Reviewerly/exHarmony huggingface dataset:
Expert system building tools based on dynamically updated knowledge

The paper presents a developed software tool that includes software systems Prolog, JavaScript, Python, as well as database management systems PostgreSQL and MySQL.With the help of the created integrated development environment, experimental versions of specialized expert systems in the field of education and health care were developed. In the field of education, a prototype of an expert system focused on the support of the discipline "development and analysis of requirements for the creation of a software product" was created. This prototype of the expert system should provide an opportunity for students to build various software systems based on the formulated requirements and rules from the knowledge base of the expert system, taking into account the specifics of the relevant subject areas. In the field of health care, a prototype of an expert system focused on supporting the processes of vaccination of patients has been created. The knowledge base of the expert system includes clinical characteristics of patients, as well as a set of rules governing the decision - making processes for vaccines.
Evaluating lexical approximation of program dependence

Complex dependence analysis typically provides an underpinning approximation of true program dependence. We investigate the effectiveness of using lexical information to approximate such dependence, introducing two new deletion operators to Observation-Based Slicing (ORBS). ORBS provides direct observation of program dependence, computing a slice using iterative, speculative deletion of program parts. Deletions become permanent if they do not affect the slicing criterion. The original ORBS uses a bounded deletion window operator that attempts to delete consecutive lines together. Our new deletion operators attempt to delete multiple, non-contiguous lines that are lexically similar to each other. We evaluate the lexical dependence approximation by exploring the trade-off between the precision and the speed of dependence analysis performed with new deletion operators. The deletion operators are evaluated independently, as well as collectively via a novel generalization of ORBS that exploits multiple deletion operators: Multi-operator Observation-Based Slicing (MOBS). An empirical evaluation using three Java projects, six C projects, and one multi-lingual project written in Python and C finds that the lexical information provides a useful approximation to the underlying dependence. On average, MOBS can delete 69% of lines deleted by the original ORBS, while taking only 36% of the wall clock time required by ORBS.
Standard operating procedures for cybercrime investigations: a systematic literature review

Traditional policing involves the identification of a victim and the establishment of an offender via way of an investigation. The investigation seeks to obtain the necessary evidence through the overt or covert collection of the evidence to support a prosecution of the offender. Overt recovery of evidence involves the obtaining of statements from victims and witnesses, CCTV recovery, photographs or videos and the forensic examination of materials for fingerprints or DNA. The covert methods involve the gathering of intelligence through Covert Human Intelligence Sources (CHIS), interception of telecommunications, infiltration of crimes groups and the use of surveillance. These approaches are inherent in policing investigative procedures and have been instilled in police officers from basic training and throughout their policing careers. The advancement in cyberspace has created new challenges for policing. Crime in cyberspace operates in a nontraditional way, and the threats and the vulnerabilities of cyberspace tend to make the use of such traditional techniques in police investigation ineffective when applied against cybercrime and cybercriminals. For example, in offences of incitement and radicalization, the victims are often so far removed from each other that traditional reactive police investigations are likely to fail to identify an offender, their location and those victims before any offences being committed and thereby any potential interventions would be too late to prevent any offences being completed. Therefore, policing needs to consider new procedures that take into account the differences in the cyber landscape and how crime is committed online. This chapter performs a systematic literature review of traditional policing with a view of identifying and proposing new procedures that can be adapted to investigate and gather evidence of cybercrimes.
Development and application of massive unstructured big data retrieval technology based on cloud computing platform

In view of the large data volume and complex structure of big data in the operation of major systems, the traditional cloud computing distributed multi-layer architecture data retrieval method has been difficult to meet the needs of big data retrieval. Therefore, related research must be centered a round the structure of complex big data is unfolded, especially unstructured big data, to shape a new data cluster infrastructure. The cluster environment needs to implement stable retrieval of unstructured data through a cloud-centric method. The system introduces an unstructured big data retrieval framework as an unstructured database, as a data retrieval engine to improve the cloud computing big data non-structure retrieval service, and obtain the key code of unstructured big data under the retrieval technology. Finally, the system is applied to the exsperimental system, and an experimental unstructured big data integration system is built to realize the unified retrieval of large-scale different business functions and business data, so as to satisfy the users to quickly retrieve from a large number of heterogeneous business systems and massive data. Claim. Through search engine technology, the use of text mining, natural language processing, information retrieval and other fields of technology to further improve the precision and recall rate of full-text search. The application of this technology can meet the needs of unified retrieval of large-scale different business data; and at the same time meet the rapid response requirements of large-scale data retrieval requests. The experimental results show that the designed system has high precision and low retrieval time in the process of retrieving unstructured big data under cloud computing, which can realize the stable retrieval of unstructured cloud data.
Impact of Human Factors in Cloud Data Breach

