Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Approach for Securing IoT Data Transmitted over Multi-RATs

In an mHealth remote patient monitoring scenario, usually control units/data aggregators receive data from the body area network (BAN) sensors then send it to the network or “cloud”. The control unit would have to transmit the measurement data to the home access point (AP) using WiFi for example, or directly to a cellular base station (BS), e.g., using the long-term evolution (LTE) technology, or both (e.g., using multi- homing to transmit over multiple radio access technologies (Multi-RATs). Fast encryption or physical layer security techniques are needed to secure the data. In fact, during normal conditions, monitoring data can be transmitted using best effort transmission. However, when real-time processing detects an emergency situation, the current monitoring data should be transmitted real-time to the appropriate medical personnel in emergency response teams. In this paper, a fast and secure approach for transmitting monitoring data over multi-RATs is proposed. The presented approach consists of benefiting of the presence of multi-RATs in order to exchange the secrecy information more efficiently while optimizing the transmission time.
Design and Development of an Assistive Hand Device for Enhancing Compatibility and Comfortability

Currently, there are plenty of hand exoskeletons for rehabilitation and assistive purpose that have been developed. They have been designed for working with patient's limbs and can be used for hand positioning and executing task which help reduce therapist's workload and give them an alternative way to rehabilitate patients. However, there are some patients with permanent impairments, which cannot improve from rehabilitation. Thus, portable assistive devices could greatly help them performing simple activities of daily living (ADL), but such device has not been fully explored, the present design of tendon routing cannot closely replicate finger orientation and motion during grasping. Improving the flexion tendon would increase the possibility of grasping for patients with hand disability. This research aims to develop a tendon routing design of a soft exoskeleton to increase compatibility with human hand, while it can execute tasks and exercises more naturally compared to normal limbs to help stroke patients to live independently. The results convinced that the proposed flexion routing can closely replicate human grasping motion with sufficient fingertip force to perform daily activities.
Unsupervised Feature Learning of Gearbox Fault Using Stacked Wavelet Auto-encoder

Unsupervised feature learning and automatic fault diagnosis of gearbox is still a major challenge. For this purpose, a novel method called stacked wavelet auto-encoder (SWAE) is proposed in this paper. Firstly, wavelet function is adopted as the nonlinear activation function to design wavelet auto-encoder, which can enhance the feature learning ability from the raw vibration signal of gearbox. Secondly, SWAE is constructed with a series of pre-trained wavelet auto-encoders for learning the deep features. Finally, the learned deep features are fed into the softmax classifier for automatic and accurate fault diagnosis. The proposed method is applied to analyze the experimental gearbox signals, and the results show that the proposed method is more effective and robust than the traditional methods.
Protein evolution revisited

ABSTRACT Antifreeze proteins (AFPs) protect marine fishes from freezing in icy seawater. They evolved relatively recently, most likely in response to the formation of sea ice and Cenozoic glaciations that occurred less than 50 million years ago, following a greenhouse Earth event. Based on their diversity, AFPs have independently evolved on many occasions to serve the same function, with some remarkable examples of convergent evolution at the structural level, and even instances of lateral gene transfer. For some AFPs, the progenitor gene is recognizable. The intense selection pressure exerted by icy seawater, which can rapidly kill unprotected fish, has led to massive AFP gene amplification, as well as some partial gene duplications that have increased the size and activity of the antifreeze. The many protein evolutionary processes described in Gordon H. Dixon’s Essays in Biochemistry article will be illustrated here by examples from studies on AFPs. Abbreviations: AFGP: antifreeze glycoproteins; AFP: antifreeze proteins; GHD: Gordon H. Dixon; SAS: sialic acid synthase; TH: thermal hysteresis
A MM Wave Cloud Cooperated and Mobility Dependant Scheme for 5G Cellular Networks

The unavoidable and dramatic increase of mobile traffic load predicted to hit future cellular networks, has operated as a catalyst for the 5th generation (5G) mobile networks to envision the support of higher data rates by a factor of 1,000 in the next 10 years. The utilization of the ultrawideband aspect of the mmWave bands has recently risen as a quite promising candidate that could support such an overwhelming demand. Armed with the exploitation of such high frequencies, several studies have proposed a logical split between the control plane (C-plane) operated by macro basestations (BSs) at the 2GHz band and the user plane (U-plane) operated by pico base stations at much higher frequencies (e.g. 3GHz or 60GHz bands). Thus, a heterogeneous cellular network (C-HetNet) is built, where macro and pico BSs could potentially function in a cooperative manner by connecting to a cloud radio access network (C-RAN). Despite the fact that such architecture provides a more efficient approach for handling signalling and user traffic, the use of mmWave bands introduces some major challenges. An appropriate user association scheme is still needed in order to successfully associate a specific user with a particular pico BS before user data transmission is initiated. It is clear that the process followed for user associations and re-associations introduces considerable latency; therefore high user equipment (UE) mobility may negatively affect user experience by demanding very frequent initiations of that process. In this paper, the author proposes a fair, user traffic off-loading mechanism, where highly mobile UEs, after a given grace period, are forced to shift the transmission of user data from the U-plane to the C-plane until the point where they become more stationary. Ultimately, this approach results in a lower amount of user re-associations needed as a trade-off to mobility and in the expense of lower data rates.
Ambience and Food as Stimuli to Diner Loyalty: the Mediating Effect of Dining Experience - (Mr. Model)

