Reviewerly/exHarmony huggingface dataset:
Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation

We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX. We present results on a suite of tasks inspired by the existing reinforcement learning literature, but remade in our engine. Additionally, we provide reimplementations of PPO, SAC, ES, and direct policy optimization in JAX that compile alongside our environments, allowing the learning algorithm and the environment processing to occur on the same device, and to scale seamlessly on accelerators. Finally, we include notebooks that facilitate training of performant policies on common OpenAI Gym MuJoCo-like tasks in minutes.
A Side Journey To Titan

The Google Titan Security Key is a FIDO U2F hardware device proposed by Google (available since July 2018) as a two-factor authentication token to sign in to applications such as your Google account. In this paper, we present a sidechannel attack that targets the Google Titan Security Key 's secure element (the NXP A700x chip) by the observation of its local electromagnetic radiations during ECDSA signatures. This work shows that an attacker can clone a legitimate Google Titan Security Key. As a side observation, we identified a novel correlation between the elliptic curve group order and the lattice-based attack success rate.
Partitioned Centerpose Network for Bottom-Up Multi-Person Pose Estimation

In bottom-up multi-person pose estimation method, grouping joint candidates into corresponding person instance is a challenging problem. In this paper, a new bottom-up method, Partitioned CenterPose (PCP) Network, is proposed to better cluster all detected joints. To achieve this goal, a novel Partition Pose Representation (PPR) is proposed which integrate person instance and body joint by joint offset. PPR leverages the center of human body and the offset between center point and body joint to encode human pose. To better enhance the relationship of body joints, we divide human body into five parts, and generate sub-PPR in each part. Based on PPR, PCP Network can detect persons and body joints simultaneously, and then grouping all body joints by joint offset. Moreover, an improved $\ell_{1}$ loss is designed to obtain more accurate joint offset. On the COCO keypoints dataset, the proposed method performs on par with the existing state-of-the-art bottom-up method in accuracy and speed.
Pixel MTF response effect on non-null interferometry

The pixel modulation transfer function response degrades the contrast of non-null interferometric surface figure measurements. We experimentally quantify this effect for spatial frequencies ranging from 0 to 363 lp/mm ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>≈<!-- ≈ --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>3.33</mml:mn> </mml:mrow> </mml:math> times the Nyquist limit). Our results show a low SNR spatial frequency band that behaves like a low-pass filter for sub-Nyquist interferometry and a stop-band filter for multiple-wavelength phase-shifting interferometry. We also introduce a multiple-mode, multiple-wavelength interferometry approach to measure optical surfaces with slope departure angles mapping to spatial frequencies in this low SNR band. The extended measurement range of this approach is achieved without using a sparse-array detector.
An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results.
Data Ethics Principles: A Comment on the House of Lords Report ‘Regulating in a Digital World’

The House of Lords select committee on communications report ‘Regulating in a Digital World’ outlines the result of their survey of experts and presents suggestions concerning digital regulation. The report adds to the growing number of government-initiated research publications that touch on the social, political, and ethical dimensions of the emerging digital technologies. In this comment piece we critically analyse sections of the report. The key takeaways are that:1. there is a lack of clarity, 2. the principles approach central to the report needs to be challenged, 3. that the suggested auditing should be specified, and, 4. a reasonable expectation of digital literary level needs to be discussed in order to address the issue of consent.
UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning

Training deep neural networks requires large scale data, which often forces users to work in a distributed or outsourced setting, accompanied with privacy concerns. Split learning framework aims to address this concern by splitting up the model among the client and the server. The idea is that since the server does not have access to client's part of the model, the scheme supposedly provides privacy. We show that this is not true via two novel attacks. (1) We show that an honest-but-curious split learning server, equipped only with the knowledge of the client neural network architecture, can recover the input samples and also obtain a functionally similar model to the client model, without the client being able to detect the attack. (2) Furthermore, we show that if split learning is used naively to protect the training labels, the honest-but-curious server can infer the labels with perfect accuracy. We test our attacks using three benchmark datasets and investigate various properties of the overall system that affect the attacks' effectiveness. Our results show that plaintext split learning paradigm can pose serious security risks and provide no more than a false sense of security.
Research of Schema Evolution and Implementation Scheme Optimization in AI-Enabled Embedded Systems

The demand of embedded artificial intelligence system for powerful computing power and diversified application scenarios will inevitably bring some new problems. This paper builds the system dynamics model of embedded system based on artificial intelligence (AI). By analyzing the causal relationship between the elements of the system dynamics model, the state equation is established, and the parameters are estimated and tested. At the same time, the influence of the model simulation experiment on the relevant factors is evaluated. The simulation results show that the proposed model is effective and efficient.
UAVs and ground-based geophysical surveys and 3D inversion when studying archeological objects in Baykal Region

