Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Understanding Complex Known-Item Requests on Reddit

Given the important role of search engines in our everyday lives, a better understanding of the information needs that guide our information seeking behavior is essential. Known-item needs form a particular type of information need and occur when a user has a limited but concrete description of an existing object and would like to (re-)find it. Most studies of know-item needs have focused on the short query representations of these needs as they occur in search engine logs. In this article, we focus on richer, more complex known-item need representations posted to six dedicated Reddit discussion forums in the casual leisure domain. An analysis of 462 known-item requests from these subreddits revealed 33 different relevance aspects of items in a variety of different domains. Some of these aspects are highly domain-specific, while others are broadly applicable across domains. The domain %of the item sought also has a strong influence on the length of the known-item requests. Our findings can be used to prioritize efforts to help existing search engines better support known-item needs, both by highlighting which aspects are easier to classify automatically and by determining which information sources should be added to a search engine's index.
Modeling and optimization of ISFET microsensor for the use of quality testing in food and pharmaceutical industry

Ion-sensitive field-effect transistor (ISFET) is one of the most promising chemical sensors that has many advantages such as high integration capability, low cost, simple interface, and high productivity. ISFET has the capability to detect various molecules such as enzymes, antibodies and proteins, but pH sensing is mainly discussed in this paper. An ISFET model is developed using the TCAD tool - COMSOL Multiphysics 5.5 to study the characteristics of the ISFET as a pH sensor. The model also used to investigate the effects of gate insulator materials, other geometry consideration and operating condition on ISFET performance. The Id-Vg characteristics, threshold voltage, electric potential, and sensitivity of the ISFET were analysed. The simulated results were verified against approximated values calculated using the surface potential equation. It was observed that the tantalum pentoxide, Ta2O5 dielectric material has the highest sensitivity, which is very near to the calculated Nernst limit and thinner oxide layer can result in smaller threshold voltage which may lead to lower the power consumption.
A Novel Weak Signal Detector Based on Power Spectrum Entropy Under Low SNR

Aiming at the problem of spectrum monitoring of low-power wideband signals in complex electromagnetic environments, this paper proposes a double threshold weak signal detector based on the information entropy of the frequency-domain power spectrum. Based on the theoretical model of information entropy, the signal's power spectrum entropy is used as the test statistic to construct a double decision threshold, which realizes the blind detection of the hidden low-power weak signal under the strong noise interference. Simulation results show that when the received signal-to-noise ratio is -16dB, the signal detection rate is greater than 80%, which is better than other traditional algorithms. Experiments in the scene of non-cooperative spectrum monitoring further prove the effectiveness of this method in practical application.
Personal Space Violation by a Robot: An Application of Expectation Violation Theory in Human-Robot Interaction

Studies of personal space between human and robot have been growing. However, little is known about the effects of personal space violation by a robot. Expectation Violation Theory (EVT) explains how people respond to personal space invasion by another person, depending on if the person is perceived as rewarding or punishing. This study aims to extend EVT to HRI and to examine the effects of personal space violation using EVT as an analytical framework. A 2 × 5 within-subject experimental design was employed. The participants were approached by two robots with different reward value (rewarding and punishing) at varying distances (threat, near, norm, slightly far, and far distances). The participants were asked to rate the robots’ perceived warmth, competence, and comfort scores after each approach to evaluate the communication outcomes after the personal space violation occurred. The results suggested that approaching distances had influences on the communication outcomes. As predicted by EVT, how each distance affected the communication outcomes differ according on robot’s reward value. The findings indicated that EVT was partially successful in predicting the effects of personal space violations depending on the reward value and approaching distance of a robot. This study has provided a partial support that EVT can be applied to HRI and contributed to understanding the effects of personal space violation by a robot. When considering personal space in HRI, perceived reward value of the robot plays an important role as it can influence the effects of the personal space violation greatly.
Design of An Approximate FFT Processor Based on Approximate Complex Multipliers

The Fast Fourier Transform (FFT) is an efficient algorithm to calculate the Discrete Fourier Transform (DFT), which is often employed in Digital Signal Processing (DSP) and communication. In FFT, complex multiplication and addition units in the butterfly module consume most of the hardware resources. Compared to the addition operation, multiplication is more complicated. In this paper, the multiplier in the complex multiplication unit of the FFT is approximated. Four Radix-4 Booth multipliers with different approximation levels are proposed to reduce the hardware complexity. The pipeline FFT and the parallel FFT based on the proposed approximate multipliers are implemented and extensively evaluated. Compared with the state-of-the-art FFT designs, the LUTs amount is reduced up to 20.3% and 29.1% for pipeline and parallel FFTs, respectively. The power is reduced up to 69.9% for pipeline FFT, and the delay is reduced up to 45.7%. Moreover, the PSNR is reduced by less than 1dB in both pipeline FFT and parallel FFT. Proved by experiment results, the overall performance of the proposed designs is better than FFT designs using other approximate multipliers.
Human Arm Motion Prediction in Reaching Movements*

