Reviewerly/exHarmony huggingface dataset:
Smart University: Digital Development Projects Based on Big Data

A Smart University (SmU) consists of a number of traditional and smart components. Undoubtedly, it has the distinctive properties of a Smart University, which are discussed in detail in V.L. Uskov and his colleagues’ works. A Smart University is constantly increasing the level of its smartness through the improvement, development and implementation of Smart Component (SmC) projects. In the process of Smart University development, big data has been accumulated. So, the management processes are characterized by the active use of analytics tools. It’s primarily done by smart analytics for work with this data. A Smart University uses the tools of intelligent systems, artificial intelligence technologies, neural networks and other Industry 4.0 technologies. The study deals with the issues of improving the management of Smart University projects through the information infrastructure development of project management. It is shown that the process of making managerial decisions in a Smart University should be built on approaches and technologies based on big data. The article also represents the conceptual vision of a projects’ formation portfolio for the development of Smart Components in a Smart University. It is shown the algorithm for using digital analytics in making management decisions based on big data. The originality of the study is based on the development of approaches to assessing the level of big data use in project management. A set of qualitative indicators has also been developed. It characterizes the level of Big Data technologies use in a management system of Smart University projects. The proposed developments can be applied to manage the university digital transformation and the transition to the concept of smart education. Expert methods were used for modeling. The proposed tools have been tested in the activities of Russian universities during the pandemic.
Summary of the Bento Packaging Activity Recognition Challenge

Human activity recognition (HAR) has a great impact on human-robot collaboration (HRC), especially in industrial works. However, it is difficult to find industrial activity data. With a goal of making the interactions between humans and robots more straightforward, we organized Bento Packaging Activity Recognition Challenge as a part of The 3rd International Conference on Activity and Behavior Computing. Here, the term Bento refers to a single-serving lunch box originated in Japan. We provided ten Bento packing activities data. The activities are performed by four subjects with a moving conveyor belt. In this work, we analyze and summarize the approaches of submission of the challenge. The challenge started on June 1st, 2021, and continued until August 25th, 2021. The participant teams used the given dataset to predict the ten activities, and they were evaluated using accuracy. The winning team used an ensemble model and achieved around 64% accuracy on testing data. To further improve the accuracy of the testing data, models particularly designed for small data with larger intra-class similarity could help.
Distributed (Correlation) Samplers: How to Remove a Trusted Dealer in One Round

Structured random strings (SRSs) and correlated randomness are important for many cryptographic protocols. In settings where interaction is expensive, it is desirable to obtain such randomness in as few rounds of communication as possible; ideally, simply by exchanging one reusable round of messages which can be considered public keys. In this paper, we describe how to generate any SRS or correlated randomness in such a single round of communication, using, among other things, indistinguishability obfuscation. We introduce what we call a distributed sampler, which enables $$n$$ parties to sample a single public value (SRS) from any distribution. We construct a semi-malicious distributed sampler in the plain model, and use it to build a semi-malicious public-key PCF (Boyle et al., FOCS 2020) in the plain model. A public-key PCF can be thought of as a distributed correlation sampler; instead of producing a public SRS, it gives each party a private random value (where the values satisfy some correlation). We introduce a general technique called an anti-rusher which compiles any one-round protocol with semi-malicious security without inputs to a similar one-round protocol with active security by making use of a programmable random oracle. This gets us actively secure distributed samplers and public-key PCFs in the random oracle model. Finally, we explore some tradeoffs. Our first PCF construction is limited to reverse-sampleable correlations (where the random outputs of honest parties must be simulatable given the random outputs of corrupt parties); we additionally show a different construction without this limitation, but which does not allow parties to hold secret parameters of the correlation. We also describe how to avoid the use of a random oracle at the cost of relying on sub-exponentially secure indistinguishability obfuscation.
On the Maximal Number of Columns of a $$\varDelta $$-modular Matrix

We study the maximal number of pairwise distinct columns in a $$\varDelta $$ -modular integer matrix with m rows. Recent results by Lee et al. provide an asymptotically tight upper bound of $$\mathcal {O}(m^2)$$ for fixed $$\varDelta $$ . We complement this and obtain an upper bound of the form $$\mathcal {O}(\varDelta )$$ for fixed m, and with the implied constant depending polynomially on m.
Sequence Recognition of Indoor Tennis Actions Using Transfer Learning and Long Short-Term Memory

