Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Privacy and Security on the Extensible Internet

The Extensible Internet (EI) is an initiative to transform the Internet architecture by introducing a new Interposition Layer between L3 and L4 for services to be deployed on. Backed by veriﬁably secure infrastructure, EI provides new opportunities to improve the privacy and security of Internet clients. In this work, we address two challenges that the Internet faces today: maintaining privacy in DNS lookups and ensuring the integrity of third-party application servers. Speciﬁcally, we design and implement services for EI to facilitate oblivious DNS and remote attestation veriﬁcation. We evaluate their execution and demonstrate how EI can help mimic and deploy these proposals without signiﬁcantly compromising on performance.
Role of Litsea cubeba Essential Oil in Agricultural Products Safety: Antioxidant and Antimicrobial Applications

The essential oil from Litsea cubeba (LCEO) has good antioxidant, antimicrobial, anti-insect properties, which gives it the potential for use as a natural additive to food resources and food products in order to prevent spoilage and extend shelf life. In this study the biological activity related to food preservation was observed. The main volatile organic compounds were geranial (39.4%), neral (29.5%), and limonene (14.3%). Antioxidant activity was 30.9%, which was equal to 167.94 µg of Trolox per mL of sample. Antimicrobial activity showed the strongest inhibition against Serratia marcescens by disk diffusion method and minimum inhibitory concentrations MIC 50 and MIC 90 were the lowest for Micrococcus luteus with values 1.46 and 3.52 µL/mL, respectively. Antimicrobial activity of the LCEO vapor phase showed strong inhibition of microorganisms on apples, pears, potatoes, and kohlrabies. Over 50% of gram-positive and gram-negative bacteria and yeasts were inhibited by a concentration of 500 µL/mL. The inhibition of microorganisms was concentration dependent. Anti-insect activity was also strong, with 100% lethality of Pyrrhocoris apterus at a concentration of 25%. These results suggest that LCEO could be potentially used as a food preservative.
Research on the SDIF Failure Principle for RF Stealth Radar Signal Design

Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal sorting algorithm, and the SDIF algorithm is a core sorting algorithm widely used in engineering. Thus, in this paper, the SDIF algorithm is first analyzed in detail. It is pointed out that the threshold function of the SDIF algorithm will fail when the signal pulse repetition interval (PRI) value obeys the interval distribution whose length is 20 times larger than the minimum interval of PRI. Secondly, the correctness of the failure principle of SDIF threshold separation is proved by the formula. Finally, the correctness is further verified by the signal design case. The principle of SDIF sorting threshold failure provides theoretical support for anti-sorting RF stealth signal design. It also complements the shortcoming of the casual design for the anti-sorting signal. Furthermore, the principle of SDIF sorting threshold failure helps improve anti-sorting signal design efficiency. Compared with the Dwell & Switch (D&S) signal and jitter signal, the anti-sorting ability of the signal designed by using the sorting failure principle is notably enhanced through simulation and experimentation.
Psychometric Properties of the Student Course Engagement Questionnaire (SCEQ): Measuring Engagement or Missing the Mark?

Despite widespread use of the Student Course Engagement Questionnaire (SCEQ; Handelsman, Briggs, Sullivan, & Towler, 2005) for the purposes of measuring college student engagement in scholarship of teaching and learning (SoTL) contexts, few studies have been published regarding its psychometric validity. The current study examined the structural and criterion validity of models proposed in the original study and in a subsequent validation study. The results indicated that neither model provided adequate fit to the current data. Concerns regarding structural, criterion, and content validity are discussed, and recommendations regarding the use of the SCEQ are provided in the context of current psychometric evidence and relevant theories of student engagement.
Head and Body Orientation Estimation with Sparse Weak Labels in Free Standing Conversational Settings

