Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Sorting of Homomorphic Encrypted Data With k-Way Sorting Network

In this study, we propose an efficient sorting method for encrypted data using fully homomorphic encryption (FHE). The proposed method extends the existing 2-way sorting method by applying the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-way sorting network for any prime <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> to reduce the depth in terms of comparison operation from <inline-formula> <tex-math notation="LaTeX">$O(\log _{2}^{2}~n)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(k\log _{k}^{2}~n)$ </tex-math></inline-formula>, thereby improving performance for <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> slightly larger than 2, such as <inline-formula> <tex-math notation="LaTeX">$k=5$ </tex-math></inline-formula>. We apply this method to approximate FHE which is widely used due to its efficiency of homomorphic arithmetic operations. In order to build up the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-way sorting network, the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-sorter, which sorts <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-numbers with a minimal comparison depth, is used as a building block. The approximate homomorphic comparison, which is the only type of comparison working on approximate FHE, cannot be used for the construction of the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-sorter as it is because the result of the comparison is not binary, unlike the comparison in conventional bit-wise FHEs. To overcome this problem, we propose an efficient <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-sorter construction utilizing the features of approximate homomorphic comparison. Also, we propose an efficient construction of a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-way sorting network using cryptographic SIMD operations. To use the proposed method most efficiently, we propose an estimation formula that finds the appropriate <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> that is expected to reduce the total time cost when the parameters of the approximating comparisons and the performance of the operations provided by the approximate FHE are given. We also show the implementation results of the proposed method, and it shows that sorting 5<sup>6</sup> = 15625 data using 5-way sorting network can be about 23.3% faster than sorting 2<sup>14</sup> = 16384 data using 2-way.
Programming Network Stack for Middleboxes with Rubik

Middleboxes are becoming indispensable in modern networks. However, programming the network stack of middleboxes to support emerging transport protocols and ﬂexible stack hierarchy is still a daunting task. To this end, we propose Rubik, a language that greatly facilitates the task of middle-box stack programming. Different from existing hand-written approaches, Rubik offers various high-level constructs for relieving the operators from dealing with massive native code, so that they can focus on specifying their processing intents. We show that using Rubik one can program the middlebox stack with minor effort, e.g. , 250 lines of code for a complete TCP/IP stack, which is a reduction of 2 orders of magnitude compared to the hand-written versions. To maintain a high performance, we conduct extensive optimizations at the middle-and back-end of the compiler. Experiments show that the stacks generated by Rubik outperform the mature hand-written stacks by at least 30% in throughput.
Secure Computation by Secret Sharing Using Input Encrypted with Random Number (Full Paper)

Typically, unconditionally secure computation using a (k, n) threshold secret sharing scheme is considered impossible when n < 2k − 1. Therefore, in our previous work, we first took the approach of finding the conditions required for secure computation under the setting of n < 2k − 1 and showed that secure computation using a secret sharing scheme can be realized with a semi-honest adversary under the following three preconditions: (1) the result of secure computation does not include 0; (2) random numbers reconstructed by each server are fixed; and (3) each server holds random numbers unknown to the adversary and holds shares of random numbers that make up the random numbers unknown to the adversary. In this paper, we show that by leaving condition (3), secure computation with information-theoretic security against a semihonest adversary is possible with k ≤ n < 2k − 1. In addition, we clarify the advantage of using secret information that has been encrypted with a random number as input to secure computation. One of the advantages is the acceleration of the computation time. Namely, we divide the computation process into a preprocessing phase and an online phase and shift the cost of communication to the preprocessing phase. Thus, for computations such as inner product operations, we realize a faster online phase, compared with conventional methods.
ATP: In-network Aggregation for Multi-tenant Learning

Distributed deep neural network training (DT) systems are widely deployed in clusters where the network is shared across multiple tenants, i.e., multiple DT jobs. Each DT job computes and aggregates gradients. Recent advances in hardware accelerators have shifted the the performance bottleneck of training from computation to communication. To speed up DT jobs’ communication, we propose ATP, a service for in-network aggregation aimed at modern multi-rack, multi-job DT settings. ATP uses emerging programmable switch hardware to support in-network aggregation at multiple rack switches in a cluster to speedup DT jobs. ATP performs decentralized, dynamic, best-effort aggregation, enables efficient and equitable sharing of limited switch resources across simultaneously running DT jobs, and gracefully accommodates heavy contention for switch resources. ATP outperforms existing systems accelerating training throughput by up to 38% 66% in a cluster shared by multiple DT jobs.
Influence of Maturity on the Content of Phenolic Compounds of Alium ursinum L.

