Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Composition of Music by LSTM

LSTM is used in different fields, such as Machine Translation, Time Series Prediction and so on. Machine Translation with LSTM uses Embedding vector for word and computes loss of two kinds of languages. And decreasing the loss of the Model and fixing it, which can translate one language to another one. Music and its lyrics are governed by rules which are declarative and nondeterministic. In this paper, compositing will be addressed with LSTM which can automatic analysis the rules of the music scores and lyrics. Automatic Composing can be looked the same as the Machine Translation. The Music score is a kind of language and the lyrics is another kind of language, which can use the Translation Model to fix it, and then to implement composing tasks.
Exploring Structured Information Systems: A Keyword Search Approach

Google search, and other search engines (e.g., Bing), provide a convenient way to find web pages that contain different keywords. Each web page is composed of loosely-structured paragraphs of text. On the other hand, a vast amount of information systems data are stored as structured data (e.g., in relational databases). In a structured database, the information about a particular entity, or the relation between different entities might be scattered across many rows and columns in different tables. These pieces of information must be stitched together to form the final answer to a user’s query. For a large database, there might be millions of answers to a search query. Thus, finding the right answer is a challenging task. In this work, we present an intuitive search system over structured information systems. This search system empower non-technical users to explore structured information systems and turn hidden information into useful insights.
Counterfeit Anomaly Using Generative Adversarial Network for Anomaly Detection

Anomaly detection aims to detect anomaly with only normal data available for training. It attracts considerable attentions in the medical domain, as normal data is relatively easy to obtain but it is rather difficult to have abnormal data especially for some rare diseases, making training a standard classifier challenging or even impossible. Recently, generative adversarial networks (GANs) become prevalent for anomaly detection and most existing GAN-based methods detect outliers by the reconstruction error. In this paper, we propose a novel framework called adGAN for anomaly detection using GAN. Unlike existing GAN-based methods, adGAN is a discriminative model, which uses the fake data generated from GAN as an abnormal class, and then learns a boundary between normal data and simulated abnormal data. Thus it is able to output the anomaly scores directly similar as one-class SVM (OCSVM), without any reconstruction process. We explicitly design adGAN with two key elements, i.e., fake pool generation and concentration loss. The fake pool is created by incrementally collecting the fake data produced by intermediate-state GAN, which are likely surrounding the normal data distribution. The concentration loss is innovatively introduced to penalize large standard deviations of discriminator outputs for normal data, aiming to make the distribution of normal data more compact and more likely to be separated from the distribution of the potential abnormal data. The trained discriminator is finally used as an anomaly detector. We evaluated adGAN on three datasets, including ab-MNIST for synthetic anomaly detection, the ISIC’2016 for skin lesion detection, and the BraTS’2017 for brain lesion detection. The extensive experiments demonstrate that adGAN is consistently superior to its competitors on all three datasets.
Proactive Wake-up Scheduler based on Recurrent Neural Networks

Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to specific traffic models. In this paper, a novel concept of wake-up scheduling is introduced to further improve the energy-efficiency of mobile devices and to deal with realistic traffic. The main idea is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically. For this, a proactive wake-up scheduler is proposed to take online decisions based on traffic prediction. Towards this end, a framework to predict packet arrivals based on recurrent neural networks is developed. Numerical results show that for given delay requirements of video, audio streaming, and mixed traffic flow, the proactive wake-up scheduler reduces the power consumption of the baseline wake-up scheme without scheduler by up to 36%, 28% and 9%, respectively.
Real-Time Optimal Scheduling of Large-Scale Electric Vehicles: A Dynamic Non-Cooperative Game Approach

In the presence of the increasing penetration of electric vehicles (EVs) and conflict of independent optimization objectives among each electric vehicle aggregator (EVA), real-time optimal scheduling (RTOS) of large-scale EVs based on dynamic non-cooperative game approach is proposed for optimal decision makings in a dynamic pricing market. First, real-time optimal scheduling framework is designed to describe the flow of energy and information. Then, equivalent model of large-scale EVs is formulated to address “curse of dimensionality” caused by a large number of decision variables. Then, the potential game theory is used to study the existence and uniqueness of the Nash equilibrium (NE) solution. Finally, a distributed approach based on alternating direction method of multipliers (ADMM) is designed to achieve the equilibrium. Case studies demonstrate that the proposed approach achieves peak load shifting and reduces cost of EVAs significantly. Furthermore, the proposed method obtains higher-quality solution compared with other methods and is more applicable for real-time optimal scheduling of large-scale EVs due to its high computation efficiency and privacy protection.
Optimal management of a solar power plant equipped with a thermal energy storage system by using dynamic programming method

