Reviewerly/exHarmony huggingface dataset:
A New Network Forensic Investigation Process Model

The procedure of the identification and analysis of electronic data is the digital forensics framework. The purpose of the procedure is to retain evidence in its primordial form by gathering, identifying and validating digital information in order to recreate events of the past. For the use of data in a court of law, the context is most appropriate. The evidence aspect of digital forensics requires strict requirements to be followed in court for cross-examination. One major pitfall in the digital forensic analysis is the possible admissibility of collected evidence in the court of law. Digital forensic analysis must comply with the quality of evidence and its admissibility to trial successfully. In this work, we proposed a new network forensic investigation process model. We precisely establish a methodology of investigation for the computer network.
SOUL: Scala Oversampling and Undersampling Library for imbalance classification

The improvements in technology and computation have promoted a global adoption of Data Science. It is devoted to extracting significant knowledge from high amounts of information by means of the application of Artificial Intelligence and Machine Learning tools. Among the different tasks within Data Science, classification is probably the most widespread overall. Focusing on the classification scenario, we often face some datasets in which the number of instances for one of the classes is much lower than that of the remaining ones. This issue is known as the imbalanced classification problem, and it is mainly related to the need for boosting the recognition of the minority class examples. In spite of a large number of solutions that were proposed in the specialized literature to address imbalanced classification, there is a lack of open-source software that compiles the most relevant ones in an easy-to-use and scalable way. In this paper, we present a novel software approach named as SOUL, which stands for Scala Oversampling and Undersampling Library for imbalanced classification. The main capabilities of this new library include a large number of different data preprocessing techniques, efficient execution of these approaches, and a graphical environment to contrast the output for the different preprocessing solutions.
Comparison of the difficulty overcoming of RFID electronic access control systems and overcoming of pin tumbler locks

At present, modern technologies are increasingly coming to the fore, including the area of entry security. Electronic access control systems that use RFID to identify people have become popular. However, as the name suggests, electricity is needed to operate them. There may be a power failure problem, causing the system to malfunction and entry is not allowed. Therefore, classic pin tumbler locks are used as a backup for electrical systems. Therefore, it is important to keep in mind the correct choice of pin tumbler locks, as these can be easily overcome, unlike the electronic access control system with RFID. The article deals with foreign research aimed at overcoming the electronic access control system with RFID and completed tests at the Faculty of Security Engineering aimed at overcoming pin tumbler locks.
Service Sector Professionals’ Perspective on Robots Doing Their Job in the Future

After a long history of industrial automation, robots are entering service fields at an accelerating rate due to the recent technological advances in robotics. Understanding the acceptance and applicability of robots is essential for successful introduction, desired benefits, and well-managed transformation of the labor market. In this work, we investigated whether service sector professionals consider robots applicable to their field compared to professionals from other sectors. We collected survey data from Finnish (N = 1817) and U.S. participants (N = 1740) and analyzed them using ordinary least squares regression. Results showed that Finnish and U.S. participants from the service sector disclosed a less positive attitude toward robots’ suitability to their own occupational field compared to participants from other fields. Younger age, technological expertise, prior experience interacting with robots at work, and positive attitude toward robots were associated with higher perceived robot suitability. Perceived robot suitability was also found to mediate the relationship between occupational sector and positive interaction attitudes. The results indicate that robots entering into service industries evokes some resistance and doubt in professionals of these fields. Increasing technological knowledge and prior experience with robots at work are central factors when introducing robots in a socially sustainable way.
Performance Comparison of Various Feature Extraction Methods for Object Recognition on Caltech-101 Image Dataset

Object recognition system helps to find the label of the object in an image. The identification of the object depends on the features extracted from the image. Features play a very important role in the object recognition system. The more relevant features an object has, the better the recognition system will be. Object recognition system mainly works in two major phases—feature extraction and image classification. Features may be the color, shape, texture, or some other information of the object. There are various types of feature extraction methods used in object recognition. These methods are classified as handcrafted feature extraction methods and deep learning feature extraction methods. This article contains a comprehensive study of various popular feature extraction methods used in object recognition system. Various handcrafted methods used in the paper are scale invariant feature transformation (SIFT), speeded-up robust feature (SURF), oriented FAST and rotated BRIEF (ORB), Shi-Tomasi corner detector, and Haralick texture descriptor. The deep learning feature extraction methods used in the paper are ResNet50, Xception, and VGG19. In this article, a comparative study of various popular feature extraction methods is also presented for object recognition using five multi-class classification methods—Gaussian Naïve Bayes, k-NN, decision tree, random forest, and XGBoosting classifier. The analysis of the performance is conducted in terms of recognition accuracy, precision, F1-score, area under curve, false positive rate, root mean square error, and CPU elapsed time. The experimental results are evaluated on a standard benchmark image dataset Caltech-101 which comprises 8677 images grouped in 101 classes.
Forms, Methods and Means of Developing Creative Activity of University Students

