Reviewerly/exHarmony huggingface dataset:
Comparing a digital and a non-digital embodied learning intervention in geometry: can technology facilitate?

Despite the rapid development of motion-based technologies, their role in embodied learning remains ambivalent. This study aimed at investigating the potential added value of motion-based technologies in the context of geometry elementary education. An explanatory sequential design was adopted, composed of two phases. First, an experiment was conducted: students in the experimental group (n = 15) participated in a digital embodied intervention while students in the comparison group (n = 16) participated in a non-digital embodied intervention, to learn about angles. Quantitative results showed that, while students’ cognitive load was not differentiated, students in the digital intervention outperformed their counterparts in terms of learning gains and emotional engagement. Qualitative analysis of interview data allowed better understanding of students’ engagement and cognitive load supporting the quantitative findings. The authors reflect on these findings and the implications for the use of motion-based technologies in embodied interventions for learning in mathematics.
A Privacy-Preserving Medical Data Sharing Scheme Based on Consortium Blockchain

Electronic medical records (EMRs) can provide medical institutes with a number of research materials, which could promote the development of medical field. In order to protect patients' privacy while achieving efficient medical data sharing, we propose a privacy-preserving medical data sharing scheme based on consortium blockchain. In the scheme, a conditional anonymous tracing mechanism is designed to protect users' privacy, in which malicious users can be tracked when illegal behavior occurs. However, as the number of users grows, masses of medical data might touch the storage bottleneck of blockchain. Therefore, an on-chain-off-chain storage model is designed to alleviate the storage burden of blockchain, in which only the metadata of EMRs are recorded in blockchain while the patients' EMRs are stored in cloud after being encrypted. In order to resist the collusion attack, an improved proxy re-encryption scheme is introduced to ensure that EMRs can only be accessed by the authorized data requester. Moreover, security analysis and simulation demonstrate that the proposed scheme is more secure and efficient.
Risk Factors in Cloud Computing Relationships: A Study in Public Organizations in Sweden

Several predictions show an increase in Cloud Computing (CC) in the coming years. The risks in CC relationships can jeopardize the CC services; therefore, this research has looked to find the risk factors in CC relationships in public organizations in Sweden. A survey research strategy has been applied and the data was collected through interviews with IT decision-makers with relevant experience in CC relationships. The study has identified security as a critical risk factor. The CC clients do not trust their providers and therefore, they do not place sensitive data in the cloud. Additionally, we have found other risks in CC that are not critical, like, asset specificity, a small number of suppliers, uncertainty, relatedness, measurement problems, competences. The reason for this result is the low complexity of services in the studied public organizations. The findings of this study are several risk factors that public organizations should be aware of for improving their relationship in CC arrangements.
Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation

We propose a novel method that tackles the problem of unsupervised domain adaptation for semantic segmentation by maximizing the cosine similarity between the source and the target domain at the feature level. A segmentation network mainly consists of two parts, a feature extractor and a classification head. We expect that if we can make the two domains have small domain gap at the feature level, they would also have small domain discrepancy at the classification head. Our method computes a cosine similarity matrix between the source feature map and the target feature map, then we maximize the elements exceeding a threshold to guide the target features to have high similarity with the most similar source feature. Moreover, we use a class-wise source feature dictionary which stores the latest features of the source domain to prevent the unmatching problem when computing the cosine similarity matrix and be able to compare a target feature with various source features from various images. Through extensive experiments, we verify that our method gains performance on two unsupervised domain adaptation tasks (GTA5→Cityscapes and SYNTHIA→Cityscapes).
Vectorial bent functions weakly/strongly outside the completed Maiorana–McFarland class