In this paper, we present a study on the impact of human factors in Cloud data breach. Data breaches in Cloud platforms result in major concerns and thus the underlying reasons for such data breaches demand investigation. An incident of data breach may occur due to several reasons. The root cause for a data breach may be related to technological factors as well as human factors. While technological factors are mostly predictable, human factors may not be. Besides, human factors are dynamic that cannot be fully quantified. This leaves a room for the attackers to compromise systems through social engineering. The presented study seeks to find the extent to which human factors are contributors for data breaches. Analyses on 20 real life incidents of Cloud data breaches are carried out, and the reasons behind those breaches are explored to understand the possible implications of human factors in Cloud breaches.
High Order Profile Expansion to tackle the new user problem on recommender systems

Collaborative Filtering algorithms provide users with recommendations based on their opinions, that is, on the ratings given by the user for some items. They are the most popular and widely implemented algorithms in Recommender Systems, especially in e-commerce, considering their good results. However, when the information is extremely sparse, independently of the domain nature, they do not present such good results. In particular, it is difficult to offer recommendations which are accurate enough to a user who has just arrived to a system or who has rated few items. This is the well-known new user problem, a type of cold-start. Profile Expansion techniques had been already presented as a method to alleviate this situation. These techniques increase the size of the user profile, by obtaining information about user tastes in distinct ways. Therefore, recommender algorithms have more information at their disposal, and results improve. In this paper, we present the High Order Profile Expansion techniques, which combine in different ways the Profile Expansion methods. The results show 110% improvement in precision over the algorithm without Profile Expansion, and 10% improvement over Profile Expansion techniques.
Libra and the Art of Task Sizing in Big-Data Analytic Systems

Despite extensive investigation of job scheduling in data-intensive computation frameworks, less consideration has been given to optimizing job partitioning for resource utilization and efficient processing. Instead, partitioning and job sizing are a form of dark art, typically left to developer intuition and trial-and-error style experimentation.
Exploiting Sparsity for Phase Unwrapping

We consider the problem of unwrapping the phase of two-dimensional interferograms, and adopt a known formulation as a sparse optimization problem. Many algorithms have been developed for solving sparse optimization problems that occur in the field of compressive sensing; in this work, we adapt one such algorithm for use in the unwrapping problem. The result is an unwrapping algorithm that gives very similar results to those of existing algorithms, but that is simpler, more reliable, and more computationally efficient.
Intelligent token-based code clone detection system for large scale source code

A code clone refers to code fragments in the source code that are identical or similar to each other. Code clones lead difficulties in software maintenance, bug fixing, present poor design and increase the system size. Code clone detection techniques and tools have been proposed by many researchers, however, there is a lack of clone detection techniques especially for large scale repositories. In this paper, we present a token-based clone detector called Intelligent Clone Detection Tool (ICDT) that can detect both exact and near-miss clones from large repositories using a standard workstation environment. In order to evaluate the scalability and the efficiency of ICDT, we use the most recent benchmark which is a big benchmark of real clones, BigCloneBench. In addition, we compare ICDT to four publicly available and state-of-the-art tools.
Patron or Poison?

For over 20 years, the academic human-computer interaction (HCI) and computer supported collaborative work (CSCW) communities have conducted research on user-facing hardware, software, and social media platforms dominated by a few key firms. Even as the contributions made by these scholars have shaped the development of these technologies, so too does industry funding play a key role in convening and supporting our community. This panel brings together HCI researchers for a reflective conversation on industry funding support for HCI.
Personalised Medicine in Critical Care Using Bayesian Reinforcement Learning

Patients with similar conditions in the intensive care unit (ICU) may have different reactions for a given treatment. An effective personalised medicine can help save patient lives. The availability of recorded ICU data provides a huge potential to train and develop the systems. However, there is no ground truth of best treatments. This makes existing supervised learning based methods are not appropriate. In this paper, we proposed clustering based Bayesian reinforcement learning. Firstly, we transformed the multivariate time series patient record into a real-time Patient Sequence Model (PSM). After that, we computed the likelihood probability of treatments effect for all patients and cluster them based on that. Finally, we computed Bayesian reinforcement learning to derive personalised policies. We tested our proposed method using 11,791 ICU patients records from MIMIC-III database. Results show that we are able to cluster patient based on their treatment effects. In addition, our method also provides better explainability and time-critical recommendation that are very important in a real ICU setting.
Traffic classification for connectionless services with incremental learning

