Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Mobility Management for 5G Ultra-Dense HetNets via Reinforcement Learning With Tile Coding Function Approximation

Mobility management is an important feature in modern wireless networks that can provide seamless and ubiquitous connectivity to mobile users. Due to the dense deployment of small cells and heterogeneous network topologies, the traditional handover control method can lead to various mobility-related problems, such as frequent handovers and handover failures. On the other hand, the mobility management’s maintenance and operation cost is also increased due to increasing node density. In this paper, an autonomous mobility management control approach is proposed to increase the mobility robustness of user equipment (UE) mobility and minimize the operational cost of mobility management. The proposed method is based on reinforcement learning, which can autonomously learn an optimal handover control policy by interacting with the environment. The function approximation approach is adopted to allow reinforcement learning to process a large state and action space. A linear function approximator is used to approximate the state-action value function. Finally, the semi-gradient state-action-reward-state-action (Sarsa) method is implemented to update the approximated state-action function and learn the optimal handover control policy. The simulation results show that the proposed method can effectively improve the mobility robustness of UE under different speed ranges. Compared with the conventional reference signal received power (RSRP) based approach, the proposed approach can reduce unnecessary handovers by about 20% and latency by 58%, while achieving near zero handover failure rate, and increasing throughput by 12%.
Polymer Solar Cells Exceeding 10% Efficiency Enabled via a Facile Star‐Shaped Molecular Cathode Interlayer with Variable Counterions

A series of star‐shaped small molecular cathode interlayer materials are synthesized for PTB7:PC71BM based polymer solar cells (PSCs), comprising neutral amino groups, quaternary ammonium ions, amino N‐oxides, and sulfobetaine ions as pendant polar functionalities, respectively. For the first time, the effect of these different pendant functional groups with or without mobile counterions on the cells' photovoltaic properties is investigated in detail. A large improvement in device performance is observed by inserting these cathode interfacial layers (CILs) between the PTB7:PC71BM active layer and the Al electrode. The CILs could effectively lower the work function of the Al cathode, increase the built‐in potential, and decrease the series resistance of the related PSCs. poly(9,9‐dioctylfluorene‐co‐N‐[4‐(3‐methyl‐propyl)]‐diphenylamine) with pendant quaternary ammonium ions shows the best cathode modification ability, giving rise to the highest power conversion efficiency of 10.1%, even better than that of the typical poly[(9,9‐bis(3′‐(N,N‐dimethylamino)propyl)‐2,7‐fluorene)‐alt‐2,7‐(9,9‐dioctylfluorene)] based device. The design strategy and structure–property relationships concluded in this work will be helpful to develop more efficient cathode interface materials for high‐performance PSCs in the future.
SPRITE: Sparsity-Aware Neural Processing Unit with Constant Probability of Index-Matching

Sparse neural networks are widely used for memory savings. However, irregular indices of non-zero input activations and weights tend to degrade the overall system performance. This paper presents a scheme to maintain constant probability of index-matching for weight and input over a wide range of sparsity overcoming a critical limitation in previous works. A sparsity-aware neural processing unit based on the proposed scheme improves the system performance up to 6.1× compared to previous sparse convolutional neural network hardware accelerators.
Duetto: Latency Guarantees at Minimal Performance Cost

The management of shared hardware resources in multi-core platforms has been characterized by a fundamental trade-off: high-performance arbiters typically employed in COTS systems offer no worst-case guarantees, while dedicated real-time controllers provide timing guarantees at the cost of significantly degrading system performance. In this paper, we overcome this trade-off by introducing Duetto, a novel hardware resource management paradigm. Duetto pairs a real-time arbiter with a high-performance arbiter and a latency estimator module. Based on the observation that the resource is rarely overloaded, Duetto executes the high-performance arbiter most of the time, switching to the real-time arbiter only in the rare cases when the latency estimator deems that timing guarantees risk being violated. We demonstrate our approach on the case study of a multi-bank memory. Our evaluation based on cycle-accurate simulations shows that Duetto can provide the same latency guarantees as the real-time arbiter with limited loss of performance compared to the high-performance arbiter.
Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis.
PLEDGER: Embedded Whole Genome Read Mapping using Algorithm-HW Co-design and Memory-aware Implementation

