Reviewerly/exHarmony huggingface dataset:
The Forensic Disclosure Model: What Should be Disclosed To, and By, Forensic Experts?

Legal disclosure issues have predominantly focused upon what evidence the prosecution provided or withheld from the defence. In this paper we expand the concept of disclosure to wider contexts in which disclosure failures may result in miscarriages of justice. We introduce a conceptual model of ‘forensic disclosure’ which addresses what information should be disclosed to forensic examiners, as well as what information should be disclosed by forensic examiners. This paper presents a holistic overview of the dynamic interactions of four categories of stakeholders: forensic services, investigation, legal, and external stakeholders. We discuss the effective implementation of the forensic disclosure model by addressing five questions: when? what? how? who? and why? should information be best given to, and by, forensic examiners in order to increase the quality of forensic decision making and to minimise bias.
Deep Centralized Cross-modal Retrieval

The mainstream of cross-modal retrieval approaches generally focus on measure the similarity between different types of data in a common subspace. Most of these methods are based on pairs or triplets of instances, which has the following disadvantages: 1) due to the high discrepancy of pairs and triplets, there might be a large number of zero-losses, and the model cannot be updated under these circumstances. 2) global information in the common subspace cannot be fully exploited to deliver cross-modal retrieval tasks. To solve the above problems, we present a novel cross-modal retrieval method, called Deep Centralized Cross-modal Retrieval (DCCMR). Specifically, we first learn a center for embeddings of each class in the common space, then a double-quadruplet-center-loss function is proposed to force the distance between samples and centers from different classes to be larger than distance from the same class. To the best of our knowledge, the proposed DCCMR could be one of the first works to utilize the combination of quadruplet loss and center loss, leading to more stable results. Comprehensive experimental results on three widely-used benchmark datasets verify that our method achieves comparable performance compared with the state-of-the-art cross-modal retrieval methods as well as the usefulness and effectiveness of our DCCMR.
Semantics-Based Decision Support for Unconventional Emergencies

Efficient situation awareness and advice can be a boon to victims of emergencies. If decision makers are better aware of the situation they are in and similar situations that experts have handled in the past, and they are provided with advice, they will be in a better position to make prompt and meaningful decisions. Unconventional emergencies like earthquakes and terrorism are large-scale, sudden-onset, and repeating in nature, and thus create massive pressure on authorities and government agencies. The impact of such a calamity is global and transcends beyond social, economic, political, or geographic borders. A paradigm shift is required, with a holistic approach encompassing all facets of disaster management. The challenge is to strive toward preparedness backed by the coordination of all stakeholders. Efficient situation awareness is hampered by the temporal and spatial locality of human communications. Semantics-based decision support suggests utilizing semantic technologies during unconventional emergencies to spread situation awareness and provide advisory support.
Proving termination by <i>k</i> -induction

We propose a novel approach to proving the termination of imperative programs by k-induction. By our approach, the termination proving problem can be formalized as a k-inductive invariant synthesis task. On the one hand, k-induction uses weaker invariants than that required by the standard inductive approach. On the other hand, the base case of k-induction, which unrolls the program, can provide stronger pre-condition for invariant synthesis. As a result, the termination arguments of our approach can be synthesized more efficiently than the standard method. We implement a prototype of our k-inductive approach. The experimental results show the significant effectiveness and efficiency of our approach.
Hover Signal-Profile Detection

The touch screen market continues to develop rapidly, capturing an increasing variety of applications. One of the more advanced features that can improve user-friendly interaction is Hover technology. This means a finger position can be determined while approaching, but not yet touching, the touchscreen or touchpad.An approaching finger can be detected using different physical principles. Currently, the most common are infrared, optical, magnetic, and capacitive sensors.Cypress Semiconductor uses capacitive sensors to detect Hover. The advantage of this technology is that a capacitive Hover solution is largely compatible with standard multi-finger touch technology, if the sense-electronics can detect these extremely small Hover signals.The use of Hover technology in automotive touchscreens, for example, can increase operational safety. This allows the driver to perform certain operations (such as changing the volume of the radio, or making/rejecting phone calls) with a simple gesture instead of reaching to the touchscreen for a specific button and thus distracting from the situation on the road.The challenge is to develop algorithms that provide stable finger detection across the touchscreen where the signal-to-noise ratio is extremely unfavorable.
Deep Learning in Healthcare

