Reviewerly/exHarmony huggingface dataset:
Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths

Privacy protection is the key to maintaining the Internet of Things (IoT) communication strategy. Steganography is an important way to achieve covert communication that protects user data privacy. Steganalysis technology is the key to checking steganography security, and its ultimate goal is to extract embedded messages. Existing methods cannot extract under known cover images. To this end, this paper proposes a method of extracting embedded messages under known cover images. First, the syndrome-trellis encoding process is analyzed. Second, a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix, while the embedding process is simulated using the cover sequence and parity-check matrix. Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion, comparing the path consistency can quickly filter the coding parameters to determine the correct matrices, and embedded messages can be extracted correctly. The proposed method does not need to embed all possible messages for the second time, improving coding parameter recognition significantly. The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.
Residual attention-based tracking-by-detection network with attention-driven data augmentation

Tracking-by-detection (TBD) is a significant framework for visual object tracking. However, current trackers are usually updated online based on random sampling with a probability distribution. The performance of the learning-based TBD trackers is limited by the lack of discriminative features, especially when the background is full of semantic distractors. We propose an attention-driven data augmentation method, in which a residual attention mechanism is integrated into the TBD tracking network as supplementary references to identify discriminative image features. A mask generating network is used to simulate changes in target appearances to obtain positive samples, where attention information and image features are combined to identify discriminative features. In addition, we propose a method for mining hard negative samples, which searches for semantic distractors with the response of the attention module. The experiments on the OTB2015, UAV123, and LaSOT benchmarks show that this method achieves competitive performance in terms of accuracy and robustness.
A modified entropy-based performance criterion for class-modelling with multiple classes

The paper presents a new proposal for a single overall measure, the diagonal modified confusion entropy (DMCEN), to assess the performance of class-models jointly computed for several classes, a versatile index regarding sensitivity and specificity, and that supports class weighting. The characteristics of the proposed figure of merit are illustrated as against other usual performance measures and show how the index is more sensitive to the variations in the class-models than similar published indexes. Besides, a benchmark value representing a random modelling is also defined for DMCEN to be used as initial level to assess the quality of the built class-models. Furthermore, systematic comparisons have been conducted by using the degree of consistency C and the degree of discriminancy D when comparing the proposed DMCEN to the usual total efficiency (a geometric mean between sensitivity and specificity). Simulations show that, for a hundred thousand sensitivity/specificity matrices with four categories, C is almost 0.7 on average, well above the needed 0.5, and there is more than 62% probability that DMCEN detects differences when the total efficiency does not. Illustration of the application of the index is shown with an experimental data set with four categories.
Efficient Filtering for Edge Extraction under Perspective Effect

Though it is generally believed that edges should be extracted at different scales when using a linear filter, it is still difficult to determine the optimal scale for each filter. In this paper, we propose a novel approach called orientation and scale tuned difference of boxes (osDoB) to solve this problem. For certain computer vision applications, such as lane marking detection, the prior information about the concerned target can facilitate edge extraction in a top-down manner. Based on the perspective effect, we associate the scale of the edge in an image with the target size in the real world and assign orientation and scale parameters for filtering each pixel. Considering the fact that it is very time-consuming to naïvely perform filters with different orientations and scales, we further design an extended integration map technology to speed up filtering. Our method is validated on synthetic and real data. The experimental results show that assigning appropriate orientation and scale parameters for filters is effective and can be realized efficiently.
Block-Diagonal Guided Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) is effective to cluster nonlinearly separable data, which uses the constructed graph to capture the structure of inherent clusters. Nevertheless, many SNMF-based clustering approaches implicitly enforce either the sparseness constraint or the smoothness constraint with the limited supervised information in the form of cannot-link or must-link in a semi-supervised manner, which may not be quite satisfactory in many applications where sparseness and smoothness are demanded explicitly and simultaneously. In this paper, we propose a new semi-supervised SNMF-based approach termed Semi-supervised Structured SNMF-based clustering (S3NMF). The method flexibly enforces the block-diagonal structure to the similarity matrix, where the sparseness and smoothness are simultaneously considered, so that we can obtain the desirable assignment matrix by simultaneously learning similarity and assignment matrices in a constrained optimization problem. We formulate S3NMF with a semi-supervised manner and utilize the indirect constraints of sparseness and smoothness by cannot-link and must-link. To effectively solve S3NMF, we present an alternating iterative algorithm with theoretically proved convergence to seek for the solution of the optimization problem. Experiments on five benchmark data sets show better performance and satisfactory stability of the proposed method.
Evolutionary Optimization of Neuro-Symbolic Integration for Phishing URL Detection

