Reviewerly/exHarmony huggingface dataset:
Discrete Approximate Information States in Partially Observable Environments

The notion of approximate information states (AIS) was introduced in [1] as a methodology for learning task-relevant state representations for control in partially observable systems. They proposed particular learning objectives which attempt to reconstruct the cost and next state and provide a bound on the suboptimality of the closed-loop performance, but it is unclear whether these bounds are tight or actually lead to good performance in practice. Here we study this methodology by examining the special case of discrete approximate information states (DAIS). In this setting, we can solve for the globally optimal policy using value iteration for the DAIS model, allowing us to disambiguate the performance of the AIS objective from the policy search. Going further, for small problems with finite information states, we reformulate the DAIS learning problem as a novel mixed-integer program (MIP) and solve it to its global optimum; in the infinite information states case, we introduce clustering-based and end-to-end gradient-based optimization methods for minimizing the DAIS construction loss. We study DAIS in three partially observable environments and find that the AIS objective offers relatively loose bounds for guaranteeing monotonic performance improvement and is sufficient but not necessary for implementing optimal controllers. DAIS may even prove useful in practice by itself or as part of mixed discrete-and continuous-state representations, due to its ability to represent logical state, to its potential interpretabilty, and to the availability of these stronger algorithms.
AutoDeepHLS: Deep Neural Network High-level Synthesis using fixed-point precision

Deep Neural Networks (DNN) have received much attention in various applications such as visual recognition, self-driving cars, health care, etc. Hardware implementation, specifically using FPGA and ASIC due to their high performance and low power consumption, is considered an efficient method. However, implementation on these platforms is difficult for neural network designers since they usually have limited knowledge of hardware. High-Level Synthesis (HLS) tools can act as a bridge between high-level DNN designs and hardware implementation. Nevertheless, these tools usually need implementation at the C level, whereas the design of neural networks is usually performed at a higher level (such as Keras or TensorFlow). In this paper, we propose a fully automated flow for creating a C-level implementation that is synthesizable with HLS Tools. Various aspects such as performance, minimal access to memory elements, data type knobs, and design verification are considered. Our results show that the generated C implementation is much more HLS friendly than previous works. Furthermore, a complete flow is proposed to determine different fixed-point precisions for network elements. We show that our method results in 25% and 34% reduction in bit-width for LeNet and VGG, respectively, without any accuracy loss.
Approximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression

In this paper, we propose a concept of approximate bisimulation relation for feedforward neural networks. In the framework of approximate bisimulation relation, a novel neural network merging method is developed to compute the approximate bisimulation error between two neural networks based on reachability analysis of neural networks. The developed method is able to quantitatively measure the distance between the outputs of two neural networks with the same inputs. Then, we apply the approximate bisimulation relation results to perform neural networks model reduction and compute the compression precision, i.e., assured neural networks compression. At last, using the assured neural network compression, we accelerate the verification processes of ACAS Xu neural networks to illustrate the effectiveness and advantages of our proposed approximate bisimulation approach.
Dynamic Quantizer Synthesis for Encrypted State-Feedback Control Systems with Partially Homomorphic Encryption

This study proposes a systematic design procedure for a dynamic quantizer in an encrypted state-feedback control system. By presenting two types of quantizers with different biases (additive and multiplicative), the authors reveal an essential condition that the stability of the control system is inherited and an overflow never occur in a quantization process. The resulting design procedure satisfies this condition and determines the type of quantizer with an appropriate parameter. Furthermore, this study experimentally validates the effectiveness of the proposed design procedure using a practical encrypted state-feedback control system with an inverted pendulum robot (LEGO MINDSTORMS EV3). This study establishes a quantizer synthesis for encrypted control systems using an multiplicative homomorphic encryption scheme.
Scooby: Improved Multi-party Homomorphic Secret Sharing Based on FHE

