Reviewerly/exHarmony huggingface dataset:
New Meet-in-the-Middle Attacks on FOX Block Cipher

Abstract FOX block cipher was designed with a Lai–Massey scheme, in which the round function uses the Substitution-Permutation-Substitution structure. A meet-in-the-middle (MITM) attack is one of the most important issues for the security of the block cipher, which consists of a precomputation phase for constructing a distinguisher and an online phase for key recovery. This paper studies the MITM attacks against FOX. The first MITM distinguishers of 5-round FOX64, 7-round FOX64-256 and 5-round FOX128 are presented when using the differential enumeration technique with truncated differential characteristics. Then, based on these distinguishers, the attacks for key recovery on 7-round FOX64, 11-round FOX64-256 and 7-round FOX128 are presented with the state-test and state-search techniques. It is shown that the attack on 11-round FOX64-256 is proposed for the first time; attacks on 7-round FOX64 and 7-round FOX128 can be improved with lower time and memory complexities compared with the currently known attacks.
Sharing Tacit Knowledge by Playing Supply Chain Collaboration Games

Tacit knowledge, which cannot be transferred to another person through mere verbalization, plays a fundamentally important role in solving society’s problems and making decisions in management. Nevertheless, earning tacit knowledge effectively and sharing tacit knowledge with other people is not easy. Learning such tacit knowledge can be achieved through experience and through learning from experience. However, only on a rare chance does one have the opportunity to experience what one wants to learn and what one should learn. The authors have created various analog business games and have used them for education to produce learning opportunities. Particularly, the authors applied Supply Chain Collaboration Games for lectures in 2014 and 2015 at Sirindhorn International Institute of Technology (SIIT) at Thammasat University. Results obtained from analyzing the students’ responses to questionnaires clarified empirically that students can learn management-related tacit knowledge effectively using analog business games.
People to People (P2P) Privacy in Mobile Devices

We are living in a digital world where people use their mobile devices to not only store their Personally Identifiable Information (PII) but also the PII of other people like friends, relatives and colleagues. Unlike the past where the governments and businesses alone were the custodians of PII, today people themselves are also the new custodians of PII. This has resulted in a new privacy breach scenario, which we call as People to People (P2P) privacy breaches, where people disseminate the PII of other people without (i) their consent and (ii) any notification to them. P2P privacy breaches have been facilitated by the ubiquitous internet and popular instant messaging applications like WhatsApp, Facebook Messenger. We conducted an online survey with 1810 participants to understand the privacy perceptions of the users related to PII. Survey data analysis shows that 68.9 % people have already indulged in a P2P privacy breach recently while 95.86 % people have chosen to be informed if their PII is shared by others. Thus, we establish that P2P privacy breach is a real concern and needs to be addressed. Analysis also shows that contact numbers and email ID, usually stored in contact cards in mobile devices, are the most shared PII categories followed by personal photographs and addresses. In this work we determine the risk associated with the contact card sharing from the mobile devices. We demonstrate the extent of privacy breach by modelling the contact card information diffusion in a directed random graph. To address this problem, we propose a collaborative P2P privacy model which detects all instances of contact card sharing from mobile devices and generates notifications for the data subjects. The notification also includes the P2P privacy breach severity.
Error Optimization in Random Number Generation Using ABC Algorithm

Optimization is an effort to find the best option in the settings of the objective function. The goal of this study is to optimize multivariable functions using Artificial Bee Colony (ABC) which is one of the most current swarm intelligence-based algorithms, simulating the foraging activity of honeybees. The performance of the ABC algorithm has been measured on the minimization process of 22 benchmark functions including the popular Griewank, Rastrigin, Sphere, and Rosenbrock. The experimental results demonstrate that the ABC algorithm has approximated the actual value with an accuracy percentage of 98.85.
Teaching Environmental Education Using an Augmented Reality World Map

Abstract The aim of this short paper is to provide readers with a comprehensive lesson plan for elementary schools, which seeks to improve digital citizenship and competency in students and teachers, by fostering digital literacy skills through an augmented reality (AR) application. The lesson plan was developed within the framework of the “Digital, Responsible Citizenship in a Connected World (DRC)” project funded under Key Action 2: Cooperation for innovation and the exchange of good practices, part of the European Union’s Erasmus+program. The DRC project aims to infuse contemporary pedagogical practices into quality lifelong learning for students and teaching professionals, including teachers, school leaders and teacher educators, across Europe. Specifically, the project aims to improve digital citizenship and competency in students and teachers by fostering digital literacy skills through education.
Superdense Coding Through Repeaterless Hybrid Network of Depolarizing Quantum Communication Channels