We focus on estimating human head and body orientations which are crucial social cues in free-standing conversational settings. Automatic estimations of head and body orientations enable downstream research about conversation involvement, influence, and other social concepts. However, in-the-wild human behavior and long interaction datasets are difficult to collect and expensive to annotate. Our approach mitigates the need for large number of training labels by casting the task into a transductive low-rank matrix-completion problem using sparsely labelled data. We differentiate our learning setting from the typical data-intensive setting required for existing supervised deep learning methods. In situations of low labelled data availability, our method takes advantage of the inherent properties and dynamics of the social scenarios by leveraging different sources of information and physical priors. Our method is (1) data efficient and uses a small number of annotated labels, (2) ensures temporal smoothness in predictions, (3) adheres to human anatomical constraints of head and body orientation differences, and (4) exploits weak labels from multimodal wearable sensors. We benchmark this method on the challenging multimodal SALSA dataset, the only large scale dataset that contains video, proximity sensors and microphone audio data. When only using 5% of all the labels as training samples, we report 65% and 76% averaged classification accuracy for head and body orientations, which is an 8% and 16% respective increase compared to previous state-of-the-art performance under the same transductive setting.
Certiﬁed Predictions using MPC-Friendly Publicly Veriﬁable Covertly Secure Commitments

We address the problem of efﬁciently and securely enabling certiﬁed predictions on deep learning models. This addresses the scenario where a party P1 owns a conﬁdential model that has been certiﬁed by an authority to have a certain property e.g. fairness. Subsequently, another party P2 wants to perform a prediction on the model with an assurance that the certiﬁed model was used. We present a solution for this problem based on MPC commitments. Our constructions operate in the publicly veriﬁable covert (PVC) security model, which is a relaxation of the malicious model of MPC, appropriate in settings where P1 faces a reputational harm if caught cheating. We introduce the notion of a PVC commitment scheme 1 and indexed hash functions to build commitment schemes tailored to the PVC framework, and propose constructions for both arithmetic and Boolean circuits that result in very efﬁcient circuits. From a practical standpoint, our constructions for Boolean circuits are 60 × faster to evaluate securely, and use 36 × less communication than baseline methods based on hashing. Moreover, we show that our constructions are tight in terms of required non-linear operations, and present a technique to amplify the security properties of our constructions that allows to efﬁciently recover malicious guarantees with statistical security.
Piecewise-smooth surface fitting onto unstructured 3D sketches

We propose a method to transform unstructured 3D sketches into piecewise smooth surfaces that preserve sketched geometric features. Immersive 3D drawing and sketch-based 3D modeling applications increasingly produce imperfect and unstructured collections of 3D strokes as design output. These 3D sketches are readily perceived as piecewise smooth surfaces by viewers, but are poorly handled by existing 3D surface techniques tailored to well-connected curve networks or sparse point sets. Our algorithm is aligned with human tendency to imagine the strokes as a small set of simple smooth surfaces joined along stroke boundaries. Starting with an initial proxy surface, we iteratively segment the surface into smooth patches joined sharply along some strokes, and optimize these patches to fit surrounding strokes. Our evaluation is fourfold: we demonstrate the impact of various algorithmic parameters, we evaluate our method on synthetic sketches with known ground truth surfaces, we compare to prior art, and we show compelling results on more than 50 designs from a diverse set of 3D sketch sources.
A Residual LSTM and Seq2Seq Neural Network Based on GPT for Chinese Rice-Related Question and Answer System

Rice has a wide planting area as one of the essential food crops in China. The problem of diseases and pests in rice production has always been one of the main factors affecting its quality and yield. It is essential to provide treatment methods and means for rice diseases and pests quickly and accurately in the production process. Therefore, we used the rice question-and-answer (Q&A) community as an example. This paper aimed at the critical technical problems faced by the agricultural Q&A community: the accuracy of the existing agricultural Q&A model is low, which is challenging to meet users’ requirements to obtain answers in real-time in the production process. A network based on Attention-ResLSTM-seq2seq was used to realize the construction of the rice question and answer model. Firstly, the text presentation of rice question-and-answer pairs was obtained using the GPT pre-training model based on a 12-layer transformer. Then, ResLSTM(Residual Long Short-Term Memory) was used to extract text features in the encoder and decoder, and the output project matrix and output gate of LSTM were used to control the spatial information flow. When the network contacts the optimal state, the network only retains the constant mapping value of the input vector, which effectually reduces the network parameters and increases the network performance. Next, the attention mechanism was connected between the encoder and the decoder, which can effectually strengthen the weight of the keyword feature information of the question. The results showed that the BLEU and ROUGE of the Attention-ResLSTM-Seq2seq model reached the highest scores, 35.3% and 37.8%, compared with the other six rice-related generative question answering models.
Update of leadership-as-practice “practice theory”: Featuring Joe Raelin Interviewed by Jenny Robinson