This study employs the dynamic programming (DP) optimization approach to maximize the daily revenue of a concentrating solar power plant (CSP) equipped with a thermal energy storage system (TES). DP guarantees the optimal solution and is easy from the computer coding point of view; therefore, it can be of great importance. Two real-life case studies are considered. The first case has a simple thermal model and is mainly chosen in order to verify the present optimization program. In this case, the present work achieves 1.3% (or US$480 /day) higher daily revenue in comparison with the literature. However, the simple thermal model of the first case study cannot appropriately simulate the actual operation of a CSP. Therefore, the optimization was applied to another case with a more detailed thermal model. The findings indicate that in order to maximize revenue, the charging and discharging of the TES should be managed such that the power block works under base load conditions (whenever it is active) and during periods of high electricity prices, regardless of the level of the daily solar radiation pattern. Furthermore, quantitative investigations show the substantial influence of the TES system on the daily revenue, especially for the case of low solar radiation pattern.
High-acuity vision from retinal image motion

A mathematical model and a possible neural mechanism are proposed to account for how fixational drift motion in the retina confers a benefit for the discrimination of high-acuity targets. We show that by simultaneously estimating object shape and eye motion, neurons in visual cortex can compute a higher quality representation of an object by averaging out non-uniformities in the retinal sampling lattice. The model proposes that this is accomplished by two separate populations of cortical neurons — one providing a representation of object shape and another representing eye position or motion — which are coupled through specific multiplicative connections. Combined with recent experimental findings, our model suggests that the visual system may utilize principles not unlike those used in computational imaging for achieving “super-resolution” via camera motion.
Hybrid Environment IoT-Mapping of Over-Tourism and Air Pollution in the Azores Archipelago

In this paper, we describe the hybridization of Microsoft Azure and Ionic for the development of a platform to track air quality in the Azores Archipelago, Portugal. The Azores region has had a sharp increase in tourism, which has led to greater public investment in sustainable tourism practices. The platform under development will access data logs from a wireless network of thermoelectric sensors, and allow users to upload data from locations outside the wireless network. The uploads will be done using a cross-platform Ionic app that is in progressive development. The app will be easily customizable and scalable across all web browsers and platforms, including desktop and mobile. The data uploaded will be stored on Microsoft Azure's Cloud infrastructure and accessible via our application. This paper focuses on the experimental setup, the collection of data pertaining to air quality and the display of the data, based on a timeline that starts from the most recent data updates and continues into the past. Data from individual sensors can also be isolated for display based on the location of the sensors. This research has been undertaken by the University of Washington, Tacoma in collaboration with the University of the Azores and the Federal University of Paraíba, Brazil.
Decentralized Collective Transport along Manifolds Compatible with Holonomic Constraints by Robots with Minimal Global Information

We present a decentralized adaptive control strategy for collective payload transport by differential-drive robots with manipulator arms. The controllers only require robots’ measurements of their own heading and velocity and their manipulator angle and angular velocity, and the only information provided to the robots is the target speed and direction of transport. The control strategy does not rely on inter-robot communication, prior information about the load dynamics and geometry, or knowledge of the number of robots and their distribution around the payload. We first design the desired manifolds of motion for the entire system such that they are compatible with the holonomic constraints between the robots and the payload. Then, we design adaptive controllers for a team of differential-drive robots that initially grasp a payload in an arbitrary configuration. We also analytically establish the stability and convergence of the system trajectories to the desired payload motion. We demonstrate the effectiveness of the proposed controllers through 3D physics simulations with realistic dynamics.
Latency and Energy Transmission Cost Optimization using BCO-aware Energy Routing for Smart Grid

In recent years the Smart Grids also known as intelligent energy systems have attracted the attention of researchers and became an active area for research. Smart Grid (SG) is a new development stage of power systems that aims to increase the efficiency of the energy transmission, to balance the demand and supply in the network and to improve the use of the distributed renewable energy sources. Since the transmitted energy have some losses in transmission path, which is the major issue in energy routing protocols. Where the main question is how to decrease the energy transmission loss in other words, how to find the efficient energy transmission path with the minimum transmission cost. Different methods and protocols have been proposed to solve the energy transmission path problem. These proposed protocols are based on traditional methods such as graph theory, game theory, autonomous systems, consensus… etc. In this paper, we have considered the problem of determining the energy efficient path as an optimization problem. In order to solve this problem, we proposed the use of swarm optimization methods in particularly the Bee Colony Optimization method. We have used the principle of bee foraging behaviour and proposed an energy routing protocol based on BCO algorithm to determine the lowest cost and latency energy path using features of power transmission and peer to peer energy market in smart grids.
The degree of internationalization of Chinese Multinationals along the belt and road initiative countries

