Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automatic Case Review System Based On Deep Learning

At present, the lawsuit documents provided by the parties during the court filing process still require to be reviewed through manual work. Faced with a large amount of lawsuit documents and limited reviewers, the review is less efficient. This paper proposes an automatic case review system based on deep learning, which can automatically review the lawsuit documents from the parties and identify the type of the case independently, instead of being manually reviewed. And it can recommend similar cases for reference by calculating the text similarity, which can help judges make fair decisions. In this paper, convolutional neural network (CNN), a deep learning model, is used to extract the key features for text classification in lawsuit documents and the features are input into the Softmax layer to classify the case. Then the system matches the case knowledge database to help the parties check for vacancies and provide suggestions, which realizes the automatic case review and improve the review efficiency.
DDoS Detection System: Using a Set of Classification Algorithms Controlled by Fuzzy Logic System in Apache Spark

Distributed denial of service (DDoS) attacks are a major security threat against the availability of conventional or cloud computing resources. Numerous DDoS attacks, which have been launched against various organizations in the last decade, have had a direct impact on both vendors and users. Many researchers have attempted to tackle the security threat of DDoS attacks by combining classification algorithms with distributed computing. However, their solutions are static in terms of the classification algorithms used. In fact, current DDoS attacks have become so dynamic and sophisticated that they are able to pass the detection system thereby making it difficult for static solutions to detect. In this paper, we propose a dynamic DDoS attack detection system based on three main components: 1) classification algorithms; 2) a distributed system; and 3) a fuzzy logic system. Our framework uses fuzzy logic to dynamically select an algorithm from a set of prepared classification algorithms that detect different DDoS patterns. Out of the many candidate classification algorithms, we use Naive Bayes, Decision Tree (Entropy), Decision Tree (Gini), and Random Forest as candidate algorithms. We have evaluated the performance of classification algorithms and their delays and validated the fuzzy logic system. We have also evaluated the effectiveness of the distributed system and its impact on the classification algorithms delay. The results show that there is a trade-off between the utilized classification algorithms’ accuracies and their delays. We observe that the fuzzy logic system can effectively select the right classification algorithm based on the traffic status.
Novel methods for distributed and privacy-preserving data stream mining

The growing number of “big” datasets present many opportunities for data mining, but also raise a variety of new challenges. Datasets may take the form of continuous streams with constantly changing patterns, they may be too widely distributed to be centralised for analysis at a single location, or they may contain sensitive values that data owners are not willing to share due to privacy concerns. Much past research has considered these issues individually, but few existing methods can address combinations of these properties. Therefore, this research develops methods for distributed and privacy-preserving data stream mining: a novel Hierarchical Distributed Stream Miner (HDSM) that learns relationships between the features of separate streams with minimal data transmission to central locations, and two data perturbation methods for privacy-preserving stream mining based on the combination of random projection, random translation, and additive noise. Experimental evaluation of HDSM demonstrates significant improvements in classification accuracy over existing distributed stream mining approaches while minimising data transmission and computational costs. HDSM’s ability to dynamically trade-off accuracy with these costs is also demonstrated. Variations of the known input-output Maximum A Posteriori (MAP) attack are developed to experimentally evaluate the data perturbation methods, and the proposed composite methods are shown to achieve a better trade-off between privacy and model accuracy than random projection alone. Finally, an approach is described for combining HDSM with data perturbation to achieve distributed privacy-preserving stream mining.
Optimization of school location-allocation using Firefly Algorithm

The zoning policy of Indonesia New Student Admission System encourages schools to accept more students from their zoning location. This condition needs to be supported by a good management to ensure that the school facilities suffice the student distribution. School location-allocation optimization problem aims to obtain optimum student distribution such that the total of students travel distance is minimized. This problem is modeled as p-median problem, and can be solved using meta-heuristic approach. In this study, firefly algorithm with two-dimensional firefly individual representation is utilized to solve South Jakarta junior school location-allocation problem. The obtained solution lowers 57.58% of student travel distance compared to the previous condition assuming that favorite schools are tend to be chosen by students.
Guiding aggregation dynamics in a swarm of agents via informed individuals: an analytical study

