamming attacks, but also leverage a jammer's power to enhance system performance for the IoT network.