Reviewerly/exHarmony huggingface dataset:
Instagram Forensic Analysis Revisited: Does anything really vanish?

Instagram has become one of the most popular and sought-after social media to be on. People of varying ages from various backgrounds use Instagram and in order to keep things exciting for their users, Instagram frequently rolls out new and exciting updates to their applications. One such feature is the “vanish mode”, that aids people to have secure and private chats. By enabling the vanish mode, people can send messages which would disappear once the vanish mode is disabled. This feature might be of interest to criminals who find ways to clear their activity trace. Disappearing messages might provide people a sense of comfort about the privacy of their chats, but poses a challenge to the investigators to track messages and obtain digital evidence to catch criminals such as child predators, drug dealers, and cyberbullies. This research focuses on analyzing this aspect of the app to identify any artifacts that could be useful in a forensic investigation. The results presented in this paper indicate the presence of vanished messages in the application database. The research also identified inconsistencies in the databases regarding the visibility of the vanished messages. Furthermore, the manner in which user-uploaded media is stored in the database was investigated, and the findings on user searches on the explore page and the shopping tab are presented. The research work adds to the existing knowledge of work done in the digital forensics field by detecting the presence of disappearing messages to aid investigations where Instagram is being analyzed.
Outlier detection using AI: a survey

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a suspicious data point that lies at an irregular distance from a population. The definition of an outlier event, however, is subjective and depends on the application and the domain (agriculture, healthcare, wireless network, etc.). It is important to detect outlier events as carefully as possible to avoid infrastructure failures, because anomalous events can cause minor to severe damage to infrastructure. For instance, an attack on a cyber-physical system, such as a microgrid may initiate voltage or frequency instability, thereby damaging a smart inverter, which involves very expensive repairing. Unusual activities in microgrids can be mechanical faults, behavior changes in the system, human or instrument errors or a malicious attack. Accordingly, and due to its variability, outlier detection (OD) is an ever-growing research field. In this chapter, we discuss the progress of OD methods using AI techniques. For that, the fundamental concepts of each OD model are introduced via multiple categories. Broad range of OD methods are categorized into six major categories: statistical-based, distance-based, density-based, clustering-based, learning-based, and ensemble methods. For every category, we discuss recent state-of-the-art approaches, their application areas, and performances. After that, a brief discussion regarding the advantages, disadvantages, and challenges of each technique is provided with recommendations on future research directions. This survey aims to guide the reader to better understand recent progress of OD methods for the assurance of AI.
DNN pruning with principal component analysis and connection importance estimation

DNN pruning reduces memory footprint and computational work of DNN-based solutions to improve performance and energy-efficiency. An effective pruning scheme should be able to systematically remove connections and/or neurons that are unnecessary or redundant, reducing the DNN size without any loss in accuracy. In this paper we show that some of the most popular pruning schemes, such as the Near Zero Weights, require an extremely time-consuming iterative process that requires retraining the DNN many times to tune the pruning hyperparameters. Then, we propose a DNN pruning scheme based on Principal Component Analysis and relative importance of each neuron's connection (PCA+DIRIE) that automatically finds the optimized DNN in one shot without requiring hand-tuning of multiple parameters. The experimental results show the effectiveness of our method on several benchmarks. Notably, on ImageNet, PCA+DIRIE can prune up to 60% of ResNet-50 with negligible impact on accuracy.
Quantum‐resistant anonymous identity‐based encryption with trable identities

Identity-based encryption (IBE), introduced by Shamir, eliminates the need for public-key infrastructure. The sender can simply encrypt a message by using the recipient's identity (such as email or IP address) without needing to look up the public key. In particular, when ciphertexts of an IBE do not reveal recipient's identity, this scheme is known as an anonymous IBE scheme. Recently, Blazy et al. (ARES '19) analysed the trade-off between public safety and unconditional privacy in anonymous IBE and introduced a new notion that incorporates traceability into anonymous IBE, called anonymous IBE with traceable identities (AIBET). However, their construction is based on the discrete logarithm assumption, which is insecure in the quantum era. In this paper, we first formalize the consistency of tracing key of the AIBET scheme to ensure that a ciphertext cannot be traced with the use of wrong tracing keys. Subsequently, we present a generic formulation concept that can be used to transform structure-specific lattice-based anonymous IBE schemes into an AIBET. Finally, we apply this concept to Katsumata and Yamada's compact anonymous IBE scheme (Asiacrypt '16) to obtain the first quantum-resistant AIBET scheme that is adaptively secure under the ring learning with errors assumption without random oracle.
Local discrimination of generalized Bell states via commutativity

