Miracle-dz/UniFAR-FTUs huggingface dataset:
LIMIT-BERT : Linguistic Informed Multi-Task BERT

In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL). LIMIT-BERT includes five key linguistic syntax and semantics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Besides, LIMIT-BERT adopts linguistics mask strategy: Syntactic and Semantic Phrase Masking which mask all of the tokens corresponding to a syntactic/semantic phrase. Different from recent Multi-Task Deep Neural Networks (MT-DNN) (Liu et al., 2019), our LIMIT-BERT is linguistically motivated and learning in a semi-supervised method which provides large amounts of linguistic-task data as same as BERT learning corpus. As a result, LIMIT-BERT not only improves linguistic tasks performance but also benefits from a regularization effect and linguistic information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT obtains new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank.
Deep Dynamic Fusion Network for Traffic Accident Forecasting

Traffic accident forecasting is a vital part of intelligent transportation systems in urban sensing. However, predicting traffic accidents is not trivial because of two key challenges: i) the complexities of external factors which are presented with heterogeneous data structures; ii) the complex sequential transition regularities exhibited with time-dependent and high-order inter-correlations. To address these challenges, we develop a deep Dynamic Fusion Network framework (DFN), to explore the central theme of improving the ability of deep neural network on modeling heterogeneous external factors in a fully dynamic manner for traffic accident forecasting. Specifically, DFN first develops an integrative architecture, i.e., with the cooperation of a context-aware embedding module and a hierarchical fusion network, to effectively transferring knowledge from different external units for spatial-temporal pattern learning across space and time. After that, we further develop a temporal aggregation neural network layer to automatically capture relevance scores from the temporal dimension. Through extensive experiments on real-world data collected from New York City, we validate the effectiveness of our framework against various competitive methods. Besides, we also provide a qualitative analysis on prediction results to show the model interpretability.
Distributed Relay Selection in Presence of Dynamic Obstacles in Millimeter Wave D2D Communication

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses and requires almost a line of sight (LOS) communication path. D2D channel condition is local to devices/user equipments (UEs) and hence is not directly visible to the base station (BS). Thus quality of the D2D channel needs to be propagated to BS by UEs which may incur some delay. Hence the solution provided by BS to UEs using this gathered channel information might become less useful to establish communication due to moving obstacles. These types of obstacles might not be known in advance and hence may cause unpredictable fluctuations to the D2D channel quality. Hence we seek to learn the D2D channels using the finite horizon partially observable Markov decision process (POMDP) framework to model the uncertainty in such kind of network environments with dynamic obstacles. The objective is to minimize delay when channel quality deteriorates, by making UEs choose locally the best possible decision between i) to continue on the current relay link on which communication is taking place or ii) to switch to another good relay by exploring other possible UEs in its locality. We derive an optimal threshold policy which tells the UE to take appropriate decision locally. Later, we give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgement failures, based on which UE make appropriate decision locally. Through extensive simulation, we demonstrate that our approach outperforms recent algorithms.
Thermodynamic laws, economic methods and the productive power of energy

Abstract Energy plays only a minor role in orthodox theories of economic growth, because standard economic equilibrium conditions say that the output elasticity of a production factor, which measures the factor's productive power, is equal to the factor's share in total factor cost. Having commanded only a tiny cost share of about 5 percent so far, energy is often neglected altogether. On the other hand, energy conversion in the machines of the capital stock has been the basis of industrial growth. How can the physically obvious economic importance of energy be reconciled with the conditions for economic equilibrium, which result from the maximization of profit or overall welfare? We show that these equilibrium conditions no longer yield the equality of cost shares and output elasticities, if the optimization calculus takes technological constraints on the combinations of capital, labor, and energy into account. New econometric analyses of economic growth in Germany, Japan, and the USA yield output elasticities that are for energy much larger and for labor much smaller than their cost shares. Social consequences are discussed.
Seasonal Catches, Size and Meristic Data for Sprat, Sprattus Sprattus, in the Severn Estuary

While the sprat, Sprattus sprattus (L.), may live for up to a total of six years, at the end of which time the total length can exceed 150 mm, the fishery in British waters is based primarily on the second, third and fourth year classes (Robertson, 1936, 1938; De Silva, 1973). Historically, the sprat was a major contributor to the fishery of the Bristol Channel and Severn Estuary (Matthews, 1933; Lloyd, 1942). Although this fishery has declined during recent decades, the sprat has remained an important component of the teleost fauna of the region. This point is illustrated by the observation that S. sprattus was by far the most abundant of all teleosts in plankton samples taken throughout the Bristol Channel between the spring and autumn of 1974 (Russell, 1980). While length–frequency data were provided by Russell (1980) for these recently spawned sprat and there is comparable information for those sprat which form the basis of the commercial fishery in the Bristol Channel (Lloyd, 1942), no such data are available for those members of the population which are known sometimes to enter the Severn Estuary in considerable numbers (Lloyd, 1941).
Analysis of Antagonistic Dynamics for Rumor Propagation

