Miracle-dz/UniFAR-FTUs huggingface dataset:
Ancient evolution of hepadnaviral paleoviruses and their impact on host genomes

Hepadnaviruses (family Hepadnaviviridae) are reverse-transcribing animal viruses that infect vertebrates. Vertebrate genomes contain DNA sequences derived from ancient hepadnaviruses, and these ‘endogenous hepatitis B viruses’ (eHBVs) reveal aspects of the long-term coevolutionary relationship between hepadnaviruses and their vertebrate hosts. Here, we use a novel, data-oriented approach to recover and analyse the complete repertoire of eHBV elements in published animal genomes. We show that germline incorporation of hepadnaviruses is exclusive to a single vertebrate group (Sauria) and that the eHBVs contained in saurian genomes represent a far greater diversity of hepadnaviruses than previously recognised. Through in-depth characterisation of eHBV elements we establish the existence of four distinct subgroups within the genus Avihepadnavirus and trace their evolution through the Cenozoic Era. Furthermore, we provide a completely new perspective on hepadnavirus evolution by showing that the metahepadnaviruses (genus Metahepadnavirus) originated >300 million years ago in the Paleozoic Era, and has historically infected a broad range of vertebrates. We also show that eHBVs have been intra-genomically amplified in some saurian lineages, and that eHBVs located at approximately equivalent genomic loci have been acquired in entirely distinct germline integration events. These findings indicate that selective forces have favoured the accumulation of hepadnaviral sequences at specific loci in the saurian germline. Our investigation provides a range of new insights into the long-term evolutionary history of reverse-transcribing DNA viruses and demonstrates that germline incorporation of hepadnaviruses has played an important role in shaping the evolution of saurian genomes.
Morph-Skyline: Skyline Queries for Virtual Knowledge Graph Access

A skyline set corresponds to the points that are non-dominated by any other points in terms of a multi-criteria function, i.e, there is no other point with values better than them in the criteria deﬁned in a multi-criteria function. Particularly, skyline queries can be used to ﬁlter the points that best meet a multi-criteria function in decision making applications over large datasets. Most of these datasets are represented in relational databases under diﬀerent schemes and data models. Integrating these datasets in a uniﬁed view may be useful for stakeholders to make more accurate decisions. As a proof of concept, we have developed Morph-Skyline, a virtual knowledge graph open source engine, which is able to automatically translate SPARQL skyline queries to equivalent ones in SQL. In this paper, we will demonstrate Morph-Skyline functionalities, and users will observe transport sector scenarios where Morph-Skyline allows specifying preferences by using skyline clause. We also evaluate the performance of Morph-Skyline against the state-of-the-art skyline methods for skyline processing over knowledge graphs. Evaluations were performed on Geonames and GTFS datasets of the subway network of Madrid with scale from 1 to 1000.
RICDaM: Recommending Interoperable and Consistent Data Models

One of the core functionalities of Knowledge Graphs is that data is not required to adhere to strictly deﬁned data models. Non-etheless, the RDF model provides speciﬁcations for publishing data on the Web that not only describe entities and relationships but also focus on enabling interoperability between datasets. However, ﬁnding the right ontologies to model a dataset is a challenge since several valid data models exist and there is no clear agreement between them. We present a demonstration of an interface that allows users to custom-ise a data model based on recommendations obtained with the RICDaM framework. This framework focuses on ranking candidates based on three metrics that measure the relevancy of the candidate, the interoperability of the neighbourhood of the candidate, and the overall consistency of the model proposed. The interface allows the user to reﬁne the recommended data model and complete it when the framework is lacking or not directly ﬁtting the intention of the user. The demo can be found at http://afel.insight-centre.org/ricdam/
A Fine-grained Complex Question Translation for KBQA

Translating natural language questions into SPARQL queries is a signiﬁcant challenge of semantic parsing based KBQA due to the gap between their representations. In this paper, we designed a ﬁne-grained complex question answering framework for KBQA, including a semantic similarity model and a neural machine translation model. Based on the above two models, we present a complex question processing algorithm to transform questions into subqueries and then process them parallelly. The experiments evaluated on benchmark datasets show that our approach is signiﬁcantly eﬀective.
Towards Knowledge Acquisition of Metadata on AI Progress

We propose an ontology to help AI researchers keep track of the scholarly progress of AI related tasks such as natural language processing and computer vision. We first define the core entities and relations in the proposed Machine Learning Progress Ontology (MLPO). Then we describe how to use the techniques in natural language processing to construct a Machine Learning Progress Knowledge Base (MPKB) that can support various downstream tasks.
An Embedding-based Approach to Completing Question Semantics