Restaurant industry is a highly lucrative industry through-out the world. Food is the basic necessity of human population and hence restaurants are not likely to fade in the future. It is also a proven fact that dining industries contribute tremendously to the growth and development of any economy. The mind-set of customers towards fine dining has undergone sea change over the past decades. Restaurants are no more a place to just buy food, but are now-a-days considered to be a place of outing where customers spend quality time with their loved ones. Due to acute competition restaurateurs face tough challenges in sustaining profitability and growth. The best strategy to sustain is to explore the opportunities arising from the express and implied needs of the diners. Hence the need to measure satisfaction and loyalty arises. Positive attitude towards a restaurant is a result of the positive experience perceived by the diner and this motivates them to be loyal towards the restaurant brand. Mehrabian and Russell have suggested a model that explains the impact of stimuli on individual response through organism effects. In this paper, the researcher has employed the MR Model to study the impact of ambience and food on diner loyalty with the mediating effect of dining experience. Therefore ambience and food play the role of stimuli, dining experience acts as the organism effect and diner loyalty is the individual response. The paper aims at giving practical tips to restaurateurs, helping them to capture new customers and create loyal diners.
Global Inequities in Precision Medicine and Molecular Cancer Research

Precision medicine based upon molecular testing is heralded as a revolution in how cancer is prevented, diagnosed, and treated. Large efforts across the world aim to conduct comprehensive molecular profiling of disease to inform preclinical models, translational research studies and clinical trials. However, most studies have only been performed in patients from high-income countries. As the burden on non-communicable diseases increases, cancer will become a pressing burden across the world, disproportionately affecting low-middle income settings. There is emerging evidence that the molecular landscape of disease differs geographically and by genetic ancestry, which cannot be explained by environmental factors alone. There is a lack of good quality evidence that characterises the molecular landscape of cancers found in low-middle income countries. As cancer medicine becomes increasingly driven by molecular alterations in high-income settings, low-income settings may become left behind. Further efforts on an international scale must be made by researchers, funders, and policymakers to ensure cancer research addresses disease across the world, so models are not limited to subtypes of disease found in high-income countries. In this review, we discuss differences found in the molecular profiles of tumours worldwide and the implication this has for the future of global cancer care. Finally, we identify several barriers currently limiting progress in this field and innovative solutions, which may address these shortcomings.
Exploring Hybrid Memory Caches in Chip Multiprocessors

Studies have shown memory needs vary significantly across applications. Recent work has explored using hybrid memory technology (SRAM+NVM) in on-chip memories of multicore processors (CMPs) to support the varied needs of diverse workloads. Such works suggest architectural modifications that require supplemental management in the memory hierarchy. Instead, we propose to deploy hybrid memory in a manner that integrates seamlessly with the existing heterogeneous multicore (HMP) architectural model, and therefore does not require any supplemental management, simply the integration of different memory technologies on-chip. We evaluate platforms with a combination of/ast (SRAM cache) and slow (STT-MRAM cache) core-types for mobile workloads.
Anthropomorphic musculoskeletal 10 degrees-of-freedom robot arm driven by pneumatic artificial muscles

ABSTRACT This paper describes the construction and design decisions of an anthropomorphic musculoskeletal robot arm actuated by pneumatic artificial muscles. This robot was designed to allow human-inspired compliant movements without the need to replicate the human body-structure in detail. This resulted in an mechanically simple design while preserving the motoric characteristics of a human. Besides the constructional details of the robot we will present two experiments to show the robots abilities regarding to its dexterity and compliance. GRAPHICAL ABSTRACT
Hardware based Two-Factor User Authentication for the Internet of Things