We present new constructions of multi-party homomorphic secret sharing (HSS) based on a new primitive that we call homomorphic encryption with decryption to shares (HEDS). Our first construction, which we call $$\mathsf {Scooby} $$ , is based on many popular fully homomorphic encryption (FHE) schemes with a linear decryption property. $$\mathsf {Scooby} $$ achieves an n-party HSS for general circuits with complexity $$O(|F| + \log n)$$ , as opposed to $$O(n^2 \cdot |F|)$$ for the prior best construction based on multi-key FHE. $$\mathsf {Scooby} $$ can be based on (ring)-LWE with a super-polynomial modulus-to-noise ratio. In our second construction, $$\mathsf {Scrappy} $$ , assuming any generic FHE plus HSS for NC1-circuits, we obtain a HEDS scheme which does not require a super-polynomial modulus. While these schemes all require FHE, in another instantiation, $$\mathsf {Shaggy} $$ , we show how in some cases it is possible to obtain multi-party HSS without FHE, for a small number of parties and constant-degree polynomials. Finally, we show that our $$\mathsf {Scooby} $$ scheme can be adapted to use multi-key fully homomorphic encryption, giving more efficient spooky encryption and setup-free HSS. This latter scheme, $$\mathsf {Casper} $$ , if concretely instantiated with a B/FV-style multi-key FHE scheme, for functions F which do not require bootstrapping, gives an HSS complexity of $$O(n \cdot |F| + n^2 \cdot \log n)$$ .
Efficient Proof of RAM Programs from Any Public-Coin Zero-Knowledge System

We show a compiler that allows to prove the correct execution of RAM programs using any zero-knowledge system for circuit satisfiability. At the core of this work is an arithmetic circuit which verifies the consistency of a list of memory access tuples in zero-knowledge. Using such a circuit, we obtain the first constant-round and concretely efficient zero-knowledge proof protocol for RAM programs using any stateless zero-knowledge proof system for Boolean or arithmetic circuits. Both the communication complexity and the prover and verifier run times asymptotically scale linearly in the size of the memory and the run time of the RAM program; we demonstrate concrete efficiency with performance results of our C++ implementation. We concretely instantiate our construction with an efficient MPC-in-the-Head proof system, Limbo (ACM CCS 2021). The C++ implementation of our access protocol extends that of Limbo and provides interactive proofs with 40 bits of statistical security with an amortized cost of 0.42 ms of prover time and 2.8 KB of communication per memory access, independently of the size of the memory; with multi-threading, this cost is reduced to 0.12 ms and 1.8 KB respectively. This performance of our public-coin protocol approaches that of private-coin protocol BubbleRAM (ACM CCS 2020, 0.15 ms and 1.5 KB per access).
Inner Product Functional Commitments with Constant-Size Public Parameters and Openings

Functional commitments (Libert et al. [ICALP'16]) allow a party to commit to a vector $$\boldsymbol{v}$$ of length n and later open the commitment at functions of the committed vector succinctly, namely with communication logarithmic or constant in n. Existing constructions of functional commitments rely on trusted setups and have either O(1) openings and O(n) parameters, or they have short parameters generatable using public randomness but have $$O(\log n)$$ -size openings. In this work, we ask whether it is possible to construct functional commitments in which both parameters and openings can be of constant size. Our main result is the construction of the first FC schemes matching this complexity. Our constructions support the evaluation of inner products over small integers; they are built using groups of unknown order and rely on succinct protocols over these groups that are secure in the generic group and random oracle model.
NIWI and New Notions of Extraction for Algebraic Languages

