Reviewerly/exHarmony huggingface dataset:
Automatic Construction of Financial Sentiment Lexicons on Bond Market

In this research, we propose an automatic construction method for financial sentiment lexicons on bond market. Research on financial sentiment lexicons that specialize on stock market has already been conducted, but it was not suitable for bond market. Therefore, in this study, we have developed a method to automatically build a financial sentiment lexicon that includes expressions on bond market. In addition, we succeeded in acquiring new expressions by using a tagging model for the learning data of a part of the automatically constructed financial sentiment lexicons. The evaluation of our method showed that the accuracy of expressions acquired by our method was 91.0% and the accuracy of the polarity assigned to the expressions by our method was 75%. Moreover, even when the automatically constructed financial sentiment lexicons were used as training data, the accuracy of newly acquired expressions was 80.0% and the accuracy of polarity assignment was 74%. It was shown that it is possible to acquire new expressions without much loss of accuracy.
UbiChromics: Enabling Ubiquitously Deployable Interactive Displays with Photochromic Paint

Pervasive and interactive displays promise to present our digital content seamlessly throughout our environment. However, traditional display technologies do not scale to room-wide applications due to high per-unit-area costs and the need for constant wired power and data infrastructure. This research proposes the use of photochromic paint as a display medium. Applying the paint to any surface or object creates ultra-low-cost displays, which can change color when exposed to specific wavelengths of light. We develop new paint formulations that enable wide area application of photochromic material. Along with a specially modified wide-area laser projector and depth camera that can draw custom images and create on-demand, room-wide user interfaces on photochromic enabled surfaces. System parameters such as light intensity, material activation time, and user readability are examined to optimize the display. Results show that images and user interfaces can last up to 16 minutes and can be updated indefinitely. Finally, usage scenarios such as displaying static and dynamic images, ephemeral notifications, and the creation of on-demand interfaces, such as light switches and music controllers, are demonstrated and explored. Ultimately, the UbiChromics system demonstrates the possibility of extending digital content to all painted surfaces.
Tangible Chromatin: Tangible and Multi-surface Interactions for Exploring Datasets from High-Content Microscopy Experiments

In biology, microscopy data from thousands of individual cellular events presents challenges for analysis and problem solving. These include a lack of visual analysis tools to complement algorithmic approaches for tracking important but rare cellular events, and a lack of support for collaborative exploration and interpretation. In response to these challenges, we have designed and implemented Tangible Chromatin, a tangible and multi-surface system that promotes novel analysis of complex data generated from high-content microscopy experiments. The system facilitates three specific approaches to analysis: it (1) visualizes the detailed information and results from the image processing algorithms, (2) provides interactive approaches for browsing, selecting, and comparing individual data elements, and (3) expands options for productive collaboration through both independent and joint work. We present three main contributions: (i) design requirements that derive from the analytical goals of DNA replication biology, (ii) tangible and multi-surface interaction techniques to support the exploration and analysis of datasets from high-content microscopy experiments, and (iii) the results of a user study that investigated how the system supports individual and collaborative data analysis and interpretation tasks.
Conductor: Intersection-Based Bimanual Pointing in Augmented and Virtual Reality

Pointing is an elementary interaction in virtual and augmented reality environments, and, to effectively support selection, techniques must deal with the challenges of occlusion and depth specification. Most of the previous techniques require two explicit steps to handle occlusion. In this paper, we propose Conductor, an intuitive, plane-ray, intersection-based, 3D pointing technique where users leverage bimanual input to control a ray and intersecting plane. Conductor allows users to use the non-dominant hand to adjust the cursor distance on the ray while pointing with the dominant hand. We evaluate Conductor against Raycursor, a state-of-the-art VR pointing technique, and show that Conductor outperforms Raycursor for selection tasks. Given our results, we argue that bimanual selection techniques merit additional exploration to support object selection and placement within virtual environments.
NoteWordy: Investigating Touch and Speech Input on Smartphones for Personal Data Capture

Speech as a natural and low-burden input modality has great potential to support personal data capture. However, little is known about how people use speech input, together with traditional touch input, to capture different types of data in self-tracking contexts. In this work, we designed and developed NoteWordy, a multimodal self-tracking application integrating touch and speech input, and deployed it in the context of productivity tracking for two weeks (N = 17). Our participants used the two input modalities differently, depending on the data type as well as personal preferences, error tolerance for speech recognition issues, and social surroundings. Additionally, we found speech input reduced participants' diary entry time and enhanced the data richness of the free-form text. Drawing from the findings, we discuss opportunities for supporting efficient personal data capture with multimodal input and implications for improving the user experience with natural language input to capture various self-tracking data.
Deep Learning based Vehicle-to-Vehicle See Through System with Auto-Start Function