The extreme boom of online social networks paves the way for rumor propagation, which may incur an economic loss and cause further public panic. Hence, there is a pressing need to develop countermeasures for reducing side effects posed by rumors. Different from the state-of-the-art work that mostly conducted micro-perspective studies, our paper focuses on a macro-perspective one. In detail, our study neglects technical details and analyzes the antagonistic dynamics between the rumormonger and the rumor suppressor, which provides a deep understanding of the overall development trend of rumor propagation. To reveal the potentials of the rumormonger and the rumor suppressor, the competence-oriented analysis is proposed, where the sufficient and necessary conditions for the existence of the Nash equilibrium in this rumor game are proved rigorously, helping us to derive steady ratios of people who trust or deny a rumor. To figure out the optimal strategy to strike back the rumormonger, the target-oriented analysis is conducted, in which the analytical solutions when the strategies of both players are static are solved and an iteration algorithm is employed to obtain the numerical solutions when their strategies are dynamic. Both numerical and real-world-data based simulations are adopted to verify the proposed competence-oriented and target-oriented analyses.
A Method of HTTP Malicious Traffic Detection on Mobile Networks

Aiming at solving the problem of HTTP malicious traffic detection on mobile networks, we propose a method of HMTD(HTTP Malicious Traffic Detection) based on the spatiotemporal sequence characteristics of traffic data. The traditional malicious traffic detection methods are relatively simple and mainly biased towards misuse detection or abnormal detection and probably suffer from a high false positive rate or false negative rate, so they are difficult to adapt to the current rapid development of the Internet. HMTD uses neural networks for malicious traffic identification, and extracts features from malicious and normal HTTP traffic, which can produce excellent detection results. HMTD utilizes CNN to extract the packet spatial characteristics in the traffic, and utilizes LSTM to extract the temporal characteristics between the packets in the traffic. The experimental results demonstrate that the proposed method can achieve an accuracy of more than 99.4% in the actual network environment and has excellent performance in terms of Precision and Recall.
Improving Monitoring of IT Systems using Machine Learning

High availability of server-bound business applications has become crucial in today’s IT landscape. It is therefore important to monitor these systems for any deviations. Machine learning can be implemented to improve IT monitoring. In this project, an anomaly detection using neural networks was implemented on multidimensional real world server data. Having a proper anomaly detection enables: faster detection of deviations; better capacity for proactive maintenance; and more rapid root cause analysis. Using machine learning to improve IT monitoring or monitoring of data from other domains in a similar format is neither a new topic in the industry nor academia. Prior work in the industry is often a black box and difficult to evaluate, and previous attempts in the academia that use a similar approach as this project implements run their experiments on different kinds of data. The neural networks were assessed using different configurations. Additionally, the ideal configuration was then compared to an established anomaly detection algorithm. The results look promising, but more data and work is needed to evaluate and tune the approach systematically. Bætt eftirlit með tölvukerfum með vélrænu gagnnámi Guðlaugur Garðar Eyþórsson
Rehabilitative Ultrasound Imaging to Study the Gastrocnemius Muscles Morphology in Patients with Genu Varum and Valgum Deformities

Introduction: Genu varum and genu valgum deformities are highly prevalent in Iran. These deformities bring about changes in the structure and function of muscles around the knees. The aim of the present investigation was to study the structure of medial and lateral gastrocnemius muscles in patients with primary genu varum and genu valgum deformities and individuals with normal knee alignment, using rehabilitative ultrasound imaging. Method and Materials : : A Honda 2100 (Honda Co., Japan) real time ultrasound scanner was used in the current the study. The dominant legs of 18 women, 6 with genu varum (mean age: 22.00±1.78 years), 6 with genu valgum (mean age: 21.67±1.03 years), and 6 with normal alignment (mean age: 21.83±1.72 years) were examined. The morphological parameters like muscle thickness, pennation angle and fascicle length of medial and lateral gastrocnemius muscle were compared among groups. Results: In participants with genu varum, the medial gastrocnemius was thinner, closer and shorter in terms of muscle thickness, pennation angle, and fascicle lengths when compared to the lateral gastrocnemius ( P <0.05). However, in participants with genu valgum, it was the other way around ( P <0.05). In participants with normal alignment, there was no statistically significant difference between the medial and lateral gastrocnemius muscle structures. Conclusions: The rehabilitative ultrasound imaging is highly recommended for studying the structure of gastrocnemius muscle. The structural differences between medial and lateral gastrocnemius muscles may be due to the long term neuromuscular adaptation to knee joint alignment in both genu varum and genu valgum deformities. Key words: Genu, Valgum, Varum, Rehabilitative, Ultrasound, Imaging
Adaptive Crawling with Cautious Users

