Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network Based Criminal Detection

Various recent advancements in deep learning models have greatly boosted the performance of semantic pattern recognition using images. Various state estimation of an individual like emotional state and other certain character features or traits can be estimated from the facial images. With this motivation, in this work we are attempting to infer criminal tendency or (crime prediction/detection) from facial images by using the learning capabilities of various deep learning architectures. More precisely two type of deep learning models we have used in this study: standard convolutional neural network(CNN) architecture and pre-trained CNN architectures, namely VGG-16, VGG-19, and Incep-tionV3. We have done a performance comparative analysis among these models for efficiently capturing criminal traits from a human face. The efficacy of the above deep learning models was evaluated on a public database, National Institute of Standards and Technology (NIST). To avoid any discrepancies, we have only used male images in this work. It was found that VGG CNN models are best performing models, especially in a limited data scenario producing the classification accuracy of 99.5% in identifying criminal faces.
On the Computational Viability of Quantum Optimization for PMU Placement

Using optimal phasor measurement unit placement as a prototypical problem, we assess the computational viability of the current generation D-Wave Systems 2000Q quantum annealer for power systems design problems. We reformulate minimum dominating set for the annealer hardware, solve the reformulation for a standard set of IEEE test systems, and benchmark solution quality and time to solution against the CPLEX optimizer and simulated annealing. For some problem instances the 2000Q outpaces CPLEX. For instances where the 2000Q underperforms with respect to CPLEX and simulated annealing, we suggest hardware improvements for the next generation of quantum annealers.
Differential Fault Attack for the Iterative Operation of AES-192 Key Expansion

Since the release of the AES encryption standard, it has been subjected to various DFA attacks. In the past time, AES-128 has been extensively studied. Therefore, later researchers turned to more research on AES variants. This paper proposes a random single-byte fault model based on AES-192. Added one byte error in the first column of the tenth round of key expansion. We show how to use four pairs of fault and correct ciphertext to crack the entire AES-192 key without a exhaustive search. We implement this attack on Modelsim simulation software. It takes less than 4 minutes to crack the key. Considering the number of error ciphertexts, the depth caused by the fault, and whether exhaustive search is required, the author's attack is the most effective DFA compared to the existing scheme in the AES-192 key plan.
Digital Mapping of Medieval Cemeteries

It has become almost standard practice that archaeological research on cemeteries is published in a similar fashion, specifically when primary sources supplement the data presented. Aside from the interpretative part, a catalog of all graves, buried individuals, and finds is published along with a map of the site and graphical depictions of the various entities. This is mostly structured within a four-level hierarchy beginning with the cemetery, the contained graves, the burials from each grave, and the finds associated with the burial. Today, even though many publications and their catalogs are based on or derived from digital data and published as open access, the outcome is often printed text such as a pdf file. Digital data that is properly structured and can be used out of the box for further analyses is rarely available. The presented article discusses how to digitize data on burials and how to provide them to the public in sustainable and comprehensible ways. Within previous and ongoing projects, the author and his team have developed a database system (OpenAtlas) that is used for the data acquisition of archaeological and anthropological research data that also maps information directly to the CIDOC CRM. Temporal and spatial fuzziness are dealt with following various concepts such as GeoJSON-T. For providing the data as Linked Open Data, the “linked places” format is used and an API provides a JSON-LD representation of each entity. Due to the “standard” approach implemented when publishing cemeteries, the data acquisition is mostly achieved by manually recording the published information in the database. In the following projects, data from several hundred Early Medieval Austrian and Czech burial sites with several thousand graves and finds have been digitized. To publicize the information, an online web application (https://thanados.net) has been developed to present and disseminate this data.
IAAS: IoT-Based Automatic Attendance System with Photo Face Recognition in Smart Campus

The existing attendance checking system uses student identification (ID) cards or mobile applications. If the attendance is processed by an automatic attendance checking system, the attendance checking process can be more convenient for both students and professors than manual attendance checking. For getting these advantages, this paper proposes an IoT-based Automatic Attendance System (IAAS). IAAS is an attendance checking system using a face recognition technology. The image data of students are collected by a capturing device (e.g., smartphone and tablet PC) and processed by the face recognition system. This system checks who are attending the class and send their attendance to an attendance database system through an email notification.
SemGloVe: Semantic Co-Occurrences for GloVe From BERT

GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. However, word pairs in the matrices are extracted from a predefined local context window, which might lead to limited word pairs and potentially semantic irrelevant word pairs. In this paper, we propose <i>SemGloVe</i>, which distills <i>semantic co-occurrences</i> from BERT into static GloVe word embeddings. Particularly, we propose two models to extract co-occurrence statistics based on either the masked language model or the multi-head attention weights of BERT. Our methods can extract word pairs limited by the local window assumption, and can define the co-occurrence weights by directly considering the semantic distance between word pairs. Experiments on several word similarity datasets and external tasks show that SemGloVe can outperform GloVe.
Recognizing Running Movement Changes with Quaternions on a Sports Watch

Sports watches are popular amongst runners but limited in terms of sensor locations (i.e., one location at the wrist). Thus, apps on the watch cannot directly sense movement changes in arbitrary body locations. This, in turn, severely limits training support services, contextual awareness and seamless interaction mechanisms. Our approach addresses this gap and connects the watch with two strategically placed inertial measurement units (IMUs) via bluetooth low energy (BLE). Our prototypical app for Wear OS receives orientation (quaternion) data and matches the sensors to the arm or leg segments using a flexible and simple procedure. We collected data from eight runners and used support vector machines (SVMs) to recognize different movements. Findings from our evaluation with different parameters indicate feasible recognition and low false-positive rates for two to three different movements, for both placements. Our approach can thus help to improve applications that support training and thus contributes to developing motion capture for personal use; it also enables movement-based interaction while running.
Object Detection, Classification and Counting for Analysis of Visual Events

The detection, classification and counting of the object (vehicle) and persons (pedestrians) in the visual event are the crucial tasks of event analysis. Two object classification systems are reported here: (i) the Fast Region-proposal Convolutional Neural Network with hyper-parameter optimization and (ii) “You Only Look Once” (YOLO). The YOLO object detection method shows improvement in the accuracy of categorizing the vehicles and persons in the video input. High-performance Video Transmission (HpVT) protocol, visual IoT device is applied for grabbing the smooth video streams in real-time. Person (pedestrian)and six classes of vehicle types in a relevant environment are detected. These types are Motorbikes, Bicycle, Bus, Truck, Van and Saloon. New object dataset collected and organized person and vehicle images from several internet websites as 5000 images. The feature extraction and segmentation are performed from the images obtained by video streaming camera. The proposed object detection, classification and counting system will provide to the application of smart tracking, smart trip planning, smart surveillance, smart tourism and pedestrian safety monitoring.
Graphon Pooling in Graph Neural Networks

Graph neural networks (GNNs) have been used effectively in different applications involving the processing of signals on irregular structures modeled by graphs. Relying on the use of shift-invariant graph filters, GNNs extend the operation of convolution to graphs. However, the operations of pooling and sampling are still not clearly defined and the approaches proposed in the literature either modify the graph structure in a way that does not preserve its spectral properties, or require defining a policy for selecting which nodes to keep. In this work, we propose a new strategy for pooling and sampling on GNNs using graphons which preserves the spectral properties of the graph. To do so, we consider the graph layers in a GNN as elements of a sequence of graphs that converge to a graphon. In this way we have no ambiguity in the node labeling when mapping signals from one layer to the other and a spectral representation that is consistent throughout the layers. We evaluate this strategy in a synthetic and a real-world numerical experiment where we show that graphon pooling GNNs are less prone to overfitting and improve upon other pooling techniques, especially when the dimensionality reduction ratios between layers is large.
Barriers for Succinct Arguments in the Random Oracle Model