Superdense coding is a fundamental protocol in quantum communication, which enables us to send two bits of information by the transmission of just a single qubit. In this paper, we consider superdense coding through a noisy quantum depolarizing channel. Following a simple calculation, we derive superdense coding capacity as a function of depolarizing probability. We extend this derivation to the scenario of repeaterless series-cascaded depolarizing channels and provide an elementary approach to evaluate the maximal intervals in which repeaters shall need to be placed in such a cascaded system. Finally, we validate the theoretical results via a Monte Carlo simulation, simultaneously providing a generalized framework. We believe that our numerical results serve as a useful benchmark and give a base to extend such simulations to other quantum channels.
A Concept of a New Knowledge Based Multimodal Affective Tutoring System

In this paper, we present a new concept of a multimodal Affective Tutoring System (mATS) based on knowledge, which is aimed at covering the entire functionalities of a human teacher - namely training and testing, and personal attitude towards every student. The system incorporates models of the student and of the task, as well as evaluation module for constant determination of the current states of both. Moreover, based on multimodal input signals and system-generated ones (performance, response time, etc.), supplemented with prior knowledge about the student (self-reported parameters, such as age, gender, level of experience, etc.) and user profiling approach mATS gives a variety of options for realization of adequate adaptive strategies.
Sequence Identification with Trees and Co-Occurrence Graphs

We evaluated a tree based approach with co-occurrence graphs for sequence identification. A tree is appended to each node of the graph to store sequences starting with the respective word. Tree parameters height, degree and replacement strategy for subtrees were varied. We used preprocessed sentences from a news-paper corpus for evaluation. Access frequency (weight) and creation/last access timepoint were recorded for each node/edge. We found that replacement strategy and degree (not height) affect meaningful sequence identification. Two main groups of results (longitudinal profiles) were identified, specific combinations showed higher rates of oblivion. Meaningful sequences were preferentially obtained with degree 15, height 15 and replacement strategies FIFO, LRU-II, MRU-I or LFU-II. In summary, we demonstrate feasibility of a tree based approach for sequence identification with co-occurrence graphs, warranting validation in independent datasets.
DNA image encryption based on the residual number system

In recent years, image data information security has gradually attracted people’s attention. In order to ensure the reliable transmission of images, this paper designs an encryption algorithm based on the residual number system for the two shortcomings of single chaotic sequence and high algorithm complexity in a large number of color image encryption algorithms. This algorithm takes advantage of the feature that the color image can be decomposed into three-layer components, and uses an associated (parallel scrambling-ordered diffusion) encryption mode for the three-layer components, making it difficult to crack any one layer of the color image alone, and With the help of the residual number system, each modular operation has natural independence, parallel characteristics, and there is no carry between each other, which increases the flexibility of the encryption method and reduces the complexity. The experimental results show that it is compared with other image encryption that uses chaos combined with DNA encoding. The encrypted histogram becomes smoother, has a higher sensitivity to the key, and the correlation between adjacent pixels of the ciphertext image is low. The encryption process is optimized to increase the transmission speed of the image.
Dynamic Image Contrast Enhancement Using Image Dependent Decomposition Method

Nowadays, the various application area of image processing demands visually rich images. But finding a quality image for preprocessing work is a challenge for researchers. The above-mentioned problem arises due to various factors like less expertise in image acquisition, poor environmental conditions, and low-quality image acquisition device. Therefore, contrast enhancement, as well as brightness preservation, is required as a preprocessing step before the analysis of images. The primary objective is to improve the clarity of the structure by adjusting the essential features. In this paper, to address the limitations of current image enhancement techniques, a bi-dimensional empirical mode decomposition (BEMD) based contrast enhancement method is proposed. Here the singular value decomposition (SVD) of the BEMD processed image and the original image are compared, and a new multiplication factor is developed. The primary objective of this paper is to limit the noise by enhancing the low-frequency coefficients only. This method preserves the high-frequency components and uses them during the reconstruction of the image. To validate the proposed method some publicly available digital images are considered. The quantitative measures like SSIM, PSNR, AMBE, and entropy ratio are evidence of the superiority of the proposed method over the state of art methods.
Study and Survey on Blockchain Privacy and Security Issues