Two new classes of bent functions derived from the Maiorana–McFarland (M) class, so-called C and D, were introduced by Carlet (1994) two decades ago. The difficulty of satisfying their defining conditions was emphasized in Mandal et al. (2016). In a recent work Zhang et al. (2017) a set of efficient sufficient conditions for specifying bent functions in C and D which are outside the completed M class, denoted by M#, was given. A natural follow up question is whether there is a possibility of extending this approach to the vectorial case. We introduce the property of vectorial bent functions that we call weakly or strongly outside M#, referring respectively to the case whether some or all nonzero linear combinations (called components) of its coordinate functions are in class C (or D) but provably outside M#. For the first time, quite different to a straightforward vectorial extension of the Maiorana–McFarland class and the class of Dillon PSap, we show the existence of several classes of vectorial bent functions whose component functions come from different classes of bent functions, mainly from M and D, and in many cases being weakly outside M#. We also address a difficult problem of specifying vectorial bent functions whose all components are in class C but provably outside M#, thus being strongly outside M#. Even though we could only specify a class of such functions whose dimension of bent vector space is only two, thus F:F2n→F22, this is the very first evidence of their existence.
Requirements for the New Normal: Requirements Engineering in a Pandemic

The COVID-19 shutdown was supposed to last for only a few weeks, or so we believed in March 2020. Along with many of us working in software and systems engineering, I absorbed that early news and assumed that any interruption to my normal professional routine would be short lived. Requirements engineering (RE) work would continue as planned, with methods and practices resilient enough to handle the pivot in our path.
Protecting the Intellectual Property of Deep Neural Networks with Watermarking: The Frequency Domain Approach

Similar to other digital assets, deep neural network (DNN) models could suffer from piracy threat initiated by insider and/or outsider adversaries due to their inherent commercial value. DNN watermarking is a promising technique to mitigate this threat to intellectual property. This work focuses on black-box DNN watermarking, with which an owner can only verify his ownership by issuing special trigger queries to a remote suspicious model. However, informed attackers, who are aware of the watermark and somehow obtain the triggers, could forge fake triggers to claim their ownerships since the poor robustness of triggers and the lack of correlation between the model and the owner identity. This consideration calls for new watermarking methods that can achieve better trade-off for addressing the discrepancy. In this paper, we exploit frequency domain image watermarking to generate triggers and build our DNN watermarking algorithm accordingly. Since watermarking in the frequency domain is high concealment and robust to signal processing operation, the proposed algorithm is superior to existing schemes in resisting fraudulent claim attack. Besides, extensive experimental results on 3 datasets and 8 neural networks demonstrate that the proposed DNN watermarking algorithm achieves similar performance on functionality metrics and better performance on security metrics when compared with existing algorithms.
Data integration by two-sensors in a LEAP-based Virtual Glove for human-system interaction

Abstract Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.
Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world. To date, all proposed approaches rely on human expertise to manually adapt a given UDA method (e.g. DANN) to a specific backbone architecture (e.g. ResNet). This dependency on handcrafted designs limits the applicability of a given approach in time, as old methods need to be constantly adapted to novel backbones.Existing Neural Architecture Search (NAS) approaches cannot be directly applied to mitigate this issue, as they rely on labels that are not available in the UDA setting. Furthermore, most NAS methods search for full architectures, which precludes the use of pre-trained models, essential in a vast range of UDA settings for reaching SOTA results.To the best of our knowledge, no prior work has addressed these aspects in the context of NAS for UDA. Here we tackle both aspects with an Adversarial Branch Architecture Search for UDA (ABAS): i. we address the lack of target labels by a novel data-driven ensemble approach for model selection; and ii. we search for an auxiliary adversarial branch, attached to a pre-trained backbone, which drives the domain alignment. We extensively validate ABAS to improve two modern UDA techniques, DANN and ALDA, on three standard visual recognition datasets (Office31, Office-Home and PACS). In all cases, ABAS robustly finds the adversarial branch architectures and parameters which yield best performances. https://github.com/lr94/abas.
Permutation Enhances Classical Communication Assisted by Entangled States

We study classical communication over a noisy quantum channel when bipartite states are preshared between the sender and the receiver, and one of the following encoding strategies are available: i) local operations; ii) local operations and one-way classical communication; iii) local operations and global permutations. Our main result is a capacity formula for strategy iii). This formula's two endpoints are the capacity formula in strategy i) and the entanglement-assisted classical capacity. Interestingly, these capacities satisfy the strong converse property, and thus the formula serves as a sharp dividing line between achievable and unachievable rates of communication. We prove that the difference between the capacities by strategy i) and strategy iii) is upper bounded by the discord of formation of the preshared state. What's more, we show that strategy ii) has no advantage over strategy i) in the weak converse regime. As examples, we derive these capacities analytically by the above strategies for some fundamental quantum channels. In some cases, the capacity of strategy iii) is strictly larger than those of strategies i) and ii) whenever entanglement assistance is available. Our results witness the power of random permutation in entanglement-assisted classical communication.
More Realistic Website Fingerprinting Using Deep Learning

