Reviewerly/exHarmony huggingface dataset:
Analysis and suggestion of double-blind review comments based on NLP technology

The double-blind review dissertation is a key link in strengthening quality supervision and inspection, promoting standardized management of degree awarding, improving internal quality assurance system, and accelerating the high-quality development of postgraduate education in the new era. Experts review the dissertation and put forward revision opinions, which largely reflect the problems existing in the dissertation, and have important reference value for the guidance of graduate dissertation. This paper analyzes the review comments received by computer dissertations through word cloud analysis, text feature extraction and other NLP technologies, summarizing common problems and puts forward corresponding suggestions, so as to provide more targeted reference for the guidance of dissertations.
Optimized Implementation of SHA-512 for 16-Bit MSP430 Microcontrollers

The enormous growth of the Internet of Things (IoT) in the recent past has fueled a strong demand for lightweight implementations of cryptosystems, i.e. implementations that are efficient enough to run on resource-limited devices like sensor nodes. However, most of today’s widely-used cryptographic algorithms, including the AES or the SHA2 family of hash functions, were already designed some 20 years ago and did not take efficiency in restricted environments into account. In this paper, we introduce implementation options and software optimization techniques to reduce the execution time of SHA-512 on 16-bit MSP430 microcontrollers. These optimizations include a novel register allocation strategy for the 512-bit hash state, a fast “on-the-fly” message schedule with low RAM footprint, special pointer arithmetic to avoid the need to copy state words, as well as instruction sequences for multi-bit rotation of 64-bit operands. Thanks to the combination of all these optimization techniques, our hand-written MSP430 Assembler code for the SHA-512 compression function reaches an execution time of roughly 40.6k cycles on an MSP430F1611 microcontroller. Hashing a message of 1000 bytes takes slightly below 338k clock cycles, which corresponds to a hash rate of about 338 cycles/byte. This execution time sets a new speed record for hashing with 256 bits of security on a 16-bit platform and improves the time needed by the fastest C implementations by a factor of 2.3. In addition, our implementation is extremely small in terms of code size (roughly 2.1k bytes) and has a RAM footprint of only 390 bytes.
IDE augmented with human-learning inspired natural language programming

Natural Language (NL) programming, the concept of synthesizing code from natural language inputs, has garnered growing interest among the software community in recent years. Unfortunately, current solutions in the space all suffer from the same problem, they require many labeled training examples due to their data-driven nature. To address this issue, this paper proposes an NLU-driven approach that forgoes the need for large numbers of labeled training examples. Inspired by how humans learn programming, this solution centers around Natural Language Understanding and draws on a novel graph-based mapping algorithm. The resulting NL programming framework, HISyn, uses no training examples, but gives synthesis accuracies comparable to data-driven methods trained on hundreds of samples. HISyn meanwhile demonstrates advantages in terms of interpretability, error diagnosis support, and cross-domain extensibility. To encourage adoption of HISyn among developers, the tool is made available as an extension for the Visual Studio Code IDE, thereby allowing users to easily submit inputs to HISyn and insert the generated code expressions into their active programs. A demo of the HISyn Extension can be found at https://youtu.be/KKOqJS24FNo.
APPLICATION OF A FUZZY CONTROLLER IN THE PROCESS OF AUTOMATED POLYETHYLENE FILM THICKNESS CONTROL

The present article aims to describe the design of a fuzzy controller used for automated control of the thickness of the extruded polyethylene film effected by the adjustment of the actuator in the cooling ring. In order to determine whether the designed controller operates properly, a model extruder was created and a simulation study was carried out. The Simulink programming environment integrated with Matlab was used for the development of the fuzzy controller and the simulation. The conducted simulation study demonstrated that the implementation of the designed controller would enable the adjustment of thickness on the perimeter of the film tube and quick reaction to possible departure in the assumed film thickness in mass production.
Deep Speaker Embeddings Based Online Diarization

This paper describes our experiments with the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) model for development of an online diarization system. For this task several UIS-RNN models based on different speaker embeddings extractors were trained. These systems were evaluated in terms of Diarization Error Rate (DER) metric on public and private test datasets. Also systems were tested on real dialogue data recorded in a bank office. Proposed online models outperform standard offline Agglomerative Hierarchical Clustering (AHC) approach and are compatible with the state-of-the-art Bayesian HMM (VBx) offline method.
Serious VR Simulation: J. Forrester's Beer Game in Virtual Reality

