Miracle-dz/UniFAR-FTUs huggingface dataset:
How Well Does Undergraduate Education Prepare Software Engineers? Perspectives of Practitioners in Bangladesh

While the sole purpose of undergraduate education is not to prepare the students for the industry, it is certainly one of its important objectives. In this work, we investigate how well the Bangladeshi software and IT-related undergraduate education prepares the students for the software industry. We conducted semi-structured interviews of twenty practitioners from the Bangladeshi software industry. During the interviews, these practitioners provided commentary on where they believe the undergraduate education system falls short, and provided their suggestions for improvement. Based on the themes discovered from the interviews, we created a survey where more than two hundred practitioners participated. The results of our work suggest that most of the practitioners believe that, while some aspects of the undergraduate education are fine, the undergraduate education system leaves its graduates largely unprepared for the software industry. In this paper, we summarize and present the practitioners' opinions on some key areas including but not limited to updating of syllabi, internships as part of the curricula, the nature, length and evaluation process of undergraduate projects, pedagogical issues, and academic practices.
Photochemistry in Organized and Constrained Media

Topological connectivity between organized assemblies physical techniques as used in organized assemblies microenvironments as viewed through photophysical probes fractal analysis of photoprocesses in reactive media of complex geometry crystals - unimolecular reactions bimolecular reactions - porous solids (organic and inorganic hosts), nonreactive surfaces, reactive surfaces, liquid crystals, micelles, monolayers, host-guest photochemistry in solution organization in biological systems 1 organization in biological systems 2 applications of organized media examples.
Energy Storage as a Service: Optimal Pricing for Transmission Congestion Relief

This paper focuses on pricing Energy Storage as a Service (ESaaS) for Transmission congestion relief (TCR). We consider a merchant storage facility that competes in an electricity market to trade energy and ancillary services on a day-to-day basis. The facility also has the opportunity to provide a firm TCR service to a regional network operator under a long-term contract. Providing the additional TCR service would impose limitations on the facility’s ability to fully harvest daily market trade opportunities. Thus, we model the opportunity costs associated with the TCR service and use it in a hybrid cost-value customized pricing technique to determine the risk-constrained optimal price of ESaaS for TCR. Given the long-term nature of the commitment to provide the TCR service, we use the Conditional Value at Risk (CVaR) metric to mitigate the long-term financial risks faced by the facility. The proposed pricing strategy enables the storage owner to estimate the additional financial gains and the associated risks that would likely result from adding the new service to its operation. Numerical simulations are provided to support the proposed methodology.
Reflective Practice in Software Development Studios: Findings from an Ethnographic Study

Over the last two decades, software educators have adopted new approaches, techniques, and tools for practical learning. Previous research has found that studio-based learning, which has been in use by some design and architecture courses, is suitable for learning the practical aspects of software engineering. The studies available recognize the presence of reflective practice in software development studios; however, they do not show empirical evidence of its contribution to learning. The goal of this study is to understand the use of reflective practice in software development studios and its contributions to the practical learning of software engineering. The qualitative data were collected using an ethnographic method through participant observation and from written students' self-reflections, which were analyzed using Cycle Coding Method supported by Atlas.ti Qualitative Data Analysis tool. The findings suggest that reflective practice promotes the emergence of new ideas while contributing to the development of skills that are valuable to software engineering professional practice. This research presents a qualitative look at how these are developed in the context of a particular software development studio.
Unsupervised Manifold Alignment for Wifi RSSI Indoor Localization

There is a wealth of analysis techniques that researchers across the world are implementing for better indoor localization. The RSSI fingerprinting is one of many techniques used for indoor and outdoor localization. In addition, other fingerprints are used to assist in the localization collected from several sources such as camera, radar, and Lidar. Ideally, a combination of these sources is used to locate the same object. Precisely, these sources are collecting the same data using different dimensions ultimately building upon one big system. Due to different dimensions set by these sources, it often becomes difficult to train the overall system to achieve the task of localization. In this paper, we propose a technique that can be used to incorporate training multiple datasets from different dimensions (e.g. Lidar, camera, and radar) into one global dataset, then train it all at once. This technique is known as the Manifold Alignment. Our proposed manifold alignment algorithm bridges the gap, allowing the inclusion of multiple datasets in our application whilst constraining the computational time and storage that would be required for the system. We assume that our technique is embedded into our system and localization is achieved through either computing our proposed Manifold Alignment algorithm over a local device, edge server, or cloud. Results in this paper show how well the Manifold Alignment Algorithm is beneficial for a localization problem where it is implemented inside a machine learning model that computes the manifold of these datasets.
An In-Depth Analysis of Cloud Block Storage Workloads in Large-Scale Production

Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from Alibaba Cloud. We study the characteristics of load intensity, spatial patterns, and temporal patterns. Also, we present a comparative study on our traces and the notable public block-level I/O traces from Microsoft Research Cambridge, and identify the commonalities and differences of the two sets of traces. Finally, we provide 15 findings and discuss their implications on load balancing, cache efficiency, and storage cluster management in a cloud block storage system. Our traces are now released for public use.
Orthogonal Waveform Design of MIMO Radar Based on Niche Genetic Algorithm

In order to optimize the orthogonal design of MIMO radar waveform, an improved algorithm based on niche genetic algorithm is proposed. The algorithm is able to eliminate individuals with similar structures in the population or data, maintain a certain distance between individuals, and effectively hold the diversity of them It thus eliminates the phenomenon of premature convergence, strengthens the global search ability of the algorithm, and improves its convergence speed. It is shown that the algorithm has both the advantages of overall search ability of genetic algorithm and the ability of the niche genetic algorithm to discover multiple optimal solutions. Simulation was given to show that the algorithm was cable to reduce the side-lobe of the auto-correlation function of the orthogonal polyphase code and the peak value of the cross-correlation function of the code with good performance of autocorrelation and cross-correlation obtained
Method for estimating target orientation in single snapshot and coherent echo space

The three-dimensional imaging sonar with small physical size and high integration can detect underwater formations and form image results with centimeter-level resolution in both the forward direction and the depth direction. Due to the limitation of physical size and signal wavelength, the horizontal azimuth resolution obtained by conventional beam-form processing has become a short board for 3D high-resolution imaging. The traditional high-resolution algorithms of MVDR and MUSIC cannot adapt to the characteristics of stratum sonar echo of single snapshot, strong coherent source and a small number of array elements. These traditional high-resolution algorithms cannot guarantee the performance and stability of azimuth resolution of targets in the horizontal direction. This paper avoids the traditional operations of estimating the co-variance matrix, counting multiple snapshot data samples, and reducing the coherence characteristics of the echo space. Through the estimation process of the maximum posterior probability criterion and the iterative convergence process of the constraint equation, the method in this paper can obtain the optimal waveform estimation of the source signal in the spatial scanning orientation. The magnitude of the waveform energy represents the spatial distribution of the target bearing. After simulation analysis and experimental data processing, the algorithm can achieve high-resolution imaging of the target orientation under the single snapshot and coherent sonar echo. It has stable processing performance and good technical innovation.
Wireless Positioning Network Location Prediction Based on Machine Learning Techniques

Developing wireless communication technology as well as the extensive usage of wireless networks imply that location applications have been made rendered necessary. Notably, this position denotes the functionality and events to estimate a node of interest's location. This study compares different machine learning (ML) approaches to locate the accurate localization of a moving tag based on wireless sensor network (WSN) as the wireless propagation medium to improve the prediction of positions. Our experimental recommends using neural networks method to design an appropriate intelligent WSN networks and seek an optimal position with high precisions. The results show the proposed method provides a prediction accuracy about 94%based on neural network approach.
Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM

In most deep learning-based load forecasting, an intact dataset is required. Since many real-world datasets contain missing values for various reasons, missing imputation using deep learning is actively studied. However, missing imputation and load forecasting have been considered independently so far. In this article, we provide a deep learning framework that jointly considers missing imputation and load forecasting. We consider a family of autoencoder/long short-term memory (LSTM) combined models for missing-insensitive load forecasting. Specifically, autoencoder (AE), denoising autoencoder (DAE), convolutional autoencoder (CAE), and denoising convolutional autoencoder (DCAE) are considered for extracting features, of which the encoded outputs are fed into the input of LSTM. Our experiments show that the proposed DCAE/LSTM combined model significantly improves forecasting accuracy no matter what missing rate or type (random missing, consecutive block missing) occurs compared to the baseline LSTM.
A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

