Reviewerly/exHarmony huggingface dataset:
The Impact of Participative Decision-Making on Organizational Commitment: A Study among Employees of Mobile Telecom Companies in Yemen

Purpose: The study aimed at examining the relationship between participative decision-making and organizational commitment among employees working in mobile telecom companies in Yemen. Approach/Methodology/Design: This study is descriptive-analytical. The study population comprised the employees working at the four mobile telecom companies in Sana'a, Yemen's capital city (Yemen Mobile, Sabafon, MTN and Y-Telecom). One hundred twenty questionnaires were collected and were valid for the data analysis. Different statistical tools, including regression analysis and correlation coefficients, were applied, and data were analyzed using SPSS. Findings: The regression analysis output shows a significant positive relationship between participative decision-making and affective commitment (β = 0.48, p &lt; 0.001). Furthermore, the regression analysis shows a significant positive relationship between participative decision making and continuance commitment (β = 0.59, p &lt; 0.001). In addition, the significance of regression analysis tested using the regression coefficients shows a significant positive relationship between participative decision-making and normative commitment (β = 0.72, p &lt; 0.001). Practical Implications: The study recommends that the companies managers need to motivate and inspire their employees to actively participate in the decision-making process to enhance their level of organizational commitment. Originality/value: The study presents evidence from a new setting where insufficient analysis has been performed to investigate the direct relationship between the variables tested.
Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos

We propose a dual system for unsupervised object segmentation in video, which brings together two modules with complementary properties: a space-time graph that discovers objects in videos and a deep network that learns powerful object features. The system uses an iterative knowledge exchange policy. A novel spectral space-time clustering process on the graph produces unsupervised segmentation masks passed to the network as pseudo-labels. The net learns to segment in single frames what the graph discovers in video and passes back to the graph strong image-level features that improve its node-level features in the next iteration. Knowledge is exchanged for several cycles until convergence. The graph has one node per each video pixel, but the object discovery is fast. It uses a novel power iteration algorithm computing the main space-time cluster as the principal eigenvector of a special Feature-Motion matrix without actually computing the matrix. The thorough experimental analysis validates our theoretical claims and proves the effectiveness of the cyclical knowledge exchange. We also perform experiments on the supervised scenario, incorporating features pretrained with human supervision. We achieve state-of-the-art level on unsupervised and supervised scenarios on four challenging datasets: DAVIS, SegTrack, YouTube-Objects, and DAVSOD. We will make our code publicly available.
Analysis of Activities based on a Single-Shoe Equipped with Force Sensors

As networked sensors become ubiquitous and integrated into the Internet-of-Things, new sensor applications provide increased safety to the public. We explore the concept of classifying personal activities using only a single shoe equipped with a low-cost constructed pressure sensor. We envision our wearable to assist in the wide-scale monitoring of citizen activity to improve smart city analytics. The pressure sensor is a polymeric foil encased with aluminum foil that provides stable readings of applied pressure. With only two detection regions: heel and toe, we assess the classification performance using a real-time implementation of a trained machine learning model. We collect training data on two categories: pedestrian movement and sports activities. We achieve an accuracy of 76%.
Simulation of Atmospheric Visibility Impairment

Changes in aerosol composition and its proportions can cause changes in atmospheric visibility. Vision systems deployed outdoors must take into account the negative effects brought by visibility impairment. In order to develop vision algorithms that can adapt to low atmospheric visibility conditions, a large-scale dataset containing pairs of clear images and their visibility-impaired versions (along with other annotations if necessary) is usually indispensable. However, it is almost impossible to collect large amounts of such image pairs in a real physical environment. A natural and reasonable solution is to use virtual simulation technologies, which is also the focus of this paper. In this paper, we first deeply analyze the limitations and irrationalities of the existing work specializing on simulation of atmospheric visibility impairment. We point out that many simulation schemes actually even violate the assumptions of the Koschmieder's law. Second, more importantly, based on a thorough investigation of the relevant studies in the field of atmospheric science, we present simulation strategies for five most commonly encountered visibility impairment phenomena, including mist, fog, natural haze, smog, and Asian dust. Our work establishes a direct link between the fields of atmospheric science and computer vision. In addition, as a byproduct, with the proposed simulation schemes, a large-scale synthetic dataset is established, comprising 40,000 clear source images and their 800,000 visibility-impaired versions. To make our work reproducible, source codes and the dataset have been released at https://cslinzhang.github.io/AVID/.
ROBOT PROCESSING EXPERIMENT USING A CHISEL BY GENERATING DYNAMIC PATHS FROM LOAD MONITORING WITH A FORCE SENSOR