The article discusses the importance of developing the creative activity of university students. The problem of the development of students’ creative activity is substantiated as an urgent social and pedagogical problem. The role of the concepts “creativity” and “activity” as concepts carrying an equal semantic load in the concept of “creative activity” is argued. The characteristics of the creative activity of a university student are given. The forms, methods and means of development of students’ creative activity are considered. The effectiveness of using the design studio as a form of developing the creative activity of university students is proved.
Semantic query expansion method based on pay-as-yougo fashion for graph model

Abstract The search requirements of users are usually vague and unclear, and the traditional keyword query method is difficult to obtain satisfactory query results. Therefore, the semantic query expansion technology uses semantic information to modify and extend the initial search requirements of users, to obtain more comprehensive and accurate query results. However, the construction cost of the extended query graph is high, and the existing research is difficult to adjust and optimize the user’s query demand in time, resulting in a poor query effect. To solve the above problems, this paper proposes a semantic expansion method based on pay-as-you-go fashion for graph model: Firstly, the initial query graph is constructed according to the user’s search requirements, and the semantic similarities between the search requirements and knowledge bases are calculated; Then, the extended sets of attribute-values and edges are generate by sorting the similarity value in descending order; Finally, all or part of the elements in the extended set are combined to generate the extended query graph. This method can dynamically adjust and optimize the initial query based on the relevant semantic information in time, and improve the query efficiency and effect.
Covert and Secure Communication Using Multiple Invisible Stego Images by Split-Logical Algorithm

The use of the web is developing for surreptitious and non-surreptitious communication with the expansion of brilliant innovations. In this circumstance, there is a need to improve the security level of the covert information. Our motive is to propose a unique algorithm by using steganography along with cryptography which provides a better solution for secret communication and improves security with more levels. In this paper, the two-level stationary wavelet transform technique is applied to cover image to acquire HH2 sub-band alongside the remaining sub-bands. The data are implanted into the HH2 sub-band of the cover image by utilizing the random pixel embedded strategy to accomplish the stego image. To improve the security of the stego image, the surreptitious text data are changed into cipher form with a novel cryptography algorithm that incorporates unique data conversion and gray-code techniques. The resultant stego image undergoes to split-logical algorithm to create numerous imperceptible stego images. The main purpose of imperceptible stego images is to provide better security to covert information and also it increases the payload capacity. The concept of generating imperceptible stego images is, each of them will be treated as a stego image for hackers. But original stego image is reconstructed by combining all generated imperceptible stego images. These imperceptible stego images are ready to oppose the diverse steganalysis assaults like visual, noise, rotational assaults, and so forth.
Resolution-Aware Deep Multi-View Camera Systems

Recognizing 3D objects with multiple views is an important problem in computer vision. However, multi view object recognition can be challenging for networked embedded intelligent systems (IoT devices) as they have data transmission limitation as well as computational resource constraint. In this work, we design an enhanced multi-view distributed recognition system which deploys a view importance estimator to transmit data with different resolutions. Moreover, a multi-view learning-based super-resolution enhancer is used at the back-end to compensate for the performance degradation caused by information loss from resolution reduction. The extensive experiments on the benchmark dataset demonstrate that the designed resolution-aware multi-view system can decrease the endpoint's communication energy by a factor of 5× while sustaining accuracy. Further experiments on the enhanced multi-view recognition system show that accuracy increment can be achieved with minimum effect on the computational cost of back-end system.
Learning Union of Integer Hypercubes with Queries

