Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Heuristics for Validating Pairwise Transactions on Cryptocurrencies

Cryptocurrencies especially bitcoin has received a lot of attention in the past year. The popularity has increased the volume of transactions in an unprecedented way. The time to complete a simple pairwise transaction from one virtual wallet to other takes time in proof-of-work where transactions are added into the Blockchain. At the same time, validating a pairwise transaction is very significant in this new technology as a payment option. Through this study, we propose a heuristic approach for validating pairwise transactions on cryptocurrencies. Our heuristic simulate all entities sending and receiving transactions among themselves. We use SHA256 algorithm to enhance our solution for pairwise transactions, creating a local Blockchain of transactions that has been used in the development of various Blockchain systems. For our experiments, we ran simulations in-file and in-memory with 2 million transactions in 290.39 and 5.34 seconds respectively. Our peak transactions per second was 6.887 using a file persistence version and 374.255 with in-memory version. Based on our experiments we conclude that is possible to improve the number of transactions processed per second increasing the size of the block as well as avoiding access to the file during the simulation. We also present some of our results implementing our algorithm in a parallel environment and hence show better performance.
An Ontology-Based Domain Modeling and Device Search in Smart Home

Smart home technology has gained more and more attention in recent years as a part of internet of things, by making living environments smarter. The technology consists of deploying different sensors and actuators and making devices intelligent inside homes. Unfortunately, the heterogeneity of devices makes sharing and reusing the collected data difficult, while the complex relation among the user, device, location, and observation within the smart home has emerged new challenges in device search. Firstly, we proposed an ontology-based smart home domain model (SHOM), secondly, we proposed a device search framework in the smart home (DSF-SH), in which rule-based reasoning, ontology-based query expansion, and Tongyici Cilin-based query expansion are combined in order to overcome the limitation of keyword-based search technique, interpret the meaning of user's queries and help upper applications find available devices by working as a middleware. Finally, a prototype is developed to demonstrate the feasibility of the DSF-SHe The results show that it significantly increases the recall, the precision, and the F-score as compare to the keyword-based search technique.
Hypergraph Matching via a Nonmonotone Newton Method

One of the basic problems in the computer vision field is to recognize the correspondence between vertices in two graphs, we propose a hypergraph matching model with a Shannon barrier function considering about the discretion property of the assignment matrix. The model is solved by a nonmonotone active set projected Newton method whose nonmonotone line search technique has a good ability to escape from the local minimizers. We evaluate our method through several experiments, which indicates that our method usually has the better matching results than others.
A New Deep Transfer Learning Model for Judicial Data Classification

For judicial analysis, usually there are enough labeled instances for one domain, but there are few or even no labeled instances in target domain. Therefore, to bridge the gap between these two domains and use the sufficient source information for target analysis is important. In this paper, we focus on developing a new deep transfer learning model to translate the source domain information for target data classification. The proposed model integrates the deep neural network (DNN) with canonical correlation analysis (CCA) to derive a deep correlation subspace for associating data across different domains. Moreover, a new objective is designed to train the whole network jointly. When the deep semantic representation is achieved, the shared features of the source domain are transferred for instance classification in the target domain. Experiments on several datasets present that the proposed method is superior to the state-of-the-art methods for deep transfer learning, which is promising for judicial data classification.
Multi-Scale Feature Learning Based on RICA for Single Training Sample Face Recognition

In many face recognition scenarios, there are three main challenges for face recognition include single training sample, partial occlusion and uneven illumination. While conventional face descriptors performed unsatisfied and deep learning networks are always time-consuming and resource-consuming. To address these challenges, a novel multi-scale feature is proposed to encoding method to extract multi-scale feature representation for face recognition in complex scenarios with single training sample. Firstly, we divide each face image into multi-scale regions and take dense samples with multi-scale patches in each region. Secondly, we introduce to learn multi-scale encoding matrixes for different face regions based on RICA. Finally, the face features will be extracted for comparing. Experimental results prove that our approach perform stably to expression, partial occlusion and uneven illumination conditions compared with the existing unsupervised methods.
Robot Navigation in Radio Beam Space: Leveraging Robotic Intelligence for Seamless mmWave Network Coverage