Following a static path, pre-calculated as the trajectory of the tool, cannot reflect potential changing states during the machining process. Therefore, adjusting to differences occurring in the environment between the assumed and actual conditions, such as variations in the physical properties of the material, is not possible. In this research, we dynamically change the tool path by monitoring the load applied to the tool during processing. Furthermore, applying our results to processing with a chisel demonstrates that parts can be machined appropriately by dynamic paths.
Sonic Becomings: Rhythmic Encounters in Interspecies Improvisation

Abstract David Rothenberg, a philosophy professor and Jazz musician, has been improvising with nonhuman animals for years, among his playing partners are birds and whales, known to be territorial animals. As Deleuze and Guattari propose that the origin of art is precisely the territorialising animal and more a function of nature than a specifically human cultural achievement, their concept of territory and rhythm offers a non-anthropocentric way of looking at these encounters. Rothenberg’s sonic experiments in resonance and interspecies interaction do not rely on language, thus I argue that the human and the nonhuman animals form a temporary joint territory via sonic rhythms and engage in a mutual becoming by forming a rhizome. His sound thinking practice thus also helps in decentralising further anthropocentric models of music and art.
Chapter 9. “You are the best!”

This study investigates relational practices in developing email conversations between interactants performing various institutional roles at a Norwegian university. Three variables were examined: conversational progression, institutional roles, and social distance. Conversational progression was found to exert an influence on increasing familiarity in closing sequences. The openings and closings in the conversations between faculty members tended to orient to familiarity to a greater extent than in most other institutional role dyads, while the widest variety and the highest frequency of relational moves outside the framing moves appeared in faculty – PhD fellow conversations. Social distance was identified as a driver of deference in both openings and closings, while its influence on the frequency of occurrence of other relational moves was limited.
Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution

The underlying basis for patent law is a quid pro quo: the inventor discloses how to make and use the invention and in exchange for that disclosure, the public gives the inventor an exclusive right to practice that invention, for a limited time. Pervasive use of Artificial Intelligence (AI) technologies such as Generative Pre-trained Transformer 3 (GPT-3) and other similar AI tools may shift the balance of power envisioned in the patent system away from the public and towards the inventor. Specifically, GPT-3 and other AI products may allow inventors to claim significantly more in their patent than they originally invented, thus inequitably enlarging their exclusive rights without benefiting the public. Patent law has built in tools such as the enablement, utility and definiteness requirements to help limit the possible unjustified expansion of patent rights caused by pervasive use of AI. Additionally, if AI wholly disrupts the quid pro quo established by Congress, then patent law may have to adjust by moving from a peripheral claiming approach back to central claiming approach.
EnergyNN: Energy Estimation for Neural Network Inference Tasks on DPU

Convolutional Neural Networks (CNNs) are increasingly becoming popular in embedded and energy limited mobile applications. Hardware designers have proposed various accelerators to speed up the execution of CNNs on embedded platforms. Deep Learning Processor Unit (DPU) is one such generic CNN accelerator for Xilinx platforms that can execute any CNN on one or more DPUs configured on an FPGA. In a period of rapid growth in CNN algorithms and the availability of multiple configurations of CNN accelerators (like DPU), the design space is expanding fast. These design points show significant trade-off in execution time, energy consumption and application performance measured in terms of accuracy. To be able to perform this trade-off, we propose a methodology for energy estimation of a CNN running on a DPU. We build an energy model using characteristics of few CNNs and use this model for energy prediction of other unseen CNNs. We evaluate our approach using 16 different standard and popular CNNs with an average prediction error of 9.9%. Energy estimation can be useful in various scheduling applications where one can choose from multiple CNNs based on its energy consumption. We demonstrate the utility of our approach in a drone that is deployed for detecting objects on the ground.
Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks

Path planning for robotic coverage is the task of determining a collision-free robot trajectory that observes all points of interest in an environment. Robots employed for such tasks are often capable of exercising active control over onboard observational sensors during navigation. We address the problem of planning robot and sensor trajectories that maximize information gain in such tasks, where the robot needs to cover points of interest with its sensor footprint. Search-based planners in general guarantee completeness and provable bounds on sub-optimality with respect to an underlying graph discretization. However, searching for kinodynamically feasible paths in the joint space of robot and sensor state variables with standard search is computationally expensive. We propose two alternative search-based approaches to this problem. The first solves for robot and sensor trajectories independently in decoupled state spaces while maintaining a history of sensor headings during the search. The second is a two-step approach that first quickly computes a solution in decoupled state spaces and then refines it by searching its local neighborhood in the joint space for a better solution. We evaluate our approaches in simulation with a kinodynamically constrained unmanned aerial vehicle performing coverage over a 2D environment and show their benefits.
Research on the Recognition Algorithm of Body Posture ELM Registration Model Based on Kinect

Aiming at the problems of low recognition accuracy and low real-time in the existing human motion recognition in the complex background, a registration model recognition algorithm of human posture limit learning machine(ELM) is proposed, which uses Kinect measurement to calculate the joint association angle. Through Kinect, the spatial coordinates of human joints are obtained. Then, three-point method is used to calculate the correlation angle of human joints, and the elm registration model corresponding to the correlation angle of the target attitude joints is defined. Finally, the movement recognition test of the elm registration model of human body posture is carried out with the joint correlation angle calculated based on Kinect real-time measurement. The experimental results show that this method has high real-time and accuracy, strong robustness and generalization ability, and can identify more pose by defining more joint angles of target pose to establish elm registration model of human pose.
Comparison of GBNN Path Planning with Different Map Partitioning Approaches

Autonomous underwater vehicles (AUVs) usually work without any commands from remote control and finish tasks on their own. Path planning is necessary and very important. The Glasius bioinspired neural network (GBNN) model has already been applied to path planning of AUVs. Generally, the model divides the environment space into many rectangular grids. However, the triangular and hexagonal grids can also be adopted to divide the environmental space. To compare among the three different map partitions, analysis and simulations are performed. The path length, steering and speed varies are compared while the receptive range of AUV keeps the same. Analysis shows that the triangular grid may be the worst and the hexagonal grid may be the best. However, in the simulations, there is a path oscillation problem in the hexagonal partition, which makes the path longer and zigzag. On the other hand, the triangular gird also has some advantages, such as minimum turning angle, while the rectangular grid can make a balance.
Vector hermeneutics: On the interpretation of vector space models of text

Abstract Scholars working in computational literary studies are increasingly making use of text-derived vector space models, by which I mean numerical models of texts that represent the distribution or modeled relations among the vocabulary extracted from these texts. These models, as this essay will argue, call for distinct modes of humanistic interpretation and explication that are related to but distinct from those that may have been used on the original source texts. While vector space models are analyzed using increasingly complicated quantitative methods and the explanation of their operation requires statistical sophistication, my emphasis on humanistic interpretation is quite intentional. This essay theorizes two major categories of vector space models, the document-term matrix and neural language models, to position these models as not merely descriptions of texts but inscriptive representational objects that perform interpretive work of their own in order to demonstrate the need for a multi-level hermeneutics in computational literary studies.
Survey on Preprocessing Techniques for Big Data Projects

In the era of big data, a vast amount of data are being produced. This results in two main issues when trying to discover knowledge from these data. There is a lot of information that is not relevant to the problem we want to solve, and there are many imperfections and errors in the data. Therefore, preprocessing these data is a key step before applying any kind of learning algorithm. Reducing the number of features to a relevant subset (feature selection) and reducing the possible values of continuous variables (discretisation) are two of the main preprocessing techniques. This paper will review different methods for completing these two steps, focusing on the big data context and giving examples of projects where they have been applied.
Narratives + Diagrams: An Integrated Approach for Externalizing and Sharing People's Causal Beliefs

Causal knowledge is of interest in many areas, such as statistics and machine learning, as it allows people and algorithms to predict outcomes and make data-driven decisions. Researchers in CSCW have proposed tools and workflows to externalize causal knowledge or beliefs from a group of people; however, most of the generated causal diagrams lack a deeper understanding of the causal mechanisms or could not capture diverse beliefs. By integrating narratives with causal diagrams, we implemented an interactive system that allows users to 1) write narratives to rationalize their perceived causal relationships, 2) visualize their causal models using directed diagrams, and 3) review and utilize others' causal diagrams and narratives. We conducted a user study (N=20) to learn how participants leveraged this integrated approach to externalize their perceived causal models for a given application context. Our results showed that the approach implemented in our tool enabled the externalization of users' causal beliefs (e.g., how and why a causal relationship might occur), allowed blind spots of individuals' causal reasoning to be revealed (e.g., learning new ideas from peers), and inspired their causal reasoning (e.g., revising or adding new causal relationships). We also identified the individual differences in people's causal beliefs and observed the impacts of showing others' causal models when one is building his/her causal diagram and narratives. This work provides practical design implications for developing collaborative tools that facilitate capturing and sharing causal beliefs.
Overlapped speech detection using phase features