Summary Ancient iron-making complexes are in many places on the western coast of the central part of Lake Baikal. The objects are located in the folded frame of the Siberian platform, represented by deeply metamorphosed rocks of the Olkhon crystalline complex. The possibilities of terrestrial and UAV magnetic surveys and electrical resistivity tomography (ERT) are considered to optimize searches and study archaeological sites with minimizing excavating by applying. They use 3D inversions of geophysical surveys’ data to localize and estimate parameters of physical-geological models of archeological and geological near-surface objects. Comparing the modeling results on a well-known archaeological site has given a chance to estimate the effectiveness of the methods used. Inversion of magnetic fields using vector scanning and elastic net regression allows receiving a geometrical image, spatial distribution of susceptibility, and remanent of furnaces by terrestrial and UAV magnetic surveys data and distinguish object artificial and geological origins.
Pasraman Branding: Tantangan Dan Strategi

Branding in the world of education is very necessary to be able to distinguish one school from another and to show the advantages of the school itself. The world of education today is not only aimed at creating a young generation who is ready to enter the world of work but also aims to sell a product. It is undeniable that currently education is also a very profitable business, this can be seen by the existence of private schools, course institutions and so on which charge very high costs and then earn very large profits. Pasraman formal as a school based on Hindu religious education must be able to compete with other schools. Formal Pasraman provides various educational facilities at free of charge. Although formal pasraman provides free education, many people do not want to send their children to formal pasraman. Therefore, to be able to attract the interest of the community, formal pasraman must have branding to be able to compete with other schools. Formal Pasraman must be able to develop quality so that a mature strategy is needed. One of them is the boarding school must be able to create a branding for the school which can then attract the interest of parents to send their children to the boarding school. Large schools can be seen from the number of students and the quality of their graduates. To be able to create a school with the best graduates, the formal pasraman must have students. What can be done by formal pasramans to attract students' interest in attending pasramans is to create contemporary branding by utilizing current digital technology.
Belief Convergence under Misspecified Learning: A Martingale Approach

We present an approach to analyze learning outcomes in a broad class of misspeciﬁed environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models, and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly speciﬁed learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). We show that these conditions can be applied, ﬁrst, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyze environments where learning is “slow,” such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly speciﬁed, vanishingly small amounts of misspeciﬁcation can generate extreme failures of learning.
An Implicit Preference-Aware Sequential Recommendation Method Based on Knowledge Graph

Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user’s preferences only depend on specific items in the current sequence and do not consider user’s implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user’s different behaviors. In order to address these above two problems, we propose an implicit preference-aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user’s implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user’s evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user’s different behaviors. Through the activation unit, the user’s preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.
Transformation of the BPMN Business Process Model into Smart Contracts for the Hyperledger Fabric Environment

Transformation of BPMN business process model will be a very important topic in the future of the Hyperledger Fabric blockchain environment. Machine transformation can increase the quality of transformation and thus reduce errors. The research paper first describes BPMN and the Hyperledger Fabric environment, what smart contracts are and why they are so important. In the second part, the raw transformation model is described, where the inputs for the transformations are BPMN and the metadata file, and the result is a smart contract written in Java that can be imported into the Hyperledger Fabric environment.
Information theoretic-based privacy risk evaluation for data anonymization

Aim: Data anonymization aims to enable data publishing without compromising the individuals’ privacy. The reidentification and sensitive information inference risks of a dataset are important factors in the decision-making process for the techniques and the parameters of the anonymization process. If correctly assessed, measuring the reidentification and inference risks can help optimize the balance between protection and utility of the dataset, as too aggressive anonymization can render the data useless, while publishing data with a high risk of de-anonymization is troublesome. Methods: In this paper, a new information theoretic-based privacy metric (ITPR) for assessing both the re-identification risk and sensitive information inference risk of datasets is proposed. We compare the proposed metric with existing information theoretic metrics and their ability to assess risk for various cases of dataset characteristics. Results: We show that ITPR is the only metric that can effectively quantify both re-identification and sensitive information inference risks. We provide several experiments to illustrate the effectiveness of ITPR. Conclusion: Unlike existing information theoretic-based privacy metrics, the ITPR metric we propose in this paper is, to the best of our knowledge, the first information theoretic-based privacy metric that allows correctly assessing both re-identification and sensitive information inference risks.
Anytime Stochastic Task and Motion Policies.

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be inexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encode agent behaviors that handle multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our method.
Challenge: A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks.

