Reviewerly/exHarmony huggingface dataset:
Improving Code Completion by Solving Data Inconsistencies in the Source Code with a Hierarchical Language Model

In the field of software engineering, applying language models to the token sequence of source code is the state-of-the-art approach to building a code recommendation system. When applying language models to source code, it is difficult for state-of-the-art language models to deal with the data inconsistency problem, which is caused by the free naming conventions of source code. It is common for user-defined variables or methods with similar semantics in source code, to have different names in different projects. This means that a model trained on one project may encounter many words the model has never seen before during another project. Those freely named variables or functions in the code will bring difficulties to the processes of training and prediction and cause a data inconsistency problem between projects. However, we discover that the syntax tree of source code has hierarchical structures. This code structure has strong regularity in different projects and can be used to combat data inconsistency. In this paper, we propose a novel Hierarchical Language Model (HLM) to improve the robustness of the state-of-the-art recurrent language model, in order to be able to deal with data inconsistency between training and testing. The newly proposed HLM takes the hierarchical structure of the code tree into consideration to predict code. The proposed HLM method generates the embedding for each sub-tree according to hierarchies and collects the embedding of each sub-tree in context, to predict the next piece of code. The experiments on inner-project and cross-project datasets indicate that the newly proposed HLM method performs better than the state-of-the-art recurrent language model in dealing with the data inconsistency between training and testing, and achieves an average improvement in prediction accuracy of 11.2%.
Development of a Dimensional Analysis Approach in Gunshot Residue Images Using Computerized Image Processing

Computer image processing is a method that uses artificial intelligence and machine learning-based general learning algorithms. With this method, objects in digital images (photos or videos) can be grouped by being perceived and detected. Computerized image processing method can be applied to almost all kinds of digital data produced with the developing technology. Nowadays, the identification and detection of gunshot residues (GSR) can be done manually by experts from the acquired images. In this study, computerized image processing method was used for the identification and dimensional analysis of gunshot residues (GSR). In this new proposed method, a dataset of 18500 digital image samples obtained from three different caliber cartridges (MKE, Gecco and S&amp;B brands) was used. From the results of the study, it has been shown that the Computer Vision Method is a successful method in the automatic dimensional classification of GSRs.
On the non-existence of a discrete power series distribution with a constant coefficient of variation

Non-existence of distributions with constant coefficient of variation(CV) is investigated within the discrete Power Series and Modified Power Series families of distributions. The development is used to revisit and comment on the problem of existence of a better but biased estimator.
Energy-Economic-Environmental Analysis of On-Grid Solar System for Electricity and Hydrogen Production at Homemade Scale: Effect of Different Shades

As an energy source, solar cells can be central to peak load control in different power companies. Hydrogen production can be cost-effective if the generated electricity is unusable. Shades reduce solar cell current and fundamentally alter panel function, making it necessary to restore panels to optimum power generation. Owing to the issues outlined in this paper, the energy-economic-environmental effect of a shade detection algorithm was examined for different shade scenarios, and domestic-scale solar hydrogen production was evaluated. As the effect of the partial shade detection algorithm on the amount of solar hydrogen production has not been investigated quantitatively so far, this issue was discussed in this study. The algorithm and laboratory equipment developed by the authors were used to detect the partial shadow. Thanks to the respected reviewer, the following sentence was added to the abstract. The analyses in HOMER V2.81 software used 20-year average data for a residential building in the city of Tehran obtained from the NASA website. The results recommended that the designed algorithm increases hydrogen production in different scenarios between 5.8% and 32.9%. The results also revealed that the designed algorithm significantly lessen the pollutants between 100 kg/year and 240 kg/year.
The edge of experiences. Towards a material ecology of augmented interiors

Augmented interiors are here to stay, yet their overall capacity to increase well-being remains unclear despite decades of technical improvements and content development. This article highlights the need to design new ecologies of spatial augmentation, grounded in materials vibrancy and able to reconnect us with ourselves, with places, and with time. To move beyond "things that glitter", information overload, and extended automation, augmented interiors ought to bring about new kinds of interior experiences that are not just novel, or more efficient, but transformative. Reflecting on [RIP]_Montevideo, an interactive installation depicting images from urban archives, it highlights the importance of edge qualities in achieving openness, arguing that a shift of focus from content to edges is essential to resolve the conflicting requirements of digitally augmented interiors, between cognition and sensibility.
Decentralized federated learning with privacy-preserving for recommendation systems