Teaching supply chain management requires a significant level of abstraction and theory. Some critical challenges are to illustrate incentive conflicts in supply chains and systems dynamics as they materialize in the bullwhip effect. An insightful solution to that end is Jay Forrester's beer game. While its board game versions suffer from lengthy instructions, counting, and bookkeeping exercises, online versions suffer less immersion. In our project, we seek to combine the best of both worlds by creating a digital beer game version in VR as a serious simulation. Pursuing a significant degree of hardware independence, we first implemented and tested a version in WebXR. Due to performance issues in a classroom trial, we implemented a Unity simulation. To feature the multiplayer setting and provide data for subsequent classroom discussion and analysis, we store all data on a web server. Students who cannot access the simulation via VR, for instance, due to motion sickness, can still participate using a standard web interface. While a comprehensive test is outstanding, we present first impressions and discuss the next steps.
A study of Chinese word separation based on bi-directional LSTM

In this paper, the method of machine learning and depth learning is applied to the field of Chinese word segmentation, and the traditional method of word segmentation based on machine learning is improved to improve word segmentation effect. In this paper, the vector corpus is quantized, and the injected LSTM adds the context relation in the language to the vector, which provides sufficient context information for the next conditional random word segmentation, thus improving the accuracy of the word segmentation. Compared with the general neural network, the advantage of LSTM is that it can preserve the dependency information of context. Compared with ordinary loop neural network, LSTM can not keep the gradient dispersion and gradient explosion to keep long distance dependence information, so as to better support word segmentation. This paper validates the proposed model on the corpus provided by the university of Beijing language, and tests and compares the effect of the traditional model on the same data set. The experimental results show that the fusion of the depth of learning characteristics and the shallow machine learning characteristics of the Chinese word segmentation compared to the traditional machine learning word segmentation, probability model word segmentation, word annotation word and word segmentation effect has improved to a certain extent. In the corpus of Peking University, our experimental results achieved 92.80% F value, which was 1% higher than that of conventional neural network segmentation method.
Integration and Development of Internet and English Teaching System

In the context of the Internet era, the rapid development of educational network informatization has led to the increasingly widespread application of Internet information technology in English teaching. This paper addresses the problems and difficulties in the application of Internet information technology in English teaching and puts forward strategies to cope with the new situation of university English teaching and management, in order to explore new research perspectives for promoting the modernization of university English teaching and the innovative development of university English teaching.
A Novel Efficient Discriminative Projection Learning for Big Data

For big data, hashing methods are currently utilized to learn hash codes; however, the performance of hashing methods degenerates rapidly as the code length increases. In contrast to the existing hashing methods, we develop a novel feature learning method named discriminant projection for big data. Specifically, the proposed projection learning method consists of two processes: optimal subset learning and discriminative projection learning. Optimal subset learning is applied to obtain a small-scale dataset; this reduces the amount of training dataset s and improves training efficiency. Discriminative projection learning is applied to learn the discriminant projection; this can make the same class samples more compact, and keep different class samples far away from each other. The proposed framework considers the discriminant information of the training data. Results of the experimental conducted with four databases clearly demonstrate the superior performance of the proposed framework.
JFT: A Robust Visual Tracker Based on Jitter Factor and Global Registration

Visual object tracking is an important yet challenging task in computer vision, whose accuracy is highly subject to the problems of camera motion/shake and occlusion. In order to solve these two challenging problems and improve the tracking accuracy of real scenes, this paper proposed a robust visual object tracker based on Jitter Factor and global registration. The proposed tracker firstly extracts the histogram of oriented gradient (HOG) features and color features of the target object to train the correlation filter. When the response map is unreliable, the proposed tracker treats the tracking problems as a global background motion and target motion problem, and then evaluates the state (tracking or missing) of the target by using Jitter Factor. If the target is assumed to be missing, global image registration and correlated Kalman filter would be applied to correct and predict the corrected target position. Experimental results on RGB-T234 show that after introducing the proposed Jitter Factor and global image registration, the correlation-filter-based trackers gained a ≥ 2.2% increase in precision rate.
Extraction of Facts from Web-Tables based on Semantic Interpretation Tabular Data

Knowledge graph engineering is a complex and time-consuming task. This process can be automated by using various types of information. Web-tables are worthy sources of relational data and can be used for knowledge graph augmentation with new facts. In this paper, we propose a semi-automated approach for knowledge graph engineering based on extracting data from web-tables. The main feature of our approach is a new method for the automatic recovery of semantics of webtables using an ontological schema included in a target knowledge graph. The proposed method consists of five stages: preprocessing, annotating named entity and literal columns, annotating relationships between columns, and extracting specific entities (facts). The approach is implemented in the form of a prototype of a web-based tool. We also present an experimental evaluation of the proposed approach and a case study in the domain knowledge graph engineering for the TALISMAN framework. The results of our experiments show the prospects of using the proposed solution to support the extraction of new facts from semantically annotated tabular data.
Design and Implementation of Course Articulation Information System