The technological advancement in VoIP technology and P2P streaming led to the development of novel applications. Most of these applications use UDP traffic. The availability of UDP services for applications such as streaming, trivial file transfer, are denied to legitimate users due to malicious traffic, intentionally created by abnormal requesting behaviour of the botnets. Categorizing the traffic is required to discriminate the malicious traffic that occur due to attacks from normal traffic for better real time resource allocation. For this purpose, this paper proposes a two level hybrid classification model based on incremental learning to detect high and low rate attacks that deny the legitimate access to connectionless services. The simulation results show that the proposed incremental learning strategy improves the classification accuracy of the proposed hybrid classifier compared to existing traditional learning methods.
Comparisons among rainfall prediction of monthly rainfall basis data in Aceh using an autoregressive moving average

Climate variability especially rainfall is an important factor in observing climate change. Extreme weather events can disrupt the rice planting calendar system which ultimately causes crop failure. The planting season for each region depends on the rainfall of an area. The purpose of this study is to determine the comparison of the predicted rainfall results in Sabang, Aceh Besar, and Aceh Tengah Municipality. Historical data for 1988-2015 were taken from BMKG. The ARIMA model was divided into three groups, namely autoregressive (AR), moving average (MA), and ARIMA (autoregressive moving average) models. The best prediction model was seen from the model that has the smallest standard error estimate (S) value. The results shows that the three regions have different rainfall than the seasonal plot visualization. The highest peaks of rainfall occurred on October and November in Aceh Besar, on November and December in Aceh Tengah, and only on December in Sabang (Weh Island). The best model for predicting rainfall for January 2016-December 2020 in Sabang was ARIMA (1,0,0) (2,0,0)[12]. The prediction model for the location of Aceh Besar in the future was ARIMA (0,0,0) (2,0,0)[12]. In Aceh Tengah, the best model for predicting January 2016-December 2020 rainfall was ARIMA (1,0,0) (2,0,2)[12]. Forecasting using ARIMA model was good for short-term forecasting, while for long-term forecasting, the accuracy of the forecasting was not good because the trends of rainfall was flat.
Semantic Knowledge Based Graph Model in Smart Cities

In smart cities, pervasive IoT devices generate an elephantine amount of multi-source heterogeneous data. The semantics helps to explore such complex datasets and drive towards higher-level insights. Later, these high-level insights are transformed to develop interlinks and associations among diverse sources of the data which leads towards knowledge discovery in a smart city. This discovery when combines with the domain knowledge using ontology-based approaches develop concepts and perceptions which initiate decision making in complex environments. However, the ontology-based approaches come up with certain limitations including an incapability to transform semi-structured data into useful knowledge, issues in handling inconsistent data, and inability to process large-scale, multi-source, and complex data of smart cities. Therefore, in this paper, we proposed a Semantic Knowledge Based Graph (SKBG) model as a solution to overcomes these limitations. The SKBG model is particularly customized to a smart city environment and purely utilizes knowledge-based graphs to incorporate any type of domain knowledge by combining diversify domains as a unit. As a result, the model works fine with diverse domain knowledge, automatically classify heterogeneous data by using machine learning techniques, handle large knowledge databases and support intelligent semantic search algorithms in smart cities. Finally, the results are summarized in the form of a knowledge graph which gives a comprehensive insight into the data.
Price Prediction of Cryptocurrency: An Empirical Study

Cryptocurrency has high volatility in market price since its inception. Existing works have explored different models to predict cryptocurrency prices. However, the accuracy is not satisfactory. In this paper, we conduct empirical study on the price forecasting. Firstly, we quantify the entropy and the conditional entropy of cryptocurrencies and stocks, respectively, and find that cryptocurrencies are more difficult to predict than stocks. Secondly, we evaluate various perspectives, including Twitter volume, Twitter sentiment and CNN-LSTM price prediction. Empirical results demonstrated the randomness in price validity, thus no single method is robust enough for cryptocurrency price prediction.
Application of Matlab Calibration Toolbox for Camera's Intrinsic and Extrinsic Parameters Solving