Website fingerprinting (WF) allows a passive local eavesdropper to monitor the encrypted channel where users search the Internet and determine which website the user is visiting from the recorded traffic. The effectiveness of using deep learning (DL) in WF attacks has been explored in recent work. However, they all are built and evaluated on one-page traces. Our goal is to explore whether deep learning can be used to handle the situations when the captured traces are not best-case for an adversary, such as partial traces and two-page traces. We aim to reduce the distance between the lab experiments and the realistic conditions. We evaluate our proposed method in both closed-world and open-world settings and found that Convolutional Neural Network (CNN) outperforms Long-Short Term Memory network (LSTM) in all scenarios. CNN also shows a great potential in predicting on a smaller number of packets. For partial trace missing 20% packets in the beginning of the trace, the accuracy is improved from 8.28% to 86.93% compared to the original DL model by adding the head detection. We then show the accuracy of predicting on two-page traces. With an overlap of 80% between two websites, we are able to achieve an accuracy of 89.25% and 74.2% for the first and second website in the closed-world evaluation, and 95.5% and 75% in the open world from our simulation. To verify our simulation results, we set up a crawler to collect both training and testing data and gathered the largest two-page traces testing dataset ever used. The results shown in the real world experiment is consistent with the simulation.
Daily Living Activity Recognition Using Wearable Devices: A Features-Rich Dataset and a Novel Approach

Automated daily living activity recognition is a relevant task since it allows to assess the health status of a subject both objectively and remotely. Having a reliable measure is important since it gives precise indications to doctors and researchers interested in evaluating the effectiveness of treatments or drugs (e.g., in the context of clinical studies). The possibility to perform this task remotely is more convenient for the patients and acquired increasing importance not only due to the current pandemic, but also because of the regularly growing population of elderly people that could benefit from remote monitoring.
3P3: Strong Flexible Privacy for Broadcasts

Privacy concerns have reached the mainstream discourse in society and already had a significant impact on research and technology. Cryptocurrencies have adopted many transaction-level privacy mechanisms to provide privacy in the persisted blockchain. Unfortunately, these are insufficient as network-level attacks can also provide privacy-breaking insights into transactions and their origins. We proposed k-Dining Cryptographers and topological methods as a basis for a privacy-preserving broadcast protocol. In this work, we present 3P3, a three-phase privacy-preserving broadcast protocol. We transformed our approach to a stronger attacker model so that it provides strong base privacy against global attackers and malicious nodes and additional privacy against common attackers, e.g., botnets. Further, we provide mechanisms to transmit almost arbitrarily long messages, reduce overhead for zero-message rounds, a more extensive analysis, and simulation results of our enhanced protocol. Our simulations show the dissemination of a message to all nodes within 1000ms in 99.9% of instances. These results hold for all network sizes, including networks of up to 10,000 participants. Bandwidth estimates also show practical applicability with usual group sizes of 10 to 30 participants.
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas

We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LTL formulas have never been seen before, yet the network performs zero-shot generalization to satisfy them. This is a novel form of multi-task learning for RL agents where agents learn from one diverse set of tasks and generalize to a new set of diverse tasks. The formulation of the network enables this capacity to generalize. We demonstrate this ability in two domains. In a symbolic domain, the agent finds a sequence of letters that is accepted. In a Minecraft-like environment, the agent finds a sequence of actions that conform to the formula. While prior work could learn to execute one formula reliably given examples of that formula, we demonstrate how to encode all formulas reliably. This could form the basis of new multitask agents that discover sub-tasks and execute them without any additional training, as well as the agents which follow more complex linguistic commands. The structures required for this generalization are specific to LTL formulas, which opens up an interesting theoretical question: what structures are required in neural networks for zero-shot generalization to different logics?
Mining Hyperproperties from Behavioral Traces