The course articulation information system construction is required by the educational modernization and high-quality education. Given this, such a system was designed by following the principle of the main leader in charge, the people-oriented principle, the principle of authority consistency, the systematic and dynamic principle, the stability principle, and the principle of easy development and maintenance. Lenovo R4900d was selected as the physical platform, J2EE as the integrated development environment and NetBeans 8.0 as the development tool to develop and apply the course articulation information system. Next, the talent training programs, curriculum syllabuses and teaching plans of secondary vocational schools and undergraduate colleges were shared, inquired and analyzed, in an effort to solve the articulation information asymmetry between secondary vocational education and undergraduate education, provide information reference for teachers to improve their teaching and enhance the educational quality.
Binary-perspective Asymmetrical Twin Gain: a Novel Evaluation Method for Question Generation

We propose a novel evaluation method for Question Generation (QG) task. It is designed to verify the quality of the generated questions in terms of different references, including not only the manually-written questions (i.e., ground truth) but also their variants. Back translation is utilized to obtain the variants, and accordingly, they generally appear as paraphrases of the ground-truth examples. In particular, an Asymmetrical Twin Gain (ATG) is proposed for binary-perspective evaluation using the existing metrics, such as BLEU and ROUGE-L, respectively. It enables both the metrics to be observed from two perspectives, including the consistency between QG results and ground-truth examples, as well as that of variants. The experiments on the publicly-available benchmark SQuAD demonstrate the reliability of ATG. More importantly, ATG is proven effective for indicating the stable QG performance. It is noteworthy that the proposed binary-perspective evaluation is explored for assisting the conventional evaluation methods, instead of replacing them. The contribute can be identified as the additional insight into the robustness of QG when some slightly-different references (e.g., paraphrases) are offered for evaluation. All the models and source codes in the experiments will be made publicly available to support reproducible research.
Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger

This paper introduces DepTrigger, a simple and effective model in low-resource named entity recognition (NER) based on multi-hop dependency triggers. Dependency triggers refer to salient nodes relative to an entity in the dependency graph of a context sentence. Our main observation is that triggers generally play an important role in recognizing the location and the type of entity in a sentence. Instead of exploiting the manual labeling of triggers, we use the syntactic parser to annotate triggers automatically. We train DepTrigger using an independent model architectures which are Match Network encoder and Entity Recognition Network encoder. Compared to the previous model TriggerNER, DepTrigger outperforms for long sentences, while still maintain good performance for short sentences as usual. Our framework is significantly more cost-effective in real business.
A New Robust Level Set Segmentation Method Based of New Saliency Driven Region (LSNSDR): Application to Medical Images

The segmentation of medical images is an important step in different applications such as visualization, quantitative analysis and image-guided surgery. Level Set method (LSM) is popular in image segmentation due to its intrinsic features for handling complex shapes and topological changes. In this paper, we present a novel method Level Set evolution (LSNSDR), wherein we propose to use both saliency map and color intensity as region external energy to motivate an initial evolution of level set function (LSF). Herein, a complete comparative quantitative study of the considered approaches was established. The behavior of these approaches was illustrated on real biomedical images from various fields.
Web Archiving as Entertainment

We want to make web archiving entertaining so that it can be enjoyed like a spectator sport. To this end, we have been working on a proof of concept that involves gamification of the web archiving process and integrating video games and web archiving. Our vision for this proof of concept involves a web archiving live stream and a gaming live stream. We are creating web archiving live streams that make the web archiving process more transparent to viewers by live streaming the web archiving and replay sessions to video game live streaming platforms like Twitch, Facebook Gaming, and YouTube. We also want to live stream gameplay from games where the gameplay is influenced by web archiving and replay performance. So far we have created web archiving live streams that show the web archiving and replay sessions for two web archive crawlers and gaming live streams that show gameplay influenced by the web archiving performance from the web archiving live stream. We have also applied the gaming concept of speedruns, where a player attempts to complete a game as quickly as possible. This could make a web archiving live stream more entertaining, because we can have a competition between two crawlers to see which crawler is faster at archiving a set of URIs.
An Efficient Quasi Comparison-free Bidirectional Architecture for Sorting Algorithm

