Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Incremental Data Capturing in Wireless Network Planning based on Log Mining

Incremental data capturing(IDC) is an important measure to improve the efficiency of parameter distribution and configuration in wireless networks to transmit the incremental part of the network planning configuration parameters. In order to solve the problems of large volume of network full-parameter files and inconvenience of wireless transmission, this paper compares and analyzes Oracle's existing incremental data capturing technology, proposes a log analysis-based incremental data capturing method and a redundancy elimination method which is suitable for wireless heterogeneous network planning. The proposed methods simplify the volume of network configuration parameter files, reduces the amount of network configuration parameters, also reduces the maintenance burden of network planners, which are suitable for realizing the distribution and update of wireless communication network planning configuration parameters, and also have certain reference value for incremental data synchronization in other wireless system's distributed databases.
Performance of various SMT Solvers in Cryptanalysis

Satisfiability (SAT) or Satisfiability Modulo Theories (SMT) are important verification tools and attracting attentions several domains and recently in cryptanalysis of ciphers. Technology of SAT solver has extraordinary advancement in the most recent decade, and the new innovation called SMT solver is also emerged as an impact of them.In this paper, we presented the applications of various SMT solvers in block cipher cryptanalysis. We formulated an algorithm for algebraic attack of block ciphers. In this attack, we represent encryption procedure of block cipher in terms of boolean representations and convert these into a suitable format (i.e. SMT-LIB and Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> py) accepted by respective SMT solvers. Our attack requires a few plain text-cipher text pairs to retrieve the master secret key. We apply the proposed procedure to demonstrate the cryptanalysis of SIMON upto certain rounds. Finally, we solve these boolean formulas using various serial and parallel SMT solvers and compared their performances.
Application Research of Big Data Technology in Engineering Cost

Abstract In view of the frequent use of information technology in practical engineering cost projects in recent years, the engineering cost industry slowly accumulated a lot of information data. The development of engineering cost industry has entered a new era of data era. The disadvantages are that there is no unified standard for project cost information data, information interconnection is difficult, the frequency of updating project cost data is slow, and the value of information has not been fully reflected. Therefore, in order to solve these problems, the most advanced information technology is applied to the construction of the project cost platform by relying on large number technology, and the project cost information data is collected, sorted out and analyzed, so that these data are full of its deserved value. The process of building a platform of the need to establish a unified standard, engineering cost data shall be carried out in accordance with the standard information collection, sorting and processing, for project cost related units to query, data model, is applied to analyze the information data at the same time, to invest in the project cost, engineering material price analysis, all staff to provide decision support for engineering cost industry. This paper USES questionnaire survey to obtain data, and analyzes the data to find that 50% of the cost information obtained comes from professional journals, and only 19% of engineering cost workers can use the Internet.
A Comparative Study on the Impact of One-Way and Two-Way Matching Strategies on the Evolution of Cloud Manufacturing Ecosystems

The supply-demand matching (SDM) strategy is an important part of the transaction mechanism design of cloud manufacturing (CMfg) platforms, which has a significant impact on the evolution trend of cloud manufacturing ecosystems (CMEs).To help CMfg platform operators choose the appropriate SDM strategy, first, the evolution process of the CME was qualitatively analyzed, and the evolution process was divided into three stages: the germination period, the growth period and the stable period.Then, three types of market agent behavior models, service demanders (SDs), service providers (SPs) and platform operators (POs), were established, and a multiagent behavior simulation experiment was conducted.Finally, the evolution of CMEs with one-way and two-way SDM strategies for POs was compared and analyzed from three aspects: the overall utilization rate of SPs, the diversity of CMEs and the total output of CMEs.Simulation experiments show that, compared with the CME that adopts the one-way SDM strategy, the CME that adopts the two-way SDM strategy is approximately 33% faster to reach ecological balance, the overall utilization rate is approximately 98.7% higher, the diversity is approximately 6% higher, and the total output is approximately 91% higher.The two-way SDM strategy that comprehensively considers the respective preferences of SDs and SPs is more conducive to the healthy development of CMEs.
Staleness and Stagglers in Distibuted Deep Image Analytics

