Reviewerly/exHarmony huggingface dataset:
Narrative Sensemaking: Strategies for Narrative Maps Construction

Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in this process. They allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model connections between storylines. As a sensemaking tool, narrative maps have applications in intelligence analysis, misinformation modeling, and computational journalism. In this work, we seek to understand how analysts construct narrative maps in order to improve narrative map representation and extraction methods. We perform an experiment with a data set of news articles. Our main contribution is an analysis of how analysts construct narrative maps. The insights extracted from our study can be used to design narrative map visualizations, extraction algorithms, and visual analytics tools to support the sensemaking process.
Knowledge-based programs as building blocks for planning

Knowledge-based programs contain both world-altering actions, which upon execution change the state of the world, and sensing actions, which upon execution change the knowledge state of the agent. Knowledge-based programming has been proposed as an alternative to planning, since programs allow solving families of planning problems. Notwithstanding, agents equipped with a variety of knowledge-based procedures can compose these procedures to achieve goals, exhibiting greater degrees of flexibility. Optimized state-of-the-art planners, unfortunately, cannot be used directly to compose programs since they require operators (not programs), defined by preconditions and effects. In this article we study how to compute preconditions and effects of knowledge-based programs in order to allow state-of-the-art planners to construct plans with knowledge-based programs as building blocks. We study the problem in the language of the situation calculus, appealing to Golog to represent our programs. To this end, we propose an offline execution semantics for Golog programs with sensing. We then propose a compilation method that transforms our action theory with programs into a new theory where programs are replaced by primitive actions. This enables us to use state-of-the-art, operator-based planning techniques to plan with programs that sense for a restricted but compelling class of problems. Finally, we discuss the applicability of these results to existing operator-based planners that support sensing and illustrate the computational advantage of planning with programs that sense via an experiment.
Optimization of echo state network behavior space based on microbial genetic algorithm

Aiming at the difficulty of selecting the parameters of the echo state network reservoir, this paper proposes a method for optimizing ESN parameters based on behavior space. Its essence is to build an ESN behavior space through generalization rank, kernel rank, and memory capacity, and use microbial genetic algorithms to find rules for reservoir parameter selection and the minimum behavior configuration required for learning tasks. This method overcomes the shortcomings of traditional ESN such as multiple parameters and long optimization time, and improves optimization efficiency and network learning performance. The experimental results show that the MGA-ESN method proposed in this paper is basically close to the optimal network structure, the learning performance is better than the GESN network, and the reasons that affect the performance of the ESN network can be explained through the behavior space.
Efficient Estimate of Low-Frequency Words’ Embeddings Based on the Dictionary: A Case Study on Chinese

Obtaining high-quality embeddings of out-of-vocabularies (OOVs) and low-frequency words is a challenge in natural language processing (NLP). To efficiently estimate the embeddings of OOVs and low-frequency words, we propose a new method that uses the dictionary to estimate the embeddings of OOVs and low-frequency words. More specifically, the explanatory note of an entry in dictionaries accurately describes the semantics of the corresponding word. Naturally, we adopt the sentence representation model to extract the semantics of the explanatory note and regard the semantics as the embedding of the corresponding word. We design a new sentence representation model to encode sentences to extract the semantics from the explanatory notes of entries more efficiently. Based on the assumption that the higher quality of word embeddings will lead to better performance, we design an extrinsic experiment to evaluate the quality of low-frequency words’ embeddings. The experimental results show that the embeddings of low-frequency words estimated by our proposed method have higher quality. In addition, both intrinsic and extrinsic experiments show that our proposed sentence representation model can represent the semantics of sentences well.
A Comprehensive Review of the Security and Privacy Issues in Blockchain Technologies

The development of blockchain technology relies on a variety of disciplines, including cryptography, mathematics, algorithms, and economic models. All cryptocurrency transactions are recorded on a digital and decentralized public ledger known as the blockchain. Customers may keep track of their crypto-transactions by looking at a chronological list rather than a centralized ledger. The blockchain's application potential is bright, and it has already produced results. In various fields, blockchain technology has been incorporated and deployed, from the earliest days of cryptocurrencies to the present day with new-age smart contracts. No comprehensive study on blockchain security and privacy has yet been done despite numerous studies in this area over the years. In this chapter, the authors talked about blockchain's security and privacy issues as well as the impact they've had on various trends and applications. This chapter covers both of these topics.
A New Approach in Controlling A Single Axis Solar Tracker Based on Photovoltaic Output