A novel low-power, high-speed quasi-comparison free bidirectional architecture for sorting algorithm is proposed in this paper. In bidirectional sorting, the set of data to be sorted is divided into low index and high index parts. Bidirectional sorting enables the tasks to be done in the higher and lower index parts simultaneously. The proposed architecture improves the performance of the bidirectional architecture by reducing power and area consumption. Along with power and area reduction, the delay has also been significantly lowered. With the modifications in the modules of bidirectional architecture, there is no need to switch from count to selection mode, which leads to a lot of power consumption in the overall architecture. The proposed bidirectional architecture is coded in Verilog HDL using Xilinx ISE and Cadence Genus synthesis solution with 45nm technology is used to synthesize the architecture. The performance of proposed architecture is compared with the existing architecture concerning the number of required sorting cycles, power, area, and delay. The results show that at the expense of the same number of sorting cycles, power consumption, are, and delay are notably reduced by the proposed architecture. With the improvements achieved in the proposed bidirectional architecture, it is an efficient and economic sorting architecture for many applications.
Object detection and tracking method based on spiking neuron network

Aiming at the problems of large parameter capacity and heavy dependence on acceleration framework for complex target detection and tracking model, the conversion method between artificial neural network and impulse neural network is studied in this work to implement target detection and tracking task based on the spiking neural network. Firstly, the convolution modules Alexnet, vgg11-5c and vgg16-8c of the full convolution twin network Siamese-FC are properly adjusted and cut to adapt with the characteristics and limitations of impulse neurons, and then the Norm-SiamFC network model is constructed and trained. Finally, the migration parameters are adopted to construct a spiking network Spiking-SiamFC for object detection and tracking, which achieves good tracking effect and occupies less resources, thus the model can be widely utilized in edge devices with limited resource storage capacity.
ICAF: Iterative Contrastive Alignment Framework for Multimodal Abstractive Summarization

Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm. Due to semantic gaps between computer vision and natural language processing, current methods often treat multiple data points as separate objects and rely on attention mechanisms to search for connection in order to fuse together. In addition, missing awareness of cross-modal matching from many frameworks leads to performance reduction. To solve these two drawbacks, we propose an Iterative Contrastive Alignment Framework (ICAF) that uses recurrent alignment and contrast to capture the coherences between images and texts. Specifically, we design a recurrent alignment (RA) layer to gradually investigate fine-grained semantical relationships between image patches and text tokens. At each step during the encoding process, crossmodal contrastive losses are applied to directly optimize the embedding space. According to ROUGE, relevance scores, and human evaluation, our model outperforms the state-of-the-art baselines on MSMO dataset. Experiments on the applicability of our proposed framework and hyperparameters settings have been also conducted.
Defense Against Query-Based Black-Box Attack With Small Gaussian-Noise

Although deep neural networks (DNNs) show un-precedented performance in various tasks, the vulnerability brought by adversarial samples to the models can incur security concerns, such as causing accidents by automatic driving, or in industrial manufacturing. Due to the discrete nature of textual data and the limitation of real-world access to the model, more and more attacks focus on iterative query attacks under black-box scenarios. The core idea is to query the models frequently to obtain the mapping relations between different input samples and the outputs, which guides the attack’s direction. Once we break down the input-output mapping relations, it will affect the attack’s query and local search process, which enables the defense against such attacks. With this motivation, we add tiny noise to the input samples to break the mapping relationship obtained by black-box attacks and we name the defense method as Gaussian Noise Perturbation Defence (GNPD). We analyze how the noise hinders the attack theoretically and demonstrate the effectiveness of the defense method on two datasets and three language models. The experimental results corroborate our analysis and our method has little impact to the performance of the original model.
db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion Planning

We consider time-optimal motion planning for dynamical systems that are translation-invariant, a property that holds for many mobile robots, such as differential-drives, cars, airplanes, and multirotors. Our key insight is that we can extend graph-search algorithms to the continuous case when used symbiotically with optimization. For the graph search, we introduce discontinuity-bounded A* (db-A*), a generalization of the A* algorithm that uses concepts and data structures from sampling-based planners. Db-A* reuses short trajectories, so-called motion primitives, as edges and allows a maximum user-specified discontinuity at the vertices. These trajectories are locally repaired with trajectory optimization, which also provides new improved motion primitives. Our novel kinodynamic motion planner, kMP-db-A*, has almost surely asymptotic optimal behavior and computes near-optimal solutions quickly. For our empirical validation, we provide the first benchmark that compares search-, sampling-, and optimization- based time-optimal motion planning on multiple dynamical systems in different settings. Compared to the baselines, kMP- db-A* consistently solves more problem instances, finds lower- cost initial solutions, and converges more quickly.