Deep learning for image analytics is widely used in many real-world applications. Due to the rapid growth in data and model size there is a need to distribute the models in multiple nodes. Distributed computing of the model helps to increase the scalability, training time and its cost effectiveness. But the distribution can lead to longer computation times in case of stale nodes. The computational time of the distributed nodes are affected by many factors like latency caused dur to communication, network connectivity, resource sharing, computational power etc. The main problem faced in case of distribution is the staleness among the worker nodes. Effect of stragglers cannot be completely avoided in distributed clusters. The failures in storage, disks, imbalanced workloads, resources sharing etc. are the main cause of stragglers. Stragglers can cause longer computation time and reduce the performance of the model. The different methods used to address this issue is described in the paper in detail. The open research problems in this field are also highlighted.
Controllable Gradient Item Retrieval

In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain attribute, given a reference item and a modification text. For example, after a customer saw a white dress, she/he wants to buy a similar one but more floral on it. The extent of ”more floral” is subjective, thus prompting one floral dress is hard to satisfy the customer’s needs. A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence. Existing item retrieval methods mainly focus on whether the target items appear at the top of the retrieved sequence, but ignore the demand for retrieving a sequence of products with gradual change on a certain attribute. To deal with this problem, we propose a weakly-supervised method that can learn a disentangled item representation from user-item interaction data and ground the semantic meaning of attributes to dimensions of the item representation. Our method takes a reference item and a modification as a query. During inference, we start from the reference item and ”walk” along the direction of the modification in the item representation space to retrieve a sequence of items in a gradient manner. We demonstrate our proposed method can achieve disentanglement through weak supervision. Besides, we empirically show that an item sequence retrieved by our method is gradually changed on an indicated attribute and, in the item retrieval task, our method outperforms existing approaches on three different datasets.
An Automated System to Predict Popular Cybersecurity News Using Document Embeddings

The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial ... | Find, read and cite all the research you need on Tech Science Press
Learning Network-Based Multi-Modal Mobile User Interface Embeddings

Rich multi-modal information - text, code, images, categorical and numerical data - co-exist in the user interface (UI) design of mobile applications. UI designs are composed of UI entities supporting different functions which together enable the application. To support effective search and recommendation applications over mobile UIs, we need to be able to learn UI representations that integrate latent semantics. In this paper, we propose a novel unsupervised model - Multi-modal Attention-based Attributed Network Embedding (MAAN) model. MAAN is designed to capture both multi-modal and structural network information. Based on the encoder-decoder framework, MAAN aims to learn UI representations that allow UI design reconstruction. The generated embedding can be applied to a variety of tasks: predicting UI elements associated with UI screens, inferring missing UI screen and element attributes, predicting UI user ratings, and retrieving UIs. Extensive experiments, including user evaluations, conducted on two datasets from RICO, a rich real-world mobile UI repository, demonstrates that MAAN out-performs other state-of-the-art models.
A Novel 3D Object Watermarking Technique Using Hash Key Cryptography

For a variety of purposes, a watermark inserts an invisible signal within the data (image, video and audio), which involves embedding copyright and caption details. First, the proposed research work outlines the implementation of 3D watermarking in this paper and then provide the basic 3D watermarking criteria. Fundamental attacks and their solutions to the watermarking of 3D geometry are then reviewed. The previous work concerned hiding the data, not distorting the signal or altering the data in order to delete a watermark. Watermarks, on the other hand, are widely used to allow use of power in signal alteration, copyright management and modification prevention. In this article a novel algorithm for image watermarking using hash key cryptography technique is proposed. The algorithm for inserting and extracting the watermark is proposed. Hash key is used in encryption and decryption of the watermark object. It is making watermarking technique more secure.
Towards Certifying Trustworthy Machine Learning Systems

Machine Learning (ML) is increasingly deployed in complex application domains replacing human-decision making. While ML has been surprisingly successful, there are fundamental concerns in wide scale deployment without humans in the loop. A critical question is the trustworthiness of such ML systems. Although there is research towards making ML systems more trustworthy, there remain many challenges. In this position paper, we discuss the challenges and limitations of current proposals. We focus on a more adversarial approach, borrowing ideas from certification of security software with the Common Criteria. While it is unclear how to get strong trustworthy assurances for ML systems, we believe this approach can further increase the level of trust.
Teaching the Scrum Master Role using Professional Agile Coaches and Communities of Practice

