Reviewerly/exHarmony huggingface dataset:
Enhanced discrimination of high-dimensional quantum states by concatenated optimal measurement strategies

The impossibility of deterministic and error-free discrimination among nonorthogonal quantum states lies at the core of quantum theory and constitutes a primitive for secure quantum communication. Demanding determinism leads to errors, while demanding certainty leads to some inconclusiveness. One of the most fundamental strategies developed for this task is the optimal unambiguous measurement. It encompasses conclusive results, which allow for error-free state retrodictions with the maximum success probability, and inconclusive results, which are discarded for not allowing perfect identifications. Interestingly, in high-dimensional Hilbert spaces the inconclusive results may contain valuable information about the input states. Here, we theoretically describe and experimentally demonstrate the discrimination of nonorthogonal states from both conclusive and inconclusive results in the optimal unambiguous strategy, by concatenating a minimum-error measurement at its inconclusive space. Our implementation comprises 4- and 9-dimensional spatially encoded photonic states. By accessing the inconclusive space to retrieve the information that is wasted in the conventional protocol, we achieve significant increases of up to a factor of 2.07 and 3.73, respectively, in the overall probabilities of correct retrodictions. The concept of concatenated optimal measurements demonstrated here can be extended to other strategies and will enable one to explore the full potential of high-dimensional nonorthogonal states for quantum communication with larger alphabets.
Survey on Automated Machine Learning (AutoML) and Meta learning

Automated Machine Learning is an area of research that has gained lots of research in the past few years. To build a high qualitymodel for Machine learning we need technical experts who have good knowledge in exploring various machines learning algorithm as well as to tune the hyperparameters efficiently. The size of data is increasing, it is observed that data scientist cannot address this challenging tasks due to lack of expertise and experience in the respective domain. So, there is a need to automate such crucial task inthe domain of machine learning. Metalearning is “learning to learn” like human expertise. This paper is initial survey of ongoing research in field of Meta Learning and AutoML.
Data Mining Approaches for Sentiment Analysis in Online Social Networks (OSNs)

IoT technology and the widespread usage of public networking platforms and apps also made it possible to use data mining in extracting useful perspectives from unorganised knowledge. In the age of big data, opinion mining may be applied as a valuable way in order to classify views into various sentiment and in general to determine the attitude of the population. Other methods to OSA have been established over the years in various datasets and evaluated in varying conditions. In this respect, this chapter highlights the scope of OMSA strategies and forms of implementing OMSA principles. Besides technological issues of OMSA, this chapter also outlined both technical problems regarding its production and non-technical issues regarding its use. There are obstacles for potential study.
ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems

Edge devices have been widely adopted to bring deep learning applications onto low power embedded systems, mitigating the privacy and latency issues of accessing cloud servers. The increasingly computational demand of complex neural network models leads to large latency on edge devices with limited resources. Many application scenarios are real-time and have a strict latency constraint, while conventional neural network compression methods are not latency-oriented. In this work, we propose a novel compact neural networks training method to reduce the model latency on latency-critical edge systems. A latency predictor is also introduced to guide and optimize this procedure. Coupled with the latency predictor, our method can guarantee the latency for a compact model by only one training process. The experiment results show that, compared to state-of-the-art model compression methods, our approach can well-fit the ‘hard’ latency constraint by significantly reducing the latency with a mild accuracy drop. To satisfy a 34ms latency constraint, we compact ResNet-50 with 0.82% of accuracy drop. And for GoogLeNet, we can even increase the accuracy by 0.3%
Integrating social influence modeling and user modeling for trust prediction in signed networks

Trust and distrust between online users play an important role in social network applications, especially in the security domain. For example, trust information can enhance social recommendation and distrust information can be used for fraud detection. However, trust prediction is challenging due to the existence and imbalance of the three kinds of social status in signed social networks (i.e., trust, distrust and no-relation). Furthermore, there are a variety types of no-relation status in reality, e.g., strangers and frenemies, which cannot be well distinguished from the other social status by existing approaches. In this paper, we propose a novel Framework of Integrating both Latent and Explicit features (FILE), to better deal with the no-relation status and hence improve the overall trust/distrust prediction performance. In particular, we design two latent features to model user's intrinsic personality. Meanwhile, we design explicit features by extending social theories, to model the external social influence from mutual neighbors. The proposed model learns the features for each user via matrix factorization with a specially designed ranking-oriented loss function. Experimental results demonstrate the superior of our approach over the state-of-the-art methods, and the effectiveness of our approach in security applications. Our work sheds light on trust prediction in signed networks as well as security applications like fraud detection.
Perceived Transparency in Optical See-Through Augmented Reality