We studied the distinguishability of generalized Bell states under local operations and classical communication. We introduced the concept of maximally commutative set (MCS), subset of generalized Pauli matrices whose elements are mutually commutative and there is no other generalized Pauli matrix that is commute with all the elements of this set. We found that MCS can be considered as a detector for local distinguishability of set $\mathcal{S}$ of generalized Bell states. In fact, we got an efficient criterion. That is, if the difference set of $\mathcal{S}$ is disjoint with or completely contain in some MCS, then the set $\mathcal{S}$ is locally distinguishable. Furthermore, we gave a useful characterization of MCS for arbitrary dimension, which provides great convenience for detecting the local discrimination of generalized Bell states. Our method can be generalized to more general settings which contains lattice qudit basis. Results in [Phys. Rev. Lett. \textbf{92}, 177905 (2004)], [Phys. Rev. A \textbf{92}, 042320 (2015)] and a recent work [arXiv: 2109.07390] can be deduced as special cases of our result.
IS CAUSAL REASONING HARDER THAN PROBABILISTIC REASONING?

Abstract Many tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages. Despite several senses in which causal reasoning is indeed more complex—both expressively and inferentially—we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to purely probabilistic problems. Thus there is no jump in computational complexity. Along the way we answer several open problems concerning the complexity of well-known probability logics, in particular demonstrating the ${\exists \mathbb {R}}$ -completeness of a polynomial probability calculus, as well as a seemingly much simpler system, the logic of comparative conditional probability.
The Classification of Clifford Gates over Qubits

We examine the following problem: given a collection of Clifford gates, describe the set of unitaries generated by circuits composed of those gates. Specifically, we allow the standard circuit operations of composition and tensor product, as well as ancillary workspace qubits as long as they start and end in states uncorrelated with the input, which rule out common "magic state injection" techniques that make Clifford circuits universal. We show that there are exactly 57 classes of Clifford unitaries and present a full classification characterizing the gate sets which generate them. This is the first attempt at a quantum extension of the classification of reversible classical gates introduced by Aaronson et al., another part of an ambitious program to classify all quantum gate sets. The classification uses, at its center, a reinterpretation of the tableau representation of Clifford gates to give circuit decompositions, from which elementary generators can easily be extracted. The 57 different classes are generated in this way, 30 of which arise from the single-qubit subgroups of the Clifford group. At a high level, the remaining classes are arranged according to the bases they preserve. For instance, the CNOT gate preserves the X and Z bases because it maps X-basis elements to X-basis elements and Z-basis elements to Z-basis elements. The remaining classes are characterized by more subtle tableau invariants; for instance, the T_4 and phase gate generate a proper subclass of Z-preserving gates.
RANet: A relation-aware network for two-view correspondence learning

Finding true correspondences from a set of putative correspondences is a basic task in computer vision. Recent advances have demonstrated that Multi Layer Perceptrons (MLPs) can handle the unordered correspondence learning problem by training a deep classifier. However, MLPs ignore the relationship between correspondences, such as geometric information, spatial information and inlier distribution information, which will lead to difficulties in modeling complex global context. To solve this issue, we propose a Relation-Aware Network (RANet), by capturing rich global information from channel and spatial dimensions, to establish reliable correspondences for feature matching. Specifically, we firstly present a Global Context Attention block by a two-branch attention structure to cooperate with MLPs for contextual information extraction. Then, we design a Relation-Aware Filter block by further exploring the channel and spatial relationship information with different connection manners. Finally, we combine two blocks to obtain an enhanced basic block with strong feature representation capacity due to the acquisition of global contextual information. Our experiments have been conducted over both indoor and outdoor datasets on the tasks of outlier removal and camera pose estimation, which demonstrate the superiority of our network that achieves the best performance compared with the state-of-the-art approaches.
Crime Prediction and Forecasting using Voting Classifier

