Miracle-dz/UniFAR-FTUs huggingface dataset:
A Context-Aware Feature Fusion Framework for Punctuation Restoration

To accomplish the punctuation restoration task, most existing approaches focused on leveraging extra information (e.g., part-of-speech tags) or addressing the class imbalance problem. Re-cent works have widely applied the transformer-based language models and signiﬁcantly improved their effectiveness. To the best of our knowledge, an inherent issue has remained ne-glected: the attention of individual heads in the transformer will be diluted or powerless while feeding the long non-punctuation utterances. Since those previous contexts, not the followings, are comparatively more valuable to the current position, it’s hard to achieve a good balance by independent attention. In this paper, we propose a novel F eature F usion framework based on two-type A ttentions (FFA) to alleviate the shortage. It intro-duces a two-stream architecture. One module involves interaction between attention heads to encourage the communication, and another masked attention module captures the dependent feature representation. Then, it aggregates two feature embeddings to fuse information and enhances context-awareness. The experiments on the popular benchmark dataset IWSLT demonstrate that our approach is effective. Without additional data, it obtains comparable performance to the current state-of-the-art models.
HIGH SCHOOL PEER BUDDY PROGRAM: IMPACT ON SOCIAL AND ACADEMIC ACHIEVEMENT FOR STUDENTS WITH DISABILITIES

The purpose of this study was to examine the Peer Buddy Program at a high school for students with disabilities (i.e., learning and behavior disabilities) in academic and social achievement. Two specific questions were addressed: Do students with learning and/or emotional/behavioral disabilities, who are participating in the Peer Buddy Program, show improvement in their use of social skills according to self-report, special and general curriculum teacher ratings on a standardized social skill rating scale? and 2) Do students with learning and/or emotional/behavioral disabilities, who are participating in the Peer Buddy Program, show improvement in academic skill use according to past to present year comparisons on grade point averages, and special education informal assessments (Fast Math, System 44, and Acuity)? Student, special and general education teachers’ ratings on the Social Skills Achievement Scale (SSIS) and student archival records data was examined through pair t-test analyses pre and post the Peer Buddy Program intervention. The results indicated that the Peer-Buddy Program had a positive impact on the social and academic achievement of high school students with learning and behavioral disabilities.
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games

We show that, for any sufficiently small fixed $\epsilon>0$, when both players in a general-sum two-player (bimatrix) game employ optimistic mirror descent (OMD) with smooth regularization, learning rate $\eta = O(\epsilon^2)$ and $T = \Omega(\text{poly}(1/\epsilon))$ repetitions, either the dynamics reach an $\epsilon$-approximate Nash equilibrium (NE), or the average correlated distribution of play is an $\Omega(\text{poly}(\epsilon))$-strong coarse correlated equilibrium (CCE): any possible unilateral deviation does not only leave the player worse, but will decrease its utility by $\Omega(\text{poly}(\epsilon))$. As an immediate consequence, when the iterates of OMD are bounded away from being Nash equilibria in a bimatrix game, we guarantee convergence to an exact CCE after only $O(1)$ iterations. Our results reveal that uncoupled no-regret learning algorithms can converge to CCE in general-sum games remarkably faster than to NE in, for example, zero-sum games. To establish this, we show that when OMD does not reach arbitrarily close to a NE, the (cumulative) regret of both players is not only negative, but decays linearly with time. Given that regret is the canonical measure of performance in online learning, our results suggest that cycling behavior of no-regret learning algorithms in games can be justified in terms of efficiency.
Evaluation of Seafood Processing Wastes in Prepared Feeds for Red Drum (Sciaenops ocellatus)

Evaluation of Seafood Processing Wastes in Prepared Feeds for Red Drum (Sciaenops ocellatus). (May 2011) Benjamin Mark Pernu, B.S., The University of Tulsa Chair of Advisory Committee: Dr. Delbert M. Gatlin III High feed costs and increasing demand for fishmeal have intensified the search for alternative protein sources which are needed to allow world aquaculture to continue expanding. A severely underused marine resource is processing wastes of various types of seafood, which are often disposed of at great cost. Therefore, this study was conducted to evaluate three different types of seafood processing wastes as potential feed ingredients for the red drum (Sciaenops ocellatus). The three processing wastes evaluated were heads and shells from Penaeid shrimp, and viscera and skeletal remains from filleted black drum (Pogonias cromis) and channel catfish (Ictaluras punctatus). These wastes were blended with soybean meal in a 40:60 ratio, dry extruded and dried to produce stable ingredients. All three byproduct meals produced had crude protein levels ranging from 45 to 50%. Two feeding trials were conducted to evaluate the different processing waste byproduct meals in comparison to menhaden fishmeal. A digestibility trial was conducted with sub-adult red drum which led to the computation of apparent digestibility coefficients (ADCs) for organic matter, protein, lipid and energy for each of the byproduct meals. Each byproduct meal had relatively high ADC values that were generally similar to those of menhaden fishmeal.
Personal knowledge management: the foundation of organisational knowledge management

