Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifted Marginal Filtering for Asymmetric Models by Clustering-Based Merging

Recently, Lifted Marginal Filtering (LiMa) has been proposed, an efﬁcient exact Bayesian ﬁltering algorithm for stochastic systems consisting of multiple, (inter-)acting entities where the sys-tem dynamics is represented by a multiset rewriting system (MRS). The core idea is to represent distributions over multisets more efﬁ-ciently than by complete enumeration, by exploiting exchangeability that naturally arises due to the MRS dynamics. However, due to sys-tem dynamics or observations of individuals, symmetry can break over time, requiring to resort to the original, much larger representation. In this paper, we propose a method to retain the lifted representation in LiMa. The method identiﬁes groups of lifted multiset states that describe a sufﬁciently similar distribution of ground multiset states for affording a representation by a single lifted state. Techni-cally, we propose a novel distance measure for lifted states that does not require to completely ground the distribution ﬁrst, and show how such a single representative for a group of lifted states can be computed. We show empirically that the error induced by this approach is signiﬁcantly smaller than by limiting the representational complexity conventionally by sampling.
TransP: A New Knowledge Graph Embedding Model by Translating on Positions*

Embedding knowledge graph into continuous space(s) is attracting more and more research attention, and lots of novel methods have been proposed. Among them, translation based methods achieved state-of-the-art experimental results. However, most of existing work ignore following two facts. First, once a relation is fixed, its linked head and tail entities will be fixed to a certain extent. Second, in a triplet, if one of its entities and the relation are fixed, the other entity’s candidates will also be fixed to a certain extent. Taking these two facts into consideration, we propose a new knowledge graph embedding model named TransP, which defines a head entity space and a tail entity space for each relation. During embedding, TransP first projects entities into these two position spaces. Then the entities in these two position spaces are further projected into a common transformation space, in which the relation is converted into two transformation matrices. A symmetrical score function is designed to connect a correct triplet’s head and tail entity in the common space. The basic idea behind this score function is that if a correct triplet holds, its head (tail) entity should be able to be converted into its tail (head) entity when taking the relation’s transformation matrix as an intermediate bridge. Viewing the transformation matrices as decoders, this process is just like a common translation process. We evaluate TransP on triplet classification task and link prediction task. Extensive experiments show that TransP achieves much better performance than other baseline models.
Firefly: An Open-source Rocket-based Intermittent Framework

Intermittent systems are ultra-low-power batteryless devices that are increasing in popularity. These systems operate with energy extracted entirely from the environment. Since most environments cannot ensure sufficient and steady power supply conditions, intermittent devices suffer from frequent power outages, where computation is interrupted due to the lack of energy. While numerous works have enabled intermittent computation via many different techniques, there are no versatile and configurable tools that ensure functionality, and rapid development/design space exploration. Therefore, in this paper, we present Firefly, an intermittent open-source framework for developing ultra-low-power devices based on the RISC-V ISA and the Rocket Chip. Firefly enables developers to modify and extend the project to satisfy their preference, i.e., memory technologies, intermittent environment configuration, and hardware modifications. By exploiting the versatility offered by the Firefly framework, we evaluate different memory technologies in an intermittent environment, showing that in many times PCRAM and RRAM will be the best choices to compose the design.
Densification and syntenic comparison of parental linkage maps in interspecific hybrids of silver carp and bighead carp

Hybrids of silver carp (Hypophthalmichthys molitrix) and bighead carp (Aristichthys nobilis) are widely accepted in aquaculture due to their better culturing performance than their parents.In order to facilitate diverse studies of the two species,such as genetic resource management and mapping of economic traits,we have constructed the parental linkage maps of the interspecific hybrids previously;however,the maps contained mainly AFLPs.In order to increase the quality of the parental maps and their applicability,they were densified with newly developed microsatellite markers in this study.For the bighead carp (female parent) and the silver carp (male parent) map,the number of assigned markers increased from 153 to 288 (including 93 new microsatellites) and from 271 to 511 (including 136 new microsatellites),respectively.The total length of the female map increased from 852.0 to 965.8 cM,while the total length of the male map increased from 952.2 to 1 049.6 cM.Syntenic comparison between bighead carp map and silver carp maps identified 22 homologous linkage groups,among them slight rearrangement of markers was found.The total length of the common intervals bounded by 76 parentshared microsatellites was almost the same (female:male=1:1),which was significantly different from that of the intraspecific hybrids of silver carp (female:male=1:0.45 [17]). In comparison with the mimic natural fertilization (intraspecific hybrids [17]),the mixed milt fertilization (interspecific hybrids,this study) caused the obvious increase of genome wide recombination rate.The hybridization of silver carp and bighead carp should not influence the recombination rate of either parent;they are morphologically similar to each other and interfertile and have identical karyoptypes.We believe that mixed milt fertilization avoide the competition among sperms of an ejaculation,enhancing genome wide recombination accordingly.This fry raising method is not favorable to the reservation of the elite haplotypes either newly formed or previouly selected,being one of the main reasons causing the depletion of culturing performance of these two species.
Deep Reinforcement Learning for Pre-caching and Task Allocation in Internet of Vehicles

