Miracle-dz/UniFAR-FTUs huggingface dataset:
On Cost-Driven Collaborative Data Caching: A New Model Approach

In this paper we consider a new caching model that enables data sharing for network services in a cost-effective way. The proposed caching algorithms are characterized by using monetary cost and access information to control the cache replacements, instead of exploiting capacity-oriented strategies as in traditional approaches. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first propose a fast off-line algorithm using dynamic programming techniques, which can generate an optimal schedule within <inline-formula><tex-math notation="LaTeX">$O(mn)$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq1-2868642.gif"/></alternatives></inline-formula> time-space complexity by using cache, migration as well as replication to serve a <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq2-2868642.gif"/></alternatives></inline-formula>-length request sequence in a <inline-formula><tex-math notation="LaTeX">$m$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq3-2868642.gif"/></alternatives></inline-formula>-node network, substantially improving the previous results. Furthermore, we also study the online form of this problem, and present an 3-competitive online algorithm by leveraging an idea of anticipatory caching. The algorithm can serve an online request in constant time and is space efficient in <inline-formula><tex-math notation="LaTeX">$O(m)$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq4-2868642.gif"/></alternatives></inline-formula> as well, rendering it more practical in reality. We evaluate our algorithms, together with some variants, by conducting extensive simulation studies. Our results show that the optimal cost of the off-line algorithm is changed in a parabolic form as the ratio of caching cost to transfer cost is increased, and the online algorithm is less than 2 times worse in most cases than its optimal off-line counterpart.
Design of a  $W$ -Band GaAs-Based SIW Chip Filter Using Higher Order Mode Resonances

In this letter, an on-chip bandpass filter is designed and implemented based on a 70-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> GaAs substrate integrated waveguide (SIW) technique for W-band integrated transceiver applications. To achieve a high-frequency selectivity, a modal bypass coupling realized by higher order mode resonators is introduced in the SIW filter, and two designable transmission zeros are generated accordingly. The presented filter has a center frequency of 92.7 GHz, with a 3-dB bandwidth of about 3.2 GHz. The measured out-of-band rejection is better than 30 dB at 90 and 95 GHz, respectively.
De-noising of GIS partial discharge signal corrupted with narrow-band noise using Shannon wavelet

Ultra high frequency(UHF) method has been widely used in GIS partial discharge monitoring for its outstanding ability of anti-interference nowadays. However, suffused narrowband noise with frequencies lying in the band of the sensor may still intrude into the monitor system, thus distorting the partial discharge monitoring signal. The acquired monitoring signal is used to identify the existence of GIS internal insulation defects, so it is of great significance to de-noise the signal. In this paper, Shannon wavelet is selected for its rectangular spectral characteristic in frequency domain, and sub-band Shannon entropy is regarded as a criterion to locate the narrow-band noise frequency. The de-noised signal is obtained by setting to zero the coefficients of sub-bands with over-low Shannon entropy. Numeric simulation experiments have been set to verify the feasibility and correctness of the proposed method.
Requirements Definition with Extended Goal Graph

Significant requirements are often discovered during discussion about tradeoffs and conflicts between stakeholders in requirements meeting. Developing a method to handle tradeoffs and conflicts becomes a breakthrough to acquire significant requirements which are difficult to elicit for requirements analysts. In this paper, the Extended Goal Graph (EGG) is proposed as a method for handling tradeoffs and conflicts by providing traceability between requirements analysis and system design. We developed the EGG system to support requirement definition process with the EGG. The system was applied to the requirement definition meeting among medical doctors and potential patients for selecting proper inspections required for diagnosing disease when shadows on lungs were found.
98-dB Gain Class-AB OTA With 100 pF Load Capacitor in 180-nm Digital CMOS Process

In this paper, a new two-stage class-AB operational trans-conductance amplifier (OTA) is proposed. Using active loads in the first stage and improved recycling structure, the effective trans-conductance of the first stage is increased. The nonlinear current mirrors boost the current of the second stage in the slewing mode, leading to the increase of the slew rate (SR). Exploiting the multi-path scheme and increasing the effective trans-conductance cause the OTA DC-gain to be enhanced. The proposed OTA has been fabricated using 1P6M 180-nm CMOS technology under 1.8 V supply voltage. The OTA exhibits 98 dB DC-gain and 21 MHz unity-gain bandwidth with a 100 pF capacitive load, consuming 3 mW from 1.8 V supply voltage.
Making Strassen Matrix Multiplication Safe