Hyperautomation can automate complex business processes, reduce human intervention and improve business operational efficiency. Recommendation systems (RS) facilitate hyperautomation greatly. However, these systems require a large amount of user data to train their machine learning (ML) models and hence user data privacy has received great attention. In this paper, we propose a decentralized federated learning framework with privacy-preserving for RS. In our framework, users train the private and public parameters locally but share the public parameters only. Extensive experiments verify that our approach is accurate and can well preserve privacy. This study is helpful for providing privacy preserving in hyperautomation.
Aerodynamic Heat Simulation Heating System Based on Fuzzy PID Control

Abstract Facing the problem of having trouble building an exact mathematical model for aerodynamic heat simulation heating system (AHSHS), the conventional PID is considered to have the characteristics of simple operation, easy parameter adjustment, and high accuracy, but it is sensitive to external disturbances and has a poor anti-interference ability. In this paper, the fuzzy PID scheme for AHSHS is introduced to conquer the above problems. Firstly, the affiliation functions are constructed according to the system output errors and deviation variations. Next, the fuzzy rule table is designed, and the parameter autotune of PID is implemented utilizing both fuzzy inference and inverse fuzzification. Finally, comparative analysis through simulation verifies that the fuzzy PID scheme is more advantageous.
The development of quadcopter using Arducopter APM 2.8 with autopilot for tracking point of drop-off goods

Abstract This research reported the development of a drone withinside the shape of a quadrotor to track positions over a certain geographic area on autopilot. The system includes an Ardupilot Mega (APM) 2.8 flight controller, Ublox NEO M8N GPS module with compass, Racerstar 920kV 2-4S Brushless Motor, Flysky Receiver FS-iA6B with FS-i6 Remote Control Transmitter, DJI F450 quadcopter frame kits with touchdown tools skid, and a LiPo Battery 3300 mAh 35C. The ground control system is set up and run through an open-source software so-known as Mission Planner. The tracking system is evolved as a payload together with BME280 sensors controlled through Arduino Uno R3 SMD. Google Maps from Mission Planner are set via waypoints. The readings from the BME280 barometer sensor are used to examine the right coordinates on the waypoint. This correction may be very crucial while a drone is implemented to a delivery system. The results show that the average error at every waypoint is 5%, which shows that one waypoint can be used as a drop-off point factor for goods to customers.
TSRL-Net: Target-aware supervision residual learning for stroke segmentation

Accurate stroke segmentation is a crucial task in establishing a computer-aided diagnostic system for brain diseases. However, reducing false negatives and accurately segmenting strokes in MRI images is often challenging because of the class imbalance and intraclass ambiguities problems. To address these issues, we propose a novel target-aware supervision residual learning framework for stroke segmentation. Considering the problem of imbalance of positive and negative samples, a creatively target-aware loss function is designed to dilate strong attention regions, pay high attention to the positive sample losses, and compensate for the loss of negative samples around the target. Then, a coarse-grained residual learning module is developed to gradually fix the lost residual features during the decoding phase to alleviate the problem of high number of false negatives caused by intraclass ambiguities. Here, our reverse/positive attention unit suppresses redundant target/background noise and allows relatively more focused highlighting of important features in the target residual region. Extensive experiments were performed on the Anatomical Tracings of Lesions After Stroke and Ischemic Stroke Lesion Segmentation public datasets, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.
GFNet: A Deep Learning Framework for Breast Mass Detection

Background: Breast mass is one of the main symptoms of breast cancer. Effective and accurate detection of breast masses at an early stage would be of great value for clinical breast cancer analysis. Methods: We developed a novel mass detection framework named GFNet. The GFNet is comprised of three modules, including patch extraction, feature extraction, and mass detection. The developed breast mass detection framework is of high robustness and generality that can be self-adapted to images collected by different imaging devices. The patch-based detection is deployed to improve performance. A novel feature extraction technique based on gradient field convergence features (GFCF) is proposed to enhance the information of breast mass and, therefore, provide useful information for the following patch extraction module. A novel false positives reduction method is designed by combining the texture and morphological features in breast mass patch. This is the first attempt at fusing morphological and texture features for breast mass false positive reduction. Results: Compared to other state-of-the-art methods, the proposed GFNet showed the best performance on CBIS-DDSM and INbreast with an accuracy of 0.90 at 2.91 false positive per image (FPI) and 0.99 at only 0.97 FPI, respectively. Conclusions: The GFNet is an effective tool for detecting breast mass.
How interesting and coherent are the stories generated by a large‐scale neural language model? Comparing human and automatic evaluations of machine‐generated text