We establish barriers on the efficiency of succinct arguments in the random oracle model. We give evidence that, under standard complexity assumptions, there do not exist succinct arguments where the argument verifier makes a small number of queries to the random oracle. The new barriers follow from new insights into how probabilistic proofs play a fundamental role in constructing succinct arguments in the random oracle model. By combining these results we obtain barriers for a large class of deterministic and non-deterministic languages. For example, a succinct argument for $$\mathrm {3SAT}$$ with few verifier queries implies an IOP with good parameters, which in turn implies a fast algorithm for $$\mathrm {3SAT}$$ that contradicts the Exponential-Time Hypothesis. We additionally present results that shed light on the necessity of several features of probabilistic proofs that are typically used to construct succinct arguments, such as holography and state restoration soundness. Our results collectively provide an explanation for "why" known constructions of succinct arguments have a certain structure.
Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian

This paper is devoted to the study of methods for information extraction (entity recognition and relation classification) from scientific texts on information technology. Scientific publications provide valuable information into cutting-edge scientific advances, but efficient processing of increasing amounts of data is a time-consuming task. In this paper, several modifications of methods for the Russian language are proposed. It also includes the results of experiments comparing a keyword extraction method, vocabulary method, and some methods based on neural networks. Text collections for these tasks exist for the English language and are actively used by the scientific community, but at present, such datasets in Russian are not publicly available. In this paper, we present a corpus of scientific texts in Russian, RuSERRC. This dataset consists of 1600 unlabeled documents and 80 labeled with entities and semantic relations (6 relation types were considered). The dataset and models are available at https://github.com/iis-research-team. We hope they can be useful for research purposes and development of information extraction systems.
Blockchain Family Deed Certificate for Privacy and Data Security

The rapid development of technology has caused some systems to have changed; most of them in the Industrial Revolution 4.0 era using new methods from various aspects of people's lives. Family Deed Certificate is a family identity that contains data about arrangements, relationships, and the number of family members. A family certificate is an essential thing for every citizen to have. However, related problems that occur are still using conventional systems that cause problems such as loss of family deed, and various manipulations of identity data. Thus, from this problem emerged a solution to guarantee all data and information security using blockchain technology. Blockchain technology is a technology for recording transactions with modern technology, which can only be added but cannot be changed or replaced. Blockchain technology can support various fields such as banking, education, health, and priorities for governance. For this research, this is applied in the field of government, which is a blockchain technology family certificate, various problems in terms of a family certificate that is a copy of a lot of family member data, and editing a deed of change, is very inflexible. With the family certificate system, blockchain technology, data security can be guaranteed so that there is no data falsification and can replace any loss on the family deed. This system uses the literature method that contains and how blockchain works. The Certificate of Family Deed on the blockchain is expected to impact the digital world positively.
Conceptual Framework for Quantum Affective Computing and Its Use in Fusion of Multi-Robot Emotions

This study presents a modest attempt to interpret, formulate, and manipulate the emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes emotion information as a superposition state, whilst unitary operators are used to manipulate the transition of emotion states which are subsequently recovered via appropriate quantum measurement operations. The framework described provides essential steps towards exploiting the potency of quantum mechanics in a quantum affective computing paradigm. Further, the emotions of multi-robots in a specified communication scenario are fused using quantum entanglement, thereby reducing the number of qubits required to capture the emotion states of all the robots in the environment, and therefore fewer quantum gates are needed to transform the emotion of all or part of the robots from one state to another. In addition to the mathematical rigours expected of the proposed framework, we present a few simulation-based demonstrations to illustrate its feasibility and effectiveness. This exposition is an important step in the transition of formulations of emotional intelligence to the quantum era.
Latent based temporal optimization approach for improving the performance of collaborative filtering

Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.
Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid

A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computational kernel execution on GPUs. This work demonstrates a 10.2x speedup over traditional layer-wise model parallelism techniques using the same number of compute units.
RETRACTED: Target detection algorithm for dance moving images based on sensor and motion capture data