There is an increasing interest in accurately predicting natural human arm motions for areas like human-robot interaction, wearable robots, and ergonomic simulations. This paper studies the problem of predicting natural fingertip and joint trajectories in human arm reaching movements. Compared to the widely-used minimum jerk model, the 5-parameter logistic model can represent natural fingertip trajectories more accurately. Based on 3520 human arm motions recorded by a motion capture system, regression learning is used to predict the five parameters representing the fingertip trajectory for a given target point. Then, the elbow swivel angle is predicted using regression learning to resolve the kinematic redundancy of the human arm at discrete fingertip positions. Finally, discrete joint angles are solved based on the predicted elbow swivel angles and then fitted to a continuous 5-parameter logistic function to obtain the joint trajectory. This method is verified using 48 test motions, and the results show that this method can generate accurate human arm motions.
Discrepancies between designs of robot communicative styles and their perceived assertiveness

A robot’s perceived assertiveness can influence how people assess its credibility and their willingness to comply with its suggestions during human-robot interaction. This study proposes a novel measurement of the perceived assertiveness of a robot using both objective assessment of a person’s compliance to a robot’s suggestions in a math quiz task and subjective assessments of a person’s perception and expectation of a robot in different hypothetical scenarios. The proposed measurement evaluates perceived assertiveness of a robot in three dimensions inspired by social behavioral studies on human-human interaction, namely social assertiveness, directiveness, and independence. We conducted an exploratory study using crowdsourcing to test the efficacy of this proposed measurement of perceived assertiveness. In particular, participants were exposed to robots that differed in terms of their anthropomorphism and their communicative styles. The communicative styles are designed following human studies of behaviors that are commonly associated with high or low assertiveness. Our results demonstrated the validity of the proposed measurement of perceived assertiveness. The observed discrepancy between the objective and subjective measurements highlights the necessity of evaluating human perception through multiple approaches. Moreover, gaps between intended and perceived robot assertiveness indicate a potential gap between human communication theories and their applicability to human-robot interaction.
Enterprise Profit Forecast Model Based on Long Short-Term Memory Neural Network

In the era of the rapid development of artificial intelligence, in order to improve the usefulness of accounting information, this paper uses Long Short-Term Memory (LSTM) neural network model and financial statement information to forecast the profit of listed companies, and compares with the results predicted by analysts. In the profit forecast task of enterprises from Shanghai and Shenzhen 300 (CSI 300), the average accuracy of LSTM model is 88.6%, which is 13.52% higher than the average accuracy of analysts' forecast. In the accuracy distribution, there is no thick tail phenomenon in the results of LSTM model, and its kurtosis is significantly higher than that of analysts' forecast, and the variance is significantly lower than that of analysts' forecast. It reveals the practical significance of the application of artificial intelligence model in financial forecasting.
A Compliant Mechanism with Progressive Stiffness for Robotic Actuation

Softer series elastic actuators display higher torque resolution and better torque transparency, which facilitates soft physical interaction of robots with their environment. Stiffer series elastic actuators favour fast, strong and powerful physical interaction between the robot and the environment. Passive compliant elements with progressive stiffness characteristic could render the actuator inherently soft at low interaction torque levels and much stiffer at higher interaction torque levels, remedying common design trade-offs of linear series elastic actuators. This paper proposes a compact solution, which realises such a passive elastic element without complex nonlinear transmissions. The solution is solely based on a number of radially oriented linear springs. The paper presents two distinct implementations using i) an assembly of concentrated tension springs and ii) a monolithic arrangement of distributed flexure beams. Detailed theoretical analyses and experimental prototype validations lead to design guidelines and confirm a cubic polynomial torque-deflection characteristic paired with low hysteresis.
Optimizing Key-Value Stores for Flash-Based SSDs via Key Reshaping

