Reviewerly/exHarmony huggingface dataset:
Extensional and Intensional Semantics of Bounded and Unbounded Nondeterminism

We give extensional and intensional characterizations of functional programs with nondeterminism: as structure preserving functions between biorders, and as nondeterministic sequential algorithms on ordered concrete data structures which compute them. A fundamental result establishes that these extensional and intensional representations are equivalent, by showing how to construct the unique sequential algorithm which computes a given monotone and stable function, and describing the conditions on sequential algorithms which correspond to continuity with respect to each order. We illustrate by defining may-testing and must-testing denotational semantics for sequential functional languages with bounded and unbounded choice operators. We prove that these are computationally adequate, despite the non-continuity of the must-testing semantics of unbounded nondeterminism. In the bounded case, we prove that our continuous models are fully abstract with respect to may-testing and must-testing by identifying a simple universal type, which may also form the basis for models of the untyped {\lambda}-calculus. In the unbounded case we observe that our model contains computable functions which are not denoted by terms, by identifying a further "weak continuity" property of the definable elements, and use this to establish that it is not fully abstract.
Unambiguity of Python Language Elements for Static Analysis

Static analysis is a technique for gathering some meaningful information during compilation-time and using it for further processing. This technique is applied in bug finding, code comprehension and in many other areas. However, static analysis gives a static view of the source and provides limited information about the dynamic behavior of a program. This limitation is generally considered to be a major barrier for dynamically typed languages, like Python, since in many situations it is hard to derive the most basic properties of variables or functions, sometimes we can’t even find the location of their definition. In this paper we present our experimental results that show this is not necessary a hard barrier in practical industrial projects. We analyzed open-source Python libraries, including the Python Standard Library itself as part of the CodeCompass code comprehension framework, and found that in a high number of the cases it was possible to decide the mentioned important features in unambiguous way. The consistent coding conventions make it possible to reduce unambiguity in static analysis of dynamic languages.
Performance analysis of AI aided embedded OFDM receiver based on RK3399 platform

Orthogonal frequency division multiplexing (OFDM) technology is widely used to deal with the multipath effect. With the rise of artificial intelligence technology, deep learning method has been initially applied in the field of channel estimation and signal detection of OFDM system. This paper proposed an AI aided embedded OFDM receiver based on RK3399 platform. The channel estimation model based on CNN and BiLSTM is constructed, and PC platform and TB-RK 3399 embedded platform are used to train and deploy the proposed deep learning network model offline and online respectively. Experimental results show that, compared with the traditional LS and MMSE channel estimation methods, the proposed channel estimation method has a great improvement in estimation error and BER performance. The semi physical receiver based on TB-RK3399 achieves similar receiving performance with PC platform.
A Security SLA Negotiation Mechanism Oriented to Mimic Cloud

Mimic cloud systems can effectively defend against security threats such as vulnerabilities and backdoors in the cloud environment, and improve the security and reliability of cloud services. However, in view of the service quality and security requirements of different users, the mimic cloud system usually has a certain degree of blindness when selecting heterogeneous executors, and it is inevitable to measure the diversity of heterogeneous executors. Based on this, a security SLA negotiation mechanism for mimic cloud is proposed, which can provide a mapping from user needs to mimic cloud resource requirements. First, the cloud platform analyzes user needs from the formal SLA template, and screens out the executors that meet the user&#x0027;s service quality requirements; furthermore, the mimic cloud platform constructs a diversified system attribute matrix to measure the diversity of heterogeneous group of executors; finally, the analytic hierarchy process is used to select a set of optimal heterogeneous executors from the pareto optimal solution set that meets the needs of users to provide services for users.
Gesture Based System for User Interface Control

The paper researches the features of the Creative Intel RealSense SR300 camera as a means of hand gesture recognition and using them to control certain application software. For this purpose, is created console application in JAVA operate the camera and hand gesture recognition that used to control the media player, photo view, change zoom and regime change maps in web-based version of Google Maps and play or pause clips, enter or exit of YouTube full screen mode.
A Capability-oriented Method for Integration of Liberal Arts Knowledge Graph in Primary Education

