Reviewerly/exHarmony huggingface dataset:
Efficient Privacy-Preserving Binary Tree Search

Binary search tree is a common structure in computer programming and is currently widely used in many scenarios, such as routing search engine in routers, massive data concurrent query, set/multiset, map in C++ STL, and Linux Such technology is applied to memory management and the like. However, with the development of network technology, people’s demand for privacy protection has gradually increased. The leakage of many key information will cause serious consequences, and also restrict the application and development of network technology. In the process of binary tree search, it is easy to have such a problem: the searched party may only want to provide the location of the searched data without exposing all the data in the entire binary search tree; at the same time, the searcher does not want to expose itself The information you need to find. This leads to issues related to the privacy protection of binary search trees. In response to the above problems, we have proposed a binary tree search solution that can protect privacy, which is used to solve the data leakage problem that may exist during the search process of the binary tree. The feature of the scheme proposed in the text is that it integrates two technologies of binary search tree and obfuscation circuit, so as to effectively protect the binary search tree and perform the search without leaking data. And the security analysis shows that the protocol we proposed is very safe and efficient, which can protect the data beneficially.
Research on Scenario Classification of Burglary Cases via BERT

Case descriptive files record the details of the crimes. For burglary cases, criminal scenarios are crucial for case analysis. This paper focuses on scenario classification task. We regard the task as a text classification problem. The BERT model is applied to tackle the problem. We additionally propose a stop-words processing algorithm which can effectively reduce the interference caused by irrelevant information in the text and speed up the model to convergence. Experiments show that the BERT model with the help of stop-words processing algorithm achieves higher performance than other four benchmarks. The F1 score finally reaches 98.83%.
Framework Design for the Retrieval of Instant Messaging in Social Media as Electronic Evidence

The rapid growth of social media features not only brings many advantages but also causes problems. Mainly related to digital evidence when cybercrime occurs. One of the social media features that are currently popular is the unsend message feature in instant messaging applications such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram. In cybercrime, the perpetrator can delete the messages and erase digital evidence, making it difficult to trace. Those artifact messages might be useful for law enforcement or forensic investigators to be used as digital evidence in court. Therefore, an effective and efficient framework is needed to guarantee the data integrity in the mobile forensic investigation process. This paper will discuss the review of several international standards on mobile forensics, namely NIST SP 800-101, ISO/IEC, and SWGDE. This paper also proposes a framework design to retrieve unsend data artifacts on social media according to official and widely used international mobile forensic standards.
Improvement of Policy Charging Control Flow Based on Internet Subscribers Behavior

Balitower has two kinds of products which are paid internet and free internet. Unified Policy Charging Control (PCC) is handling both internets traffic, which is paid internet and free internet to have more flexibility in managing various product. Technically both traffic will get the same treatment by PCC to refer to 3GPP Standard in accordance to deletion of the session. 3GPP has arranged that deleting a session on PCRF can only be done if there is an initiation from a different node. This rule is suitable to be applied for the paid subscriber which is expected to be always active until run out of credit or expired, while to keep session for free internet will depending on subscriber behaviour. Based on free internet data traffic from first weeks till twelfth of 2020, it was found that 70% of the subscriber to access the internet was less than five times a week and more than 60% of subscriber-only accesses within 5 minutes. By referring to that data, it proposed to have automatic session deletion for free internet in PCRF to allow other customers to access the internet without having to add cost for license upgrade.
A Methodology for Analyzing Information Needs in Construction

There is a dearth of research on information needs in construction, though they are the basis for developing information systems. This study aims to develop a methodology for analyzing information needs in construction. The proposed methodology consists of three steps: 1) firstly, activity theory is employed to help define the context in which information needs arise; 2) secondly, interview is employed to identify information needs in each activity; and 3) finally, the identified information needs are analyzed and structured. A case study was conducted to illustrate the application of this methodology. The proposed methodology provides a practical approach for information system developers to identify and analyze information needs in construction.
Adversarial Shared-Private Attention Network for Joint Slot Filling and Intent Detection

