Reviewerly/exHarmony huggingface dataset:
Gaze+Lip: Rapid, Precise and Expressive Interactions Combining Gaze Input and Silent Speech Commands for Hands-free Smart TV Control

As eye-tracking technologies develop, gaze becomes more and more popular as an input modality. However, in situations that require fast and precise object selection, gaze is hard to use because of limited accuracy. We present Gaze+Lip, a hands-free interface that combines gaze and lip reading to enable rapid and precise remote controls when interacting with big displays. Gaze+Lip takes advantage of gaze for target selection and leverages silent speech to ensure accurate and reliable command execution in noisy scenarios such as watching TV or playing videos on a computer. For evaluation, we implemented a system on a TV, and conducted an experiment to compare our method with the dwell-based gaze-only input method. Results showed that Gaze+Lip outperformed the gaze-only approach in accuracy and input speed. Furthermore, subjective evaluations indicated that Gaze+Lip is easy to understand, easy to use, and has higher perceived speed than the gaze-only approach.
Dean leadership efficacy and the faculty teaching and research efficacy: a case study at Herat University, Afghanistan

As a pioneer study on academic leadership efficacy in university context, this mixed-method study aimed at exploring the association between dean leadership efficacy in the areas of action efficacy and self-regulation efficacy and the faculty level of self-efficacy in terms of teaching and research at a public university in Afghanistan. The study employed a mixed-method design to collect the required data in two different phases. First, two sets of questionnaires were used to collect the quantitative data. Second, semi-structured interviews were conducted with six faculty members to investigate their perceptions of dean leadership and the faculty efficacy so as to triangulate the quantitative results. The total participants of this study were 126 faculty members (120 for quantitative and 6 for qualitative). The results showed the dean leaders were perceived to have a medium level of leadership efficacy and the faculty were also reported with almost the same level of teaching efficacy but low level of research efficacy. A significant positive association was also found between the dean leaders and the faculty members’ levels of efficacy in leadership, teaching and research. Accordingly, universities’ stakeholders were addressed with certain implications about dean leadership as well as the faculty levels of efficacy.
El proceso de aprendizaje de la asignatura de Biología en modalidad blended learning

The teaching of biology demands the constant search for innovative learning strategies that allow the achievement of established purposes in the curricular programs. There are a large number of learning strategies in blended settings, so it is essential to identify their scope in a blended context due to the contingency that is taking place. The objective of this research is to evaluate if the learning process of 2ndgrade biology students is accomplished on the technopedagogical design operable on the Teams platform in the blended learning modality. The research is mixed, with an explanatory scope. It starts with obtaining qualitative data related to the performance criteria of each student, and subsequently, they are quantitatively analyzed to identify the appropriation of knowledge to the specific content of Biology. The results make crucial contributions to the educational field since it provides an instructional design based on strategies that foster the learning of Biology in a blended learning modality according to the current contingency needs as well as evaluation instruments that demonstrate the achievement of the purposes raised according to a competency-based educational program.
Rap-Net: Coarse-To-Fine Multi-Organ Segmentation With Single Random Anatomical Prior

Performing coarse-to-fine abdominal multi-organ segmentation facilitates extraction of high-resolution segmentation minimizing the loss of spatial contextual information. However, current coarse-to-refine approaches require a significant number of models to perform single organ segmentation. We propose a coarse-to-fine pipeline RAP-Net, which starts from the extraction of the global prior context of multiple organs from 3D volumes using a low-resolution coarse network, followed by a fine phase that uses a single refined model to segment all abdominal organs instead of multiple organ corresponding models. We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model. To train and evaluate our method, a clinical research cohort consisting of 100 patient volumes with 13 organs well-annotated is used. We tested our algorithms with 4-fold cross-validation and computed the Dice score for evaluating the segmentation performance of the 13 organs. Our proposed method using single auto-context outperforms the state-of-the-art on 13 models with an average Dice score 84.58% versus 81.69% (p<0.0001).
VoiSense

