Reviewerly/exHarmony huggingface dataset:
Characterizing Robotic and Organic Query in SPARQL Search Sessions

SPARQL, as one of the most powerful query languages over knowledge graphs, has gained significant popularity in recent years. A large amount of SPARQL query logs have become available and provided new research opportunities to discover user interests, understand query intentions, and model search behaviors. However, a significant portion of the queries to SPARQL endpoints on the Web are robotic queries that are generated by automated scripts. Detecting and separating these robotic queries from those organic ones issued by human users is crucial to deep usage analysis of knowledge graphs. In light of this, in this paper, we propose a novel method to identify SPARQL queries based on session-level query features. Specifically, we define and partition SPARQL queries into different sessions. Then, we design an algorithm to detect loop patterns, which is an important characteristic of robotic queries, in a given query session. Finally, we employ a pipeline method that leverages loop pattern features and query request frequency to distinguish the robotic and organic SPARQL queries. Differing from other machine learning based methods, the proposed method can identify the query types accurately without labelled data. We conduct extensive experiments on six real-world SPARQL query log datasets. The results demonstrate that our approach can distinguish robotic and organic queries effectively and only need \(7.63 \times 10^{-4}\) s on average to process a query.
Developing Complex Safety Critical Systems in Complex Supply Chains

The emergence of high performance high complexity automotive systems for autonomous driving involves introducing complex supply chains to the system design and managing them in a structured way. Based on current estimates a fully autonomous car could require up to 1 billion lines of code with a code base involving dozens of suppliers. This paper identifies and discusses the complexities involved when such a complex safety critical system is designed using a high number of interacting safety elements that have been designed out of context of the target system by a multitude of suppliers. The paper details the complexities of the integration task. It argues in favour of introducing additional error containment boundaries and safety mechanisms to help manage the integration complexity.
Lattice-based Concurrent Signatures in the Standard Model

We present a lattice-based concurrent signature construction as a post-quantum solution to fair exchange of digital signatures. Our construction has the following two desirable features: (1) it is adaptively secure in the standard model, that is, its security does not rely on the random oracle assumption, and (2) it achieves anonymity, a stronger security notion than ambiguity. That is, before the release of the keystone, from a third party's point of view, a signature in our construction could be generated by anyone, not just limited to two parties involved in the exchange. We obtain these results by applying a novel idea to the lattice-based signatures due to Boyen.
Data Analytics Implementation for Surabaya City Emergency Center

Quick response service and emergency reports handling is one of the main aspects in the data-driven government system, oriented to people service in the city of Surabaya through an emergency center called as Command Center 112. Our idea is to implement descriptive and predictive analytics to be able to provide a detailed picture of the intensity of the number of reports of each category and sub-district in the city of Surabaya as well as make predictions to find out future public report projections by analyzing spatial and temporal data. For descriptive analysis, we apply the unsupervised learning method with agglomerative hierarchical clustering combined with K-Means clustering for centroid initialization. After the data is preprocessed, such as imputation and data structure improvement, the data is then transformed into a report number format for each month and category, then segmented with the K-Means clustering hierarchical model, this model will get 3 final labels. These labels will be projected (grounding) to the level of intensity of community reports in the month and category, ranging from the low, medium and high categories. As for the prediction model, in this study we use combination of timeseries prediction methods, such as Exponential Smoothing, Moving Average and Auto Regressive Integrated Moving Average (ARIMA) by modifying the parameters according to the characteristics of movement, trends and seasonal data. We applied the model that we proposed for research purposes with a dataset of reports from the people of Surabaya to the Command Center 112 in 2019 with a total of 169,937 data.
Tutorial: Static Analysis at Scale with Muse

In this tutorial we will describe how to use the Muse program analysis platform to perform cloud-based static analysis experimentation at scale. We will leverage two key capabilities of Muse: 1) user-provided custom analysis tools can be easily plugged into the system and 2) APIs are available to initiate analysis and collect analysis results. We will show how to make use of these capabilities by guiding attendees through the process of integrating and experimenting with a tool of their choice. Attendees will come away from the tutorial knowing how to make use of Muse to facilitate and accelerate their program analysis research.
What are the differences between group and individual modeling when learning BPMN?

