Reviewerly/exHarmony huggingface dataset:
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>δ</mml:mi></mml:math> -Quench Measurement of a Pure Quantum-State Wave Function

The measurement of a quantum state wave function not only acts as a fundamental part in quantum physics but also plays an important role in developing practical quantum technologies. Conventional quantum state tomography has been widely used to estimate quantum wave functions, which usually requires complicated measurement techniques. The recent weak-value-based quantum measurement circumvents this resource issue but relies on an extra pointer space. Here, we theoretically propose and then experimentally demonstrate a direct and efficient measurement strategy based on a $\ensuremath{\delta}$-quench probe: by quenching its complex probability amplitude one by one ($\ensuremath{\delta}$ quench) in the given basis, we can directly obtain the quantum wave function of a pure ensemble by projecting the quenched state onto a postselection state. We confirm its power by experimentally measuring photonic complex temporal wave functions. This new method is versatile and can find applications in quantum information science and engineering.
A Feasible Quantum Protocol for Private Set Intersection Cardinality

Private Set Intersection Cardinality (PSI-CA) is to compute privately the cardinality of the intersection of two sets, and there are many practical applications of PSI-CA, e.g., data mining and data analysis with the privacy protection. The current classical protocols for PSI-CA could not resist the attacks of quantum computers. In this paper, we present a novel quantum protocol for PSI-CA based on the basic laws of quantum mechanics, which can resist well-known quantum attacks. This protocol utilizes single photons as quantum resources and only needs to apply simple single-photon operators and measurements. Therefore, it is more feasible to implement this protocol, compared with the related quantum protocols, which require entangled states and other complicated operators.
CONFIDENCE-AWARE PEDESTRIAN TRACKING USING A STEREO CAMERA

Abstract. Pedestrian tracking is a significant problem in autonomous driving. The majority of studies carries out tracking in the image domain, which is not sufficient for many realistic applications like path planning, collision avoidance, and autonomous navigation. In this study, we address pedestrian tracking using stereo images and tracking-by-detection. Our framework comes in three primary phases: (1) people are detected in image space by the mask R-CNN detector and their positions in 3D-space are computed using stereo information; (2) corresponding detections are assigned to each other across consecutive frames based on visual characteristics and 3D geometry; and (3) the current positions of pedestrians are corrected using their previous states using an extended Kalman filter. We use our tracking-to-confirm-detection method, in which detections are treated differently depending on their confidence metrics. To obtain a high recall value while keeping a low number of false positives. While existing methods consider all target trajectories have equal accuracy, we estimate a confidence value for each trajectory at every epoch. Thus, depending on their confidence values, the targets can have different contributions to the whole tracking system. The performance of our approach is evaluated using the Kitti benchmark dataset. It shows promising results comparable to those of other state-of-the-art methods.
Quantum Rifling: Protecting a Qubit from Measurement Back Action

Quantum mechanics postulates that measuring the qubit's wave function results in its collapse, with the recorded discrete outcome designating the particular eigenstate that the qubit collapsed into. We show that this picture breaks down when the qubit is strongly driven during measurement. More specifically, for a fast evolving qubit the measurement returns the time-averaged expectation value of the measurement operator, erasing information about the initial state of the qubit, while completely suppressing the measurement back-action. We call this regime `quantum rifling', as the fast spinning of the Bloch vector protects it from deflection into either of its eigenstates. We study this phenomenon with two superconducting qubits coupled to the same probe field and demonstrate that quantum rifling allows us to measure either one of the qubits on demand while protecting the state of the other from measurement back-action. Our results allow for the implementation of selective read out multiplexing of several qubits, contributing to the efficient scaling up of quantum processors for future quantum technologies.
Is online fraud just fraud? Examining the efficacy of the digital divide

Purpose Fraud is not a new offence. However, the recent evolution and proliferation of technologies (predominantly the internet) has seen offenders increasingly use virtual environments to target and defraud victims worldwide. Several studies have examined the ways that fraud is perpetrated with a clear demarcation between terrestrial and cyber offences. However, with moves towards the notion of a “digital society” and recognition that technology is increasingly embedded across all aspects of our lives, it is important to consider if there is any advantage in categorising fraud against the type of environment it is perpetrated in. This paper aims to discuss these issues. Design/methodology/approach This paper examines the perceived utility of differentiating online and offline fraud offences. It is based upon the insights of thirty-one professionals who work within the “fraud justice network” across London, UK and Toronto, Canada. Findings It highlights both the realities faced by professionals in seeking to ether maintain or collapse such a differentiation in their everyday jobs and the potential benefits and challenges that result. Practical implications Overall, the paper argues that the majority of professionals did not feel a distinction was necessary and instead felt that an arbitrary divide was instead a hindrance to their activities. However, while not useful on a practical front, there was perceived benefit regarding government, funding and the media. The implications of this moving forward are considered. Originality/value This paper provides new insights into how fraud justice network professionals understand the distinction between fraud offences perpetrated across both online and offline environments.
Stochastic Double Deep Q-Network