Recognizing tennis actions during a practice session can be widely used as the coaching assistant. Accurate classification of actions is tricky, with a massive similarity among different actions. Hybrid deep neural networks composed of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) such as Long Short-term Memory (LSTM) are widely employed while dealing with spatial and temporal features. We leverage transfer learning as the spatial feature extractor, allowing weights extracted by training over a massive dataset. Transfer learning from three pre–trained models such as InceptionResNetV2, ResNet152V2, and Xception are utilized in this work. Two approaches are exercised to determine the best one: (a) developing a single hybrid CNN–LSTM model and (b) passing extracted CNN features to a single LSTM-based model. Experimental results prove the effectiveness of the latter approach for recognizing indoor tennis actions. The publicly available THETIS dataset is employed for all the evaluations and approaches, which contains 12 different tennis actions performed in indoor practice sessions. The Xception–based model outperforms other models by attaining 75% accuracy.
Enhanced Scientific Workflow Scheduling in Cloud System

In today’s technological world, cloud has taken a long stride towards success for providing maximum throughput as well as high qualitative services to its consumers. Yet, approaches for maximizing the utilization of cloud resources is at peak demand. In the world of ‘pay-as-per-need’ and ‘pay-as-per-use’, consumer’s satisfaction depends on several factors. Each cloud service provider focuses on maximum utilization with minimum consumption of cloud resources. In this paper, a framework has been proposed which is using a better approach for scheduling a workflow in cloud system. In complete process, while scheduling workflow tasks, deadline constraint is important parameter. Also total execution time, total execution cost and response time are important parameters which require to be minimized.
The impact of innovation on customer satisfaction in the commercial banks: Business performance as a mediating variable

Innovation is an important variable, and it has received less attention. The purpose of this study is to examine the impact of innovation on customer satisfaction. The study also examines the business performance as a mediator between innovation and customer satisfaction. The study collected data using a questionnaire. A total of 387 responses were collected and analyzed using AMOS. The findings show that innovation positively affected customer satisfaction and business performance. Business performance affected customer satisfaction. Business performance also mediated the effect of innovation on customer satisfaction. Decision makers in the banking industry are suggested to increase the level of innovation to improve the business performance and customer satisfaction.
Knowledge Lock: Overcoming Catastrophic Forgetting in Federated Learning

Federated Learning (FL) aims to train machine learning models by decentralized data without direct data sharing. Nevertheless, the heterogeneity of data across FL participants has significantly prevented federated models from competitive performance. In this paper, we consider this issue as the consequence of knowledge forgetting, since the local update process in FL may result in catastrophic forgetting of the knowledge learned from other participants. Motivated by the recent advance in incremental learning techniques, we address this issue by overcoming the sever knowledge forgetting caused by data isolation. We propose a novel method called FedKL (Federated Learning with Knowledge Lock), in which knowledge distillation techniques are employed to maintain the previously learned knowledge. Our extensive experiment results demonstrate that FedKL achieves superior performance than prior methods, with over 3.4% and 3.5% accuracy improvements on CIFAR-10 and CIFAR-100 respectively, compared with the popular FL algorithm FedAvg. Furthermore, we also explore the benefits of introducing shared exemplars (a fraction of local data) to FedKL. In the experiments, we select and share 10 samples per class for FedKL and the baseline methods. As a result, FedKL obtains 2.56% accuracy increase on CIFAR-10, instead of the marginal improvements on prior methods (less than 1.5%
A Hybrid CNN Real-Time Object Identification and Classification Approach for Autonomous Vehicles

Autonomous Vehicles (AVs) are progressively attaining attention globally. It consists of clear and transparent potential future in the upcoming generations and it is estimated that it will intensely differ the transportation as we know it by today. An automated driving system is a complicated and complex association of several constituents. The identification and classification of any object and traffic road sign is an important job for any autonomous vehicle at the same time achieving the 100% accuracy is also difficult and complex task. In this paper, we have put forward the proposal for hybrid CNN deep learning model for real-time identification and classification of objects and simulation-based results proved that the modified version CNN model performed very well with accuracy more than 95%.
EpiGRAM: Practical Garbled RAM