The effectiveness of classification based on motion capture data to identify the human skeleton dance types. The goal is based on the body joint's information is to perform the characteristic posture obtained by the ultra-high Kinect sensor, identifying for each dance. The proposed Target Detection (TD) algorithm is used to dance moving object identification based on the improved Gaussian Mixture Model (GMM). The proposed Target Detection (TD) algorithm based on a Gaussian Mixture Model is a widely used method for modeling background from a Kinect sensor moving objects. The used data set contains six folk dance sequences and their variations. Gesture recognition scheme using a plurality of time constraints, spatial information, and spatial distribution characteristics to create a training data set appropriate application. The Gaussian Mixed Model distribution background model account, the algorithm, and their frame difference can be extracted in straight lines to obtain target dance areas with less background and background photo station under motor damage conditions. Through real-time Target Detection (TD) algorithm dance moving images based on the Gaussian Mixture Model (GMM), these two algorithms effectively detect dance moving image targets.
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We solve the stabilization problem on the n-sphere in the presence of conic constraints. We use the stereographic projection to map this problem to the classical navigation problem on Rn in the presence of spherical obstacles. As a consequence, any obstacle avoidance algorithm for navigation in the Euclidean space can be used to solve the given problem on the n-sphere. We illustrate the effectiveness of the approach using the kinematics of the reduced attitude model on the 2-sphere.
bi-HPTM: An Effective Semantic Matchmaking Model for Web Service Discovery

Analyzing textual semantics in matching user query and service description is critical for Web service discovery. Existing works mostly extract the features of the description and query independently, downgrading them into word-level calculation, which can not jointly extract the accurate semantics. For this issue, this work explores a way to enable the semantic matching for the contents (including words, phrases, or sentences) in query and the sentences in service description, by mapping words and sentences into the same semantic space. Specifically, we propose an unsupervised Bayesian probabilistic model, bi-Directional Hybrid Priors Topic Model (bi-HPTM), to capture the textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible operation to build the semantic links from the queries to service descriptions. Meanwhile, the textual semantics generated by bi-HPTM is highly interpretable that help to understand the user requirements. The proposed model is examined by ProgrammableWeb. Experimental results demonstrate that bi-HPTM outperforms state-of-the-art methods for semantic service discovery on service classification and retrieval. The visualizations of the nearest-neighbored queries and descriptions show the insights of our model on capturing the latent semantics of Web services.
AI Explainability 360 Toolkit

As machine learning algorithms make inroads into our lives and society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. Moreover, these stakeholders, whether they be government regulators, affected citizens, domain experts, or developers, present different requirements for explanations. To address these needs, we introduce AI Explainability 3601, an open-source software toolkit featuring eight diverse state-of-the-art explainability methods, two evaluation metrics, and an extensible software architecture that organizes these methods according to their use in the AI modeling pipeline. Additionally, we have implemented enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, accessible versions of algorithms to guidance material to help users navigate the space of explanations along with tutorials and an interactive web demo to introduce AI explainability to practitioners. Together, our toolkit can help improve transparency of machine learning models and provides a platform to integrate new explainability techniques as they are developed.
Evaluating Concurrent Executions of Multiple Function-as-a-Service Runtimes with MicroVM

Serverless computing and public Function-as-a-Service (FaaS) systems are gaining significant attention because they help easily build a highly available system. With recent advances in micro virtual machines (microVM), the internal architecture of FaaS systems substantially changes. This paper focuses on a thorough investigation of the recent improvement in public FaaS systems concerning numerous concurrent executions. The adoption of microVM has changed the nature of FaaS, especially for runtime reservations. As a result, the performance degradation has decreased significantly compared to the previous generation FaaS, as shown in the experiment.
Spectrogram-Based Classification Of Spoken Foul Language Using Deep CNN

Excessive content of profanity in audio and video files has proven to shape one's character and behavior. Currently, conventional methods of manual detection and censorship are being used. Manual censorship method is time consuming and prone to misdetection of foul language. This paper proposed an intelligent model for foul language censorship through automated and robust detection by deep convolutional neural networks (CNNs). A dataset of foul language was collected and processed for the computation of audio spectrogram images that serve as an input to evaluate the classification of foul language. The proposed model was first tested for 2-class (Foul vs Normal) classification problem, the foul class is then further decomposed into a 10-class classification problem for exact detection of profanity. Experimental results show the viability of proposed system by demonstrating high performance of curse words classification with 1.24-2.71 Error Rate (ER) for 2-class and 5.49-8.30 F1- score. Proposed Resnet50 architecture outperforms other models in terms of accuracy, sensitivity, specificity, F1-score.