Models that describe the business processes adopted by an organization are widely used in industry, especially in consultancy services for improvements or new solutions based on system information. Learning business process modeling (e.g., Business Process Modeling Notation - BPMN) is a fundamental skill for Information System undergraduate students to master. Nevertheless, learning how to model BPMN is not a trivial task for students. Some studies have highlighted the importance of providing opportunities for students to learn how to develop the ”AS IS” and ”TO-BE” models in groups. Group modeling aims to contribute to the student’s learning process by providing opportunities for discussion and reflection with their classmates. Although there are previous studies that mention initiatives in which BPMN activities are performed in teams or groups, there is still no consolidated knowledge as to whether group modeling is better than individual modeling. This paper presents an experimental study designed to verify whether there are differences between modeling business processes in groups or individually. The experimental study was conducted with undergraduate students in Information Systems courses, evaluating the quality of the BPMN (”AS IS” and ”TO-BE”) models produced by these students in terms of completeness and correctness. Unlike expected, in most cases group modeling showed results similar to those of individual modeling. These results and insights motivate future analysis of how students can best learn business process modeling technique.
Fine-grained Multi-label Sexism Classification Using Semi-supervised Learning

Sexism, a pervasive form of oppression, causes profound suffering through various manifestations. Given the rising number of experiences of sexism reported online, categorizing these recollections automatically can aid the fight against sexism, as it can facilitate effective analyses by gender studies researchers and government officials involved in policy making. In this paper, we explore the fine-grained, multi-label classification of accounts (reports) of sexism. To the best of our knowledge, we consider substantially more categories of sexism than any related prior work through our 23-class problem formulation. Moreover, we present the first semi-supervised work for the multi-label classification of accounts describing any type(s) of sexism wherein the approach goes beyond merely fine-tuning pre-trained models using unlabeled data. We devise self-training based techniques tailor-made for the multi-label nature of the problem to utilize unlabeled samples for augmenting the labeled set. We identify high textual diversity with respect to the existing labeled set as a desirable quality for candidate unlabeled instances and develop methods for incorporating it into our approach. We also explore ways of infusing class imbalance alleviation for multi-label classification into our semi-supervised learning, independently and in conjunction with the method involving diversity. Several proposed methods outperform a variety of baselines on a recently released dataset for multi-label sexism categorization across several standard metrics.
The Five Purposes of Value Modeling

Data driven and experimental development practices provide effective means for companies to adopt a customer and market-centric way-of-working. In online companies, controlled experimentation is the primary technique to measure how customers respond to variants of deployed software. Over the recent years, and due to increasing connectivity and data collection from products in the field, these practices are being adopted also in software-intensive embedded systems companies. In these companies, experiments are run on selected instances of the system or as comparisons of previously computed data to ensure value delivery to customers, improve quality and explore new value propositions. However, to utilize the benefits of data- driven and experimental development practices, companies need to define what value factors to optimize for. For highly complex embedded systems with thousands of parameters, and with people at different levels in the organization having different opinions about the value of features, this is a challenging task. In this paper, we report on longitudinal multi-case study research in which we explore value modeling as a technique to help people in development, in product management and on the business level to align interests and agree on value factors. Based on this work, we identify five purposes of value modeling and how this technique helps accelerate critical activities in an organization. The contribution of this paper is three-fold. First, we provide empirical evidence for how value modeling is an effective technique to help companies define what to optimize for. Second, we identify five purposes of value modeling. Third, we identify the key challenges that the case companies experience when applying value modeling.
CLOUD RESOURCES AND SERVICES FOR TRAINING OF FUTURE SPECIALIST OF INFORMATION TECHNOLOGIES: SELECTION CRITERIA, CASE STUDIES

The article presents the criteria and indicators for the selection of cloud resources and services for the training of future IT professionals. The cloud resources and services that are expedient to use in the process of training future IT professionals to provide them with training materials, services for practical tasks, tools for self-control and reflection are analyzed. Resources and services that were considered: academic resources and massive open online courses (MOOCs), platforms and automated programming systems, online programming labs, services and resources for teamwork, software development platforms, tools for management and modeling, professional community. In this reserch, a comparative characteristic of selected cloud resources and services based on certain criteria and indicators is given. In the expert evaluation of the identified criteria were involved 23 experts, who are scientific and pedagogical workers and have practical experience in training future specialists in the subject area 12 “Information Technologies” and specialties “Computer Science”, “Software Engineering” and “Computer Engineering”. At various stages of the research, between 58 and 109 students were involved in the pedagogical experiment. As a result of the research, the classification of cloud resources and services for the training of future IT specialists are justified, the educational and methodological selection criteria for each type of resources and services and the weight of each criterion are used using the expert method are defined. Examples and results of the use of certain cloud resources and services in the process of training future IT specialists are given. Keywords
Herbrand Proofs and Expansion Proofs as Decomposed Proofs