The purpose of this small-scale study was to explore how people perceived Personal Knowledge Management (PKM), whether people were aware of the PKM concept, and how PKM can have an impact on organisational knowledge management and productivity. A questionnaire survey with quantitative and qualitative questions was used. The study revealed that a majority (63%) of respondents were not aware of the PKM concept; 33% were aware, while 2% had a vague idea about it. Eighty three (83%) felt that it was important to manage personal knowledge and that PKM could increase individual productivity and organisational performance. The major recommendations included creating awareness about PKM. It should be at the heart of each employee-development programme, alignment of personal and organisational goals and adequate facilities and training in PKM.
Bayesian Nonparametric Vector Autoregressive Models via a Logit Stick-breaking Prior: an Application to Child Obesity

Overweight and obesity in adults are known to be associated with risks of metabolic and cardiovascular diseases. Because obesity is an epidemic, increasingly aﬀecting children, it is important to understand if this condition persists from early life to childhood and if diﬀerent patterns of obesity growth can be detected. Our motivation starts from a study of obesity over time in children from South Eastern Asia. Our main focus is on clustering obesity patterns after adjusting for the eﬀect of baseline information. Speciﬁcally, we consider a joint model for height and weight patterns taken every 6 months from birth. We propose a novel model that facilitates clustering by combining a vector autoregressive sampling model with a dependent logit stick-breaking prior. Simulation studies show the superiority of the model to capture patterns, compared to other alternatives. We apply the model to the motivating dataset, and discuss the main features of the detected clusters. We also compare alternative models with ours in terms of predictive performances.
Reducing overestimating and underestimating volatility via the augmented blending-ARCH model

SVR-GARCH model tends to “backward eavesdrop” when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-GARCH model has achieved good performance in terms of various performance measurements, trading opportunities, peak or trough behaviors in the time series are all hampered by underestimating or overestimating the volatility. We propose a blending ARCH (BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem and make the prediction towards better peak or trough behaviors.The method is illustrated using real data sets including SH300 and S&P500. The empirical results obtained suggest that the augmented and blending models improve the volatility forecasting ability.
Real-time Rendering and Editing of Scattering Effects for Translucent Objects

The photorealistic rendering of the transparent effect of translucent objects is a hot research topic in recent years. A real-time photorealistic rendering and material dynamic editing method for the diffuse scattering effect of translucent objects is proposed based on the bidirectional surface scattering reflectance function's (BSSRDF) Dipole approximation. The diffuse scattering material function in the Dipo le approximation is decomposed into the product form of the shape-related function and the translucent material-related function through principal component analysis; using this decomposition representation, under the real-time photorealistic rendering framework of pre-radiative transmission and the scattering transmission to realize real-time editing of translucent object materials under various light sources. In addition, a method for quadratic wavelet compression of precomputed radiative transfer data in the spatial domain is also proposed. Using the correlation of surface points in the spatial distribution position, on the premise of ensuring the rendering quality, the data is greatly compressed and the rendering is efficiently improved. The experimental results show that the method in this paper can generate a highly realistic translucent effect and ensure the real-time rendering speed.
Active Intelligent Reflecting Surface Assisted Secure Air-to-Ground Communication with UAV Jittering

Unmanned Aerial Vehicles (UAV)-enabled communication is a promising solution for secure air-to-ground (A2G) networks due to the additional secure degrees of freedom afforded by mobility. However, the jittering characteristics caused by the random airflow and the body vibration of the UAV itself have a non-negligible impact on the performance of UAV communication. Considering the impact of UAV jittering, this paper propose a robust and secure transmission design assisted by an novel active intelligent reflecting surface (IRS),where the reflecting elements in IRS not only adjust the phase shift but also amplify the amplitude of signals. Specifically, under the worst-case secrecy rate constraints, we aim to minimize the transmission power by the robust joint design of active IRS's reflecting coefficient and beamforming at the UAV-borne base station (UBS). However,it is challenging to solve the joint optimization problem due to its non-convexity. To tackle this problem, the non-convex problem is reformulated with linear approximation for the channel variations and linear matrix inequality transformed by S-procedure and Schur's complement. Then, we decouple this problem into two sub-problems, namely, passive beamforming and active IRS's reflecting coefficient optimization, and solve them through alternate optimization (AO). Finally, the numerical results demonstrate the potential of active IRS on power saving under secure transmission constraints and the impact of UAV jittering.
Thin-disk laser multi-pass amplifier