Abstract We study the problem of learning a finite union of integer (axis-aligned) hypercubes over the d -dimensional integer lattice, i.e., whose edges are parallel to the coordinate axes. This is a natural generalization of the classic problem in the computational learning theory of learning rectangles. We provide a learning algorithm with access to a minimally adequate teacher (i.e. membership and equivalence oracles) that solves this problem in polynomial-time, for any fixed dimension d . Over a non-fixed dimension, the problem subsumes the problem of learning DNF boolean formulas, a central open problem in the field. We have also provided extensions to handle infinite hypercubes in the union, as well as showing how subset queries could improve the performance of the learning algorithm in practice. Our problem has a natural application to the problem of monadic decomposition of quantifier-free integer linear arithmetic formulas, which has been actively studied in recent years. In particular, a finite union of integer hypercubes correspond to a finite disjunction of monadic predicates over integer linear arithmetic (without modulo constraints). Our experiments suggest that our learning algorithms substantially outperform the existing algorithms.
Critical Analysis and Evaluation of Current Digital Healthcare Solutions

Digitalization of health, healthcare and healthcare delivery unequivocal have positive consequences contributing to health system goals, but they also might have some negative ones which have to be avoided or mitigated. The scope of this paper is to introduce the necessity of the assessment of Health Technology Assessment (HTA) and to identify the most appropriate and important criteria needed for the evaluation and comparison of functional digital healthcare solutions. It aims to outline consistent criteria against which digital health technologies and services can be evaluated during the prototyping phase, but also during long-term operation.
Rigorous Performance Analysis of Hyper-heuristics

We provide an overview of the state-of-the-art in the time complexity analysis of selection hyper-heuristics for combinatorial optimisation. These algorithms aim at automating the optimisation process by using a set of low-level heuristics and a machine learning mechanism to decide online which heuristic is the most appropriate one at the current stage. We mainly focus on work that establishes the performance gains that simple and sophisticated hyper-heuristics can achieve compared to the low-level heuristics applied in isolation, and that compares the expected runtime of the hyper-heuristics against the best possible one achievable with the given set of low-level heuristics. We cover examples where mixing heuristics is necessary, as well as others where learning from the past performance of the applied heuristics is crucial for the algorithms to be efficient. We emphasise that simple and sophisticated hyper-heuristics from the literature can achieve optimal performance for some standard unimodal and multimodal benchmark functions. Problem characteristics are highlighted for which more or less machine learning sophistication is required, and insights are provided of how a rigorous theory can guide the design of more efficient hyper-heuristics.
QoA4ML - A Framework for Supporting Contracts in Machine Learning Services

Important service-level constraints in machine learning (ML) services must be communicated and agreed among relevant stakeholders. Due to the lack of studies and support, it is unclear which and how ML-specific attributes and constraints should be specified and assured in service contracts for ML services. This paper examines service contracts in the three stakeholders engagement model of ML services. We identify key ML-specific attributes that should be specified and monitored for the ML service provider, ML consumer and ML infrastructure provider. Based on that, we propose QoA4ML (Quality of Analytics for ML) as a framework to support ML-specific service contracts. QoA4ML includes an ML-specific service contract specification, monitoring utilities and a contract observability service. To illustrate the usefulness of QoA4ML, we present real-world examples for contract terms and policies, monitoring and contract evaluation with dynamic ML services in predictive maintenance.
Training Deep Neural Networks in 8-bit Fixed Point with Dynamic Shared Exponent Management

The increase in complexity and depth of deep neural networks (DNNs) has created a strong need to improve computing performance. Quantization methods for training DNNs can effectively improve computation throughput and energy efficiency of hardware platforms. We have developed an 8-bit quantization training method representing the weight, activation, and gradient tensors in an 8-bit fixed point data format. The shared exponent for each tensor is managed dynamically on the basis of the distribution of the tensor elements calculated in the previous training phase, not in the current training phase, which improves computation throughput. This method provides up to 3.7 -times computation throughput compared with FP32 computation without accuracy degradation.
Explore the New Path of Piano Teaching in University under the Background of Internet Information

with the steady development of modern society, all walks of life under the background of Internet information have realized the corresponding change and innovation, reflects the updated economic development model, in the economy, politics, education, science and technology and other links have reflected a good effect. As the trend of The Times, in the Internet economy under the background of piano teaching in colleges and universities are also put forward higher request, need to be combined with the actual situation to find a new path to improve the piano piano teaching aspects of the teaching mode, create a good teaching atmosphere, perfect the teaching system, based on the current time background to reflect more have effect of innovation education concept, And actively explore the network and information teaching methods to create a more advanced new normal of education, so as to make the effect of piano teaching more obvious, guide students, so that students have a deeper understanding of piano knowledge.
Sample Regenerating Particle Filter Combined With Unequal Weight Ensemble Kalman Filter for Nonlinear Systems

