Miracle-dz/UniFAR-FTUs huggingface dataset:
Bi-Level Attention Model for Sentiment Analysis of Short Texts

Short text is an important form of information dissemination and opinion expression in various social media platforms. Sentiment analysis of short texts is beneficial for the understanding of customers’ emotional state, obtaining customers’ opinions and attitudes toward events, information and products, however, is difficult because the sparsity of the short-text data. Unlike the traditional methods using the external knowledge, this paper proposes a bi-level attention model for sentiment analysis of short texts, which does not rely on external knowledge to deal with the data sparsity. Specifically, at word level, our model improves the effect of word representation by introducing latent topic information into word-level semantic representation. Neural topic model is used to discover the latent topic of the text. A new topic-word attention mechanism is presented to explore the semantics of words from the perspective of topic–word association; At the sequence level, a secondary attention mechanism is used to capture the relationship between local and global sentiment expression. Experiments on the ChnSentiCorp-Htl-ba-10000 and NLPCC-ECGC datasets validate the effectiveness of the BAM model.
Assessment and prediction of fishery water quality using electrochemical sensor array carried by UAV

Traditional fishery water assessment and prediction rely on collecting water samples and sending them to inspection in different time periods, and then carrying out physical and chemical analysis and testing, it requires a long period and consumes more human resources, which is not only cumbersome but also inefficient. Aiming at the above problems, a method for assessing and predicting fishery water quality using electrochemical sensor array such as pH, dissolved oxygen and ammonia nitrogen carried by unmanned aerial vehicle (UAV) is proposed. The T-shaped floating structure of the UAV can realize the take-off and landing of water surface. The sensors in the structure carry out real-time detection in the process of UAV floating. The sensor data is transmitted to the server backstage by wireless network. Then the time series analysis is applied to assess and predict the water quality data. Finally, the conclusion is displayed on the application program. The results show that the scheme can assess fishery water quality efficiently and has high prediction accuracy (95.1%).
A Convolutional Neural Network Approach for Classification of LPWAN Technologies: Sigfox, LoRA and IEEE 802.15.4g

This paper presents a Convolutional Neural Network (CNN) approach for classification of low power wide area network (LPWAN) technologies such as Sigfox, LoRA and IEEE 802.15. 4g. Since the technologies operate in unlicensed sub-GHz bands, their transmissions can interfere with each other and significantly degrade their performance. This situation further intensifies when the network density increases which will be the case of future LPWANs. In this regard, it becomes essential to classify coexisting technologies so that the impact of interference can be minimized by making optimal spectrum decisions. State-of-the-art technology classification approaches use signal processing approaches for solving the task. However, such techniques are not scalable and require domain-expertise knowledge for developing new rules for each new technology. On the contrary, we present a CNN approach for classification which requires limited domain-expertise knowledge, and it can be scalable to any number of wireless technologies. We present and compare two CNN based classifiers named CNN based on in-phase and quadrature (IQ) and CNN based on Fast Fourier Transform (FFT). The results illustrate that CNN based on IQ achieves classification accuracy close to 97% similar to CNN based on FFT and thus, avoiding the need for performing FFT.
Understanding Performance Gains of Accelerator-Rich Architectures

The power and utilization walls in today's processors have led to a recent focus on accelerator-rich architectures (ARAs), which include a sea of customized accelerators with orders-of-magnitude performance and energy gains. Meanwhile, some researchers wonder how the reported large gains are achieved, considering that ARAs use a similar memory hierarchy to conventional processors. In this paper we conduct an in-depth analysis of ARAs with a key focus on the memory access component not studied in prior work. Based on our experimental results, we observe that ARAs achieve performance gains from both computation and memory access customization. For computation customization, ARAs not only exploit the coarse-grained parallelism as conventional processors do, but also uniquely customize a deep processing pipeline without instruction overhead. For memory access customization, ARAs exploit a tile-based read-compute-write execution model that both reduces the number of memory accesses and improves the memory-level parallelism (MLP). We quantitatively evaluate the performance impact of such factors and surprisingly find that 1) memory access customization plays a bigger role in the performance improvement than computation customization, and 2) the dominating contributor to the ARA memory access performance improvement is the improved MLP rather than the widely-expected memory access reduction. Indeed, we find that existing GPU accelerators also benefit from the improved MLP through different techniques. The unique customized deep processing pipeline of ARAs further provide an average of 1.4x speedup over GPUs. Moreover, on average, ARAs are 18x more energy efficient over GPUs. We hope this understanding can help future ARA design and adoption.
WMixNet: An Energy-Scalable and Computationally Lightweight Deep Learning Accelerator