Estimation bias seriously affects the performance of reinforcement learning algorithms.The maximum operation may result in overestimation, while the double estimator operation often leads to underestimation.To eliminate the estimation bias, these two operations are combined together in our proposed algorithm named stochastic double deep Q-learning network (SDDQN), which is based on the idea of random selection.A tabular version of SDDQN is also given, named stochastic double Q-learning (SDQ).Both the SDDQN and SDQ are based on the double estimator framework.At each step, we choose to use either the maximum operation or the double estimator operation with a certain probability, which is determined by a random selection parameter.The theoretical analysis shows that there indeed exists a proper random selection parameter that makes SDDQN and SDQ unbiased.The experiments on Grid World and Atari 2600 games illustrate that our proposed algorithms can balance the estimation bias effectively and improve performance.
Session Resumption Protocols and Efficient Forward Security for TLS 1.3 0-RTT

Abstract The TLS 1.3 0-RTT mode enables a client reconnecting to a server to send encrypted application-layer data in “0-RTT” (“zero round-trip time”), without the need for a prior interactive handshake. This fundamentally requires the server to reconstruct the previous session’s encryption secrets upon receipt of the client’s first message. The standard techniques to achieve this are session caches or, alternatively, session tickets . The former provides forward security and resistance against replay attacks, but requires a large amount of server-side storage. The latter requires negligible storage, but provides no forward security and is known to be vulnerable to replay attacks. In this paper, we first formally define session resumption protocols as an abstract perspective on mechanisms like session caches and session tickets. We give a new generic construction that provably provides forward security and replay resilience, based on puncturable pseudorandom functions (PPRFs). We show that our construction can immediately be used in TLS 1.3 0-RTT and deployed unilaterally by servers, without requiring any changes to clients or the protocol. To this end, we present a generic composition of our new construction with TLS 1.3 and prove its security. This yields the first construction that achieves forward security for all messages, including the 0-RTT data. We then describe two new constructions of PPRFs, which are particularly suitable for use for forward-secure and replay-resilient session resumption in TLS 1.3. The first construction is based on the strong RSA assumption. Compared to standard session caches, for “128-bit security” it reduces the required server storage by a factor of almost 20, when instantiated in a way such that key derivation and puncturing together are cheaper on average than one full exponentiation in an RSA group. Hence, a 1 GB session cache can be replaced with only about 51 MBs of storage, which significantly reduces the amount of secure memory required. For larger security parameters or in exchange for more expensive computations, even larger storage reductions are achieved. The second construction combines a standard binary tree PPRF with a new “domain extension” technique. For a reasonable choice of parameters, this reduces the required storage by a factor of up to 5 compared to a standard session cache. It employs only symmetric cryptography, is suitable for high-traffic scenarios, and can serve thousands of tickets per second.
Language as an Abstraction for Hierarchical Deep Reinforcement Learning

Solving complex, temporally-extended tasks is a long-standing problem in reinforcement learning (RL). We hypothesize that one critical element of solving such problems is the notion of compositionality. With the ability to learn concepts and sub-skills that can be composed to solve longer tasks, i.e. hierarchical RL, we can acquire temporally-extended behaviors. However, acquiring effective yet general abstractions for hierarchical RL is remarkably challenging. In this paper, we propose to use language as the abstraction, as it provides unique compositional structure, enabling fast learning and combinatorial generalization, while retaining tremendous flexibility, making it suitable for a variety of problems. Our approach learns an instruction-following low-level policy and a high-level policy that can reuse abstractions across tasks, in essence, permitting agents to reason using structured language. To study compositional task learning, we introduce an open-source object interaction environment built using the MuJoCo physics engine and the CLEVR engine. We find that, using our approach, agents can learn to solve to diverse, temporally-extended tasks such as object sorting and multi-object rearrangement, including from raw pixel observations. Our analysis reveals that the compositional nature of language is critical for learning diverse sub-skills and systematically generalizing to new sub-skills in comparison to non-compositional abstractions that use the same supervision.
Soma Bits - Mediating Technology to Orchestrate Bodily Experiences