The creation of policing strategies and the implementation of crime prevention and control programmes rely heavily on crime prediction. In India, the number of cognizable offences are gradually rising. These offences include those that are covered by the Indian Penal Code as well as those covered by a variety of Special and Local Laws. In this study, various machine learning algorithms are used to analyze data related to crime which will give the behaviors in crime over an area, that might be helpful in crime prevention. Classification Algorithms such as KNN, Random Forest, Adaptive Boosting Classifier, Gradient Boosting Classifier, and Extra Trees Classifier were tested individually on a crime dataset based in South Bangalore. Later, these five algorithms were combined to achieve better results using Voting Classifier Technique in order to balance out their individual weaknesses.
Reliability of Trust Management Systems in Cloud Computing

Cloud computing is an innovation that conveys administrations like programming, stage, and framework over the web. This computing structure is wide spread and dynamic, which chips away at the compensation per-utilize model and supports virtualization. Distributed computing is expanding quickly among purchasers and has many organizations that offer types of assistance through the web. It gives an adaptable and on-request administration yet at the same time has different security dangers. Its dynamic nature makes it tweaked according to client and supplier’s necessities, subsequently making it an outstanding benefit of distributed computing. However, then again, this additionally makes trust issues and or issues like security, protection, personality, and legitimacy. In this way, the huge test in the cloud climate is selecting a perfect organization. For this, the trust component assumes a critical part, in view of the assessment of QoS and Feedback rating. Nonetheless, different difficulties are as yet present in the trust the board framework for observing and assessing the QoS. This paper talks about the current obstructions present in the trust framework. The objective of this paper is to audit the available trust models. The issues like insufficient trust between the supplier and client have made issues in information sharing likewise tended to here. Besides, it lays the limits and their enhancements to help specialists who mean to investigate this point.
Contextual Convolutional Neural Networks

We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost compared to the standard convolution. CoConv is inspired by neuroscience studies indicating that (i) neurons, even from the primary visual cortex (V1 area), are involved in detection of contextual cues and that (ii) the activity of a visual neuron can be influenced by the stimuli placed entirely outside of its theoretical receptive field. On the one hand, we integrate CoConv in the widely-used residual networks and show improved recognition performance over baselines on the core tasks and benchmarks for visual recognition, namely image classification on the ImageNet data set and object detection on the MS COCO data set. On the other hand, we introduce CoConv in the generator of a state-of-the-art Generative Adversarial Network, showing improved generative results on CIFAR-10 and CelebA. Our code is available at https://github.com/iduta/coconv.
Alternative to third-party cookies

Many popular websites give users the ability to sign up for their services, which requires personally identifiable information (PII). However, these websites embed third-party tracking and advertising resources, and as a consequence, the authentication flow can intentionally or unintentionally leak PII to these services. Since a user can be identified with PII, trackers can use it for tracking purposes, leading to further privacy leaks when cross-site, cross-browser, and cross-device tracking occur.
Heterogeneous Systolic Array Architecture for Compact CNNs Hardware Accelerators

Compact convolutional neural networks have become a hot research topic. However, we find that the systolic array accelerators are extremely inefficient in dealing with compact models, especially when processing depthwise convolutional layers in the neural networks. To make systolic arrays more efficient for compact convolutional neural networks, we propose the heterogeneous systolic array (HeSA) architecture. It introduces heterogeneous processing elements that support multiple modes of dataflow, which can further exploit the reuse data chance of depthwise convolutional layers and without changing the scale or structure of the nave systolic array. By increasing the utilization rate of processing elements in the array, the HeSA improves the performance, throughput, and energy efficiency compared to the standard baseline. In addition, we design the flexible buffer structure for the communication between the computing array and external buffer. Through flexible routing, the HeSA can achieve a large-scale array design with maintaining high processing elements utilization rate and low communication costs. Based on our evaluation with typical workloads, the HeSA improves the utilization rate of the computing resource in depthwise convolutional layers by 4.5 - 11.2 and acquires 1.6 - 3.1 total performance speedup compared to the standard systolic array architecture. In the large-scale array design, the HeSA can reduce the data traffic by 40% while maintaining the same performance as the scaling-out method. By improving the on-chip data reuse chance and reducing data traffic, the HeSA saves over 20% in energy consumption. Meanwhile, the area of the HeSA is basically unchanged compared to the baseline due to its simple design.
A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.
Predicting PV Power Generation using SVM Regression