Various advanced and mission-critical applications are enabled by the emerging technologies in fifth-generation (5G) mobile communication systems. To ensure improved quality of experience (QoE) of users, 5G and beyond networks require ultra-reliable low-latency communications (URLLC). The successful realization of the URLLC entails the advent of new technological concepts. Therefore, this article presents an overview of the enabling techniques for the URLLC. Classification of the enabling techniques is done and an extensive review of the literature is presented to identify the state-of-the-art techniques, limitations, and the potential approaches for alleviating the limitations. It is observed that artificial intelligence (AI)-enabled edge computing and caching solutions are widely explored as promising techniques to effectively guarantee low latency and reliable content acquisition while reducing redundant network traffic and improving the QoE. Therefore, we present a classification of the AI-enabled edge caching solutions and discuss various mechanisms of the caching agents. In particular, we investigate the use of deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) algorithms. Subsequently, we analyze the performance of the state-of-the-art edge caching schemes and demonstrate the performance gains of FL frameworks over conventional centralized and decentralized DL and DRL frameworks. We confirm that FL edge caching is a viable mechanism in 5G and beyond networks. On the other hand, it is shown that the IEEE 802.1 time sensitive networking and the emerging IETF deterministic networking standards present effective mechanisms when deterministic networks with bounded ultra-low latency are considered. Finally, we present the open issues and opportunities for further research.
Profiling Bots and Fake News Spreaders at PAN’19 and PAN’20 : Bots and Gender Profiling 2019, Profiling Fake News Spreaders on Twitter 2020

This paper synthesizes our participation in the CLEF conference 2020 regarding the Profiling Fake News Spreaders on Twitter task, organized at the PAN lab on digital text forensics and stylometry. Our team obtained one of the two best performing results with an average accuracy of 0.7775 –0.7350 for English and 0.8200 for Spanish. In summary, we submit a Support Vector Machine (SVM) classifier trained with character and word n-gram to determine whether the author of a tweet feed is keen to be a spreader of fake news. Besides, we present our results as the best performing team in Bots and Gender Profiling 2019 with an average accuracy of 0.8805. Its goal was to determine if the author of a tweet feed was a bot or human, followed by identifying her/his gender in the case of humans.
Traffic Aware Beamformer Design for Flexible TDD-Based Integrated Access and Backhaul

Integrated access and backhaul (IAB) network consists of base station (BS), relay nodes (RNs), and user-equipments (UEs), where BS and RNs exchange UE data via wireless in-band backhaul while sharing the same frequency-time resources with access links. In this paper, a flexible time-division-duplex (TDD)-based IAB network is considered where RNs and BS are assigned to distinct uplink (UL) or downlink (DL) transmission modes to mitigate conventional half-duplex (HD) loss at RNs. An iterative beamformer design is proposed to manage the resulting cross-channel interference and to allocate wireless backhaul and access resources jointly over two consecutive data delivery intervals required for communications between the BS and UEs through HD RNs. Dynamic traffic behavior is handled via weighted queue minimization objective, and user-specific UL/DL queues are also introduced at RNs to guarantee reliable end-to-end data delivery. Bi-directional forward-backward training via spatially precoded over-the-air pilot signaling is employed to allow decentralized beamformer design across all the nodes. A novel user allocation method is proposed to assign UEs to BS or RNs based only on long-term channel statistics and some practical IAB limitations. The numerical examples illustrate the superior system performance of the considered flexible IAB in comparison to the conventional HD relaying system.
Antirotational plating combined with bone grafting for nonisthmal femoral shaft nonunion after intramedullary nailing: a retrospective study

Objective: Exchange nailing for aseptic femoral shaft nonunion is currently a standard orthopedic treatment modality. However, according to recent studies there is occasionally a high failure rate when exchange nailing is used. In the present study, augmentative locked plating and bone graft was used as an alternative method for treating such cases. The purpose of this study was to report the treatment outcomes of antirotational plating (ARP) combined with bone grafting with a retained nail left in situ for nonisthmal femoral shaft nonunion after femoral nailing. Materials and Methods: Fifty-three patients presenting with nonisthmal femoral shaft nonunion after femoral nailing were selected (according to inclusion criteria) for a retrospective study from April 2012 to June 2016. All patients received treatment with ARP entailing a nail left in situ and combined with bone grafting. Main result measures include union time, union rate, and operation time, amount of bleeding during operation. Follow-ups, including radiologic and clinical function examinations, were carried out at 1, 2, 3, 4, 6, and 12 months after surgery and then once a year, to monitor callus growth and functional recovery of the injured limb. Results: All patients achieved bone union by 6.4 months clinically (4–8 mo) on average and 5.2 months radiologically (2–16 mo), without any intervening measures during follow-up. Mean operation time was 99.3±27.8 minutes (range=50–215 min), and mean blood loss was 494.9±281.3 mL (20–1400 mL). The average follow-up time was 18.7 months (12–26 mo). All patients in the current study achieved bone union (100%). We observed no postoperative complications, such as failure of internal fixation, infections, angular or rotational malunion. Physicians removed the intramedullary nail and plate from 5 patients 11–16 months after the operation without any refracture. During the last follow-up, all patients could flex their knees >100 degrees. Conclusions: AR with a nail left in situ, combined with bone grafting, is an effective treatment option for nonisthmal femoral shaft nonunions.
The Relationship of Transcranial Magnetic Stimulation With Sleep and Plasticity

Neuroplasticity is an area of expanding interest in psychiatry. Plasticity and metaplasticity are processes contributing to the scaling up and down of neuronal connections, and they are involved with changes in learning, memory, mood, and sleep. Effective mood treatments, including repetitive transcranial magnetic stimulation (rTMS), are reputed to work via changes in neuronal circuitry. This article explores the interrelatedness of sleep, plasticity, and rTMS treatment. A PubMed-based literature review was conducted to identify all available studies examining the relationship of rTMS, plasticity, and sleep. Key words used in this search included “TMS,” “transcranial magnetic stimulation,” “plasticity,” “metaplasticity,” “sleep,” and “insomnia. ” Depressed mood tends to be associated with impaired neural plasticity, while antidepressant treatments can augment neural plasticity. rTMS impacts plasticity, yielding long-lasting effects, with differing impacts on the waking and sleeping brain. Higher quality sleep promotes plasticity and learning. Reports on the sleep impact of high-frequency and low-frequency rTMS are mixed. The efficacy of rTMS may rely on brain plasticity manipulation, enhanced via the stimulation of neural circuits. Total sleep time and sleep continuity are sleep qualities that are likely necessary but insufficient for the homeostatic plasticity driven by slow-wave sleep. Understanding the relationship between sleep and rTMS treatment is likely critical to enhancing outcomes.
Sunscreen and Tanning Bed Use in High-Risk College-Aged Students

ABSTRACT Skin cancer is the most prevalent cancer in the United States. Contributing factors include phenotypic exposure to ultraviolet radiation, lack of protective measures, and using high-risk behaviors. Protective measures (sunblock/protective clothing) are not popular with college-aged individuals, whereas tanning bed use is highly prevalent. The purpose of this study was to describe phenotypic risk factors for skin cancer, application of sunscreen, and tanning bed use in college students. A convenience sample of 673 students (55% male) completed a descriptive survey during winter, spring, and summer months. Researchers collected information about phenotypic risk factors using the Fitzpatrick Skin Scale, use of sunscreen, previous tanning bed use, and gender. Furthermore, 9.5% of college-aged individuals reported using sunscreen. Sunscreen use varied by season (p = .009) but not by skin type. Women reported higher sunscreen use (p = .0001). In addition, 12.6% disclosed a history of tanning bed use (M = 1.2). Male participants were more likely to use tanning beds during winter months (p = .015). Skin type did not influence tanning bed use. College-aged individuals are not sufficiently protecting themselves from the sun. More attention is needed to help melano-compromised individuals, especially men, understand how risk factors influence the development of skin cancer.
Relation of Individual Time Management Practices and Time Management of Teams

Full research paper—Team configuration, work practices, and communication have a considerable impact on the outcomes of student software projects. This study observes 150 college students who first individually solve exercises and then carry out a class project in teams of three. All projects had the same requirements. We analyzed how students’ behavior on individual pre-project exercises predict team project outcomes, investigated how students’ time management practices affected other team members, and analyzed how students divided their work among peers. Our results indicate that teams consisting of only low-performing students were the most dysfunctional in terms of workload balance, whereas teams with both low-and high-performing students performed almost as well as teams consisting of only high-performing students. This suggests that teams should combine students of varying skill levels rather than allowing teams with only low performers or letting students to form teams without constraints. We also observed that individual students’ poor time management practices impair their teammates’ time management. This underlines the importance of encouraging good time management practices. Most teams reported that they divided tasks in a way that is beneficial for the acquisition of technical skills rather than collaboration and communication skills. Only a few teams assigned tasks so that students would have worked only on tasks they already knew and thus felt most comfortable to work with.