The emerging millimeter-wave (mmWave) networking technology promises to unleash a new wave of multi-Gbps wireless applications. However, due to high directionality of the mmWave radios, maintaining stable link connection remains an open problem. Users' slight orientation change, coupled with motion and blockage, can easily disconnect the link. In this paper, we propose miDroid, a robotic mmWave relay that optimizes network coverage through wireless sensing and autonomous motion/rotation planning. The robot relay automatically constructs the geometry/reflectivity of the environment, by estimating the geometries of all signal paths. It then navigates itself along an optimal moving trajectory, and ensures continuous connectivity for the client despite environment/human dynamics. We have prototyped miDroid on a programmable robot carrying a commodity 60 GHz radio. Our field trials demonstrate that miDroid can achieve nearly full coverage in dynamic environment, even with constrained speed and mobility region.
What Do Older Adults and Clinicians Think About Traditional Mobility Aids and Exoskeleton Technology?

Mobility impairments can prevent older adults from performing their daily activities, which highly impacts quality of life. Powered exoskeletons, which are wearable robotic devices, can assist older adults by providing additional support to compensate for age-related decline in muscle strength. To date, little is known about the opinions and needs of older adults regarding exoskeletons, as current research primarily focuses on the technical development of exoskeleton devices and on groups with more severe mobility impairments such as people with spinal cord injuries. Therefore, the aim of this article is to inform the design of exoskeletons for older adults from a person-centered perspective. Interviews were conducted with seven older adults and six clinicians. Results indicated that exoskeletons can be a valuable addition to existing mobility devices to support older adults during walking and other actions necessary to perform their daily activities. Although reactions towards the concept of exoskeleton technology were positive, older adults did not currently perceive the need for an exoskeleton device. Exoskeletons for older adults should be easy to use; preferably, users should be able to put the device on and take it off independently. Moreover, the appearance of the exoskeleton should be as inconspicuous as possible, as most older adults do not like to advertise their need for assistive devices. At this point in time, the willingness to use exoskeleton technology will depend on personal needs and preferences.
ERSCC: Enable Efficient and Reliable Screen-Camera Communication

Each camera on digital devices is made of hundreds of thousands of sensors, with which it can separate and capture the light from spatial points in a fine-grain manner, enabling high-rate data transfer from the digital display to the camera, i.e. screen-camera communication. Compared with RF (Radio Frequency) technologies, visible light approaches are more convenient and secure. In this work, we design a mobile-to-mobile screen-camera communication system to handle the rolling shutter issue and increase the reliability. First, a time-domain self-restoration coding scheme is proposed to identify mixed frames for doubling the coding efficiency. Second, we design a feedback channel for reliability. It is further integrated with an adaptive scheme to achieve robustness in challenging conditions. We implement the system prototype on the Android platform and conduct extensive experiments. The results show that our system increases the throughput by an order-of-magnitude (16x) compared with existing rolling-shutter solutions and two times (1.8x) over the color-barcode system.
4-aminopyridinium hydrogen maleate.

The title compound, C(5)H(7)N(2)(+). C(4)H(3)O(4)(-), crystallizes in space group P2(1) with one ion pair in the asymmetric unit. The hydrogen maleate anion possesses nearly planar geometry and displays an extremely short intramolecular O--H.O hydrogen bond, with an O.O distance of 2.4198 (19) A. Classical N-H.O hydrogen bonds, together with short C--H.O contacts, generate an extensive hydrogen-bonding network.
An Overview of Potential Authentication Threats and Attacks on Internet of Things(IoT): A Focus on Smart Home Applications

Internet of things (IoT) are finding their wide use in various domains. IoT implementation in the Smart home domain is one that is complex as the devices that are being used in such platforms are of different sizes and have different computational capacity. The ability to ensure security is enforced on such devices rests on how proper the authentication processes are executed. It is against this background that this paper is formulated, to give a detailed review of the potential authentication threats and attacks on IoT in the Smart home domain in particular. The main ideas on the potential authentication threats and attacks on IoT in Smart home applications, presented in this paper are largely informed by the detailed literature review of related work in the domain of IoT.
Mean Field Theory of Activation Functions in Deep Neural Networks

We present a Statistical Mechanics (SM) model of deep neural networks, connecting the energy-based and the feed forward networks (FFN) approach. We infer that FFN can be understood as performing three basic steps: encoding, representation validation and propagation. From the meanfield solution of the model, we obtain a set of natural activations -- such as Sigmoid, $\tanh$ and ReLu -- together with the state-of-the-art, Swish; this represents the expected information propagating through the network and tends to ReLu in the limit of zero noise.We study the spectrum of the Hessian on an associated classification task, showing that Swish allows for more consistent performances over a wider range of network architectures.
Eating disorders: lessons from a cross-cultural study