Education is one of the major problems in people's livelihood in China nowadays, especially in field of basic primary education. Personalized learning is a very important research and application topic in field of primary education, in which the construction of knowledge graph is the core and foundation of application. This paper focuses on constructing and integrating the knowledge graph of liberal arts subject in field of primary education. First, the liberal arts concepts are extracted and subject ontologies are constructed. Second, multi-disciplinary ontology is integrated to construct the conceptual model of field of Liberal arts. Third, the method of acquiring data layer of knowledge graph is discussed and implemented. At last, the application of knowledge graph in field of primary education is listed, and the practice of this method provides ideas for the construction of knowledge graph in field of primary education, and also knowledge graph of liberal arts can provide support for personalized education applications.
System Architecture of Unobtrusive Sensors for Supporting Home Care and Independet Living

The paper deals with an implementation of unobtrusive sensors installed in home environment for continuous monitoring of functional-health parameters of the observed persons. A multi-tier architecture links sensory devices through sensor-data concentrators to a home server. Automated sensory measurements are supported by a concept of sensoractivated events, event-driven data transmission and processing by a dedicated application interface. Its logic and data structures are revealed. Examples of three typical execution scenarios are given and a short description depicts clinical installation of proposed system for testing purposes.
Application of digital image correlation algorithm in evaluation of alkali resistant glass for rubidium atomic clock

Because of the obvious deformation difference for the alkali resistant glass tubes with different internal stress, in this paper, a new method for evaluating the internal stress and annealing quality of alkali resistant glass tubes based on digital image correlation algorithm is proposed. According to the deformation change rate of alkali resistant glass tube that obtained by digital image correlation algorithm, it can directly determine whether the internal stress meets the production standard. The alkali resistant glass tubes which have undergone abnormal(annealing time less than 5 hours and annealing temperature lower than 630 ℃) and normal annealing operation were selected as samples of experimental group and control group. The samples of these two groups were heated to 200 ℃ and then cooled to room temperature. The displacement fields between deformed images and the initial image are calculated by digital image correlation algorithm and the mean values change rate of the displacement fields are regard as the evaluated basis for the internal stress and annealing quality. Since the new method has many advantages such as simple, efficient and non-contact and only two continuous pictures are required in the detection process, it has been applied in the actual industrial production testing. In addition, this method provides a new approach for the internal stress detection of other solid materials.
Bayesian Optimization-Based Analysis and Planning Approach for Self-Adaptive Cyber-Physical Systems

Modern cyber-physical systems (CPSs) operate in dynamic and uncertain environments or operational contexts. Therefore, it is necessary to design systems that self-adapt according to context changes at run-time. However, making a decision on the optimal adaptation in a changing and uncertain context is a complex task. This paper proposes a modular approach for analysis and planning, which generates the optimal system adaptations based on individual sub-decisions. Each sub-decision corresponds to a model @ RT that deals with specific aspects of the context relevant for the concrete adaptation. As a proof-of-concept, we introduce a multi-robot use case to show the possible performance gains of the suggested approach compared with non-adaptive analysis and planning.
BDMPathfinder: a tool for exploring attack paths in models defined by Boolean logic Driven Markov Processes

Security officers employ adversarial modelling techniques to drive analysis over complex attack surfaces. One technique for modelling safety and security is Attack Trees (AT) that uses logic gates to address the likelihood of malicious actions and outcomes. However, attack progression over time is not considered in AT analysis. To cope with this, the formalism of Boolean logic Driven Markov Processes (BDMP) extends AT where triggered transitions connect the sub-trees pertaining the hierarchy. BDMP is embedded with Markovian processes notions where modellers decorate transitions with likely timestamps to compute path probabilities. The time attackers take to complete any given malicious incursion stretches over a range of possibilities. Those durations are often difficult to cope due to a wealth of intangible characteristics such as adversaries' technical abilities, tool adequacy, quickness to devise vulnerability exploits, or countermeasures or defences in place in targeted infrastructures. The current BDMP analysis pipeline is sequential and generates a single output for one mission time. We propose BDMPathfinder, a tool that iterates over multiple durations to compute the totality of path attacks for BDMP models. We show its properties and trade-offs in a comprehensive case study exercising most common BDMP primitives by plotting the paths and probabilities altogether.
Image Super-Resolution Reconstruction Based on Improved Dense Residual Network

In order to solve the problems of low image feature utilization, low quality and blurred texture details after reconstruction by super-resolution technology, a network model based on a combination of dense residual blocks and multi-scale convolutional layers is proposed. Firstly, dense residual blocks are introduced to enable the model to make more use of feature information and to improve the quality of the reconstructed image; then multi-scale convolutional layers are used to extract information of different scales to enhance the learning ability of the network; then Charbonnier loss is used as the loss function; Finally, the network structure is optimized by removing the batch normalization layer in the network model. Compared the reconstruction results of our model in this article with classic methods such as SRCNN, FSRCNN, and SRResNet on test data sets such as set5 and set14, the experimental results show that the image reconstructed by this model has a certain improvement in objective evaluation indicators and subjective visual effects on the test data set.
Targets Detection Using Multiple Foveas