Strassen's recursive algorithm for matrix-matrix multiplication has seen slow adoption in practical applications despite being asymptotically faster than the traditional algorithm. A primary cause for this is the comparatively weaker numerical stability of its results. Techniques that aim to improve the errors of Strassen stand the risk of losing any potential performance gain. Moreover, current methods of evaluating such techniques for safety are overly pessimistic or error prone and generally do not allow for quick and accurate comparisons. In this paper we present an efficient technique to obtain rigorous error bounds for floating point computations based on an implementation of unum arithmetic. Using it, we evaluate three techniques - exact dot product, fused multiply-add, and matrix quadrant rotation - that can potentially improve the numerical stability of Strassen's algorithm for practical use. We also propose a novel error-based heuristic rotation scheme for matrix quadrant rotation. Finally we apply techniques that improve numerical safety with low overhead to a LINPACK linear solver to demonstrate the usefulness of the Strassen algorithm in practice.
Sleep Stage Scoring of Single-Channel EEG Signal based on RUSBoost Classifier

Automatic sleep stages detection in medical applications using an intelligent method is one of the most important issues in recent years which leads to reducing the workload of specialist in analyzing sleep data with visual inspection. In this paper, a single-channel EEG-based algorithm is presented for automatic recognition of sleep stages using complete ensemble empirical mode decomposition (CEEMD), hybrid genetic algorithm (GA) and neural network. First, the EEG signal is decomposed into IMFs using CEEMD and statistical and temporal features are extracted. For dimensionality reduction of feature space, a hybrid GA and multi-layer back propagation neural network is applied. Then, McNemar’s test is used to validate the features. The RUSBoost algorithm performs the final classification on these optimized features and on average, the accuracies of classification for 2 up to 6 classes are 98.10%, 95.12%, 93.09%, 90.16% and 91.0% and Kappa Cohen coefficients are 0.98, 0.95, 0.93, 0.89 and 0.91, respectively. It is shown that the proposed method has better performance in classification of sleep stages compared to the previous studies.
Effects of GPS Spoofing on Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are no longer exclusively military and scientific solutions. These vehicles have been growing in popularity among hobbyist and also as industrial solutions for specific activities. The flying characteristics and the absence of a crew on board of these devices allow them to perform a wide variety of activities, which can be inaccessible to humans or may threat their life. Despite the advantages, they also bring up major concerns regarding security breaches in the flight controller software, which may lead to security (e.g., vehicle hijacking by attackers), safety (e.g., crashing the vehicle into a planned area or building), or privacy (e.g., eavesdropping or stealing video footage) problems. GPS spoofing is one the main threat of UAVs. The predictability and knowledge of GPS signal properties, create conditions to attackers to assume control of the UAV and use it for their own objectives. In this paper the GPS spoofing effect on UAV is analyzed through a series of tests, under a simulation environment. The results are shown as deviation from the original trajectory and attack success, and analyzed over time and by attack type.
ER-CRLB Analysis for Indoor Multi-Antenna Localization System : (Invited Paper)

Using channel state information (CSI) values to achieve fine grained localization has received much attention in the recent years. However, the impacts of signal power, the non-line-of-sight, and the scale of the playing field have not been investigated yet. In this paper, we propose an extended recursive Cramér-Rao lower bound (ER-CRLB) method as a tool to analyze the performance of multi-antenna based wireless indoor localization systems. The Fisher information matrix of the channel state information based wideband wireless system is formulated, in which the unknown estimated vector is classified as two parts: the estimated vector and the auxiliary vector. The observations are the CSI values which indicate the attenuation and the propagation delay. We employ the ER-CRLB to analyze the impacts of geometry impact, NLOS identification, power control and prior target state information. The simulation results indicate that the power control scheme is more essential than related prior information.
iNKT Cell Activation Exacerbates the Development of Huntington's Disease in R6/2 Transgenic Mice

Huntington's disease (HD) is an inherited neurodegenerative disorder which is caused by a mutation of the huntingtin (HTT) gene. Although the pathogenesis of HD has been associated with inflammatory responses, if and how the immune system contributes to the onset of HD is largely unknown. Invariant natural killer T (iNKT) cells are a group of innate-like regulatory T lymphocytes that can rapidly produce various cytokines such as IFNγ and IL4 upon stimulation with the glycolipid α-galactosylceramide (α-GalCer). By employing both R6/2 Tg mice (murine HD model) and Jα18 KO mice (deficient in iNKT cells), we investigated whether alterations of iNKT cells affect the development of HD in R6/2 Tg mice. We found that Jα18 KO R6/2 Tg mice showed disease progression comparable to R6/2 Tg mice, indicating that the absence of iNKT cells did not have any significant effects on HD development. However, repeated activation of iNKT cells with α-GalCer facilitated HD progression in R6/2 Tg mice, and this was associated with increased infiltration of iNKT cells in the brain. Taken together, our results demonstrate that repeated α-GalCer treatment of R6/2 Tg mice accelerates HD progression, suggesting that immune activation can affect the severity of HD pathogenesis.
Deep Feature Ranking for Person Re-Identification