Abstract Evaluation of the narrative text generated by machines has traditionally been a challenge, particularly when attempting to evaluate subjective elements such as interest or believability. Recent improvements in narrative machine text generation have been largely driven by the emergence of transformer‐based language models, trained on massive quantities of data, resulting in higher quality text generation. In this study, a corpus of stories is generated using the pre‐trained GPT‐Neo transformer model, with human‐written prompts as inputs upon which to base the narrative text. The stories generated through this process are subsequently evaluated through both human evaluation and two automated metrics: BERTScore and BERT Next Sentence Prediction, with the aim of determining whether there is a correlation between the automatic scores and the human judgements. The results show variation in human evaluation results in comparison to modern automated metrics, suggesting further work is required to train automated metrics to identify text that is defined as interesting by humans.
Prediction of the Factors Affecting the Permanence of Knowledge in Mathematics Using Soft Computing

In this scientific research work, the possibilities of applying artificial intelligence of neural networks, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS) methodology will be presented and explored as a support in teaching mathematics in predicting the durability of knowledge. The results of the research show that it is through the use of these sophisticated technologies that students&rsquo; achievements in mathematics can be improved, and that research in this direction is very much needed.
Serials Spoken Here

This quarter’s column includes a report on transitioning a highly selective title to open access from the UKSG 45th Annual Conference in Telford held on May 30 and June 1, 2022. There are also five reports from the NASIG 37th Annual Conference held in Baltimore, MD June 5–8, 2022. These report topics include standards related to electronic resource management, identifying problems with link resolver URLs, access frameworks developed by libraries, serials weeding using GreenGlass, and communicating the importance of diversity in collections with vendors. Lastly, there is a report from the Acquisitions Institute, held May 21–24, 2022 in Timberline Lodge, Oregon that highlights diversity and equity themes from multiple sessions.
Vibro-acoustics time domain FEM-BEM analysis of a Titian Stradivari violin: the role of the bridge

Abstract The acoustic behaviour of the violin is defined during manufacturing since the vibrational acoustic properties of the instrument are related to the top plate’s vibrational modes in a direct way. An important component of the violin assembly is the bridge. The bridge transfers the excitation forces from the strings to the soundboard via the bridge feet and acts as a sound filter. In this study the Titian Stradivari violin CAD geometry of the soundboard, including the f-holes and bass bar, and the bridge is modelled, and time domain coupled finite element method-boundary element method, vibro-acoustic, simulations are performed to analyse the interference of the instrument’s sound with its structural components. A comparison of the vibroacoustic behaviour for the soundboard with and without the bridge enlightens the crucial role of the bridge. This initial computational approach will be extended, with similar studies on the role of every structural component of the violin individually, aiming to decode the full violin soundbox vibroacoustic behaviour and provide key insights for violinmakers on how structural changes affect the quality of the emitted sound.
280 characters to the White House: predicting 2020 U.S. presidential elections from twitter data

This nation-shaping election of 2020 plays a vital role in shaping the future of the U.S. and the entire world. With the growing importance of social media, the public uses them to express their thoughts and communicate with others. Social media have been used for political campaigns and election activities, especially Twitter. The researchers intend to predict presidential election results by analyzing the public stance toward the candidates using Twitter data. Previous researchers have not succeeded in finding a model that simulates well the U.S. presidential election system. This manuscript proposes an efficient model that predicts the 2020 U.S. presidential election from geo-located tweets by leveraging the sentiment analysis potential, multinomial naive Bayes classifier, and machine learning. An extensive study is performed for all 50 states to predict the 2020 U.S. presidential election results led by the state-based public stance for electoral votes. The general public stance is also predicted for popular votes. The true public stance is preserved by eliminating all outliers and removing suspicious tweets generated by bots and agents recruited for manipulating the election. The pre-election and post-election public stances are also studied with their time and space variations. The influencers' effect on the public stance was discussed. Network analysis and community detection techniques were performed to detect any hidden patterns. An algorithm-defined stance meter decision rule was introduced to predict Joe Biden as the President-elect. The model's effectiveness in predicting the election results for each state was validated by the comparison of the predicted results with the actual election results. With a percentage of 89.9%, the proposed model showed that Joe Biden dominated the electoral college and became the winner of the U.S. presidential election in 2020.
Networking in next generation disaggregated datacenters