Data collection is a major hurdle faced by the researchers, particularly in the field of wearable computing, as it requires significant amount of time and manual effort. Although wearable devices such as, smartwatches have sophisticated sensing capabilities today and are being popularly used by CPS researchers, collecting data from the sensors embedded on the smartwatch remains a challenging job. The fundamental limitation of the smartwatches is the small form factor, which makes it difficult for the user to input any data collection preferences or labels for the data collection. The existing apps/systems that takes such inputs manually from the user are therefore ineffective for the smartwatches. In this paper, we present the idea of using voice interaction capabilities of a smartwatch for conveniently collecting and labeling sensor data from the available sensing modalities. We present VoiSense, a voice interactive smartwatch based system which intelligently gathers the user preferences and data labels with voice interaction and facilitates convenient data collection without requiring any expertise or significant manual effort.
Techno-economic comparison between photovoltaic systems with solar trackers and fixed structure in “El Valle de Sula”, Honduras

Abstract Solar energy has the greatest energy potential among all types of renewable energies. In 2018, the annual demand for electricity worldwide was 22,964 TWh. Daily, earth receives about 174 PW in the form of solar irradiance. This means that, if all this radiation could be harnessed, in approximately 8 minutes the electricity needed to meet the global annual electricity demand could be generated. Therefore, the need arises to implement photovoltaic systems that take better advantage of the solar resource, but in turn provide greater economic profitability. This article aims to evaluate the technical and economic feasibility of photovoltaic systems with solar trackers and compare them with fixed structure systems in “El Valle de Sula”, Honduras, Central America. The research was able to determine the levelized cost of electricity (LCOE) of each type of photovoltaic system from the generation of electricity in municipalities comprising the study area and the costs associated with initial investment, and operation and maintenance of each photovoltaic system. This research was carried out by implementing a methodology with a quantitative approach, using data collection to test hypotheses, based on numerical measurement and statistical analysis. Considering the value of the land, the photovoltaic system with a horizontal axis tracker has the lowest LCOE in 14 of the 18 municipalities of “El Valle de Sula”. Without taking into account the value of the land, the system with the lowest LCOE in all municipalities of this region is the system with a horizontal axis tracker, then the system with a dual-axis tracker and finally the system with fixed structure.
A Geometric Approach to Homomorphic Secret Sharing

An (n, m, t)-homomorphic secret sharing (HSS) scheme allows n clients to share their inputs across m servers, such that the inputs are hidden from any t colluding servers, and moreover the servers can evaluate functions over the inputs locally by mapping their input shares to compact output shares. Such compactness makes HSS a useful building block for communication-efficient secure multi-party computation (MPC).
Efficient access methods for very large distributed graph databases

indexing Subgraph isomorphism Large scale processing a b s t r a c t Subgraph searching is an essential problem in graph databases, but it is also challenging due to the involved subgraph isomorphism NP-Complete sub-problem.Filter-Then-Verify (FTV) methods mitigate performance overheads by using an index to prune out graphs that do not fit the query in a filtering stage, reducing the number of subgraph isomorphism evaluations in a subsequent verification stage.Subgraph searching has to be applied to very large databases (tens of millions of graphs) in real applications such as molecular substructure searching.Previous surveys have identified the FTV solutions GraphGrepSX (GGSX) and CT-Index as the best ones for large databases (thousands of graphs), however they cannot reach reasonable performance on very large ones (tens of millions graphs).This paper proposes a generic approach for the distributed implementation of FTV solutions.Besides, three previous methods that improve the performance of GGSX and CT-Index are adapted to be executed in clusters.The evaluation shows how the achieved solutions provide a great performance improvement (between 70% and 90% of filtering time reduction) in a centralized configuration and how they may be used to achieve efficient subgraph searching over very large databases in cluster configurations.
Pupil Tracking Under Direct Sunlight

Pupil tracking in a bright outdoor environment is challenging due to low eye image quality and reduced pupil size in response to bright light. In this study we present research to develop robust outdoor pupil tracking without the need for shading the eyes. We first investigate the effect of camera post-processing settings in order to find values that enhance image quality for the purpose of pupil tracking under direct, oblique and overcast sunlight illuminations. We then tested the performance of the state-of-the-art pupil tracking techniques under these extreme real-world outdoor lighting conditions. Our results suggest that a key goal should be maintaining the contrast between iris and pupil to support accurate estimation of pupil position regardless of the overall eye image quality.
One-class anomaly detection via novelty normalization