Today, with the increasing human population, the need for transportation is also increasing. These increasing transportation needs bring along some problems such as traffic density and traffic accidents. It is known that many of these accidents are caused by incorrect overtaking on single-lane roads and highways. In this research study, a solution proposal for such wrongful overtaking situations is discussed. The most important of these situations is that the vehicle to be overtaken is obstructing the view. In this study, by establishing a WLAN-based communication system between vehicles, it is discussed to eliminate the related loss of vision by image transfer between each vehicles. Unlike existing systems, this image transfer process is equipped with an automatic trigger with artificial intelligence. When overtaking is required, it is determined by means of deep networks whether vision loss occurs or not. As a main essence of this work, If vision loss is detected, the image transfer process between vehicles starts automatically and informs the driver. As a result of the real-time tests, it has been observed that it works successfully within the processing time required for the system to process 20 fps image data at a distance of 100 meters.
Staff Training Ontology for Humanitarian Organizations

Due to the high number of conflicts and wars worldwide, unfortunately large numbers of people are internally displaced or become refugee outside their countries. Humanitarian organizations try to support these affected individuals (children, adults and elderly) by providing basic needs such as food, shelter, education and health services. In order to effectively support internally displaced individuals (IDP) and refugees, humanitarian organizations require highly skilled and trained staff. Since, employees' level of skills and knowledge affect the quality of the humanitarian work, many humanitarian organizations have staff capacity building services. Specific websites or systems are used for online training of the staff. As it is difficult for international humanitarian organizations to provide the same quality of training for large number of staff in different countries, there is a need for standardization of staff online training activities within humanitarian organizations. Therefore, in this paper, we propose a new staff training ontology for humanitarian organizations that can provide machine-processable metadata about staff online training and capacity building activities. In addition, we created a new vocabulary for training subjects of humanitarian organization. Using the proposed ontology, knowledge level and skills of the staff can be monitored and suitable staff member for a particular humanitarian activity can be queried using SPARQL queries. In addition, we apply extensive Semantic Web Rule Language (SWRL) rules to infer knowledge about training activities of individuals and trainers.
Improvement of Solar Energy Efficiency Using Solar Tracking and Artificial Intelligence

Numerous kinds of solar producing devices, such as concentrators and flat-plate arrays, need that the sun be followed in order to function. The experimental technique used to model the process that must be handled or the plant with unknown characteristics is an essential part of process identification. This article aims to find and simulate a two-axis solar tracking system utilizing artificial neural networks. In order to manage the placement of the solar tracker, a fuzzy logic controller with and without self-tuning scaling factors has been researched and implemented. To establish which kind of controller was best in terms of performance, logic controllers were compared with proportional integral and derivative controllers. We used a number of tools and software, including MATLAB and Simulink, as well as real-time workshop equipment, to construct the environment.
Effect of Gamma Radiation and Borax on Mechanical Properties of Polyvinyl Alcohol and Chitosan Blend Film

The creation and characterisation of biodegradable blend films based on chitosan and polyvinyl alcohol for application in a range of packaging is described. The compatibility between the chitosan and PVA polymers was good. Composite films had a compact and homogeneous structure, according to the morphology analysis. The mechanical test result of PVA/CH at concentrations 5% showed, that The higher values of TS recorded in sample (p1, with 40 MPa) while the lower values appeared in sample (p9, with 22.09 MPa), the TS decreased gradually as the amount of PVA increased in blend film. While the blend film of pure Chitosan exhibits a poor mechanical strength which makes it a poor candidate for packaging but Blending CH with PVA together improved tensile strength. PVA/CH at concentrations 10%. Showed the higher TS values appeared in sample (C1 with 36.64MPa) while the lower values of TS appeared in sample (C9 with 24.4 MPa). In comparison with pure PVA have the lower TS than all blend film. The result of mechanical properties after addition of borax solution revealed that the Borax improves the E at max and TS of blend films. Mechanical properties results of CH/PVA/Tio2, Showed that the TS and E at max were increased after introducing of Tio2 into the polymer matrix. The P1 sample have been chosen according to their possess acceptable TS, E at max to be subjected to gamma radiation at different doses 2.3, 3.5, 4.5, 6, 7.5 kGy. The results demonstrated, that the increasing the radiation doses lead to increasing in TS of blend film, following increasing in E at max, the highest TS of the irradiated films is observed at 7.5.kGy dose.
Implementation of Socket Programming Simulation Using Quantum Communication Technologies