With the development of Internet of Vehicles and 5G network, there is an increasing demand for services from vehicle users. Mobile edge computing offers a solution, that is, processing tasks on the edge server to improve user quality of experience (QoE). However, given the constant changes in the location of users on fast-moving vehicles, it remains a challenge on how to efficiently and stably transmit data. To address it, a method of pre-caching and task allocation based on deep reinforcement learning is proposed in this paper. The files requested by vehicle users are pre-cached on roadside units (RSUs), and transmission tasks are dynamically allocated to vehicle to vehicle (V2V) transmission and vehicle to roadside unit (V2R) transmission based on the speed of transmission. To be specific, pre-caching and task allocation are modeled as Markov decision processes (MDP). Then, Deep Deterministic Policy Gradient (DDPG) is applied to determine the optimal ratio of pre-caching and task allocation. The performance of the algorithm in different situations is analyzed through simulation and it is compared with other algorithms. It is found that DDPG can maximize the data reception rate of fast-moving vehicles, thereby improving the QoE of vehicle users.
Detection and classification of malware based on FastText

Nowadays, the Internet has penetrated into every corner of people's lives. It brings convenience to my life as well as certain risks. Millions of new types of malware appear every day, affecting thousands or even millions of home computer users. And attackers can use fully automated design and reuse malware, which makes the threshold for cybercrime lower and lower. Therefore, we urgently need a detection technology that can be applied to the current rapidly changing malware ecosystem. Based on the fact that the implementation of software functions must use Windows API functions, this paper proposes to dynamically extract the API call sequence patterns of different categories of malware, then used FastText as the classifier and word representation. The model is applied to two open malware datasets, and the experimental results show that the proposed method has high detection rate and low false alarm rate, which proves that it can effectively detect and classify malware.
Flexibility Robust Optimal Operation Strategy for Cross-Regional Interconnected Power Under Load-Source Coordination

Ultra High Voltage (UHV) transmission technology is the main regional power interconnection method in China. In addition, renewable energy sources (RES) in China are characterized by centralized distribution. In view of the cross-regional flexible scheduling of large-scale grid-connected RES, we build a day-ahead optimization model which considers the generation economy and robustness of transmission for each regional grid. The model considers the spatial clustering effect of RES and loads, uses robust optimization theory to construct uncertainty sets, and introduces uncertain spatial constraint parameters to compensate for the insufficiency of traditional robust optimization. Furthermore, a co-evolutionary algorithm filter-based is proposed to solve above the mixed-integer non-convex nonlinear programming model. Taking the modified IEEE-39 nodes as an example, the results show that the system cost can be reduced by 21.9% by selecting the confidence probability of uncertain variables of both load and source under stable operation, while the prediction accuracy has a significant impact on the system cost when the confidence probability is smaller, and a better operation scheme can be obtained by increasing the space constraint parameters. Finally, the feasibility of the proposed model and algorithm are verified on the Hami power grid in Xinjiang, China.
Predicting a Hit Song with Machine Learning: Is there an apriori secret formula?

Thought to be an ever-changing art form, music has been a form of recreational entertainment for ages. The music industry is constantly making efforts for songs to be a hit and earn considerable revenues. It could be an interesting exercise to predict a song making it to top charts from a mathematical perspective. While several studies have looked into factors after a song is released, this research looks at apriori parameters of a song to predict the success of a song. Data sources available from multiple platforms are combined to create a dataset that has technical parameters of a song and sentimental analysis of the lyrics. Four machine learning algorithms (Logistic Regression, Decision Trees, Naïve Bayes and Random Forests) to answer the question-Is there a magical formula for the prediction of hit songs? It was found that there are elements beyond technical data points that could predict a song being hit or not. This paper takes a stand that music prediction is yet not a data science activity.
Multi-Class Cardiovascular Diseases Diagnosis from Electrocardiogram Signals using 1-D Convolution Neural Network