Abstract The reduction of undecidable first-order logic to decidable propositional logic via Herbrand’s theorem has long been of interest to theoretical computer science, with the notion of a Herbrand proof motivating the definition of expansion proofs. In this paper we construct simple deep inference systems for first-order logic, both with and without cut, such that ‘decomposed’ proofs—proofs where the contractive and non-contractive behaviour of the proof is separated—in each system correspond to either expansion proofs or Herbrand proofs. Translations between proofs in this system, expansion proofs and Herbrand proofs are given, retaining much of the structure in each direction.
Digital Transformation Journeys

Big Data and Digitization are among the most talked about concepts of the last years. Whereas Big Data has more and more concrete outcomes, Digitization is still seen as far away future. Nowadays, however, the question is not anymore how to prepare for the upcoming Digitization. The question companies and organizations have to ask themselves is how to adapt to and in today's Digitizing World. A correct translation of this strategy is equally important. It requires a dynamic silver lining, compatible with interactive work clusters and a hybrid organization. Typically, this results in a new way of working and an appropriate approach to power this way of working. The way of working needs to be adapted to the redefined borders between reactivity and proactivity, the need for real-time service and contextual adaptation. It needs to be designed for agility. To provide value, an organic compromise has to be found that answers the need for creative freedom, and the need for a solid structure where guidance, management, and development of human capabilities is possible in a structured way.
Research on a Data Desensitization Algorithm of Blockchain Distributed Energy Transaction Based on Differential Privacy

Distributed energy trading is a bilateral transaction of electricity directly involved by power-producers, operators, power-sellers, and power-large-users. The blockchain essence is a decentralized, distributed storage technology with natural adaptability in distributed energy transactions. Applying blockchains to distributed energy transactions requires addressing data desensitization first. Aiming at the data characteristics in energy trading, this paper proposes an energy blockchain data desensitization algorithm based on differential privacy. The algorithm randomizes the query results by adding different distributed noise data. Experiments show that the algorithm can better integrate distributed energy services and improve the security and data storage efficiency of blockchain system data.
Practical Suggestions to Successfully Adopt the CMMI V2.0 Development for Better Process, Performance, and Products

A significant number of the organizations in systems and software development industry are interested in the Capability Maturity Model Integration (CMMI) for better processes, performance, and products. The CMMI Institute has lately released version 2 (V2.0) of the CMMI and all interested organizations need to do the transition. This transition journey obliges some planned and devoted efforts. In this paper, I share the relevant experiences in adopting the new version of the model and the specifics of the pertinent transition journey for an organization. Specifically, I purposefully give information about how new and most significantly changed practice areas can be addressed. Besides, I give details about the novel gap analysis report template that I crafted to be used in analyzing the gaps to become compliant. Moreover, the original transition plan template that I created to manage and implement the transition process is provided. I think these noble distillations and experiences are going to be beneficial for organizations in successfully adopting the new model for better processes, performance, and products.
ECDet: an efficient convolutional network for real-time object detection

This paper introduces a lightweight convolutional neural network, called ECDet, for real-time accurate object detection. In contrast to recent advances of lightweight networks that prefer to use pointwise convolution for changing the number of feature map’s channel, ECDet makes an effort to design equal channel block for constructing the whole backbone network architecture. Meanwhile, we deploy depth-wise convolution to compress the feature pyramid network (FPN) detection head. The experiments show that ECDet only has 3.19 M model size and needs only 3.48B FLOPs with a 416×416 input image. Our method has a 5% improvement in accuracy compared to YOLO Nano, and it requires less computation. The comprehensive experiments demonstrate that our model achieves promising results in terms of available speed and accuracy trade-off on PASCAL VOC 2007 datasets.
Topic Analysis by Exploring Headline Information