Garbled RAM (GRAM) is a powerful technique introduced by Lu and Ostrovsky that equips Garbled Circuit (GC) with a sublinear cost RAM without adding rounds of interaction. While multiple GRAM constructions are known, none are suitable for practice, due to costs that have high constants and poor scaling. We present the first GRAM suitable for practice. For computational security parameter $$\kappa $$ and for a size-n RAM that stores blocks of size $$w = \varOmega (\log ^2n)$$ bits, our GRAM incurs amortized $$O(w \cdot \log ^2 n \cdot \kappa )$$ communication and computation per access. We evaluate the concrete cost of our GRAM; our approach outperforms trivial linear-scan-based RAM for as few as 512 128-bit elements.
Lossless Data Compression Method Using Deep Learning

A method of lossless data compression on various data formats using predefined algorithms and machine learning is claimed in this chapter. The proposed system makes use of predefined algorithms and a neural network for encoding and decoding. As it uses machine learning (sequence to sequence) model, it makes the system adaptive and the compression ratio keeps improving over time with more and more data. Use of machine learning ensures that different file formats can be compressed within the same system. Compressed data obtained after the implementation of predefined algorithms will be used as output labels for the ML model, and the original data will be the input labels for the ML model. For decompression, the compressed data is used as input, and the original data is used as output labels. The system can be deployed as a cloud-based application in which the user can input data and store it in a cloud database. The proposed system achieves 74% accuracy on a batch comprising 50,000 training data. Higher accuracy can be achieved by training the model on more data.
A Trustworthy, Reliable, and Lightweight Privacy and Data Integrity Approach for the Internet of Things

Data integrity and authenticity are among the key challenges faced by the interacting devices of Internet of Things (IoT). The resource-constrained nature of sensor-embedded devices makes it even more difficult to design lightweight security schemes for these networks. In view of limited resources of the IoT devices, this article proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Initially, a trusted authority generates and assigns identities (IDs) and mask them to servers and clients, also known as member devices, in an offline phase. These IDs are utilized to prevent possible infiltration of the adversary device(s). Next, every device ensures the authenticity of requesting devices using a sophisticated challenge, which is encrypted using a 128-b secret key, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{i}$</tex-math></inline-formula> . Each device expects a reply from the intended destination device for resolving the encrypted challenge within the defined timeframe, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$i.e., \bigtriangleup T$</tex-math></inline-formula> . Moreover, authenticity of the requesting device is verified through the stored IDs, which are shared in the offline phase. Simulation results have verified the exceptional performance of the proposed authentication scheme against field proven approaches in terms of computational and communication costs.
Face Expression Recognition Using SVM and KNN Classifier with HOG Features

For communication between humans and machines, just like a human-to-human interaction, machines should recognize human facial expressions. A lot of research has been carried out on facial expression recognition in the last two decades, but it is still challenging due to variation in face image parameters like pose variation, different illumination, alignment, occlusion, etc. The recognition accuracy of expressions depends on features extracted and the classifier used. Different features extraction methods reflect different features of a face image. The histogram of the Gradients method is presented in this paper to extract features reflecting edge directions. The experiment was performed on the JAFEE database. The features are computed by dividing face images into cells, and it detects the shape and appearance of a local object by computing the local and edge direction. Support Vector Machine and K-Nearest-Neighbour algorithms are applied for the classification of features. The performance of classifiers is compared with the recognition accuracy and processing time required. It is observed that the recognition accuracy of the K-Nearest Neighbour algorithm is more than the Support Vector Machine and the processing time required is less.
Parallel $$\mathcal{O}(log(n))$$ Computation of the Adjacency of Connected Components

Connected Component Labeling (CCL) is a fundamental task in pattern recognition and image processing algorithms. It groups the pixels into regions, such that adjacent pixels have the same label while pixels belonging to distinct regions have different labels. The common linear-time raster scan CCL techniques have a complexity of $$\mathcal{O}(image-size)$$ in a 2D binary image. To speed up the procedure of the CCL, the paper proposes a new irregular graph pyramid. To construct this pyramid, we use a new formalism [1] that introduces an order of the pixels in the base grid to detect the redundant edges through the hierarchical structure. These redundant edges, unlike the usual methods of constructing the irregular pyramid, are removed before contracting the edges. This not only simplifies the construction processes but may decrease memory consumption by approximately half. To perform the CCL task efficiently the proposed parallel algorithm reduces the complexity to $$\mathcal{O}(log(n))$$ where the n is the diameter of the largest connected component in the image. In addition, using an efficient combinatorial structure the topological properties of the connected components including adjacency of CCs, multi-boundaries and inclusions are preserved. Finally, the mathematical proofs provide fully parallel implementations and lead to efficient results in comparison with the state-of-the-art.
Neuronlike spiking dynamics in asymmetrically driven dissipative nonlinear photonic dimers