A phishing attack is defined as a type of cybersecurity attack that uses URLs that lead to phishing sites and steals credentials and personal information. Since there is a limitation on traditional deep learning to detect phishing URLs from only the linguistic features of URLs, attempts have been made to detect the misclassified URLs by integrating security expert knowledge with deep learning. In this paper, a genetic algorithm is proposed to find combinatorial optimization of logic programmed constraints and deep learning from given 13 components, which are 12 rule-based symbol components and a neural component. The genetic algorithm explores numerous searching spaces of combinations of 12 rules with deep learning to get an optimal combination of the components. Experiments and 10-fold cross-validation with three different real-world datasets show that the proposed method outperforms the state-of-the-art performance of \(\beta \)-discrepancy integration approach by achieving a 1.47% accuracy and a 2.82% recall improvement. In addition, a post-analysis of the proposed method is performed to justify the feasibility of phishing URL detection via analyzing URLs that are misclassified from either the neural or symbolic networks.
The Relative Importance of Social Cues in Immersive Mediated Communication

Effective interpersonal communication is important to maintain relationships and build trust, empathy, and confidence. In this digital age, communication has become mediated, which filters out many of the social cues that are essential to facilitate interpersonal communication. This paper investigates the extent to which social cues influence social presence in mediated, bidirectional, multiparty interaction. Literature related to six social cues – paralinguistic cues, linguistic cues, body language, eye movements, facial expressions, and proxemic cues – was reviewed. These cues were ranked based on how relevant they are in creating a sense of social presence in mediated social communication (MSC). The most relevant cue was eye movements, followed by facial expression and linguistic cues, and lastly, body language and proxemic cues. Paralinguistic cues could not be ranked due to sparse literature in the context of MSC. Further research is required to better understand how social cues can be incorporated into MSC systems.
The Use of Counting Peaks Method for the Purpose of Smoothing Filtering Efficiency Assessment in Analysis of Electroencephalography Data

In this paper an innovative counting peaks method applied for the purpose of efficiency assessment of smoothing filtering applied on electroencephalography data was presented. The proposed method gave promising results and can be treated as an introductory method for intelligent machine-based reasoning. The analysed data were obtained from the open source data-base and recorded with the OpenBCI EEG headset. The comparison focused on Moving Average and Savitzky-Golay (SG) filter. The best results were obtained with the use of the Moving Average filtering.
Lambda Computatrix (LC)—Towards a Computational Enhanced Understanding of Production and Management

This paper describes why and how the Artificial Intelligence (AI) discipline will affect decisions and what is the difference to decisions in the past, focusing on the application to industrial production. From this analysis with a global economic systemic towards universal model, there will be given a globally emerging company structures outlook, concerning their decision situation, and way how to decide in future rightly. For this purpose, universal logical tools, that implement the lambda calculus for quantification logic may be useful. We define these as Lambda Computatrix (LC). Examples are Theorem Provers (TP) like Isabelle or Lambda-Prolog. They help to decide precisely to reach company, societal and environmental goals. The key issue of the LC is the universal approach of computation, which is connecting graph-theoretically potentially every node with each other one by a communication edge. LC hence enables increasingly intelligent communication, informationally and materially in terms of logistics and production processes.
Wisdom of the IoT Crowd: Envisioning a Smart Home-based Nutritional Intake Monitoring System

Obesity and overweight are two factors linked to various health problems that lead to death in the long run. Technological advancements have granted the chance to create smart interventions. These interventions could be operated by the Internet of Things (IoT) that connects different smart home and wearable devices, providing a large pool of data. In this work, we use IoT with different technologies to present an exemplary nutrition monitoring intake system. This system integrates the input from various devices to understand the users’ behavior better and provide recommendations accordingly. Furthermore, we report on a preliminary evaluation through semi-structured interviews with six participants. Their feedback highlights the system’s opportunities and challenges.
WeakCounter: Acceleration-based Repetition Counting of Actions with Weakly Supervised Learning