As Scrum currently is the most popular Agile method, most universities teaching software development teach it. A Scrum Team consists of a Scrum Master, a Product Owner, and developers. The Scrum Master role is vital in the team. However, looking at the industry, companies have difficulties both in hiring good Scrum Masters, and in convincing their developers or other personnel to take that role. Agile trainers do offer short Scrum Master training courses. However, we lack information on how to train Scrum Masters in university education. In this paper, we report how we arranged a one semester long Scrum Master training course. During the course, students worked as Scrum Masters under the guidance of professional agile coaches and collaborated in a Scrum Master Community of Practice. We collected data by conducting 13 post-course semi-structured interviews with Scrum Masters, and by analyzing the learning diaries of 37 students from three course instances. Team-specific Agile coaches supported their Scrum Master first by example, then by observing the Scrum Masters in action and providing feedback and ideas, and finally by helping the students to reflect. Students found the coaches extremely useful, and wished they had utilized them more. The Scrum Master community of practice worked best when students self-organized, shared practices and tools, and provided peer support when facing problems. Finally, students non-technical backgrounds, as well as female students performed better as Scrum Masters than computer science and male students.
Prospect of Voiceprint Recognition Based on Deep Learning

Abstract As a biometric technology, voiceprint recognition is as unique as human fingerprint and pupil, so voiceprint recognition has great potential in practical application. Aiming at the feature extraction method and voiceprint recognition method, this paper first introduces the principle of voiceprint recognition, traditional MFCC, LPDA and other feature parameters and their performance; secondly, the traditional voiceprint recognition methods such as GMM and GMM-SVM are introduced, as well as their shortcomings and improvement schemes. Aiming at the shortcomings of voiceprint recognition system based on traditional algorithms in accuracy, robustness and real-time, this paper introduces the role of deep learning neural network in different stages of voiceprint recognition, and introduces the characteristics and network structure of some typical algorithms based on deep learning. Finally, according to the advantages and disadvantages of deep learning in voiceprint recognition, the development prospect and challenges of voiceprint recognition technology are analysed.
Transdisciplinary Principles of Narrative Discourse as a Basis for the Use of Big Data Communicative Properties

The results of modern scientific reseach are scientific products that represent a large number of passive knowledge systems in the form of unstructured and poorly structured information arrays. The transdisciplinarity of these knowledge systems as a metacategory that takes into account the hyperproperties of big data (Big Data) is discusses. Reflection that implements the principles of integration, systematization and ensures the integrity of their behaviour; recursion that implements the category of repeatability of forms of their operational display during activation; reduction, based on which the principle of decomposition of these forms is realized are defined.The directions of cognitive interpretation of these hyperproperties concerning interactive integrated use of scientific production narrative as a passive network knowledge system are defined. Cognitive interpretation is implemented in the structural analysis of information resources, providing interaction with information resources and definition of mechanisms for identifying criteria for rational choice of appropriate contexts that determine the conditions for the integration of knowledge systems. This approach to solving the problem of interactive integrated use of knowledge systems, as a large narrative of the content of scientific results, provides the implementation of a component architecture of cognitive services of its integrated interactive use.
ACKEM: Automatic Classification, Using KNN Based Ensemble Modeling

Over the last couple of years, Deep Learning (DL) methods for objects and features classification have been shown to overcome previous state-of-the-art classification techniques in multiple areas, such as image classification and speech recognition. In our previous paper MESRS – Model Ensemble Speech Recognition System, we have described a unique speech recognition system for automatic classification of voice commands. The work described in this paper, presents a novel method for classification that continues our previous work by extending the system-supported input to the image space, not just the audio space. Aside from supporting multiple input types, this paper also describes an automated method of models ensemble based on the K-Nearest Neighbors algorithm. The automatic method of ensemble selection was added in order to improve the system’s running times and achieve the highest possible accuracy results. The work in this paper shows that applying dynamic input-based classification over multiple architectures can significantly improve the final classification results. Since different models with different architectures could achieve different results on different inputs, the task of producing the best results could be achieved by selecting the best fitted model for the given input. This method was tested over multiple datasets including Chest X Ray Pneumonia Dataset, Malaria Cells Dataset, Road Potholes Dataset, and the Voice Commands Dataset which also served us in our previous work. This paper proves that our method works and has the ability to improve the classification quality on top of all of the above datasets. Our results were compared with previous results obtained by similar works on top of the above datasets and a significant improvement was shown for all of the tested datasets. These findings prove the effectiveness of our method and motivate us to develop it further, in order to achieve even better results in future work.
Research on 3D Object Detection Method Based on Convolutional Attention Mechanism