Blockchain-based technology is becoming increasingly popular and is now used to solve a wide range of tasks. And it's not all about cryptocurrencies. Even though it's based on secure technology, a blockchain needs protection as well. The risks of exploits, targeted attacks, or unauthorized access can be mitigated by the instant incident response and system recovery. Blockchain technology relies on a ledger to keep track of all financial transactions. Ordinarily, this kind of master ledger would be a glaring point of vulnerability. Another tenet of security is the chain itself. Configuration flaws, as well as insecure data storage and transfers, may cause leaks of sensitive information. This is even more dangerous when there are centralized components within the platform. In this chapter, the authors will demonstrate where the disadvantages of security and privacy in blockchain are currently and discuss how blockchain technology can improve these disadvantages and outlines the requirements for future solution.
Cost and routing of CV quantum networks

We investigate the capabilities provided by highly multimode entangled quantum systems with arbitrary shapes, evaluate the cost of their experimental realization and devise an entanglement routing protocol to allow quantum communications between arbitrary nodes.
Siamese Network and Facial Ratios for Deformed Facial Matching

Facial Recognition is a technique that uses the face and its features to identify/verify a person. It is a Biometric Application, which is playing an integral role in today’s world in a wide variety of areas like Criminal Identification, Visitor Verification, and many other Real Time Identification systems. In this paper, we present a system that does facial matching for deformed faces. The proposed method combines the extraction of high-level feature representations using Deep Learning and the calculation of facial ratios using Image Processing to generate a robust model that can be used to identify/verify deformed faces. Whereas traditional face recognition systems show poor results when there is a face deformation. We use a function that tells us how similar or how different the two input images are. So, for this Siamese network is used. This network takes in an image as input and gives its feature vector. Then, we find the distance between the computed feature vectors of the two images which will give us a similarity score. Next, we use an Image Processing technique to calculate the facial ratios from macro features like eyes, lips, etc. These facial ratios are calculated based on facial landmarks spread across the face. We use multiple facial ratios, so even if deformities occur in a part of the face it will not have much effect on the system rendering it immune to facial changes. Hence, this paper tries to close the gap in the previously devised facial matching methods which were not designed for deformed faces.
Investigation of Segmentation Performances of Superpixel Algorithms for Noisy Images

Superpixels, used espically in image and video segmentation applications, are the visiual structures composed of pixels having same color, intensity and texture behavior. In this work, segmentation performances of superpixel algorithms for noisy images are investigated. Moreoever, the effect of denoising to segmentation accuracy is also investigated. For this purpose, most common three superpixel algorithms are implemented to the images selected from image segmentation database. As a result of efforts made, noise sensitivity and segmentation performance of superpixel algorithms are investigated. Also, the effect of denoising process as a preprocessing step to the segmentation performance is also examined.
Unsafe Maneuver Classification From Dashcam Video and GPS/IMU Sensors Using Spatio-Temporal Attention Selector

In this paper, we propose a novel deep learning architecture to classify unsafe driving maneuvers from dashcam and IMU data. Such architecture processes the output of an object detection algorithm in combination with raw video frames and GPS/IMU data. At the core of the architecture there is a novel Spatio-Temporal Attention Selector (STAS) module, which (1) extracts features describing the evolution of each object in the scene over time and (2) leverages multi-head dot product attention to select the relevant ones, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the dangerous ones or the ones in danger, to perform classification. We also introduce a simple but effective methodology to increase the benefit of fine-tuning the backbone network. Our method is shown to achieve higher performance than other approaches in the literature applying attention over single frames.
A Bacterial Foraging Algorithm with Adaptive Step Length According to Adaptability Changes

Bionic algorithm is an important theoretical basis and indispensable method of modern science, which is widely used in various fields. Among them, bacterial foraging algorithm(BFA) is more flexible than other evolutionary algorithms, adapts to solve many practical problems, and has strong adaptability and robustness. However, BFA is more complex and the calculation amount is large. From the point of view of the natural selection behavior of bacteria tending to be suitable for the environment and avoid harm, this paper puts forward a improved BFA that realizes adaptive step according to the change of environmental adaptability value, and uses the initial setting step as a relative reference value to dynamically adjust the step forward of each bacteria. Using the Resenbrock function as the evaluation function, the test results show that the improved BFA converges faster, solves with higher precision and has better stability.
Adaptive Graph Convolutional Network with Prior Knowledge for Action Recognition

Skeleton-based action recognition has been paid more and more attention in recent years. Previous researches mainly depend on CNNs or RNNs to capture dependencies among sequences. Recently, graph convolution networks are widely used due to its extraordinary ability to exploit node relationships. We propose a new GCN-based model named PK-GCN which utilizes prior knowledge to design learnable node connections. It can be proved that models can learn adaptive connections by itself, because the node connections can be learned with random initialization. The prior knowledge can be used to design node connections by selecting prominent pairs of joints in actions. By combining the proposed methods above, PK-GCN achieves the best performance in ablation study. Compared with other single-stream GCN-based models, PK-GCN on two large-scale datasets NTU-RGB+D and Kinetics achieves state-of-the-art results.
Construction of Open Information Resource Service Platform in Ji Lin Province Local Universities Under the Background of Regional Development