This study presents WeakCounter, which is a weakly supervised method for the repetition counting of a human action using a body-worn inertial sensor. WeakCounter is composed of two novel components: i) an attention-based network that can be trained on a weak label, which is defined to specify only the number of repetitions of an action included in an input data segment in this study, and ii) label diversification that enhances training of the network.
DetectX—Adversarial Input Detection Using Current Signatures in Memristive XBar Arrays

Adversarial input detection has emerged as a prominent technique to harden Deep Neural Networks(DNNs) against adversarial attacks. Most prior works use neural network-based detectors or complex statistical analysis for adversarial detection. These approaches are computationally intensive and vulnerable to adversarial attacks. To this end, we propose DetectX - a hardware friendly adversarial detection mechanism using hardware signatures like Sum of column Currents (SoI) in memristive crossbars (XBar). We show that adversarial inputs have higher SoI compared to clean inputs. However, the difference is too small for reliable adversarial detection. Hence, we propose a dual-phase training methodology: Phase1 training is geared towards increasing the separation between clean and adversarial SoIs; Phase2 training improves the overall robustness against different strengths of adversarial attacks. For hardware-based adversarial detection, we implement the DetectX module using 32nm CMOS circuits and integrate it with a Neurosim-like analog crossbar architecture. We perform hardware evaluation of the Neurosim+DetectX system on the Neurosim platform using datasets-CIFAR10(VGG8), CIFAR100(VGG16) and TinyImagenet(ResNet18). Our experiments show that DetectX is 10x-25x more energy efficient and immune to dynamic adversarial attacks compared to previous state-of-the-art works. Moreover, we achieve high detection performance (ROC-AUC > 0.95) for strong white-box and black-box attacks. The code has been released at https://github.com/Intelligent-Computing-Lab-Yale/DetectX
A Framework for Studying Coordinated Behaviour Applied to the 2019 Philippine Midterm Elections

This paper covers a hybrid framework for studying coordinated social media behaviour. The study focuses on social media activity during the 2019 Philippine National and Local Elections. With the use of social media information obtained from the CrowdTangle platform, the research is able to extract necessary post detail information that can be used to determine coordinated behaviour. The study tags posts are coordinated if they have shared media content between five or more posts within a 1-minute period. This should point to a high degree of coordination. The results are then visualized using the Fruchterman–Reingold algorithm using the Networkx library to see the various clusters. This gives us a reasonable amount of data to further explore and act upon. This framework was able to extract a number of Facebook accounts that showed a high degree of coordinated behaviour. These accounts refer to media that are no longer available which is not characteristic of reputable content.
Linguistic complexity in teachers' assessment of German essays in high stakes testing

In national educational standards, linguistic complexity is one of the core aspects characterizing the demands of reading material and student writing quality. In Germany, teachers are supposed to consider linguistic complexity in their assessment of language arts Abitur examinations, the high stakes test qualifying for higher education admission in Germany. In the present study, we investigated if and how teachers consider linguistic complexity in their assessment of writing quality. To systematically identify a range of linguistic complexity aspects, we conducted computational linguistic analyses of student essays (N = 344) written during Abitur examinations in the subject German. Experienced teachers (N = 33) then regraded a subsample of the essays (n = 16) and rated their linguistic complexity. We used mixed-effects models to study the relationships between computational measures of linguistic complexity, teachers’ assessment of writing quality (language, content, and overall grade), and their linguistic complexity rating. Results confirm that teachers’ complexity rating and their assessment of the language aspect of writing quality is related to the computed linguistic complexity features, primarily measures of syntactic complexity and language use (word frequency measures). But findings also show that teachers have difficulties in separating linguistic complexity from aspects of content and language accuracy.
A Modified Echo State Network for Time Independent Image Classification