Abstract In 3D object detection, the illumination and occlusion of the input point cloud data lead to inaccurate feature extraction, and the maximum pooling method destroys the information structure of the point cloud, leading to the problem of weak local feature expression. This paper proposes a 3D object detection method based on Convolutional Attention Mechanism (CAM). CAM first adds an attention mechanism to the first and last layers of the traditional feature extraction network structure, then fuses the feature information of different layers, and finally performs normalization operations. Experimental results show that this method has achieved better results on the KITTI and SUN-RGB data sets compared with mainstream algorithms DoBEM, MV3D, DSS, COG, 2D-driven, and FPNet. The mAP index has increased by 0.6%-12.5%. While CAM realizes the fusion of local and global information, it significantly improves the accuracy of object detection in illuminated and occluded scenes.
Cyber Threat in Public Sector: Modeling an Incident Response Framework

There is a high probability that a cyber-attack would cause havoc, probably impacting a company's resources and assets, its clients, time and brand value. An incident response framework aims to control this damage and recover as smoothly as possible. As most organizations have encountered cyber-attacks at some point of their corporate life cycle, a formidable and a well-developed defense is the most excellent way to protect a company. As the types of information breaches upsurge, the absence of an customized response plan could lead to extensive interruption and recovery periods and high cost. In today's era of digital and mobile use in a globally integrated society, it always pays to have an incident response framework for an organization. Therefore, this paper explores the evolution and design modeling of the incident response framework.
Challenges of Trajectory Planning with Integrator Models on Curved Roads

In the context of trajectory planning for autonomous vehicles, a widely used vehicle model relies on linear integrator dynamics. We consider planning with this model type, with a focus on the requirement to account for curved road topologies. As our analysis reveals, this generally gives rise to non-convex, coupled constraints on the vehicle's states and inputs, which impedes computationally efficient planning. We propose a method to resolve this issue by modification of the non-convex constraints. This modification is based on inner approximations of sub-level sets of nonlinear functions, which are obtained by quantifier elimination. The efficacy of the method is demonstrated in two example scenarios.
Unsupervised Page Area Detection Approach for the Unconstrained Chronic Handwritten Modi Document Images

The sparse old age historical Modi documents are archived in government and non-government sectors from long time. These documents are pasted on card-sheet like material for preservation. In the processes of intelligent archaic Modi document recognition system there is a need to detect the actual page area before to other preprocessing stages. Over the past period there has been a growing interest in addressing of object detection of document images or removal of clutter noise. However, no comprehensive studies have been exists which are work on the proposed problem statement. This paper presents page boundary extraction and page area detection approach for highly degraded chronic handwritten Modi document images. This algorithm is based on the analysis of the gray level frequency of the document image with statistical scrutiny. The ground-truth region-based evaluation methods are used to evaluate and compare the results. Actual page area of the ancient handwritten Modi document images is detected 97% proficiently without affecting its originality. The proposed approach is robust and efficient compared with state-of-art techniques.
JomLinerapy: Therapist on Your Palm

The awareness of mental health issues has arisen currently with the concern of their mental state situation. People are attending therapist consultation to help to reduce the feeling of depression, anxiety, and stress. This research aims to create a mobile application for patients that are restricted by physical boundaries. Another purpose of the research hopes to overcome the inconvenience brought by traditional face-to-face consultation and therapy, it also hopes to achieve cost-saving and increase efficiency and raise awareness. Research shows that most of the students in private universities are quite aware of the issues regarding mental health. Students from private universities were chosen due to exposure to technology using survey and interview. University students born around the 90s have more mature thinking and the exposure to advanced technology is greater than the adult that born in the 80s. Managerial and practical implications are discussed further.
Computing Coordinated Motion Plans for Robot Swarms: The CG:SHOP Challenge 2021

We give an overview of the 2021 Computational Geometry Challenge, which targeted the problem of optimally coordinating a set of robots by computing a family of collision-free trajectories for a set S of n pixel-shaped objects from a given start configuration to a desired target configuration.