The Soma Bits are a prototyping toolkit that facilitates Soma Design. Acting as an accessible ‘sociodigital material’ Soma Bits allow designers to pair digital technologies, with their whole body a ...
BAP: A Batch and Auditable Privacy Preservation Scheme for Demand Response in Smart Grids

Advancing network technologies allow the setup of two-way communication links between energy providers and consumers. These developing technologies aim to enhance grid reliability and energy efficiency in smart grids. To achieve this goal, energy usage reports from consumers are required to be both trustworthy and confidential. In this paper, we construct a new data aggregation scheme in smart grids based on a homomorphic encryption algorithm. In the constructed scheme, obedient consumers who follow the instruction can prove their data consumption's adjustment by using a range proof protocol. Additionally, we propose a new identity-based signature algorithm in order to ensure authentication and integrity of the constructed scheme. By using this signature algorithm, data usage reports are verified in real time. Extensive simulations demonstrate that our scheme outperforms other data aggregation schemes.
YNU_DYX at SemEval-2019 Task 5: A Stacked BiGRU Model Based on Capsule Network in Detection of Hate

This paper describes our system designed for SemEval 2019 Task 5 "Shared Task on Multilingual Detection of Hate".We only participate in subtask-A in English.To address this task, we present a stacked BiGRU model based on a capsule network system.In order to convert the tweets into corresponding vector representations and input them into the neural network, we use the fastText tools to get word representations.Then, the sentence representation is enriched by stacked Bidirectional Gated Recurrent Units (BiGRUs) and used as the input of capsule network.Our system achieves an average F 1 -score of 0.546 and ranks 3rd in the subtask-A in English.
Coherence-assisted non-Gaussian measurement-device-independent quantum key distribution

Non-Gaussian operations on two mode squeezed vacuum states (TMSV) in continuous variable measurement device independent quantum key distribution (CV-MDI-QKD) protocols have been shown to effectively increase the total transmission distances drastically. In this paper we show that photon subtraction on a two mode squeezed coherent (PSTMSC) state can further improve the transmission distances remarkably. To that end we also provide a generalized covariance matrix corresponding to PSTMSC, which has not been attempted before. We show that coherence, defined as the amount of displacement of vacuum state, along with non-Gaussianity can help improve the performance of prevalent CV-MDI-QKD protocols. Furthermore, since we use realistic parameters, our technique is experimentally feasible and can be readily implemented
Standing on Shoulders or Feet? The Usage of the MSR Data Papers

Introduction: The establishment of the Mining Software Repositories (MSR) Data Showcase conference track has encouraged researchers to provide more data sets as a basis for further empirical studies. Objectives: Examine the usage of the data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Methods: Data track papers were collected from the MSR Data Showcase and through the manual inspection of older MSR proceedings. The use of data papers was established through citation searching followed by reading the studies that have cited them. Data papers were then clustered based on their content, whereas their citations were classified according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. Results: We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of citations. MSR data papers are cited less than other MSR papers. A considerable number of the citations stem from the teams that authored the data papers. Publications providing repository data and metadata are the most frequent data papers and the most often cited ones. Mobile application data papers are the least common ones, but the second most frequently cited. Conclusion: Data papers have provided the foundation for a significant number of studies, but there is room for improvement in their utilization. This can be done by setting a higher bar for their publication, by encouraging their use, and by providing incentives for the enrichment of existing data collections.
Feature fusion method using BoVW framework for enhancing image retrieval

The bag-of-visual words (BoVW) has been applied to myriad of recognition problems in computer vision such as object recognition, scene classification and image retrieval due to its scalability and high precision. However, their performance is subservient in certain datasets, especially in natural image datasets, mainly due to the lack of consideration of image cues such as colour, texture etc. which are not prime features while computing invariant descriptors, on which BoVW models are generally built on. Hence, this study describes a multi-cue fusion approach for BoVW framework, exploiting both early and late fusion methods, to improve the retrieval performance, mainly in natural image datasets. For this, a composite edge and colour descriptor is proposed to describe the local regions of the image along with the invariant feature descriptor Speeded Up Robust Features (SURF). Independent vocabularies are built based on these descriptors and images in the dataset are encoded to form two histograms using the respective vocabularies. The histograms are further fused to characterise the image. The retrieval is carried out by matching the histograms. Experimental results show that significant increment in the average precision can be attained by combining the proposed descriptor with invariant descriptors.
Novel term weighting schemes for document representation based on ranking of terms and Fuzzy logic with semantic relationship of terms