Image classification is typically performed with highly trained feed-forward machine learning algorithms like deep neural networks and support vector machines. The image can be treated as a time-series input when applied to the network multiple times, opening the way for recurrent neural networks to perform tasks like image classification, semantic segmentation and auto-encoding. With this approach, ultra-fast training, network optimization, and short-term memory effects allows for dynamic, low-volume datasets to be quickly learned without heavy image pre-processing or feature extraction; the main limitation being that input images need labeled output images for training, as is also true of most standard approaches. In this work, the MNIST handwritten digit dataset is used as a benchmark to evaluate metrics of a modified Echo State Network for static image classification. The image array is passed through a noise filter multiple times as the Echo State Network converges to a classification. This highly dynamic approach easily adapts to sequential image (video) tasks like object tracking and is effective with small datasets. Classification rates reach 95.3% with sample size of 10000 handwritten digits and training time of approximately 5 minutes. Progression of this research enables discrete image and time-series classification under a single algorithm, with low computing power and memory requirements.
Forward-Secure Revocable Identity-Based Encryption

For identity-based encryption (IBE), if a user’s private key is compromised, the security of his/her ciphertexts will fail completely. Revocation capability provides an effective way to mitigate above harm, so that the adversary cannot access to future ciphertexts anymore. However, current revocable IBE schemes do not provide any means to guarantee the security of the user’s previous ciphertexts. In this paper, we propose a new cryptographic primitive, namely forward-secure revocable identity-based encryption (FS-RIBE), to address this issue. In FS-RIBE, when the event of full exposure of the user’s current private key occurs, the forward security can guarantee that the user’s private keys prior to this remain secure, while the revocation capability further guarantees that the adversary cannot obtain any valid decryption keys for future times. We provide formal definition and security model for FS-RIBE, and give a generic construction that is secure under the security model from (Hierarchical) IBE. Finally, we show some results of instantiations from various IBE and Hierarchical IBE schemes.
Explainable Abusive Language Classification Leveraging User and Network Data

Online hate speech is a phenomenon with considerable consequences for our society. Its automatic detection using machine learning is a promising approach to contain its spread. However, classifying abusive language with a model that purely relies on text data is limited in performance due to the complexity and diversity of speech (e.g., irony, sarcasm). Moreover, studies have shown that a significant amount of hate on social media platforms stems from online hate communities. Therefore, we develop an abusive language detection model leveraging user and network data to improve the classification performance. We integrate the explainable AI framework SHAP (SHapley Additive exPlanations) to alleviate the general issue of missing transparency associated with deep learning models, allowing us to assess the model’s vulnerability toward bias and systematic discrimination reliably. Furthermore, we evaluate our multimodel architecture on three datasets in two languages (i.e., English and German). Our results show that user-specific timeline and network data can improve the classification, while the additional explanations resulting from SHAP make the predictions of the model interpretable to humans.
Benchmarking 50-Photon Gaussian Boson Sampling on the Sunway TaihuLight

Boson sampling is expected to be an important milestone that will demonstrate quantum computational advantage (or quantum supremacy). This work establishes the benchmarking of Gaussian boson sampling (GBS) with threshold detection based on the Sunway TaihuLight supercomputer. To achieve the best performance and provide a competitive scenario for future quantum computing studies, the selected simulation algorithm is fully optimized based on a set of innovative approaches, including a parallel framework with almost perfect load balance and an instruction-level optimizing scheme based on a shortest-path-based instruction scheduling. In addition, data precision is carefully processed by an integer-instruction-based and multiple-precision fixed-point implementation, including 128- and 256-bit precison mode, which can be appropriately selected based on an adaptive precision optimizing scheme. Based on these methods, a highly efficient parallel quantum sampling algorithm is designed. The largest run enables us to obtain one Torontonian function of a <inline-formula><tex-math notation="LaTeX">$100\times 100$</tex-math></inline-formula> submatrix from 50-photon GBS within 20 hours in 128-bit precision and 2 days in 256-bit precision. To our knowledge, this was the largest quantum computing simulation based on Boson Sampling by using modern supercomputers.
DisCERN: Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods

Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of "actionable features". We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN outperformed DiCE by minimising both the number of feature changes and the amount of change necessary to create good counterfactual explanations.