With over 6000 known genetic disorders, genomics is a key driver to transform the current generation of healthcare from reactive to personalized, predictive, preventive and participatory (P4) form. High throughput sequencing technologies produce large volumes of genomic data, making genome reassembly and analysis computationally expensive in terms of performance and energy. In this paper, we propose an algorithm-hardware co-design driven acceleration approach for enabling translational genomics. Core to our approach is a Pyopencl based tooL for gEnomic workloaDs tarGeting Embedded platforms (PLEDGER). PLEDGER is a scalable, portable and energy-efficient solution to genomics targeting low-cost embedded platforms. It is a read mapping tool to reassemble genome, which is a crucial prerequisite to genomics. Using bit-vectors and variable level optimisations, we propose a low-memory footprint, dynamic programming based filtration and verification kernel capable of accelerated parallel heterogeneous executions. We demonstrate, for the first time, mapping of real reads to whole human genome on a memory-restricted embedded platform using novel memory-aware preprocessed data structures. We compare the performance and accuracy of PLEDGER with state-of-the-art RazerS3, Hobbes3, CORAL and REPUTE on two systems: 1) Intel i7-8750H CPU + Nvidia GTX 1050 Ti, 2) Odroid N2 with 6 cores: 4xCortex-A73 + 2xCortex-A53 and Mali GPU. PLEDGER demonstrates persistent energy and accuracy advantages compared to state-of-the-art read mappers producing up to 11× speedups and 5.9× energy savings compared to state-of-the-art hardware resources.
A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/low-frequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major preprocessing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the employed success measures and appropriate constitution of the ECG signal database also play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.
FTApprox: A Fault-Tolerant Approximate Arithmetic Computing Data Format

Approximate computing (AC) is an effective energy-efficient method for error-resilient applications. The essence behind AC is to reduce energy consumption by slightly sacrificing computation accuracy purposefully while providing quality-acceptable results. On the other hand, soft error is a common problem during program execution and may cause unacceptable outputs or catastrophic failure to the system. As AC introduces errors and soft errors are mitigated by fault-tolerant mechanisms, they have conflict goals and contradictory approaches. To the best of our knowledge, there is no previous efforts to consider the two at the same time. In this paper, we study the problem of AC with soft errors in order to guarantee the safe execution of the program while reducing energy (by AC). More specifically, we propose FTApprox, a fault-tolerant approximate arithmetic computing data format, to enable the detection and correction of SEs. As an approximate data format, FTApprox can use 16 bits to approximate any 32-bit integers and fixed-point numbers, and will select only the most significant part of operands for AC at runtime. Energy saving is obtained by converting 32-bit arithmetic operations to 8-bit operations. Meanwhile, for soft errors such as random bit flips, FTApprox not only can detect all single bit flips and most 2-bit flips, it can also correct most of these errors. The experimental results show that FTApprox has significant resistance against soft errors while providing 66.4%-79.6% energy saving.
Subgraph Decoupling and Rescheduling for Increased Utilization in CGRA Architecture

When coarse-grained reconfigurable array (CGRA) architecture is shifting towards general-purpose, some complex control flows, such as nested loop, conditional branch and data dependence, may embarrass it and reduce the processing element (PE) array utilization by breaking the intact dataflow graph (DFG) into multiple regions with inconsistent control regions. This paper proposes subgraph decoupling and rescheduling, which decouples the inconsistent regions into control-independent subgraphs. Each subgraph can be rescheduled with zero-cost domino context switching and parallelized to fully utilize the PE resources. Then, we propose lightweight hardware changes based on general CGRA architecture to enable our design. The experiment results show that our proposal can improve the performance and energy efficiency by 1.35× and 1.18× over a static-mapped CGRA (Plasticine), and by 1.27× and 1.45× over an instruction-driven CGRA (TIA).
Model-Based Control Can Improve the Performance of Artificial Cilia

Artificial cilia are a prominent example of physical intelligence. Their mechanical properties are often designed so to achieve desired motions in response to very simple actuation patterns. Yet, variability in the mechanical properties are inherent in these systems. This may critically disrupt the input-output relation, resulting in a final behavior completely different from the desired one. In this Communication we investigate the possibility of designing a robotic brain that helps the cilium to maintain its physical intelligence. We achieve that by closing a model-based control loop which tracks the position of the end effector while compensating for drag forces. We propose experiments to characterize our model, and extensive simulations validating the results in different conditions. This work is intended as a proof of concept, which will be further expanded in future work.
Control of Mobile Robots: Regulate the Velocity Within a Specific Shape