In this paper, we present a lightweight CNN model named MixNet which is easily scalable to different energy requirements in embedded platforms. The MixNet model uses two extreme bit-precisions that efficiently balances the model accuracy and energy consumption. The energy consumption in processing MixNet is managed by controlling the ratio between high-precision (16bit) and low-precision (1bit) paths. Since only two bit-precisions are required in designing a hardware accelerator, the control logic becomes simpler compared to other multi-precision accelerators. In addition, a reconfigurable multiplier is proposed to enable highly parallel MixNet computations for faster prediction and/or training. Overall, the energy efficiency in terms of run-time per unit power improves by 1.75 ~1.94 × over the recently proposed reduced-precision CNN model.
Towards Real Time Radiotherapy Simulation

We propose a novel reconfigurable hardware architecture to implement Monte Carlo based simulation of physical dose accumulation for intensity-modulated adaptive radiotherapy. The long term goal of our effort is to provide accurate dose calculation in real-time during patient treatment. This will allow wider adoption of personalised patient therapies which has the potential to significantly reduce dose exposure to the patient as well as shorten treatment and greatly reduce costs. The proposed architecture exploits the inherent parallelism of Monte Carlo simulations to perform domain decomposition and provide high resolution simulation without being limited by on-chip memory capacity. We present our architecture in detail and provide a performance model to estimate execution time, hardware area and bandwidth utilisation. Finally, we evaluate our architecture on a Xilinx VU9P platform as well as the Xilinx Alveo U250 and show that three VU9P based cards or two Alevo U250s are sufficient to meet our real time target of 100 million randomly generated particle histories per second.
Experimental Evaluation of Timed Finite State Machine Based Test Derivation

Finite State Machines (FSMs) are widely used for deriving tests with guaranteed fault coverage for various kinds of discrete and hybrid systems such as telecommunication protocols and services. A Timed FSM (TFSM) seems to be a natural extension of an FSM annotated with timed variables that allows to adequately describe a system under test for which time aspects are important. However, most of existing test derivation methods for timed automata with several clocks return very long test suites. We propose to consider FSMs with a single clock for deriving test suites with guaranteed fault coverage and this can be a compromise between the model complexity and adequacy. A test is derived based on FSM abstraction of a given TFSM and is complete with respect to an established fault model. The experiments were performed for randomly generated TFSMs as well as for TFSMs which are used for service description and they clearly show that a proposed method returns high quality tests with the length that is close to those returned by classical FSM based methods. For service TFSMs we also evaluate the test suite length for different fault domains, the length of preset distinguishing and homing sequences which are widely used for minimizing FSM based test suites with guaranteed fault coverage.
Identifying Risks of the Internet Finance Platforms Using Multi-Source Text Data

With the explosion of the Internet Finance Platforms, identifying the risks of these platforms is of growing significance, which can help discover problematic platforms in time and ensure the healthy development of the Internet finance industry. In this paper, we design a risk index system to measure the quantitative risk of the Internet finance platforms, and propose a deep neural network based model, CBiGRU-RI, to identify the risks of the platforms using multi-source text data. We conducted comparative experiments with various baseline models on real-world data. The experimental results show that our proposed model can identify the risks of platforms more effectively than the baseline methods.
Simultaneous Multi-Dataset PET Image Reconstruction Using a Progressive Mutually Weighted Quadratic Penalty

There are many contexts in which a patient is scanned with positron emission tomography (PET) multiple times, for example in oncology treatment monitoring scans, or to obtain complementary information from multiple radiotracers. As is common in PET, such scans often suffer from high noise due to the counts-limited nature of PET acquisitions. Reduction of this noise while preserving scan-specific features by sharing information between datasets is the subject of ongoing research. In this work we adapt a recently proposed joint PET- MR reconstruction method to the multi-dataset PET context in order to reduce PET image noise across data-series. The method calculates similarity weights between neighbouring voxels in each image at each iteration and uses these to update the weighted quadratic penalty (WQP) for use in the next iteration. By iterating the two-step process of weights calculation and a penalised PET reconstruction update, the mutually weighted quadratic penalty (MWQP) method is defined. The proposed method was applied to a 2-scan, 2D, [18F]fluorodeoxyglucose brain cancer treatment response simulation study. Results indicate that using the MWQP method allows for reduced reconstructed image noise (up to 89%) while avoiding bias within scan-unique features, such as tumours. In this respect the method is superior to maximum-likelihood expectation maximisation reconstructions with a Gaussian smooth, total-variation regularised reconstructions, and traditional guided image reconstruction techniques that only allow a one-way transfer of information between datasets. Reconstructed visual image quality reflects the quantitative results, demonstrating lower image-wide noise and higher tumour contrast These results suggest that using a MWQP reconstruction approach improves reconstructed image quality beyond that achieved with conventional methods. Future work will involve further characterisation of the method and testing on clinical datasets.
Detecting Alpha-induced Radioluminescence in the UVC Wavelength Range Using a UVTron Flame Sensor, and the Effect of a Gas Flow on Detection Rates as Compared to an Air Atmosphere