We give an efficient construction of a computational non-interactive witness indistinguishable (NIWI) proof in the plain model, and investigate notions of extraction for NIZKs for algebraic languages. Our starting point is the recent work of Couteau and Hartmann (CRYPTO 2020) who developed a new framework (CH framework) for constructing non-interactive zero-knowledge proofs and arguments under falsifiable assumptions for a large class of languages called algebraic languages. In this paper, we construct an efficient NIWI proof in the plain model for algebraic languages based on the CH framework. In the plain model, our NIWI construction is more efficient for algebraic languages than state-of-the-art Groth-Ostrovsky-Sahai (GOS) NIWI (JACM 2012). Next, we explore knowledge soundness of NIZK systems in the CH framework. We define a notion of strong f-extractability, and show that the CH proof system satisfies this notion. We then put forth a new definition of knowledge soundness called semantic extraction. We explore the relationship of semantic extraction with existing knowledge soundness definitions and show that it is a general definition that recovers black-box and non-black-box definitions as special cases. Finally, we show that NIZKs for algebraic languages in the CH framework cannot satisfy semantic extraction. We extend this impossibility to a class of NIZK arguments over algebraic languages, namely quasi-adaptive NIZK arguments that are constructed from smooth projective hash functions.
On the Related-Key Attack Security of Authenticated Encryption Schemes

Related-key attacks (RKA) are powerful cryptanalytic attacks, where the adversary can tamper with the secret key of a cryptographic scheme. Since their invention, RKA security has been an important design goal in cryptography, and various works aim at designing cryptographic primitives that offer protection against related-key attacks. At EUROCRYPT’03, Bellare and Kohno introduced the first formal treatment of related-key attacks focusing on pseudorandom functions and permutations. This was later extended to cover other primitives such as signatures and public key encryption schemes, but until now, a comprehensive formal security analysis of authenticated encryption schemes with associated data (AEAD) in the RKA setting has been missing. The main contribution of our work is to close this gap for the relevant class of nonce-based AEAD schemes. To this end, we revisit the common approach to construct AEAD from encryption and message authentication. We extend the traditional security notion of AEAD to the RKA setting and consider an adversary that can tamper with the key $$ K _{e}$$ and $$ K _{m}$$ of the underlying encryption and MAC, respectively. We study two security models. In our weak setting, we require that tampering will change both $$ K _{e}$$ and $$ K _{m}$$ , while in our strong setting, tampering can be arbitrary, i.e., only one key might be affected. We then study the security of the standard composition methods by analysing the nonce-based AEAD schemes N1 (Encrypt-and-MAC), N2 (Encrypt-then-MAC), and N3 (MAC-then-Encrypt) due to Namprempre, Rogaway, and Shrimpton (EUROCRYPT’14). We show that these schemes are weakly RKA secure, while they can be broken under a strong related-key attack. Finally, based on the N3 construction, we give a novel AEAD scheme that achieves our stronger notion.
Streaming and Unbalanced PSI from Function Secret Sharing

Private Set Intersection (PSI) is one of the most useful and well-studied instances of secure computation, with many variants and applications. In this work, we present new solutions to PSI and a weighted variant in which the output is the sum of the weights of keywords in the intersection. Our protocols apply to the semi-honest, two-server model and are optimized for the unbalanced case, where one of the sets is much larger than the other, and for a dynamic streaming setting, in which sets can evolve over time. Our protocols make use of Function Secret Sharing (FSS) to aggregate numerical payloads associated with the intersection while minimizing interaction and computational overhead. They avoid the use of public-key cryptography, giving simple and concretely efficient protocols for unbalanced PSI. In the dynamic setting, we use queuing theory to eliminate leakage with minimal overhead while ensuring low wait times, giving efficient streaming unbalanced PSI.
Uncertain motion tracking via target-objectness proposal and memory validation