Weighting and normalization are the most important factor that may affect the text representation significantly. This paper presents two novel term weighting schemes to represent text documents, namely, i). Term-weighting scheme for document representation based on Term Frequency - Ranking of Term Frequency (TF-RTF) and ii). Term-weighting scheme for document representation based on Term Frequency - Ranking of fuzzy logic with semantic relationship of terms (TF-RFST). The ranking of each term in a document provides its priority of the document and uses these priorities for document representation in TF-RTF. In TF-RFST, each term is represented based on its frequency and the frequency of semantic related terms for that term. Hence, the ranking of each term is based on the combined frequencies of the term and its semantic related terms with a specific weighting scheme. With appropriate weighting schemes such as TF-RFT and TF-RFST, the proposed methods provide better clustering performance in terms of accuracy, entropy, recall and F-Measure than previously suggested methods, such as word count, Term Frequency-Inverse Document Frequency (TF-IDF), Term Frequency-Inverse Corpus Frequency (TF-ICF), Multi Aspect TF (MATF), BM25 and BM25F. Experiments carried out on the Reuters-8, Reuters-52 and WebKB data sets with K-means and K-means++ clustering algorithms for demonstrate the effectiveness of the proposed term weighting schemes.
Modified relay selection and circuit selection for faster Tor

Users of the Tor anonymity system suffer from less-than-ideal performance, in part because circuit building and selection processes are not tuned for speed. In this study, the authors examine both the process of selecting among pre-built circuits and the process of selecting the path of relays for use in building new circuits to improve performance while maintaining anonymity. First, the authors show that having three pre-built circuits available allows the Tor client to identify fast circuits and improves median time to first byte (TTFB) by 15% over congestion-aware routing, the current state-of-the-art method. Second, they propose a new path selection algorithm that includes broad geographic location information together with bandwidth to reduce delays. In shadow simulations, 20% faster median TTFB and 11% faster median total download times over congestion-aware routing for accessing web page-sized objects were found. The proposed security evaluations show that this approach leads to better or equal security against a generic relay-level adversary compared to Tor, but increased vulnerability to targeted attacks. The authors explore this trade-off and find settings of the proposed system that offers good performance, modestly better security against a generic adversary, and only slightly more vulnerability to a targeted adversary.
Algoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah

The decision on financing approval in sharia cooperatives has a high risk of the inability of customers to pay their credit obligations at maturity or referred to as bad credit. To maintain and minimize risk, an accurate method is needed to determine the financing agreement. The purpose of this study is to classify sharia cooperative loan history data using the Naïve Bayes algorithm, Decision Tree and SVM to predict the credibility of future customers. The results showed the accuracy of Naïve Bayes algorithm 77.29%, Decision Tree 89.02% and the highest Support Vector Machine (SVM) 89.86%.
Knowledge Distillation with Feature Maps for Image Classification

The model reduction problem that eases the computation costs and latency of complex deep learning architectures has received an increasing number of investigations owing to its importance in model deployment. One promising method is knowledge distillation (KD), which creates a fast-to-execute student model to mimic a large teacher network. In this paper, we propose a method, called KDFM (Knowledge Distillation with Feature Maps), which improves the effectiveness of KD by learning the feature maps from the teacher network. Two major techniques used in KDFM are shared classifier and generative adversarial network. Experimental results show that KDFM can use a four layers CNN to mimic DenseNet-40 and use MobileNet to mimic DenseNet-100. Both student networks have less than 1% accuracy loss comparing to their teacher models for CIFAR-100 datasets. The student networks are 2–6 times faster than their teacher models for inference, and the model size of MobileNet is less than half of DenseNet-100’s.
Computing skew left braces of small orders

We improve Algorithm 5.1 of [Math. Comp. 86 (2017) 2519–2534] for computing all nonisomorphic skew left braces, and enumerate left braces and skew left braces of orders up to 868 with some exceptions. Using the enumerated data, we state some conjectures for further research.
Semi-supervised online structure learning for composite event recognition

Online structure learning approaches, such as those stemming from statistical relational learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.