The aim of the study was to evaluate the content of polyphenolic compounds in anatomical parts of bear garlic (Allium ursinum L.) in different stages of maturity. Identification and quantification of polyphenolic compounds were determined in the examined samples using ultra-performance liquid chromatography photodiode detector-quadrupole/time of flight-mass spectrometry (UPLC-PDA-Q/TOF-MS) method. Compounds identified in extracts of leaves, roots, steams and roots garlic include 32 kaempferol derivatives and 4 caffeic acid derivatives (occurring only in the roots). Fourteen compounds were identified in bear garlic for the first time. Total phenolic content varied from 10.9 in roots to 1477 mg/100 g DW in leaves garlic in March, and garlic in June were content from 6.5 in roots to 1195.9 mg/100 g DW in leaves. Practical Applications It has been well documented that plant by-products, such as leaves, stems, flowers and roots contain high levels of various health-enhancing substances, e.g., phenolic compounds. The utilization of by-products has become an important aspect in waste management in contributing to more sustainable production in the food and pharmaceutical industries. This review presents the phenolic profile of bear garlic leaves, stems, flowers and roots. These valuable compounds in Allium ursinum could be applied to great effect in the formulation of food and nutritional supplements.
An Improved 3D Registration Method of Mobile Augmented Reality for Urban Built Environment

3D registration plays a pivotal role in augmented reality (AR) system. The existing methods are not suitable to be applied directly in the mobile AR system for the built environment, with the reasons of poor real-time performance and robustness. This paper proposes an improved 3D registration method of mobile AR for built environment, which is based on SURFREAK and KLT. This method increases the building efficiency of algorithm descriptors and maintains the robustness of the algorithms. To implement and evaluate the registration method, a smart phone-based mobile AR system for built environment is developed. The experimental result shows that the improved method is endowed with higher real-time performance and robustness, and the mobile AR 3D registration can realize a favorable performance and efficiency in the complex built environment. The mobile AR system could be used in building recognition and information augmentation for built environment and further to facilitate location-based games, urban heritage tourism, urban planning, and smart city.
Improving HCI with Brain Input: Review, Trends, and Outlook

In the field of HCI, researchers from diverse backgrounds have taken a broad view of application domains that could benefit from brain signals, both by applying HCI methods to improve interfaces using brain signals (e.g., human-centered design and evaluation of brain-based user interfaces), as well as integrating brain signals into HCI methods (e.g., using brain metrics in user experience evaluation). Recent advances in brain sensing technologies, new analysis methods, and hardware improvements have opened the door for such research, which will accelerate with the increased commercialization of wearable technology containing brain sensors. In this monograph, we examine brain signals from an HCI perspective, focusing on work that makes an HCI-related contribution. We pursue three main goals. First, we give a primer for HCI researchers on the necessary technology, the possibilities, and limitations for using brain signals in user interfaces. Second, we systematically map out the research field by constructing a taxonomy of applications, input paradigms, and interface designs. For this purpose, we reviewed more than 100 publications in major HCI conferences and journals. Finally, we identify gaps and areas of emerging work to lay a foundation for future research on HCI for and with brain signals. Erin T. Solovey and Felix Putze (2021), “Improving HCI with Brain Input: Review, Trends, and Outlook”, Foundations and Trends® in Human–Computer Interaction: Vol. 13, No. 4, pp 298–379. DOI: 10.1561/1100000078. The version of record is available at: http://dx.doi. org/10.1561/1100000078
This paper is included in the Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation.

Several emerging evolving graph application workloads demand support for efﬁcient ad-hoc analytics—the ability to perform ad-hoc queries on arbitrary time windows of the graph. We present T EGRA , a system that enables efﬁcient ad-hoc window operations on evolving graphs. T EGRA allows efﬁcient access to the state of the graph at arbitrary windows, and signiﬁcantly accelerates ad-hoc window queries by using a compact in-memory representation for both graph and intermediate computation state. For this, it leverages persistent data structures to build a versioned, distributed graph state store, and couples it with an incremental computation model which can leverage these compact states. For users, it exposes these compact states using Timelapse, a natural abstraction. We evaluate T EGRA against existing evolving graph analysis techniques, and show that it signiﬁcantly outperforms state-of-the-art systems (by up to 30 × ) for ad-hoc window operation workloads.
Prism: Proxies without the Pain