Harvesting energy from the sun is a trend in this era. One of the strategies in collecting this solar energy is using a solar panel with a solar tracker to find an optimum value of this power. There are many strategies to track sunlight movement both on the single-axis or double axis. In this paper, a new method in finding an optimum energy collection is proposed. This new approach applies a mini solar tracker driven by a micro servo motor as a sunlight sensor. This method’s strategy is to multiply the output voltage and current from the mini solar panel to get a maximum power value at a specific degree for driving the larger servo motor at larger PV. As a result, energy supply to the larger motor is reduced. The results show that this strategy can find optimum power from the sun’s energy.
Secure Comparison Protocol for Promoting Business to Business Collaboration on the Blockchain

To promote Industry 4.0 and Society 5.0 and so on, it is important to connect and share data so that every member can trust it. Blockchain technology is currently attracting attention as the most advanced tool and has been used in the financial field and so on. In this paper, we focus on the industrial field, using homomorphic encryption and blockchain technique. Each company on the supply chain can exchange confidential information on encrypted data and utilize the data for their own business.
Delineation of sulfide mineralization over a part of western margin of greenstone belt of the Dhanjori Basin, Singhbhum Craton from petrophysical, petrographic and geophysical studies

The Archean greenstone belt occupying the Dhanjori basin is a potential target for exploring resources of various precious metals and sulfide minerals like other Precambrian greenstone belts. Thick alluvium cover and limited outcrops in the survey area pose challenges in mineral exploration strategies. This work presents an integrated study of petrophysics, petrography and geophysics (gravity and magnetic survey) over an identified conductor in the western margin of the Dhanjori basin, which aims to delineate anomalous sulfide-bearing zones. The present analysis of petrophysical parameters (bulk density, and volume magnetic susceptibility) of various litho-units including dolerite, metabasalt, magnetite, chert and metasediments (quartzite and quartz pebble conglomerate (QPC)) indicate the occurrences of sulfide minerals. Petrographic studies of the smoky and fuchsitic quartzites also suggest sulfide mineral phases (chalcopyrite, and pyrite). EPMA studies on these metasediments reveal the presence of sulfide minerals (pyrite, chalcopyrite, millerite, bornite and pyrrhotite) and sulfosalts (gersdorffite, and cobaltite), along with traces of gold and silver. The 3D inversion of gravity and magnetic data identified significant geophysical signatures corresponding to possible subsurface sulfide mineralization and have helped in the reduction of the target area. This work also demonstrates the utility of integrated investigations in identifying deep-seated mineralization, which may be helpful in exploring analogous prospective Precambrian greenstone belts.
Application and Optimization of Enterprise Financial Sharing Service Center Based on Cloud Computing

With the development of information technology, the financial sharing service center has become an inevitable trend of enterprise development, which shows great advantages and also produces many problems. These problems have brought great pressure to the financial management of the company, which not only makes the financial management risk of the enterprise rise, but also increases the financial management cost of the enterprise to a certain extent. Large enterprise groups urgently need to find a way out to solve the financial management crisis caused by the above problems. The optimization research of establishing financial sharing center based on cloud computing in the financial field has become the mainstream trend of financial sharing services for enterprises to provide decision support. Under the background of big data era, this paper expounds the necessity and practical problems faced by the transformation of financial shared service center to data center, and puts forward corresponding optimization suggestions based on cloud computing technology, in order to provide reference for the current enterprises to optimize the construction of financial shared service center.
Towards A Theory of Shared Understanding of Non-Functional Requirements in Continuous Software Engineering