In the context of the Lamb shift measurement in muonic helium [1,2,3,4] we developed a thin-disk laser composed of a Q-switched oscillator and a multi-pass amplifier delivering pulses of 150 mJ at a pulse duration of 100 ns. Its peculiar requirements are stochastic trigger and short delay time (< 500 ns) between trigger and optical output [5]. The concept of the thin-disk laser allows for energy and power scaling with high efficiency. However the single pass gain is small (about 1.2). Hence a multi-pass scheme with precise mode matching for large beam waists (w = 2 mm) is required. Instead of using the standard 4f design, we have developed a multi-pass amplifier with a beam propagation insensitive to thermal lens effects and misalignments. The beam propagation is equivalent to multiple roundtrips in an optically stable resonator. To support the propagation we used an array of 2 x 8 individually adjustable plane mirrors. Astigmatism has been minimized by a compact mirror placement. Precise alignment of the kinematic array was realized using our own mirror mount design. A small signal gain of 5 for 8 passes at a pump power of 400 W was reached. The laser was running for more than 3 months without the need of realignment. Pointing stability studies is also reported here.
Title AC hot-carrier-induced degradation in NMOSFET ' s with N 2 O-based gate dielectrics

Frequency-dependent ac-stress-induced degradation in NMOSFET’s with N 2O-grown and N2O-nitrided gate oxides was investigated. Suppressed device degradation is observed in both N2O-based devices as compared to SiO 2 device for frequency up to 100 kHz, which is attributed to nitrogen incorporation in the gate oxides. Moreover, when comparing the two N 2O-based oxides, N2O-grown oxide device exhibits enhanced degradation than N2O-nitrided oxide device. Charge pumping measurements reveal that N2O-nitrided oxide has better immunity to interfacestate and neutral-electron-trap generation under dynamic stress.
Energy Efficient Placement of ML-Based Services in IoT Networks

The Internet of Things (IoT) is gaining momentum in its quest to bridge the gap between the physical and the digital world. The main goal of the IoT is the creation of smart environments and self-aware things that help to facilitate a variety of next generation services. Huge volumes of data are expected to be collected by the connected sensors/things, which in traditional cases are processed centrally by large data centers in the core network that will inevitably lead to excessive trans-portation power consumption as well as added latency overheads. Instead, fog computing has been proposed by researchers from industry and academia to extend the capability of the cloud right to the point where the data is collected at the sensing layer. This way, primitive tasks that can be hosted in IoT sensors do not need to be sent all the way to the cloud for processing. In this paper, we propose energy efficient embedding of generic smart services over a cloud-fog network using a Mixed Integer Linear Programming (MILP) optimization model. We exploit virtualization in our framework to abstract Machine learning (ML) services that have multiple interconnected components such as Deep Neural Networks (DNN) layers. The different layers are composed into a set of VMs interconnected by virtual links. We study the single VM allocation solution and the multiple VM allocation solution at the IoT layer. The results showed that the latter solution is far more superior as a maximum power saving of 65% can be achieved.
A Survey about application based on RF signals

With the popularity of commercial wireless devices, the research based on RF signal has developed rapidly in the past decade. RF signals have great advantages in environmental perception. Wireless signals are transmitted through the transmitter. With the propagation of these signals in the medium, they are reflected from different objects and people in space to the receiver. In this process, rich environmental perception information is carried. These ubiquitous perceptual information has rich application significance and bright prospects in the fields of indoor positioning, motion perception, behavior recognition, human-computer interaction, fall detection, health monitoring, smart home, search and rescue and so on. This paper will discuss and analyze the current research results by combing some existing literature and the research status of relevant researchers in relevant fields at this stage, so as to understand the future development trend of RF signal based applications.
Sharing the Spotlight: Co-presenting with a Humanoid Robot

Public speaking is important in the sciences, but poor quality presentations are common, as are high rates of public speaking anxiety. In this work we explore the use of a mobile humanoid robot as a co-presenter that can share the stage with a scientist giving an oral presentation. We conducted a within-subjects experiment comparing presentations given with and without the robot and impacts on public speaking anxiety and speaker confidence. We found that participants reported significantly greater confidence and lower public speaking anxiety when co-presenting with the robot, compared to when they presented on their own, without the robot. Audiences accepted scientific presentations given with the robot, rating these presen-tations significantly greater than neutral on presentation quality.
Learning Socially Appropriate Robo-waiter Behaviours through Real-time User Feedback