A question can be correctly answered, which has complete semantics; that is, it contains all basic semantic elements. In practical, questions are not always complete due to users’ ambiguous representation of their intents. Unfortunately, there is few research work on this problem. In this paper, we present an embedding-based approach to completing question semantics by inspiring from knowledge graph completion based on our proposed representation of a complete basic question as unique type and subject and multiple possible constraints
Spread the Good Around! Information Propagation in Schema Matching and Entity Resolution for Heterogeneous Data

In this short paper we describe the experience from our entries to the Entity Resolution (ER) and Schema Matching (SM) challenges of the Second DI2KG workshop. Altogether we study four solutions, two domain-specific and two based on machine learning (ML). Concerning ER, we find that through ample data cleaning/extraction, simple matching rules can already achieve a high f1 score (0.921). However, we note the limited generalization power of such kind of solutions. For ML-ER, by reducing data cleaning/extraction, generic ML models resulted unsuccessful out of the box; but by increasing it, models resulted redundant compared to simple rules. For SM, we report less competitive f1 scores, establishing the need for more appropriate methods than those attempted. Based on our experience we confirm the importance of automating data cleaning/extraction as a goal towards general data integration methods that would be more portable across datasets. We venture that for highly heterogeneous schemas, a promising approach could be to evolve collective integration with ML & graph-based methods, incorporating strategies based on information propagation.
Flow-Sensitive Type-Based Heap Cloning

By respecting program control-ﬂow, ﬂow-sensitive pointer analysis promises more precise results than its ﬂow-insensitive counterpart. However, existing heap abstractions for C and C++ ﬂow-sensitive pointer analyses model the heap by creating a single abstract heap object for each memory allocation. Two runtime heap objects which originate from the same allocation site are imprecisely modelled using one abstract object, which makes them share the same imprecise points-to sets and thus reduces the beneﬁt of analysing heap objects ﬂow-sensitively. On the other hand, equipping ﬂow-sensitive analysis with context-sensitivity, whereby an abstract heap object would be created (cloned) per calling context, can yield a more precise heap model, but at the cost of uncontrollable analysis overhead when analysing larger programs. This paper presents TypeClone , a new type-based heap model for ﬂow-sensitive analysis. Our key insight is to diﬀerentiate concrete heap objects lazily using type information at use sites within the program control-ﬂow (e.g., when accessed via pointer dereferencing) for programs which conform to the strict aliasing rules set out by the C and C++ standards. The novelty of TypeClone lies in its lazy heap cloning: an untyped abstract heap object created at an allocation site is killed and replaced with a new object (i.e. a clone), uniquely identiﬁed by the type information at its use site, for ﬂow-sensitive points-to propagation. Thus, heap cloning can be performed within a ﬂow-sensitive analysis without the need for context-sensitivity. Moreover, TypeClone supports new kinds of strong updates for ﬂow-sensitive analysis where heap objects are ﬁltered out from imprecise points-to relations at object use sites according to the strict aliasing rules. Our method is neither strictly superior nor inferior to context-sensitive heap cloning, but rather, represents a new dimension that achieves a sweet spot between precision and eﬃciency. We evaluate our analysis by comparing TypeClone with state-of-the-art sparse ﬂow-sensitive points-to analysis using the 12 largest programs in GNU Coreutils. Our experimental results also conﬁrm that TypeClone is more precise than ﬂow-sensitive pointer analysis and is able to, on average, answer over 15% more alias queries with a no-alias result.
Vein-Based Biometric Verification Using Densely-Connected Convolutional Autoencoder

In this letter, we propose a vein-based biometric verification system relying on deep learning. A novel approach consisting of a convolutional neural network (CNN), trained in a supervised manner, cascaded with an auto-encoder, trained in an unsupervised way, is here exploited. In more detail, a novel densely-connected convolutional autoencoder is here used on top of backbone CNNs. This architecture aims at increasing the discriminative capability of the features generated from hand vein patterns. Experimental tests on finger, palm, and dorsal veins show that the proposed approach leads to an improvement of the recognition rates with respect to the use of the sole CNNs for feature extraction. The achieved performance are superior to the current state of the art in vein biometric verification.
MEMO Box: Health Assistant for Depression With Medicine Carrier and Exercise Adjustment Driven by Edge Computing

According to the report by the World Health Organization, depression, as the leading disabling disease in the world, has aroused widespread social concern. However, the shortage of medical and nursing staff in psychiatry conflicts with the rising nursing needs of depressed patients. First, depression is a chronic mental illness, its cure course is relatively long, and stable drug treatment and healthy living habits are keys to the curing process. Second, the disease feature where depression patients have stronger emotional needs and more sensitive mental states is ignored in existing health monitoring systems. In consideration of the above two aspects, a health management assistant is proposed in this paper, called MEMO box system, that focuses on emotion and takes smart medicine box as carrier. Specifically, the MEMO box system is composed of electronic medicine box and smart applications of phones, and electronic medicine box can collect the multi-mode data of patients, including their medication behaviors, daily activities, physical exercise data, and so on, which provides a data basis for the health assistant. Emotion recognition and sports detection algorithms are deployed in the edge cloud, which enables health service requests from patients to get quick response. With the cooperation of hardware and software in this system, patients are urged to take medicines on time to effectively control their conditions, and emotion cognition and exercise adjustment recommendation can be realized for depression patients through physiological data, thus providing patients with empathic sports recommendations.
A Random Compensation Scheme for 5G Slicing Under Statistical Delay-QoS Constraints