Self-organised aggregation, the formation of large clusters of independent agents, is an important process in swarm robotics systems since it is the prerequisite for more complex collective behaviours. Previous work on self-organised aggregation focused on the study of the individual mechanisms required to allow a swarm to form a single aggregate. In this paper, we discuss an analytical model which looks at the possibility to use the concept of informed individuals to allow the swarm to distribute on different aggregation sites according to proportions of individuals at each site arbitrarily chosen by the designer. Informed individuals are opinionated agents that selectively prefer an aggregation site and avoid to rest on the non-preferred sites. We study environments with two aggregation sites, and consider two different scenarios: one in which the informed individuals are equally distributed in numbers between the two sites; and one in which informed individuals for one type of site are three times more numerous than those on the other site. Our objective is to find out whether and for what range of model parameters the swarm distributes between the two sites according to the relative distribution of informed agents among the two sites. The analysis of the model shows that the designer capability to exploit informed individuals to control how the swarm aggregates depends on the environmental conditions. For intermediate values of the site carrying capacity, a small minority of informed individuals is able to guide the dynamics as desired by the designer. We also show that the larger the site carrying capacity the larger the total proportion of informed individuals required to lead the swarm to the desired distribution of individuals between the two sites.
Encroachment in Data Processing using Big Data Technology

the nature of big data is now growing and information is present all around us in different kind of forms. The big data information plays crucial role and it provides business value for the firms and its benefits sectors by accumulating knowledge. This growth of big data around all the concerns is high and challenge in data processing technique because it contains variety of data in enormous volume. The tools which are built on the data mining algorithm provides efficient data processing mechanisms, but not fulfill the pattern of heterogeneous, so the emerging tools such like Hadoop MapReduce, Pig, SPARK, Cloudera, Impala and Enterprise RTQ, IBM Netezza and Apache Giraphe as computing tools and HBase, Hive, Neo4j and Apache Cassendra as storage tools useful in classifying, clustering and discovering the knowledge. This study will focused on the comparative study of different data processing tools, in big data analytics and their benefits will be tabulated.
Battlespace Awareness: Heterogeneous Sensor Maps of Large Scale, Complex Environments

Abstract : Robots require high-quality maps-internal representations of their operating workspace-to localize, path plan, and perceive their environment. Until recently, these maps were restricted to sparse, 2D representations due to computational, memory, and sensor limitations. With the widespread adoption of high-quality sensors and graphics processors for parallel processing, these restrictions no longer apply: dense 3D maps are feasible to compute in real time (i.e., at the input sensor's frame rate). This thesis presents the theory and system to create large-scale dense 3D maps (i.e., reconstruct continuous surface models) using only sensors found on modern autonomous automobiles: 2D laser, 3D laser, and cameras. We demonstrate our system fusing data from both laser and camera sensors to reconstruct 7.3 km of urban environments. We evaluate the quantitative performance of our proposed method through the use of synthetic and real-world datasets. With only stereo camera inputs, our regularizer reduces the 3D reconstruction metric error between 27% to 36%with a final median accuracy ranging between 4 cm to 8 cm.
JOTE: Joint Offloading of Task and Energy in Fog-Enabled IoT Networks

Fog computing is considered to be a promising solution to enable latency-critical applications in Internet of Things (IoT). In this paper, we consider a fog-enabled IoT network where the task node can offload both the tasks and the energy to the helper nodes by applying the simultaneous wireless information and power transfer (SWIPT) technology. The task node first offloads energy to the helper nodes and then offloads the tasks to the helper nodes according to a time division multiple access (TDMA) protocol and an optimized ordering. In order to jointly minimize the task execution delay and the energy consumption, we formulate a combinatorial optimization problem. A low-complexity algorithm is then proposed to solve it based on the decomposed sub-problems. Additionally, we characterize the sufficient condition under which the joint task and energy offloading becomes beneficial. We also derive the offloading probability when taking into account the random fading channels. We further show that it is always desirable to offload both the task and the energy from the task node when the number of helper nodes goes to infinity. Numerical results are provided to validate our proposed algorithm and theoretical results.
Internet of Things Architecture Based Cloud for Healthcare