With increased global demands for renewable energy, the prediction of photovoltaic (PV) power generation becomes more necessary for efficient power scheduling and energy usage. Since PV generation is heavily dependent on weather conditions, using historical weather data and machine learning techniques to predict solar power generation is a popular approach to solving the problem. In this paper, we focus on the Support Vector Machine (SVM) to create a regression model capable of forecasting PV generation one hour ahead with two cases: fair and cloudy weather. The SVM model is trained by minimizing the one-step prediction (15-minute ahead) error, and then its hyperparameters are tuned using GridSearchCV and a validation dataset. The final performance of the resulting model is evaluated on the testing dataset for one-hour ahead prediction.
Research on Intelligent Vehicle Path Planning Method Based on Improved RRT Algorithm

This paper studies the problem of intelligent vehicle path planning based on improved RRT algorithm. Firstly, the extended target bias strategy is used to make the random point sampling of RRT algorithm biased to the target point. Secondly, based on the vehicle kinematics model, the random point sampling area is limited to the feasible fan-shaped range in front of the vehicle that is greater than the step size d. Thirdly, in the process of random point expansion, considering the actual size of vehicles and obstacles, the separation axis law is used to detect the collision risk of vehicles and surrounding obstacles in real time. Finally, based on the Matlab simulation platform, different RRT algorithms are used for simulation and analysis in different environments, and the improved RRT path planning algorithm is verified on the ROS intelligent vehicle platform. The experimental results show that compared with the basic RRT algorithm, the improved RRT algorithm reduces the average number of sampling points by 51. 8 percent, and the search time is reduced by 41. 7 percent in the right-angle turn road environment. In the s-shaped road environment, the average number of tree nodes is reduced by 54.3 percent, and the search time is reduced by 42 percent. In the maze-type road environment, the average number of tree nodes is reduced by 70 percent, and the search time is reduced by 52.2 percent. In the dynamic obstacle environment, the real-time performance of dynamic obstacle avoidance is good and the path is smooth. This paper considers the improved RRT algorithm of vehicle kinematics and geometric size constraints. The search efficiency and smoothness are improved, and the generated path meets the requirements of intelligent vehicle path tracking.
Chinese Position Segmentation Based on ALBERT- BiGRU-CRF Model

Aiming at the problems of poor parsing effect and poor generalization ability in Chinese position segmentation using current neural network models, this paper proposes a Chinese position segmentation method based on ALBERT-BiGRU-CRF model. The method first uses the ALBERT pre-training model to pre-train the Chinese location information to obtain the context information in all layers, enhance the semantic representation ability of the Chinese location information, and then extract the feature information of the vector through the BiLSTM model, and finally decode it through the CRF model to obtain the global Optimal labeling sequence. Experimental results show that on the basis of different numbers and regions of Chinese location information data sets, the ALBERT-BiGRU-CRF model has better word segmentation accuracy and F1 value on all test sets than the current commonly used neural network models, and the highest can be achieved. 93.91% and 93.96%. Using the ALBERT-BiGRU-CRF model to segment Chinese location information not only effectively improves the accuracy of Chinese location information analysis and polysemous word analysis, but also has better generalization capabilities.
An Approach Toward Numerical Data Augmentation and Regression Modeling Using Polynomial-Kernel-Based SVR

The paper is devoted to solving the actual problem of processing short datasets. The authors developed a new numerical data augmentation procedure that is based on the usage of polynomial-kernel-based SVR. The generalization property of the SVR method has increased during regression analysis of short datasets by using the following procedure. In addition, the paper presents the author's prediction procedure based on the extended dataset. This procedure provides a significant increase of the accuracy of the SVR with a polynomial kernel. The simulation of the method is based on the usage of a real short set of medical data. The optimal parameters of the method are selected; the efficiency of its work is compared with the existing methods of this class. The lowest errors of the developed method in comparison with the existing ones are established. Prospects for further research are presented in terms of improving the proposed approach and the new areas of its practical application.