Target detection enables running a robotic task. However, their limited resources make large amount of data processing harder. Image foveation is an approach that can reduce processing demand by reducing the amount of data to be processed. However, as an important visual stimulli can be attenuated by this reduction, some strategy should be applied in order to keep/recover awareness of it. This work compares gradient descent (potential field), maximum likelihood, multilateration, trilateration, and barycentric coordinates to solve this problem in a multiple dynamic and fixed foveas context. Our results demonstrate that the proposed methodology detects the target converging with an average euclidian distance of 51 pixels from the target&#x2019;s center position.
An Approach to Objective Optimization of High-dimensional Data

Objective optimization is crucial in industrial production. However, for high-dimensional data, it often needs to take a long time and is full of challenges to get an approximate optimal solution which is very close to the optimum. Therefore, an approach to solve objective optimization problems of high-dimensional data is proposed by combining gradient descent and clustering, with simulated annealing. Considering the optimization problem underneath coal blending in electric field as a background, the proposed approach is compared with the traditional simulated annealing algorithm and that combined with gradient descent. Experiments shows that the proposed approach can improve the average matching degree by 1.94% and 2.14%, and the best matching degree by 0.08% and 0.09%, compared to that based on the traditional simulated annealing algorithm and that combined with superimposed gradient descent respectively. Experiments and analyses also show that the proposed approach can reduce time consumption largely, and has a great improvement in stability, accuracy of the optimal solution as well as the efficiency of solution searching.
ARTPHIL: Reversible De-identification of Free Text Using an Integrated Model

Organisations that collect and maintain individual data face the challenge of preserving privacy and security when using, archiving, or sharing these data. De-identification tools are essential for minimising the privacy risk. However, current data de-identification and anonymisation methods are widely used to alter the original data in a way that cannot be recovered. This results in data distortion and, hence, the substantial loss of knowledge within the data. To address this issue, this paper introduces the concept of reversible data de-identification methods to de-identify unstructured health data under the Health Insurance Portability and Accountability Act (HIPAA) guidelines. The model integrates Philter [9], the state-of-the-art tool for extracting personal identifiers from free-text, to detect confidential information and encrypt them with E-ART, lightweight encryption algorithm E-ART [10]. The performance of the proposed model ARTPHIL is evaluated using i2b2 data corpus in terms of recall, precision, F-measure and execution time. The results of the experiment are consistent with the recent de-identification method with recall of 96.93%. More importantly, the original data can be recovered, if needed, and authenticated.
Named entity recognition of legal documents based on cascade model

Aiming at the problems of long legal document entities and the lack of annotated data, a cascade model that integrates the characteristics of characters and words is proposed. The traditional NER is decomposed into two cascaded subtasks: the entity recognition and the attribute recognition. The model obtains the vector representation of text character-level and word-level through BERT and Self-attention, respectively. Then the BiLSTM is used to obtain the internal features of the serialized text. Subsequenly, CRF is used to select the entity's optimal tag sequence and the attributed optimal tag sequence. Finally, these two sequences are spliced to obtain the optimal marker sequence. In order to improve the utilization rate of the data, the label linearization is introduced for the data enchancement. The results show that the method is superior to traditional models, can effectively extract named entities of legal documents, and has the vital practical significance.
Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes. Antigens are produced by plasma cells to combat bacteria and viruses and prevent inflammation and illness. Multiple myeloma is a plasma cell cancer that starts in the bone marrow and causes the formation of abnormal plasma cells. Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. Furthermore, because the ultimate decision is based on human sight and opinion, there is a possibility of error in the result. The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears. For recognizing purposes, we have used Mask-Recurrent Convolutional Neural Network, and for detection purposes, Efficient Net B3 has been used. There are already many studies on white blood cell cancer, but very few with both segmentation and classification. We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. Also, a new data set has been made from the multiple myeloma data sets, which has been used in our classification model. This research focuses on hybrid segmentation models and increases the accuracy level of the classification model. Both of our models are trained pretty well, where the Mask-RCNN model gives a mean average precision (mAP) of 93% and the Efficient Net B3 model gives 94.68% accuracy. The result of this research indicates that the Mask-RCNN model can recognize multiple myeloma and Efficient Net B3 can distinguish between myeloma and nonmyeloma cells and beats most of the state of the art in myeloma recognition and detection.
Less false detections, fewer identity switches: methods for the improvement of deep sort