Optical see-through (OST) displays overlay rendered images to the real-world background creating augmented reality (AR). But the blending between the rendering and the real-world background introduces perceived transparency. The increased luminance on the rendering decreases the background contrast, and reduces the perceived transparency, which is not incorporated in existing color appearance models or display color management pipelines. We studied the perceived transparency in AR focusing on the interaction between the rendering and the patterned background in various luminance, contrast, and wave forms. In addition to AR contrast, we also examined simulated contrast modulation by changing the luminance amplitude. Two psychophysical experiments were conducted to quantify the perceived transparency. The first experiment measured a perceived transparency scale using direct scaling, and the second experiment evaluated the transparency equivalency between two methods of contrast modulation. The result showed that the two methods evoke different transparency perceptions. The background contrast affects the perceived AR transparency significantly, while the background luminance and wave form do not. We proposed a model predicting the perceived transparency based on the first experiment result from AR luminance and background contrast. The model was verified with the second experiment and showed good prediction. Our model presented a new perceptual dimension in OST AR and can possibly be incorporated into color management pipeline to improve AR image quality.
Language history on fast forward: Innovations in heritage languages and diachronic change

Abstract There has been a substantial amount of research on heritage language acquisition and diachronic change. Although recent work has increasingly pointed to parallels between those two areas, it remains unclear how systematic these are. In this paper, we provide a bird's eye view, illustrating how patterns of diachronic change are mirrored in heritage language grammars. In doing so, we focus on one of the best-described grammaticalization processes – namely, the formation of articles from demonstratives and numerals, reviewing studies on heritage varieties which mirror those processes. Based on this review, we make two main points: that change in heritage language can be predicted based on established diachronic scenarios, and that heritage languages often amplify incipient changes in the baseline. After discussing a number of attested changes in a bilingual context, we identify directions for future research in the domain of determiners in heritage languages.
Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions

Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separability or boundary shapes, as well as group morphology. Many techniques can transform the data in order to find better features, but few focus on specifically reducing data complexity. Most data transformation methods mainly treat the dimensionality aspect, leaving aside the available information within class labels which can be useful when classes are somehow complex. This paper proposes an autoencoder-based approach to complexity reduction, using class labels in order to inform the loss function about the adequacy of the generated variables. This leads to three different new feature learners, Scorer, Skaler and Slicer. They are based on Fisher's discriminant ratio, the Kullback-Leibler divergence and least-squares support vector machines, respectively. They can be applied as a preprocessing stage for a binary classification problem. A thorough experimentation across a collection of 27 datasets and a range of complexity and classification metrics shows that class-informed autoencoders perform better than 4 other popular unsupervised feature extraction techniques, especially when the final objective is using the data for a classification task.
A gradient ascent algorithm based on possibilistic fuzzy C-Means for clustering noisy data

Real-world data are often corrupted by noise and outliers, which are originated from different procedures such as data collection, storage, and processing. Noise and outliers decrease the quality of clustering and lead to the inaccurate and misplaced cluster centers. In this paper, we propose a new algorithm called Improved Possibilistic Fuzzy C-Means (IPFCM) to cluster noisy data. First, initial cluster centers are calculated by Possibilistic Fuzzy C-Means (PFCM) which do not match dense regions of the data. Then, the domain is divided to some subdomains and each data point is assigned to a sub-domain. The cluster centers are iteratively moved towards high-density regions by maximizing a novel cluster validity index. In the proposed method, a Gaussian membership function is defined on each cluster to weight the data. Then, the sum of weights in each cluster is calculated. The product of these values is considered as the validity index. Since division of the domain is changed with moving the cluster centers, this procedure is repeated until the convergent criterion is satisfied. Cluster analysis performed on six synthetics, nine real benchmarks datasets shows the superiority of IPFCM over some previous clustering algorithms such as Fuzzy C-Means (FCM), PFCM, Kernel Fuzzy C-Means (KFCM), Noise Clustering (NC), and Generalized Entropy based Possibilistic Fuzzy C-Means (GEPFCM). The clustering results of near-fault ground motion data indicate that the cluster centers identified by IPFCM are well separated from each other, while those for PFCM are close to each other in some datasets. Moreover, the results show that the impact of noisy data on the proposed index and consequently cluster analysis decreases as the noisy data get away from the cluster centers which is one of the advantages of using IPFCM algorithm.
The Effect of Google Search on Software Security: Unobtrusive Security Interventions via Content Re-ranking