Anomaly detection is an important task in many real-world applications, such as within cybersecurity and surveillance. As with most data these days, the size and dimensionality of the data within these fields are constantly growing, which makes it essential to develop an approach that can both accurately and efficiently identify anomalies within these datasets. In this paper, we address the problem of one-class anomaly detection, where after training on a singular class, we try to determine whether or not inputs belong to that said class. Most of the currently existing methods have limitations in which the criterion of the novel class relies solely on the reconstruction error term. We attempt to break away from this restriction by proposing the use of an autoencoder network with a normalization term. We pair this with an additive novelty scoring module during the training procedure as a way to determine the difference between a given image and our determined normal class, therefore improving the efficiency of our model. We evaluate our model on MNIST, CIFAR-10, and Fashion-MNIST, three popular datasets for image classification, and compare the results against other various state-of-the-art models to determine the efficacy of our efforts. Our model not only outperforms the existing methods, but it also gives us a narrower range of AUCs for the tested classes, suggesting a stark improvement in both accuracy and precision. Moreover, we discover that introducing this “Novelty Normalization” concept into our model allows us to expand its usage into multiclass scenarios without a steep drop in accuracy.
Machine Learning Techniques for Accountability

Artificial intelligence systems have provided us with many everyday conveniences. We can easily search for information across millions of web‐pages via text and voice. Paperwork processing is increasingly automated. Artificial intelligence systems flag potentially fraudulent credit‐card transactions and filter our e‐mail. Yet these artificial intelligence systems have also experienced significant failings. Across a range of applications, including loan approvals, disease severity scores, hiring algorithms, and face recognition, artificial‐intelligence‐based scoring systems have exhibited gender and racial bias. Self‐driving cars have had serious accidents. As these systems become more prevalent, it is increasingly important that we identify the best ways to keep them accountable.
Ghost-Light-3dnet: Efficient Network For Heart Segmentation

Accurate 3D whole heart segmentation provides more details of the morphological and pathological information that could help doctors with more effective patient-specific treatments. 3D CNN network has been recognized as an important role in accurate volumetric segmentation. Typically, 3D CNN network has a large number of parameters as well as the floating point operations (FLOPs), which leads to heavy and complex computation. In this paper, we introduce an efficient 3D network (Ghost-Light-3DNet) for heart segmentation. Our solution is characterized by two key components: First, inspired by GhostNet in 2D, we extend the Ghost module to 3D which can generate more feature maps from cheap operations. Second, a sequential separable conv with residual module is applied as a light plug-and-play component to further reduce network parameters and FLOPs. For evaluation, the proposed method is validated on the MM-WHS heart segmentation Challenge 2017 datasets. Compared to state-of-the-art solution using 3D UNet-like architecture, our Ghost-Light-3DNet achieves comparable segmentation accuracy with the 2. 18x fewer parameters and 4. 48x less FLOPs, respectively.
From Employable to Fully Operational: The Need for Training of Computer Science Graduates

For graduates in computer science and informatics to get employment as IT professionals, there is a need for their education to provide the competence sought after by employers being aligned with the discipline. To become fully operational in the organization, the candidates typically also need to further develop their competence there, engaging in activities, and becoming familiar with the practices in the company. For a university offering master’s degrees in computer science and informatics, it is important to know the employers’ view of the relevance of the study programs and what is possibly considered to be lacking. Also, it is essential to know whether the missing part needed to become fully operational should be provided by the university. In this paper, we investigate these questions by asking employers of master students in IT. We use the framework of modes of identification by Wenger-Trayner and Wenger-Trayner to find out if the graduates are aligned with the discipline, engaged in activities, and able to imagine their future in an IT-position. A combination of in-depth interviews and a survey is used. Analysis of the findings shows that employers overall find the candidates’ competence from the university to be adequate. We argue that collaboration between university and industry is essential to this success, pointing to a set of key steps in the process from entering a study program to becoming fully operational in work life.
Wireless Cybersecurity Education: A Focus on Curriculum

Higher education is increasingly called upon to enhance cyber education, including hands-on "experiential" training. The good news is that additional tools and techniques are becoming more available, both in-house and through third parties, to provide cyber training environments and simulations at various features and price points. However, the training thus far has only focused on "traditional" Cybersecurity that lightly touches on wireless in undergraduate and master's degree programs, and certifications. The purpose of this research is to identify and recognize nascent cyber training emphasizing a broader spectrum of wireless security and encourage curricular development that includes critical experiential training. Experiential wireless security training is important to keep pace with the growth in wireless communication mediums and associated Internet of Things (IoT) and Cyber Physical System (CPS) applications. Cyber faculty at a university offering undergraduate and master's Cybersecurity degrees authored this paper; both degrees are offered to resident as well as online students.
Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification

Deep transfer learning aims at dealing with challenges in new tasks with insufficient samples. However, when it comes to few-shot learning scenarios, due to the low diversity of several known training samples, they are prone to be dominated by specificity, thus leading to one-sidedness local features instead of the reliable global feature of the actual categories they belong to. To alleviate the difficulty, we propose a cross-modal few-shot contextual transfer method that leverages the contextual information as a supplement and learns context awareness transfer in few-shot image classification scenes, which fully utilizes the information in heterogeneous data. The similarity measure in the image classification task is reformulated via fusing textual semantic modal information and visual semantic modal information extracted from images. This performs as a supplement and helps to inhibit the sample specificity. Besides, to better extract local visual features and reorganize the recognition pattern, the deep transfer scheme is also used for reusing a powerful extractor from the pre-trained model. Simulation experiments show that the introduction of cross-modal and intra-modal contextual information can effectively suppress the deviation of defining category features with few samples and improve the accuracy of few-shot image classification tasks.
Cognitive function vs. accessible authentication

The most common authentication mechanism, the password, requires a user to recall a secret. Users take this memorisation, or cognitive function, test on a daily basis in order to gain access to systems and devices. This mechanism's design has received much scrutiny and there is a common realization that security and usability are key considerations. In this paper, we consider a third, emergent aspect: that of accessibility. Using a qualitative approach, we explore the challenges current password-based approaches pose to people with dyslexia, a relatively common cognitive disability, highlighting several issues. Following draft web accessibility guidelines, we also evaluate alternative authentication mechanisms. We observe a lack of consideration for accessibility in the area of authentication and offer suggestions for future research.
The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor

Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation from historical student data, as well as research showing positive impacts on student learning. However, there is a lack of research exploring the possibility of extending data-driven methods to provide higher-level assistance. Therefore, we modified our data-driven Next-Step hint generator to provide Waypoints, hints that are a few steps ahead, representing problem-solving subgoals. We hypothesized that Waypoints would benefit students with high prior knowledge, and that Next-Step hints would most benefit students with lower prior knowledge. In this study, we investigated the influence of data-driven hint type, Waypoints versus Next-Step hints, on student learning in a logic proof tutoring system, Deep Thought, in a discrete mathematics course. We found that Next-Step hints were more beneficial for the majority of students in terms of time, efficiency, and accuracy on the posttest. However, higher totals of successfully used Waypoints were correlated with improvements in efficiency and time in the posttest. These results suggest that Waypoint hints could be beneficial, but more scaffolding may be needed to help students follow them.
A Unified Framework for Flexible Playback Latency Control in Live Video Streaming

Live video streaming has seen tremendous growth in the past decade. An important fact in live streaming is that the demand for low playback-latency inherently conflicts with the desire for high QoE. This requires different types of live services to seek different latency-QoE tradeoffs according to their service-requirements. However, our investigations revealed that it is fundamentally difficult for existing streaming algorithms to keep consistent latency in changing network conditions, let alone achieve the service-desired latency-QoE tradeoff. To tackle the challenge, this article develops a novel framework called Flexible Latency Aware Streaming (FLAS) that not only can achieve consistent low latency, but also control the latency-QoE tradeoff flexibly. Specifically, FLAS generates a set of adaptation logics offline, each optimized for a candidate tradeoff point, then selects the most appropriate one to run online. We first show how FLAS can be applied to optimizing the existing algorithms, then developed a novel Genetic Programming approach to fully exploit FLAS's potential. Extensive evaluations show that FLAS can precisely control latency all the way down to 1s and achieve substantially higher QoE than state-of-the-arts. FLAS can be readily implemented into real streaming platforms, offering a practical and reliable solution for live-streaming services.
EyeTell: Tablet-based Calibration-free Eye-typing using Smooth-pursuit movements

Gaze tracking technology, with the increasingly robust and lightweight equipment, can have tremendous applications. To use the technology during short interactions, such as in public displays or hospitals to communicate non-verbally after a surgery, the application needs to be intuitive without requiring a calibration. Gaze gestures such as smooth-pursuit eye movements can be detected without calibration. We report the working performance of a calibration-free eye-typing application using only the front-facing camera of a tablet. In a user study with 29 participants, we obtained an average typing speed of 1.27 WPM after four trials and a maximum typing speed of 1.95 WPM.