For the ground mobile robot, the moving speed is a crucial concern when it comes to the applications like, vison-based object detection, map building. In this case, a controller that can strictly regulate the robot speed with the undesired trajectory is need. A mobile robot control strategy is proposed to achieve a satisfactory trajectory tracking task, and at the same time, achieve the speed regulation or limitation task. Compared to traditional methods, the proposed method no longer needed extra output calculation or saturation to the control output, which will avoid the unexpected oscillations and errors. Moreover, the proposed controller has the advantage of that, even if the provided reference signal is larger, or say, violates the desired boundary, the proposed controller can still be able to drive the robot speed within the desired boundary. In addition, the boundary regulation can be ensured, when the given reference signals are not ideal. In this paper, the given reference signal is not considered to be ideal. Moreover, the stabilization and tracking speed regulation problem has been conducted, and the simulation results are also provided with the demonstration of its superiority over the traditional mobile robot control methods.
Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining

Depression is one of the most growing health disorders, generating social and economic problems. The affective computing models focus on analyzing unique user posts, not observing temporal behavior patterns, which are essential to track changes and the evolution of emotional behavior and user context, that involves the persistent analysis of feelings and characteristics over time. This article proposes the TROAD framework for longitudinal recognition of sequential patterns from depressive users on social media. The framework identifies the best interval to analyze every user activity, extracts emotional and contextual features from user data, and models the features into time windows to recognize sequential patterns from depressive user behavior. The main characteristics of the users found in the top-10 rules are negative emotions: violence, pain, shame, depression, sadness, and silence. We obtained strong sequence patterns with a minimum of 70% of support, 81% of confidence, and 69% regarding sequential confidence, considering periods of silence between users’ posts. Without considering silent periods, the rules showed 70%, 86%, and 38% of support, confidence, and sequential confidence. TROAD computational approach is a promising tool for clinical specialists in human behavior.
Mobile Augmented Reality Integrating Fingertip Haptic Devices and Wrist-Worn Visual Displays

In the context of Mobile Augmented Reality (MAR), hand-based interaction with virtual environments is still an open challenge. Virtual objects projected on the smartphone screen are on a different layer than the camera-captured images, hence the user’s hand cannot get in contact with virtual entities. In this work, we present an improvement to a previously published proof-of-concept that aims at overcoming current MAR limitations by mapping the hand in the virtual reference. To this end, we developed an ad hoc ring interface for finger tracking and haptic feedback, designed with wearability in mind. A wrist-worn display compacts the hardware on a single-arm, leaving the other completely free. The overall system is affordable thanks to the use of the smart-phone as core technology and provides "on-demand" augmented reality, tailored for daily activities scenarios. Eighteen users were enrolled to compare the wristband based system and the standard hand-held smartphone montage. Quantitative and qualitative results show that our system was positively received by the subjects, that completed the experimental task achieving similar performance in the two conditions but expressed their preference for the wrist-worn display.
MT for Subtitling: Investigating professional translators' user experience and feedback

This paper presents a study of machine translation and post-editing in the ﬁeld of audiovisual translation. We analyse user experience data collected from post-editing tasks completed by twelve translators in four language pairs. We also present feedback provided by the translators in semi-structured interviews. The results of the user experience survey and thematic analysis of interviews shows that the translators’ impression of post-editing subtitles was on average neutral to somewhat negative, with segmentation and timing of subtitles identiﬁed as a key factor. Finally, we discuss the implications of the issues arising from the user experience survey and interviews for the future development of automatic subtitle translation.
Real-Time Look-Ahead Distance Optimization for Smooth and Robust Steering Control of Autonomous Vehicles *