This article constitutes an interview between a new researcher of the field of Leadership-as-Practice (L-A-P), Jenny Robinson, with one of the co-creators of the field, Joe Raelin. It is dedicated to providing an update and refinement of leadership-as-practice “practice theory,” which has gone through a fair degree of transformation since this journal’s first article on the subject in 2008. The call for such an update is precipitated by the need for emerging L-A-P researchers to appreciate the subject’s conceptual boundaries for more consistent and integrated exploration. In particular, L-A-P claims to differentiate not only from other plural traditions in leadership but from other “as-practice” approaches in the wider management field. Some of the other distinctions covered in this article comprise the role of theory in L-A-P, its contribution to leadership research and leadership development, its connection to other related fields, and its phenomenological, ethical, democratic, and post-humanistic foundations.
Research on Influencing Factors of Provincial Energy Efficiency in China Based on the Spatial Panel Model

The Super-SBM model was used first to assess the energy efficiency of 30 Chinese provinces from 2012 to 2017. After that, an energy efficiency spatial correlation test was conducted, and finally, the influencing elements of energy efficiency were analyzed using a geographic panel model. The findings show that the amount of regional economic development has a substantial positive impact on energy efficiency, whereas the level of regional urbanization and the severity of environmental restrictions have a considerable negative impact on energy efficiency in China’s provinces. Other regions’ energy structure and technical innovation have a substantial positive spillover effect on the region’s energy efficiency, whereas other regions’ economic development and foreign direct investment have a significant negative spillover effect on the region’s energy efficiency.
Comparison of physiological and perceptual load between uke and tori after intermittent osaekomi-waza exercise in judo

ABSTRACT This study investigated differences in physiological and perceptual responses between uke and tori during intermittent osaekomi-waza exercise. Twenty-one male judo athletes voluntarily participated in this study. Following anthropometric measurements and 1 min uchi-komi test, athletes executed 20 s × 6 sets (1:½ work rest ratio) intermittent osaekomi-waza exercise with kesa-gatame as both uke and tori. Athletes’ heart rate and lactate concentration were monitored at rest, 1 min, and 6 min following the exercise while the athletes classified the rate of perceived exertion just after the exercise. During the experiment, the changes in heart rate were different between positions with higher values in uke position compared to tori position after the intermittent exercise (F2,40 = 4.28, p = 0.03). Moreover, athletes in the uke position stated a higher rating of perceived exertion than athletes in the tori position (p = 0.01). However, blood lactate responses were found to be similar between positions (p > 0.05). Intermittent osaekomi-waza exercises resulted in similar metabolic load for uke and tori positions, while higher cardiovascular load was observed in the uke position compared to the tori position. Thus, it can be concluded that the athletes in uke position are exposed to more cardiovascular load compared to those in tori position and present higher perceptual responses.
SISTEM PENDUKUNG KEPUTUSAN PEMBELIAN NOTEBOOK MENGGUNAKAN LOGIKA FUZZY TAHANI