In the distributed Internet of Things (IoT) architecture, sensors collect data from vehicles, home appliances and office equipment and other environments. Various objects contain the sensor which process data, cooperate and exchange information with other embedded devices and end users in a distributed network. It is important to provide end-to-end communication security and an authentication system to guarantee the security and reliability of the data in such a distributed system. Two-factor authentication is a solution to improve the security level of password-based authentication processes and immunized the system against many attacks. At the same time, the computational and storage overhead of an authentication method also needs to be considered in IoT scenarios. For this reason, many cryptographic schemes are designed especially for the IoT; however, we observe a lack of laboratory hardware test beds and modules, and universal authentication hardware modules. This paper proposes a design and analysis for a hardware module in the IoT which allows the use of two-factor authentication based on smart cards, while taking into consideration the limited processing power and energy reserves of nodes, as well as designing the system with scalability in mind.
Creation of Materialized Views Based on Neural Network Algorithm

Materialized view (MV) is one of many technical optimizations like index and fragmentation. This technique ensures a similar answer for a workload. In other word, it is used to prohibit the repetition of the same computes and reduce the execution time. In this paper, we will present an algorithm based on neural networks and business intelligent to avoid repeating the creation of MV process. This algorithm has two phases: The first phase is the learning of the logic between the arrived queries and the materialized views as the final solution. However, the second phase allows the prediction of a new solution associated to the new workload. In order to calculate the similarity between the vectors (query-attributes), we use the Jaccard Index. Finally, an experimental work is done to validate our approach.
Efficient Uplink from Vehicular Micro Cloud Solutions to Data Centers

One of the most recent applications in the vehicular networking domain is distributed data processing using cars as sensors of information. In recent work, the concept of vehicular cloud computing has been explored to provide the necessary scalability and to improve the communication between clusters of cars being called vehicular micro clouds and other participants (cars, bicyclists, pedestrians). In order to provide a bigger picture and also to interconnect such micro clouds, data centers or cloud servers are considered bridging the gap. We study the uplink capabilities from connected cars to such data centers. Options include direct LTE uplinks from all cars, selected use of Roadside Units (RSUs) with back-end connectivity or LTE uplinks from the vehicular micro clouds, and finally hybrid solutions taking network quality and available channel resources into account. Our findings clearly show the advantages of such hybrid solutions both in terms of throughput as well as of optimizing operational costs.
Poor coherence in older people's speech is explained by impaired semantic and executive processes

The ability to speak coherently is essential for effective communication but declines with age: older people more frequently produce tangential, off-topic speech. The cognitive factors underpinning this decline are poorly understood. We predicted that maintaining coherence relies on effective regulation of activated semantic knowledge about the world, and particularly on the selection of currently relevant semantic representations to drive speech production. To test this, we collected 840 speech samples along with measures of executive and semantic ability from 60 young and older adults, using a novel computational method to quantify coherence. Semantic selection ability predicted coherence, as did level of semantic knowledge and a measure of domain-general executive ability. These factors fully accounted for the age-related coherence deficit. Our results indicate that maintaining coherence in speech becomes more challenging as people age because they accumulate more knowledge but are less able to effectively regulate how it is activated and used.
Energy Efficient Hybrid Precoding for Cooperative Multicell Multiuser Massive MIMO Systems with Multiple Base Station Association

Massive multiple-input multiple-output (massive MIMO) and the millimeter wave (mmWave) communication are known to be among the key technologies for the fifth generation (5G) mobile networks. The implementation of massive MIMO with the mmWave architecture requires the utilization of hybrid precoding technique with a low dimensional digital precoder and a high dimensional analog precoder. In this paper, we investigate the hybrid precoding design problem for the case of cooperative multicell multiuser massive MIMO system. This problem is formulated with the consideration of the user and base station (BS) association problem, and we propose an iterative algorithm to solve the formulated problem. The precoding problem is solved using the Eigen precoding algorithm whereas the Lagrangian based approach is proposed for the solution of the association problem. Simulation results show that our proposed solution can achieve higher energy efficiency and convergence rate.
Audio Feedback Associated With Body Movement Enhances Audio and Somatosensory Spatial Representation

In the last years, the positive impact of sensorimotor rehabilitation training on spatial abilities has been taken into account, e.g., providing evidence that combined multimodal compared to unimodal feedback improves responsiveness to spatial stimuli. To date, it still remains unclear to which extent spatial learning is influenced by training conditions. Here we investigated the effects of active and passive audio-motor training on spatial perception in the auditory and proprioceptive domains on 36 healthy young adults. First, to investigate the role of voluntary movements on spatial perception, we compared the effects of active vs. passive multimodal training on auditory and proprioceptive spatial localization. Second, to investigate the effectiveness of unimodal training conditions on spatial perception, we compared the impact of only proprioceptive or only auditory sensory feedback on spatial localization. Finally, to understand whether the positive effects of multimodal and unimodal trainings generalize to the untrained part, both dominant and non-dominant arms were tested. Results indicate that passive multimodal training (guided movement) is more beneficial than active multimodal training (active exploration) and only in passive condition the improvement is generalized also on the untrained hand. Moreover, we found that combined audio-motor training provides the strongest benefit because it significantly affects both auditory and somatosensory localization, while the effect of a single feedback modality is limited to a single domain, indicating a cross-modal influence of the two domains. Therefore, the use of multimodal feedback is more efficient in improving spatial perception. These results indicate that combined sensorimotor signals are effective in recalibrating auditory and proprioceptive spatial perception and that the beneficial effect is mainly due to the combination of auditory and proprioceptive spatial cues.
Effect of Annotation on Multiple-Player-Tracking Algorithms

