Reviewerly/exHarmony huggingface dataset:
Towards a Unified Approach to Black-Box Constructions of Zero-Knowledge Proofs

General-purpose zero-knowledge proofs for all $$\mathsf {NP} $$ languages greatly simplify secure protocol design. However, they inherently require the code of the underlying relation. If the relation contains black-box calls to a cryptographic function, the code of that function must be known to use the ZK proof, even if both the relation and the proof require only black-box access to the function. Rosulek (Crypto'12) shows that non-trivial proofs for even simple statements, such as membership in the range of a one-way function, require non-black-box access. We propose an alternative approach to bypass Rosulek's impossibility result. Instead of asking for a ZK proof directly for the given one-way function f, we seek to construct a new one-way function F given only black-box access to f, and an associated ZK protocol for proving non-trivial statements, such as range membership, over its output. We say that F, along with its proof system, is a proof-based one-way function. We similarly define proof-based versions of other primitives, specifically pseudo-random generators and collision-resistant hash functions. We show how to construct proof-based versions of each of the primitives mentioned above from their ordinary counterparts under mild but necessary restrictions over the input. More specifically, Our results open up the possibility that general-purpose ZK proofs for relations that require black-box access to the primitives above may be possible in the future without violating their black-box nature by instantiating them using proof-based primitives instead of ordinary ones.
Translate, then Parse! A Strong Baseline for Cross-Lingual AMR Parsing

In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures: given a sentence in any language, we aim to capture its core semantic content through concepts connected by manifold types of semantic relations.Methods typically leverage large silver training data to learn a single model that is able to project non-English sentences to AMRs.However, we find that a simple baseline tends to be overlooked: translating the sentences to English and projecting their AMR with a monolingual AMR parser (translate+parse, T+P).In this paper, we revisit this simple two-step baseline, and enhance it with a strong NMT system and a strong AMR parser.Our experiments show that T+P outperforms a recent stateof-the-art system across all tested languages: German, Italian, Spanish and Mandarin with +14.6, +12.6, +14.3 and +16.0 Smatch points.
Synthesizing a human-like voice is the easy way

Deep-learning based technologies produce speech that is almost indistinguishable from humans. However, focusing on producing human-like voices poses ethical, security and societal issues. Considering the flexibility and the regression power of new technologies based on deep-learning, it is now time to consider a new type of synthesis: natural non-human-like speech synthesis. This paper aims to convince you that such research opens new research directions, that it brings another perspective to address human-like speech challenges, and that enough material is available to start to investigate non-human-like speech.
Generic Multi-label Annotation via Adaptive Graph and Marginalized Augmentation

Multi-label learning recovers multiple labels from a single instance. It is a more challenging task compared with single-label manner. Most multi-label learning approaches need large-scale well-labeled samples to achieve high accurate performance. However, it is expensive to build such a dataset. In this work, we propose a generic multi-label learning framework based on Adaptive Graph and Marginalized Augmentation (AGMA) in a semi-supervised scenario. Generally speaking, AGMA makes use of a small amount of labeled data associated with a lot of unlabeled data to boost the learning performance. First, an adaptive similarity graph is learned to effectively capture the intrinsic structure within the data. Second, marginalized augmentation strategy is explored to enhance the model generalization and robustness. Third, a feature-label autoencoder is further deployed to improve inferring efficiency. All the modules are jointly trained to benefit each other. State-of-the-art benchmarks in both traditional and zero-shot multi-label learning scenarios are evaluated. Experiments and ablation studies illustrate the accuracy and efficiency of our AGMA method.
Temporal Scheduling and Optimization for Multi-MAV Planning

Multi-MAV trajectory planning is essentially a high dimensional path and motion planning problem. The main challenge of this problem beyond individual MAV trajectory planning is collision avoidance for dynamic obstacles, which is usually achieved by spatial planning or spatial and temporal combined planning. In this paper, however, we address this problem by planning only in time domain, which can naturally decouple the static and dynamic obstacle collision avoidance. By introducing the time scheduling graph and constructing time-irrelevant collision range pair as high dimensional time domain static obstacles, we formulate this problem as a convex programming problem. Starting from arbitrary static object collision free trajectories, we propose a novel approach to achieve near optimal, kinodynamically feasible, dynamic collision-free re-scheduled trajectory planning. We also propose a sufficient condition to guarantee the existence of solutions.
DAVE: Dynamic Adaptive Video Encoding for Real-time Video Streaming Applications