We present an approach which combines the sample regenerating particle filter (SRGPF) and unequal weight ensemble Kalman filter (UwEnKF) to obtain a more accurate forecast for nonlinear dynamic systems. Ensemble Kalman filter assumes that the model errors and observation errors are Gaussian distributed. Particle filter has demonstrated its ability in solving nonlinear and non-Gaussian problems. The main difficulty for the particle filter is the curse of dimensionality, a very large number of particles is needed. We adopt the idea of the unequal weight ensemble Kalman filter to define a proposal density for the particle filter. In order to keep the diversity of particles, we do not apply resampling as the traditional particle filter does, instead we regenerate new samples based on a posterior distribution. The performance of the combined sample regenerating particle filter and unequal weight ensemble Kalman filter algorithm is evaluated using the Lorenz 63 model, the results show that the presented approach obtains a more accurate forecast than the ensemble Kalman filter and weighted ensemble Kalman filter under Gaussian noise with dense observations. It still performs well in case of sparse observations though more particles are required. Furthermore, for non-Gaussian noise, with an adequate number of particles, the performance of the approach is much better than the ensemble Kalman filter and more robust to noise with nonzero bias.
Strong Feature Fusion Networks for Person Re-Identification

With the development of deep learning, the performance of person Re-Identification (Re-ID) has been significantly improved.It's still a challenging task due to the challenges coming from large variations on persons such as occlusion, background clutter, pose, illumination and detection failure, etc.To retrieve true pedestrians, robust feature expression is significant.Instead of using external cues, this paper takes advantage of robust alignment features and metric learning.First, from the aspect of feature extraction, there were three contributions.(i) Embeded a spatial transformer network in the network architecture, which is called ResNet_STN in this paper, which can solve the problem of local spatial semantic feature inconsistency, accurately express the main characteristics of the target, and achieve pedestrian alignment.(ii) Designed a strong feature fusion network based on the aligned local features, which is named a Strong Feature Fusion Module (SFFM) and can make full use of the connection between semantic information to extract detailed features of images.Then, from the aspect of metric loss function, one contribution was put forward.(iii) Proposed a Ranking Matrix (RM) method to select local 第 2 期 刘玉杰, 等: 基于增强特征融合网络的行人重识别方法 233triplet samples and compute local triplet loss.We combined a regularized classification loss to train the network to unleash the discrimination ability of the learned strong representations of this network architecture.Finally, the proposed method with the existing re-ranking algorithm to further improves Rank-1 and mAP retrieval accuracy.Experimental results on Market-1501 dataset demonstrate the effectiveness of our proposed method.
Tell Me When Users Leave: Predicting Users’ Abandonment of A Task-Oriented Chatbot Service using Explainable Deep Learning

Task-oriented chatbots have been widely used by businesses to support users in accomplishing predefined tasks. Yet, conversation breakdowns could result in users abandoning the chatbot service. Detecting or early predicting signals of users' chatbot abandonment could help businesses know when to provide assistance. Based on an annotated conversation log involving 1,837 users, we built two models, one end-to-end model built on top of pre-trained BERT models, and the other being an attention-based deep learning model trained from 102 different handcrafted features derived from annotated messages. The former achieved an AUROC of 90%. The latter explainable model, despite the extra effort of adding annotations, achieved a higher AUROC of 95.7% and provided additional insights into important features indicative of service abandonment, such as input types, error types, and presence of users' information-request within recently exchanged messages.
Informatization as a Condition for the Development of Academic Disciplines by Students of a Higher Educational Institution

The article enlightens the role of information technology in the educational process of a modern higher educational institution. The authors of the work talk about the need to apply new methods and models of communication in their work. The authors investigate the forms of communication of participants in the educational process used in distance learning, suggesting that information resource portals are a promising area of educational communications. The purpose of the study is to analyze effective forms of e-learning. The authors note the prospects for the development of e-learning. The conclusions made by the authors of the article make it possible to assert the need to apply new educational models and technologies in the activities of teachers of higher educational institutions.
Review for the Solar Radiation Forecasting Methods Based on Machine Learning Approaches

Abstract Predictions of solar potential for these systems’ production are important, whether they ensure sound activity or the perfect control of an energy discharge heading to the solar system. It is important to base the prediction on solar irradiance before predicting solar systems performance. The measurement of solar radiation elements is a very significant criterion for applications of solar energy. Several globalized solar radiation prediction modes can be done in the two major categories: cloud imagery with physical models and machine learning techniques are correlated. In this paper, the methods used to predict solar radiation are explained with machine learning algorithms.