Summary Nowadays, datacenters lean on a computer‐centric approach based on monolithic servers which include all necessary hardware resources (mainly CPU, RAM, network, and disks) to run applications. Such an architecture comes with two main limitations: (1) difficulty to achieve full resource utilization and (2) coarse granularity for hardware maintenance. Recently, many works investigated a resource‐centric approach called disaggregated architecture where the datacenter is composed of self‐content resource boards interconnected using fast interconnection technologies, each resource board including instances of one resource type. The resource‐centric architecture allows each resource to be managed (maintenance, allocation) independently. LegoOS is the first work which studied the implications of disaggregation on the operating system, proposing to disaggregate the operating system itself. They demonstrated the suitability of this approach, considering mainly CPU and RAM resources. However, they did not study the implication of disaggregation on network resources. We reproduced a LegoOS infrastructure and extended it to support disaggregated networking. We show that networking can be disaggregated following the same principles, and that classical networking optimizations such as DMA, DDIO, or loopback can be reproduced in such an environment. Our evaluations show the viability of the approach and the potential of future disaggregated infrastructures.
The Formation of the Beginning of Dialogue in Anonymous Online Communication

The main purpose of the article is to track and analyze the correlation of technical and semantic aspects of anonymous interaction on the web. The initial phase of communication between two interlocutors in the “Anonymous VKontakte chat” is empirically investigated. Combining the conceptual and methodological resources of conversational analysis and membership categorization analysis, as well as the theory of affordances adapted to technical objects (in this case, anonymous chat), the author defends the concept of the order of statements of anonymous interlocutors for successful interaction. Most visitors use the chat to search for short-term romantic and/or sexual communication, therefore, at the beginning of the correspondence, the interlocutors often seek to identify each other's gender identity using typical statements. The importance of having the first row is noted, since it is he who, with a high degree of probability, gives the priority right to ask a question about the gender identity of the interlocutor. However, this right does not negate the fact that sooner or later (rather sooner) the owner of the first turn will have to answer the questions of the interlocutor, so the anonymous interaction under consideration, through the communicative efforts of actors organized in the next order, tends to information symmetry.
A General Condition of Structural Distinguisher

Abstract This paper proposes a general construction method for structural distinguishers, based on the recently proposed ‘multiple-of-n property’ of a subspace trail. The multiple-of-n property refers to the fact that by appropriate choices of difference for a number of input pairs, it is possible to make sure that the number of times that the difference of the resulting output pairs lie in a particular subspace is always a multiple of n. We weaken the condition for constructing the structural distinguisher from the exact subspace trail to the general subspace trail and this general condition is applied to the SPN cryptographic algorithm, allowing a six-round structural distinguisher to be obtained for the first time. In this way, the best six-round Midori-64 distinguisher is constructed, with its data complexity being identical to Midori’s five-round structural distinguisher. We also applied this new condition to the SKINNY algorithm, extending its structural distinguisher from five to six rounds.
Multi‐graph learning‐based software defect location

Abstract Software quality is key to the success of software systems. Modern software systems are growing in their worth based on industry needs and becoming more complex, which inevitably increases the possibility of more defects in software systems. Software repairing is time‐consuming, especially locating the source files related to specific software defect reports. To locate defective source files more quickly and accurately, automated software defect location technology is generated and has a huge application value. The existing deep learning‐based software defect location method focuses on extracting the semantic correlation between the source file and the corresponding defect reports. However, the extensive code structure information contained in the source files is ignored. To this end, we propose a software defect location method, namely, multi‐graph learning‐based software defect location (MGSDL). By extracting the program dependency graphs for functions, each source file is converted into a graph bag containing multiple graphs (i.e., multi‐graph). Further, a multi‐graph learning method is proposed, which learns code structure information from multi‐graph to establish the semantic association between source files and software defect reports. Experiments' results on four publicly available datasets, AspectJ, Tomcat, Eclipse UI, and SWT, show that MGSDL improves on average 3.88%, 5.66%, 13.23%, 9.47%, and 3.26% over the competitive methods in five evaluation metrics, rank@10, rank@5, MRR, MAP, and AUC, respectively.
Timed concurrent language for argumentation with maximum parallelism

Abstract The timed concurrent language for argumentation (tcla) is a framework to model concurrent interactions between communicating agents that reason and take decisions through argumentation processes, also taking into account the temporal duration of the performed actions. Time is a crucial factor when dealing with dynamic environments in real-world applications, where agents must act in a coordinated fashion to reach their own goals. However, modelling complex interactions and concurrent processes may be challenging without the help of proper languages and tools. In this paper, we discuss the use of tcla for practical purposes and provide a working implementation of the language, endowed with a user interface available online, that serves the dual purpose of aiding the research in this field and facilitating the development of multi-agent systems based applications.