Traditional correlation filter trackers only rely on a search window to localize the object in each frame. However, such approaches are prone to fail in case of e.g.uncertain motion or distractor, where a simple motion model alone is insufficient for robust tracking. To address this problem, we propose a novel tracking architecture that can utilize target-objectness proposals (TOP) effectively. Firstly, we reformulate the Markov Chains Monte Carlo method as a region proposal mechanism from a new perspective. It integrates learning features into the feature space sampling strategy for global object candidates boxes generator. Secondly, to improve the quality of candidates boxes, we constructe objectness labels to guide the sampling process. With the help of objectness information, this interaction of spatial constraints increases the effectiveness of region proposals. In the end, a unified tracking framework is designed to enable sampling and regression strategies to exploit and complement each other to cope with uncertain motion tracking. The proposed TOP tracker performs favorably against advanced trackers, especially in uncertain motion scenarios, on four benchmark datasets including OTB-2013, OTB-2015, Temple Color-128 and UAV-123.
Gesture MNIST: A New Free-Hand Gesture Dataset

We present a unimodal, comprehensive, and easy-to-use dataset for visual free-hand gesture recognition. We call it GestureMNIST because of the 28 $$\times $$ 28 grayscale format of its images, and because the number of samples is approximately 80,000, similar to MNIST. Each of the six gesture classes is composed of a sequence of 12 images taken by a 3D camera. As a peculiarity w.r.t. other datasets, all sequences are recorded by a single person, ensuring high sample uniformity and quality. A particular focus is to provide a vision-based dataset that can be used “out of the box” for sequence classification without any preprocessing, segmentation, and feature extraction steps. We present classification experiments on GestureMNIST with different types of DNNs, establishing a performance baseline for sequence classification algorithms. We place particular emphasis on ahead-of-time classification, i.e., the correct identification of a gestures class before the gesture is completed. It is shown that CNN and LSTM-based deep learning achieves near-perfect performance, whereas ahead-of-time classification performance offers ample scope for future research with GestureMNIST. GestureMNIST contains visual samples only, but other modalities, namely acceleration and sound data, are available upon request.
Contrastive Learning for Session-Based Recommendation

Session-based recommendation predicts the next item to recommend by modeling session sequences. Existing methods usually treat this task as a sequential prediction task. Though these methods have achieved promising results, they still suffer from the data sparsity problem. Inspired by recent success of contrastive learning on natural language processing and recommendation, we propose a novel multi-task model called Contrastive Learning for Session-based Recommendation (CLSR). Based on the sequential prediction task, CLSR performs a contrastive learning task to get more accurate session representations. CLSR constructs augmented data from original data according to a global item transition graph to maintain users' intentions. Extensive experiments performed on three public datasets demonstrate the effectiveness of our proposed approach.
Self-supervised Anomaly Detection by Self-distillation and Negative Sampling

Detecting whether examples belong to a given in-distribution or are out-of-distribution (OOD) requires identifying features that are specific to the in-distribution. In the absence of labels, these features can be learned by self-supervised representation learning techniques under the generic assumption that the most abstract features are those which are statistically most over-represented in comparison to other distributions from the same domain. This work shows that self-distillation of the in-distribution training set together with contrasting against negative examples derived from shifting transformation of auxiliary data strongly improves OOD detection. We find that this improvement depends on how the negative samples are generated, with the general observation that negative samples that keep the statistics of lower level features but change the global semantics result in higher detection accuracy on average. For the first time, we introduce a sensitivity score using which we can optimise negative sampling in a systematic way in an unsupervised setting. We demonstrate the efficiency of our approach across a diverse range of OOD detection problems, setting new benchmarks for unsupervised OOD detection in the visual domain.
Heterogeneous Graph Neural Network for Multi-behavior Feature-Interaction Recommendation

Graph neural network has great advantage in learning vector representation of users and items for modern recommender systems. Modeling user-item interaction bipartite graph is helpful for learning the collaborative signals between users and items. However, this modeling scheme ignores the influence of the objectively existing attribute information of item itself, and cannot well explain why users focus on items. A feature interaction-based graph convolutional collaborative filtering algorithm, Attention Interaction Graph Collaborative Filtering (ATGCF) is proposed to address the limitations of existing works in this paper. It can deeply explore user preferences for multi-feature items. Firstly, we inject user and multi-feature into a user-feature interaction layer composed of multi-head-attention and fully connected layers to capture the user potential preference for multi-feature. Then we build a user-features-item bipartite graph and design the corresponding graph aggregation layer to obtain the high-order connectivity between the three. Experiment results on three real-world datasets verify the effectiveness of our model in exploiting multi-feature data, and ATGCF algorithm outperforms the other baselines in terms of recall, precision and NDCG evaluation metrics. Moreover, further experiments demonstrate that our model can achieve better results with less data.
A new fitness function in genetic programming for classification of imbalanced data