The Internet of Things (IoT) contains smart devices placed in different environments, connected with each other across networks and Internet. The integration between Things and Cloud Computing (CC) for monitoring and permanent storage is required for future IoT applications. Therefore, this paper proposes IoT architecture based Cloud for healthcare network when patients are remotely monitored by their family and physicians. This proposed architecture is different from the traditional IoT architecture that consists of Things, getaways, middleware, and application layers which in turn need connectivity insurance between them. The proposed architecture is designed and configured using Cisco Packet Tracer version 7.0 over two sites: Site 'A' located at smart home and site 'B' located at the smart hospital. The results show that the IoT based Cloud enhances the patient life style by using smart sensors and mobile application, as well as the physicians can remotely monitor the data in real time.
A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms

This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O3). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal–oxide O3 sensors, 25 electro-chemical O3 sensors, 25 electro-chemical NO2 sensors, and 60 temperature and relative humidity sensors. As ozone is a seasonal pollutant, which appears in summer in Europe, the biggest challenge is to calibrate the sensors in a short period of time. In this paper, we compare four calibration methods in the presence of a large dataset for model training and we also study the impact of a limited training dataset on the long-range predictions. We show that the difficulty in calibrating these sensor technologies in a real deployment is mainly due to the bias produced by the different environmental conditions found in the prediction with respect to those found in the data training phase.
SiamesePointNet: A Siamese Point Network Architecture for Learning 3D Shape Descriptor

We present a novel deep learning approach to extract point‐wise descriptors directly on 3D shapes by introducing Siamese Point Networks, which contain a global shape constraint module and a feature transformation operator. Such geometric descriptor can be used in a variety of shape analysis problems such as 3D shape dense correspondence, key point matching and shape‐to‐scan matching. The descriptor is produced by a hierarchical encoder–decoder architecture that is trained to map geometrically and semantically similar points close to one another in descriptor space. Benefiting from the additional shape contrastive constraint and the hierarchical local operator, the learned descriptor is highly aware of both the global context and local context. In addition, a feature transformation operation is introduced in the end of our networks to transform the point features to a compact descriptor space. The feature transformation can make the descriptors extracted by our networks unaffected by geometric differences in shapes. Finally, an N‐tuple loss is used to train all the point descriptors on a complete 3D shape simultaneously to obtain point‐wise descriptors. The proposed Siamese Point Networks are robust to many types of perturbations such as the Gaussian noise and partial scan. In addition, we demonstrate that our approach improves state‐of‐the‐art results on the BHCP benchmark.
The Development Of Computer Aided Design Learning Model In Civil Engineering Vocational Education Study Program In Universitas Negeri Semarang

Computer Aided Design (CAD) learning model in the Civil Engineering Vocational Education program in Universitas Negeri Semarang, especially 2-dimensional modeling, continues to be developed so that learning outcomes are accepted by the standards the business / construction services industry. Development of learning outcomes is inseparable from efforts to develop learning model that is used during the learning process. The development of learning models in this study focused on the development of learning methods, learning materials and CAD learning evaluation techniques. This study used the research and development using the Dick and Carrey design. The research sample was 34 students who took Computer Graphic course. The results showed that the three aspects: the development of methods, the development of teaching materials and materials and the development of evaluation techniques were categorized as excellent and very excellent. The CAD learning method was very suitable to be developed through conceptual learning methods, direct practice learning and case study learning. The use of this method was based on the needs faced in the classroom (situational). This means that the use of this method could be used at once, or varied according to the condition in the class. The use of learning equipment is one of the keys for successful learning development. The learning equipment developed in this study included the use of hand-outs, modules and supplements. The use of all three was very successful to support optimal learning outcomes. The use of all three was also aimed to facilitate the diverse learning needs of students. The development of evaluation techniques in learning included three domains: cognitive, psychomotor and affective domain. The cognitive domain was divided into two aspects: construction knowledge and the use of software. The development of evaluation techniques showed that the ability of students in each domain was in the excellent and very excellent category.
Application of Change-Point Detection in Image Retrieval