The current rapid development of science and technology, the steady progress of university information process, from the previous individuals to the library manual access to materials, to now anytime, anywhere access to the information platform of book management, can be said to be a major change, to bring great convenience to people. Although there are more and more versions of the library management system, most of them are only general-purpose, not combined with the actual situation and needs of the regional library, and even the functions of some systems are not perfect. Therefore, it is necessary to establish an open information resource service platform in colleges and universities. First of all, the business process and functional requirements of a university library are deeply analyzed, and the application closest to practical work is completed, which improves the efficiency of library management. Then, from the actual point of view, in-depth analysis of each functional module in the system is combined with the given various charts to a university library management information system in-depth analysis. Finally, SQL Server2008 is used as the design of database management software, mainly considering the characteristics of high security and large storage capacity of SQL Server2008. The design and implementation of this system effectively integrate the daily management and business ability of the library, basically meet the actual needs of daily work, and greatly improve the efficiency of the librarian’s work. This paper aims at the design and realization of the management information system of a university library and designs and implements a set of transportation line in the B/S architecture, modeling with UML, database using SQL Server2008 management information system.
An Efficient and Scalable Distributed Hypergraph Processing System

The recent increase in the amount of graph data has drawn our attention to distributed graph processing systems scalable to large-scale inputs. Although distributed-memory processing is generally less efficient than shared-memory processing because of the communication costs and program complexity, state-of-the-art distributed graph processing systems, such as Gemini, have achieved a comparable efficiency by using lightweight graph partitioning and load balancing. However, the achievement of both scalability and efficiency in hypergraph processing remains an open issue because distributed hypergraph processing systems have not been extensively studied. In this paper, we propose a distributed hypergraph processing system based on Gemini that achieves both scalability and efficiency. Our system outperformed the state-of-the-art shared-memory hypergraph processing system Hygra from several folds to tens of folds on a single-node computer. In addition, it showed speedup in processing a large-scale dataset on a multi-node computer.
High-speed logging cable telemetry system transceiver circuit design based on FPGA

The telemetry system is critical in the application of geophysical exploration technology. The efficiency and accuracy of the entire logging system will be directly affected by whether the signals captured by the downhole acquisition equipment can be transferred reliably and at a high rate to the surface receiver for data processing. Now, for the problems of slow cable transmission speed, signal attenuation, and severe distortion, the transmission system is studied in this paper based on orthogonal frequency division multiplexing (OFDM) technology. The logging cable telemetry system's overall plan is designed, and the OFDM communication system is completed according to IEEE802.11a physical layer standard, detailing the transmitter and receiver's circuit structure, functional roles, and implementation methods, and instituting each transmitter and receiver module using XILINX Field Programmable Gate Arrays (FPGA). Scrambler, convolutional encoder, interleaver, 16QAM modulator, IFFT module, insert CP module, and training sequence module is all included in the transmitter. Time synchronizer, CP removal module, FFT module, deinterleaver, demodulator, Viterbi decoder, and descrambler are all included in the receiver.
The Design of Multimedia Sports Teaching System Based on B/S Mode

Computer network multimedia-assisted teaching has become a modern teaching method and one of the important ways for educators to improve teaching efficiency. Because of the particularity of physical education, it is more urgent to use multimedia teaching system to assist physical education. In sports teaching, the application of network-assisted teaching is less. This paper designs and studies the multimedia sports teaching system, optimizes the design of the sports teaching system with B/S mode, and proves the feasibility of the multimedia sports teaching system by comparing with the experiment of the conventional teaching method, which has certain popularization.
Knowledge-Based Multiple Lightweight Attribute Networks for Zero-Shot Learning

Zero-shot learning aims to recognize unseen objects. From the start of zero-shot learning research, attributes have played an important role. However, previous attribute-based methods do not fully exploit attributes and their relationships. In order to overcome these drawbacks, we propose a new framework. This framework consists of the convolutional neural network, fully connected networks and the attribute knowledge graph to make classification. This framework incorporates knowledge and is suitable for incremental learning. Also, the framework treats different attributes unequally according to their relationships, which further improves the recognition accuracy. Experiments show that this framework has achieved the comparable accuracy on the AWA2 dataset. At the same time, trainable parameters are reduced by about 100 times compared to previous attribute-based methods.