Using socket programming, which plays a major role in the communication between devices and people, data is transmitted instantly over predetermined ports. By the evolution of technology, the size of the data is increasing due to the increase in the quality of the data. As a result, there are losses and delays in data transfers. According to the research, it is assumed that applications and systems to be developed using quantum technologies will be useful in solving these problems. In this study, firstly information was given about the mechanism in classical socket programming and the problems encountered in this mechanism. Then, by giving information about quantum communication and the methods used in this communication, the quantum solutions to these problems were examined, the application was coded in the quantum programming language and the communication between two ports with quantum socket programming was simulated. The results obtained from this simulation was compared with the classically obtained results. Communication simulation between sockets was performed using teleportation and super-dense coding in quantum computers.
GAN and IEC Approach for Image Generation

Generative adversarial networks (GANs) are capable of learning deep representations efficiently without requiring a significant amount of annotation. Through Backpropagation, signals are derived from one network by another through a competitive process. This article presents a machine learning framework that incorporates generative adversarial networks (GANs) and interactive evolutionary computation (IEC) to generate images and to resample those images for the detection of fake images. In our study, we found that GANs trained on a particular domain could produce reliable and compact phenotypic maps. Through the use of a user research method, participants able to produce images closely matched target images, demonstrating the advantages of this unique approach.
Textual Adversarial Attacks on Named Entity Recognition in a Hard Label Black Box Setting

Named entity recognition is a key task in the field of natural language processing, which plays a key role in many downstream tasks. Adversarial examples attack based on hard label black box is to generate adversarial examples which make the model classification wrong under the condition that only the decision results of the model are obtained. However, at present, there is little research on adversarial examples attack in hard-label black box setting for named entity recognition task. Influenced by adversarial examples attacks in hard-label black box settings in text classification task, we apply genetic algorithm to adversarial examples attacks in named entity recognition task. In this paper, we first randomly generate the initial adversarial examples, and shorten the search space to a certain extent, and then use genetic algorithm to continuously optimize the examples, and finally generate high quality adversarial examples. Experiments and analysis show that the adversarial examples generated in the hard label black box setting can effectively reduce the accuracy of the model.
A Modular Reasoning Approach to Knowledge Graph

The construction and application of Knowledge Graph require effective reasoning support. However, the standard reasoning engines can not effectively deal with large-scale Knowledge Graphs because they load and compute Knowledge Graphs as a whole. This paper proposes a modular reasoning approach to Knowledge Graph. Firstly, the facts in the Knowledge Graph are partitioned into modules according to the predicate type and entity. Then the concepts and attributes involved in the fact module are used as seed signatures to extract the ontology module from the schema. During the reasoning procedure, the reasoning engine partially loads fact modules and the related ontology modules. Experiments show that the proposed approach can deal with large-scale Knowledge Graphs in a modular way with less time and memory.
Thai Question-Answering System Using Pattern-Matching Approach

Teaching concepts in Thailand's universities have abruptly changed, due to the advancement of the COVID-19 pandemic, including changes in classroom to online formats, as well as administrative difficulties. The research herein, therefore, addresses these concerns, presenting a Thai question-answering system using the pattern-matching approach. Our case study covers course information, teaching timetable, teacher schedule, and course supplements. We classified the questions into six categories according to type and acknowledged typical expressions which matched to question patterns. We use RegEx® to match a defined pattern. When a response did not match, we used word embedding to transform the question into a vector and then calculated the cosine similarity to identify the most similar pattern. The system can then generate a corresponding SQL command to query the answer from the database. We evaluated the accuracy of the proposed system with the collected data resulted in an accuracy rate of 82%.
Face Recognition Based Student Attendance System

Based on face recognition, a system is used to make a presence review system this system is known as face recognition system. The program measures the presence of each student by taking a picture with a portable computer webcam. Then the image that is captured further processed via personal computer, an instructor is capable to check the student's destination automatically at no additional effort and cost. The system itself updates people and then whether you are / are not in the excel sheet. An excel sheet used for updating presence is written over and over again when the system is in use. The user must therefore enter the presence in memory that can be used in near future also. Data obtained from website, can therefore be concluded that the presence-based presence system works satisfactorily. It is clear that the current system shows better performance in terms of speed, lower false positive level and higher accuracy. In this paper, we have implemented facial recognition program using the Main Component Analysis and Eigen facial technique. The system effectively detects a person's face and works well with a variety of facial expressions.
The 2022 Top 10 IT Issues: The Higher Education We Deserve

In ShortEDUCAUSE’s Top 10 IT issues identify and describe technology’s contributions to higher education’s most strategic opportunities and pressing priorities.In 2022, institutional leaders are developing a shared, technology-enabled transformational vision and strategy for the institution.Student success is another major area of attention and investment for technology and data in 2022. Institutions are embracing students as primary stakeholders and optimizing their offerings to meet students’ needs.The pandemic jump-started digital transformation in higher education. Institutions are using technology to reshape institutional business models and culture to anticipate and serve the current and emergent needs of learners, communities, and employers.
Bagging-based cross-media retrieval algorithm

It is hard to come up with a strong learning algorithm with high cross-media retrieval accuracy, but finding a weak learning algorithm with slightly higher accuracy than random prediction is simple. This paper proposes an innovative Bagging-based cross-media retrieval algorithm (called BCMR) based on this concept. First, we use bootstrap sampling to take a random sample from the original set. The amount of sample abstracted by bootstrapping is set to be the same as the amount of sample abstracted by the original dataset. Secondly, 50 bootstrap replicates are used to train 50 weak classifiers. In our experiments, we used homogeneous individual classifiers and eight different baseline methods. Last but not least, we generate the final strong classifier from 50 weak classifiers using the sample voting integration strategy. Using collective wisdom, we can get rid of bad decisions, giving the integrated model a much better generalization ability. Extensive experiments on three datasets show that BCMR can significantly improve cross-media retrieval accuracy.
Trust-free service measurement and payments for decentralized cellular networks

Decentralized cellular networks have emerged to increase network accessibility by distributing infrastructure ownership over independent entities. Unlike the centralized setting, these architectures can allow users to connect to any untrusted base station without prior subscription. However, verification of the service is necessary in the absence of trust for commensurate payments by the user. Further, any method of verification must be non-intrusive and reliably agreed upon by the involved parties. To this end, we describe two-sided measurements where both the users and the providers independently assess the cellular service. We find that reconciling measurements from different layers of the cellular stack for a diverse set of matching observations is challenging but not impossible. Hence, new use cases such as a decentralized slicing marketplace, and contract-free roaming can be enabled by two-sided measurements. We envision applying two-sided measurements to real-time, on-demand network slicing and present an architecture that is capable of offering, as well as verifying, such slices in a scalable manner.
Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

The cluster target brings a serious challenge to the traditional multisensor multitarget tracking algorithm because of its large number of members and the cooperative interaction between members. Using multiradar joint tracking cluster target is an alternative method to solve the problem of cluster target tracking, but it inevitably brings the problem of radar-target assignment and tracking information fusion. Aiming at the problem of radar-target assignment and tracking information fusion, a joint tracking method based on graph-long short-term memory neural nets (Graph-LSTMs) is proposed. Firstly, we use multivariable stochastic differential equations (SDE) to model the cooperative interaction of cluster members and transform the derived state space model of cluster members into the same form as the constant velocity (CV) motion model, and the target state equation of cluster which can be used for Bayesian filtering iteration is established. Secondly, based on the detection relationship between radars and cluster members, we introduce the detection confirmation matrix and propose a radar-target assignment method to achieve multiple measurements of single member and detection coverage of all cluster members. Then, each radar uses δ-GLMB filter to estimate the motion state of the assigned targets. Finally, on the basis of spatial discretization, the labels of multiple estimates of cluster member states are obtained. We use the designed Graph-LSTMs to learn the cooperative relationship between target states to fuse the labels and obtain better tracking effect. The experimental results show that the proposed method effectively simulates the cluster motion and realizes the joint estimation of cluster target motion state by multiradar. Our method makes up for the defect that a single radar cannot stably track adjacent multiple targets and achieves better estimation fusion effect than the expectation-maximization (EM) algorithm and mean method.