Spoken language understanding plays an important role in the dialogue systems, and in such systems, intent detection and slot filling tasks are used to extract semantic components. In previous works on spoken language understanding, many ways that from traditional pipeline methods to joint models have been investigated. The features from these methods are usually extracted from one dataset that cannot jointly optimize databases with different distributions. In this paper, we propose a new adversarial shared-private attention network that learns features from two different datasets with shared and private spaces. The proposed adversarial network trains the shared attention network so that the shared distributions of two datasets are close, thereby reducing the redundancy of the shared features, which helps to alleviate the interference from the private and shared space. A joint training strategy between intent detection and slot filling is also applied to enhance the task relationship. Experimental results on public benchmark corpora, called ATIS, Snips and MIT, show that our proposed models significantly outperform other methods on intent accuracy, slot F1 measure and sentence accuracy.
The Green Logistics Idea Using Vacuum Insulation Panels (VIPs) For Freezer Logistics Box in Normal Truck

This research presents the green logistics using vacuum insulation panels (VIPs) for freezer logistics box in normal trucks. The materials were Vacuum Insulation Panels (VIPs) with polyurethane foam box compared to polyurethane foam box that thermal conductivity for polyurethane foam and Vacuum Insulation Panels (VIPs) at &gt; 20 and &lt; 7 mW/m.K. The vacuum Insulation Panels (VIPs) with polyurethane foam box design with VIP inside the polyurethane foam protect some impact from losing vacuum that loss thermal resistance. According to the result, after 24 hours, the ice-cream temperature of polyurethane foam box that lost 6 Degree Celsius, calculated as 30.00%. Besides, the ice-cream temperature of that vacuum Insulation Panels (VIPs) with a polyurethane foam box has lost 1.5 Degrees Celsius, counted as 7.5%, and compared with stating temperature at -20 Degrees Celsius. The result represents ice-cream quality after testing that shows the ice-cream condition in VIP Box still freezing, and ice cream condition in the normal box is melt. This research can be applied to a cold room or freezer room in supermarkets, factories, distribution centers, and VIPs that can result from electricity saving for compressors or cold trucks and freezer trucks that can get the result of fuel-saving for engines.
MPE model: a method for the ideal parameters selection based on multiple regression in YHGSM

In the Yin-he Global Spectral Model (YHGSM) developed by the National University of Defense Technology, an issue to be studied is that how to determine the parameters and achieve the ideal execution time in the model. Based on the multiple regression analysis, we firstly construct a quantitative relationship between the parameters and the execution time in the model by giving a set of random parameters and its execution time in advance. Then we establish the multiple regression equation between them. In this way, we finally establish the model parameter evaluation (MPE) model. In the new model, by giving more random parameters, we can predict their corresponding execution time. On this basis, given a critical value at which the execution time may be accepted, when the execution time is less than the critical value and the model can be operated normally, the determined parameters at this time are the ideal parameters that we recognize.
Representing a Quantum Fourier Transform, Based on a Discrete Model of a Quantum-Mechanical System

This paper presents a discrete model of the quantum fast Fourier transform (QFFT) defined over a quantum mechanical system that includes N basis states (QMS(N)). In this case, the QMS(N) is defined based on a known discrete model described using 2(N − 1) parameters, of which (N − 1) parameter describes the amplitude components of the basic states of the QMS(N), and (N − 1) parameter describes the phase components. QFFT is modelled discretely by specifying the same type of operations, which are described as the known quantum gates: A single-qubit Hadamard gate and a two-qubit gate with a controlled phase. Each of these valves affects the specified parameters of the QMS(N) N/2 times. This representation allows you to display the impact of the QFFT on the QMS(N) by varying only N of 2(N − 1) of its parameters. In addition, simulation of the effects of N/2 Hadamard gates and N/2 controlled-phase gates on the specified parameters of the discrete QMS(N) model can be performed in parallel. This circumstance allows us to perform distributed modeling of the impact of QFFT on the QMS(N) when using multiprocessor computing systems, both existing and prospective, both specialized and general-purpose.
Examples and Applications on Artificial Neural Networks

The chapter discusses examples and applications from real-life which utilize artificial neural networks. The chapter starts with simple linearly separable problems such as the issue of a credit card, logical functions such as AND, OR, and NOT from the Boolean algebra and shows that how a single neuron solves such problem efficiently by providing details of data, learning mechanism, and neural network structures. However, there are a few real-life problems that can be solved using a single neuron. In reality, often problems are much more complex and require many neurons. For such a problem, advanced models of artificial neural networks need to be used. Examples such as sales prediction, mobile selection, detection of flu and other viral diseases, student’s aptitude testing, course selection, job selection, etc. are discussed in detail with the network architecture as well as training data sets. Various business domains such as tours and travels, properties and real estate, finance, agriculture, education, etc. are explored and applications of the artificial neural networks are demonstrated with input/output encoding, the architecture of the network, and sample training data. These examples undergo supervised learning as well as unsupervised learning mechanisms. Further, the practical applications on various domains such as the application of the Support Vector Machines (SVM) for content writers are also demonstrated.
Incorporating Agile Methodologies into DoD Software Sustainment Operations