Notebook adalah perangkat komputer bagi pengguna yang dinamis. Memiliki fungsi seperti komputer secara umum. Dalam pembelian sebuah notebook, banyak hal yang dapat dijadikan parameter penilaian untuk mendukung keputusan. Dalam hal ini, seseorang bisa saja memiliki parameter yang berbeda dengan orang lainnya. Terdapat permasalahan dimana sesorang bisa rumit dalam menentukan notebook mana yang akan dibelinya, oleh karena banyaknya spesifikasi dan harga dari tipe-tipe notebook yang variatif, sementara calon pembeli terkadang kurang dapat memberikan spesifikasi yang jelas terhadap kebutuhan dan keinginannya kepada penjual. Penggunaan metode fuzzy database dengan model Tahani pada sebuah sistem pendukung keputusan merupakan salah satu jalan pemecahan masalah yang dapat menangani hal tersebut, dimana bahasa alami yang sering digunakan sehari-hari yang bersifat relatif, kualitatif, dan tidak presisi akan menjadi input kriteria pada sistem oleh pengguna. Sehingga pada akhir prosesnya, pengguna akan mendapatkan daftar notebook yang direkomendasikan berdasarkan kriteria masukannya. Hasil menunjukkan bahwa sistem pendukung keputusan ini dapat membantu pengguna untuk mendapatkan alternatif notebook yang dapat direkomendasikan berdasarkan kriteria yang digunakan pengguna dalam memilih sebuah notebook dengan model fuzzy tahani.
Evolutionary optimization of robot morphology and control - Using evolutionary algorithms in the design of a six legged robotic platform

Evolutionary robotics has yet to achieve mainstream status within the robotics research community, and among the reasons for this is the relative low maturity of the field. Most of the current research is still done in the scope of fundamental research, and has yet to be used to solve a wide range of real world problems, although much of it shows great promise. Conventionally designed and controlled robots solve an ever increasing number of tasks. For evolutionary robotics to catch up with, or even outperform traditional robotics techniques, a wide array of functional real life robots built on the foundation of evolutionary techniques is required. This thesis proposes an evolutionary framework for evolving both morphology and control for a six legged robot. It features a parameterized 3D model with adaptable servo placement, base size, leg lengths, and a possibility for adding two more legs or tool holders to the front. It is also integrated into a simulation environment for evolutionary experiments. The algorithm tested is able to produce a varied set of solutions with different weights and speeds, and shows promise for solving more complex tasks or fitness functions. The thesis also tests whether co-evolution of control and morphology is a feasible technique for robot design, by comparing the performance of a manually designed instance of the robot to two evolved models, both in simulation and reality. Machine learning is also used to lessen the reality gap present between the simulations and the physical experiments. Coevolution of control and morphology shows a significant improvement over the manually designed morphology and gait, producing a robot which is 3% lighter and 49% quicker.
Indicators Predicting Inpatient Mortality in Post-Stroke Patients Admitted to a Chronic Care Hospital: A Retrospective Pilot Study

Evidence concerning the mortality of post-stroke patients admitted to a chronic-phase hospital seems to be lacking. This pilot study aimed to identify mortality-related clinical variables in the admission of post-stroke patients from a retrospective perspective. A group of 38 non-survival stroke patients and another group of 46 survival stroke patients in a chronic-phase ward of the single center were recruited. Clinical variables including age, sex, stroke type, and Barthel index (BI) score were collected. The difference in the age and BI scores on admission were statistically significant between the two groups (p < 0.01). Polytomous logistic regression analysis revealed that age (odds ratio = 1.09, p = 0.03, and 95% confidence interval: 1.01–1.07), male sex (odds ratio = 5.04, p = 0.01, and 95% confidence interval: 1.39–18.27), and BI scores on admission (odds ratio = 0.90, p = 0.01, and 95% confidence interval: 0.83–0.97) could be prognostic variables. The percentage of correct classification was 83.3%. Age, male sex, and BI scores on admission may be prognostic indicators. The result of this study could lay the groundwork for palliative care for such a clinical population.
An investigation of environmental awareness and practice among a sample of undergraduate students in Belize