According to the mathematical model of the camera, the formula of camera parameter calibration algorithm is derived. The Matlab calibration toolbox is used to design a simulated checkerboard grid according to the requirements. The coordinates of the corners of the checkerboard grid in space and the coordinates in the image plane are used to calibrate the algorithm. The calibration algorithm is given, and the simulation results and error analysis are given to obtain the optimization results. The internal parameters of the camera and the external parameters in different states are obtained to realize the calibration of the camera.
Optimised Feature Selection for Identification of Carcinogenic Leukocytes Using Weighted Aggregation Based Transposition PSO

In this paper, an automated leukocyte identification and classification methodology using microscopic blood smear images is proposed. After performing a colour based clustering for the identification of leukocytes, a large set of features are derived from the identified leukocytes. Thereafter a weighted aggregation-based transposition particle swarm optimisation technique (WATPSO) is proposed to select an optimised feature subset which is sufficient enough for classifying carcinogenic blast cells and normal cells efficiently. The proposed method is applied to a benchmark acute lymphoblastic leukaemia (ALL) data set and 97.30% test accuracy is achieved using only 6 optimal features. Experimental results demonstrate the usefulness of the proposed methodology in terms of high test accuracy and less number of informative selected features. The proposed method can be used as a decision support structure for haematologists for early diagnostic prediction of ALL.
Towards a QoS-aware Cyber Physical Networking Middleware Architecture

Cyber-physical systems (CPSs) are used in various application fields. In those, multiple CPSs can cooperate by exchanging information (processed sensor data, derived data, etc.) to fullfill a task. Thus, cyber-physical networking plays an essential role. The communication in a cyber-physical network (CPN) as well as with the local sensors and actuators is complex. The applications of CPSs may use different programming abstractions, e.g. callbacks, streams, etc. for the interaction. Also CPNs are used in changing environments. Thus, they are afflicted with dynamic non-functional requirements. Due to this, an adaptive communication is important to enforce the resulting Quality of Service (QoS) constrains. Classical middleware architectures do not cover all of these aspects. In this paper we propose an architectur for a middleware that is designed to the needs of cyber-physical networking. Therefore, we strive to integrate multiple programming abstractions and communication protocols in an easy to use middleware. Also, we focus on the QoS-aspects of the communication to sensors, actuators and other CPS. Those constrains require an end-to-end view of system and a communication architecture that adapts to the changing available communication infrastructure in CPNs and correspondingly in CPSs. Therefore, this approach eases the development, test and maintenance of CPSs and CPNs.
Answering SPARQL queries on the web of data through zero-knowledge link traversal

Link traversal has emerged as a SPARQL query processing method that exploits the Linked Data principles to dynamically discover data relevant for answering a query by dereferencing online Web resources (URIs) at query execution time. While several approaches for such a lookup-based query evaluation method have been proposed, there exists no analysis of the types (patterns) of queries that can be directly answered on the Web of Data through a "zero-knowledge" approach, i.e., without accessing local or remote endpoints and without a-priori knowledge of available data sources. In this paper, we first provide a method for examining if a SPARQL query can be answered through zero-knowledge link traversal and analyse a large corpus of real SPARQL query logs for finding the frequency and distribution of answerable and non-answerable query patterns. Subsequently, we provide an algorithm for transforming answerable queries to SPARQL-LD queries that bypass the endpoints, as well as a method to estimate their evaluation cost which can be useful for deciding on the query execution strategy to follow. We report experimental results about the efficiency of the transformed queries and discuss the benefits and the limitations of this query evaluation method.
Expert System for Dota 2 Character Selection Using Rule-Based Technique

Dota 2 is a multiplayer online battle arena strategy-based game developed by Valve Corporation that is currently holding the highest prize money for its international tournament event, which is The International. Due to numerous numbers of characters with each having different strengths and weaknesses, Dota 2 players are having difficulties in selecting a suitable character when playing the game. This research aims to develop an expert system to suggest a suitable character of Dota 2 that can identify the best possibility for Dota 2 character selection using the rule-based technique, to construct an expert system for Dota 2 character selection using the rule-based technique, and to test user evaluation feedback on usability aspects. User evaluation feedback on usability aspects is used as a testing method for this system to retrieve information regarding system performance. Based on the analysis of the testing, this project has passed all the usability levels and usability values required. As a conclusion, the system can help Dota 2 players to select a suitable character for their line-up when playing the game so that they would not face any difficulty as encountered by those who do not know how to select the character properly.