As for the topic representation in standard topic models, the words that appear in a document are considered with the same weight under the assumption of ‘bag of words’. The word-topic assignment will lean to the high-frequency words and ignore the influence of the low-frequency words. As a result, it will ultimately impact on the performance of topic representation. Generally, the statistical information obtained from the whole document collection can be used to improve this situation. In addition, headlines of some kind of documents, such as news articles, usually summarize the important elements in the document, and the words in headlines are more appropriate to represent the topics. However, few previous studies consider the headline rich information, which is significant for topic modeling. In this paper, we propose a new headline-based topic model in order to accomplish a well-formed topic description. Experimental results on three widely used datasets show that the proposed headline-based modeling scheme achieves lower perplexity.
Asynchronous Bundled-Data Pipeline Cryptographic Architecture with RDI Countermeasure

This work proposed an Asynchronous Bundled-Data Pipelined Architecture for the Data Encryption Standard (DES) Algorithm using Random Delay Insertion (RDI) Countermeasure. The objective was to achieve robustness against Differential Power Analysis (DPA). This architecture was implemented in 4 different ways varying according some architectural-level decisions. All versions of this architecture presented robustness against DPA by means of time-misalignment in the power traces. This protection was achieved at the cost of an average area increasement of 8.455 times and an average throughput increasement of 17 times when compared to a regular synchronous implementation. Comparing with an asynchronous implementation in literature, the architecture achieved a reduction of 37.367% in area overhead and an increseament of 10.8 times in the throughput ratio.
Multimedia Document Mining using Sequential Multimedia Feature Patterns

Recent years have witnessed the expeditious progress in multimedia technology and rapid growth of multimedia documents. The enormous amount of multimedia documents require sophisticated multimedia mining methods to analyze and utilize the multimodal information. The multimodal objects of a multimedia document are described by the patterns of features. The feature pattern sequences are used to identify the contextual information of the multimedia documents. In this paper, we propose an approach for the discovery of sequential feature patterns from the multimedia documents. The sequential multimedia feature pattern mining generates the multimedia class sequential rules that are used to classify the multimedia documents. The efficiency of the proposed sequential multimedia feature pattern mining is evaluated by experimenting with four datasets of multimedia documents. Experimental results demonstrate that the proposed sequential feature pattern mining can be efficiently used for the knowledge mining from multimedia documents.
Early Detection of Superficial Basal-Cell Carcinoma Skin Cancer with Extraction Method ABCD Feature Based on Android

Basal Cell Carcinoma (KSB) is a deadly skin cancer that has become one of the most common diseases. This disease generally occurs in areas of the skin that are often exposed to sunlight such as the face and neck. Basal cell carcinoma usually appears after more than 40 years of age, although it can also be found in children and adolescents rarely. If not treated immediately, basal cell carcinoma will spread locally, resulting in substantial tissue damage which causes impaired function. So we need the right steps in handling it. The purpose of designing this application is to detect basal cell carcinoma skin cancer on an Android-based device. In Paper, the authors discusses ways to overcome this problem by using the ABCD Feature extraction and K-Nearest Neighbor (KNN) as a classification. This application has a user friendly application display because it was developed using a platform that can be used by all circles of science, so users do not need a qualified IT skills. The results of this study are beneficial to the community, which can be used by the community and can find out whether or not KSB is detected or not. From the results of tests that have been done, the results of feature extraction accuracy get an accuracy of 91,6%.
Post-Quantum Blockchains

Abstract Chapter 10 deals with the fact that quantum computers will break all current practical digital signature schemes once large-scale quantum computers become reality. The chapter starts with an outline of the major cryptographic primitives that are considered to be quantum-safe and compare their efficiency and usability for blockchain networks. For this, it compares the basic factors of the most popular classical public-key schemes and some chosen post-quantum approaches. This is followed by an introduction to hash-based cryptosystems. Based on Lamport-Diffie one-time signatures, it shows how hash-based signature schemes work and how they can be transformed to multi-signature schemes.
Change is not the only constant

Abstract Developments in information technology during the past 40 years have presented new opportunities for information designers and also posed new challenges. Computer controlled displays are now so ubiquitous that most people in everyday life need to interact with information shown on screens. Many of the characteristics of these readers have not changed: they forget things, get distracted, make assumptions, etc. Research has confirmed that designing an interaction style to reduce the demands made on people’s sensory and cognitive processes helps them accomplish tasks more easily. There has undoubtedly been progress, particularly with mobile devices. Graphical user interfaces and touch screens can make interactions feel intuitive, almost conversational, but interface glitches remain. These arise both from the tension inherent in designing for experienced and novice users, and from design decisions often involving people with differing priorities. The features of interfaces will continue to change but the challenge of finding ways to support people’s sensory, cognitive and conative characteristics will continue.