Abstract Climate change is causing rapid environmental deterioration in the Caribbean. Therefore, empowering citizens to become environmentally literate is crucial. This study examined environmental awareness (EA) and practice (EP) among a sample (n=207) of Belizean undergraduate students. An Environmental Knowledge Indicator (High = 18-20; Moderate = 14-17; Low = 10-13; Poor = 7-9; Very Poor= < 6) and an Environmentally Responsible Behaviour scale (High = 72-80; Moderate = 56-71; Low = 40-55; Poor = 26-39; Very Poor= < 25) were used to collect data. Results revealed low levels of environmental awareness (M=13.5), and poor environmentally responsible practice (M=33.4). There was no statistically significant relationship between EA and EP. Males had statistically higher EA scores (M =14.06) than females (M= 12.49), t (205) = 3.53, p =. 001. They also had statistically higher EP scores (M=35.11) than females (M = 32.15), t (203) = 2.07, p<. 05). Natural Science students had statistically higher EA scores (M = 13.93) than Social Science students (M= 12.41), t (205) = -3.51, p=. 001. They also had statistically higher EP scores (M = 34.97) than Social Science students (M= 31.82), t (203) = -2.23, p<. 05. These results may suggest inadequate exposure to Education for Sustainable Development models.
Effectiveness of Oxygen during Sintering of Silver Thin Films Derived by Nanoparticle Ink

Silver nanoparticle (NP) inks have been widely used in the ink-jet printing field because of their excellent properties during low-temperature sintering. However, the organic dispersant used to prevent the aggregation and sedimentation of NPs can hinder the sintering process and result in the high resistivity of sintered films. In this study, silver thin films derived from silver NP ink with polyvinylpyrrolidone (PVP) dispersant were sintered in different atmospheres of pure nitrogen, air, and pure oxygen. The effect of the oxygen content in the sintering atmosphere on the thermal properties of the ink, the electrical resistivity and microstructure of the sintered films, and the amount of organic residue were studied by using differential scanning calorimetry, the four-point probe method, scanning electron microscopy, Fourier transform infrared spectroscopy, etc. The mechanism of optimizing the film resistivity by influencing the decomposition of the PVP dispersant and the microstructure evolution of the silver thin films through the sintering atmosphere was discussed. The results demonstrated that an oxygen-containing atmosphere could be effective for silver NPs in two ways. First, the oxygen content could enhance the diffusion ability of silver atoms, thus accelerating the stage transition of microstructural evolution at low temperatures. Second, the oxygen content could enable the PVP to decompose at a temperature much lower than in conditions of pure nitrogen, thus helping to finalize the densification of a silver film with a low resistivity of 2.47 μΩ·cm, which is approximately 1.5-fold that of bulk silver. Our findings could serve as a foundation for the subsequent establishment of ink-jet printing equipment and the optimization of the sintering process for printing silver patterns on flexible substrates.
Facial Expression Recognition from Multi-Perspective Visual Inputs and Soft Voting

Automatic identification of human facial expressions has many potential applications in today’s connected world, from mental health monitoring to feedback for onscreen content or shop windows and sign-language prosodic identification. In this work we use visual information as input, namely, a dataset of face points delivered by a Kinect device. The most recent work on facial expression recognition uses Machine Learning techniques, to use a modular data-driven path of development instead of using human-invented ad hoc rules. In this paper, we present a Machine-Learning based method for automatic facial expression recognition that leverages information fusion architecture techniques from our previous work and soft voting. Our approach shows an average prediction performance clearly above the best state-of-the-art results for the dataset considered. These results provide further evidence of the usefulness of information fusion architectures rather than adopting the default ML approach of features aggregation.
Seeking optimal parameters for achieving a lightweight reservoir computing: A computational endeavor

Reservoir computing (RC) is a promising approach for model-free prediction of complex nonlinear dynamical systems. Here, we reveal that the randomness in the parameter configurations of the RC has little influence on its short-term prediction accuracy of chaotic systems. This thus motivates us to articulate a new reservoir structure, called homogeneous reservoir computing (HRC). To further gain the optimal input scaling and spectral radius, we investigate the forecasting ability of the HRC with different parameters and find that there is an ellipse-like optimal region in the parameter space, which is completely beyond the area where the spectral radius is smaller than unity. Surprisingly, we find that this optimal region with better long-term forecasting ability can be accurately reflected by the contours of the $ l_{2} $-norm of the output matrix, which enables us to judge the quality of the parameter selection more directly and efficiently.