Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-microphone voice activity and single-talk detectors based on steered-response power output entropy

Voice activity detection (VAD), namely determining whether a speech signal is active or inactive, and single talk detector (STD), namely detecting that only one speaker is active, are important building blocks in many speech processing applications. A speaker-localization stage (such as the steered response power (SRP)) is often concurrently implemented on the same device.In this paper, the spatial properties of the SRP are utilized for improving the performance of both the voice activity detector (VAD) and the STD. We propose to measure the entropy at the SRP output and compare with the typical entropy of noise-only frames. This feature utilizes spatial information and may therefore become advantageous in nonstationary noise environments. The STD can then be implemented by determining local minimum values of the entropy measure of the SRP.The proposed VAD was tested for a single speaker with two cases, directional background noise with changing level and with a background music source. The proposed STD was tested using real recordings of two concurrent speakers.
A Study of NoSQL Database for Enterprises

Popularization of big data makes the enterprise need to store more and more data. The enterprise's database must access data as fast as possible, but RDB has the speed limitation due to the join operation. Many enterprises have changed to use NoSQL database which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, 15 categories of NoSQL databases will be analysed to find out the characteristics of every category. Some principles are proposed to choose an appropriate NoSQL database for different types of enterprises.
Flavanone-3-Hydroxylase Plays an Important Role in the Biosynthesis of Spruce Phenolic Defenses Against Bark Beetles and Their Fungal Associates

Conifer forests worldwide are becoming increasingly vulnerable to attacks by bark beetles and their fungal associates due to the effects of global warming. Attack by the bark beetle Ips typographus and the blue-stain fungus it vectors (Endoconidiophora polonica) on Norway spruce (Picea abies) is well known to induce increased production of terpene oleoresin and polyphenolic compounds. However, it is not clear whether specific compounds are important in resisting attack. In this study, we observed a significant increase in dihydroflavonol and flavan-3-ol content after inoculating Norway spruce with the bark beetle vectored fungus. A bioassay revealed that the dihydroflavonol taxifolin and the flavan-3-ol catechin negatively affected both I. typographus and E. polonica. The biosynthesis of flavan-3-ols is well studied in Norway spruce, but little is known about dihydroflavonol formation in this species. A flavanone-3-hydroxylase (F3H) was identified that catalyzed the conversion of eriodictyol to taxifolin and was highly expressed after E. polonica infection. Down-regulating F3H gene expression by RNA interference in transgenic Norway spruce resulted in significantly lower levels of both dihydroflavonols and flavan-3-ols. Therefore F3H plays a key role in the biosynthesis of defense compounds in Norway spruce that act against the bark beetle-fungus complex. This enzyme forms a defensive product, taxifolin, which is also a metabolic precursor of another defensive product, catechin, which in turn synergizes the toxicity of taxifolin to the bark beetle associated fungus.
A Novel Skin-Stretch Haptic Device for Intuitive Control of Robotic Prostheses and Avatars

Without proprioception, i.e., the intrinsic capability of a body to perceive its own limb position, completing daily life activities would require constant visual attention and it would be challenging or even impossible. This situation is similar to the one experienced after limb amputation and in robotic tele-operation, where the natural sensory-motor loop is broken. While some promising solutions based on skin stretch sensory substitution have been proposed to restore tactile properties in these conditions, there is still room for enhancing the intuitiveness of stimulus delivery and integration of haptic feedback devices within user's body. To contribute to this goal, here, we propose a wearable device based on skin stretch stimulation, the Stretch-Pro, which can provide proprioceptive information on artificial hand aperture. This system can be suitably integrated in a prosthetic socket or can be easily worn by a user controlling remote robots. The system can imitate the stretching of the skin that would naturally occur on the intact limb, when it is used to accomplish motor tasks. Two versions of the system are presented, with one and two actuators, respectively, which deliver the stretch stimulus in different ways. Experiments with able-bodied participants and a preliminary test with one prosthesis user are reported. Results suggest that Stretch-Pro could be a viable solution to convey proprioceptive cues to upper limb prosthesis users, opening promising perspectives for tele-robotics applications.
Design of a Truncated Circular Microstrip Patch Antenna with Slot for GPS Application