The electrocardiogram (ECG) is an important signal in the health informatics for the detection of cardiac abnormalities. There have been several researches on using machine learning techniques for analyzing ECG. However, they need additional computation owning to ECG signals challenges. We introduce a new architecture of 1-D convolution neural network (CNN) to diagnose arrhythmia diseases automatically. The proposed architecture consists of four convolution layers, three pooling layers, and three fully connected layers evaluated on the arrhythmia dataset. All previous researches are conducted to classify healthy people from people with Arrhythmia disease. In this paper, we propose to go further multiclass classification with two classes of cardiac diseases and one class of healthy people. The results are compared with common 1-D CNN and seven different classifiers. The experimental results demonstrate that the proposed architecture is superior to existing classifiers and also competitive with state of the art in terms of accuracy.
A Power Control based Handshake-Competition MAC Protocol for Underwater Acoustic Networks

Underwater acoustic sensor networks are research hotspots in the areas of cognitive oceans and military reconnaissance. The long propagation delay, limited Energy, and low bit rate pose great challenges to the MAC protocol design for the underwater acoustic networks. So in order to solve the problems of the limited energy of underwater acoustic sensor networks and the short lifetime of nodes, a Power Control based Handshake-Competition MAC Protocol for Underwater Acoustic Networks (PC-MAC) is proposed and analyzed. The protocol dynamically adjusts the transmission power according to the channel environment and the distance between nodes. The protocol reduces the energy consumption of data transmission, and ultimately achieves the purpose of prolonging the service life of nodes. The simulation results show that the proposed protocol can significantly improve the energy efficiency of the network.
Design of the Kibble balance for the QEMMS

The design status of the Kibble balance for the Quantum Electro-Mechanical Metrology Suite is provided. The balance is being developed with the objective to obtain a simple and robust design while performing high precision measurements. Aspects related to the vacuum vessel and balance mechanism are described. This includes some design information and simulation results.
A Two-Level Noise-Tolerant Model for Relation Extraction with Reinforcement Learning

Distant supervision has been widely used to automatically label data for relation extraction, but inevitably suffers from wrong labeling problems. Existing methods solve the noisy problem by merely focusing on one aspect, either at the sentencelevel or the bag-level. However, none consider the two levels as a whole. In this paper, we propose a deep reinforcement learning model to solve the noisy problem at both the bag level and the sentence level. For a bag, i.e., a set of sentences containing the same pair of entities, the sentence-level extractor serves as an agent which predicts the label for each sentence, and then determines the label for the bag. The bag-level extractor provides a delayed reward to the agent, and iteratively promotes its performance. The experimental results show that our two-level denoising model effectively improves the performance of distant supervision relation extraction compared to previous methods.
A Hybrid Heuristic Community Detection Approach

Community detection is a very important concept in many disciplines such as sociology, biology and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks. In fact, the analysis of this data make it possible to extract new knowledge about groups of individuals, their communication modes and orientations. This knowledge can be exploited in marketing, security, Web usage and many other decisional purposes. Community detection problem $(C\mathcal{DP})$ is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper we propose a hybrid heuristic approach that does not need any prior knowledge about the number or the size of each community to tackle the $C\mathcal{DP}$. This approach is evaluated on real world networks and the result of experiments show that the proposed algorithm outperforms many other algorithms according to the modularity $(\mathcal{Q})$ measure.
Comparison of the Programmable Quantum Current Generator and an Ultrastable Low-Noise Current Amplifier

The 2019 revision of the International System of Units motivated different practical realizations of the ampere according to the corresponding Mise en pratique. Independently, the national metrology institutes of France and of Germany realized the unit ampere by using Ohm's law and practical realizations of volt and ohm by means of Josephson or quantum Hall effects, respectively. In this work, we present the results of the first direct comparison of both setups with the focus on currents at the level of $50\ \mu \mathrm{A}$ and 50 nA. The relative deviations are $(4\pm 3)\times 10^{-7}$ and $(14\pm 6.5)\times 10^{-7}$ respectively.
Selective Information Transmission using Convolutional Neural Networks for Cooperative Underwater Surveillance

Cooperation among multiple autonomous surface and underwater vehicles is an important capability for detection and tracking of underwater objects. Cooperative autonomy in the underwater environment, however, is challenged by the communication bandwidth. In this work, we propose a selective communication scheme that underpins collaborative surveillance under communication constraints. This scheme classifies signal reflections of sonar pulses that are detected by on-board sensor processing as contacts with the object of interest or background using a convolutional neural network. This network is trained using previously labelled contact spectrograms obtained during three sea trials carried out between 2016–2018. The classification scores at the CNN output are ordered to select the few contacts that the underwater modem bandwidth allows for transmission to the network. First, we evaluate the accuracy of the data-driven information selection scheme using recall scores and similar performance measures. Then, we find the accuracy in Bayesian recursive filtering (tracking) of these contacts for different communication rates using established error metrics. The results suggest that the selective scheme yields a favourable surveillance performance communication cost trade-off.
Effects of Cunninghamia lanceolata plantations at different developmental stages on soil microbial community structure

By the method of denaturing gradient gel electrophoresis(DGGE),the polymorphism of soil bacterial 16S rRNA and fungal 28S rRNA specific fragments in 3-,11-,17-,21-,and 24-year old Cunninghamia lanceolata plantations was studied,with the effects of C.lanceolata plantation's development stage on soil microbial community structure analyzed.The Shannon diversity index(H)and richness(S)showed that the soil microbial community diversity increased significantly with the development of C.lanceolata plantation,but was still significantly lower than that in secondary broad-leaved forests(P0.05). Cluster analysis(UPGMA)indicated that the soil fungal community similarity in different aged C.lanceolata plantations was less than 60%,whereas that of soil bacterial community similarity was up to 65%,suggesting that the shift of soil fungal community structure with the development of C.lanceolata plantation was more obvious than that of soil bacterial community structure.The soil microbial diversity had significant correlations with soil available N content and C/N ratio(P0.05). This study indicated that long-term silviculture with pure plantations could affect soil microbial community via altering soil properties,which in turn,could affect the nutrient cycling in forest ecosystem and the plantation productivity.
Relative Localization in Multi-Robot Systems Based on Dead Reckoning and UWB Ranging

Combining dead reckoning and Ultra-Wideband (UWB) ranging information to achieve relative localization (RL) becomes prevalent in recent years. However, two main problems, i.e., pose initialization and distributed implementation, are rarely investigated in practical multi-robot systems. In this paper, a novel two-stage RL method is proposed to fill this gap, wherein an initialization strategy, using robot-to-robot measurements acquired at different vantage points during robot motion to determine initial pose, and a consensus-based distributed particle filter (DPF), fusing statistics from local robot and neighbors to realize RL, are designed. In our system, by computing the pairwise relative pose between all robots in a team, the initialization strategy can determine an estimate of the initial pose of all robots with respect to a common reference frame and the corresponding covariance. The consensus-based DPF allows determining the relative pose of all robots with significantly reduced computing costs. Experiments results on a team of differentially driven mobile robots show the effectiveness of the initialization strategy and highlight the low computation cost of the proposed approach.
FPGA based Compressive Sensing Framework for Video Compression on Edge Devices

Compressive Sensing (CS) is an emerging signal processing methodology, which compresses the signal being acquired at the time of sensing. As it relies on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Recent advancements in CS made us able to capture compressed videos and images on a sensory level in the camera. As CS cameras are still in the research phase, it is not commercially available. In this paper, we propose a simple FPGA based hardware architecture which makes conventional cameras to produce the compressive sensed video. Here the proposed hardware architecture emulates the sensing methodology of pixel-wise coded exposure imaging for 13x compression, which can be modified based on applications. Our framework does not require any changes to the conventional image sensor as it feeds off the video from the same sensor, this is a huge advantage over previous works in CS video compression. Our implementation results show that the proposed method is effective and easily adaptable to any conventional camera. The proposed framework can be deployed on edge devices with low-cost conventional camera to reduce the communication data rate and help in saving the significant power.
An Empirical Study on the Ability Relationships Between Programming and Testing

Under the software quality management mechanism, developers are generally required to review and test their own code firstly to ensure that the submitted code meets specific quality standards. At the same time, with the popularity of test-driven development (TDD) and extreme programming (XP), programming and testing are complementary in the process of software development, i.e., software testing has become as important as programming. Despite its importance, there is no empirical study that investigates the ability relationships between programming and testing. This article presents such a study, where we designed software tasks to investigate the ability of programming and testing. We distributed the program tasks to software vocational students and analyzed the results from multiple dimensions. Our main findings show that (i) almost half of the developers with strong programming ability do not have a good testing ability; (ii) some developers with weak programming ability can do well in testing; (iii) compared with programming ability, testing fundamentals have a greater impact on the testing ability; and (iv) most developers can do well at finding bugs but lack experience in writing test scripts.