This study assesses the degree of internationalization of Chinese firms along the Belt and Road initiative countries. Most of the extant studies of the Belt and Road initiative have been qualitative, and where there have been quantitative studies, they have usually been at the aggregate level, and only a handful have used firm-level data to study initiative. Using a composite measure of the degree of internationalization, DOIBRI, that composed of variables capturing the performance, structural and attitudinal dimensions of internationalization, comparative analysis of State-owned enterprises and privately owned enterprises turned up counter-intuitive results. Firstly, given that state ownership could be positively associated with the degree of internationalization of firms and because of the significance of the Belt and Road initiative, we expected the State-owned enterprises to dominate the DOIBRIrankings. We assessed the firms, and contrary to expectations, privately owned firms had a higher average degree of internationalization. Furthermore, we expected both state-owned enterprises and privately-owned enterprises to have similar levels of psychic dispersion. However, state-owned enterprises were more psychically dispersed. Suggesting that along the belt and road countries, the advantages of state ownership of Chinese multinationals may be attenuated.
Relationship between Mobilephone Addiction and Network Media Literacy of College Students

The mobilephone addiction has been increasing serious among the college students with the growth of network mobile technology and the development of a large number of APP software in recent years. In order to study the mobilephone addiction of college students and the relationship with network media literacy. The Scale for Mobilephone Addiction Tendency of College Students and the Self-compiled Questionnaire for Network Media Literacy of College Students are used in the research. 1,020 college students have been surveyed by questionnaire through random cluster sampling. The results showed that: First, The total score of mobilephone addiction for college students was $40.37 \pm 11.40$, and the proportion of high tendency for mobilephone addiction was 24.40%. Second, Two demographic variables, namely homeplace and only child or not, had the significant difference in the score of mobilephone addiction, but the difference of other variables was nonsignificant. Third, The mobilephone addiction and information construction capabilities of college students were negatively correlated, namely that with higher score of information construction capabilities, the score of mobilephone addiction would be lower, and vice versa. Therefore, The improvement of information construction capabilities is beneficial to solving the mobilephone addiction.
Ground Vehicle Localization using Road Profile Feature *

The road profile is a good map to identify vehicle position when signals from environmental sensors generally utilized in map-based localization are unreliable, because the road profile excites the vehicle responses. This paper proposes map-based localization using road profile. The concept of road profile-based localization is to find the current position by matching current features with a pre-indexed map. A feature is defined a spectrogram in the distance and frequency domain that is transformed from the road profile. The road profile-based map is constructed position information in dominant feature domain, and the position information is stochastic information of possible positions of each dominant feature. A vehicle builds the current spectrogram with the road profile estimated from only inertial sensor signals and extracts the position information of most similar dominant feature. Current vehicle position is monotonic and continuous with respect to the previous position among the position candidates, and the probability of the previous position is reflected into the position candidates using the Kalman filter. The algorithm shows accuracy within just meter-level error in the given spectrogram resolution.
Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid

This paper presents a novel algorithm for detecting pavement cracks from mobile laser scanning (MLS) data. The algorithm losslessly transforms MLS data into a regular grid structure to adopt the proven image-based methods of crack extraction. To address the problem of lacking topology, this study assigns a two-dimensional index for each laser point depending on its scanning angle or acquisition time. Next, crack candidates are identified by integrating the differential intensity and height changes from their neighbors. Then, morphology filtering, a thinning algorithm, and the Freeman codes serve for the extraction of the edge and skeleton of the crack curves. Further than the other studies, this work quantitatively evaluates crack shape parameters: crack direction, width, length, and area, from the extracted crack points. The F1 scores of the quantity of the transverse, longitudinal, and oblique cracks correctly extracted from the test data reached 96.55%, 87.09%, and 81.48%, respectively. In addition, the average accuracy of the crack width and length exceeded 0.812 and 0.897. Experimental results demonstrate that the proposed approach is robust for detecting pavement cracks in a complex road surface status. The proposed method is also promising in serving the extraction of other on-road objects.
Secure Resource Allocation for Polarization-Based Non-Linear Energy Harvesting Over 5G Cooperative Cognitive Radio Networks