Real-time video streaming applications have become tremendously popular in recent years, such as remote control and video conferencing applications. A key characteristic that differentiates these applications from traditional live streaming applications is that these applications have a very low-latency requirement for interactivity. The stricter low-latency requirement brings many challenges: the video has to be encoded in a real-time manner; the substantial resources on the server or cloud cannot be utilized for encoding; and the adaptation strategies in live streaming applications are not adequate for real-time video streaming, such as adaptive bitrate selection (ABR). In addition, the video perceptual quality of current real-time video streaming systems is usually sacrificed to meet the very low-latency requirement.To address these challenges, in this paper, a new real-time video streaming protocol, DAVE (Dynamic Adaptive Video Encoding for real-time video streaming applications), is proposed. In the proposed real-time video streaming system, captured video frames are encoded with different configurations. Since the video encoding configuration determines the video data size, quality, and encoding time, we first conduct an experimental study on the impact of each configuration parameter. Based on our experimental findings, we then propose a super resolution based video encoding configuration selection algorithm which does not use a fixed strategy to determine the encoding configurations as in existing real-time video streaming systems but uses a reinforcement learning based model to learn the optimal video encoding configuration that includes the configuration of both regular video encoding parameters and the up-scale of super resolution models. As a result, DAVE can optimize the performance of real-time video streaming systems based on user Quality of Experience (QoE) metrics. To the best of our knowledge, this is the first work that incorporates super resolution and reinforcement learning in the protocol design for real-time video streaming systems. Extensive evaluations show that DAVE can substantially improve the video perceptual quality by 15% and can also reduce the end-to-end latency by 20%, as compared with existing systems<sup>1</sup>.
Deep Learning Techniques for Explainable Resource Scales in Collectible Card Games

In collectible card games, developers face the challenge of creating new, and interesting cards that are not too strong or game-breaking, retaining the game’s overall balance. Over time, this becomes challenging due to the sheer volume of the published content. In this article, we propose a framework for generating models capable of recommending resource scales, a pivotal point in balancing. We evaluate the usage of several state-of-the-art neural architectures to learn representations for text followed by gradient boosting decision trees to incorporate remaining features. Throughout our analysis, we present various explanation tools that should empower game developers, and aid them with new insights. In particular, we present the sets of words that drive the model in diverse situations, such as when it was inaccurate by a small margin. We also exhibit instances where textual features cannot give an accurate prediction, requiring additional information. Our method achieves a mean reciprocal rank of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+0.8$</tex-math></inline-formula> when evaluated on popular card games, even though superficially identical cards might have distinct costs.
An Overview of Reading Habits and Medium Preference Among University Students

Reading is a fundamental skill in the learning process. Reading improves the thinking ability by giving new concepts and ideas and enhances vocabulary and language which is essential in verbal communication. The habit of reading is one of the strongest predictors of academic success. Due to the advancement in ICT, the reading habits of students have changed. This paper reviews current literature on the reading habits and reading medium preferences among university students with particular attention to the advantages and disadvantages of reading on paper vs on-screen. The reviewed literature (N = 17, published between 2015 and 2020) revealed several characteristics of students’ reading habits and medium of reading. The findings show a decline in reading books and an inclination towards online materials. However, a preference for printed materials persists in academic reading.
Weighted Clusterwise Linear Regression based on adaptive quadratic form distance

The standard approach to Clusterwise Regression is the Clusterwise Linear Regression method. This approach can lead to data over-fitting, and it is not able to distinguish linear relationships in groups of observations well separated in the space of explanatory variables. This paper presents a Weighted Clusterwise Linear Regression method to obtain homogeneous clusters of observations while maintaining a proper fitting for the response variable, by the minimization of an optimization criterion that combines a k-means-like criterion (based on an adaptive quadratic form dissimilarity) in x-space and the criterion of minimum squared residuals of Regression Analysis. The adaptive metric allows automatic weighing or take into account the correlation between explanatory variables under multiple constraints types. We explore six constraints types. Experiments with synthetic and benchmark datasets corroborate the usefulness of the proposed method.
Do additional optima speed up evolutionary algorithms?

Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT ∪ S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT.
Image Compression Based on a Partially Rotated Discrete Cosine Transform With a Principal Orientation

Image transforms are necessary for image and video compression.Analytic transforms are powerful in compacting natural signals for wider exploitation.Various methods have been introduced to represent such data as a small number of bases, and several of these methods use machine learning, usually based on sparse coding, to outperform analytic transforms.They show sufficient data compaction abilities.However, these methods focus only on data compaction and reconstruction performance, without considering computational issues during implementation.We introduce a new framework for a more efficient transform based on a two-dimensional discrete cosine transform (DCT) and its characteristics.We aimed to improve the data compaction ability of transforms to levels better or similar to that of the DCT and other data-driven transforms, with fast and efficient implementation.We focused on the properties of the DCT, including horizontal and vertical directional information, and approximated its direction using the transform.Our framework was designed by rotating some of the DCT bases to fit this direction.As expected, our framework achieves a transform design with minimized computation for efficient implementation.It does not require an iterative algorithm or brute-force methods to find the best transform matrix or other parameters, thereby making it much faster than other methods.Our framework is 10 times faster than the steerable DCT (SDCT) and twice as fast as the eight-level SDCT with minimum performance reduction.Experimental validation with various images indicates that the proposed method sufficiently approaches the performance of the other transforms despite faster implementation.
Knowledge socialism in the COVID-19 era: A collective exploration of needs, forms, and possibilities

The inspiration for this collective writing project began with a digital conference entitled 'Knowledge Socialism, COVID-19 and the New Reality of Education' held at Beijing Normal University. In this conference and through this article, multiple researchers spread across six continents have engaged in the collaborative task of outlining emerging innovations and alternative contingencies towards education, international collaboration, and digital reform in this time of global crisis. Trends associated with digital education, knowledge openness, peer production, and collective intelligence as articulated by Michael A. Peters' conception of Knowledge Socialism are given careful analysis and exploration. Some of the members of this collective endeavor to identify problems, others, begin to draw boxes around potential solutions. Overall, this article engages with real world challenges and innovations that look beyond dominant neoliberal trends in the knowledge economy to build bridges toward novel possibilities in this era of rapid digital change.
Efficient full data-path width and serialized hardware structures of SPONGENT lightweight hash function

In this paper, two efficient low-cost and high-throughput structures of the SPONGENT lightweight hash function are presented. Two structures are called full data-path width (parallel) and 4-bit serialized. The serialized architecture is designed by using one multi-task shift register in the round computations with minimum hardware resources. The area consumed in this structure is lower than that of the full data-path structure but the number of clock cycles is increased. The speed computation of the full data-path is higher compared to the 4-bit serialized structure of the SPONGENT hash function because the data are computed in a parallel form. To improving the timing characteristics, we implement the S-box block as the complex block in the SPONGENT hash function based on an Area×Delay optimized circuit. A large number of gates, in the structure, have been implemented by 2-input NAND and 2-input NOR gates in order to reduce delay and area. The performance measurement of the proposed structures is performed by evaluating the parameters such as area consumption, computation time, critical path delay (CPD), throughput, and throughput/area. The implementation results are achieved for all variants of the SPONGENT hash function in 180 nm CMOS technology. The results of area consumption (for 4-bit serialized structure) and throughput (for full data-path structure) show improvements compared to previous works. For area-constrained applications, the proposed structure with a lower data-path width is an appropriate choice. The full data-path width structure can be used for the high-speed and high-throughout cryptographic applications.
Word Significance Analysis in Documents for Information Retrieval by LSA and TF-IDF using Kubeflow

The capital investment for automated analysis of electronic documents has enhanced rapidly since the growth of text categorization and classification. In recent times, various works have been done in the context of text mining and information retrieval. This paper proposes a new method for information retrieval using latent semantic analysis (LSA) and tf-idf also known as term frequency—inverse document frequency. The proposed method helps to identify the most and least significant words from sentences of the paragraph obtained from a document by splitting the sentences to make the most significant words a query of each sentence. If the word is of the highest priority, then the document will be saved in the drive as a star file. The star file consists of the highlighted less significant words in that document and an array of higher probability words for the respective word at the top of its sentence. The proposed system has been implemented by using a machine learning pipeline, Kubeflow. Based on the analysis of the estimated parameters and calculations of the output graph slope in the system, the model gave a hearty probability of 0.9427 that refers to the accuracy of the system.
SSLayout360: Semi-Supervised Indoor Layout Estimation from 360° Panorama

Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction. In this work, we explore the intersection of these two fields to advance the research objective of enabling more accurate 3D indoor scene modeling with less labeled data. We propose the first approach to learn representations of room corners and boundaries by using a combination of labeled and unlabeled data for improved layout estimation in a 360° panoramic scene. Through extensive comparative experiments, we demonstrate that our approach can advance layout estimation of complex indoor scenes using as few as 20 labeled examples. When coupled with a layout predictor pre-trained on synthetic data, our semi-supervised method matches the fully supervised counterpart using only 12% of the labels. Our work takes an important first step towards robust semi-supervised layout estimation that can enable many applications in 3D perception with limited labeled data.
On the Assessment of Robustness of Telemedicine Applications against Adversarial Machine Learning Attacks

Telemedicine applications have been recently evolved to allow patients in underdeveloped areas to receive medical services. Meanwhile, machine learning (ML) techniques have been widely adopted in such telemedicine applications to help in disease diagnosis. The performance of these ML techniques, however, are limited by the fact that attackers can manipulate clean data to fool the model classifier and break the truthfulness and robustness of these models. For instance, due to attacks, a benign sample can be treated as a malicious one by the classifier and vice versa. Motivated by this, this paper aims at exploring this issue for telemedicine applications. Particularly, this paper studies the impact of adversarial attacks on mammographic image classifier. First, mamographic images are used to train and evaluate the accuracy of our proposed model. The original dataset is then poisoned to generate adversarial samples that can mislead the model. For this, structural similarity index is used to determine similarity between clean images and adversarial images. Results show that adversarial attacks can severely fool the ML model.
Pupil Size Prediction Techniques Based on Convolution Neural Network

The size of one’s pupil can indicate one’s physical condition and mental state. When we search related papers about AI and the pupil, most studies focused on eye-tracking. This paper proposes an algorithm that can calculate pupil size based on a convolution neural network (CNN). Usually, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses as the best fitting shapes. This paper uses the major and minor axes of an ellipse to represent the size of pupils and uses the two parameters as the output of the network. Regarding the input of the network, the dataset is in video format (continuous frames). Taking each frame from the videos and using these to train the CNN model may cause overfitting since the images are too similar. This study used data augmentation and calculated the structural similarity to ensure that the images had a certain degree of difference to avoid this problem. For optimizing the network structure, this study compared the mean error with changes in the depth of the network and the field of view (FOV) of the convolution filter. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. According to the results, the mean error of the pupil length is 5.437% and the pupil area is 10.57%. It can operate in low-cost mobile embedded systems at 35 frames per second, demonstrating that low-cost designs can be used for pupil size prediction.
Trust and Security of Embedded Smart Devices in Advanced Logistics Systems

This paper addresses security and risk management of hardware and embedded systems across several applications. There are three companies involved in the research. First is an energy technology company that aims to leverage electric- vehicle batteries through vehicle to grid (V2G) services in order to provide energy storage for electric grids. Second is a defense contracting company that provides acquisition support for the DOD's conventional prompt global strike program (CPGS). These systems need protections in their production and supply chains, as well as throughout their system life cycles. Third is a company that deals with trust and security in advanced logistics systems generally. The rise of interconnected devices has led to growth in systems security issues such as privacy, authentication, and secure storage of data. A risk analysis via scenario-based preferences is aided by a literature review and industry experts. The analysis is divided into various sections of Criteria, Initiatives, C-I Assessment, Emergent Conditions (EC), Criteria-Scenario (C-S) relevance and EC Grouping. System success criteria, research initiatives, and risks to the system are compiled. In the C-I Assessment, a rating is assigned to signify the degree to which criteria are addressed by initiatives, including research and development, government programs, industry resources, security countermeasures, education and training, etc. To understand risks of emergent conditions, a list of Potential Scenarios is developed across innovations, environments, missions, populations and workforce behaviors, obsolescence, adversaries, etc. The C-S Relevance rates how the scenarios affect the relevance of the success criteria, including cost, schedule, security, return on investment, and cascading effects. The Emergent Condition Grouping (ECG) collates the emergent conditions with the scenarios. The generated results focus on ranking Initiatives based on their ability to negate the effects of Emergent Conditions, as well as producing a disruption score to compare a Potential Scenario's impacts to the ranking of Initiatives. The results presented in this paper are applicable to the testing and evaluation of security and risk for a variety of embedded smart devices and should be of interest to developers, owners, and operators of critical infrastructure systems.