Healthcare sectors of different types and specialties are increasing day by day and kept as a data pool called Electronic health records. This pool of data that healthcare owned is also enormously increasing and becomes unused without the intervention of a proper trained model for further medical analysis. Artificial Intelligence, machine learning, and deep learning are some of the known technologies that can help the end user to analyze and provide recommendations. Through the advent of this innovative technology, hierarchical learning, or deep structured learning is going to be done on the existing EHRs using layered algorithmic architecture for a complete data analysis within a short duration of time. With successful experimental results and enormous applications areas, deep learning has the potential to change the challenges pertaining to healthcare.
Deep Neural Network-Based Approach to Identify the Crime Related Twitter Posts

Crime prediction is an important task to reduce criminal activities in the society. We can minimize the harm by identifying the crimes hotspot before any crime happens using a crime prediction model. Thus, crime prediction is becoming a hot topic among researches. We can identify crime patterns by analyzing historical crime data and predict future crimes. Researchers used different sources to get crimes related data to generate the prediction model. But, some crimes are unregistered. In this paper, we used twitter posts to detect crimes. People share information around their environment via Twitter posts. Here, we proposed the Bidirectional Encoder Representations from Transformers (BERT) approach to identify the crime-related posts. Our approach outperformed the existing approaches by obtaining 92.8% accuracy.
Impact of Augmented Reality as an ICT tool to Deliver Engineering Education Content

Augmented reality has found itself as an excellent candidate to deliver content for education. There is a definite need for improved visualization when it comes to Engineering education. AR could present itself as a good candidate to do so. This paper presents the use of Augmented Reality for engineering education. The need for visualization finds itself at the core of imparting Engineering education and there are various platforms to do so. We continue the work presented in an earlier publication where we have implemented our smartbooks concept for delivering an engaging AR experience. We have presented the use of AR for delivering content for three fundamental engineering subjects: Engineering mechanics, engineering drawing and electrical engineering. We have also explored a different user interface to improve our AR content delivery. Examples for each are demonstrated and so are their use cases. Measuring the impact of using AR was a particular challenge with variable usage across students. We have defined certain metrics (engagement factor) that help us correlate usage with impact. We have measured the impact of our AR implementation through well designed questionnaires, and used the academic scores of the candidates as an output metric to measure impact. The objective of this research is to present a case for the use of AR in being a front-runner ICT technology for delivering engineering education.
Advances in the use of domain engineering to support feature identification and generation of information visualizations

Information visualization tools are widely used to better understand large and complex datasets. However, to make the most out of them, it is necessary to rely on proper designs that consider not only the data to be displayed, but also the audience and the context. There are tools that already allow users to configure their displays without requiring programming skills, but this research project aims at exploring the automatic generation of information visualizations and dashboards in order to avoid the configuration process, and select the most suitable features of these tools taking into account their contexts. To address this problem, a domain engineering, and machine learning approach is proposed.
Research on Traffic Dispatching Scheme of Multi-service Data Center for 5G Smart Grid

Abstract Aiming at the flow scheduling problem of 5G power network multi-service data center, this paper designs and proposes a multi-service-oriented multi-factor flow scheduling scheme. Based on the shortest path algorithm, this paper improves and perfects the traffic scheduling strategy, and introduces a set of scoring mechanism. The number of hops, the number of large flows and the remaining bandwidth of each path are used as scoring indicators, and the path with the highest comprehensive score is selected. As the forwarding path of traffic. Based on the Mininet simulation platform, use Ryu to control to generate probability model traffic to simulate data center network traffic. The experimental results show that the scheme improves the network throughput and bandwidth utilization, and improves the performance of the data center network.
Embedding Models for Episodic Knowledge Graphs

In recent years a number of large-scale triple-oriented knowledge graphs have been generated and various models have been proposed to perform learning in those graphs. Most knowledge graphs are static and reflect the world in its current state. In reality, of course, the state of the world is changing: a healthy person becomes diagnosed with a disease and a new president is inaugurated. In this paper, we extend models for static knowledge graphs to temporal knowledge graphs. This enables us to store episodic data and to generalize to new facts (inductive learning). We generalize leading learning models for static knowledge graphs (i.e., Tucker, RESCAL, HolE, ComplEx, DistMult) to temporal knowledge graphs. In particular, we introduce a new tensor model, ConT, with superior generalization performance. The performances of all proposed models are analyzed on two different datasets: the Global Database of Events, Language, and Tone (GDELT) and the database for Integrated Conflict Early Warning System (ICEWS). We argue that temporal knowledge graph embeddings might be models also for cognitive episodic memory (facts we remember and can recollect) and that a semantic memory (current facts we know) can be generated from episodic memory by a marginalization operation. We validate this episodic-to-semantic projection hypothesis with the ICEWS dataset.
Grounding Knowledge Acquisition with Ontology Explanation: A Case Study