Object storage systems, which store data in a ﬂat name space over multiple storage nodes, are essential components for providing data-intensive services such as video streaming or cloud backup. Their bottleneck is usually either the compute or the network bandwidth of customer-facing frontend machines, despite much more such capacity being available at backend machines and in the network core. Prism addresses this problem by combining the ﬂexibility and security of traditional frontend proxy architectures with the performance and resilience of modern key-value stores that optimize for small I/O patterns and typically use custom, UDP-based protocols inside a datacenter. Prism uses a novel connection hand-oﬀ protocol that takes the advantages of a modern Linux kernel feature and programmable switch, and supports both unen-crypted TCP and TLS, and a corresponding API for easy integration into applications. Prism can improve throughput by a factor of up to 3.4 with TLS and by up to 3.7 with TCP, when compared to a traditional frontend proxy architecture.
Effect of corticosteroid therapy in patients with an acute exacerbation of chronic obstructive pulmonary disease receiving ventilatory support

Background Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable disease characterized by persistent respiratory symptoms and airflow limitation that is due to airway and/or alveolar abnormalities, usually caused by significant exposure to noxious particles or gases. The chronic airflow limitation that characterizes COPD is caused by a mixture of small airway disease (e.g. obstructive bronchiolitis) and parenchymal destruction (emphysema), the relative contributions of which vary from person to person. Aim The aim of the study was to determine the effect of systemic corticosteroids in patients with severe acute exacerbations of COPD admitted to the ICU receiving ventilatory support. Patients and methods A total of 100 patients with acute exacerbations of COPD leading to hypoxemia and respiratory acidosis with pH less than 7.35 and PaCO2 more than 45 mmHg admitted to the ICU who were receiving ventilator support (invasive or noninvasive mechanical ventilation) were included. Results It was noticed that steroid group had significantly lower duration of mechanical ventilation (4.67±2.76 vs. 2.76±1.11 days; P=0.01), ICU stay (5.33±2.87 vs. 7.89±3.36 days; P=0.04), and hospital stay (11.65±3.89 vs. 16.67±4.44 days; P=0.03). Conclusion Corticosteroid therapy was associated with significantly lower duration of mechanical ventilation, ICU stay, and hospital stay.
Real-Time Video Emotion Recognition Based on Reinforcement Learning and Domain Knowledge

Multimodal emotion recognition in conversational videos (ERC) develops rapidly in recent years. To fully extract the relative context from video clips, most studies build their models on the entire dialogues which make them lack of real-time ERC ability. Different from related researches, a novel multimodal emotion recognition model for conversational videos based on reinforcement learning and domain knowledge (ERLDK) is proposed in this paper. In ERLDK, the reinforcement learning algorithm is introduced to conduct real-time ERC with the occurrence of conversations. The collection of history utterances is composed as an emotion-pair which represents the multimodal context of the following utterance to be recognized. Dueling deep-Q-network (DDQN) based on gated recurrent unit (GRU) layers is designed to learn the correct action from the alternative emotion categories. Domain knowledge is extracted from public dataset based on the former information of emotion-pairs. The extracted domain knowledge is used to revise the results from the RL module and is transformed into other dataset to examine the rationality. The experimental results on datasets show that ERLDK achieves the state-of-the-art results on weighted average and most of the specific emotion categories.
Improved fuzzy clustering algorithm for image segmentation based on low-rank prior

Image segmentation is a basic problem of medical image analysis and an auxiliary method for disease diagnosis. However, the complexity of medical images makes image segmentation diﬃcult. In recent decades, fuzzy clustering algorithms are preferable due to its simplicity and eﬃciency. However, fuzzy clustering algorithms are sensitive to noise. To solve this problem, many algorithms with non-local information have been proposed, which performed well but with low eﬃciency. In this paper, an improved fuzzy clustering algorithm by utilizing non-local self-similarity and low-rank prior for image segmentation is proposed. Firstly, cluster center initialization is performed based on peak detection. Then, the pixel correlation model between corresponding pixels is constructed, and the similar pixel set is retrieved. To improve eﬃciency and the robustness, a novel objective function combining non-local information and low-rank prior is designed in the proposed algorithm. Experiments on synthetic images and medical images illustrate that the algorithm can improve eﬃciency greatly while achieving satisfactory results.
Spoken Language Technology for North-East Indian Languages

The North-East India hosts a myriad of languages that belong to three different language families and that have salient phonological inventories. Ofﬁcially, of the 99 non-scheduled languages, 60 are spoken in North-East India. Among these 60, 34 languages have more than 50,000 speakers. However, these languages do not have enough linguistic resources for language technology development. Many of them do not even have detailed linguistic descriptions that would be helpful in understanding the challenges that lay ahead in building language technology based tools in the languages. In this paper, we provide an overview of the languages of North-East India and outline a few phonetic features distinct to some of the languages. We also provide an overview of the attempts to build spoken language technology for some of these languages and ﬁnally we conclude by outlining the challenges in building speech technology in these languages and suggest some approaches to overcome the challenges.
Application of the Theory on Planned Behaviour to Green Hotel Visit Intention: An Extension of Self-Identity