Person re-identification plays a critical part in many surveillance applications. Due to complicated illumination environments and various viewpoints, it is still a challenging problem to extract robust features. To solve this issue, we propose a novel deep feature ranking scheme. Our main contribution is to rank achieved deep features, which are obtained by classic deep learning model, and set the sort order number as our feature vector, named as ordinal deep features (ODFs). Person re-identification results are acquired by ranking person candidates by measuring distance based on ODFs. Since applying for rank orders rather than original feature values, our method achieves robust results, especially under the situation of viewpoints shift. Comprehensive experiments are carried out to demonstrate the significance of the proposed feature. Meanwhile, comparative experiments are applied over the publicly available dataset, our method achieves promising performance and outperforms the state of the art methods. Moreover, we applied the proposed feature in the scenario of image classification and discussed the effectiveness.
Detection Possibilities of Depression and Parkinson’s disease Based on the Ratio of Transient Parts of the Speech

Speech production is a complex and complicated brain process. Thus the speech of subjects who suffer from neurological or psychological disease such as Parkinson’s disease or depression differ from the normal speech from healthy speakers. It is characteristic, that their speech tempo is slower, and their articulation is inadequate. These alterations modify the speech product to become monotone and reduce the rate of rapid and significant changes. In this study it was investigated how the ratio of transient parts is affected in case of depression and Parkinson’s disease. Overall 321 speech samples were examined from healthy, depressed and Parkinson’s diseased speakers. The transient parts were detected in their speech using an automatic transient detection method, and based on the detection, the ratio of the transient parts (RoT) were calculated. It was found that the mean value of RoT in case of depressed and Parkinson’s diseased speakers decreased by 9-10% compared to healthy speakers. The RoT was used as the only input of classification experiments, and it achieved quite promising results for separation of healthy and non-healthy speakers with accuracy of 81%.
Speech Chain for Semi-Supervised Learning of Japanese-English Code-Switching ASR and TTS

Code-switching (CS) speech, in which speakers alternate between two or more languages in the same utterance, often occurs in multilingual communities. Such a phenomenon poses challenges for spoken language technologies: automatic speech recognition (ASR) and text-to-speech synthesis (TTS), since the systems need to be able to handle the input in a multilingual setting. We may find code-switching text or code-switching speech in social media, but parallel speech and the transcriptions of code-switching data, which are suitable for training ASR and TTS, are generally unavailable. In this paper, we utilize a speech chain framework based on deep learning to enable ASR and TTS to learn code-switching in a semi-supervised fashion. We base our system on Japanese-English conversational speech. We first separately train the ASR and TTS systems with parallel speech-text of monolingual data (supervised learning) and perform a speech chain with only code-switching text or code-switching speech (unsupervised learning). Experimental results reveal that such closed-loop architecture allows ASR and TTS to learn from each other and improve the performance even without any parallel code-switching data.
Aspect-based Query Expansion for Search Results Diversification

Query expansion is a prominent method to reformulate the under-specified and ambiguous query that helps to retrieve documents. This paper is aimed at diversifying web documents using a novel query expansion method. Our method leverage query suggestions and completions for pooling the terms which are used for final expansion. We then group similar candidates into the clusters. In this regards, a soft clattering algorithm is applied to the candidates, where each cluster represents different query aspect. The query is then expanded using the terms selected from cluster labels. Multiple lexical and semantic features are introduced to compute the relevancy between a query and the document. The lexical features use the textual contents of the query and document while, the semantic ones leverage the word-level semantics in word2vec model. Finally, a weighted linear ranking approach is applied to rank the documents retrieved for the query using the extracted features. The experimental results on Clueweb09 document collection using TREC 2012 Web Track queries clearly demonstrate that our proposed aspect-based query expansion method is effective to diversify the retrieved documents and outperformed baseline and some known related methods in terms of diversity metrics ERR-IA, α-nDCG and NRBP at the cut of 20.
Learning Connectivity Patterns via Graph Kernels for fMRI-Based Depression Diagnostics

It has long been known that patients with depression exhibit abnormal brain functional connectivity patterns, that are often studied from a graph-theoretic perspective. However, while certain simpler graph features have been examined, little has been done in the direction of advanced feature learning methodologies such as network embeddings. Our work aims to extend the understanding of importance of graph-based features for medical applications by evaluating the recently proposed anonymous walk embeddings (AWE) in difficult depression classification problems. For two challenging datasets, we obtain performance gains and investigate the learned vector representations. Our results indicate that using AWE-based features is a promising new direction for medical applications.
A Novel Application of Magnesium Di-Boride Superconducting Energy Storage to Mitigate the Power Fluctuations of Single-Phase PV Systems