SYNOPSIS Five hundred and eighty schoolgirls living in a small North Indian industrial town were screened using the EAT-26. Although twenty-nine per cent scored above the threshold for the questionnaire (≥ 20), closer scrutiny of the pattern of responses revealed misinterpretations of a large number of questions possibly on a conceptual basis. Linguistic and conceptual pitfalls inherent to cross-cultural epidemiological research are discussed with reference to previous work.
Non-Contact Method of Heart Rate Using a Webcam

Aiming at the defects of traditional heart rate measurement, a blood volume pulse (BVP) signal and estimate heart rate estimate is proposed based on tree component analysis (TCA) and complete total empirical mode decomposition (CEEMD). The region of interest is selected from facial video, which is taken by non-contact camera, and then is separated by TCA to obtain noisy BVP. BVP signal is decomposed into different scale signal which is computed by spectrum analysis to get heart rate. The experiment indicates that the method is consistent with the pulse oximeter, and the 95 % confidence interval of this measurement system is[-1.5,1.5]. Benefiting from property of simplicity and non-contact, this method can be applied to live detection. Moreover, the method can avoid the selection of relevant parameters and reduce the workload of the operators.
Effective floating-point analysis via weak-distance minimization

This work studies the connection between the problem of analyzing floating-point code and that of function minimization. It formalizes this connection as a reduction theory, where the semantics of a floating-point program is measured as a generalized metric, called weak distance, which faithfully captures any given analysis objective. It is theoretically guaranteed that minimizing the weak distance (e.g., via mathematical optimization) solves the underlying problem. This reduction theory provides a general framework for analyzing numerical code. Two important separate analyses from the literature, branch-coverage-based testing and quantifier-free floating-point satisfiability, are its instances. To further demonstrate our reduction theory’s generality and power, we develop three additional applications, including boundary value analysis, path reachability and overflow detection. Critically, these analyses do not rely on the modeling or abstraction of floating-point semantics; rather, they explore a program’s input space guided by runtime computation and minimization of the weak distance. This design, combined with the aforementioned theoretical guarantee, enables the application of the reduction theory to real-world floating-point code. In our experiments, our boundary value analysis is able to find all reachable boundary conditions of the GNU sin function, which is complex with several hundred lines of code, and our floating-point overflow detection detects a range of overflows and inconsistencies in the widely-used numerical library GSL, including two latent bugs that developers have already confirmed.
Android Evaluation Methodology in Automotive Electronics

Automotive electronics have been developing fast since it came up. It is very common to apply Android system to automotive electronics because its functionality is becoming more and more complicated. However, the open-source nature of Android can cause many safety issues, especially in this area which has very high requirements for system safety and stability. This paper presents a novel methodology which can help to evaluate the performance, stability and safety of Android system used in automotive electronics. It also provides a demonstration show how to use this methodology and how to optimize the Android system based on the evaluation results.
A Benchmark of Visual Storytelling in Social Media

Media editors in the newsroom are constantly pressed to provide a"like-being there" coverage of live events. Social media provides a disorganised collection of images and videos that media professionals need to grasp before publishing their latest news updated. Automated news visual storyline editing with social media content can be very challenging, as it not only entails the task of finding the right content but also making sure that news content evolves coherently over time. To tackle these issues, this paper proposes a benchmark for assessing social media visual storylines. The SocialStories benchmark, comprised by total of 40 curated stories covering sports and cultural events, provides the experimental setup and introduces novel quantitative metrics to perform a rigorous evaluation of visual storytelling with social media data.
Impact of academic exposure on health status of university students.