We demonstrate neuronlike spiking dynamics in a dissipative Bose-Hubbard dimer model, describing two coupled nonlinear Kerr cavities with a finite photon lifetime and under coherent pumping of just one cavity. Spiking dynamics appear due to the excitable nature of the system. In this context, excitable excursions in the phase space correspond to spikes in the temporal evolution of the cavity intensities, i.e., of the number of photons. In our case, excitability is mediated by the destruction of an oscillatory state in a global homoclinic bifurcation. In this type of excitability (known as type I) the period of the oscillatory state diverges when approaching the bifurcation. Beyond this point, the system exhibits excitable dynamics under the application of suitable perturbations. We have also characterized the effect that additive Gaussian noise has on the spiking dynamics, showing that the system undergoes a coherence resonance for a given value of the noise strength.
A hybrid approach to accelerate the classification accuracy of cervical cancer data with class imbalance problems

Cervical cancer is one of the most ubiquitous gynaecological disorders worldwide. While most of the research conducted over the past decade has focused on this, diagnosis widely suffers due to imbalanced class distribution problem over a time. Therefore, oversampling approach combined with unsupervised algorithm is proposed in this research work. Several existing oversampling techniques are used to address the class distribution problem, but noise among the data increases gradually and no new knowledge is added, which leads to overfitting. Hence, proposed technique combines Principal component Analysis (PCA) with Majority weighted Minority oversampling technique (MWMOTE). Also, proposed framework is tested by using two complete imbalanced data sets. For performance evaluation, evaluation metrics like recall, precision and F-measure are utilised. As a result, our proposed novel automated framework produces a better accuracy compared to existing approaches.
Het2Hom: Representation of Heterogeneous Attributes into Homogeneous Concept Spaces for Categorical-and-Numerical-Attribute Data Clustering

Data sets composed of a mixture of categorical and numerical attributes (also called mixed data hereinafter) are common in real-world cluster analysis. However, insightful analysis of such data under an unsupervised scenario using clustering is extremely challenging because the information provided by the two different types of attributes is heterogeneous, being at different concept hierarchies. That is, the values of a categorical attribute represent a set of different concepts (e.g., professor, lawyer, and doctor of the attribute "occupation"), while the values of a numerical attribute describe the tendencies toward two different concepts (e.g., low and high of the attribute "income"). To appropriately use such heterogeneous information in clustering, this paper therefore proposes a novel attribute representation learning method called Het2Hom, which first converts the heterogeneous attributes into a homogeneous form, and then learns attribute representations and data partitions on such a homogeneous basis. Het2Hom features low time complexity and intuitive interpretability. Extensive experiments show that Het2Hom outperforms the state-of-the-art counterparts.
Declaration-based Prompt Tuning for Visual Question Answering

In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e.g., masked language modeling (MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream task (e.g., VQA) via a brand-new objective function, e.g., answer prediction. However, the inconsistency of the objective forms not only severely limits the generalization of pre-trained VL models to downstream tasks, but also requires a large amount of labeled data for fine-tuning. To alleviate the problem, we propose an innovative VL fine-tuning paradigm (named Declaration-based Prompt Tuning, abbreviated as DPT), which fine-tunes the model for downstream VQA using the pre-training objectives, boosting the effective adaptation of pre-trained models to the downstream task. Specifically, DPT reformulates the VQA task via (1) textual adaptation, which converts the given questions into declarative sentence form for prompt-tuning, and (2) task adaptation, which optimizes the objective function of VQA problem in the manner of pre-training phase. Experimental results on GQA dataset show that DPT outperforms the fine-tuned counterpart by a large margin regarding accuracy in both fully-supervised (2.68%) and zero-shot/fewshot (over 31%) settings. All the data and codes will be available to facilitate future research.
Evolutionary Approach to Security Games with Signaling

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings. In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs).