Reviewerly/exHarmony huggingface dataset:
Identify-Based Outsourcing Data Auditing Scheme with Lattice

To reduce the burden and overhead of local storage, more and more organizations and individuals chosen to outsourcing data to cloud servers. Cloud users can access cloud data through any network. At the same time, as users lose actual control over the data, the data security in the cloud server is exposed to great risks. Therefore, how to ensure the integrity and security of cloud data has become an open issue. Around this problem, researchers have put forward many plans. However, these schemes are based on traditional cryptography, they cannot resist quantum attacks and have high computational complexity. It cannot meet the needs of interconnection of everything in the future. To solve this problem, we propose an identity-based outsourcing data integrity auditing scheme with lattice, which has forward security. Our proposed scheme is based on lattice cryptosystem, which not only has higher security and computational efficiency, but also has forward security. In the proposed scheme, the data owner can designate a proxy to outsource the data, thus reducing the local computing burden. In addition, we analyze the security of the scheme through the assumption of ISIS problem, which shows the theoretical security of the scheme. Theoretical analysis and comparison indicate that our proposed scheme is more secure and practical.
Frogs Sound Detection to Control The Population of Frogs as Pests Using Mel-frequency Cepstral Coefficient-Vector Quantization (MFCC-VQ) Algorithm

The marsh frogs that lost their habitat will become pests that are very detrimental to the fish cultivators. A technological approach is mandatory to assure the fish ponds to always be free from the frogs' presence by detecting the frog sound as a sign of frog existence in the breeding pond. This research implemented Mel Frequency Cepstral Coefficients- Vector Quantization (MFCC-VQ) algorithm to identify the frog's sound. The captured sound signal features will be extracted using Mel Frequency Cepstral Coefficients (MFCC) algorithm. The extracted sound signal will be identified using Vector Quantization (VQ) algorithm. The result of this research is the further the distance of the microphone from the sound source and the noisier environmental conditions in the detection process, the smaller the accuracy percentage. This study's highest accuracy is 99.6% at a distance of 1 meter and 83.2% at a distance of 11 meters in normal circumstances. Through the test, it can also be concluded that the further distance of the microphone from the source, the smaller the percentages of specificity and sensitivity, and the slower the processing speed, although the difference in speed per meter is not too significant.
Criteria for Accepting Software Requirements Negotiation Tools in the Software Industry

Requirements negotiation is a centralized process of making a decision in order to resolve conflicts in the requirements of the stakeholder. The negotiation will enable the shared vision of software to be developed among the heterogeneous stakeholder in the software industry to be achieved. Many process models used for the negotiation of stakeholder’s requirements have been proposed for the software industry by the research community, yet the acceptance of these process models is discouraging. This study tends to investigate the inadequate adoption of requirements negotiation process models. Further, it finds the acceptance criteria for the software industry to adopt requirements negotiation models. Finding shows that the software industries do not adopt the process models. The perceived usefulness, perceived ease of use and many more criteria have been identified through the literature review on the general criteria of software systems acceptance.
Papercode

Paper can be a powerful and flexible user interface that lets programmers read through large amounts of code. Using off-the-shelf equipment, how can we generate a paper-based UI that supports code review, annotation, and teaching? To address this question, we ran formative studies and developed Papercode, a system that formats source code for printing on standard paper. Users can interact with that code on paper, make freehand annotations, then transfer annotations back to the computer by taking photos with a normal phone camera. Papercode optimizes source code for on-paper readability with tunable heuristics such as code-aware line wraps and page breaks, quick references to function and global definitions, moving comments and short function calls into margins, and topologically sorting functions in dependency order.
BCRL: Long Text Friendly Knowledge Graph Representation Learning

The sparse data and large computational overhead in the use of large-scale knowledge graphs have caused widespread attention to Knowledge Representation Learning (KRL) technology. Although many KRL models have been proposed to embed structure information, their ability to accurately represent newly added entities or entities with few relations is significantly insufficient. In some studies, the introduction of textual information has partially solved this problem. However, most existing text-enhanced models only consider the shallow description information of the entities, and ignore the relation mention information between entities, and deep semantic information between sentences and words, which is not optimized for long texts supplementary information like Wikipedia. In this paper, we proposed a long text friendly structure-text joint KRL model, named BCRL (BERT and CNN Representation Learning), which can effectively explore rich semantics embedded in entity description and relation mention text taking Wikipedia as supplementary information. For the obtained text of entity description and relation mention, the model first uses the BERT model to generate sentence vector representation respectively. Then it uses a convolutional neural network with an attention mechanism to select valid information in the text and obtain the overall vector representation of the text. Finally, the gate mechanism is used to combine the structure-based and the text-based vectors to generate the final joint representation. We evaluated the performance of our BCRL model on link prediction tasks using FB15K and WN18 datasets. The experimental results show that BCRL outperforms structure-only models and text-enhanced models in most cases, and has significant advantages in complex relation representation.
The Research on Mobile Robot Path Routing Based on PID Algorithm

This paper mainly studies the path route of the mobile robot, and controls the left and right wheel drive of the robot by two AT89C51 chips. The PID algorithm is used to rectify the robot's route. Through programming, it completes the automatic control and rectification of the robot's path, then it can walk accurately according to the predetermined route.
Data Distributed Storage Scheme in Internet of Things Based on Blockchain

With the explosive growth of Internet of Things data, the storage of large amounts of data has become a problem that plagues people. These IoT devices generally do not have powerful computing and storage capabilities. Most of them assume the role of data collection and information transfer, which also brings challenges to data storage. And a large proportion of the large amount of data is the user’s privacy data, so under the premise of storing a large amount of data, we must also ensure the security of the data. In view of the above problems, this article proposes an effective IoT data storage model based on secret sharing, which effectively guarantees the security of IoT data, and proposes a secure distributed storage of IoT data based on blockchain. This solution has the advantages of a complete blockchain. Under the supervision of the edge computing nodes of the entire network, it can ensure that each transaction data is not tampered with, and it can also obtain complete original data.
Regulation of cyber security: A glimpse into the Indian it regime

Cyber crimes are committed by cyber criminals with the help of technology who tend to use ‘Dark-Web’ for their illegal hacking and nefarious acts to take place. In the era of technology expanding at a fast pace, with the help of IoT, cyber criminals penetrate within the security measures of our smart devices easily and commit crime. The most frequent modus operandi of these cyber criminals is to initiate cyber crime through emails. The present article discussed about the different kind of cyber crimes along with the relevant case laws in the Indian IT Law regime.
Knowledge-Based Development and Economic Diversification: The Case of Qatar

Qatar is privileged to have massive natural gas reserves compared to a small population. This massive wealth in gas reserves coupled with the fact that Qatar has a small population, has led Qatar to be ranked among the countries with the highest per capita income in the world. However, continuing reliance on the hydrocarbon revenues will expose the nation to several risks, including overdependence and volatile energy prices, in addition to the “peak oil” risks. In view of this, economic diversification through investments in non-energy sectors becomes an essential element of future economic planning. Alternatively, knowledge is considered to be a key factor in constant economic growth and a knowledge-based economy (KBE) is a vital source of global economic growth that manages and facilitates the process of innovation which converts knowledge into wealth. This chapter highlights the top-down implementation of the transitions towards a KBE in Qatar in order to diversify away from the overdependence on a single finite source of income and invest in non-energy sectors. It illustrates the public policies related to KBE (QNV2030, QNDS 2011–2016 and QNR 2012) and highlights the alignment between them; it also emphasizes the national priorities in R&D in the Qatar entrepreneurship and innovation ecosystem. Furthermore, it provides an analysis of Qatar’s performance and competitiveness worldwide and within the GCC countries that share similar social-economic factors. It also extracts the performance trends and identifies the strengths, weaknesses and challenges that Qatar is facing while transforming into a KBE with reference to the related global indexes; Global Competitiveness Index (GCI), Global Innovation Index (GII) and Global Entrepreneurship Index (GEI).
An Evolutionary Analytic Center Classifier

Classification is an essential task in the field of Machine Learning, where developing a classifier that minimizes errors on unknown data is one of its central problems. It is known that the analytic center is a good approximation of the center of mass of the version space that is consistent with the Bayes-optimal decision surface. Therefore, in this work, we propose an evolutionary algorithm, relying on the convexity properties of the version space, that evolves a population of perceptron classifiers in order to find a solution that approximates its analytic center. Hyperspherical coordinates are used to guarantee feasibility when generating new individuals and enabling exploration to be uniformly distributed through the search space. To evaluate the individuals we consider using a potential function that employs a logarithmic barrier penalty. Experiments were performed on real datasets, and the obtained results indicate concrete possibilities for applying the proposed algorithm for solving practical problems.
HS-Pilot: Heap Security Evaluation Tool Model Based on Atomic Heap Interaction

To evaluate heap security, researchers have designed evaluation tools that automatically locate heap vulnerabilities. Most of these tools define heap interactions as heap misuses that are bugs, such as overflow in a target heap allocator, and verify whether each combination of heap interactions can be used as an exploit. However, this definition of heap interactions requires preliminary work by a user possessing evaluation tools and specialized knowledge-the user needs to manually do much work to find which heap misuses exist in the target heap allocator. In addition, because the existing heap misuses vary according to target heap allocators and versions, this preliminary work must be performed on each heap implementation. That is, the current definition of heap interaction cannot be generalized to all heap implementations. In this article, we propose a novel heap security evaluation model, called Heap Security Pilot (HS-Pilot), to overcome the preliminary work load and the dependency of heap misuse in heap implementation. In HS-Pilot, a heap interaction is newly defined as the modification of heap metadata, based on the idea that any heap misuse can be represented by a sequence of heap metadata, i.e. combination of heap interactions used by HS-Pilot. Consequently, the heap interactions in HS-Pilot can be applied to all heap implementations without specialized knowledge, and therefore, are more general than that in existing heap evaluation tools. Our evaluation shows that HS-Pilot can cover the analysis range of other evaluation tools, and is able to detect 14 known types of heap exploitation against heap allocator ptmalloc and all types of heap exploitation found by a state-of-the-art evaluation tool.
HOSENet: Higher-Order Semantic Enhancement for Few-Shot Object Detection

Few-shot object detection is to detect objects of novel categories from only a few annotated examples and has recently attracted attention. Existing methods focus on designing new training strategies on a widely used object detection network, while ignoring the importance of network representation ability for this problem. This paper proposes a novel network model named HOSENet, which introduces a higher-order semantic enhancement module (e.g. second-order pooling) in the forward process of the network. The entire network is first trained with base class data and then fine-tuned by support images to detect objects of novel classes. Experiments on PASCAL VOC and MS COCO show that the detection precision of our method in novel classes is far superior to other competitive methods without compromising the detection precision in base classes.
A manufacture-friendly design framework for optical neural networks

We introduced a Python-based design framework for manufacture-friendly optical ANN. It provides the cross-level interoperability between the photonic circuit chip layout and the neural networks infrastructure to enable the optical ANN with better tolerance to the device-by-device or chip-by-chip deviation. It allows a wide range of abstract on levels to describe the behavior of optical ANN: from the lowest-level functionality of manipulating the properties and arrangement of individual phase shifters on a photonic circuit chip, to the highest-level features of designing optical ANN via PyTorchlike development-library as well as its optimization with the well-established machine learning algorithms such as backpropagation. On all the levels, the physical design of the photonic circuit chip can be integrated and synchronized with the construction of the neural networks accounting the influences of the fabrication-deviation with the assistance of IPKISS, a Python-based tool for photonic circuit design. As a demonstration, we use our framework to design the LeNet-5 networks, which can be executed on the photonic circuit chip with non-uniformed grating coupling efficiency. Our LeNet-5 networks achieves the precision around 97.5% for MNIST task.
Working Under the IID Assumption

In this thesis, we are interested in analysing the behaviour of multi-round boxes when such boxes are used to play many non-local games, e.g., while running a cryptographic protocol. In the previous chapter we discussed the different models of multi-round boxes (the parallel and sequential ones). As we saw, their behaviour can be quite complex.
COMET-OCEP: A Software Process for Research and Development

Research and development (R&D) activities are employed to find new theories commonly derived in software products; but the gap between the proof-of-concept software and the final implementation is commonly hard to narrow. In a previous work, a combination of the COMET (collaborative object modeling and architectural design method) and OCEP (Open Community engagement model) methodologies was successfully applied to the development of a person re-identification system. In this paper, the COMET-OCEP software process is conceptually related to other processes, and its guidelines are detailed. Additionally, the advantages of the COMET-OCEP process are highlighted through the analysis of a test case.
Event Recognition on Public Safety Events Using Deep Learning Models

Event recognition is an important part of event extraction task. This paper generates pre-trained Chinese word vectors from Baidu Encyclopedia, Wikipedia_zh, People's Daily News and Sogou News using word2vec tools. Then two typical deep learning models, BiLSTM-CRF and IDCNN-CRF, are constructed to recognize trigger words and event types. The experimental results show that: 1. The word vectors pre-trained from Sogou News benefit model training and prediction; 2.On Chinese Emergency Corpus, deep learning models are generally superior to machine learning models in event recognition task; 3. The IDCNN-CRF model achieves an optimal F1 value of 76.4% for trigger word recognition, which also has the best training efficiency compared with other methods.
Figure-Background Relations in Black and White Illustration as a Section of Teaching Book Art on the Art-Graphic Faculty of MSPU

The problem with the study below is contradiction between the need to introduce the young generation to the culture of books as an aesthetic phenomenon and the lack of development of methods for studying the composition of illustrations in art and pedagogical education. The research methods used were the study of literature on the given topic and the practice of teaching the art of the book at the AGF MPSU for ten years. During the theoretical part of the study we examined the perception of black and white, laws of visual perception (similarity, neighborhood, closure, continuity, inertia and primary forms) and features of construction of the relations "figure - ground" in one-figure, two-figure and multi-figure compositions. The result of the practical part of the study was the system of compositional exercises on the theme ""figure - background relations" in black and white illustration". The system of exercises below is not final and can be expanded and refined by the author during further scientific and pedagogical activities.
Insights collaboration space

Next to technical expertise, the ability to effectively communicate and collaborate with a large number of different stakeholders with varying technical backgrounds has always been one of the vital skills of good software architects. So far, software architects have had to mainly interact with domain experts, UX designers, managers, testers, operators, and developers, with developers being one of the most important stakeholder groups for them. In the future, we believe, there will be a new group of stakeholders equally important to developers: data scientists. So the ability to effectively communicate and collaborate with data scientists will become a paramount skill of software architects. Data-driven services will be a large driver for innovation and new business models in the upcoming years. However, the design of those services in a quality that is acceptable to users will require much closer collaboration of the different disciplines than in traditional software engineering projects. In terms of software engineering, it is crucial to facilitate this challenging cross-disciplinary collaboration in the best possible way. As a result of a research cooperation between John Deere and Fraunhofer IESE, we contribute the ICSpace app, an insights collaboration space for cross-disciplinary teams jointly working on the design of data-driven services.
An Unsupervised Approach for 3D Face Reconstruction from a Single Depth Image

In this paper, we propose a convolutional encoder network to learn a mapping function from a noisy depth image to a 3D expressive facial model. We formulate the task as an embedding problem and train the network in an unsupervised manner by exploiting the consistent fitting of the 3D mesh and the depth image. We use the 3DMM-based representation and embed depth images to code vectors concerning facial identities, expressions, and poses. Without semantic textural cues from RGB images, we exploit geometric and contextual constraints in both the depth image and the 3D surface for reliable mapping. We combine the multi-level filtered point cloud pyramid and semantic adaptive weighting for fitting. The proposed system enables the 3D expressive face completion and reconstruction in poor illuminations by leveraging a single noisy depth image. The system realizes a full correspondence between the depth image and the 3D statistical deformable mesh, facilitating landmark location and feature segmentation of depth images.
Multi-classifier Guided Discriminative Siamese Tracking Network

Recent siamese trackers regard the tracking task as a similarity learning problem. However, these trackers ignore the background information and result in lacking the discriminability. Besides, they can not adapt to some challenges owing to the fixed template during tracking. To address these problems, we design a multi-classifier guided discriminative siamese tracking network, which consists of three parts: siamese base tracker, multi-classifier module and an online template update mechanism with bounding box selection strategy. Specifically, the siamese base tracker is used to generate classification scores and regression offsets. Then, we utilize the ResNet50 and SeNet50 to extract the features for training the classifiers online. Besides, we replace the original loss function in online module with a re-weighted loss to balance the sample weights. After that, we fuse their classification scores maps, combine with the scores map from siamese base tracker to improve its discriminatibility and boost regression accuracy. Moreover, the fused classification scores map not only can improve the discriminability but also can guide the template update. Finally, we add a bounding box selection strategy based on the template update mechanism to get more accurate results. The extensive experiments on OTB2015, VOT2018, GOT-10k and VOT2019 demonstrate the competitive performance of our tracker against the state-of-the-art trackers.
Translations of public notices in Macao

A public notice is a notice released by governments or other institutions offering “directing, prompting, restricting or compelling” (Lu 2004: 38) information to the public. Public notices can be divided into single-modal and multimodal ones. The former only contains the language, while the latter usually includes language and image and is widely used in our daily life. In multimodal public notices, language and image cooperate to achieve meaning potentials. This chapter aims to investigate the translated public notices in Macao from a multimodal perspective. With a self-built corpus of multimodal public notices in Macao, based on the visual social semiotics (Kress and van Leeuwen 1996, 2001, 2006), it first analyzes the verbal realisation of appellative function in the translation of public notices, and then examines the visual realisation of interpersonal function in the images of public notices. Finally, it discusses the multimodal cooperation between language and image in public notices.