OBJECTIVE To assess the impact of academic life on health status of university students. METHODS Longitudinal study including 154 undergraduate students from the Universidade de Aveiro, Portugal, with at least two years of follow-up observations. Sociodemographic and behavioral characteristics were collected using questionnaires. Students' weight, height, blood pressure, serum glucose, serum lipids and serum homocysteine levels were measured. Regression analysis was performed using linear mixed-effect models, allowing for random effects at the participant level. RESULTS A higher rate of dyslipidemia (44.0% vs. 28.6%), overweight (16.3% vs. 12.5%) and smoking (19.3% vs. 0.0%) was found among students exposed to the academic life when compared to freshmen. Physical inactivity was about 80%. Total cholesterol, high density lipoprotein-cholesterol (HDL-C), triglycerides, systolic blood pressure, and physical activity levels were significantly associated with gender (p<0.001). Academic exposure was associated with increased low density lipoprotein-cholesterol (LDL-C) levels (about 1.12 times), and marginally with total cholesterol levels (p = 0.041). CONCLUSIONS High education level does not seem to have a protective effect favoring a healthier lifestyle and being enrolled in health-related areas does not seem either to positively affect students' behaviors. Increased risk factors for non-transmissible diseases in university students raise concerns about their well-being. These results should support the implementation of health promotion and prevention programs at universities.
Using forum in an organizational learning context

Information and Communication Technologies have a growing impact on long life learning. Organizational Learning is an increasingly important area of research that concerns the way organizations learn and thus augment their competitive advantage, innovativeness, and effectiveness. Within the project MEMORAe2.0, we are interested by the capitalization of knowledge in the context of an organization, but also by the externalization and capitalization of tacit knowledge that's why we use a forum in an organizational learning context. We developed the E-MEMORAe2.0 environment which is based on the concept of learning organizational memory.
Obstructed Target Tracking in Urban Environments

Accurate tracking in urban environments necessitates target birth, survival, and detection models that quantify the impact of terrain and building geometry on the sequential estimation procedure. Current efforts assume that target trajectories are limited to fixed paths, such as road networks. In these settings a single airborne platform with a downward-facing camera is capable of fully observing a target, outside of a few obstructed regions that can be determined a priori (e.g. tunnels). However, many practical target types are not necessarily restricted to road networks and thus require knowledge of azimuthal shadowed regions to the sensor. In this paper, we propose the integration of geospatial data for an urban environment into a particle filter realization of a random finite set target tracking algorithm. Specifically, we use 3D building polygons to compute the azimuthal shadowed regions with respect to deployed sensor location. The particle filter predict and update steps are modified such that (1) target births are assumed to occur in line-of-sight (LOS) regions, (2) targets do not move into obstructions, (3) true target detections only occur in LOS regions. The localization error performance improvement for a single target Bernoulli filter under these modifications is presented using freely available building vector data of New York City.
Automated Machine Learning: State-of-The-Art and Open Challenges

With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models. In the last few years, several techniques and frameworks have been introduced to tackle the challenge of automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH) in the machine learning domain. The main aim of these techniques is to reduce the role of the human in the loop and fill the gap for non-expert machine learning users by playing the role of the domain expert. In this paper, we present a comprehensive survey for the state-of-the-art efforts in tackling the CASH problem. In addition, we highlight the research work of automating the other steps of the full complex machine learning pipeline (AutoML) from data understanding till model deployment. Furthermore, we provide comprehensive coverage for the various tools and frameworks that have been introduced in this domain. Finally, we discuss some of the research directions and open challenges that need to be addressed in order to achieve the vision and goals of the AutoML process.
Nikolaus Wenger refined locomotion after complete spinal cord injury Closed-loop neuromodulation of spinal sensorimotor circuits controls

, 255ra133 (2014); 6 Sci Transl Med et al. Nikolaus Wenger refined locomotion after complete spinal cord injury Closed-loop neuromodulation of spinal sensorimotor circuits controls Editor's Summary fluid motor control and prevent fatigue when rehabilitating patients with SCI. of leg movement and, in turn, a more natural locomotion. Closed-loop neuromodulation of spinal circuits could impart control devised a computational system that integrated feedback and feed-forward models for closed-loop, continuous while in a body support system, walking upright (bipedal) on a treadmill, or climbing stairs. From this information, they rats complete SCI epidural electrical stimulation and then mapped their leg movements and sensorimotor responses to move freely, without worrying about adjusting electrical pulse width, amplitude, or frequency. The authors gave −− or, in their study, the paralyzed rat −− system that will essentially ''auto-tune'' the device, allowing the paralyzed patient have created a closed-loop et al. for broad translation of these technologies to injured patients. In response, Wenger modulation requires constant observation and patient-specific adjustments, which are not ideal for fluid movement or devices, which electrically stimulate the brain or spinal cord, causing movement in the legs. Currently, tuning such Patients with spinal cord injury (SCI) and paralysis may soon be outfitted with so-called neuromodulation Closing the Loop on Neuroprosthetic Control