Deep sort algorithm is a multi-object tracking algorithm with high tracking accuracy and speed. However, due to the lack of detection filter and the association stage of a single frame, the accuracy of multi-object tracking is remaining enhancement. In this paper, we propose a DO-Adaptive NMS algorithm to filter the detections, and combine the K nearest neighbor algorithm with the intersection of union algorithm to sharpen features of the trajectories. Besides, we put forward a weighted algorithm of motion information and appearance information, which takes the disappear time of trajectories into consideration. Experiments show that the methods mentioned above all perform better than the original algorithm.
Research on Inscription Rubbings Text Detection Based on Deep Learning

It is of great practical significance to use advanced digital and intelligent technology to digitally apply and effectively protect China's ancient cultural heritage and resolve the problems that cannot be solved by manpower in a faster and more efficient way. Based on the YOLOv4 target detection algorithm, this paper effectively expands the collected data according to the data characteristics of inscription rubbings and text images, and carries out many training and verification tests. The accuracy of the detection model is 92.3%, so as to improve the speed and quality of rubbing arrangement of inscriptions.
Immersive Experience with Non-Player Characters Dynamic Dialogue

Non-Player Character (NPC) is one of the important elements in the game, because NPCs can liven up the atmosphere in the game by means of intense interaction with players with various functions. This has an impact on the game experience which is more immersive than the game being played. This study provides an overview so that NPCs are able to have dynamic dialogue with players, and this study also discusses chatbots as a communication technology that is currently emerging and its impact when combined with NPC.
Design of Patient Rehabilitation Training and Rest Monitoring Device

The rehabilitation center is a department set up by the hospital for sports medicine. When patients undergo rehabilitation training, they need to be trained and rested in strict accordance with the doctor's requirements. However, sometimes there are too many patients and the number of doctors and nurses is limited, so each patient cannot be fully taken care of. Therefore, the training of patients who do not have the concept of time or the rest time is too long or too short, which will cause unsatisfactory medical effects. The components of the patient rehabilitation training and rest time monitoring device are STM32F103C8T6 microcontroller core board, buttons, travel switch and buzzer. The status of the travel switch distinguishes whether the patient is resting (lying down or sitting down) or training (standing). The function of the button is to set the patient's training and rest time. When the patient's training time does not reach the specified value, if the patient is resting, the buzzer will alarm and monitor the rest time. If the patient is training at this time, the buzzer will not act and monitor the training time; when the patient's rest time reaches the specified value, the beeper The buzzer will alarm and monitor the rest time. If the patient is training at this time, the buzzer will not act and monitor the training time.
Information communication technology and physics teachers usage

influence of ICT utilization on teachers’ effectiveness in teaching of physics was investigated. This study was conducted in Calabar Municipality Local Government Area of Cross River State. The survey design was adopted, the target population comprised of 48 secondary schools, 107 physics teachers and 2000 physics students in SS II class in Secondary School, Calabar Municipality. The samples of the study were selected using the simple random sampling procedure. It employed questionnaires and physics achievement test (PAT) as a guide in data collection. The data collected were analyzed using Pearson Correlation. And the study showed that three variables: knowledge accessibility and utilization of ICT were related to the academic achievement of the physics students. This study recommended among others that physics teachers should make use of technology in their lessons to enhance learners understanding of concepts.
SQL in Python

This chapter introduces the reader to the way the database language, “Structured Query Language,” can be accessed and used from a Python program. It will be demonstrated how to create the database file. When this is done, the reader will be able to create database tables. Then, it will be shown how to insert data into the table, how to amend data in the table, how to delete data from the table, and how to print out data held in the table.
A Numerical Real Time Web Tracking and Scrapping Strategy Applied to Analysing COVID-19 Datasets

With the huge amount of data collected from the web, it is hard to manually analyze and extract useful incites from tables, matrices, or rows of data. Therefore, we need a way to represent these data (maps or graphs) to analyze/interpret them. Data visualization makes it easier to identify trends, patterns, and outliers with large datasets. Data Analytics is used to analyze raw data to make conclusions about the information. In this paper, we tackled web scrapping strategies to get COVID-19 data from the web, and then we went through some data analysis and exploration using EDA.