In the furtherance of technological evolution, the wireless communication system has its own contribution by providing an expanded number of technological devices among which microstrip antenna occupies a dynamic area. Microstrip antenna field has a variety of features and Global Positioning System (GPS) application is one of them. In this research work, a design has been proposed for civilian GPS (GPS L2 1.2276 GHz Band) utility purpose. The model is a circular patch having a slot and truncation on two sides which has been simulated using Computer Simulation Technology (CST) Microwave Studio Suite software. The results obtained from the simulation shows an impressive impedance matching having a return loss of around −31dB, RHCP (Right Hand Circular Polarization) on the desired frequency with a bandwidth coverage of nearly 41 MHz and also an overall gain of 6.55dBi. All these simulated outcomes from this unique proposed design satisfactorily suit with the GPS antenna characteristics.
Flexible 3-D Helix Fabrication by In-Situ SEM Micromanipulation System

The helical microstructure has been widely applied to various industries, including the mechanical manufacture, electronics, and biomedical engineering. Due to the miniature size of the helical microstructure, the low manufacture precision will highly degrade its working performance. Although current manufacture methods can realize its fabrication with relatively high accuracy, the used material is constrained to several specific conditions. To address the above problems, this article proposes a laconic and high-precision three-dimensional (3-D) manufacture method for the helical microstructure based on the microrobotic system without the limitation of materials. In this article, the microrobotic system with six degrees-of-freedom is developed for manufacture. Then, the image processing operation for providing feedback information is introduced. Third, the trajectory of each individual robotic module is designed according to the structure's certain size. After that, the control system with feedforward and double feedback is reported to realize the accurate manufacture. Finally, the experimental results demonstrate that the manufacture precision can reach up to 2 $\mu$m with the standard deviation (STD) of 1.06. This research will pave a new path for the high-precision 3-D manufacturing at small scale, and have long-term impacts on robot-aided manufacturing system with high potential for commercialization in the future.
Backing Up Control of a Self-Driving Truck-Trailer Vehicle with Deep Reinforcement Learning and Fuzzy Logic

This paper presents a control strategy for autonomous truck-trailer systems based on deep reinforcement learning (Deep RL). The deep deterministic policy gradient (DDPG) is used for training the controller using a reward function defined in terms of the desired final state of the system. A fuzzy-logic approach is employed to avoid the truck-trailer jackknife state. Simulation results show that the designed controller exhibits similar performance to state-of-the-art controllers such as the linear-fuzzy controller but with a much simpler design process.
Evaluating Feasibility of Image-Based Cognitive APIs for Home Context Sensing

Cognitive API is API of emerging AI-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement smart and affordable context sensing services in a smart home. However, since the existing APIs are trained for general-purpose image recognition, they may not be of practical use in specific configuration of smart homes. In this paper, we therefore propose a method that evaluates the feasibility of cognitive APIs for the home context sensing. In the proposed method, we exploit document similarity measures to see how well tags extracted from given images characterize the original contexts. Using the proposed method, we evaluate practical APIs of Microsoft Azure, IBM Watson, and Google Cloud for recognizing 11 different contexts in our smart home.
Tracking Student Performance in Introductory Programming by Means of Machine Learning

large amount of digital data is being generated across a wide variety of fields and Data Mining (DM) techniques are used transform it into useful information so as to identify hidden patterns. One of the key areas of the application of Education Data Mining (EDM) is the development of student performance prediction models that would predict the student’s performance in educational institutions. We build a model which can notify students (in introductory programming course) about their probable outcomes at an early stage of the semester (when evaluated for 15% grades). We applied 11 Machine Learning algorithms (from 5 categories) over a data source using WEKA and concluded that Decision Tree (J48) is giving higher accuracy in terms of correctly identified instances, F-Measure rate and true positive detections. This study will help to the students to identify their probable final grades and modify their academic behavior accordingly to achieve higher grades.
Pixel-wise Illumination Correction Algorithms for Relative Color Constancy Under the Spectral Domain