This paper presents an optimization problem for the look-ahead distance used in lateral automated vehicle control. A relation is formulated to describe the sensitivity of the control input steering speed $\dot \delta $ with respect to the variation of the look-ahead distance L. The nonlinear relation is written as an LPV model using the grid-based approach. The aim is to design a controller which minimizes the steering speed $\dot \delta $ by optimizing an additional look-ahead distance Ld that is added to the nominal look-ahead distance. Such action generates a smooth/stable vehicle motion when subjected to high oscillations due to noises, large lateral errors, etc. The proposed solution is based on the Linear Parameter Varying (LPV) control approach, where an output-feedback dynamic controller is designed based on Linear Matrix Inequalities (LMIs). The control synthesis is carried out using the grid-based approach combined with the ${\mathcal{H}_\infty }$ control problem. Simulation results show the tracking performance and the smoothness of the steering input, when the vehicle is subjected to successive large lateral errors, which provides a comfortable riding.
Signatures of a spinning supermassive black hole binary on the mas-scale jet of the quasar S5 1928+738 based on 25 years of VLBI data

In a previous work, we have identified the spin of the dominant black hole of a binary from its jet properties. Analysing Very Long Baseline Array (VLBA) observations of the quasar S5 1928+738, taken at 15-GHz during 43 epochs between 1995.96 and 2013.06, we showed that the inclination angle variation of the inner (<2 mas) jet symmetry axis naturally decomposes into a periodic and a monotonic contribution. The former emerges due to the Keplerian orbital evolution, while the latter is interpreted as the signature of the spin-orbit precession of the jet emitting black hole. In this paper, we revisit the analysis of the quasar S5 1928+738 by including new 15-GHz VLBA observations extending over 29 additional epochs, between 2013.34 and 2020.89. The extended data set confirms our previous findings which are further supported by the flux density variation of the jet. By applying an enhanced jet precession model that can handle arbitrary spin orientations κ with respect to the orbital angular momentum of a binary supermassive black hole system, we estimate the binary mass ratio as ν = 0.21 ± 0.04 for κ = 0 (i.e. when the spin direction is perpendicular to the orbital plane) and as ν = 0.32 ± 0.07 for κ = π/2 (i.e. when the spin lies in the orbital plane). We estimate more precisely the spin precession velocity, halving its uncertainty from ( − 0.05 ± 0.02)○ yr−1 to ( − 0.04 ± 0.01)○ yr−1.
An Interaction Strategy for Safe Human Co-Working with Industrial Collaborative Robots

Within the framework of Industry 4.0, robotics is experiencing a rapprochement between humans and robots. To achieve this, the barriers that have historically separated humans from industrial robots are being eliminated with the main objective of increasing performance by taking advantage of the capabilities in which each one excels. The success of collaborative robotics is possible once safety is ensured, adding the need and importance of a proper human-robot communication interface. In this context, there are many safety techniques in current collaborative robots, but these are not sufficient to guarantee high industrial production and quality products. Non-verbal communication using the hands is one of the most widely used techniques in the industrial environment for the exchange of human-human information. Thus, this work presents a safety solution based on the creation of a new layer that allows the avoidance of human-robot collisions and includes a robust and safe human-robot communication interface. Advanced automatic learning and computer vision techniques have been used in its development, which was experimental analyses and validated in a co-work scenario involving an industrial collaborative robot and an operator.
ManiHD: Efficient Hyper-Dimensional Learning Using Manifold Trainable Encoder

Hyper-Dimensional (HD) computing emulates the human short memory functionality by computing with hyper-vectors as an alternative to computing with numbers. The main goal of HD computing is to map data points into sparse high-dimensional space where the learning task can perform in a linear and hardware-friendly way. The existing HD computing algorithms are using static and non-trainable encoder; thus, they require very high-dimensionality to provide acceptable accuracy. However, this high dimensionality results in high computational cost, especially over the realistic learning problems. In this paper, we proposed ManiHD that supports adaptive and trainable encoder for efficient learning in high-dimensional space. ManiHD explicitly considers non-linear interactions between the features during the encoding. This enables ManiHD to provide maximum learning accuracy using much lower dimensionality. ManiHD not only enhances the learning accuracy but also significantly improves the learning efficiency during both training and inference phases. ManiHD also enables online learning by sampling data points and capturing the essential features in an unsupervised manner. We also propose a quantization method that trades accuracy and efficiency for optimal configuration. Our evaluation of a wide range of classification tasks shows that ManiHD provides 4.8% higher accuracy than the state-of-the-art HD algorithms. In addition, ManiHD provides, on average, 12.3× (3.2×) faster and 19.3× (6.3×) more energy-efficient training (inference) as compared to the state-of-the-art learning algorithms.