The DoD is increasingly employing agile methods in efforts to rapidly produce software. However, the specific domain of agile methodologies as applied to the sustainment of embedded systems remains in need of further research. One initial area examined here is the relationship between agile principles and the policies and practices used by DoD sustainment organizations. It is shown that current sustainment policy for the Air Force Sustainment Center is compatible with agile principles. Challenges specific to sustainment operations can be resolved through careful consideration of the scope and organizational levels at which agile principles and practices are implemented.
Measuring semantic relatedness between words using related synsets in African Wordnet

Words should not be treated as if all they do is fulfil roles; they can also have relationships. In our everyday talk, we normally give the meanings of words in terms of their relationships. The approach for measuring semantic relatedness between words is determined by means of a set of words that are closely related to them, and these are normally referred to as the set of determiner words. This article examines the measures of semantic relatedness to which words are associated by means of the variety of types of semantic relationships – such as synonymy, hyponymy, function and association. This relationship is considered within the context of African Wordnet (AWN), which provides South African indigenous languages with a platform to access a machine-readable lexical database organised by meaning. This article argues that computing semantic relatedness using African Wordnet could be helpful in developing an understanding of the meaning of related words for use in different African languages.
Using of deep convolutional neural networks for visual features extraction in multiple objects tracking task

The article explores the task of matching visual features when tracing objects on a video sequence. The authors conducted a comparative analysis of existing methods for extracting visual object features and similarity estimation for the re-identification of objects. An algorithm that compares several approaches for feature extraction and similarity estimating has been developed. The authors carried out an experimental assessment of the speed and accuracy of the algorithms using the MOT-16 and PETS 2007 datasets. It is shown that the most accurate estimates of the similarity of objects are achieved by calculating the modified value of the normalized cross-correlation function between features derived from the neural network average pooling layer.
Contrastive Learning with Hallucinating Data for Long-Tailed Face Recognition

Face recognition has been well studied over the past decades. Most existing methods focus on optimizing the loss functions or improving the feature embedding networks. However, the long-tailed distribution problem, i.e, most of the samples belong to a few identities, while the remaining identities only have limited samples, is less explored, where these datasets are not fully utilized. In this paper, we propose a learning framework to balance the long-tailed distribution problem in public face datasets. The proposed framework learns the diversity from head identity samples to generate more samples for identifying persons' identities in the tail. The generated samples are used to finetune face recognition models through a contrastive learning process. The proposed framework can be adapted to any feature embedding networks or combined with different loss functions. Experiments on both constrained and unconstrained datasets have proved the efficiency of the proposed framework.
Design and Implementation of High Performance Cryptographic Service Framework Based on TrustZone

Due to its favorable isolation and security features, TrustZone technology has been widely used in mobile payment, cloud computing, Internet of Things, etc. However, the cryptographic algorithm provided by the trusted execution environment such as Open-TEE and OP-TEE is inadequate, which brings inconvenience to the development of trusted applications. In addition, the system mainly uses the software algorithms to provide cryptographic services and does not make full use of hardware cryptographic resources, which results in the low efficiency of cryptographic service. To Figure out these two issues, aiming at features of domestic Phytium processors, this paper establishes a unified cryptographic service framework in the TrustZone platform to build a more rich and complete cryptographic algorithm library. Meanwhile, it makes full use of the hardware cryptographic resources of the processor platform to improve the performance of cryptographic service. Test result shows that the proposed framework can bring significant convenience to the development of trusted applications. Meanwhile, compared with the original cryptographic service performance, the cryptographic service provided by the new framework can achieve significant performance improvement.
P-BBA: A Master/Slave Parallel Binary-based Algorithm for Mining Frequent Itemsets in Big Data

Frequent itemsets mining is an effective but computational expensive technique especially when dealing with big datasets. Hence, the need for a customizable algorithm to work with big datasets in a reasonable time becomes a necessity. The Binary-based Technique Algorithm (BBT) used a binary representation of the database transactions as well as binary operations in order to simplify the process of identifying the frequent patterns as well as reduce the memory consumption. However, BBT algorithm still suffer the problem of low performance in terms of execution times when dealing with big data. This is due to the fact that the BBT algorithm was designed to run as a single thread of execution. Therefore, there is a need to improve the performance of the Binary-based Technique Algorithm (BBT). In this research, we proposed a Parallel Binary-Based Algorithm (P-BBA) towards solving the above mentioned problem. The objective of the proposed P-BBA is to process big datasets by developing collaborative threads that would work together concurrently and collaboratively and generates the list of frequent itemsets within an acceptable time frame. The algorithm is designed using a Master/Slave thread model to fits in Apache Spark distributed platform. The performance will be evaluated based on the total execution time.
Multiscale Convolutional Fusion Network for Non-Lambertian Photometric Stereo