Knowledge validation is still a challenge when constructing knowledge-based systems. It is one of the major reasons for user rejection and disagreement between project participants. Systematic and periodic reviews of the domain ontology, with a formal agreement of the whole development team (including the experts) are a recommended good practice. Nevertheless, these reviews do not guarantee system success. This paper presents a case study of the construction process of a knowledge-based system. The process involved a group of experts with varied work experience. A great deal of negotiation happened during knowledge acquisition meetings, which took place during a 6-month period. After each meeting, changes in the ontology were verified through a web-based questionnaire, from which either consensual agreement was reached (and changes implemented) or the need for a new meeting was ascertained. An explanatory review at the beginning of each meeting further solidified the understanding of all participants. This cyclic process led to a final version of the ontology, ratified by all participants. This model supports diagnosis and prediction of failures in mechanical drilling rigs in oil exploration sites. Unexpectedly, during system trials, experts disagreed with results, which raised questions about the validity of the domain ontology. The system’s explanation module provided a cornerstone for a reflective process that helped identify inconsistencies and corrections needed. These reflections led to adjustments to the ontology, and a reflection about previous decisions and element definitions. Explanations, derived from the ontology and instantiated using real scenarios, shed light on knowledge gaps and semantic inconsistencies of the domain model. In this paper we have three main goals: (1) to present our ontology construction process; (2) to highlight a particular situation where results were inadequate; and (3) to show how the explanation system helped experts and knowledge engineers identify gaps. We also present lessons learned from the whole process, that may apply in other situations.
Express-method of determining the water-yielding capacity of natural water sludge in modern dewatering technologies

Abstract Water treatment of surface sources for the purposes of public water service provision is impossible without waste build-up – sludge of water treatment plants. This kind of waste poses a considerable threat to the environment as it represents a complex organomineral structure with a high concentration of chemical reagents used for source water clarification and decolourization. In the present day, it is still international practice to discharge wastewater into surface water objects or onto the land surface, which causes severe damage to the natural environment. A tightening of environmental regulations encourages housing and utility services sector to undertake measures related to waste processing (waste dewatering). To implement these purposes mechanical machines (centrifuges, decanting tanks, filter presses, vacuum filters) are widely applied. An initial sludge quality, more particularly a filterability indicator – specific resistance to filtration – is a critical factor for the successful functioning of these machines. The higher the specific resistance to filtration, the worse the sludge gives away moisture, the more reagents and (or) mineral additives should be used before sending sludge for dewatering. Determining indicator of specific resistance to filtration using standard methods is quite a long process requiring the use of laboratory equipment. In this article, the author proposes an express-method enabling to determine within a short time span the water-yielding capacity of sludge based on the capillary suction speed of filtrate, standing out from its components. This method allows simplifying the start-up and commissioning of the modern sludge dewatering equipment as well as operation on the whole.
Developing “conceptual knowledge” descriptors for CEFR-based proficiency levels

Abstract This article proposes a framework for the integration of “conceptual knowledge” descriptors’ into CEFR (Common European Framework of Reference for languages)-based proficiency levels. Research on the difficulties faced by second language (L2) learners when learning an additional language has shown that apart from the pure linguistic and communicative errors, a further type of error, namely conceptual errors hinders communication in the target language. Conceptual errors are a manifestation of ill-developed conceptual knowledge, that is the ability to express oneself in an L2 while using the L2 conceptual system. Although there exists a number of studies linking conceptual knowledge and its constituent parts to native-like fluency, relatively little is present in the CEFR. Hence, the goal of this paper is twofold, first to introduce and clarify “conceptual knowledge” and second (and most importantly) to develop conceptual knowledge descriptors’ and sample activities for each CEFR level ranging from A2 to C2.
Some properties of various types of matrix factorization