Building shared understanding of requirements is key to ensuring downstream software activities are efficient and effective. Non-functional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. Research has shown NFRs are, in practice, poorly defined and difficult to verify, especially in agile environments. Continuous software engineering (CSE) practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to NFRs. However, the level of shared understanding achieved across an organization is not well-understood. This dissertation will build the foundations towards a theory of the complex and intricate relationship between shared understanding of NFRs and CSE.
Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.
Graph Signal Denoising Method Using the K-Nearest Neighbors Found by Dijkstra's Algorithm

In this paper, graph signal denoising problem is investigated. First, conventional graph signal denoising method using graph Laplacian matrix (GLM) is described to show that a big matrix inversion is needed in this method. To reduce computational load, a modified Dijkstra's algorithm is presented to find the K-nearest neighbors (K-NN) of a given vertex in the graph and a local graph Laplacian matrix (LGLM) of the sub-graph around this vertex is constructed by using the K-NN information and graph adjacency matrix. Then, based on the local smoothness property of graph signal and LGLM, the denoised signal at the given vertex can be computed by a Cramer's rule method. Finally, real temperature data is used to show the effectiveness of the proposed denoising method and performance comparison with conventional method is made.
Effective splicing localization based on noise level inconsistencies

In the era of digital technology, it becomes easy to share photographs and videos using smartphones and social networking sites to their loved ones. On the other hand, many photo editing tools evolved to make it effortless to alter multimedia content. It makes people accustomed to modifying their photographs or videos either for fun or extracting attention from others. This altering brings a questionable validity and integrity to the kind of multimedia content shared over the internet when used as evidence in Journalism and Court of Law. In multimedia forensics, intense research work is underway over the past two decades to bring trustworthiness to the multimedia content. This paper proposes an efficient way of identifying the manipulated region based on Noise Level inconsistencies of spliced mage. The spliced image segmented into irregular objects and extracts the noise features in both pixel and residual domains. The manipulated region is then exposed based on the cosine similarity of noise levels among pairs of individual objects. The experimental results reveal the effectiveness of the proposed method over other state-of-art methods.
A Study of Relevant Parameters Influencing Code Smell Prioritization in Object-Oriented Software Systems

Code smells are indicators of some design flaws in the software code. The evolutionary property of an object-oriented software product increases the number of code smells with every release of a version of the product. These code smells like to hamper the quality of the software system. During software maintenance, it becomes cost and effort-intensive to eliminate such a large number of smells due to time and budget constraints. This demands the prioritization of code smells where the developers are with top severe smells to save time and effort. Before proposing any prioritization approach, it becomes important to Figure out the need for code smell elimination using different refactoring activities. Following this, it is imperative to understand the role of different relevant elements in code smell prioritization. To address this need, this paper highlights the drawbacks of code smells as well as provides an overview of several prioritization-related elements such as factors, subject programs, performance metrics, and detection tools. This would help the researchers in getting a preliminary understanding of various parameters that are crucial for proposing and validating code smell prioritization approaches.
Stealthy Data Exfiltration via TCP Sequence Numbers based Covert Channel

Ex-filtration of confidential data from a compromised network node become increasingly difficult given modern day defenses, such as deep packet inspection filters, next generation firewalls, anomaly based behavioral intrusion detection systems, and data loss/leak prevention systems. There are various proposed methods in the literature to establish covert channels for stealthy data extraction, however most of them cause anomalies in the network traffic and could be flagged by the state-of-the art defensive network technologies. In this paper, we describe a novel approach, along with a proof-of-concept implementation, for creating a covert channel that fits well within the bounds of RFC-793, utilizing Transmission Control Protocol (TCP) Sequence Numbers. This method encodes the data to be ex-filtrated within TCP sequence numbers, and adds random bytes as payload to each packet to keep the communication compatible with TCP. By designing the proposed data ex-filtration channel to function within RFC standards, we aimed to increase our ability to blend into the regular network traffic and hide from network monitoring tools. We tested our method against an open source intrusion detection system, Snort, and was able to evade detection.
Data Mining to Identify Anomalies in Public Procurement Rating Parameters