We address secure resource allocation for the energy harvesting (EH) based 5G cooperative cognitive radio networks (CRNs). To guarantee that the size-limited secondary users (SUs) can simultaneously send the primary user’s and their own information, we assume that SUs are equipped with orthogonally dual-polarized antennas (ODPAs). In particular, we propose, develop, and analyze an efficient resource allocation scheme under a practical non-linear EH model, which can capture the nonlinear characteristics of the end-to-end wireless power transfer (WPT) for radio frequency (RF) based EH circuits. Our obtained numerical results validate that a substantial performance gain can be obtained by employing the non-linear EH model.
Non linear time compression of clear and normal speech at high rates

We compare a series of time compression methods applied to normal and clear speech. First we evaluate a linear (uniform) method applied to these styles as well as to naturally-produced fast speech. We found, in line with the literature, that unprocessed fast speech was less intelligible than linearly compressed normal speech. Fast speech was also less intelligible than compressed clear speech but at the highest rate (three times faster than normal) the advantage of clear over fast speech was lost. To test whether this was due to shorter speech duration we evaluate, in our second experiments, a range of methods that compress speech and silence at different rates. We found that even when the overall duration of speech and silence is kept the same across styles, compressed normal speech is still more intelligible than compressed clear speech. Compressing silence twice as much as speech improved results further for normal speech with very little additional computational costs.
Association of low CD4/CD8 ratio with Adverse Cardiac Mechanics in Lymphopenic HIV-Infected Adults.

Gordon Mansergh, PhD Rob Stephenson, PhD Sabina Hirshfield, PhD Patrick Sullivan, PhD, DVM Division of HIV/AIDS Prevention Centers for Disease Control and Prevention Atlanta, GA Department of Systems, Population and Leadership School of Nursing University of Michigan Ann Arbor, MI Department of Medicine SUNY Downstate Health Sciences University Brooklyn, NY Department of Epidemiology Rollins School of Public Health Emory University Atlanta, GA
SALSA: A Domain Specific Architecture for Sequence Alignment

The explosion of genomic data is fostering research in fields such as personalized medicine and agritech, raising the necessity of providing more performant, power-efficient and easy-to-use architectures. Devices such as GPUs and FPGAs, deliver major performance improvements, however, GPUs present no-table power consumption, while FPGAs lack programmability. In this paper, we present SALSA, a Domain-Specific Architecture for sequence alignment that is completely configurable, extensible and is based on the RISC-V ISA. SALSA delivers good performance even at 200 MHz, outperforming Rocket, an open-source core, and an Intel Xeon by factors up to 350x in performance and 790x in power efficiency.
Pyruvate kinase activity and protein concentration in 'd'Anjou' pear fruit with cork spot.

An assay for pyruvate kinase (PK) was tested as a diagnostic tool for cork spot, a major physiological disorder in pear (Pyrus communis L. cv. d'Anjou) fruit. PK activity and Ca and protein concentrations were measured in peel of normal and affected fruit during selected months in 2 years. Protein concentrations was more closely associated with cork spot than PK activity or Ca concentration. These preliminary results suggested the PK assay was a poor diagnostic tool for cork spot
What Perception Do Students Have About the Gamification Elements?

The use of the mechanics and elements of games in non-ludic environments (Gamification) has increased in non-ludic contexts in various areas including education. This study focused on the perception that students have of the different elements of gamification in three different experiences, after which students were asked to answer a survey about the gamified elements of the course in which they participated. In the three experiences, Feedback, levels, points and missions stand out as the best valued, despite the fact that, for example, “time limit” were little valued by the students, it can be useful in the design of a gamified experience, but its use must be well regulated to avoid possible negative aspects and favor a good experience for the student. On the other hand, though badges were also been well valued by students in the longest experiences, in the shortest experience, it was one of the worst rated elements. Finally, the elements that students liked the least of all those analyzed, were the leaderboard and the blocked content.
Research on the New Mode of MOOC and Flipping Classroom Teaching Method in Classroom Teaching of "Network Database"

The teaching mode of "flipping classroom" course is adopted to reform the course of "Network Database", and according to the teaching knowledge point, the teaching PPT, teaching video, micro-course and other kinds of teaching resources are made. Enrich the MOOC online teaching resource library for the course of Network Database. According to the characteristics of running schools and students' personality of computer major, taking vocational ability as the core of curriculum, combining the mode of flipping classroom teaching, and according to the characteristics of the mixed teaching mode of flipping classroom, the course knowledge module of Network Database is established. Under the guidance of mixed teaching theory, based on the professional direction, adopting MOOC and flipping classroom as the basic teaching mode of "Network Database" course, the students can change from passive to active under the mode of flipping classroom course. To cultivate students' ability of innovation and independence, to improve the quality of personnel training and to promote teaching reform.