Matrix factorizations or matrix decompositions are methods that represent a matrix as a product of two or more matrices. There are various types of matrix factorizations such as LU factorization, Cholesky factorization, singular value decomposition etc. Matrix factorization is widely used in pattern recognition, image denoising, data clustering etc. Motivated by these applications, some properties and applications of various types of matrix factorizations are studied. One of the purposes of matrix factorization is to ease the computation. Thus, comparisons in term of computation time of various matrix factorizations in different areas are carried out.
A caching‐enabled light control scheme for aerobics video

The video transmission traffic has account for 60% Internet traffic, and how to optimize the video transmission efficiency has become a major challenge. Especially, the aerobics video is strongly subjected to both temporal locality and spatial locality, and the related contents are also dynamically changeable with the precise and complex movements. Thus, in the age of multimedia, the transmission optimization of aerobics video has the considerable research value. In this paper, a transmission control scheme for aerobics video is proposed. This scheme has the light feature, which is embodied by one heuristic video compression method. Then, this paper realizes the effective congestion control based on the adaptive rate design. Meanwhile, video compression and congestion control are performed in the simultaneous way, and they are completed by using the explored in‐network cache. Given this, the simple cache allocation method is presented at last. Two baselines are compared by evaluating packet loss rate, transmission time and delay jitter, and the experiment results demonstrate the efficiency of the proposed transmission scheme.
School From Home Situation in Indonesia: an Exploratory Data Analysis of Indonesian Tweet Data

The outbreak of the novel coronavirus has reached Indonesia, and the Indonesian government had to implement a social distancing policy to prevent its transmission because there is still no vaccine for this disease until now. This approach's effect is that work, school, and worship activities are carried out from home. Using tweet data in the Indonesian language and by exploring the data using EDA techniques, this research attempts to describe how the community reacts to the implementation of the “school from home” policy. Furthermore, using the same technique, this study also tries to show some preprocess steps that might be required to handle data with unique characteristics and what kind of algorithm is most appropriate for processing this data.
Application of software testing methodology based on quality criteria and expert assessments to mobile applications

Abstract In this paper, we propose a technique for choosing between a manual, automated and semi- automated approaches to software testing. The great desire of each person to achieve maximum comfort in each of the spheres of life has also affected the international web. The user, wanting to always stay online, uses the phone as a communicator. This led to the emergence of mobile Internet. When staying away from home or during travel and business trips, you can connect using a tablet or mobile phone instead of a laptop. The efficiency and functionality of “mini” computers would not be brought to such a high level without specialized applications. Properly chosen testing methodology improves the quality of mobile applications.
Efficient Features for Effectively Detection of Leukemia Cells

Leukemia the disease of blood forming cells is a cancer that usually begin in the bone marrow. It results in high numbers of abnormal blood cells usually affecting the leukocytes, or white blood cells of a body. Oncologists and researchers are still working on appropriate reasons behind the cause of leukemia and its early detection as well. The contemporary techniques to detect leukemia are usually time consuming, laborious and subject oriented. In this research we presented a novel technique to detect the leukemia at its early stage. Detection of leukemia through images is quick and cost effective as there is no need of advanced lab testing and experts with in-depth knowledge. To identify whether the disease is acute or chronic, algorithmic techniques depend on the affected white blood cells. In our work, color filter is used as a preprocessing step to detect the region of interest that is white blood cells of ALL-IDB dataset. Then the structural feature (wavelet and curvelet descriptor) is used to detect the important features. This feature vector is trained on KNN and SVM classifier to check the correctness rate of this algorithm. Our approach achieved the accuracy of 92.7% which proved better in comparison with existing techniques in this field of medical research.
Instance-Based Classification Through Hypothesis Testing

Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as an optimization problem and do not address the issue of statistical significance. In this paper, we formulate the binary classification problem as a two-sample testing problem. More precisely, our classification model is a generic framework that is composed of two steps. In the first step, the distance between the test instance and each training instance is calculated to derive two distance sets. In the second step, the two-sample test is performed under the null hypothesis that the two sets of distances are drawn from the same cumulative distribution. After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value. Essentially, the presented classification method can be regarded as an instance-based classifier based on hypothesis testing. The experimental results on 38 real data sets show that our method is able to achieve the same level performance as several classic classifiers and has significantly better performance than existing testing-based classifiers. Furthermore, we can handle outlying instances and control the false discovery rate of test instances assigned to each class under the same framework.