For most people from all ages and genders, participation in sports becomes part of their life, especially participation in soccer matches, which are considered a symbol of healthy living and active attitudes of families. Analyzing soccer matches, similar to analyzing other sports, is a challenging job for the coaches and trainers, as well as for the audience, due to the fast motion of players in some situations during the match and occlusions. That is why computer vision techniques are used to tackle these problems. In this paper, we test an efficient, simple and available annotating tool to label and generate the ground truth data of the interested targets (players or the ball) on a soccer field, which helps to assess the performance of the tracking techniques and achieve their goals. This method is tested on two different sequences of soccer datasets. The annotation results are tested by using four tracking algorithms based on context-aware correlation filters. The tracking results on both sequences that were annotated by the experts and annotated by this method were very similar, which shows that this robust method outperforms the state-of-the-art annotating techniques.
Sequence to Sequence Pattern Learning Algorithm for Real-Time Anomaly Detection in Network Traffic

Network intrusions can be modeled as anomalies in network traffic in which the expected order of packets and their attributes deviate from regular traffic. Algorithms that predict the next sequence of events based on previous sequences are a promising avenue for detecting such anomalies. In this paper, we present a novel multi-attribute model for predicting a network packet sequence based on previous packets using a sequence-to-sequence (Seq2Seq) encoder-decoder model. This model is trained on an attack-free dataset to learn the normal sequence of packets in TCP connections and then it is used to detect anomalous packets in TCP traffic. We show that in DARPA 1999 dataset, the proposed multi-attribute Seq2Seq model detects anomalous raw TCP packets which are part of intrusions with 97 % accuracy. Also, it can detect selected intrusions in real-time with 100% accuracy and outperforms existing algorithms based on recurrent neural network models such as LSTM.
Smart Choices for Deviceless and Device-Based Manipulation in Immersive Virtual Reality

The choice of a suitable method for object manipulation is one of the most critical aspects of virtual environment design. It has been shown that different environments or applications might benefit from direct manipulation approaches, while others might be more usable with indirect ones, exploiting, for example, three dimensional virtual widgets. When it comes to mid-air interactions, the success of a manipulation technique is not only defined by the kind of application but also by the hardware setup, especially when specific restrictions exist. In this paper we present an experimental evaluation of different techniques and hardware for mid-air object manipulation in immersive virtual environments (IVE). We compared task performances using both deviceless and device-based tracking solutions, combined with direct and widget-based approaches. We also tested, in the case of freehand manipulation, the effects of different visual feedback, comparing the use of a realistic virtual hand rendering with a simple cursor-like visualization.
Effects of Hand Training During the Aftereffect Period of Low-Frequency rTMS in Subacute Stroke Patients

Objective To investigate the effects of hand training using low-frequency repetitive transcranial magnetic stimulation (rTMS) within the aftereffect period on hand function in patients with subacute stroke. Methods The subacute stroke patients with hand weaknesses were divided randomly into two groups. Patients in the intervention group underwent hand training within the aftereffect period, that is, immediately after receiving low-frequency rTMS treatment. Patients in the control group underwent hand training 2 hours after the low-frequency rTMS treatment. A manual function test (MFT) for ‘grasp and pinch’ and ‘hand activities’; a manual muscle test (MMT) for ‘grasp’, ‘release’, and ‘abductor pollicis brevis (APB)’; and the Modified Ashworth Scale for finger flexion were performed and measured before and immediately after combined therapy as well as 2 weeks after combined therapy. Results Thirty-two patients with hand weakness were enrolled in this study. The intervention group patients showed more improvements in grasp MMT and MMT APB tested immediately after combined therapy. However, the changes in all measurements were not significantly different between the two groups 2 weeks after the combined therapy. In both groups, hand functions improved significantly immediately after combined therapy and 2 weeks after combined therapy. Conclusion Hand training immediately after low-frequency rTMS showed more rapid improvement in the motor power of hands than hand training conducted 2 hours after low-frequency rTMS. Our results suggest that conducting hand training immediately after low-frequency rTMS could be an improved useful therapeutic option in subacute stroke patients.