In this paper, we approach the image retrieval problem from a different angle by converting the problem into a changepoint detection problem. An algorithm is introduced for detecting multiple change-points in distributions of a sequence of independently distributed random variables. By using this algorithm, a procedure is given for image retrieval. The proposed method is evaluated via two examples, which show that the proposed method for the image retrieval has satisfactory performance in terms of the quality of the retrieved images. Keywords–Change-point detection; Image retrieval; Contamination; Outliers
Parameter Estimation Using the Python Package pymcmcstat

A Bayesian approach to solving inverse problems provides insight regarding model limitations as well as the underlying model and observation uncertainty. In this paper we introduce pymcmcstat, which provides a wide variety of tools for estimating unknown parameter distributions. For scientists and engineers familiar with least-squares optimization, this package provides a similar interface from which to expand their analysis to a Bayesian framework. This package has been utilized in a wide array of scientific and engineering problems, including radiation source localization and constitutive model development of smart material systems.
Assessment of Model Validation, Calibration, and Prediction Approaches in the Presence of Uncertainty

Model validation is the process of determining the degree to which a model is an accurate representation of the true value in the real world. The results of a model validation study can be used to either quantify the model form uncertainty or to improve/calibrate the model. However, the model validation process can become complicated if there is uncertainty in the simulation and/or experimental outcomes. These uncertainties can be in the form of aleatory uncertainties due to randomness or epistemic uncertainties due to lack of knowledge. Four different approaches are used for addressing model validation and calibration: 1) the area validation metric (AVM), 2) a modified area validation metric (MAVM) with confidence intervals, 3) the standard validation uncertainty from ASME V&V 20, and 4) Bayesian updating of a model discrepancy term. Details are given for the application of the MAVM for accounting for small experimental sample sizes. To provide an unambiguous assessment of these different approaches, synthetic experimental values is generated from computational fluid dynamics simulations of a multi-element airfoil. A simplified model is then developed using thin airfoil theory. This simplified model is then assessed using the synthetic experimental data. Each of these validation/calibration approaches are assessed for the ability to tightly encapsulate the true value in nature at locations both where experimental results are provided and prediction locations where no experimental data are available.
From embodied interaction to compositional referential communication: A minimal agent-based model without dedicated communication channels

Referential communication is a ”representation-hungry” behavior, and the bee waggle dance is a classical example of referential communication in nature. We used an evolutionary robotics approach to create a simulation model of a minimalist example of this situation. Two structurally identical agents engage in embodied interaction such that one of them can find a distant target in 2D space that only the other could perceive. This is a challenging task: during their interaction the agents must disambiguate translational and communicative movements, allocate distinct behavioral roles (sender versus receiver), and switch behaviors from communicative to target seeking behavior. We found an evolutionary convention with compositionality akin to the waggle dance, correlating duration and angle of interaction with distance and angle to target, respectively. We propose that this behavior is more appropriately described as interactive mindshaping, rather than as the transfer of informational content.
ACCESSIBILITY, BIASES AND ETHICS IN CHATBOTS AND INTELLIGENT AGENTS FOR EDUCATION

The use of chatbots is increasingly common in universities around the world to support both educational and administrative tasks. There is also a growing awareness of the importance of inclusive education and, as a consequence, the need to comply with accessibility requirements regarding web contents and web interface (WCAG). These requirements are also supported by specific laws in most countries. But such awareness does not yet reach the interfaces of conversational agents or chatbots that are being developed. Within the framework of the European project ACACIA, co-funded by the Erasmus+ program of the European Union, Artemisa, a chatbot has been created dedicated to fight against sexual harassment and search for volunteers to promote the acceptance of diversity and tolerance, has been created using a platform that facilitates the generation and management of this type of artificial intelligences. But to what extent Artemisa is an accessible chatbot or not? Is it ethically acceptable to make use of a tool that is intended to support inclusiveness but presents accessibility barriers to some users? What is the current state in terms of accessibility compliance of chatbots that work on social networks? This article seeks to answer this and other questions related to accessibility in Chatbots, conversational agents and virtual assistant’s. Based on the answer to these questions it follows that there is a need for training in interculturalism and web accessibility to combat the biases that present such entities, which are endowed with artificial intelligence and, unfortunately, in some cases causing serious damage to some people.