In Online Social Networks (OSNs), privacy issue is a growing concern as more and more users are sharing their candid personal information and friendships online. One simple yet effective attack aims at private user data is to use socialbots to befriend the users and crawl data from users who accept the attackers' friend requests. With the attackers involving, individual users' preference and habit analysis is available, hence it is easier for the attackers to trick the users and befriend them. To better protect private information, some cautious, high-profile users may refer to their friends' decisions when receiving a friend request. The aim for this paper is to analyze the vulnerability of OSN users under this attack, in a more realistic setting that the high profile users having a different friend request acceptance model. Specifically, despite the existing probabilistic acceptance models, we introduce a deterministic linear threshold acceptance model for the cautious users such that they will only accept friend requests from users sharing at least a certain number of mutual friends with them. The model makes the cautious users harder to befriend with and complicates the attack. Although the new problem with multiple acceptance models is non-submodular and has no performance guarantee in general, we introduce the concept of adaptive submodular ratio and establish an approximation ratio under certain conditions. In addition, our results are also verified by extensive experiments in real-world OSN data sets.
Design of Microscale Piezoelectric Energy Harvesting System

This paper presents a design of microscale piezoelectric energy harvesting system, AC/DC converter is used to convert the AC power generated from piezoelectric to DC power, then DC/DC is used to convert the DC voltage to suitable voltage, Analog to digital converter is used to convert the analog signal produced from AC/DC to 8 bits digital signal then feeds to digital control oscillator that produced the clock for DC/DC and hence guarantees maximum point power tracking of all system based on the input signal.
Revisiting Depth Image Fusion with Variational Message Passing

The running average approach has long been perceived as the best choice for fusing depth measurements captured by a consumer-grade RGB-D camera into a global 3D model. This strategy, however, assumes exact correspondences between points in a 3D model and points in the captured RGB-D images. Such assumption does not hold true in many cases because of errors in motion tracking, noise, occlusions, or inconsistent surface sampling during measurements. Accordingly, reconstructed 3D models suffer unpleasant visual artifacts. In this paper, we visit the depth fusion problem from a probabilistic viewpoint and formulate it as a probabilistic optimization using variational message passing in a Bayesian network. Our formulation enables us to fuse depth images robustly, accurately, and fast for high quality RGB-D keyframe creation, even if exact point correspondences are not always available. Our formulation also allows us to smoothly combine depth and color information for further improvements without increasing computational speed. The quantitative and qualitative comparative evaluation on built keyframes of indoor scenes show that our proposed framework achieves promising results for reconstructing accurate 3D models while using low computational power and being robust against misalignment errors without post-processing.
A Band-Pass Noise-Shaping Modulator Using the Error-Feedback Structure on a 10-bit SAR ADC

This paper presents a band-pass noise-shaping (NS) successive-approximation-register (SAR) analog-to-digital converter (ADC) with a second-order modulator which is comprised of a fully passive switched-capacitor (SC) filter and a gain-boosted dynamic amplifier. It employs the error-feedback (EF) structure in a simple method to implement the complicated noise transfer function (NTF) zeros, which enhances the noise-shaping capability by minimally modifying a standard SAR ADC. Designed in 28 nm CMOS process, the prototype of proposed NS-SAR ADC achieves 76.84 dB signal-to-noise and distortion ratio (SNDR) with the bandwidth between 20 and 40 MHz and the FoMw is only 2.24 fJ/step at the oversampling ratio (OSR) of 5.
MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction

Critiquing-based recommender systems aim to elicit more accurate user preferences from users' feedback toward recommendations. However, systems using a graphical user interface (GUI) limit the way that users can critique the recommendation. With the rise of chatbots in many application domains, they have been regarded as an ideal platform to build critiquing-based recommender systems. Therefore, we present MusicBot, a chatbot for music recommendations, featured with two typical critiquing techniques, user-initiated critiquing (UC) and system-suggested critiquing (SC). By conducting a within-subjects (N=45) study with two typical scenarios of music listening, we compared a system of only having UC with a hybrid critiquing system that combines SC with UC. Furthermore, we analyzed the effects of four personal characteristics,musical sophistication (MS), desire for control (DFC), chatbot experience (CE), and tech savviness (TS), on the user's perception and interaction of the recommendation in MusicBot. In general, compared with UC, SC yields higher perceived diversity and efficiency in looking for songs; combining UC and SC tends to increase user engagement. Both MS and DFC positively influence several key user experience (UX) metrics of MusicBot such as interest matching, perceived controllability, and intent to provide feedback.
IPv6-Based Smart Grid Communication over 6LoWPAN