One of the key issues in photometric stereo is the extension of its application in real world objects which shows non-Lambertian reflectance. This letter proposes a multi-scale weighted convolutional fusion network with deep learning architecture to realize high-precision perception of non-Lambertian surfaces under arbitrary illumination conditions. A multi-scale convolutional fusion module is designed to strengthen the photometric physics and the utilization of the neighborhood features at the same time so as to overcome shadows and distinguish multiple materials. In order to further deal with the problem of arbitrary illumination conditions, a multi-resolution polar coordinate division method is proposed to integrate the input image information and fully utilizes the multi-scale convolution. Both syntheses and real-world experiments verifies the performance of the proposed method in recovery accuracy and computational efficiency.
A Study on Presentation Method of Video Thumbnail on SNS Using Micro-Moments

It is necessary to conduct research find out effective format of video thumbnails that users gain information in Micro-Moments when video thumbnails are being presented to users. The methodology we used is to find elements that influence their decisions by providing content that can be recognized and that has an interesting element while the participants are using SNS in a short period. The textual information and fast-forwarding format were effective to comprehend contents regardless of intensity and amount of movement. Authors believe that it would be better for the audience that contents can be intuitively recognized as video clips because comprehension of con-tents and interests can be changed by audiences’ interests when contents have many moving elements. However, fast-forwarding is an effective method to attract audiences’ interests for contents that have subtle moving elements. Therefore, the contents that have subtle moving elements can in-duce fast-forwarding. This research of video clips on SNS administers micro-moments that can increase in contents understanding and thumbnail method that the audience can effectively recognize contents. In terms of understanding content, it was effective for the audience to increase under-standing by a combination of fast-forwarding and textual information.
Mapping the Contribution of Electronic Theses and Dissertations by the Universities of Assam to INFLIBNET Shodhganga Project: A Study

Electronic Theses and Dissertations (ETDs) are considered as generally significant and broadly utilized assets by the researchers in higher educational institutions. The current paper assesses the contribution of Universities of Assam in India's ETD Database Shodhganga. In order to share the research information among the Universities, the Shodhganga Project was initiated by INFLIBNET Centre, which providing free access to theses and dissertations through the archive. In this project, 12 Universities of Assam have marked MoU for Shodhganga and just 10 universities have submitted their research reports.
Brick Assembly Networks: An Effective Network for Incremental Learning Problems

Deep neural networks have achieved high performance in image classification, image generation, voice recognition, natural language processing, etc.; however, they still have confronted several open challenges that need to be solved such as incremental learning problem, overfitting in neural networks, hyperparameter optimization, lack of flexibility and multitasking, etc. In this paper, we focus on the incremental learning problem which is related with machine learning methodologies that continuously train an existing model with additional knowledge. To the best of our knowledge, a simple and direct solution to solve this challenge is to retrain the entire neural network after adding the new labels in the output layer. Besides that, transfer learning can be applied only if the domain of the new labels is related to the domain of the labels that have already been trained in the neural network. In this paper, we propose a novel network architecture, namely Brick Assembly Network (BAN), which allows a trained network to assemble (or dismantle) a new label to (or from) a trained neural network without retraining the entire network. In BAN, we train labels with a sub-network (i.e., a simple neural network) individually and then we assemble the converged sub-networks that have trained for a single label together to form a full neural network. For each label to be trained in a sub-network of BAN, we introduce a new loss function that minimizes the loss of the network with only one class data. Applying one loss function for each class label is unique and different from standard neural network architectures (e.g., AlexNet, ResNet, InceptionV3, etc.) which use the values of a loss function from multiple labels to minimize the error of the network. The difference of between the loss functions of previous approaches and the one we have introduced is that we compute a loss values from node values of penultimate layer (we named it as a characteristic layer) instead of the output layer where the computation of the loss values occurs between true labels and predicted labels. From the experiment results on several benchmark datasets, we evaluate that BAN shows a strong capability of adding (and removing) a new label to a trained network compared with a standard neural network and other previous work.