Many important specifications of hardware and software systems, such as secure information flow and determinism are expressible only as hyperproperties. In contrast to the well-studied class of trace properties, which specify sets of valid runs (aka traces) of a system, hyperproperties can specify relations that must hold between the traces of a system. While hyperproperties have many applications, primarily in security verification, coming up with hyperproperties for SoC validation is challenging. In this paper, we work toward addressing this challenge by introducing a framework for mining hyperproper-ties from execution traces of SoC designs. We introduce novel algorithms based on coverage-guided fuzzing that enable the generation of good input traces for the hyperproperty miner. We also present novel optimistic and pessimistic semantics for Hyper Linear Temporal Logic (HyperLTL) that enable principled evaluation of HyperLTL formulas over finite traces. Finally, we propose algorithms for scalably evaluating non-trivial satisfaction of candidate hyperproperties on sets of traces. Experiments on a small but realistic SoC design show the framework is effective in identifying useful hyperproperties.
Long-Term Anticipation of Activities with Cycle Consistency

With the success of deep learning methods in analyzing activities in videos, more attention has recently been focused towards anticipating future activities. However, most of the work on anticipation either analyzes a partially observed activity or predicts the next action class. Recently, new approaches have been proposed to extend the prediction horizon up to several minutes in the future and that anticipate a sequence of future activities including their durations. While these works decouple the semantic interpretation of the observed sequence from the anticipation task, we propose a framework for anticipating future activities directly from the features of the observed frames and train it in an end-to-end fashion. Furthermore, we introduce a cycle consistency loss over time by predicting the past activities given the predicted future. Our framework achieves state-of-the-art results on two datasets: the Breakfast dataset and 50Salads.
A Two-Stage Unsupervised Deep Learning Framework for Degradation Removal in Ancient Documents

Processing historical documents is a complicated task in computer vision due to the presence of degradation, which decreases the performance of Machine Learning models. Recently, Deep Learning (DL) models have achieved state-of-the-art accomplishments in processing historical documents. However, these performances do not match the results obtained in other computer vision tasks, and the reason is that such models require large datasets to perform well. In the case of historical documents, only small datasets are available, making it hard for DL models to capture the degradation. In this paper, we propose a framework to overcome issues by following a two-stage approach. Stage-I is devoted to data augmentation. A Generative Adversarial Network (GAN), trained on degraded documents, generates synthesized new training document images. In stage-II, the document images generated in stage-I, are improved using an inverse problem model with a deep neural network structure. Our approach enhances the quality of the generated document images and removes degradation. Our results show that the proposed framework is well suited for binarization tasks. Our model was trained on the 2014 and 2016 DIBCO datasets and tested on the 2018 DIBCO dataset. The obtained results are promising and competitive with the state-of-the-art.
A Survey of Emotion Analysis in Text Based on Deep Learning

With the rapid development of mobile Internet, and the popularization of e-commerce and social networks, people have changed from the simple users of network information to the main publishers of network information. Thus, a large number of various network data have been generated, and a large part of these data contains negative emotions. Mining these data can make us better understand the views and positions of netizens, and help to grasp the key information of network public opinion. In this paper, we provide an introduction for the background knowledge of emotion analysis, including different definitions and classification methods of emotion. Then, we summarize the related models of deep learning, as well as the main emotion analysis methods in text based on deep learning, and make a detailed introduction and comparison on these methods. Finally, we enumerate the challenges of emotion analysis in text, and the future research trend for emotion analysis.
Matching of Matching-Graphs - A Novel Approach for Graph Classification

Due to fast developments in data acquisition, we observe rapidly increasing amounts of data available in diverse areas. Simultaneously, we observe that in many applications the underlying data is inherently complex, making graphs a very useful and adequate data structure for formal representation. A large amount of graph based methods for pattern recognition have been proposed. Many of these methods actually rely on graph matching. In the present paper a novel encoding of graph matching information is proposed. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs. In an empirical evaluation we show that it can be highly beneficial to focus on these stable parts of graphs during graph classification.