The demand for green products and services has increased in recent years, as customers are more concerned about global warming and environmental pollution. As a result, more companies focus on introducing environmental friendly solutions such as green concept hotels to meet consumers demand. Since he whole concept of green hotel is still novel in market, this article aims to investigate the relationship between attitude, subjective norms, perceived behavioural control, self-identity and intention to visit green hotels. The findings show that self-identity can enhance the predictive powers of the Theory of Planned Behaviour as it explains an additional variance of 9%. This findings can be useful the players in hospitality industry to promote their offering in market by using more effective marketing tools. Furthermore, this research enhances a growing body of knowledge that recognise self-identity as a genuine addition to the Theory of Planned Behaviour.
Women constructing leadership identities in academia: Intersection of gender and culture

This paper is drawn from a PhD dissertation on women’s experiences of academic leadership in higher education in Pakistan. It aims to document women’s voices from higher education about the discursive construction of their leadership identities within the dominant masculine academic culture. The data presented here is derived from ten in-depth interviews carried out with women leaders from co-educational universities located in Rawalpindi and Islamabad, Pakistan during the year 2019. The empirical data reveals that women are expected to exercise masculine attributes for representing themselves as successful leaders. However, they feel comfortable acting in their feminine roles by being flexible and accommodating. Women do not consider their gender as an obstacle in constructing leadership identities, yet the masculine academic culture does not accept them in their feminine style. Resultantly, women encounter numerous challenges in constructing their leadership identities. This paper argues that leadership identities of women are discursively produced and reproduced in universities to legitimise masculine discourses. In light of the findings, it is suggested that universities need to develop gender-sensitive policies to encourage and celebrate women’s leadership in academia.
Auto Drone: Modifying a DIY Drone Kit for Autonomous Flight Auto Drone: Modifying a DIY Drone Kit for Autonomous Flight

: With the addition of an Arduino microprocessor, a basic drone build was modified to obtain autonomous flight. By wiring Arduino and receiver in series with each other, the Arduino draws power from the main drone battery. The signal from the receiver is intercepted by the Arduino and a manually coded pulse position modulation signal is sent out to the flight controller. Through experimental testing using Betaflight configurator, it was found that the PPM signal requires 35000 µs of end pause between two frames of data. Any less, lead to unsteady errors in the flight controller’s ability to read the coded signal. By adding more time between frames there would be a decrease in performance caused by additional input delay and latency in the internal commands reaching the flight controller. Aux switches were utilized on the transmitter to code a manual to automatic switch, such that a drone pilot can quickly and easily transition in and out of the coded autonomous flight mode. The flight mode coded is based on feedback control using a GPS module as the primary sensor. The flight mission consists of keeping the drone at a level altitude with an additional switch on the transmitter that adds 5 ft to the altitude. A drone pilot could keep the drone in place without requiring constant input into the transmitter and simply flip a switch back and forth to raise then lower the drone’s desired altitude. When coding in a specific error from the expected value in the control loop, the throttle, pitch, roll, and yaw values recorded in Betaflight corresponded to the coded correction model. The GPS module altitude data proved too imprecise in testing to do flight testing outdoors. With more time, a barometer altimeter could be added to replace the GPS module as the sensor in the feedback loop. With the success of the core code and wiring of the Arduino, new flight missions can be coded and simply inserted into the existing Arduino code.
Thomas Moore’s Lalla Rookh: Iran and Ireland a Sororal Reflection in light of Irish Orientalism

The West-East love affair and especially with the Persian empire has always been the same, following one unflinching ethos: the former Hellenizes the latter and, as a result, remodeling it either by injecting an inherently western trajectory into the core of the East, or by philosophizing and then reevaluating its national, ideological and cultural grand narratives. By exploring Thomas Moore’s Lalla Rookh (1817) as the main point of departure for cultural interventions in the orient, especially Persia or the land of Ēran or Aryāns as a conspicuous source of insight, and crisscross my reading of Moore’s colonial look at Iran with Joyce’s subtle reading of oriental subjects and orientalism this article examines the ways in which the West has historically been re-introducing a different East in the form of an exotic Orient to not only its local audience but those abroad. To this end, this article will introduce and then instrumentalize the concept of Essentialist Irish Orientalism to shed light on an ever-growing interest of the West to meet and re-evaluate the East in the general and Iran in particular. Hence, the paper will first engage with an Irish orientalist reading of the East, especially Iran, by drawing upon theories of Said, Deleuze, Freud and Fanon.