For the communication scenarios of heterogeneous arrivals in the fifth generation cellular networks, slicing is believed to be an effective way to provide quality-of-service (QoS) provisioning. However, the reports on an effective segmentation of resources for random and bursty arrival are rare. Relying on the bandwidth estimation and service design, we give a simple but effective scheme for the slicing of bandwidths named two-hole leaky bucket random service mechanism. The service in our scheme comprises two parts, the specific basic service and shared random compensation service. Accordingly, the mechanism of resource allocation is just like leaky bucket with two holes, one of which has a random leak rate. To guarantee the statistical QoS of heterogeneous traffic flows, effective bandwidth/capacity theory is introduced into the slicing scheme. By establishing function equations, we deduce the compensation probability of each slice, which is taken as the slicing adjustment factor. Simulation results prove that the random compensation scheme can satisfy the statistical QoS requirements of heterogeneous traffic flows. Although the scheme is proved to be effective against two slices, the proposed model is applicable to general multiple heterogeneous arrivals.
Battery Modeling and Parameter Extraction for Drive Cycle Loss Evaluation of a Modular Battery System for Vehicles Based on a Cascaded H-Bridge Multilevel Inverter

This article deals with the modeling and the parameterization of the battery packs used in cascaded H-bridge multilevel propulsion inverters. Since the battery packs are intermittently conducting the motor currents, the battery cells are stressed with a dynamic current containing a substantial amount of low-order harmonic components up to a couple of kHz, which is a major difference in comparison to a traditional two-level inverter drive. Different models, such as pure resistive and dynamic RC-networks, are considered to model the energy losses for different operating points (OPs) and driving cycles. Using a small-scale setup, the models’ parameters are extracted using both a low-frequency, pulsed current, and an electrochemical impedance spectroscopy (EIS) sweep. The models are compared against measurements conducted on the small-scale setup at different OPs. Additionally, a drive cycle loss comparison is simulated. The simple resistive model overestimates the losses by about 20% and is, thus, not suitable. The dynamic three-time-constant model, parameterized by a pulsed current, complies with the measurements for all analyzed OPs, especially at low speed, with a maximum deviation of 3.8%. Extracting the parameters using an EIS seems suitable for higher speeds, though the losses for the chosen OPs are underestimated by 1.5%–7.9%.
ECS2: A Fast Erasure Coding Library for GPU-Accelerated Storage Systems with Parallel & Direct IO

As data volume keeps increasing at a rapid rate, there is an urgent need for large, reliable, and cost-effective storage systems. Erasure coding has drawn increasing attention because of its ability to ensure data reliability with higher storage efficiency, and it has been widely adopted in many distributed and large-scale storage systems, such as Azure cloud storage and HDFS. However, the storage efficiency of erasure code comes at the price of higher computing complexity. While many studies have shown the coding computations can be significantly accelerated using GPU, the overhead of data transfer between storage devices and GPUs become a new performance bottleneck. In this work, we designed and implemented, ECS2, a fast erasure coding library on GPU -accelerated storage to let users enhance their data protection with transparent IO performance and file system like programming interface. By taking advantage of the latest GPUDirect technology supported on Nvidia GPU, our library is able to bypass CPU and host memory copy from the IO path, so that both the computing and IO overhead from coding can be minimized. Using synthetic IO workload based on real storage system trace, we show that the IO latency can be reduced by 10% ∼ 20% with GPUDirect technology, and the overall IO throughput of a storage system can be improved up to 70%.
Smart Glass for Awareness of Important Sound to People with Hearing Disability

People with hearing disability find it difficult to establish a communication specially because most of the communication mediums that people naturally use are vocal. Hearing impaired people sometimes risk their life in cases like busy roads or run by high speed vehicles. Furthermore, they are unable to hear people calling them. Even if they understand that they are being called or they hear any sudden noise, they can't figure out the direction of the sound which is out of their regular vision. Recent technologies can be used to help them. In this paper we propose a smart glass that will enable them to understand any sudden sound by separating the noise using active noise cancellation and recognize the sound by speech recognition, notify that they are being called and provide them the direction of sound using binaural cue. This glass can also be used for old people or people with lower hearing ability. Which can be integrated with modern commercial smart glasses for better performance and lower price.
An Equivalent Transmit ADBF Technique for Phased-MIMO Radar