Google Search is where most developers start their Web journey looking for code examples to reuse. It is highly likely that code that is linked to the top results will be among those candidates that find their way into production software. However, as a large amount of secure and insecure code has been identified on the Web, the question arises how the providing webpages are ranked by Google and whether the ranking has an effect on software security. We investigate how secure and insecure cryptographic code examples from Stack Overflow are ranked by Google Search. Our results show that insecure code ends up in the top results and is clicked on more often. There is at least a 22.8% chance that one out of the top three Google Search results leads to insecure code. We introduce security-based re-ranking, where the rank of Google Search is updated based on the security and relevance of the provided source code in the results. We tested our re-ranking approach and compared it to Google's original ranking in an online developer study. Participants that used our modified search engine to look for help online submitted more secure and functional results, with statistical significance. In contrast to prior work on helping developers to write secure code, security-based re-ranking completely eradicates the requirement for any action performed by developers. Our intervention remains completely invisible, and therefore the probability of adoption is greatly increased. We believe security-based re-ranking allows Internet-wide improvement of code security and prevents the far-reaching spread of insecure code found on the Web.
LRW-CRDB: Lossless Robust Watermarking Scheme for Categorical Relational Databases

In 2002, Agrawal and Kiernan defined six basic requirements, including preventing illegal watermark embedding and authentication, reversibility, robustness, and others, which must be satisfied when a reversible watermark is designed for relational databases. To meet these requirements, in this paper, a lossless watermarking scheme for a categorical relational database called LRW-CRDB (lossless robust watermarking for categorical relational databases) is proposed. In our LRW-CRDB scheme, the database owner needs to generate two secret embedding keys, K1 and K2, in advance. Then, two reference sets are generated based on two different secret embedding keys and a symmetry-based data hiding strategy, and then these are used for the watermark embedding phases. Experimental results confirmed that our LRW-CRDB scheme successfully detects 100% of hidden watermarks, even when more than 95% of the watermarked relational database has been deleted. In other words, the robustness of our proposed LRW-CRDB scheme outperforms other existing schemes under a variety of possible attacks, such as alteration, sorting, deletion, and mix-match attacks.
On the Impossibility of Purely Algebraic Signatures

The existence of one-way functions implies secure digital signatures, but not public-key encryption (at least in a black-box setting). Somewhat surprisingly, though, efficient public-key encryption schemes appear to be much easier to construct from concrete algebraic assumptions (such as the factoring of Diffie-Hellman-like assumptions) than efficient digital signature schemes. In this work, we provide one reason for this apparent difficulty to construct efficient signature schemes.
Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation

We consider a new problem of adapting a human mesh reconstruction model to out-of-domain streaming videos, where the performance of existing SMPL-based models is significantly affected by the distribution shift represented by different camera parameters, bone lengths, backgrounds, and occlusions. We tackle this problem through online adaptation, gradually correcting the model bias during testing. There are two main challenges: First, the lack of 3D annotations increases the training difficulty and results in 3D ambiguities. Second, non-stationary data distribution makes it difficult to strike a balance between fitting regular frames and hard samples with severe occlusions or dramatic changes. To this end, we propose the Dynamic Bilevel Online Adaptation algorithm (DynaBOA). It first introduces the temporal constraints to compensate for the unavailable 3D annotations and leverages a bilevel optimization procedure to address the conflicts between multi-objectives. DynaBOA provides additional 3D guidance by co-training with similar source examples retrieved efficiently despite the distribution shift. Furthermore, it can adaptively adjust the number of optimization steps on individual frames to fully fit hard samples and avoid overfitting regular frames. DynaBOA achieves state-of-the-art results on three out-of-domain human mesh reconstruction benchmarks.
Understanding and identifying the use of emotes in toxic chat on Twitch