Current Humanoid Service Robot (HSR) behaviours mainly rely on static models that cannot adapt dynamically to meet individual customer attitudes and preferences. In this work, we focus on empowering HSRs with adaptive feedback mechanisms driven by either implicit reward, by estimating facial affect, or explicit reward, by incorporating verbal responses of the human ‘customer’. To achieve this, we first create a custom dataset, annotated using crowd-sourced labels, to learn appropri-ate approach (positioning and movement) behaviours for a Robo-waiter. This dataset is used to pre-train a Reinforcement Learning (RL) agent to learn behaviours deemed socially appropriate for the robo-waiter. This model is later extended to include separate implicit and explicit reward mechanisms to allow for interactive learning and adaptation from user social feedback. We present a within-subjects Human-Robot Interaction (HRI) study with 21 participants implementing interactions between the robo-waiter and human customers implementing the above-mentioned model variations. Our results show that both explicit and implicit adaptation mechanisms enabled the adaptive robo-waiter to be rated as more enjoyable and sociable, and its positioning relative to the participants as more appropriate compared to using the pre-trained model or a randomised control implementation.
A Machine Learning Based Framework for Continuous Defect Prediction in CI/CD pipelines

Recent advances in information technology has led to an increasing number of applications to be developed and maintained daily by product teams. Ensuring that a software application works as expected and that it is absent of bugs requires a lot of time and resources. Thanks to the recent adoption of DevOps methodologies, it is often the case where code commits and application builds are centralized and standardized. Thanks to this new approach, it is now possible to retrieve log and build data to ease the development and management operations of product teams. However, even if such approaches include code control to detect unit or integration errors, they do not check for the presence of logical bugs that can raise after code builds. For such reasons in this work we propose a framework for continuous defect prediction based on machine learning algorithms trained on a publicly available dataset. The framework is composed of a machine learning model for detecting the presence of logical bugs in code on the basis of the available data generated by DevOps tools and a dashboard to monitor the software projects status. We also describe the serverless architecture we designed for hosting the aforementioned framework.
Effects of Colored LEDs in Robotic Storytelling on Storytelling Experience and Robot Perception

Social robots can use biomimetic modalities such as gestures to convey emotions. The use of colored light for emotion expression is also possible, but rarely explored. In this paper, colored LEDs are used in addition to contextual gestures to communicate emotions in robotic storytelling. Results show that adding colored light to the storytelling did not improve the recipients' transportation into the story. Their cognitive absorption was significantly decreased. The users' perception of the NAO robot was not influenced by colored lights except for animacy which was higher using only white LEDs. Problems might have been mismatches between colors and emotions, the lack of emotional gestures, and the too small light emission from NAO's eye LEDs. Since smart rooms lighting can affect the users' whole visual field, they could enhance emotional effects. Thus, future studies should investigate the integration of a robotic storyteller in smart rooms allowing for smart light control.
Costly Portfolio Adjustment

This paper studies the dynamic optimization problem of a household when portfolio adjustment is costly. The analysis is motivated by the observation that on an annual basis, less than 71% of stockholders typically adjust their portfolio of common stocks. We use this, and related observations, to estimate the parameters of household preferences and portfolio adjustment costs. We find significant adjustment costs, beyond the direct costs of buying and selling assets. These adjustment costs and the consequent inactivity in portfolio adjustment imply that inferences drawn about household risk aversion and the elasticity of intertemporal substitution are biased: household risk aversion is lower compared to other estimates and it is not equal to the inverse of the elasticity of intertemporal substitution.
DETECTION OF TNT-CONTAMINATION IN SPIKED-SOIL SAMPLES USING SPME AND GC / MS

Soil-contamination from toxic explosive residues at military bases throughout the world, is a major environmental concern. A strong candidate for TNT soil-contamination within Greece, is the military base/airport of Maleme in Crete. While soil sampling was in progress new routes of trace analysis for explosives in soil were explored. Solid-phase micro-extraction (SPME) was used to selectively preconcentrate analytes prior to GC/MS. In the preliminary experiments (effectuated on water-spiked samples) optimization of the SPME technique was achieved by controlling parameters such as sampling method, salt content and sample agitation. As immersion of the SPME fiber was found to be more efficient for extracting most analytes of interest, a pretreatment step was introduced for the soil samples which simply converted them to water samples. The developed SPME protocol was able to screen explosives in spiked-soils, in concentrations well below the certified reporting and detection limits.