In order to meet mobile cellular users' ever-increasing network usage, today's 4G and 5G networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This Challenge paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing computation for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, and operational cost, and deployment timelines surrounding quantum technology. Armed with these data, we analyze and project the quantitative performance targets future quantum hardware must meet in order to provide a computational and power advantage over silicon hardware, while matching its whole-network spectral efficiency. Our quantitative analysis predicts that with quantum hardware operating at a 140 $\mu$s problem latency and 4.3M qubits, quantum computation will achieve a spectral efficiency equal to silicon while reducing power consumption by 40.8 kW (45% lower) in a representative 5G base station scenario with 400 MHz bandwidth and 64 antennas, and an 8 kW power reduction (16% lower) using 2.2M qubits in a 200 MHz-bandwidth 5G scenario.
Eye Movements and Picture Recognition: Contribution or Embellishment

This chapter re-evaluates the role of saccades and fixations during recognition, not from the standpoint of challenging their normal occurrence, but as a challenge to assumptions about the essential nature of the fixation/saccadic sequences to the recognition process. Stark and Ellis provide a very elaborate explanation of the qualitative role of the saccade/fixation sequence. In their view, eye movements are controlled by cognitive models already present in the brain and although each individual provides their own preferred order of searching, that order remains quite consistent between pictures or when making different observations on subsequent presentations of the same picture. The measurement technique and algorithm determine the accuracy and precision of eye movement measurement, but the frequent occurrence of what appears to be a completely idiosyncratic, unrepeatable eye movement pattern delimits the availability of the appropriate models to describe the data.
Optimization Strategy of English Classroom Education Ecosystem Based on Mobile Learning System

In order to overcome the limitations of traditional English teaching mode and the disadvantages of mobile teaching classroom, this paper proposes a novel optimization strategy of English classroom education ecosystem based on mobile learning system. This optimization strategy combines a variety of mobile learning devices. From the perspective of educational ecology, this paper analyzes the optimization methods of English classroom education ecosystem from three dimensions: students, teachers and learning environment. The results show that the optimization strategy can break through the limitations of traditional English teaching mode, scientifically solve the imbalance of education ecosystem in mobile teaching classroom, and build a good English learning environment for teachers and students.
The Round Complexity of Quantum Zero-Knowledge.

We study the round complexity of zero-knowledge for QMA (the quantum analogue of NP). Assuming the quantum quasi-polynomial hardness of the learning with errors (LWE) problem, we obtain the following results:2-Round statistical witness indistinguishable (WI) arguments for QMA.4-Round statistical zero-knowledge arguments for QMA in the plain model, additionally assuming the existence of quantum fully homomorphic encryption. This is the first protocol for constant-round statistical zero-knowledge arguments for QMA.2-Round computational (statistical, resp.) zero-knowledge for QMA in the timing model, additionally assuming the existence of post-quantum non-parallelizing functions (time-lock puzzles, resp.).
On-Line English Learning in The Perception of Twelfth Grade Students at SMA Negeri 3 Palu

The study discusses students' perception over on-line English Learning from the twelfth grade students at SMA Negeri 3 Palu.This study uses qualitative research approach with online survey method through Kobbo Collect for collecting the data.86 twelfth grade students were chosen as the sample from 431 population by using the convenience sampling technique.Two English teachers were involved in this study as secondary data.The data was collected using a 25 items close-ended questionnaire and 25 questions open-ended statement for teachers interview question items divided into 6 variables,The general perception on on-line learning, Students' achievement, Teachers' Competence, Learning Media and Tools, Parental Support and Financial.The findings of the study were suggested that on-line English learning based on students' perception generally still considered far from expected.On-line learning causes a decrease on students' English grades.The burdensome of homework and project from the English teacher in on-line English learning do not accompanied with good material and media that used by the English teacher.The teacher implies their bad performance due to the fact that there was no training session regarding on-line learning before.The lack of proper electronic devices and powerless internet from the network provider also had both teacher and students.Parents were still lack of understanding regarding the on-line learning process that cause interference during the learning session.Although on-line learning using digital media but somehow, students still have to pay for English books, including a way to provide a good internet connection to successfully conducting on-line learning.
New Approaches for Quantum Copy-Protection.

Quantum copy-protection uses the unclonability of quantum states to construct quantum software that provably cannot be pirated. copy-protection would be immensely useful, but unfortunately, little is known about achieving it in general. In this work, we make progress on this goal, by giving the following results:We show how to copy-protect any program that cannot be learned from its input-output behavior relative to a classical oracle. This construction improves on Aaronson (CCC 2009), which achieves the same relative to a quantum oracle. By instantiating the oracle with post-quantum candidate obfuscation schemes, we obtain a heuristic construction of copy-protection.We show, roughly, that any program which can be watermarked can be copy detected, a weaker version of copy-protection that does not prevent copying, but guarantees that any copying can be detected. Our scheme relies on the security of the assumed watermarking, plus the assumed existence of public-key quantum money. Our construction is publicly detectable and applicable to many recent watermarking schemes.
Text Augmentation for Language Models in High Error Recognition Scenario

We examine the effect of data augmentation for training of language models for speech recognition. We compare augmentation based on global error statistics with one based on per-word unigram statistics of ASR errors and observe that it is better to only pay attention the global substitution, deletion and insertion rates. This simple scheme also performs consistently better than label smoothing and its sampled variants. Additionally, we investigate into the behavior of perplexity estimated on augmented data, but conclude that it gives no better prediction of the final error rate. Our best augmentation scheme increases the absolute WER improvement from second-pass rescoring from 1.1 % to 1.9 % absolute on the CHiMe-6 challenge.