Alpha-induced radioluminescence provides a potential avenue for the detection of alpha-emitting materials from a distance far greater than the travel of alpha-particles themselves. This work details experiments carried out into the detection of this radioluminescence in the ultraviolet C wavelength range (180-280 nm) using an off-the-shelf flame sensor, the UVTron (Hamamatsu, Japan). There is less interference from natural and artificial background lighting in the ultraviolet C wavelength range than at other ultraviolet wavelengths. A UVTron flame sensor (R9533, Hamamatsu, Japan), which is sensitive only in the ultraviolet C wavelength range, was used to detect the presence of a 6.95 MBq 210Po source at a distance of approximately 20 mm. The signal (0.3280 counts per second) was over 147 times that of the background, which was very low (2.224 × 10-3 counts per second) under the general laboratory/commercial lighting conditions. The limit of detection, where the signal can be distinguished from background, can be calculated to be approximately 240 mm under these conditions, assuming a standard 1/r2, which is much greater than the alpha particle travel. Gas was flowed over the alpha sample to determine if this would enhance the radioluminescence and hence the detection by the UVTron. Gases of Ar, Xe, Ne, N2, Kr and P10 were tested, all of which increased the signal detected by the UVTron sensor. The greatest increase was found to be in a flow of Xe, which greater than doubled the counts per second of the detector in one instance. The ability of the UVTron to detect the radioluminescence from alpha-emitting materials and the enhancement which may be possible using a flow of gas, indicate the potential of the UVTron sensor for inclusion in an alpha-emitting materials detection system which could be operated at a distance in the field, for example for nuclear decommissioning characterisation purposes or nuclear security applications.
A Compact Self-Capacitance Sensing Analog Front-End for a Touch Detection in Low-Power Mode

A novel self-capacitance transition sensing method is presented for low-power touch detection using a capacitive touch-screen. While maintaining a voltage level, an additional electric charge is additionally required when a touch-input is newly added; the amount of charge is used for detection. Accordingly, the proposed current mirroring voltage-level regulation (CM-VLR) circuit senses the transition of self-capacitance of the touch-screen and detects motions of the touch-object. Only one CM-VLR cell is used to scan the entire touch-screen. Thus low-power readout and high integrated-circuit area efficiency are achieved. Moreover, the proposed self-capacitance sensing method does not require an offset-calibration step through a charge-sharing-based voltage generation and the offset-coverage capacitor. Fabricated in a 180-nm CMOS process, and the CM-VLR cell occupies 0.12 mm2. At a 120-Hz report rate, the proposed analog front-end (AFE) detects touch-input at a 32-dB SNR while dissipating 2.1 mW.
Fault-Tolerant Control of IPMSMs Based on an Modified Sliding Mode Observer

This paper proposes a fault-tolerant control system for interior permanent magnet synchronous motor (IPMSM) drives operating in the middle and high speed regions. The proposed control system is capable of dealing with encoder's failures, hence increase the reliability and performance of PMSM. Sensor's setup of the control system consists of an actual sensor (encoder's model) and the virtual sensor (Sliding Mode Observer, SMO). The work focuses on performance of SMO when the encoder fails in low speed operation. In normal operation, position and speed signals from the encoder will be sent to the controller. When the encoder's failures are detected, the estimated values from the SMO are used. Presence of the noise in SMO observer operation affects dramatically to the stability of the control system. To reduce the noise low pass filters are added to the observer design. Finally, phase-locked loop (PLL) technique has been used to estimate of rotor's position and velocity. The feasibility of the proposed controller is confirmed via simulations of IPMSM drive model in MATLAB/Simulink software.
CloudBC-A Secure Cloud Data acess Management system

Data security and Storage Management becomes the most promising task for any IT operation in the future. The business and service provider’s mange to finds the technology like cloud computing for pooled computing resources. The enormous amounts of research works are initiated in parallel to protect data on the cloud environment. This leads to the strong evidence of unanswered questions related to the data security over the cloud data centres. The Cloud computing model used as a primary technology in many IT business operations across the millions of users. To ensure the data security and modification over the data stored on the cloud considered as a major issue among the community cloud users. The Proposed research work aims to protect the cloud data by implementing the new approach using blockchain technology. The Hybrid approach to combine the BlockChain(BC) Technology with cloud computing to ensure the data security among the community cloud users. is Hybrid approach will restrict the malicious data access and modification on the cloud servers. The secure hash blocks are generated and maintained by the cloud storage devices enhance the data security on the cloud. The block chains allow the user to trace back the malicious data access and modifications.
A Research on Automated Guided Vehicle Indoor Localization System Via CSI