Key-Value store (KV store) is becoming widely popular in both academia and industry due to its fast performance and simplicity in data management. To improve the performance of KV stores, recent Serial Advanced Technology Attachment (SATA) and Non-Volatile Memory express (NVMe) Solid-State Drives (SSDs) have been widely adopted. In contrast to the existing Hard-Disk Drives (HDDs), SSDs have unique characteristics which must be carefully considered to exploit the full performance. For example, due to the erase before write constraint, the access pattern of workloads impacts the performance and endurance of SSDs. Thus, the performance of SSD with the sequential workload is higher than that with the random workload. In this paper, we propose a key reshaping technique to improve the performance of KV stores with high performance storage devices. By reshaping keys, our scheme allows KV stores to process the random insert requests into sequential insert requests, improving request processing and Input/Output (I/O) performance. Our experimental results show that the proposed scheme can improve the performance of KV store by up to 106% and 281% compared with the existing scheme, in the case of SATA and NVMe SSDs, respectively.
A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity

Objective: We present a statistical model for extracting physiologic characteristics from electrodermal activity (EDA) data in observational settings. Methods: We based our model on the integrate-and-fire physiology of sweat gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval structure. At the core of our model-based paradigm is a subject-specific amplitude threshold selection process for EDA pulses based on the statistical properties of four right-skewed models including the IG. By performing a sensitivity analysis across thresholds and fitting all four models, we selected for IG-like structure and verified the pulse selection with a goodness-of-fit analysis, maximizing capture of physiology at the time scale of EDA responses. Results: We tested the model-based paradigm on simulated EDA time series and data from two different experimental cohorts recorded during different experimental conditions, using different equipment. In both the simulated and experimental data, our model-based method robustly recovered pulses that captured the IG-like structure predicted by physiology, despite large differences in noise level. In contrast, established EDA analysis tools, which attempted to estimate neural activity from slower EDA responses, did not provide physiological validation and were susceptible to noise. Conclusion: We present a computationally efficient, statistically rigorous, and physiology-informed paradigm for pulse selection from EDA data that is robust across individuals and experimental conditions, yet adaptable to varying noise level. Significance: The robustness of the model-based paradigm and its physiological basis provide empirical support for the use of EDA as a clinical marker for sympathetic activity in conditions such as pain, anxiety, depression, and sleep states.
Evaluating Distances in Tactile Human-Drone Interaction

The increasing autonomy and presence of Unmanned Aerial Vehicles (UAVs), especially quadrotors, in everyday applications requires in-depth studies of proxemics in Human-Drone Interaction (HDI) and novel methods of user interaction suitable for different distances. This paper presents a user study (N=32) that evaluates proxemics with a miniature quadrotor (92 mm wheelbase) from four directions (front, back, left, right) in a seated setting investigating preferred approach directions and distances in future home or workplace scenarios. The goal of this study is to determine if humans are willing to allow flying robots of that size and mechanical appearance to approach close enough to enable tactile interaction. Moreover, the participants' inclination to physically interact with the quadrotor is examined. Studies evaluating proxemics in HDI are highly dependent on repeatable results and actually flying robots. In most comparable studies, the quadrotors used did not fly freely or at all, but were moved, manually controlled, or flew barely repeatable trajectories due to unstable onboard navigation. Only few studies have used pose estimation systems that ensure smooth and reproducible trajectories and thus reliable findings of the studies. For this reason, in addition to the presented study and its results, an insight into the used testbed is provided, that also integrates full skeleton pose estimation rather than tracking participants with only a single marker.
Differentiable Ranking Metric Using Relaxed Sorting for Top-K Recommendation

Most recommenders generate recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-items of high scores. Since sorting is not differentiable and is difficult to optimize with gradient descent, it is nontrivial to incorporate it in recommendation model training despite its relevance to top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> recommendations. As a result, inconsistency occurs between existing learning objectives and ranking metrics of recommenders. In this work, we present the Differentiable Ranking Metric (DRM) that mitigates the inconsistency between model training and generating top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> recommendations, aiming at improving recommendation performance by employing the differentiable relaxation of ranking metrics via joint learning. Using experiments with several real-world datasets, we demonstrate that the joint learning of the DRM objective and existing factor based recommenders significantly improves the quality of recommendations.
A Novel Individual Variable Step-Size Subband Adaptive Filter Algorithm Robust to Impulsive Noises