Smart Grid is a major element of the Smart City concept that enables two-way communication of energy data between electric utilities and their consumers. These communication technologies are going through sharp modernization to meet future demand growth and to achieve reliability, security, and efficiency of the electric grid. In this paper, we implement an IPv6 based two-way communication system between the transformer agent (TA), installed at local electric transformer and various customer agents (CAs), connected to customer's smart meter. Various homes share their energy usage with the TA which in turn sends the utility's recommendations to the CAs. Raspberry Pi is used as hardware for all the CAs and the TA. We implement a self-healing mesh network between all nodes using OpenLab IEEE 802.15. 4 chips and Routing Protocol for Low-Power and Lossy Networks (RPL), and the data is secured by RSA/AES keys. Several tests have been conducted in real environments, inside and outside of Carleton University, to test the performance of this communication network in various obstacle settings. In this paper, we highlight the details behind the implementation of this IPv6-based smart grid communication system, the related challenges, and the proposed solutions.
Thai word segmentation on social networks with time sensitivity

Social network service like Twitter is one of the important social networks that has had a huge impact on Thai culture.It has changed the behavior of many Thai people from using televisions to using computers or smart phones regularly.Thai people also share their experiences and get information such as news on social networks. With the increasing number of micro-blog messages that are originated and discussed over social networks, Thai word segmentation is becoming a compelling research issue as it is an important task in natural language processing. However, the existing Thai segmentation approaches are not designed to deal with short and noisy messages like Twitter. In this paper, we proposed Thai word segmentation on social networks approach by exploit both the local context (in tweets) and the global context from Thai Wikipedia.We evaluate our approach based on a real-world Twitter dataset. Our experiments show that the proposed approach can effectively segment Twitter messages over the baseline.
Simois: A Scalable Distributed Stream Join System with Skewed Workloads

Many BigData applications require to perform quick join operations on different large-scale real-time data streams. The key challenge to design an efficient stream join system is how to reasonably partition the streaming data among distributed processing nodes to avoid high density of join computation. However, the skewed distribution of real world streams raises great challenges for streaming data partitioning in distributed stream join systems. Existing hash based partitioning schemes incur significant load imbalance which leads to low system throughput and long processing latency, while shuffling based strategies incur redundant join computation and much more communication. To address this issue, in this paper, we propose and implement a scalable distributed stream join system, Simois, which shuffles the potential top heavy-load keys while hashing the others. However, how to identify the keys which lead to the heavy workload imbalance is challenging, because the heavy workload is determined by the current joint status of two streams, and the distribution of the two streams may change with time. To solve this problem, we design a novel efficient exponential counting scheme for identifying the keys with the heaviest workload in the two dynamic streams. The proposed exponential counting scheme needs extremely low computation and space cost, so that it can be well implemented in a stream processing system. Moreover, we design a popularity decline algorithm to make our design adaptive to the highly dynamic changes of streams. We implement Simois on top of Apache Storm and conduct comprehensive experiments using large-scale real world traces. Experiment results show that Simois improves the system throughput significantly by 52% and reduces the average latency by 37%, compared to existing state-of-the-art designs.
Critical analysis of genetic algorithm based IDS and an approach for detecting intrusion in MANET using data mining techniques

Intrusion can be defined as any action which leads to the compromise of availability, confidentiality or integrity of the system. Intrusion detection system is the mechanism which detects those malicious actions and takes the appropriate security actions. Due to the varied number of network behaviors and the rapid development of attack fashions, it has become essential to use the fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. Most of the Intrusion Detection Systems (IDSs) are rule based. In this paper, we have explained genetic algorithm approach to the intrusion detection system. Similarly the data mining approach in intrusion detection is also explained. Both the approaches are analyzed with their respective merits and demerits. The principles behind intrusion detection in MANET (Mobile Adhoc Networks) have also been given. A hybrid approach has also been proposed which considers the principles of the various IDSs. An imperative study of the various intrusion detection systems has been proposed. Keywords-Data mining, Genetic algorithm, Intrusion Detection, IDS, Intrusion Detection System,,MANET