The superconducting magnetic energy storage (SMES) device has been known as one of the most promising energy storage device as the superconducting coil shows almost zero electrical resistance. With a proper power conditioning circuit, it can have a super-fast unlimited charging/discharging cycle and handle a high pulse power. This feature becomes very attractive for power system applications as it can be utilized to solve the problems of voltage fluctuation, frequency deviation, and power supply disruption. This paper presents a novel concept for the integration of a low-cost micro-SMES (μ-SMES) unit with a residential photovoltaic (RPV) system to mitigate the solar photovoltaic output power intermittency. The low-cost magnesium di-boride (MgB2) superconductor wire is used to design the μ-SMES unit. The performance of a 10-kW RPV-SMES is evaluated in the PSIM software environment.
Knowledge-Graph Based Multi-Target Deep-Learning Models for Train Anomaly Detection

The state-of-art image segmentation algorithms can be applied to accurately localize objects by using deep convolutional neural networks (CNN). In this paper, we consider the anomaly detection problem encountered in a train wheel system. We propose a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task. Moreover, we use the knowledge graph approach to establish a semantic consistency matrix by quantifying the spatial relationship between various components. We show that by establishing a knowledge graph of the normally operating systems, we are able to identify a faulty component effectively.
Emotion Recognition by Facial Features using Recurrent Neural Networks

This paper presents emotion recognition models using facial expression features. By detecting the face in videos and extracting local characteristics (landmarks) to generate the geometric-based features to discriminate between a set of five emotion expressions (amusement, anger, disgust, fear, and sadness) for videos from BioVid Emo database. The classification operation is done using different machine learning models including random forest (RF), support vector machines (SVM), k-nearest neighbors (KNN) and recurrent neural network (RNN), then the evaluation operation is done to generate different discrimination rates that reached up to 82% to discriminate between anger and disgust emotions.
Optimizing Content Dissemination for Real-Time Traffic Management in Large-Scale Internet of Vehicle Systems

As an application of “smart transport” for Internet of Things, Internet of Vehicle (IoV) has emerged as a new research field based on vehicular ad hoc networks (VANETs). With the development of smart vehicles and the integration of sensors, applications of traffic management and road safety in large-scale IoV systems have drawn great attentions. By sensing events occurred on roads, vehicles can broadcast messages to inform others about traffic jams or accidents. However, the store-carry-and-forward transmission pattern may cause a large transmission delay, making the implementation of large-scale traffic management difficult. In this paper, we put forward a feasible solution to minimize the response time for traffic management service, by enabling real-time content dissemination based on heterogeneous network access in IoV systems. We first design a crowdsensing-based system model for large-scale IoV systems. Then, a cluster-based optimization framework is investigated to provide timely responses for traffic management. Specifically, we estimate the message transmission delay by stochastic theory, which can provide a guideline for the next-hop relay selection in our delay-sensitive routing scheme. Furthermore, network performances are evaluated based on two city-road maps, and performance metrics, containing average delivery delay, average delivery ratio, average communication cost, and access ratio, demonstrate the superiority of our system. Finally, we conclude our work and discuss the further work.
Out-of-Domain Slot Value Detection for Spoken Dialogue Systems with Context Information

This paper proposes an approach to detecting-of-domain slot values from user utterances in spoken dialogue systems based on contexts. The approach detects keywords of slot values from utterances and consults domain knowledge (i.e., an ontology) to check whether the keywords are-of-domain. This can prevent the systems from responding improperly to user requests. We use a Recurrent Neural Network (RNN) encoder-decoder model and propose a method that uses only in-domain data. The method replaces word embedding vectors of the keywords corresponding to slot values with random vectors during training of the model. This allows using context information. The model is robust against over-fitting problems because it is independent of the slot values of the training data. Experiments show that the proposed method achieves a 65% gain in F1 score relative to a baseline model and a further 13 percentage points by combining with other methods.
Shift-Collapse Acceleration of Generalized Polarizable Reactive Molecular Dynamics for Machine Learning-Assisted Computational Synthesis of Layered Materials

Reactive molecular dynamics is a powerful simulation method for describing chemical reactions. Here, we introduce a new generalized polarizable reactive force-field (ReaxPQ+) model to significantly improve the accuracy by accommodating the reorganization of surrounding media. The increased computation is accelerated by (1) extended Lagrangian approach to eliminate the speed-limiting charge iteration, (2) shift-collapse computation of many-body renormalized n-tuples, which provably minimizes data transfer, (3) multithreading with round-robin data privatization, and (4) data reordering to reduce computation and allow vectorization. The new code achieves (1) weak-scaling parallel efficiency of 0.989 for 131,072 cores, and (2) eight-fold reduction of time-to-solution (T2S) compared with the original code, on an Intel Knights Landing-based computer. The reduced T2S has for the first time allowed purely computational synthesis of atomically-thin transition metal dichalcogenide layers assisted by machine learning to discover a novel synthetic pathway.