The awarding of public procurement processes is one of the main causes of corruption in governments, due to the fact that in many cases, contracts are awarded to previously agreed suppliers (favouritism); for this selection, the qualification parameters of a process play a fundamental role, seeing as due to their manipulation, bidders with high prices win, causing prejudice to the state. This study identifies processes with anomalies and generates a model for detecting possible corruption in the assignment of process qualification parameters in public procurement. A multi-phase model was used (the identification of anomalies and generation of the detection model), which uses different algorithms, such as clustering (K-Means), Self-Organizing map (SOM), Support Vector Machine (SVM) and Principal Component Analysis (PCA). SOM was used to determine the level of influence of each rating parameter, K-Means to create groups by clustering, semi-supervised learning with SVM and PCA to generate a model to detect anomalies in the processes. By means of a case study, four groups of processes were obtained, highlighting the presence of the group “null economic offer” where the values for the economic offer do not exceed 1%, and a greater weight is given to other qualification parameters, which include direct contracting. The processes in this cluster are considered anomalous. Following this methodology, a semi-supervised learning model is built for the detection of anomalies, which obtains an accuracy of 95%, allowing the detection of procedures where the aim is to benefit a particular supplier by means of the qualification assignment parameters.
Incremental code updates exploitation as a basis for return oriented programming attacks on resource-constrained devices

Code-reuse attacks pose a threat to embedded devices since they are able to defeat common security defenses such as non-executable stacks. To succeed in his code-reuse attack, the attacker has to gain knowledge of some or all of the instructions of the target firmware/software. In case of a bare-metal firmware that is protected from being dumped out of a device, it is hard to know the running instructions of the target firmware. This consequently makes code-reuse attacks more difficult to achieve. This paper shows how an attacker can gain knowledge of some of these instructions by sniffing the unencrypted incremental updates. These updates exist to reduce the radio reception power for resource-constrained devices. Based on the literature, these updates are checked against authentication and integrity, but they are sometimes sent unencrypted. Therefore, it will be demonstrated how a Return-Oriented Programming (ROP) attack can be accomplished using only the passively sniffed incremental updates. The generated updates of the R3diff and Delta Generator (DG) differencing algorithms will be under assessment. The evaluation reveals that both of them can be exploited by the attacker. It also shows that the DG generated updates leak more information than the R3diff generated updates. To defend against this attack, different countermeasures that consider different power consumption scenarios are proposed, but yet to be evaluated.
On the formation of environmental consciousness among students of an agricultural university

Abstract The paper analyzes the phenomenon of environmental awareness and educational technology of its formation among the students of an agricultural university. The definition of ecological consciousness is used as the ability to build a relationship with the surrounding natural world in accordance with the law of harmonious and sustainable development of a modern technogenic society. At the same time, the work puts forward an understanding of environmental consciousness as a factor that builds a person’s relationship with the social world, which determines the worldview maturity of a person himself as a person. The latter makes environmental awareness not only an important component of environmental safety, but a guarantor of the existence of culture as a whole, in connection with which there is an urgent need to develop a strategy for the formation of environmental awareness among young people. The special role of educational institutions in the formation of environmental awareness as an important component of the educational process is indicated. The experience of research and teaching and educational work of teachers of the Department of Botany and Meadow Ecosystems and the Department of Humanities of the Tver State Agricultural Academy on the formation of environmental awareness among students is presented. One of the effective forms of extracurricular and research work is analyzed - project activity, an innovative method that contributes to the development of ecological consciousness and ecological lifestyle of students. Reconstructed the main stages of a pilot project demonstrating the benefits of growing environmentally friendly vegetable products. An educational project - “The World of a Russian Estate”, which forms the basic values necessary to preserve the ecological consciousness of young people, is presented.
Exploratory Study of Machine Learning Algorithms in Recommender Systems

Recommender systems are widely used by various companies today to help create personalized experiences for users or consumers. These systems incorporate a lot of artificial intelligence techniques, one of which is machine learning. Several machine learning algorithms/techniques are applied for recommender systems to work appropriately. This study looks at the processes that are carried out for recommender systems to work and analyzes the various machine learning algorithms/techniques applied in the recommendation process and their pros and cons. Using companies such as Amazon, Netflix, and Instagram as case studies, we would also look at the general benefits and drawbacks of recommender systems and identify ways to improve these systems for future use.