The latest advances in NLP (natural language processing) have led to the launch of the much needed machine-driven toxic chat detection. Nevertheless, people continuously find new forms of hateful expressions that are easily identified by humans, but not by machines. One such common expression is the mix of text and emotes, a type of visual toxic chat that is increasingly used to evade algorithmic moderation and a trend that is an under-studied aspect of the problem of online toxicity. This research analyzes chat conversations from the popular streaming platform Twitch to understand the varied types of visual toxic chat. Emotes were sometimes used to replace a letter, seek attention, or for emotional expression. We created a labeled dataset that contains 29,721 cases of emotes replacing letters. Based on the dataset, we built a neural network classifier and identified visual toxic chat that would otherwise be undetected through traditional methods and caught an additional 1.3% examples of toxic chat out of 15 million chat utterances.
Noise-resistant quantum communications using hyperentanglement

Quantum information protocols are being deployed in increasingly practical scenarios, via optical fibers or free space, alongside classical communications channels. However, entanglement, the most critical resource to deploy to the communicating parties, is also the most fragile to the noise-induced degradations. Here we show that polarization-frequency hyperentanglement of photons can be effectively employed to enable noise-resistant distribution of polarization entanglement through noisy quantum channels. In particular, we demonstrate that our hyperentanglement-based scheme results in an orders-of-magnitude increase in the SNR for distribution of polarization-entangled qubit pairs, enabling quantum communications even in the presence of strong noise that would otherwise preclude quantum operations due to noise-induced entanglement sudden death. While recent years have witnessed tremendous interest and progress in long-distance quantum communications, previous attempts to deal with the noise have mostly been focused on passive noise suppression in quantum channels. Here, via the use of hyperentangled degrees of freedom, we pave the way toward a universally adoptable strategy to enable entanglement-based quantum communications via strongly noisy quantum channels.
A Robust Approach for Privacy Data Protection: IoT Security Assurance Using Generative Adversarial Imitation Learning

With the increasing importance of data security, privacy protection has gradually risen to a strategic position, especially IoT data privacy protection. The concern for data security has become a national strategy. The discovery of potential risks of privacy data is of great significance, such as the risk of data privacy leakage, data security vulnerabilities, etc. In this article, starting from the privacy data protection mechanism in the Industrial Internet of Things (IIoT) scenario, we proposed a method based on generative adversarial imitation learning (GAIL) to discover the privacy data security risks in IIoT by training privacy protection agents using a large amount of expert data on privacy protection. Finally, our proposed method is validated by relevant simulation experiments, and the results show that our proposed method has wide generalizability and reliability to obtain the maximum payoff of the agents and thus, reduce the risk of data security leakage.
Developing decision support for cybersecurity threat and incident managers

Cybersecurity threat and incident managers in large organizations, especially in the financial sector, are confronted more and more with an increase in volume and complexity of threats and incidents. At the same time, these managers have to deal with many internal processes and criteria, in addition to requirements from external parties, such as regulators that pose an additional challenge to handling threats and incidents. Little research has been carried out to understand to what extent decision support can aid these professionals in managing threats and incidents. The purpose of this research was to develop decision support for cybersecurity threat and incident managers in the financial sector. To this end, we carried out a cognitive task analysis and the first two phases of a cognitive work analysis, based on two rounds of in-depth interviews with ten professionals from three financial institutions. Our results show that decision support should address the problem of balancing the bigger picture with details. That is, being able to simultaneously keep the broader operational context in mind as well as adequately investigating, containing and remediating a cyberattack. In close consultation with the three financial institutions involved, we developed a critical-thinking memory aid that follows typical incident response process steps, but adds big picture elements and critical thinking steps. This should make cybersecurity threat and incident managers more aware of the broader operational implications of threats and incidents while keeping a critical mindset. Although a summative evaluation was beyond the scope of the present research, we conducted iterative formative evaluations of the memory aid that show its potential.