Many real-world problems have an uneven distribution of data over different classes. The imbalanced nature of data impacts the performance of classifiers. The higher counts of majority class samples influence the learning abilities of well-known classifiers. Genetic programming (GP) algorithm based on natural evolution also impacts if the data's nature is imbalanced. The fitness function plays a pivotal role and impacts almost each building block of the GP framework. GP with the standard fitness function produces under-fitted and biased classifiers. Therefore, this paper has proposed a new fitness function in GP to classify the imbalanced data. The proposed method is used to classify nine imbalanced problems: ABL-18, ABL-9-18, BAL, YEAST2, YEAST1, ABL-9, ION, WDBC, and SPECT. The imbalanced factor of benchmark problems varies from 99:1 to 59:41. The proposed method's performance is compared with K-Nearest-Neighbourhood (KNN) and the standard fitness function-based GP methods. The GP with newly proposed fitness function gives average AUC values for ABL-18(99:1), ABL-9-18(94:6), BAL(92:8), YEAST2(89:11), YEAST1(84:16), ABL-9(83:17), ION(64:36), WDBC(63:37), and SPECT(59:41) as 0.714, 0.812, 0.975, 0.916, 0.768, 0.654, 0.872, 0.939, and 0.704, respectively, which are higher than KNN and the standard fitness function-based GP methods. The result outcomes prove the superiority of the proposed method.
Modeling quantum light interference on a quantum computer

Modelling of photonic devices traditionally involves solving the equations of light-matter interaction and light propagation, and it is restrained by their applicability. Here we demonstrate an alternative modelling methodology by creating a "quantum copy" of the optical device in the quantum computer. As an illustration, we simulate quantum interference of light on a thin absorbing film. Such interference can lead to either perfect absorption or total transmission of light through the film, the phenomena attracting attention for data processing applications in classical and quantum information networks. We map behaviour of the photon in the quantum interference experiment to the evolution of a quantum state of transmon, a superconducting charge qubit of the IBM quantum computer. Details of the real optical experiment are flawlessly reproduced on the quantum computer. We argue that superiority of the "quantum copy" methodology shall be apparent in modelling complex multi-photon optical phenomena and devices.
On new results on extremal graph theory, theory of algebraic graphs, and their applications

New explicit constructions of infinite families of finite small world graphs of large girth with well-defined projective limits which is an infinite tree are described. The applications of these objects to constructions of LDPC codes and cryptographic algorithms are shortly observed. We define families of homogeneous algebraic graphs of large girth over the commutative ring K. For each commutative integrity ring K with |K| &gt; 2, we introduce a family of bipartite homogeneous algebraic graphs of large girth over K formed by graphs with sets of points and lines isomorphic to Kn, n &gt; 1, and cycle indicator ≥ 2n + 2 such that their projective limit is well defined and isomorphic to an infinite forest.
Outlier detection in traffic data set

The article presents a comparison of three outlier detection methods - Local Outlier Factor, Isolation Forest, and One Class Support Vector Machine over traffic data set. The data are obtained from virtual detectors based on road cameras. Subsets corresponding to hours of the day for working days and holidays are formed. The algorithms are applied over each subset and the dispersion of purified and raw data is compared via coefficient of variation in percentage. Experimental results show that the algorithms handle the working day data set well, but have difficulty with the holidays datasets. When processing the holidays data sets Isolation Forest shows the best results, which confirms one of its main advantages - applicability over small data sets.