The purpose of adaptive digital beam-forming (ADBF), is to obtain the biggest gain in the expected direction, at the same time suppress interfering signals automatically. For phased-multiple-input multiple-output (MIMO) radar applied to large array, the overall ADBF process suffers from heavy computing burden. This is because the signal processing is conducted on the virtual array. Therefore, an ADBF algorithm should consider not only high output signal-to-noise-plus-interference ratio (SINR), but how to reduce computational complexity. This paper presents an equivalent transmit ADBF process technique whose essence is to separate the overall ADBF into a digital beam-forming in receiver and a ADBF in equivalent transmitter. Theoretical analysis and simulations show that this method guarantees the high output SINR and greatly reduces the computational complexity.
Dynamic Gesture Recognition Based on 3D Separable Convolutional LSTM Networks

Dynamic gesture recognition, as an important component of human-computer interaction (HCI), has attracted the attention of many researchers. However, there are still many challenges in the actual gesture recognition task, such as the non-rigid properties of the hand, the acquisition of different semantic information through spatial and motion information at different locations, and the changes of light and background in the surrounding environment can ultimately affect the extraction of gesture features. Since the essence of dynamic gesture recognition is classification and recognition, how to extract the spatio-temporal features of dynamic gestures will have a significant impact on the final recognition and classification of gestures. In recent years, three-dimensional convolutional neural networks have achieved excellent performance in motion recognition, but traditional three-dimensional convolution is redundant in the feature extraction process. In order to reduce the resource consumption, this paper adopts 3D separable convolution as an alternative solution. To further extract semantic and action information of gestures, we combine attentional mechanisms and long- and short-term memory To further extract semantic and action information of gestures, we combine attentional mechanisms and long- and short-term memory (LSTM) networks for gesture recognition in this paper. We conducted experiments on the ChaLearn Large-Scale Gesture Recognition Dataset (IsoGD), and the experimental results validate the effectiveness of our method.
Thirteen-Level Modified Packed U-Cell Multilevel Inverter for Renewable-Energy Applications

Multilevel inverters are getting more interest to be used in several applications due to their various advantages compared to the classical inverters. In this paper, a modified packed U-cell inverter is proposed to provide thirteen-level smooth waveform at the output. The proposed inverter uses eight switches and three DC sources, which is very less compared to the classical inverters. The efficient multi-carrier PWM switching technique is employed to control the operation of the inverter and to reduce the current harmonics. Simulations are carried out using Matlab/Simulink package to investigate the performance of the proposed inverter. The improvement in the output waveform and the reduced harmonic distortion are pointed out, which prove the efficiency of the proposed inverter.
Conditions for Increasing User’s Active Power in Intermittent Duty Cycle Operation

The features of the operation of an improved consumer power supply system with a pulsed power supply are considered. Special aspects of that system consist in the fact that the inductive load of the consumer is shunted by a semiconductor element, locked during the duration of the voltage pulse of the source and opened during a pause, meanwhile to the supply line, an electronic switch inserted, which opened during the voltage pulse from source and lockdown during a pause. It was proved that with such an algorithm of operation, the reactive energy of the inductive consumer provides an increase in its active power and decrease consumed current from the source.
PADL: a Language for the Operationalization of Distributed Analytical Pipelines over Edge/Fog Computing Environments

In this paper we introduce PADL, a language for modeling and deploying data-based analytical pipelines. The novelty of this language relies on its independence from both the infrastructure and the technologies used on it. Specifically, this descriptive language aims at embracing all the particularities and constraints of high-demanding deployment models, such as critical restrictions regarding latency, privacy and performance, by providing fully-compliant schemas for implementing data analytical workloads. The adoption of PADL provides means for the operationalization of these pipelines in a reproducible and resilient fashion. In addition, PADL is able to fully utilize the benefits of Edge and Fog computing layers. The feasibility of the language has been validated with an analytical pipeline deployed over an Edge computing environment to solve an Industry 4.0 use case. The promising results obtained therefrom pave the way towards the widespread adoption of our proposed language when deploying data analytical pipelines over real application scenarios.
X-Controlled Memristive Devices for Automatic Gain Control in RC Oscillators

Classical memristive systems, also referred to as the extended memristors, are generalized to memristive systems whose internal state can be controlled via the voltage or current of an auxiliary port. The usefulness of these proposed devices is illustrated on the example of AGC (Automatic Gain Control) of RC oscillators. The parameters of the corresponding memristive systems required for a smooth startup and low harmonic distortion of the oscillation signal are formulated. It is shown that this concept includes some hitherto employed methods of gain control as special cases, and that its key added value consists in the possibility of nanoscale implementation.