Obviously, we cannot deny the important role that the Automated Guided Vehicles (AGV) play in warehouse and logistic industry nowadays. Managing and monitoring the AGV system became more and more important for every company. However, so far, most of its precise navigating systems have been based on optical solution such as laser triangulation, image processing, line-follower, QR code scanner, etc. which is only suitable for a dust-free environment, not likely to cement warehouse, etc. In this paper we would like to inform a nonoptical based method to localize AGVs in the indoor environment via Channel State Information (CSI), which could be effortlessly extracted from normal wireless card in every WiFi interface. Although these technique was quite well-received by telecommunication fields, very few of those made use on real AGV application. We prefer using CSI to applying Received Signal Strength Indicator (RSSI) because of its robust accuracy in the indoor environment which had been tested in our first paper. And finally, we also try to optimize the data collection through an API server application so that we can put forward to deep learning techniques to train the system more accuracy.
An Efficient Deep Neural Network Multilayer Perceptron Based Classifier in Healthcare System

The amount of data being stored in various repositories has been growing rapidly. In order to uncover hidden information from such data, there is a need for information extraction techniques. This resulted in the use of Data Mining and Machine Learning tasks such as Classification. One among the industries producing huge number of records on a daily basis is the Healthcare system. Data associated with medical field deal with the diagnosis of patients, higher accuracy in decision making is expected. In this work, we deal with the analysis of Cleveland Heart Disease data which contains 303 instances of patients records. Four classifiers are applied on the processed data to measure the accuracy of classification. Our results show that Deep Neural Net MLP classifier is able to diagnose the status of a patient with more accuracy.
Investigation of knowledge transfer approaches to improve the acoustic modeling of Vietnamese ASR system

It is well known that automatic speech recognition ( ASR ) is a resource consuming task. It takes sufficient amount of data to train a state-of-the-art deep neural network acoustic model. As for some low-resource languages where scripted speech is difficult to obtain, data sparsity is the main problem that limits the performance of speech recognition system. In this paper, several knowledge transfer methods are investigated to overcome the data sparsity problem with the help of high-resource languages. The first one is a pretraining and fine-tuning ( PT/ FT) method, in which the parameters of hidden layers are initialized with a well trained neural network. Secondly, the progressive neural networks ( Prognets ) are investigated. With the help of lateral connections in the network architecture, Prognets are immune to forgetting effect and superior in knowledge transferring. Finally, bottleneck features ( BNF ) are extracted using cross-lingual deep neural networks and serves as an enhanced feature to improve the performance of ASR system. Experiments are conducted in a low-resource Vietnamese dataset. The results show that all three methods yield significant gains over the baseline system, and the Prognets acoustic model performs the best. Further improvements can be obtained by combining the Prognets model and bottleneck features.
Overview on SNIFFPHONE: a portable device for disease diagnosis

We present SNIFFPHONE, a handy and easy-to-use device that allows the non-invasive detection of gastric diseases. It analyzes the user’s exhaled breath using specifically developed gas sensors. The device is coupled to a smartphone, which governs the breath analysis process, sends the data measurements to an external data analysis server, and finally gives feedback to the user. In this work, we describe the SNIFFPHONE device and the general platform under development.
CNN-based DGA Detection with High Coverage

Attackers often use domain generation algorithms (DGAs) to create various kinds of pseudorandom domains dynamically and select a part of them to connect with command and control servers, therefore it is important to automatically detect the algorithmically generated domains (AGDs). AGDs can be broken down into two categories: character-based domains and wordlist-based domains. Recently, methods based on machine learning and deep learning have been widely explored. However, much of the previous work perform well in detecting one kind of DGA families but poorly in classifying another kind. A general detection system which is applicable to both kinds of domains still remains a challenge. To address this problem, we propose a novel real-time detection method with high accuracy as well as high coverage. We first convey a domain name into a sequence of word-level or character-level components, then design a deep neural network based on temporal convolutional network to extract the implicit pattern and classify the domain into two or more categories. Our experimental results demonstrate that our model outperforms state-of-the-art approaches in both binary classification and multi-class classification, and shows a good performance in detecting different kinds of DGAs. Besides, the high training efficiency of our model makes it adjust to new malicious domains quickly.
Development of quartz crystal microbalance based sensor for real-time ozone monitoring

This paper presents the development of quartz crystal microbalance based sensor for the real-time monitoring of ozone (O3) in workplaces in the range of 10–100 ppbv (part per billion per volume). Quartz crystal microbalances are interesting because of their capability to detect the adsorption of gaseous compounds on the crystal coating. Here, we focused on the polybutadiene performance as coating material in terms of stability, sensibility and reproducibility under real conditions. Chemical sensors were exposed to low concentration of ozone in a dedicated controlled test atmosphere device in order to determine the influence of several parameters: temperature, humidity, concentration and sampling flow rate. Results show that our detection device can detect 7 ppb in few minutes and does not undergo decrease of sensibility under moisture.