This paper proposes a novel individual variable step-size subband adaptive filter algorithm robust to impulsive noises. A fixed step-size subband adaptive filter algorithm that is robust against impulsive noises is newly derived by obtaining the optimal solution from a constrained optimization problem through the Lagrange multiplier. In addition, in order to further improve the convergence performance of the proposed algorithm, the weight update formula with a single fixed step size is modified to have multiple individual step sizes. By analyzing its mean-square-deviation (MSD), the optimal individual step size is designed. Simulation results show that the proposed algorithm outperforms the algorithms robust to impulsive noises in the literature.
On the Reuse of Motor Unit Filters in High Density Surface Electromyograms Recorded at Different Contraction Levels

We analyzed the efficiency of Motor Unit (MU) tracking across different experimentally recorded contraction levels of Biceps Brachii (BB), Tibialis Anterior (TA), First Dorsal Interosseous (FDI) and Abductor Digiti Minimi (ADM) muscles and in simulated conditions. We used the Convolution Kernel Compensation (CKC) algorithm to estimate the MU filters from high-density electromyograms (HDEMGs) at contraction levels ranging from 5 % to 70 % of Maximum Voluntary Contraction (MVC), and applied these MU filters to all the recorded contractions levels of the same muscle. For each MU filter estimation-application pair we assessed the number of identified MUs, Pulse-to-Noise Ratio (PNR), Mean Discharge Rate (MDR) and MUAP peak-to-peak (P2P) amplitude, normalized by HDEMG P2P amplitude. In simulated conditions, we also calculated the Precision ( $Pr$ ) and Sensitivity ( $Se$ ) of MU firing identification. We also reported the number of jointly identified MUs for all possible contraction level pairs. The results in simulated conditions confirmed the efficiency of MU filter transfer from low to high contraction levels. The large majority of MUs identified at lower contraction levels were tracked successfully at higher contraction levels, though many of them were not directly identified at higher contraction levels. Sensitivities of identified MU firings were above 97 %, decreasing only by about 1 % when transferring the MU filters from low to high contraction levels. In the experimental conditions the efficiency of the MU tracking depended significantly on the investigated muscle. The highest efficiency was demonstrated in the FDI muscle, where about 90 % and 70 % of MUs identified at the contraction level of MU filter estimation were also tracked at 10 % and 20 % higher contraction level, respectively. These figures decreased to 47 % and 20 % of MUs in TA, and to 21 % and 18 % of MUs in the BB muscle. The observed differences between the MU filter transfer efficiencies are likely caused by the differences in the size and anatomy of the investigated muscles.
Teleoperated Robot Coaching for Mindfulness Training: A Longitudinal Study

Social robots are becoming incorporated in daily human lives, assisting in the promotion of the physical and mental wellbeing of individuals. To investigate the design and use of social robots for delivering mindfulness training, we develop a teleoperation framework that enables an experienced Human Coach (HC) to conduct mindfulness training sessions virtually, by replicating their upper-body and head movements onto the Pepper robot, in real-time. Pepper’s vision is mapped onto a Head-Mounted Display (HMD) worn by the HC and a bidirectional audio pipeline is set up, enabling the HC to communicate with the participants through the robot. To evaluate the participants’ perceptions of the teleoperated Robot Coach (RC), we study the interactions between a group of participants and the RC over 5 weeks and compare these with another group of participants interacting directly with the HC. Growth modelling analysis of this longitudinal data shows that the HC ratings are consistently greater than 4 (on a scale of 1 5) for all aspects while an increase is witnessed in the RC ratings over the weeks, for the Robot Motion and Conversation dimensions. Mindfulness training delivered by both types of coaching evokes positive responses from the participants across all the sessions, with the HC rated significantly higher than the RC on Animacy, Likeability and Perceived Intelligence. Participants’ personality traits such as Conscientiousness and Neuroticism are found to influence their perception of the RC. These findings enable an understanding of the differences between the perceptions of HC and RC delivering mindfulness training, and provide insights towards the development of robot coaches for improving the psychological wellbeing of individuals.
Stock Market Analysis Using Linear Regression and Decision Tree Regression

In business, the Stock market or Share market is a more perplexing and sophisticated way to do business. Every business owner wants to reduce the risk and make an immense profit using an effective way. The bank sector, brokerage corporations, small ownerships, all depends on this very body to earn profit and reduce risks. However, using the machine learning algorithm of this paper to predict the future stock price and shuffle by using subsist algorithms and open source libraries to assist in inventing this unsure format of business to a bit more predictable. The proposed system of this paper works in two methods - Linear Regression and Decision Tree Regression. Two models like Linear Regression and Decision Tree Regression are applied for different sizes of a dataset for revealing the stock price forecast prediction accuracy. Moreover, the authors of this paper have revealed some development that could be the club to acquire better validity in these approaches.