Simultaneous speech of multiple speakers is known as overlapped speech, which causes problems for speech recognition and speaker diarization systems. The present work uses previously less utilized signal phase information in the task of overlapped speech detection. In this context, Instantaneous Frequency Cosine Coefficient (IFCC) and Modified Group Delay Cepstral Coefficient (MGDCC) features are explored. IFCC captures the time-varying phase characteristics, while MGDCC represents the frequency-varying information of the phase spectrum. A Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based classifier is used for the classification. The present work uses synthetically generated overlapped speech from the GRID corpus. The proposed method is benchmarked against three baseline approaches that use magnitude spectrum features. It is observed that the combination of IFCC and MGDCC features with CNN-LSTM classifier provides better performance than the baselines. The combination of phase features with magnitude-based MFCC feature provides the best performance, indicating the importance of complementary information. The present study also investigates the effect of segment duration, genders, and number of simultaneous speakers on the overlapped speech detection system. Finally, the proposed method is also evaluated on real overlapped data from the AMI corpus.
Smart Home Assistance for humans with episodic memory decline problems using a fuzzy rule-based mechanism: The case of stove usage

Nowadays, the fast pace of life is causing problems to many people concerning lapses of attention or episodic memory decline. These problems are to blame for many annoying incidents, like forgetting where the car is parked, to extremely serious situations, like an accident due to a fire provoked from a possible failure to remember to turn off the oven. In view of these problems, the aim of this paper is to assist humans with episodic memory decline problems or lapses of attention in carrying out everyday tasks and protect them from the dangers that their memory impairment may cause at home. Particularly, this paper presents a fuzzy inference system for smart homes that monitors the user’s everyday activity and calculates the degree of the emergency that is caused by an inconsistency in the monitored person’s actions and generates the appropriate alert. The input of the fuzzy inference system consists of variables that are associated with the monitored person’s activity like time, moves and apparatus and performs goal recognition and problem diagnosis in the sequence of actions. The presented system uses fuzzy sets and fuzzy rules to generate alert messages that notify the monitored person about the next action that s/he has to do in order to complete a particular activity. This paper presents of stove usage as a case study of research.
Advancements in Healthcare: Multi-Agent Based Intelligent Sensor Approach

The paper studies the applications of multi-agent systems and sensors in healthcare. It goes through the current advancements in the field by introducing the literature and discussing applications using the technology in healthcare. The paper proposes a multi-agent system in a healthcare environment. It then conclude by discussing the merits and limitations of the technologies involved in the domain and their future scope.Bisen, Kushagra Singh
Paving the path toward genomic privacy with secure imputation

Ensuring the privacy of participants in genomic studies is a critical responsibility of the biomedical community. Accurate and efficient implementations of secure genotype imputation highlight practical approaches to safeguard sensitive genomic data that can be adapted for numerous bioinformatics applications.
Code Edit Recommendation Using a Recurrent Neural Network

When performing software evolution tasks, developers spend a significant amount of time looking for files to modify. By recommending files to modify, a code edit recommendation system reduces the developer’s navigation time when conducting software evolution tasks. In this paper, we propose a code edit recommendation method using a recurrent neural network (CERNN). CERNN forms contexts that maintain the sequence of developers’ interactions to recommend files to edit and stops recommendations when the first recommendation becomes incorrect for the given evolution task. We evaluated our method by comparing it with the state-of-the-art method MI-EA that was developed based on the association rule mining technique. The result shows that our proposed method improves the average recommendation accuracy by approximately 5% over MI-EA (0.64 vs. 0.59 F-score).
Practising Reading Fluency with Virtual Voice Assistants: Theoretical Perspectives and Experiences

The ability to read fluently is important the participation in social and public life, the main reason for primary and secondary school students to acquire this ability. The Multilingual Readers’ Theatre is a method used to teach reading fluency by means of reading aloud. In the Multilingual Readers’ Theatre, students actively and cooperatively learn to read dialogical texts, with different reading roles distributed among the students. This method requires two or more students to take on the roles in a dialogical story text which is read aloud in theatre. As part of the reading training, students are required to practice their reading role outside of the classroom; however, some students lack a reading partner for practice at home. This paper demonstrates the Amazon Alexa application (Skill) designed to fill this gap by serving as a reading model, reading partner, and provider of student feedback for the purpose of reading fluency training. This paper reports on the conception, development and evaluation of a Skill prototype. The main aim is to investigate the degree of user acceptance and willingness for future use as response to the prototype design concept development.