Achieving color constancy between and within images, i.e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition. Most current methods attempt to solve this by illumination correction on perceptual color spaces. In this paper, we proposed two pixel-wise algorithms to achieve relative color constancy by working under the spectral domain. That is, the proposed algorithms map each pixel to the reflectance ratio of objects appeared in the scene and perform illumination correction in this spectral domain. Also, we proposed a camera calibration technique that calculates the characteristics of a camera without the need of a standard reference. We show that both of the proposed algorithms achieved the best performance on nonuniform illumination correction and relative illumination matching respectively compared to the benchmarked algorithms.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this article, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (QF1) is higher than the second one (QF2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches.
User-Controlled Computations in Untrusted Computing Environments

Computing infrastructures are challenging and expensive to maintain. This led to the growth of cloud computing with users renting computing resources from centralized cloud providers. There is also a recent promise in providing decentralized computing resources from many participating users across the world. The compute on your own server model hence is no longer prominent. But, traditional computer architectures, which were designed to give a complete power to the owner of the computing infrastructure, continue to be used in deploying these new paradigms. This forces users to completely trust the infrastructure provider on all their data. The cryptography and security community research two different ways to tackle this problem. The first line of research involves developing powerful cryptographic constructs with formal security guarantees. The primitive of functional encryption (FE) formalizes the solutions where the clients do not interact with the sever during the computation. FE enables a user to provide computation-specific secret keys which the server can use to perform the user specified computations (and only those) on her encrypted data. The second line of research involves designing new hardware architectures which remove the infrastructure owner from the trust base. The solutions here tend to have better performance but their security guarantees are not well understood. This thesis provides contributions along both lines of research. In particular, • We develop a (single-key) functional encryption construction where the size of secret keys do not grow with the size of descriptions of the computations, while also providing a tighter security reduction to the underlying computational assumption. This construction supports the computation class of branching programs. Previous works for this computation class achieved either short keys or tighter security reductions but not both. • We formally model the primitive of trusted hardware inspired by Intel’s Software Guard eXtensions (SGX). We then construct an FE scheme in a strong security model using this trusted hardware primitive. We implement this construction in our system Iron and evaluate its performance. Previously, the constructions in this model relied on heavy cryptographic tools and were not practical. • We design an encrypted database system StealthDB that provides complete SQL support. StealthDB is built on top of Intel SGX and designed with the usability and security limitations of SGX in mind. The StealthDB implementation on top of Postgres achieves practical performance (30% overhead over plaintext evaluation) with strong leakage profile against adversaries who get snapshot access to the memory
Kinematic Modeling and State Estimation of Exploration Rovers

State estimation is crucial for exploration rovers. It provides the pose and velocity of the rover by processing measurements from onboard sensors. Classical wheel odometry only employs encoder measurements of the two wheels in the differential drive. As a consequence, input noise can lead to large uncertainties in the estimated results. Also, the estimation models used in classical wheel odometry are nonlinear, and the linearization process that propagates the mean and covariance of the estimated state introduces additional errors in the process. This letter puts forward a novel wheel odometry approach for six-wheeled rovers. A kinematic model is formulated to relate the velocity of the wheels and the chassis, and later used to develop the corresponding estimation model. The components of the velocity of the chassis, decomposed in the chassis-fixed coordinate frame, are selected as the system state in the estimation, which results in a linear model. The motions of all wheels are fused together to provide the measurements. Wheel slip is considered random Gaussian noise in this kinematic model. The continuous-time Kalman filter is employed to process the model. Experimental validation with six-wheeled rover prototypes was used to confirm the validity of the proposed approach.
Transcriptional and Proteomic Choreography Under Phosphorus Deficiency and Re-supply in the N2 Fixing Cyanobacterium Trichodesmium erythraeum

The N2 fixing cyanobacterium Trichodesmium is a critically important organism in oligotrophic marine ecosystems, supplying “new” nitrogen (N) to the otherwise N-poor tropical and subtropical regions where it occurs. Low concentrations of phosphorus (P) in these regions can constrain Trichodesmium distribution and N2 fixation rates. Physiological characterization of a single species in a mixed community can be challenging, and ‘omic approaches are increasingly important tools for tracking nutritional physiology in a taxon-specific manner. As such, studies examining the dynamics of gene and protein markers of physiology (e.g., nutrient stress) are critical for the application and interpretation of such ‘omic data in situ. Here we leveraged combined transcriptomics, proteomics, and enzyme activity assays to track the physiological response of Trichodesmium erythraeum IMS101 to P deficiency and subsequent P re-supply over 72 h of sampling. P deficiency resulted in differential gene expression, protein abundance, and enzyme activity that highlighted a synchronous shift in P physiology with increases in the transcripts and corresponding proteins for hydrolyzing organic phosphorus, taking up phosphate with higher affinity, and modulating intracellular P demand. After P deficiency was alleviated, gene expression of these biomarkers was reduced to replete levels within 4 h of P amendment. A number of these gene biomarkers were adjacent to putative pho boxes and their expression patterns were similar to a sphR response regulator. Protein products of the P deficiency biomarkers were slow to decline, with 84% of the original P deficient protein set still significantly differentially expressed after 72 h. Alkaline phosphatase activity tracked with proteins for this enzyme. With the rapid turnover time of transcripts, they appear to be good biomarkers of a P stress phenotype, whereas proteins, with a slower turnover time, may better reflect cellular activities. These results highlight the importance of validating and pairing transcriptome and proteome data that can be applied to physiological studies of key species in situ.
Progress in EBV Vaccines

The Epstein-Barr virus (EBV) is a ubiquitous pathogen that imparts a significant burden of disease on the human population. EBV is the primary cause of infectious mononucleosis and is etiologically linked to the development of numerous malignancies. In recent years, evidence has also been amassed that strongly implicate EBV in the development of several autoimmune diseases, including multiple sclerosis. Prophylactic and therapeutic vaccination has been touted as a possible means of preventing EBV infection and controlling EBV-associated diseases. However, despite several decades of research, no licensed EBV vaccine is available. The majority of EBV vaccination studies over the last two decades have focused on the major envelope protein gp350, culminating in a phase II clinical trial that showed soluble gp350 reduced the incidence of IM, although it was unable to protect against EBV infection. Recently, novel vaccine candidates with increased structural complexity and antigenic content have been developed. The ability of next generation vaccines to safeguard against B-cell and epithelial cell infection, as well as to target infected cells during all phases of infection, is likely to decrease the negative impact of EBV infection on the human population.
Low-Order Response Modeling for Wind Farm-MTDC Participating in Primary Frequency Controls

A low-order and system-level response model for wind farms (WFs) and voltage source converter based high-voltage direct current (VSC-HVDC) participating in primary frequency regulation using virtual synchronous machine (VSM) control is developed and validated. The VSM control with virtual power system stabilizer (PSS) function is presented being applicable to all multi-terminal HVDC (MTDC) VSCs. In this context, a general low-order response model (LRM) for presenting the WF-MTDC participating in the frequency control of main AC system is readily established. This modeling method derives transfer function based block diagram offering an illustrative insight into dynamic interaction between AC frequencies and DC voltages. Such a model can also provide a convenient way performing control system designing and parameter tuning. As a paradigm, LRM-based linear quadratic regulation optimization is applied to obtain the droop and damping gains for feasible frequency regulation. The effectiveness of the LRM method is verified through comparisons of the simulation results obtained from different models, controller designs, and parameter tuning approaches, which are all performed on a five-terminal VSC-HVDC connecting WF-side and main AC systems.
CSIP

Intersection management is one of the main challenging issues in road safety because intersections are a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur around intersection areas, which, in turn, has led to 8,500 fatalities and approximately 1 million injuries every year. With vehicles expected to become self-driving, the question is whether high throughput can be obtained through intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [8] was recently proposed in the literature to address this situation. Under this protocol, automated and connected vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors and/or control system failure. In this article, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP). CSIP is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicular distance to meet safety requirements in the presence of GPS inaccuracies and control failure. The inter-vehicular distances under CSIP are much more acceptable and comfortable to human passengers due to longer inter-vehicular distances that do not cause fear. With CSIP, the inter-vehicular distances can also be changed at each intersection to account for different traffic volumes, GPS accuracy levels, and geographical layout of intersections. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.